![Preface
Six years ago, we published a volume on Imaging Flow Cytometry in the Methods in
Molecular Biology series [1] and would now like to extend the presentation of the capabilities
of modern cytometry development in this series from a broader perspective.
In recent years, flow cytometry has demonstrated significant progress in technology
(data acquisition) and data analysis. Improvement of flow cytometry is now directed to
overcoming limitations of the traditional flow cytometry technology in the number of colors
(labels) that could be detected simultaneously and the inability to capture images of each cell
(object) during analysis. Two branches are emerging: imaging flow cytometry (IFC) is
gaining popularity, and spectral flow cytometry (SFC) instruments became commercially
available just several years ago. This volume aims to present an overview of spectral cyto-
metry, recently developed protocols in the IFC area, and several protocols developed by
using FlowCam – a special imaging cytometer whose potential in basic research is still
underestimated.
Multi-laser flow cytometers made it possible to detect up to 15–18 colors [2–4];
however, the compensation tables, in this case, become critically large, and detection is
usually limited only to relatively bright populations. It means that despite the many colors
used, not all subpopulations, for example in bone marrow probes, could be distinguished
unambiguously. This limitation has recently been overcome by introducing spectral instru-
ments equipped with PMT arrays (altogether, it has up to 186 PMTs), with each detector
assigned to the narrow part of the spectrum. The development of special spectral unmixing
algorithms for data analysis improved the discrimination of the populations stained with
dyes having similar fluorescent spectra compared to the spectral compensation algorithms in
conventional cytometry. Spectral cytometry allowed to extend of the multicolor panel
beyond 40 colors [5]. The most prominent advantage of SFC is its ability to analyze
autofluorescent spectra in detail. This feature gives new capabilities to the analysis of
heterogeneous populations of blood cells [6–8] and will become an indispensable tool for
the analysis of algae and cyanobacteria.
Another problem of conventional flow cytometry is the inability to take pictures of
individual cells. Information about objects in the flow cell of a conventional cytometer is
limited by its light scatter properties. For example, doublets cannot always be resolved from
singlets making analysis of the rare events extremely difficult. Visualization of individual cells
in the brightfield mode and in fluorescent channels during population analysis was resolved
by introducing Imagestream-100 and later Imagestream X and Mark II models (Amnis)
with the time-delayed camera(s). Imagestream instrument (Amnis) was developed for the
analysis of relatively small cells similar to those in the conventional flow cytometry and used
the cuvette 250 μm in diameter and 875 μm in depth. It allows to obtain high-resolution
images of cells during flow experiments in multiple fluorescent channels with high sensitiv-
ity. The image-enabled intelligent high-speed cell sorting of single cells is under develop-
ment [9, 10].
A separate line of evolution of the flow instruments resulted in the introduction of
FlowCam, designed for the analysis of relatively large objects. FlowCam was initially
developed for the analysis of phytoplankton and some zooplankton and later became a
useful instrument for the evaluation of aggregated particles in pharma industries. Specific
vii](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-11-320.jpg)
![Chapter 1
Development of Spectral Imaging Cytometry
Ivan A. Vorobjev, Aigul Kussanova, and Natasha S. Barteneva
Abstract
Spectral flow cytometry is a new technology that enables measurements of fluorescent spectra and light
scattering properties in diverse cellular populations with high precision. Modern instruments allow simul-
taneous determination of up to 40+ fluorescent dyes with heavily overlapping emission spectra, discrimina-
tion of autofluorescent signals in the stained specimens, and detailed analysis of diverse autofluorescence of
different cells—from mammalian to chlorophyll-containing cells like cyanobacteria. In this paper, we review
the history, compare modern conventional and spectral flow cytometers, and discuss several applications of
spectral flow cytometry.
Key words Spectral cytometry, Flow cytometry, Fluorescence spectra, Aurora cytometer, Sony spec-
tral analyzer, Autofluorescence, Spectral unmixing, Virtual filtering
1 Introduction
Flow cytometry began its development in the middle of the twen-
tieth century and has established itself as one of the major func-
tional methods widely used by scientists and clinicians. As it
developed, flow cytometry in the twenty-first century diverges
into the following directions: (1) Conventional flow cytometry
and fluorescent activated cell sorting (FACS); (2) Imaging flow
cytometry; (3) Spectral flow cytometry (spectral FCM).
Conventional cytometry allows studying the size, granularity,
and several fluorescent signals of individual cells or particles at the
rate of 1000 events per second. Imaging flow cytometry, a hybrid
technology, which combines the principles of flow cytometry and
microscopy, allows obtaining an image of each cell and thus collects
galleries of images along with light scatter and fluorescent signals.
However, its throughput is significantly less than conventional flow
cytometry [1]. Spectral FCM, which is based on spectroscopy,
made it possible to record the full spectrum of every single cell
during measurements and now operates at a rate similar to conven-
tional flow cytometry. Both imaging flow cytometry and spectral
Natasha S. Barteneva and Ivan A. Vorobjev (eds.), Spectral and Imaging Cytometry: Methods and Protocols,
Methods in Molecular Biology, vol. 2635, https://doi.org/10.1007/978-1-0716-3020-4_1, © The Author(s) 2023
3](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-19-320.jpg)
![FCM allow sophisticated offline analysis of the specimens. Recent
technical advances in multicolor cytometry were focused on detect-
ing and analyzing cellular subpopulations with complex immuno-
phenotypes participating in the immune response to diseases
and/or vaccine response [2, 3]. Besides, significant progress in
the decomposition of complex fluorescent spectra was introduced
by Rosetti and co-authors [4], which could improve spectral
unmixing and detection of autofluorescence. It will allow better
separation of negative, dim, and positive populations using multi-
color labeling.
4 Ivan A. Vorobjev et al.
2 Development of Spectral Flow Cytometry
Wade and colleagues made one of the first attempts to extract full
emission spectra during flow cytometry analysis in 1979 [5]. They
used a grating spectrograph and projected the spectrum of the
fluorescent signal onto TV type Vidicon detector (objects: Anacyc-
tis nidulans—chlorophyll and phycobilin, 600–750 nm; 3T3 fibro-
blasts from Balb/c mice, propidium iodide (PI) and fluorescamine
staining) [5]. The recorded signal from individual cells has a very
low signal-to-noise (S/N) ratio, and reasonable spectra were
obtained only by averaging recordings from hundreds of cells
(fibroblasts).
The next configuration of the spectral flow cytometer was
based on a photomultiplier tube (PMT) as a detector and grating
monochromator. This cytometer had a 10 nm bandwidth spectral
resolution, and signal detection was performed when cells were
running through the cuvette at a rate of hundreds of events per
second [6]. The system eliminated problems of the noisy back-
ground using PMT with adjustable gain and offset as a detector
(analyzed objects—fixed rat thymocytes, stained with Hoechst
33258) [6]. However, the spectrum measured was only between
400 and 600 nm, not including far-red and infrared
(IR) wavelengths.
Buican [7] described in 1990 a “real-time FT spectrometer”
that was an interferometer-based spectral detector using PMT with
minimal time needed for the recording of the spectrum (only
3.2 μs). However, this instrument was never used as a commercially
available cytometer. Subsequently, several more spectral systems
were created in an attempt to obtain spectra from short measure-
ments using conventional cytometers in 1990–2000. Thus, Gauci
and co-authors described configuration with the prism and
512-element intensified photodiode array based on the FACS IV
laser flow cytometer. They analyzed spectra obtained from Dictyos-
telium discoideum spores stained with Cy3, fluorescein isothiocya-
nate, R-phycoerythrin (R-PE), and calibration beads [8]. This
system was relatively slow (operating at 62.5 Hz) and not sensitive](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-20-320.jpg)
![enough to show individual spectra of the labeled cells [8]. Further-
more, Asbury et al. (1996) [9] were the first group able to obtain
the fluorescent spectrum using a standard flow cytometer (Cytoma-
tion, USA) with a monochromator attached in front of PMT used
as a detector of fluorescent signal. However, this was not a real
spectral FCM yet. The monochromator was operating
sequentially—for each wavelength (spectral point), 100 events
were recorded. Then monochromator was shifted to the adjacent
position, recording another 100 events and so on. The overall
spectrum (400–800 nm) was built up from measurements made
on 20,000 particles.
Spectral Imaging Cytometry 5
At the beginning of the multicolor analysis, the sensitivity of
flow cytometers and confocal microscopes in the far-red and IR
parts of the spectrum was limited by the low sensitivity of PMTs at
wavelengths beyond 650 nm [10]. The use of avalanche photodi-
ode detectors (APD) led to substantially better S/N performance
over the PMT in the red and near-IR spectral regions. Changing
conventional PMTs to APD and APD arrays [11, 12] made it
possible to achieve reasonable S/N for multichannel detectors
using short-time exposures even in near IR (wavelengths up to
800–900 nm) [13]. An alternative type of detector was used by
Isailovic and colleagues [14]. Their instrument (single-cell fluores-
cence spectrometer) was based on ICCD (intensified charge couple
device) detector and used a 5–20 ms exposure time, thus coming
close to the real spectral FCM. Using this instrument, they demon-
strated that measurement of individual spectra with a spectral reso-
lution of 6.5 nm from fluorescently labeled E. coli expressing GFP
and non-fluorescent apo-subunits of R-PE gives more accurate
results compared to the measurement of bulk spectra.
Since the beginning of the twenty-first century, various systems
have achieved sufficient sensitivity for recording a spectrum of
fluorescent signals from a single cell in a reasonably short time.
The next step in the development of spectral flow cytometers
became possible when computer speed accelerated and paralleled
recording of multiple signals with high frequency was achieved on a
standard PC. Rapid registration of fluorescent spectra was done
using parallel data recording and digital processing. These instru-
ments were based on multidetector arrays, where emission light is
split and projected onto the grid of PMTs or APDs. A flow cyt-
ometer equipped with 32-channel Hamamatsu multi-anode PMT
able to collect spectral information in not more than 5 μs was built
in Purdue University Cytometry Laboratory and later patented by
Purdue University [15, 16]. This instrument allowed a digitization
rate of up to 75,000 complete a 32-channel spectra per second at
14 bits dynamic range for uniformly (in time) presented events.
The system was based on an EPICS Elite cell sorter (Beckman
Coulter, USA) equipped with argon (488 nm) and HeNe](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-21-320.jpg)
![(633 nm) lasers [16]. This system achieved a speed of 3000 random
events per second; however, the sensitivity was lower than that of
conventional filter-based detectors.
6 Ivan A. Vorobjev et al.
A similar system based on a modified BD FACSCalibur cyt-
ometer equipped with argon-ion laser and 100 W mercury lamp
was built by Goddard and co-authors [17] using a grating spectro-
graph and Hamamatsu CCD array with 80% quantum yield. The
spectra analyzed by this instrument were in the range of
500–800 nm. This instrument allowed recording spectra with
great linearity, making spectral subtraction to remove background
signals from labeled specimens such as Rayleigh scattering, Raman
light scatter, and even cell autofluorescence feasible. Also, the
sensitivity of the instrument was significantly lower (10–30 times)
than that of the conventional cytometer [17].
Alternative spectral cytometry systems used a charge-coupled
device (CCD) camera as a detector to measure spectra from single
cells and beads [17–19]. In 2012 Nolan’s group [20] developed
spectral FCM instruments and data analysis algorithms suitable for
everyday use. Their two systems were based on FACSCanto
equipped with 405 and 488 nm lasers and using EM-CCD (elec-
tron-multiplying CCD) detector (11.3 nm resolution in the
500–800 nm range) and Coulter Elite cytometer using 785 nm
laser for IR emission (at 3.23 nm resolution in 790–930 nm range).
Their spectral flow cytometers used a holographic grating and
EM-CCD detector for high-speed spectra detection. Customized
software was developed for the spectral unmixing and production
of spectra-derived parameters for individual cells.
Instrument calibration and data analysis were very complicated
at these early stages of spectral FCM development (circa 2012)
[21]. Instrument design was not standardized, requiring thorough
spectral calibration for each instrument. Also, different instruments
used different data formats, making cross-platform spectral analysis
tricky. In the first spectral cytometers, spectral unmixing was per-
formed through the least square unmixing algorithm or indirectly
through principal component analysis [22]. Overall comparing the
spectral data obtained by different instruments was practically
impossible. So far, at that time, the advantages of spectral FCM
over conventional multichannel flow cytometry were impossible to
use in many applications. The next step was done when commer-
cially available spectral cytometers with standardized parameters
appeared.
The system patented by Purdue University was licensed by
Sony Inc., which is producing the first-generation commercial
spectral cytometry system (sometimes named hyperspectral
cytometer)—the Sony SP6800 Spectral Analyzer was announced
at the end of 2012 and came to the market in 2014. Also, in 2014
Cytek Biosciences (USA) developed and soon released its Aurora](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-22-320.jpg)
![8 Ivan A. Vorobjev et al.
4 Advances and Limitations of Spectral Flow Cytometry
A critical review of the latest advances and remaining problems in
spectral FCM was published recently [23]. The essential aspects of
spectral FCM are that instrument performance in the case of Cytek
Aurora strongly depends on the characteristics of each filter (total—
of 64 filters). For example, a thorough check uncovered two out-
of-specification filters in the commercial instrument that precluded
efficient separation of eFluor450 from BV421 and SB436
[23]. Other issues dealt with laser delay and titration of antibodies.
In the case of spectral FCM, titration of antibodies is more compli-
cated because of living and dead cells in the same tube. Authors
suggest using live and dead cell markers along with a standard set of
CD markers, making titration a multistep process. This process can
be described as inversed to FMO (fluorescence minus one) controls
used in conventional multicolor cytometry. The sequence of sug-
gested tests for titration is the following: viability dye, major mar-
kers like CD45, lineage-specific markers (CD3, CD19, etc.), and
finely more specific markers to identify small subpopulations of
blood cells [23].
5 Development of Spectral Unmixing Algorithms
The significant advantage of spectral measurements against conven-
tional flow cytometry is its ability to make a detailed comparison of
fluorescent spectra from individual cells (objects) in a heteroge-
neous population. Multiparametric cytometry often has bleed-
through problems due to the overlapping spectra of fluorophores.
To identify and characterize complex interactions of multiple cell
types, it is necessary to analyze a significant number of fluorescent
labels simultaneously. Fluorescence signals were initially analyzed as
a linear combination of reference spectra with algorithms extracting
the weight of individual spectra (linear unmixing) [24]. Identifica-
tion of heavily overlapping spectra can be performed to a limited
extent using the spectral compensation procedure, and instead,
spectral unmixing was introduced. Spectral unmixing refers to a
group of techniques that attempt to determine how much each
fluorophore contributes to the observed emission spectrum. It was
initially suggested for microscopy [25] and later applied in flow
cytometry [21, 26]. Spectral unmixing in cytometry allows analysis
of the simultaneous labeling of cells with several fluorophores
and/or fluorescent proteins. Spectral unmixing methods have
been developed extensively for the remote sensing analysis of
hyperspectral data [27, 28]; however, some key differences make
many unmixing algorithms unsuitable for spectral cytometry:
(a) the number of fluorophores used for cellular staining is known](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-24-320.jpg)
![a priori, though the number of autofluorescent signals can be
unknown; (b) remote sensing spectral analysis is focused on blind
unmixing of source signals while in spectral cytometry it is possible
to use reference spectra to define emission spectral endmembers.
Spectral Imaging Cytometry 9
6 Spectral Unmixing Problems
The fluorophores originated from algal photosynthetic apparatus
such as PE, APC, and PERCP have broad and overlapping spectra,
and to some degree, can be excited by violet laser (405 nm excita-
tion) [29]. Synthetic dyes such as Alexa Fluor and Cyan families are
small organic fluorophores that do not exhibit much crossbeam
excitation. Most spectral unmixing algorithms cannot separate a
signal from background noise or autofluorescence. Autofluores-
cence is a common, undesired signal arising from endogenous
fluorophores contained in the cells or extracellular matrix (i.e.,
NAD(P)H, flavine adenine nucleotide (FAD), lipids, collagen, elas-
tin, and other common fibrous proteins, porphyrins) [30] often
with wide emission spectra [31]. One of the major endogenous
fluorophores inside cells is a mitochondrial NADH (Exc./Em.
350/460 nm) [32], declining with cellular injury. Cellular samples
may contain different types of autofluorescent molecules, and it is
challenging to predict their distribution since they can change in
time (the cell is dying or becoming apoptotic). Spectral unmixing
for subtracting autofluorescence is possible using the non-negative
matrix factorization variant of spectral unmixing, which exploits
spectra obtained at the different excitation wavelengths [4, 33].
7 Comparison of Spectral Unmixing and Spectral Compensation
Despite extensive development in cytometry, the compensation
stays based upon the classical algorithms, using the single controls
approach developed by Bagwell and Adams [34], with some recent
developments [35, 36]. Two methods of separating fluorophore
signals in multicolor cytometry were recently compared by Niewold
and colleagues [35]. One of the major limitations of spectral com-
pensation is the increased spread of compensated signals compared
to the original ones that diminish the ratio between mean/median
values of positive and negative populations [37]. Particularly it
precludes discrimination between negative and dim populations.
For some highly overlapping fluorophores, spectral unmixing
algorithms made it possible to resolve the two fluorescence signals
where spectral compensation did not. Unmixing in spectral cyt-
ometers gives less spreading, which is important when using
numerous (panels >16) fluorophores [35]. However, if the cyt-
ometer uses optical filters (Aurora spectral analyzer, Cytek, USA),](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-25-320.jpg)
![the quality of these filters plays a crucial role in the spreading when
unmixing similar spectra [3]. The commercially available filters
might slightly deviate from the characteristics provided by the
supplier and, thus, sometimes, do not adequately exclude the fluo-
rescent emission of other fluorophores and/or autofluorescent
molecules that overlap with the desired signal. In commercial spec-
tral cytometers from SONY, instead of the optical filters, specialized
prism-based optics are used to measure and separate emissions from
different fluorophores [38].
10 Ivan A. Vorobjev et al.
Another advantage of spectral unmixing in spectral cytometry is
better extracting of autofluorescence signal that could be treated as
an additional fluorophore [36], while compensation cannot be
applied to autofluorescence until its spectrum is recorded.
8 Comparison of Spectral Cytometry and Mass Cytometry
Flow cytometry allows analysis of up to 25–40 parameters at a rate
of several thousands of events per second. On the other hand, mass
cytometry, currently a competitor to spectral FCM, allows typing of
various immune cells on panels from 14 to 42 parameters with
minimal overlap between channels and without autofluorescence
[39–42]. Despite these benefits, broader practical applications of
mass cytometry are affected by limitations such as slow collection
rates (300–500 events/s vs. several thousand events/s. with con-
ventional cytometry) and total cost of experimentation/
ownership [43].
9 Differences and Similarities Between Spectral and Conventional Flow Cytometry
The common feature of spectral and conventional cytometry is the
observation of a single cell. The full spectrum of a single event can
be detected under the action of hydrodynamic focusing, where the
cell passes an interrogation point and is excited by a collinear or
non-collinear laser system. Subsequently, the detection of the emis-
sion signal for these two systems is fundamentally different. Spec-
trum detection became possible because of a unique emitting
optical system. This system uses prisms and gratings to disperse
fluorescence light, while a conventional cytometer splits fluorescent
signal using bandpass, short pass, and long pass filters (Fig. 2).
Prisms as dispersive optics in spectral FCM propagate light in a
non-linear manner, unlike gratings that propagate light into a
detector in a linear manner. Moreover, spectral cytometry to detect
the full spectrum uses an array of detectors such as CCDs and
multianode PMTs, while in most conventional configurations, sep-
arate PMT is utilized in each forward scatter (FSC), side scatter
(SSC), and fluorescence channel.](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-26-320.jpg)
![development of numerous fluorescent proteins with similar spectra
[44]. Emission spectra of these proteins overlap significantly and
thus cannot be distinguished by conventional fluorescent micros-
copy or FCM using dichroic mirrors and even highly selective
bandpass filters [38].
12 Ivan A. Vorobjev et al.
This principle of spectral FCM operation is used with commer-
cial spectral cytometry companies but with some differences in
optical layout. The Sony spectral analyzer separates the emitted
light with a set of prisms before sending it to 32-channel PMT
arrays. To capture the fluorescence spectrum, the Cytek Aurora
system employs multiple APDs with a unique set of filters in front
of each APD. The possibility of obtaining a full emission spectrum
with commercial spectral analyzers allowed new combinations of
fluorochromes, which due to the significant spectra overlap, are not
used together in conventional cytometry. Moreover, spectral FCM
allows using more fluorochromes per experiment. To address the
existing gap in commercially available fluorochromes, new dyes are
necessary, and this need started to be addressed [45]. Another
advantage of Spectral FCM is extracting the autofluorescence
(AF) of cells and using it as a separate parameter(s) [46], allowing
better signal resolution and even a comparison of different auto-
fluorescent parameters [47].
10 Applications of Spectral FCM
Major problems of conventional flow cytometry can be solved
using the spectral FCM: (1) enhanced number of fluorescent para-
meters used in a single tube (hematology, minimal residual disease
(MRD)); (2) subtraction of fluorescent signal with the improve-
ment of S/N ratio and detailed analysis of autofluorescence signal
for analysis of unlabeled cells. The enhanced number of fluorescent
channels is critical for analyzing small biopsies such as bone marrow
aspirates in MRD. Subtraction of autofluorescence is particularly
helpful for the analysis of cells with a high level of autofluorescence,
such as myocytes, macrophages, brain cells, and hepatocytes. Pri-
mary cells are heterogeneous, and each subpopulation may require
assigning its autofluorescence as a separate fluorophore and
performing additional spectral unmixing [3].
11 Current Applications: Multi-parametric Spectral Cytometry
Nevertheless, certain studies were already made at the early stage of
spectral cytometry. In 2015 Futamura and co-workers [38]
described an analysis of lymphocyte migration from the individual
lymph node (within 24 h) and using photoconvertible protein, and](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-28-320.jpg)
![11-color labeling showed that CD69 low naive T cell subset was
replaced in lymph node faster than CD69 high memory T-cell
subsets [36]. Schmutz and co-authors (2016) [48], using a
two-laser Sony SP6800 instrument (405 and 488 nm), demon-
strated by detailed fluorescence-minus-one control (FMO) that
while the staining index (SI) for individual dyes in spectral FCM
was the same as in conventional FCM, spectral FCM gives much
better discrimination of dyes with similar fluorescent properties.
Spectral FCM allowed discrimination of dyes with the same peak
fluorescence intensity when the overall spectra were different and
dyes with similar spectra but shifted for 10–20 nm peaks using
Kaluza software (YFP versus GFP; both proteins versus
FITC) [48].
Spectral Imaging Cytometry 13
Besides, spectral FCM allowed discrimination of lymphocytes
among the cells isolated from the tissues with high autofluores-
cence. Complete elimination of autofluorescent signal makes it
possible to discriminate dye-positive and dye-negative cells using
dyes with emission spectra close to the autofluorescent spectra for
further analysis [48].
The Sony SP6800 Spectral Cell Analyzer instrument utilized
a 32 multianode PMT (Hamamatsu), and spectrum separation is
achieved through a complex prism-based monochromator.
SONY Inc. demonstrated a prototype instrument and reported
on hyperspectral technology during the ISAC Congress in Seat-
tle in 2012 and announced the launch of the new hyperspectral
flow cytometer product—an SP6800 Spectral Cell Analyzer—
in 2012.
In some applications, the multiplexing by spectral tags may not
require spectral unmixing. In this setting, it may be beneficial to
classify the spectra directly instead of classification based on
unmixed intensities. Many techniques may be utilized here, includ-
ing unsupervised data reduction (using, for example, principal
component analysis, independent component analysis, or factor
analysis) or supervised techniques (such as neural networks or
support vector machines).
Advantages of spectral cytometry such as a large number of
studied parameters in one panel with better resolution due to the
removal of the autofluorescence signal and a rate of several thou-
sand events per second (Sony SP6800 10,000–20,000 events/
second, Cytek Aurora 35,000 events/s), have led to an increase in
the practical use of spectral cytometers in immunophenotyping.
One of the first multicolor panels (nine colors) was created by
Futamura and co-authors [38] at the presentation of the Spectral
Analyzer SP 6800 to study the movement of KikGr protein after
photoconversion in the inguinal lymph node cells. The remaining
immune cells, after photoconversion, changed their emission from
green to red (KikGrGreen-KikGrRed) while migrated cells stayed](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-29-320.jpg)
![green. In this experiment, the emission spectra of fluorochromes
and fluorescent proteins, which strongly overlapped with each
other, were separated using spectral unmixing (EGFP/FITC/
KikGr-Green, KikGr-Red/PE, KikGr-Green/Venus, EGGP/
Venus, KikGr-Red/mKO2) [36]. It would be difficult to apply
this panel in conventional flow cytometry, and with the spectral
analyzer, it became possible to separate and eliminate the low and
high levels of autofluorescence that were found in the mouse
splenocytes with strong expression of F4/80 marker (major mac-
rophage biomarker, APC labeled) [38]. Solomon and co-authors
[49] used a 15-fluorochrome panel and spectral FCM to describe
the aging of the bone marrow in mice.
14 Ivan A. Vorobjev et al.
The separation of lasers at the Cytek spectral flow cytometer
allowed the creation of 30–40 multicolor cytometric panels. The
40-color panel OMIP-069 with Aurora for identifying T cells, B
cells, NKT—like cells, monocytes, and dendritic cells was reported
recently [50]. This panel is effective in the study of the immune
response with low sample volume [50]. In this panel, with spectral
cytometry, it became possible to use dyes that have a strong overlap
of the emission signal between them (PE/FITC, PE-Alexa Fluor
700/PerCP-eFluor 710, BUV 496/eFluor 450, SuperBright
436). Using data acquired by a 3-laser 38-color Aurora (Cytek,
USA) spectral cytometer and analyzed by Kaluza and FlowJo soft-
ware, Chen and co-authors [51] demonstrated that SFC allows
distinguishing subsets of myeloid cells when using one tube with
24-color staining more precise compared to the standard 3*8-color
panel. By automated clustering, malignant cells from patients with
minimal residual disease (MRD) were distinguished from rare nor-
mal mast cells and basophils. In the early study, Murphy and col-
leagues [52] conducted a similar study for typing human peripheral
blood mononuclear cells (PBMCs), but separate panels have been
developed for the determination of T cells (23 colors) and B cells
(22 colors). Schmutz and co-authors [48] described a 19 colors
panel for the separation of murine splenocytes into B-, T-, NK-, and
dendritic cells.
A new generation of SONY spectral cytometers—ID7000 also
has a combination of separate lasers (for sequential excitation). It
allows the use of multicolor panels, such as a 28-colors panel for
immune-profiling of COVID-19 patients [53]. Two highly auto-
fluorescent fetal liver stromal subsets were clearly discriminated
using spectral unmixing with autofluorescence assigned as an inde-
pendent parameter [47]. The use of other multicolor panels for
immunophenotyping with a spectral cytometer is summarized in
Table 1.](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-30-320.jpg)
![(continued)
Spectral Imaging Cytometry 15
Table 1
Multicolor immunophenotyping panels examined by spectral FCM-type of analysis
Colors number Instrument Cells types References
40 Aurora Human PBMCs—CD4 T cells,
CD8 T cells, regulatory T cells,
γδ T cells, NKT-like cells, B cells,
NK cells, monocytes,
dendritic cells
Park et al. 2020 [54]
22–23 Aurora Human PBMCs—T and B cells Murphy et al., 2019 [52]
11 Sony SP 6800 KikGR expressing mice—T cells Futamura et al., 2015 [38]
Up to 9 Sony SP 6800 Measure of CD71 expression in
pDC, CD 103+
CD11b-
DC,
CD103-
CD11b+
DC, AM, GR,
BC, TC, NK, etc. in lung, liver,
small intestine, Peyer’s patches,
mesenteric lymph nodes, spleen,
thymus, bone marrow, blood
from mouse
Lippitsch et al., 2017 [55]
12 Sony SP 6800 Mouse bladder cells—CD45, NK
cells, neutrophils, macrophages,
eosinophils
Rousseau et al., 2016 [56]
14 Aurora Human PBMC—CD14, CD169
monocytes
Affandi et al., 2020 [57]
Up to 7 Aurora Urokinase-type plasminogen
activator receptor-targeted
CART T cells
Amor et al., 2020 [58]
19 Sony SP 6800 Murine splenocytes—B, T, NK,
dendritic, myeloid spleen cells
Schmutz et al., 2016 [48]
12 Aurora using
SpectroFlo
2.2
CD8+
T-cell and B-cell Turner et al., 2020 [59]
14 Aurora using
SpectroFlo
Mouse hematopoietic stem and
progenitor compartments
Solomon et al., 2020 [49]
14 Aurora Leukocytes, neutrophils,
eosinophils, NK cells, NKT cells,
CD4+
T-cells, CD8α+
T cells,
PDCs, B cells, cDC, microglia,
Ly6Chigh, and Ly6Clow
infiltrating monocytes
Niewold et al., 2020 [35]
12 Aurora Composition and activation of
circulating leukocytes in COVID-
19 and influenza PBMCs
(peripheral blood mononuclear
cells (PBMCs))
Mudd et al., 2020 [60]](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-31-320.jpg)
![Table 1
(continued)
Colors number Instrument Cells types References
18 Aurora T-cells subsets, the R-based pipeline
using fluorescence minus one
(FMO) controls
Fox et al., 2020 [61]
22 Aurora
(3 lasers)
Splenocytes—B cells, CD4 and
CD8 T-cells, neutrophils, NK
cells, DCs, and monocytes.
Comparison with mass cytometry
Ferrer-Font et al., 2020 [62]
9 Aurora Placental mesenchymal stem/
stromal cells
Boss et al., 2020 [63]
24 Aurora
(3 lasers)
Subsets of myeloid cells for MRD
analysis
Chen et al., 2020 [51]
Up to 10 Aurora
(5 lasers)
Detection of murine gamma herpes
virus 68 cells
Riggs et al., 2021 [46]
3 channel FRET
detection
Aurora
(4 lasers)
Spectral unmixing for improved
FRET detection
Henderson et al., 2021 [64]
23 Aurora 23 colors for placental mesenchymal
cells analysis
Boss et al., 2021 [65]
37 Aurora 45 different subpopulations, PBMC
from SARS-CoV-2 infected
patients
Fernandez et al., 2022 [66]
Up to 11 in one
panel
ID7000 Senolytic vaccination to eliminate
senescent cell in mice
Suda et al., 2021 [67]
Autofluorescence
parameters
ID7000 Autofluorescence spectra analysis Peixoto et al., 2021 [47]
9 colors for genes
initially
identified by
RNA-sequencing
SP6800/
ID7000
CLEC12A, CD1a, CD86, CCL18,
CCL17, CCL22, CD115, CD88
and CD85d
Costa et al., 2022 [68]
5 fluorescent
proteins
SP6800
(SONY)
Bacterial phytochromes with far-red
and near-infrared emission
[69]
Autofluorescence
multiple
ID7000 autofluorescence multiple murine
lung cells populations
[70]
12 Two Major Types of Spectral FCM Analysis: Virtual Filtering and Spectral
Unmixing
Spectral unmixing is the most used and considered to be the most
powerful approach, but it requires a thorough recording of auto-
fluorescent controls from heterogeneous cellular populations.
Sophisticated spectral unmixing with commercially available soft-
ware allows robust separation from 4+ to 20+ fluorochromes.
Another less powerful but more universal approach is virtual filter-
ing. It was initially demonstrated in the phytoplankton study](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-32-320.jpg)
![[71, 72]. Spectral cytometry allowed effective selection of “filtering
off” autofluorescent part of spectra, which may overlap with fluo-
rescent signals in the multiparametric analysis of multiple taxa of
algae [71]. It mimics the interchange of hardware filters in the PMT
channels in a standard flow cytometer. In conventional cytometry,
changing optical filters means manipulation with hardware, and
some optical bandpass filters may not be available on the market.
With SFC, we can make a large selection of virtual filters after the
sample is recorded [72].
Spectral Imaging Cytometry 17
The use of multiple fluorescent conjugates and dyes/pigments
significantly affected cytometric analysis facilitating multivariate
analysis, dimensionality reduction algorithms based on stochastic
neighbor embedding (SNE), unsupervised cluster analysis, and
cell-subset identification programs such as SPADE, CITRUS,
FlowSOM, CellCNN, and viSNE [73–77]. An alternative to clus-
tering algorithms is principal component analysis (PCA), which
is widely used in other areas of biology. Recently, Ogishi and
co-authors [78] introduced iMUBAC (integration of multi-batch
cytometry datasets) using unsupervised cell-type identification
across multiple batches.
13 Conclusions
Currently, the spectral cytometer becomes a superior alternative to
the conventional cytometer since it allows the acquisition of fluo-
rescent dyes and proteins without the limitations of hardware optics
and detectors. It leads to reducing the complexity of multi-color
panel design and allows easy acquisition of more than 20 colors
with good discrimination of bright, dim, and negative cellular
subpopulations. The latest multi-laser (up to seven lasers) commer-
cially available spectral cytometer ID7000 (SONY) allows the
detection and analysis of up to 40 fluorescent parameters. Spectral
FCM or full-spectrum cytometry can subtract autofluorescence
from signals generated by dyes without increasing spread, besides,
it allows acquire autofluorescence as separate parameter(s). Spectral
FSM allows detailed analysis of the autofluorescence that might be
especially useful for analyzing phytoplankton where a strong auto-
fluorescent signal from chlorophyll precludes using fluorescently
labeled dyes/antibodies and for highly autofluorescent cells
(macrophages, myeloid progenitors, infected cells, etc.). Available
libraries of emission spectra of the numerous standard fluorophores
make single-stained controls unnecessary. The limitations of the
spectral deconvolution approach in Spectral FCM are related to
the use of tandem dyes or the inability to use ratiometric probes.
The new generation of multi-laser Spectral FSM instruments initi-
ates a breakthrough in cytometric analysis and the replacement of
conventional cytometers. Full-spectrum cell sorters and
co-registering spectra with images of cells can be foreseen in the
near future.](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-33-320.jpg)
![Chapter 2
Using Virtual Filtering Approach to Discriminate Microalgae
by Spectral Flow Cytometer
Natasha S. Barteneva, Aigul Kussanova, Veronika Dashkova,
Ayagoz Meirkhanova, and Ivan A. Vorobjev
Abstract
Fluorescence methods are widely used for the study of marine and freshwater phytoplankton communities.
However, the identification of different microalgae populations by the analysis of autofluorescence signals
remains a challenge. Addressing the issue, we developed a novel approach using the flexibility of spectral
flow cytometry analysis (SFC) and generating a matrix of virtual filters (VF) which allowed thorough
examination of autofluorescence spectra. Using this matrix, different spectral emission regions of algae
species were analyzed, and five major algal taxa were discriminated. These results were further applied for
tracing particular microalgae taxa in the complex mixtures of laboratory and environmental algal popula-
tions. An integrated analysis of single algal events combined with unique spectral emission fingerprints and
light scattering parameters of microalgae can be used to differentiate major microalgal taxa. We propose a
protocol for the quantitative assessment of heterogenous phytoplankton communities at the single-cell
level and monitoring of phytoplankton bloom detection using a virtual filtering approach on a spectral flow
cytometer (SFC-VF).
Key words Spectral flow cytometry, Phytoplankton, ID7000, Virtual filtering, Spectral flow cyt-
ometer, Cyanobacteria
1 Introduction
The development of spectral flow cytometry (SFC) expanded our
ability to characterize heterogeneous cell populations because of
the high spectral resolution achieved by this instrument [1].
The key advantage of spectral flow cytometry (SFC) is that a
measurement of a set of emission spectra using different excitation
wavelengths is done from individual cells with rates of hundreds
and thousands of events per sec [1, 2]. Moreover, SFC analysis
makes possible additional differentiation of heterogeneous algal
mixtures by size and granularity in a manner similar to conventional
flow cytometry (FCM) [1]. The emission spectrum information for
every single cell could be combined with light scattering data
Natasha S. Barteneva and Ivan A. Vorobjev (eds.), Spectral and Imaging Cytometry: Methods and Protocols,
Methods in Molecular Biology, vol. 2635, https://doi.org/10.1007/978-1-0716-3020-4_2, © The Author(s) 2023
23](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-39-320.jpg)
![through sequential gating on combinations of standard dot plots
and histograms. The populations could now be separated not only
by using conventional fluorescent conjugated antibodies but by
also using the autofluorescent signal from unstained cells [3].
24 Natasha S. Barteneva et al.
Since the spectral unmixing algorithm is based on the record of
single stained probes [4], it cannot be directly applied to the natural
algal probes having bright autofluorescence from different sources,
and another approach has to be considered.
In 2019 we developed a novel “virtual filtering” approach
(SFC-VF) based on the spectral flow cytometry analysis and use
of variable regions of algal autofluorescence spectra in combination
with light scattering-related separation of algal populations based
on algae cellular size and granularity [5]. We applied SFC-VF to
differentiate and characterize microalgae taxa in binary and multi-
component mixtures as well as natural environmental microalgae
assemblages and were able: (1) to differentiate microalgal cells from
different phytoplankton taxa with a similar combination of pig-
ments; and (2) to remove fluorescence signal from contaminating
sources using light scatter gating. Moreover, unlike FCM, SFC
makes it possible to separate individual algal cells presented in
heterogenous algal populations (such as cryptophytes) based on
their unique spectral data.
The SFC-VF method relies on identifying of the most variable
regions of the spectra of the mixtures of algal strains analyzed
pairwise and on creating a matrix of SFC fluorescent channels
corresponding to those regions. Spectral differences between single
algal strains (morphology—Fig. 1, left column) were captured by
both spectral flow cytometer Sony SP6800 (Sony Biotechnology
Inc., USA, 405 nm and 488 nm excitation) and spectrofluorimeter
(Fig. 1, right column). However, the spectrofluorimeter provided
an averaged signal from the population of algal cells, debris, and
fluorescent organic matter. The separation of algal mixtures based
on the conventional FCM approach and a filter combination used
for algal analysis (such as phycoerythrin (PE) bandpass
575/25 nm) versus allophycocyanin (APC) bandpass
(660/20 nm) was complicated by the heterogeneity of algal
populations.
In the SFC-VF approach, the sensitivity of chlorophyll-
associated channels (CH 24–30) captured on the SP6800 was
switched to the minimal level. Then, the non-chlorophyll-based
spectral differences (from accessory pigments) in the 420–650 nm
wavelength range became prominent, enabling better discrimina-
tion of algal strains (Fig. 2). Further SFC analysis of algal cultures
was continued with the reduced intensity of chlorophyll-associated
channels.
Mixtures of algal cultures were analyzed in a pairwise manner
generating different algal combinations. Initially, several variants of](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-40-320.jpg)
![sp. culture were mixed with environmental samples and analyzed
using SFC-VF. A combination of the virtual filters CH 22 (405 nm
excitation) and V1-2 (405 nm excitation) enabled the best separa-
tion of Aphanizomenon sp. population in the 1:1 mixture of Apha-
nizomenon sp. and environmental sample (50% Aphanizomenon
cells: 50% pond sample) and was used for the analysis of other
volume ratios. Spectra of Aphanizomenon sp. cells could be traced
in the mixture containing as little as 0.5% proportion relative to the
total volume (see Note 1).
28 Natasha S. Barteneva et al.
Fig. 4 Spectral analysis of five algal cultures Aphanizomenon sp., C. pyrenoidifera, D. divergens, Cyclotella sp.
and Chlorella sp. Mixed together. C. pyrenoidifera and Cyclotella sp. populations were separated within the
mixture based on CH 12–14 and CH 32 (488 nm excitation) filters (Step 1). Then unseparated part of the
mixture (marked Unknown 1) was gated and projected onto CH 4–15 (405 nm excitation) versus CH
32 (488 nm excitation) dot plot to discriminate the cell population of Aphanizomenon sp. (Step 2). Conse-
quently, the unidentified population (Unknown 2) was gated and visualized on a combination of CH 24–28 and
CH 30 (488 nm excitation) filters to detach the last two populations of D. divergens and Chlorella sp. with very
similar spectral profiles (Step 3)
In conventional cytometry, optical bandpass filters are used to
separate fluorescent signals during instrument detection. Optimi-
zation of fluorescence detection and decreasing the acquisition of
signal coming from a region with a high level of autofluorescence
(e.g., GFP signal from cellular autofluorescence in a green-range
region) require the replacement of a standard optical filter with a
modified one [6]. The SFC-VF approach allows the creation of
“virtual bandpass filters” with no hardware modification and with-
out spectral unmixing. As a result, it was possible to narrow or
widen the spectral signal that is taken into consideration from ~10
to ~300 nm bandwidth (for the SP6800 instrument) and to achieve
significant discrimination of algal populations.](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-44-320.jpg)
![3.2 Spectrocyto-
fluorimetric
32 Natasha S. Barteneva et al.
2.2 List of
Microalgae Cell
Cultures
Microalgae cell cultures from major microalgae taxa, including
Cyclotella sp. CCMP334, Chlorella sp. CCMP251, Dinobryon
divergens CCMP3055, Cryptomonas pyrenoidifera CCMP1177,
Aphanizomenon sp. CCMP2764, Acutodesmus obliquus SAG
276-1, and Porphyridium sordidum SAG 114.79 were obtained
from the National Center for Marine Algae and Microbiota
(NCMA; Bigelow Laboratory for Ocean Sciences, USA) and Göt-
tingen University’s collection of algal cultures (Germany).
2.3 Reagents 1. Microalgae cell culture media: (1) DY-V medium; (2) L1
medium; (3) L1 derivative, L1–11 psu medium.
2. Eight peak beads (Sony Biotechnology Inc., USA).
3. Align Check beads (Sony Biotechnology Inc., USA).
4. 12 × 75 mm round-bottom Falcon polystyrene tubes.
3 Methods
3.1 Cultivation of
Algae Cultures
Freshwater cultures D. divergens, Aphanizomenon sp., and
C. pyrenoidifera were maintained in DY-V medium (modified
from Lehman and co-authors [7]) at 14 °C and 20 °C, respectively,
under 150 μmoles/m2
/s light irradiance and 12/12 L/D cycle.
Chlorella sp. and Cyclotella sp were maintained in L1 medium and
L1 derivative, L1–11 psu medium, respectively, at 14 °C under
150 μmoles/m2
/s light irradiance and 12/12 L/D cycle. 1. Two
or more phytoplankton cell cultures (e.g., Chlorella
sp. CCMP1177, Acutodesmus obliquus SAG-276-1 and Porphyr-
idium sordidum SAG 114.79) were used for experiments with
ID7000 spectral flow cytometer.
1. Prior to the analysis, spin down each microalgae culture and
resuspend it in a small volume. Count algal cells (microscope).
For spectral cytometry analysis, 1000 μL volume of each cul-
ture should be used to analyze single culture controls.
Acquisition of
Microalgal Samples
2. Spectral analysis of algal cell cultures for ID 6800:
3. Prepare mixtures using 500 μL volume of each culture to
analyze 10 pairwise culture mixtures and 200 μL volume of
each culture to analyze a mixture of all five cultures together
(ratio 1:1:1:1:1). Alternatively, mix Chlorella sp. and Acutodes-
mus obliquus cultures with relatively equal cell densities in 1:1,
1:10 volume ratio making up to 250 μL sample. The next steps
are accommodated for the use of the ID7000 spectral system
(Sony Biotechnlogy Inc., USA).
4. Turn on the spectral flow cytometer and run autocalibration
using calibration beads. Use Ultra Rainbow calibration beads
(Spherotech, USA or Sony Biotechnology Inc., USA) for auto-
matic calibration.](https://image.slidesharecdn.com/92988-250527050703-6acc332d/85/Spectral-and-Imaging-Cytometry-Methods-and-Protocols-2nd-Edition-Natasha-S-Barteneva-Ivan-A-Vorobjev-48-320.jpg)
Spectral and Imaging Cytometry Methods and Protocols 2nd Edition Natasha S Barteneva Ivan A Vorobjev
- 1. Spectral and Imaging Cytometry Methods and Protocols 2nd Edition Natasha S Barteneva Ivan A Vorobjev install download https://ebookmeta.com/product/spectral-and-imaging-cytometry- methods-and-protocols-2nd-edition-natasha-s-barteneva-ivan-a- vorobjev/ Download more ebook from https://ebookmeta.com
- 2. We believe these products will be a great fit for you. Click the link to download now, or visit ebookmeta.com to discover even more! Protein Arginylation Methods and Protocols 2ed 2nd Edition Anna S Kashina https://ebookmeta.com/product/protein-arginylation-methods-and- protocols-2ed-2nd-edition-anna-s-kashina/ Antibiotics Methods and Protocols 2nd Edition Peter Sass https://ebookmeta.com/product/antibiotics-methods-and- protocols-2nd-edition-peter-sass-2/ Peroxisomes Methods and Protocols 2nd edition Michael Schrader https://ebookmeta.com/product/peroxisomes-methods-and- protocols-2nd-edition-michael-schrader/ Mathematics for Computer Graphics (Undergraduate Topics in Computer Science), 6th Edition 2022 John Vince https://ebookmeta.com/product/mathematics-for-computer-graphics- undergraduate-topics-in-computer-science-6th-edition-2022-john- vince/
- 3. Philosophy of Sculpture Historical Problems Contemporary Approaches 1st Edition Kristin Gjesdal https://ebookmeta.com/product/philosophy-of-sculpture-historical- problems-contemporary-approaches-1st-edition-kristin-gjesdal-2/ Aviation Week Space Technology April 24 May Volume 185 Number 8 2023 1st Edition Collective Of Authors https://ebookmeta.com/product/aviation-week-space-technology- april-24-may-volume-185-number-8-2023-1st-edition-collective-of- authors/ Shattered Mirror Circus of Glass 1 1st Edition Lydia Reeves Xu Ji Westin https://ebookmeta.com/product/shattered-mirror-circus-of- glass-1-1st-edition-lydia-reeves-xu-ji-westin/ Samsung Digital Key Applet Samsung Electronics Co. https://ebookmeta.com/product/samsung-digital-key-applet-samsung- electronics-co-2/ British Modernism and Chinoiserie 1st Edition Anne Witchard Editor https://ebookmeta.com/product/british-modernism-and- chinoiserie-1st-edition-anne-witchard-editor/
- 4. 2022 CFA Exam Prep SchweserNotes Level II Book 4 Fixed Income Derivatives and Alternative Investments 1st Edition Kaplan Schweser https://ebookmeta.com/product/2022-cfa-exam-prep-schwesernotes- level-ii-book-4-fixed-income-derivatives-and-alternative- investments-1st-edition-kaplan-schweser/
- 5. Spectraland Imaging Cytometry Natasha S. Barteneva Ivan A.Vorobjev Editors Methods and Protocols SecondEdition Methods in Molecular Biology 2635
- 6. M E T H O D S I N M O L E C U L A R B I O L O G Y Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK For further volumes: http://www.springer.com/series/7651
- 7. For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-by- step fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.
- 8. Spectral and Imaging Cytometry Methods and Protocols Second Edition Edited by Natasha S. Barteneva Department of Biology, School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan; Brigham Women’s Hospital, Harvard University, Boston, MA, USA Ivan A. Vorobjev Department of Biology, School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan
- 9. Editors Natasha S. Barteneva Department of Biology School of Sciences and Humanities Nazarbayev University Astana, Kazakhstan Brigham Women’s Hospital Harvard University Boston, MA, USA Ivan A. Vorobjev Department of Biology School of Sciences and Humanities Nazarbayev University Astana, Kazakhstan ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-3019-8 ISBN 978-1-0716-3020-4 (eBook) https://doi.org/10.1007/978-1-0716-3020-4 © Springer Science+Business Media, LLC, part of Springer Nature 2016, 2023 Chapters 1, 2, and 5 are licensed under the terms of the Creative Commons Attribution 4.0 International License (http:/ /creativecommons.org/licenses/by/4.0/). For further details see license information in the chapter. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.
- 10. Dedication In memory of Aleksandra Bergman-Evstafieva: “The crocodile cannot turn its head. Like all science, it must always go forward with all-devouring jaws” – Pyotr Kapitsa v
- 11. Preface Six years ago, we published a volume on Imaging Flow Cytometry in the Methods in Molecular Biology series [1] and would now like to extend the presentation of the capabilities of modern cytometry development in this series from a broader perspective. In recent years, flow cytometry has demonstrated significant progress in technology (data acquisition) and data analysis. Improvement of flow cytometry is now directed to overcoming limitations of the traditional flow cytometry technology in the number of colors (labels) that could be detected simultaneously and the inability to capture images of each cell (object) during analysis. Two branches are emerging: imaging flow cytometry (IFC) is gaining popularity, and spectral flow cytometry (SFC) instruments became commercially available just several years ago. This volume aims to present an overview of spectral cyto- metry, recently developed protocols in the IFC area, and several protocols developed by using FlowCam – a special imaging cytometer whose potential in basic research is still underestimated. Multi-laser flow cytometers made it possible to detect up to 15–18 colors [2–4]; however, the compensation tables, in this case, become critically large, and detection is usually limited only to relatively bright populations. It means that despite the many colors used, not all subpopulations, for example in bone marrow probes, could be distinguished unambiguously. This limitation has recently been overcome by introducing spectral instru- ments equipped with PMT arrays (altogether, it has up to 186 PMTs), with each detector assigned to the narrow part of the spectrum. The development of special spectral unmixing algorithms for data analysis improved the discrimination of the populations stained with dyes having similar fluorescent spectra compared to the spectral compensation algorithms in conventional cytometry. Spectral cytometry allowed to extend of the multicolor panel beyond 40 colors [5]. The most prominent advantage of SFC is its ability to analyze autofluorescent spectra in detail. This feature gives new capabilities to the analysis of heterogeneous populations of blood cells [6–8] and will become an indispensable tool for the analysis of algae and cyanobacteria. Another problem of conventional flow cytometry is the inability to take pictures of individual cells. Information about objects in the flow cell of a conventional cytometer is limited by its light scatter properties. For example, doublets cannot always be resolved from singlets making analysis of the rare events extremely difficult. Visualization of individual cells in the brightfield mode and in fluorescent channels during population analysis was resolved by introducing Imagestream-100 and later Imagestream X and Mark II models (Amnis) with the time-delayed camera(s). Imagestream instrument (Amnis) was developed for the analysis of relatively small cells similar to those in the conventional flow cytometry and used the cuvette 250 μm in diameter and 875 μm in depth. It allows to obtain high-resolution images of cells during flow experiments in multiple fluorescent channels with high sensitiv- ity. The image-enabled intelligent high-speed cell sorting of single cells is under develop- ment [9, 10]. A separate line of evolution of the flow instruments resulted in the introduction of FlowCam, designed for the analysis of relatively large objects. FlowCam was initially developed for the analysis of phytoplankton and some zooplankton and later became a useful instrument for the evaluation of aggregated particles in pharma industries. Specific vii
- 12. features of the FlowCa flow cells of different d However, FlowCam is m camera, and its fluoresc image gallery is created Astana, Kazakhstan Natasha S. Barteneva Ivan A. Vorobjev m are interchangeable objectives with different magnification and iameters allowing analysis of planktonic organisms up to 600 μM. ainly addressed to the analysis in the brightfield mode using a color ent capabilities are limited by not more than two channels. The by this instrument offline using special software. viii Preface The basic knowledge and techniques of IFC have been well documented in our previous volume (2016). Two aspects are considered in the current volume – the development of rapidly emerging spectral cytometry and some applications of imaging flow cytometry. This new volume is organized into three parts. The first part provides an introduction to state-of-the-art spectral cytometry. In the first chapter, the authors review a relatively short history of spectral cytometry development and discuss its advantages compared to conven- tional cytometry. The second chapter demonstrates the possibility of discriminating differ- ent phytoplankton species based on cell autofluorescence and provides a detailed protocol of virtual filtering. At first glance, the absence of chapters on a detailed description of the new SFC techniques may seem like an oversight for a volume having “Spectral Flow Cytometry” in the title. However, given the rapidly evolving nature of SFC and the recent introduction of new instruments, we expect special volumes dedicated to this novel technology will likely be forthcoming in the next 2–3 years. The second part describes several novel applications of imaging flow cytometry using Imagestream instrumentation for semi-quantitative and quantitative analysis in different experimental models. Chapters reflect ongoing IFC advances in quantitative analysis of pathogens (Legionella pneumophila), analysis of multi- nuclearity, quantitative biodosimetry, and autophagy protocols. Moreover, methods for quantification of specific organelles, such as Golgi complex and inflammasomes have been added to the current volume. The third part contains detailed protocols for handling and using the FlowCam imaging flow cytometer from the supplier and research protocol for the studies of phytoplankton communities. We are extremely grateful to all authors who provided chapters for this volume during the difficult time of the COVID-19 epidemics. Last but not least, we would like to thank Professor John Walker, Editor of the Methods in Molecular Biology series, for his unlimited guidance and help. We believe that this volume will be a valuable source for a wide audience looking for new approaches in cytometry. The development of methods that will become instrumental in spectral cytometry is continuing, whereas the IFC is becoming a matured cytometry method. This is truly a fascinating time to be involved in cytometry, as spectral and imaging cytometry continues to evolve at an amazing speed.
- 13. References Preface ix 1. Imaging Flow Cytometry: Methods and Protocols (Methods in Molecular Biology vol.1389), 1st Edition, 2016. Eds. Natasha S. Barteneva, Ivan A, Vorobjev. pp. 308. 2. Moncunill G, Han H, Dobano C, McElrath MJ, De Rosa SC (2014) Pan-leukocyte immunopheno- typic characterization of PBMC subsets in human samples. Cytometry A 85: 995–998. https:/ /doi. org/10.1002/cyto.a.22580. 3. https:/ /www.beckman.kz/resources/reading-material/application-notes/18-color-human-blood- phenotyping-flow-cytometry 4. https:/ /www.agilent.com/cs/library/applications/application-osmotic-fragility-novocyte-5994-102 9en-agilent.pdf 5. Sahir F, Mateo JM, Steinhoff M, Siveen KS (2020) Development of a 43 color panel for the characterization of conventional and unconventional T-cell subsets, B cells, NK cells, monocytes, dendritic cells, and innate lymphoid cells using spectral flow cytometry. Cytometry 2020:1–7. https:/ /doi.org/10.1002/cyto.a.24288 6. Peixoto MM, Soares-da-Silva F, Schmutz S, Mailhe M-P, Novault S, Cumano A, Ait-Mansour C (2022) Identification of fetal liver stroma in spectral cytometry using the parameter autofluorescence. Cytometry A 2022. https:/ /doi.org/10.1002/cyto.a.24567. 7. Adusei KM, Ngo TB, Alfonso AL, Lokwani R, DeStefano S, Karkanitsa M, Spathies J, Goldman SM, Dearth CL, Sadtler KN (2022) Development of a high-color flow cytometry panel for immunologic analysis of tissue injury and reconstruction in a rat model. Cells Tissues Organs. https:/ /doi.org/10. 1159/000524682 8. Heieis GA, Patente TA, Tak T, Almeida L, Everts B (2022) Spectral flow cytometry reveals metabolic heterogeneity in tissue macrophages. BioRxiv. doi: https:/ /doi.org/10.1101/2022.05.26.493548 9. Schraivogel D, Kuhn TM, Rauscher B, Rodrı́guez-Martı́nez M, Paulsen M, Owsley K, Middlebrook A, Tischer C, Ramasz B, Ordoñez-Rueda D, Dees M (2022) High-speed fluorescence image–enabled cell sorting. Science 375: 315–320. doi: https:/ /doi.org/10.1126/science.abj3013 10. Salek M, Li N, Chou HP, Sinai K, Jovic A, Jacobs K, Johnsson C, Lee E, Chang C, Nguyen P, Mei J. (2022) Sorting of viable unlabeled cells based on deep representations links morphology to multio- mics. Research Square. Preprint. doi: https:/ /doi.org/10.21203/rs.3.rs-1778207/v1
- 14. Contents Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii PART I SPECTRAL FLOW CYTOMETRY 1 Development of Spectral Imaging Cytometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Ivan A. Vorobjev, Aigul Kussanova, and Natasha S. Barteneva 2 Using Virtual Filtering Approach to Discriminate Microalgae by Spectral Flow Cytometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Natasha S. Barteneva, Aigul Kussanova, Veronika Dashkova, Ayagoz Meirkhanova, and Ivan A. Vorobjev PART II IMAGING FLOW CYTOMETRY: IMAGESTREAM SYSTEMS 3 Imaging Flow Cytometric Analysis of Primary Bone Marrow Erythroblastic Islands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Joshua Tay, Kavita Bisht, Ingrid G. Winkler, and Jean-Pierre Levesque 4 Imaging Flow Cytometry of Legionella-Containing Vacuoles in Intact and Homogenized Wild-Type and Mutant Dictyostelium . . . . . . . . . . . . . . . . . . . . 63 Amanda Welin, Dario Hüsler, and Hubert Hilbi 5 Imaging Flow Cytometry of Multi-Nuclearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Ivan A. Vorobjev, Sultan Bekbayev, Adil Temirgaliyev, Madina Tlegenova, and Natasha S. Barteneva 6 The Imaging Flow Cytometry-Based Cytokinesis-Block MicroNucleus (CBMN) Assay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Ruth C. Wilkins, Matthew Rodrigues, and Lindsay A. Beaton-Green 7 High-Throughput γ-H2AX Assay Using Imaging Flow Cytometry . . . . . . . . . . . 123 Younghyun Lee, Qi Wang, Ki Moon Seong, and Helen C. Turner 8 Label-Free Identification of Persistent Particles in Association with Primary Immune Cells by Imaging Flow Cytometry . . . . . . . . . . . . . . . . . . . . 135 Bradley Vis, Jonathan J. Powell, and Rachel E. Hewitt 9 “Immuno-FlowFISH”: Applications for Chronic Lymphocytic Leukemia. . . . . . 149 Henry Y. L. Hui, Wendy N. Erber, and Kathy A. Fuller 10 Quantifying Golgi Apparatus Fragmentation Using Imaging Flow Cytometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Inbal Wortzel and Ziv Porat 11 Flow Imaging of the Inflammasome: Evaluating ASC Speck Characteristics and Caspase-1 Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Abhinit Nagar and Jonathan A. Harton xi
- 15. xii Contents 12 Quantitative Analysis of Latex Beads Phagocytosis by Human Macrophages Using Imaging Flow Cytometry with Extended Depth of Field (EDF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Ekaterina Pavlova, Daria Shaposhnikova, Svetlana Petrichuk, Tatiana Radygina, and Maria Erokhina PART III IMAGING FLOW CYTOMETRY: FLOWCAM 13 FlowCam 8400 and FlowCam Cyano Phytoplankton Classification and Viability Staining by Imaging Flow Cytometry . . . . . . . . . . . . . . . . . . . . . . . . . 219 Kathryn H. Roache-Johnson and Nicole R. Stephens 14 Optimizing FlowCam Imaging Flow Cytometry Operation for Classification and Quantification of Microcystis Morphospecies . . . . . . . . . . . . 245 Dmitry Malashenkov, Veronika Dashkova, Ivan A. Vorobjev, and Natasha S. Barteneva Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
- 16. Contributors NATASHA S. BARTENEVA • Department of Biology, School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan; Brigham Women’s Hospital, Harvard University, Boston, MA, USA; The EREC, Nazarbayev University, Astana, Kazakhstan LINDSAY A. BEATON-GREEN • Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, ON, Canada SULTAN BEKBAYEV • School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan KAVITA BISHT • Mater Research Institute, The University of Queensland, Woolloongabba, QLD, Australia VERONIKA DASHKOVA • School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan; School of Digital Sciences and Engineering, Nazarbayev University, Astana, Kazakhstan; PhD Program in Science, Engineering and Technology, Nazarbayev University, Astana, Kazakhstan WENDY N. ERBER • Translational Cancer Pathology Laboratory, School of Biomedical Sciences (M504), The University of Western Australia, Crawley, WA, Australia; PathWest Laboratory Medicine, Nedlands, WA, Australia MARIA EROKHINA • Faculty of Biology, Lomonosov Moscow State University, Moscow, Russian Federation KATHY A. FULLER • Translational Cancer Pathology Laboratory, School of Biomedical Sciences (M504), The University of Western Australia, Crawley, WA, Australia JONATHAN A. HARTON • Department of Immunology and Microbial Disease, Albany Medical College, Albany, NY, USA RACHEL E. HEWITT • Department of Veterinary Medicine, University of Cambridge, Cambridge, UK HUBERT HILBI • Institute of Medical Microbiology, University of Zürich, Zürich, Switzerland HENRY Y. L. HUI • Translational Cancer Pathology Laboratory, School of Biomedical Sciences (M504), The University of Western Australia, Crawley, WA, Australia DARIO HÜSLER • Institute of Medical Microbiology, University of Zürich, Zürich, Switzerland AIGUL KUSSANOVA • School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan; Core Facilities, Nazarbayev University, Astana, Kazakhstan YOUNGHYUN LEE • Laboratory of Biological Dosimetry, National Radiation Emergency Medical Center, Korea Institute of Radiological and Medical Sciences, Seoul, Republic of Korea; Department of Biomedical Laboratory Science, College of Medical Sciences, Soonchunhyang University, Asan, Republic of Korea JEAN-PIERRE LEVESQUE • Mater Research Institute, The University of Queensland, Woolloongabba, QLD, Australia; Translational Research Institute, Woolloongabba, QLD, Australia DMITRY MALASHENKOV • Department of Biology, School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan AYAGOZ MEIRKHANOVA • School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan ABHINIT NAGAR • Program in Innate Immunity, Division of Infectious Diseases and Immunology, School of Medicine, University of Massachusetts, Worcester, MA, USA xiii
- 17. xiv Contributors EKATERINA PAVLOVA • Faculty of Biology, Lomonosov Moscow State University, Moscow, Russian Federation SVETLANA PETRICHUK • National Medical Research Center for Children’s Health, Laboratory of Experimental Immunology and Virology, Moscow, Russian Federation ZIV PORAT • Flow Cytometry Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel JONATHAN J. POWELL • Department of Veterinary Medicine, University of Cambridge, Cambridge, UK TATIANA RADYGINA • National Medical Research Center for Children’s Health, Laboratory of Experimental Immunology and Virology, Moscow, Russian Federation KATHRYN H. ROACHE-JOHNSON • Yokogawa Fluid Imaging Technologies, Scarborough, ME, USA MATTHEW RODRIGUES • Luminex Corporation, Seattle, WA, USA KI MOON SEONG • Laboratory of Biological Dosimetry, National Radiation Emergency Medical Center, Korea Institute of Radiological and Medical Sciences, Seoul, Republic of Korea DARIA SHAPOSHNIKOVA • Faculty of Biology, Lomonosov Moscow State University, Moscow, Russian Federation NICOLE R. STEPHENS • Yokogawa Fluid Imaging Technologies, Scarborough, ME, USA JOSHUA TAY • Mater Research Institute, The University of Queensland, Woolloongabba, QLD, Australia ADIL TEMIRGALIYEV • School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan MADINA TLEGENOVA • National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan HELEN C. TURNER • Center for Radiological Research, Columbia University Irving Medical Center, New York, NY, USA BRADLEY VIS • Department of Veterinary Medicine, University of Cambridge, Cambridge, UK IVAN A. VOROBJEV • Department of Biology, School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan; National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan; A.N. Belozersky Insitute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, Russian Federation; Biological Faculty, Lomonosov Moscow State University, Moscow, Russian Federation QI WANG • Center for Radiological Research, Columbia University Irving Medical Center, New York, NY, USA; Radiation Oncology, Columbia University Irving Medical Center, New York, NY, USA AMANDA WELIN • Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden RUTH C. WILKINS • Consumer and Clinical Radiation Protection Bureau, Health Canada, Ottawa, ON, Canada INGRID G. WINKLER • Mater Research Institute, The University of Queensland, Woolloongabba, QLD, Australia INBAL WORTZEL • Children’s Cancer and Blood Foundation Laboratories, Departments of Pediatrics, and Cell and Developmental Biology, Drukier Institute for Children’s Health, Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- 18. Part I Spectral Flow Cytometry
- 19. Chapter 1 Development of Spectral Imaging Cytometry Ivan A. Vorobjev, Aigul Kussanova, and Natasha S. Barteneva Abstract Spectral flow cytometry is a new technology that enables measurements of fluorescent spectra and light scattering properties in diverse cellular populations with high precision. Modern instruments allow simul- taneous determination of up to 40+ fluorescent dyes with heavily overlapping emission spectra, discrimina- tion of autofluorescent signals in the stained specimens, and detailed analysis of diverse autofluorescence of different cells—from mammalian to chlorophyll-containing cells like cyanobacteria. In this paper, we review the history, compare modern conventional and spectral flow cytometers, and discuss several applications of spectral flow cytometry. Key words Spectral cytometry, Flow cytometry, Fluorescence spectra, Aurora cytometer, Sony spec- tral analyzer, Autofluorescence, Spectral unmixing, Virtual filtering 1 Introduction Flow cytometry began its development in the middle of the twen- tieth century and has established itself as one of the major func- tional methods widely used by scientists and clinicians. As it developed, flow cytometry in the twenty-first century diverges into the following directions: (1) Conventional flow cytometry and fluorescent activated cell sorting (FACS); (2) Imaging flow cytometry; (3) Spectral flow cytometry (spectral FCM). Conventional cytometry allows studying the size, granularity, and several fluorescent signals of individual cells or particles at the rate of 1000 events per second. Imaging flow cytometry, a hybrid technology, which combines the principles of flow cytometry and microscopy, allows obtaining an image of each cell and thus collects galleries of images along with light scatter and fluorescent signals. However, its throughput is significantly less than conventional flow cytometry [1]. Spectral FCM, which is based on spectroscopy, made it possible to record the full spectrum of every single cell during measurements and now operates at a rate similar to conven- tional flow cytometry. Both imaging flow cytometry and spectral Natasha S. Barteneva and Ivan A. Vorobjev (eds.), Spectral and Imaging Cytometry: Methods and Protocols, Methods in Molecular Biology, vol. 2635, https://doi.org/10.1007/978-1-0716-3020-4_1, © The Author(s) 2023 3
- 20. FCM allow sophisticated offline analysis of the specimens. Recent technical advances in multicolor cytometry were focused on detect- ing and analyzing cellular subpopulations with complex immuno- phenotypes participating in the immune response to diseases and/or vaccine response [2, 3]. Besides, significant progress in the decomposition of complex fluorescent spectra was introduced by Rosetti and co-authors [4], which could improve spectral unmixing and detection of autofluorescence. It will allow better separation of negative, dim, and positive populations using multi- color labeling. 4 Ivan A. Vorobjev et al. 2 Development of Spectral Flow Cytometry Wade and colleagues made one of the first attempts to extract full emission spectra during flow cytometry analysis in 1979 [5]. They used a grating spectrograph and projected the spectrum of the fluorescent signal onto TV type Vidicon detector (objects: Anacyc- tis nidulans—chlorophyll and phycobilin, 600–750 nm; 3T3 fibro- blasts from Balb/c mice, propidium iodide (PI) and fluorescamine staining) [5]. The recorded signal from individual cells has a very low signal-to-noise (S/N) ratio, and reasonable spectra were obtained only by averaging recordings from hundreds of cells (fibroblasts). The next configuration of the spectral flow cytometer was based on a photomultiplier tube (PMT) as a detector and grating monochromator. This cytometer had a 10 nm bandwidth spectral resolution, and signal detection was performed when cells were running through the cuvette at a rate of hundreds of events per second [6]. The system eliminated problems of the noisy back- ground using PMT with adjustable gain and offset as a detector (analyzed objects—fixed rat thymocytes, stained with Hoechst 33258) [6]. However, the spectrum measured was only between 400 and 600 nm, not including far-red and infrared (IR) wavelengths. Buican [7] described in 1990 a “real-time FT spectrometer” that was an interferometer-based spectral detector using PMT with minimal time needed for the recording of the spectrum (only 3.2 μs). However, this instrument was never used as a commercially available cytometer. Subsequently, several more spectral systems were created in an attempt to obtain spectra from short measure- ments using conventional cytometers in 1990–2000. Thus, Gauci and co-authors described configuration with the prism and 512-element intensified photodiode array based on the FACS IV laser flow cytometer. They analyzed spectra obtained from Dictyos- telium discoideum spores stained with Cy3, fluorescein isothiocya- nate, R-phycoerythrin (R-PE), and calibration beads [8]. This system was relatively slow (operating at 62.5 Hz) and not sensitive
- 21. enough to show individual spectra of the labeled cells [8]. Further- more, Asbury et al. (1996) [9] were the first group able to obtain the fluorescent spectrum using a standard flow cytometer (Cytoma- tion, USA) with a monochromator attached in front of PMT used as a detector of fluorescent signal. However, this was not a real spectral FCM yet. The monochromator was operating sequentially—for each wavelength (spectral point), 100 events were recorded. Then monochromator was shifted to the adjacent position, recording another 100 events and so on. The overall spectrum (400–800 nm) was built up from measurements made on 20,000 particles. Spectral Imaging Cytometry 5 At the beginning of the multicolor analysis, the sensitivity of flow cytometers and confocal microscopes in the far-red and IR parts of the spectrum was limited by the low sensitivity of PMTs at wavelengths beyond 650 nm [10]. The use of avalanche photodi- ode detectors (APD) led to substantially better S/N performance over the PMT in the red and near-IR spectral regions. Changing conventional PMTs to APD and APD arrays [11, 12] made it possible to achieve reasonable S/N for multichannel detectors using short-time exposures even in near IR (wavelengths up to 800–900 nm) [13]. An alternative type of detector was used by Isailovic and colleagues [14]. Their instrument (single-cell fluores- cence spectrometer) was based on ICCD (intensified charge couple device) detector and used a 5–20 ms exposure time, thus coming close to the real spectral FCM. Using this instrument, they demon- strated that measurement of individual spectra with a spectral reso- lution of 6.5 nm from fluorescently labeled E. coli expressing GFP and non-fluorescent apo-subunits of R-PE gives more accurate results compared to the measurement of bulk spectra. Since the beginning of the twenty-first century, various systems have achieved sufficient sensitivity for recording a spectrum of fluorescent signals from a single cell in a reasonably short time. The next step in the development of spectral flow cytometers became possible when computer speed accelerated and paralleled recording of multiple signals with high frequency was achieved on a standard PC. Rapid registration of fluorescent spectra was done using parallel data recording and digital processing. These instru- ments were based on multidetector arrays, where emission light is split and projected onto the grid of PMTs or APDs. A flow cyt- ometer equipped with 32-channel Hamamatsu multi-anode PMT able to collect spectral information in not more than 5 μs was built in Purdue University Cytometry Laboratory and later patented by Purdue University [15, 16]. This instrument allowed a digitization rate of up to 75,000 complete a 32-channel spectra per second at 14 bits dynamic range for uniformly (in time) presented events. The system was based on an EPICS Elite cell sorter (Beckman Coulter, USA) equipped with argon (488 nm) and HeNe
- 22. (633 nm) lasers [16]. This system achieved a speed of 3000 random events per second; however, the sensitivity was lower than that of conventional filter-based detectors. 6 Ivan A. Vorobjev et al. A similar system based on a modified BD FACSCalibur cyt- ometer equipped with argon-ion laser and 100 W mercury lamp was built by Goddard and co-authors [17] using a grating spectro- graph and Hamamatsu CCD array with 80% quantum yield. The spectra analyzed by this instrument were in the range of 500–800 nm. This instrument allowed recording spectra with great linearity, making spectral subtraction to remove background signals from labeled specimens such as Rayleigh scattering, Raman light scatter, and even cell autofluorescence feasible. Also, the sensitivity of the instrument was significantly lower (10–30 times) than that of the conventional cytometer [17]. Alternative spectral cytometry systems used a charge-coupled device (CCD) camera as a detector to measure spectra from single cells and beads [17–19]. In 2012 Nolan’s group [20] developed spectral FCM instruments and data analysis algorithms suitable for everyday use. Their two systems were based on FACSCanto equipped with 405 and 488 nm lasers and using EM-CCD (elec- tron-multiplying CCD) detector (11.3 nm resolution in the 500–800 nm range) and Coulter Elite cytometer using 785 nm laser for IR emission (at 3.23 nm resolution in 790–930 nm range). Their spectral flow cytometers used a holographic grating and EM-CCD detector for high-speed spectra detection. Customized software was developed for the spectral unmixing and production of spectra-derived parameters for individual cells. Instrument calibration and data analysis were very complicated at these early stages of spectral FCM development (circa 2012) [21]. Instrument design was not standardized, requiring thorough spectral calibration for each instrument. Also, different instruments used different data formats, making cross-platform spectral analysis tricky. In the first spectral cytometers, spectral unmixing was per- formed through the least square unmixing algorithm or indirectly through principal component analysis [22]. Overall comparing the spectral data obtained by different instruments was practically impossible. So far, at that time, the advantages of spectral FCM over conventional multichannel flow cytometry were impossible to use in many applications. The next step was done when commer- cially available spectral cytometers with standardized parameters appeared. The system patented by Purdue University was licensed by Sony Inc., which is producing the first-generation commercial spectral cytometry system (sometimes named hyperspectral cytometer)—the Sony SP6800 Spectral Analyzer was announced at the end of 2012 and came to the market in 2014. Also, in 2014 Cytek Biosciences (USA) developed and soon released its Aurora
- 23. spectral flow cytometer. Nowadays, two companies are concerned with the production of commercial models of spectral cytometers: (1) Sony Biotechnology (spectral cell analyzers SA 3800, SP 6800, ID 7000); (2) Cytek Biosciences (Cytek Aurora and Northern Lights instruments). In summary, recent advances in hardware, detectors, and computer analysis algorithms resulted in commer- cially available spectral FCM instrumentation. Spectral Imaging Cytometry 7 3 Current Spectral Cytometry Instruments Modern Sony ID7000 instrument supports up to 7 lasers and can use up to 168 detectors (in 7 laser configuration) covering the spectral range from 360 to 920 nm with ~10 nm resolution. Specialized InGaAs PMTs are used for efficient capturing of the IR signals. Aurora Cytek spectral cytometer measures fluorescence in up to 64 fluorescent channels (in the 5-lasers instrument— 16UV + 16 V + 14B + 10YG + 8R) across the APD detector arrays (Fig. 1). Each channel uses a special bandpass filter with about 10–15 nm bandwidth, reflecting all wavelengths outside of its transmission band. The full spectral range is 400–900 nm. In both types of instruments, lasers excite the specimen sequentially. Fig. 1 Three laser Aurora Cytek instrument—optical setup. Fluorescence signal is delivered to the sets of detectors (V for violet excitation, B for blue excitation, and R for red excitation). Notice that SSC signal is measured for each laser, and the number of APD detectors is different. Laser beams are spatially separated at the conventional cytometer. Picture was modified from figures given at Aurora Cytek website (https://cytekbio. com>pages>aurora-cs)
- 24. 8 Ivan A. Vorobjev et al. 4 Advances and Limitations of Spectral Flow Cytometry A critical review of the latest advances and remaining problems in spectral FCM was published recently [23]. The essential aspects of spectral FCM are that instrument performance in the case of Cytek Aurora strongly depends on the characteristics of each filter (total— of 64 filters). For example, a thorough check uncovered two out- of-specification filters in the commercial instrument that precluded efficient separation of eFluor450 from BV421 and SB436 [23]. Other issues dealt with laser delay and titration of antibodies. In the case of spectral FCM, titration of antibodies is more compli- cated because of living and dead cells in the same tube. Authors suggest using live and dead cell markers along with a standard set of CD markers, making titration a multistep process. This process can be described as inversed to FMO (fluorescence minus one) controls used in conventional multicolor cytometry. The sequence of sug- gested tests for titration is the following: viability dye, major mar- kers like CD45, lineage-specific markers (CD3, CD19, etc.), and finely more specific markers to identify small subpopulations of blood cells [23]. 5 Development of Spectral Unmixing Algorithms The significant advantage of spectral measurements against conven- tional flow cytometry is its ability to make a detailed comparison of fluorescent spectra from individual cells (objects) in a heteroge- neous population. Multiparametric cytometry often has bleed- through problems due to the overlapping spectra of fluorophores. To identify and characterize complex interactions of multiple cell types, it is necessary to analyze a significant number of fluorescent labels simultaneously. Fluorescence signals were initially analyzed as a linear combination of reference spectra with algorithms extracting the weight of individual spectra (linear unmixing) [24]. Identifica- tion of heavily overlapping spectra can be performed to a limited extent using the spectral compensation procedure, and instead, spectral unmixing was introduced. Spectral unmixing refers to a group of techniques that attempt to determine how much each fluorophore contributes to the observed emission spectrum. It was initially suggested for microscopy [25] and later applied in flow cytometry [21, 26]. Spectral unmixing in cytometry allows analysis of the simultaneous labeling of cells with several fluorophores and/or fluorescent proteins. Spectral unmixing methods have been developed extensively for the remote sensing analysis of hyperspectral data [27, 28]; however, some key differences make many unmixing algorithms unsuitable for spectral cytometry: (a) the number of fluorophores used for cellular staining is known
- 25. a priori, though the number of autofluorescent signals can be unknown; (b) remote sensing spectral analysis is focused on blind unmixing of source signals while in spectral cytometry it is possible to use reference spectra to define emission spectral endmembers. Spectral Imaging Cytometry 9 6 Spectral Unmixing Problems The fluorophores originated from algal photosynthetic apparatus such as PE, APC, and PERCP have broad and overlapping spectra, and to some degree, can be excited by violet laser (405 nm excita- tion) [29]. Synthetic dyes such as Alexa Fluor and Cyan families are small organic fluorophores that do not exhibit much crossbeam excitation. Most spectral unmixing algorithms cannot separate a signal from background noise or autofluorescence. Autofluores- cence is a common, undesired signal arising from endogenous fluorophores contained in the cells or extracellular matrix (i.e., NAD(P)H, flavine adenine nucleotide (FAD), lipids, collagen, elas- tin, and other common fibrous proteins, porphyrins) [30] often with wide emission spectra [31]. One of the major endogenous fluorophores inside cells is a mitochondrial NADH (Exc./Em. 350/460 nm) [32], declining with cellular injury. Cellular samples may contain different types of autofluorescent molecules, and it is challenging to predict their distribution since they can change in time (the cell is dying or becoming apoptotic). Spectral unmixing for subtracting autofluorescence is possible using the non-negative matrix factorization variant of spectral unmixing, which exploits spectra obtained at the different excitation wavelengths [4, 33]. 7 Comparison of Spectral Unmixing and Spectral Compensation Despite extensive development in cytometry, the compensation stays based upon the classical algorithms, using the single controls approach developed by Bagwell and Adams [34], with some recent developments [35, 36]. Two methods of separating fluorophore signals in multicolor cytometry were recently compared by Niewold and colleagues [35]. One of the major limitations of spectral com- pensation is the increased spread of compensated signals compared to the original ones that diminish the ratio between mean/median values of positive and negative populations [37]. Particularly it precludes discrimination between negative and dim populations. For some highly overlapping fluorophores, spectral unmixing algorithms made it possible to resolve the two fluorescence signals where spectral compensation did not. Unmixing in spectral cyt- ometers gives less spreading, which is important when using numerous (panels >16) fluorophores [35]. However, if the cyt- ometer uses optical filters (Aurora spectral analyzer, Cytek, USA),
- 26. the quality of these filters plays a crucial role in the spreading when unmixing similar spectra [3]. The commercially available filters might slightly deviate from the characteristics provided by the supplier and, thus, sometimes, do not adequately exclude the fluo- rescent emission of other fluorophores and/or autofluorescent molecules that overlap with the desired signal. In commercial spec- tral cytometers from SONY, instead of the optical filters, specialized prism-based optics are used to measure and separate emissions from different fluorophores [38]. 10 Ivan A. Vorobjev et al. Another advantage of spectral unmixing in spectral cytometry is better extracting of autofluorescence signal that could be treated as an additional fluorophore [36], while compensation cannot be applied to autofluorescence until its spectrum is recorded. 8 Comparison of Spectral Cytometry and Mass Cytometry Flow cytometry allows analysis of up to 25–40 parameters at a rate of several thousands of events per second. On the other hand, mass cytometry, currently a competitor to spectral FCM, allows typing of various immune cells on panels from 14 to 42 parameters with minimal overlap between channels and without autofluorescence [39–42]. Despite these benefits, broader practical applications of mass cytometry are affected by limitations such as slow collection rates (300–500 events/s vs. several thousand events/s. with con- ventional cytometry) and total cost of experimentation/ ownership [43]. 9 Differences and Similarities Between Spectral and Conventional Flow Cytometry The common feature of spectral and conventional cytometry is the observation of a single cell. The full spectrum of a single event can be detected under the action of hydrodynamic focusing, where the cell passes an interrogation point and is excited by a collinear or non-collinear laser system. Subsequently, the detection of the emis- sion signal for these two systems is fundamentally different. Spec- trum detection became possible because of a unique emitting optical system. This system uses prisms and gratings to disperse fluorescence light, while a conventional cytometer splits fluorescent signal using bandpass, short pass, and long pass filters (Fig. 2). Prisms as dispersive optics in spectral FCM propagate light in a non-linear manner, unlike gratings that propagate light into a detector in a linear manner. Moreover, spectral cytometry to detect the full spectrum uses an array of detectors such as CCDs and multianode PMTs, while in most conventional configurations, sep- arate PMT is utilized in each forward scatter (FSC), side scatter (SSC), and fluorescence channel.
- 27. Spectral Imaging Cytometry 11 Fig. 2 The differences and similarities between spectral and conventional cytometry. Conventional cytometry: optical part – dichroic mirrors and bandpass filters. Light collection – reflection, transmission, blocking. Detectors – photo- multipliers (PMT). Spectral flow cytometry: optical part – grating or prisms. Light collection - dispersion. Detectors – multianode PMTs or CCD Further development of the real-time spectral FCM allowing measurement of emission spectra in the flow cell with the fre- quency typical to that of the standard flow cytometer (about 10,000 events per second) as well as the use of the spectral detectors in fluorescent microscopy was stimulated by the
- 28. development of numerous fluorescent proteins with similar spectra [44]. Emission spectra of these proteins overlap significantly and thus cannot be distinguished by conventional fluorescent micros- copy or FCM using dichroic mirrors and even highly selective bandpass filters [38]. 12 Ivan A. Vorobjev et al. This principle of spectral FCM operation is used with commer- cial spectral cytometry companies but with some differences in optical layout. The Sony spectral analyzer separates the emitted light with a set of prisms before sending it to 32-channel PMT arrays. To capture the fluorescence spectrum, the Cytek Aurora system employs multiple APDs with a unique set of filters in front of each APD. The possibility of obtaining a full emission spectrum with commercial spectral analyzers allowed new combinations of fluorochromes, which due to the significant spectra overlap, are not used together in conventional cytometry. Moreover, spectral FCM allows using more fluorochromes per experiment. To address the existing gap in commercially available fluorochromes, new dyes are necessary, and this need started to be addressed [45]. Another advantage of Spectral FCM is extracting the autofluorescence (AF) of cells and using it as a separate parameter(s) [46], allowing better signal resolution and even a comparison of different auto- fluorescent parameters [47]. 10 Applications of Spectral FCM Major problems of conventional flow cytometry can be solved using the spectral FCM: (1) enhanced number of fluorescent para- meters used in a single tube (hematology, minimal residual disease (MRD)); (2) subtraction of fluorescent signal with the improve- ment of S/N ratio and detailed analysis of autofluorescence signal for analysis of unlabeled cells. The enhanced number of fluorescent channels is critical for analyzing small biopsies such as bone marrow aspirates in MRD. Subtraction of autofluorescence is particularly helpful for the analysis of cells with a high level of autofluorescence, such as myocytes, macrophages, brain cells, and hepatocytes. Pri- mary cells are heterogeneous, and each subpopulation may require assigning its autofluorescence as a separate fluorophore and performing additional spectral unmixing [3]. 11 Current Applications: Multi-parametric Spectral Cytometry Nevertheless, certain studies were already made at the early stage of spectral cytometry. In 2015 Futamura and co-workers [38] described an analysis of lymphocyte migration from the individual lymph node (within 24 h) and using photoconvertible protein, and
- 29. 11-color labeling showed that CD69 low naive T cell subset was replaced in lymph node faster than CD69 high memory T-cell subsets [36]. Schmutz and co-authors (2016) [48], using a two-laser Sony SP6800 instrument (405 and 488 nm), demon- strated by detailed fluorescence-minus-one control (FMO) that while the staining index (SI) for individual dyes in spectral FCM was the same as in conventional FCM, spectral FCM gives much better discrimination of dyes with similar fluorescent properties. Spectral FCM allowed discrimination of dyes with the same peak fluorescence intensity when the overall spectra were different and dyes with similar spectra but shifted for 10–20 nm peaks using Kaluza software (YFP versus GFP; both proteins versus FITC) [48]. Spectral Imaging Cytometry 13 Besides, spectral FCM allowed discrimination of lymphocytes among the cells isolated from the tissues with high autofluores- cence. Complete elimination of autofluorescent signal makes it possible to discriminate dye-positive and dye-negative cells using dyes with emission spectra close to the autofluorescent spectra for further analysis [48]. The Sony SP6800 Spectral Cell Analyzer instrument utilized a 32 multianode PMT (Hamamatsu), and spectrum separation is achieved through a complex prism-based monochromator. SONY Inc. demonstrated a prototype instrument and reported on hyperspectral technology during the ISAC Congress in Seat- tle in 2012 and announced the launch of the new hyperspectral flow cytometer product—an SP6800 Spectral Cell Analyzer— in 2012. In some applications, the multiplexing by spectral tags may not require spectral unmixing. In this setting, it may be beneficial to classify the spectra directly instead of classification based on unmixed intensities. Many techniques may be utilized here, includ- ing unsupervised data reduction (using, for example, principal component analysis, independent component analysis, or factor analysis) or supervised techniques (such as neural networks or support vector machines). Advantages of spectral cytometry such as a large number of studied parameters in one panel with better resolution due to the removal of the autofluorescence signal and a rate of several thou- sand events per second (Sony SP6800 10,000–20,000 events/ second, Cytek Aurora 35,000 events/s), have led to an increase in the practical use of spectral cytometers in immunophenotyping. One of the first multicolor panels (nine colors) was created by Futamura and co-authors [38] at the presentation of the Spectral Analyzer SP 6800 to study the movement of KikGr protein after photoconversion in the inguinal lymph node cells. The remaining immune cells, after photoconversion, changed their emission from green to red (KikGrGreen-KikGrRed) while migrated cells stayed
- 30. green. In this experiment, the emission spectra of fluorochromes and fluorescent proteins, which strongly overlapped with each other, were separated using spectral unmixing (EGFP/FITC/ KikGr-Green, KikGr-Red/PE, KikGr-Green/Venus, EGGP/ Venus, KikGr-Red/mKO2) [36]. It would be difficult to apply this panel in conventional flow cytometry, and with the spectral analyzer, it became possible to separate and eliminate the low and high levels of autofluorescence that were found in the mouse splenocytes with strong expression of F4/80 marker (major mac- rophage biomarker, APC labeled) [38]. Solomon and co-authors [49] used a 15-fluorochrome panel and spectral FCM to describe the aging of the bone marrow in mice. 14 Ivan A. Vorobjev et al. The separation of lasers at the Cytek spectral flow cytometer allowed the creation of 30–40 multicolor cytometric panels. The 40-color panel OMIP-069 with Aurora for identifying T cells, B cells, NKT—like cells, monocytes, and dendritic cells was reported recently [50]. This panel is effective in the study of the immune response with low sample volume [50]. In this panel, with spectral cytometry, it became possible to use dyes that have a strong overlap of the emission signal between them (PE/FITC, PE-Alexa Fluor 700/PerCP-eFluor 710, BUV 496/eFluor 450, SuperBright 436). Using data acquired by a 3-laser 38-color Aurora (Cytek, USA) spectral cytometer and analyzed by Kaluza and FlowJo soft- ware, Chen and co-authors [51] demonstrated that SFC allows distinguishing subsets of myeloid cells when using one tube with 24-color staining more precise compared to the standard 3*8-color panel. By automated clustering, malignant cells from patients with minimal residual disease (MRD) were distinguished from rare nor- mal mast cells and basophils. In the early study, Murphy and col- leagues [52] conducted a similar study for typing human peripheral blood mononuclear cells (PBMCs), but separate panels have been developed for the determination of T cells (23 colors) and B cells (22 colors). Schmutz and co-authors [48] described a 19 colors panel for the separation of murine splenocytes into B-, T-, NK-, and dendritic cells. A new generation of SONY spectral cytometers—ID7000 also has a combination of separate lasers (for sequential excitation). It allows the use of multicolor panels, such as a 28-colors panel for immune-profiling of COVID-19 patients [53]. Two highly auto- fluorescent fetal liver stromal subsets were clearly discriminated using spectral unmixing with autofluorescence assigned as an inde- pendent parameter [47]. The use of other multicolor panels for immunophenotyping with a spectral cytometer is summarized in Table 1.
- 31. (continued) Spectral Imaging Cytometry 15 Table 1 Multicolor immunophenotyping panels examined by spectral FCM-type of analysis Colors number Instrument Cells types References 40 Aurora Human PBMCs—CD4 T cells, CD8 T cells, regulatory T cells, γδ T cells, NKT-like cells, B cells, NK cells, monocytes, dendritic cells Park et al. 2020 [54] 22–23 Aurora Human PBMCs—T and B cells Murphy et al., 2019 [52] 11 Sony SP 6800 KikGR expressing mice—T cells Futamura et al., 2015 [38] Up to 9 Sony SP 6800 Measure of CD71 expression in pDC, CD 103+ CD11b- DC, CD103- CD11b+ DC, AM, GR, BC, TC, NK, etc. in lung, liver, small intestine, Peyer’s patches, mesenteric lymph nodes, spleen, thymus, bone marrow, blood from mouse Lippitsch et al., 2017 [55] 12 Sony SP 6800 Mouse bladder cells—CD45, NK cells, neutrophils, macrophages, eosinophils Rousseau et al., 2016 [56] 14 Aurora Human PBMC—CD14, CD169 monocytes Affandi et al., 2020 [57] Up to 7 Aurora Urokinase-type plasminogen activator receptor-targeted CART T cells Amor et al., 2020 [58] 19 Sony SP 6800 Murine splenocytes—B, T, NK, dendritic, myeloid spleen cells Schmutz et al., 2016 [48] 12 Aurora using SpectroFlo 2.2 CD8+ T-cell and B-cell Turner et al., 2020 [59] 14 Aurora using SpectroFlo Mouse hematopoietic stem and progenitor compartments Solomon et al., 2020 [49] 14 Aurora Leukocytes, neutrophils, eosinophils, NK cells, NKT cells, CD4+ T-cells, CD8α+ T cells, PDCs, B cells, cDC, microglia, Ly6Chigh, and Ly6Clow infiltrating monocytes Niewold et al., 2020 [35] 12 Aurora Composition and activation of circulating leukocytes in COVID- 19 and influenza PBMCs (peripheral blood mononuclear cells (PBMCs)) Mudd et al., 2020 [60]
- 32. Table 1 (continued) Colors number Instrument Cells types References 18 Aurora T-cells subsets, the R-based pipeline using fluorescence minus one (FMO) controls Fox et al., 2020 [61] 22 Aurora (3 lasers) Splenocytes—B cells, CD4 and CD8 T-cells, neutrophils, NK cells, DCs, and monocytes. Comparison with mass cytometry Ferrer-Font et al., 2020 [62] 9 Aurora Placental mesenchymal stem/ stromal cells Boss et al., 2020 [63] 24 Aurora (3 lasers) Subsets of myeloid cells for MRD analysis Chen et al., 2020 [51] Up to 10 Aurora (5 lasers) Detection of murine gamma herpes virus 68 cells Riggs et al., 2021 [46] 3 channel FRET detection Aurora (4 lasers) Spectral unmixing for improved FRET detection Henderson et al., 2021 [64] 23 Aurora 23 colors for placental mesenchymal cells analysis Boss et al., 2021 [65] 37 Aurora 45 different subpopulations, PBMC from SARS-CoV-2 infected patients Fernandez et al., 2022 [66] Up to 11 in one panel ID7000 Senolytic vaccination to eliminate senescent cell in mice Suda et al., 2021 [67] Autofluorescence parameters ID7000 Autofluorescence spectra analysis Peixoto et al., 2021 [47] 9 colors for genes initially identified by RNA-sequencing SP6800/ ID7000 CLEC12A, CD1a, CD86, CCL18, CCL17, CCL22, CD115, CD88 and CD85d Costa et al., 2022 [68] 5 fluorescent proteins SP6800 (SONY) Bacterial phytochromes with far-red and near-infrared emission [69] Autofluorescence multiple ID7000 autofluorescence multiple murine lung cells populations [70] 12 Two Major Types of Spectral FCM Analysis: Virtual Filtering and Spectral Unmixing Spectral unmixing is the most used and considered to be the most powerful approach, but it requires a thorough recording of auto- fluorescent controls from heterogeneous cellular populations. Sophisticated spectral unmixing with commercially available soft- ware allows robust separation from 4+ to 20+ fluorochromes. Another less powerful but more universal approach is virtual filter- ing. It was initially demonstrated in the phytoplankton study
- 33. [71, 72]. Spectral cytometry allowed effective selection of “filtering off” autofluorescent part of spectra, which may overlap with fluo- rescent signals in the multiparametric analysis of multiple taxa of algae [71]. It mimics the interchange of hardware filters in the PMT channels in a standard flow cytometer. In conventional cytometry, changing optical filters means manipulation with hardware, and some optical bandpass filters may not be available on the market. With SFC, we can make a large selection of virtual filters after the sample is recorded [72]. Spectral Imaging Cytometry 17 The use of multiple fluorescent conjugates and dyes/pigments significantly affected cytometric analysis facilitating multivariate analysis, dimensionality reduction algorithms based on stochastic neighbor embedding (SNE), unsupervised cluster analysis, and cell-subset identification programs such as SPADE, CITRUS, FlowSOM, CellCNN, and viSNE [73–77]. An alternative to clus- tering algorithms is principal component analysis (PCA), which is widely used in other areas of biology. Recently, Ogishi and co-authors [78] introduced iMUBAC (integration of multi-batch cytometry datasets) using unsupervised cell-type identification across multiple batches. 13 Conclusions Currently, the spectral cytometer becomes a superior alternative to the conventional cytometer since it allows the acquisition of fluo- rescent dyes and proteins without the limitations of hardware optics and detectors. It leads to reducing the complexity of multi-color panel design and allows easy acquisition of more than 20 colors with good discrimination of bright, dim, and negative cellular subpopulations. The latest multi-laser (up to seven lasers) commer- cially available spectral cytometer ID7000 (SONY) allows the detection and analysis of up to 40 fluorescent parameters. Spectral FCM or full-spectrum cytometry can subtract autofluorescence from signals generated by dyes without increasing spread, besides, it allows acquire autofluorescence as separate parameter(s). Spectral FSM allows detailed analysis of the autofluorescence that might be especially useful for analyzing phytoplankton where a strong auto- fluorescent signal from chlorophyll precludes using fluorescently labeled dyes/antibodies and for highly autofluorescent cells (macrophages, myeloid progenitors, infected cells, etc.). Available libraries of emission spectra of the numerous standard fluorophores make single-stained controls unnecessary. The limitations of the spectral deconvolution approach in Spectral FCM are related to the use of tandem dyes or the inability to use ratiometric probes. The new generation of multi-laser Spectral FSM instruments initi- ates a breakthrough in cytometric analysis and the replacement of conventional cytometers. Full-spectrum cell sorters and co-registering spectra with images of cells can be foreseen in the near future.
- 34. 18 Ivan A. Vorobjev et al. Acknowledgments Work was supported by the Ministry of Health of the Republic of Kazakhstan under the program-targeted funding of the Ageing and Healthy Lifespan research program (IRN: 51760/Ф-М Р-19) and AP08857554 (Ministry of Education and Science, Kazakhstan) to IAV. NSB was funded by CRP 16482715 and SSH2020028 grants from Nazarbayev University, and AP14872088 MES grant (Kazakhstan). References 1. Imaging flow cytometry: methods and proto- cols (2016) Barteneva NS, Vorobjev IA (eds) Methods in molecular biology, vol 1389. Humana, New York, 295pp 2. Wang W, Su B, Pang L et al (2020) High- dimensional immune profiling by mass cytome- try revealed immunosuppression and dysfunc- tion of immunity in COVID-19 patients. Cell Mol Immunol 17:650–652. https:/ /doi.org/ 10.1038/s41423-020-0447-2 3. Maucourant C, Filipovic I, Ponzetta A, Aleman S, Cornilett M et al (2020) Natural killer cell immunophenotypes related to COVID-19 disease severity. Sci Immunol 5: eabd6832. https://doi.org/10.1126/ sciimmunol.abd68 4. Rossetti BJ, Wilbert SA, Welch JLM, Borisy GG, Nagy JG (2020) Semi-blind sparse affine spectral unmixing of autofluorescence- contaminated micrographs. Bioinformatics 36:910–917. https:/ /doi.org/10.1093/bioin formatics/btz674 5. Wade CG, Rhyne RH, Woodruff WH et al (1979) Spectra of cells in flow cytometry using a vidicon detector. J Histochem Cyto- chem 27:1049–1052. https:/ /doi.org/10. 1177/27.6.110874 6. Steen HB, Stokke T (1986) Fluorescence spec- tra of cells stained with a DNA-specific dye, measured by flow cytometry. Cytometry 7: 104–106. https:/ /doi.org/10.1002/cyto. 990070117 7. Buican, TN (1990) Real-time Fourier trans- form spectrometry for fluorescence imaging and flow cytometry. Proceedings of the SPIE, bioimaging and two-dimensional spectroscopy 1205:126–133. https:/ /doi.org/10.1117/ 12.17787 8. Gauci MR, Vesey G, Narai J et al (1996) Obser- vation of single-cell fluorescence spectra in laser flow cytometry. Cytometry 25:388–393. https:/ /doi.org/10.1002/(sici)1097-0320 (19961201)25:4<388::aid-cyto11>3.0. co;2-r 9. Asbury CL, Esposito R, Farmer C, van den Engh G (1996) Fluorescence spectra of DNA dyes measured in a flow cytometer. Cytometry 24:234–242. https:/ /doi.org/10.1002/(sici) 1097-0320(19960701)24:3<234::aid- cyto6>3.0.co;2-h 10. Lawrence WG, Varadi G, Entine G et al (2008) Enhanced red and near infrared detection in flow cytometry using avalanche photodiodes. Cytometry A 73A:767–776. https:/ /doi.org/ 10.1002/cyto.a.20595 11. Zhao S, Wu X, Chen Y, et al (2011) High gain avalanche photodiode (APD) arrays in flow cytometer opitical system. 2011 international conference on multimedia technology, pp 2151–2153. https:/ /doi.org/10.1109/icmt. 2011.6002457 12. Yamamoto M (2017) Photon detection: cur- rent status. In: Single cell analysis. Springer, Singapore, pp 227–242. https:/ /doi.org/10. 1007/978-981-10-4499-1_10 13. Nolan JP, Condello D, Duggan E, Naivar M, Novo D. (2013) Visible and near infrared fluo- rescence spectral cytometry. Cytometry Part A; 83A:253–264. https:/ /doi.org/10.1002/ cyto.a.22241 14. Isailovic D, Li H-W, Phillips GJ, Yeung ES (2005) High-throughput single-cell fluores- cence spectroscopy. Appl Spectrosc1 59:221– 2 2 6 . h t t p s : // d o i . o r g / 1 0 . 1 3 6 6 / 0003702053085124
- 35. 15. Spectral Imaging Cytometry 19 Robinson JP, Rajwa B, Gregori G et al (2005) Multispectral cytometry of single bio-particles using a 32-channel detector. Proc SPIE 5692: 359–365. https://doi.org/10.1117/12. 591365 16. Gregori G, Patsekin V, Rajwa B, Jones J, Ragheb K, Holdman C, Robinson JP (2012) Hyperspectral cytometry at the single-cell level using a 32-channel photodetector. Cytometry A 81A:35–44. https:/ /doi.org/10.1002/cyto. a.21120 17. Goddard G, Martin JC, Naivar M et al (2006) Single particle high resolution spectral analysis flow cytometry. Cytometry A 69A:842–851. https:/ /doi.org/10.1002/cyto.a.20320 18. Watson DA, Brown LO, Gaskill DF et al (2008) A flow cytometer for the measurement of Raman spectra. Cytometry A 73A:119–128. https:/ /doi.org/10.1002/cyto.a.20520 19. Nolan JP, Sebba DS (2011) Surface-enhanced Raman scattering (SERS) cytometry. Methods Cell Biol:515–532. https:/ /doi.org/10.1016/ b978-0-12-374912-3.00020-1 20. Nolan JP, Condello D, Duggan E et al (2012) Visible and near infrared fluorescence spectral flow cytometry. Cytometry A 83A:253–264. https:/ /doi.org/10.1002/cyto.a.22241 21. Nolan JP, Condello D (2013) Spectral flow cytometry. Curr Protoc Cytom 63: 1.27.1–1.27.13. https:/ /doi.org/10.1002/ 0471142956.cy0127s63 22. Sanders CK, Mourant JR (2013) Advantages of full spectrum flow cytometry. J Biomed Opt 18:037004. https:/ /doi.org/10.1117/1.jbo. 18.3.037004 23. McCausland M, Lin Y-D, Nevers T et al (2021) With great power comes great responsibility: high-dimensional spectral flow cytometry to support clinical trials. Bioanalysis 13:1597– 1616. https://doi.org/10.4155/bio- 2021-0201 24. de Juan A, Tauler R (2021) Multivariate curve resolution: 50 years addressing the mixture analysis problem – a review. Anal Chim Acta 1145:59–78. https:/ /doi.org/10.1016/j.aca. 2020.10.051 25. Zimmermann T, Rietdorf J, Pepperkok R (2003) Spectral imaging and its applications in live cell microscopy. FEBS Lett 546:87–92. https:/ /doi.org/10.1016/S0014-5793(03) 00521-0 26. Garini Y, Young IT, McNamara G (2006) Spectral imaging: principles and applications. Cytometry A 69A:735–747. https:/ /doi.org/ 10.1002/cyto.a.20311 27. Benachir D, Deville Y, Hosseini S, Karoui MS (2020) Blind unmixing of hyperspectral remote sensing data: a new geometrical method based on a two-source sparsity con- straint. Remote Sens 12:3198. https:/ /doi. org/10.3390/rs12193198 28. Wei J, Wang X (2020) An overview on linear unmixing of hyperspectral data. Math Probl Eng 2020:1–12. https:/ /doi.org/10.1155/ 2020/3735403 29. Kronick MN (1986) The use of phycobilipro- teins as fluorescent labels in immunoassay. J Immunol Methods 92:1–13. https:/ /doi.org/ 10.1016/0022-1759(86)90496-5 30. Monici M (2005) Cell and tissue autofluores- cence research and diagnostic applications. Biotechnol Annu Rev:227–256. https:/ /doi. org/10.1016/s1387-2656(05)11007-2 31. Croce AC, Bottiroli G (2014) Autofluores- cence spectroscopy and imaging: a tool for bio- medical research and diagnosis. Eur J Histochem 58:2461. https:/ /doi.org/10. 4081/ejh.2014.2461 32. Chance B (2004) Mitochondrial NADH redox state, monitoring discovery and deployment in tissue. Methods Enzymol:361–370. https:/ / doi.org/10.1016/s0076-6879(04)85020-1 33. Rakotomanga P, Soussen C, Khairallah G, Amoroux M, Zaytsev S et al (2019) Source separation approach for the analysis of spatially resolved multiply excited autofluorescence spectra during optical clearing of ex vivo skin. Opt Express 10:3410–1424. https:/ /doi.org/ 10.1364/BOE.10.003410 34. Bagwell CB, Adams EG (1993) Fluorescence spectral overlap compensation for any number of flow cytometry parameters. Ann N Y Acad Sci 677:167–184. https:/ /doi.org/10.1111/j. 1749-6632.1993.tb38775.x 35. Niewold P, Ashhurst TM, Smith AL, King NJ (2020) Evaluating spectral cytometry for immune profiling in viral disease. Cytometry A 97:1165–1179. https:/ /doi.org/10.1002/ cyto.a.24211 36. Roca CP, Burton OT, Gergelits V et al (2021) AutoSpill is a principled framework that simpli- fies the analysis of multichromatic flow cytome- try data. Nat Commun 12:1–16. https:/ /doi. org/10.1038/s41467-021-23126-8 37. Nguyen R, Perfetto S, Mahnke YD et al (2013) Quantifying spillover spreading for comparing instrument performance and aiding in multi- color panel design. Cytometry A 83A:306–315. https:/ /doi.org/10.1002/ cyto.a.22251
- 36. 20 Ivan A. Vorobjev et al. 38. Futamura K, Sekino M, Hata A et al (2015) Novel full-spectral flow cytometry with multi- ple spectrally-adjacent fluorescent proteins and fluorochromes and visualization of in vivo cel- lular movement. Cytometry A 87:830–842. https:/ /doi.org/10.1002/cyto.a.22725 39. Baumgart S, Peddinghaus A, Schulte-Wrede U et al (2016) OMIP-034: comprehensive immune phenotyping of human peripheral leu- kocytes by mass cytometry for monitoring immunomodulatory therapies. Cytometry A 91:34–38. https:/ /doi.org/10.1002/cyto.a. 22894 40. Jaracz-Ros A, Hémon P, Krzysiek R et al (2018) OMIP-048 MC: quantification of cal- cium sensors and channels expression in lym- phocyte subsets by mass cytometry. Cytometry A 93:681–684. https:/ /doi.org/10.1002/ cyto.a.23504 41. Brodie TM, Tosevski V, Medová M (2018) OMIP-045: characterizing human head and neck tumors and cancer cell lines with mass cytometry. Cytometry A 93:406–410. https:/ /doi.org/10.1002/cyto.a.23336 42. Dusoswa SA, Verhoeff J, Garcia-Vallejo JJ (2019) OMIP-054: broad immune phenotyp- ing of innate and adaptive leukocytes in the brain, spleen, and bone marrow of an orthoto- pic murine glioblastoma model by mass cyto- metry. Cytometry A 95:422–426. https:/ /doi. org/10.1002/cyto.a.23725 43. Iyer A, Hamers AAJ, Pillai AB (2022) CyTOF® for the masses. Front Immunol 13: 815828. https:/ /doi.org/10.3389/fimmu. 2022.815828 44. Chudakov DM, Matz MV, Lukyanov S, Lukya- nov KA (2010) Fluorescent proteins and their applications in imaging living cells and tissues. Physiol Rev 90:1103–1163. https:/ /doi.org/ 10.1152/physrev.00038.2009 45. Seong Y, Nguyen DX, Wu Y, Thakur A, Harding F, Nguyen TA (2022) Novel PE and APC tandems: additional near-infrared fluoro- chromes for use in spectral flow cytometry. Cytometry A:1–11. Epub 2 Feb 2022. https:/ /doi.org/10.1002/cyto.a.24537 46. Riggs JR, Medina EM, Perrenoud LJ, Bonilla DL, Clambey ET, van Dyk LF, Berg LJ (2021) Optimized detection of acute MHV68 infec- tion with a reporter system identifies large peri- toneal macrophages as a dominant target of primary infection. Front Microbiol 12: 656979. https:/ /doi.org/10.3389/fmicb. 2021.656979 47. Peixoto MM, Soares-da-Silva F, Schmutz S, Mailhe M-P, Novault S, Cumano A, Ait-Mansour C (2022) Identification of fetal liver stromal subsets in spectral cytometry using the parameter autofluorescence. Cyto- metry A. Epub 2 May 2022. https:/ /doi.org/ 10.1002/cyto.a.24567 48. Schmutz S, Valente M, Cumano A, Novault S (2016) Spectral cytometry has unique proper- ties allowing multicolor analysis of cell suspen- sions isolated from solid tissues. PLoS One 11: e0159961. https:/ /doi.org/10.1371/journal. pone.0159961 49. Solomon M, DeLay M, Reynaud D (2020) Phenotypic analysis of the mouse hematopoie- tic hierarchy using spectral cytometry: from stem cell subsets to early progenitor compart- ments. Cytometry A 97:1057–1065. https:/ / doi.org/10.1002/cyto.a.24041 50. Park LM, Lannigan J, Jaimes MC (2020) Omip-069: forty-color full spectrum flow cyto- metry panel for deep immunophenotyping of major cell subsets in human peripheral blood. Cytometry A 97:1044–1051. https:/ /doi.org/ 10.1002/cyto.a.24213 51. Chen M, Wang H, Fu M et al (2020) One tube 24 color full spectral flow cytometry and multi- dimensional software to study the maturation pattern and antigen expression of the myeloid. Blood 136:13–14. https:/ /doi.org/10.1182/ blood-2020-140600 52. Murphy KA, Bhamidipati K, Rubin SJS et al (2019) Immunomodulatory receptors are dif- ferentially expressed in B and T cell subsets relevant to autoimmune disease. Clin Immunol 209:108276. https:/ /doi.org/10.1016/j. clim.2019.108276 53. Chen MC, Lai KS, Chien KL, Teng ST, Lyn YR et al (2021) Impact of placenta-derived mesen- chymal stem cells treatment on patients with severe lung injury caused by COVID-19 pneu- monia: clinical and immunological aspect. Res Square. Preprint. https:/ /doi.org/10.21203/ rs.3.rs-1013382/v1 54. Park LM, Lannigan J, Jaimes MC (2020) Forty-color full spectrum flow cytometry panel for deep immunophenotyping of major cell subsets in human peripheral blood. Cyto- metry A 97A:1044–1051. https:/ /doi.org/10. 1002/cyto.a.24213 55. Lippitsch A, Chukovetskyi Y, Baal N et al (2017) Unique high and homogenous surface expression of the transferrin receptor CD71 on murine plasmacytoid dendritic cells in different tissues. Cell Immunol 316:41–52. https:/ /doi. org/10.1016/j.cellimm.2017.03.005 56. Rousseau M, Goh HMS, Holec S et al (2016) Bladder catheterization increases susceptibility to infection that can be prevented by prophy- lactic antibiotic treatment. JCI Insight 1: e88178. https:/ /doi.org/10.1172/jci.insight. 88178
- 37. Spectral Imaging Cytometry 21 57. Affandi AJ, Grabowska J, Olesek K et al (2020) Selective tumor antigen vaccine delivery to human CD169+antigen-presenting cells using ganglioside-liposomes. Proc Natl Acad Sci U S A 117:27528–27539. https:/ /doi.org/10. 1073/pnas.2006186117 58. Amor C, Feucht J, Leibold J et al (2020) Seno- lytic car T cells reverse senescence-associated pathologies. Nature 583:127–132. https:/ / doi.org/10.1038/s41586-020-2403-9 59. Turner JS, Zhou JQ, Han J et al (2020) Human germinal centres engage memory and naive B cells after influenza vaccination. Nature 586:127–132. https:/ /doi.org/10.1038/ s41586-020-2711-0 60. Mudd PA, Crawford JC, Turner JS et al (2020) Distinct inflammatory profiles distinguish COVID-19 from influenza with limited con- tributions from cytokine storm. Sci Adv 6: eabe3024. https:/ /doi.org/10.1126/sciadv. abe3024 61. Fox A, Dutt TS, Karger B, Rojas M, Obregon- Henao A et al (2020) CYTO-feature engineer- ing: a pipeline for flow cytometry analysis to uncover immune populations and associations with disease. Sci Rep 10:7651. https:/ /doi. org/10.1038/s41598-020-64516-0 62. Ferrer-Font L, Mayer JU, Old S et al (2020) High-dimensional data analysis algorithms yield comparable results for mass cytometry and spectral flow cytometry data. Cytometry A 97:824–831. https:/ /doi.org/10.1002/ cyto.a.24016 63. Boss AL, Brooks AES, Chamley LW, James JL (2020) Influence of culture media on the deri- vation and phenotype of fetal-derived placental mesenchymal stem/stromal cells across gesta- tion. Placenta 101:66–74. https:/ /doi.org/10. 1016/j.placenta.2020.09.002 64. Henderson J, Havranek O, Ma MCJ, Herman V, Kupcova K, Chrbolkova T et al (2021) Detecting F€ orster resonance energy transfer in living cells by conventional and spec- tral cytometry. Cytometry A:1–17. https:/ / doi.org/10.1002/cyto.a.24472 65. Boss AL. Damani T, Chamley LW, James JL, Brooks AES (2021) The origins of placental mesenchymal stromal: full spectrum flow cyto- metry reveals mesencgymal heterogeneity in first trimester placentae, and phenotypic con- vergence in culture. BioRxiv. Preprint. https:/ / doi.org/10.1101/2021.12.21.473551 66. Fernandez MA, Alzayat H, Jaimes MC, Kharraz Y, Requena G, Mendez P (2022) High-dimensional immunophenotyping with 37-colors panel using full-spectrum cytometry. Methods Mol Biol 2386:43–60. https:/ /doi. org/10.1007/978-1-0716-1771-7_4 67. Suda M, Shimizu I, Katsuumi G et al (2021) Senolytic vaccination improves normal and pathological age-related phenotypes and increases lifespan in progeroid mice. Nat Aging 1:1117–1126. https:/ /doi.org/10. 1038/s43587-021-00151-2 68. Costa B, Becker J, Krammer T, Mulenge F, Duran V et al (2022) HCMV exploits STING signaling and counteracts IFN and ISG induc- tion to facilitate dendritic cell infection. Res Square. Preprint, https:/ /doi.org/10.21203/ rs.3.rs-953016/v1 69. Telford WG, Shcherbakova DM, Buschke D et al (2015) Multiparametric flow cytometry using near-infrared fluorescent proteins engi- neered from bacterial phytochromes. PLoS One 10:e0122342. https:/ /doi.org/10. 1371/journal.pone.0122342 70. Wanner N, Barnhart J, Apostolakis N, Zlojutro V, Asosingh K (2022) Using the auto- fluorescence finder on the Sony ID7000TM spectral cell analyzer to identify and unmix multiple highly autofluorescent murine lung populations. Front Bioeng Biotechnol 10: 827987. https:/ /doi.org/10.3389/fbioe. 2022.827987. eCollection 2022 71. Dashkova V, Segev E, Malashenkov D et al (2016) Microalgal cytometric analysis in the presence of endogenous autofluorescent pig- ments. Algal Res 19:370–380. https:/ /doi. org/10.1016/j.algal.2016.05.013 72. Barteneva NS, Dashkova V, Vorobjev I (2019) Probing complexity of microalgae mixtures with novel spectral flow cytometry approach and “virtual filtering”. BioRxiv. Preprint. https:/ /doi.org/10.1101/516146 73. Qiu P, Simonds EF, Bendall SC, Gibbs KD Jr, Bruggner RV et al (2011) Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol 29:886– 891. https:/ /doi.org/10.1038/nbt.1991 74. Amir ED, Davis KL, Tadmor MD, Simonds EF, Levine JH et al (2013) viSNE enables visu- alization of high-dimensional single-cell data and reveals phenotypic heterogeneity of leuke- mia. Nat Biotechnol 31:545–552. https:/ /doi. org/10.1038/nbt.2594 75. Van Gassen S, Callebaut B, Van Helden MJ, Lambrecht BN, Demeester P, Dhaene T, Sayes Y (2015) FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87:636–645. https:/ /doi.org/10.1002/cyto.a.22625
- 38. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http:/ /creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. 22 Ivan A. Vorobjev et al. 76. Nowicka M, Krieg C, Weber LM, Hartmann FJ, Guglietta S et al (2017) CyTOF workflow: differential discovery in high-throughput high- dimensional cytometry datasets. F1000Res 6: 7 4 8 . h t t p s : // d o i . o r g / 1 0 . 1 2 6 8 8 / f1000research.11622.2 77. Bruggner RV, Bodenmiller B, Dill DL, Tibshir- ani RJ, Nolan GP (2014) Automated identifi- cation of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci USA 111: E2770–E2777 78. Ogishi M, Yang R, Gruber C, Zhang SJ, Pelham S, Spaan AN et al (2020) Multi-batch cytometry data integration for optimal immu- nophenotyping. J Immunol 206:206–213. https://doi.org/10.1101/2020.07.14. 202432
- 39. Chapter 2 Using Virtual Filtering Approach to Discriminate Microalgae by Spectral Flow Cytometer Natasha S. Barteneva, Aigul Kussanova, Veronika Dashkova, Ayagoz Meirkhanova, and Ivan A. Vorobjev Abstract Fluorescence methods are widely used for the study of marine and freshwater phytoplankton communities. However, the identification of different microalgae populations by the analysis of autofluorescence signals remains a challenge. Addressing the issue, we developed a novel approach using the flexibility of spectral flow cytometry analysis (SFC) and generating a matrix of virtual filters (VF) which allowed thorough examination of autofluorescence spectra. Using this matrix, different spectral emission regions of algae species were analyzed, and five major algal taxa were discriminated. These results were further applied for tracing particular microalgae taxa in the complex mixtures of laboratory and environmental algal popula- tions. An integrated analysis of single algal events combined with unique spectral emission fingerprints and light scattering parameters of microalgae can be used to differentiate major microalgal taxa. We propose a protocol for the quantitative assessment of heterogenous phytoplankton communities at the single-cell level and monitoring of phytoplankton bloom detection using a virtual filtering approach on a spectral flow cytometer (SFC-VF). Key words Spectral flow cytometry, Phytoplankton, ID7000, Virtual filtering, Spectral flow cyt- ometer, Cyanobacteria 1 Introduction The development of spectral flow cytometry (SFC) expanded our ability to characterize heterogeneous cell populations because of the high spectral resolution achieved by this instrument [1]. The key advantage of spectral flow cytometry (SFC) is that a measurement of a set of emission spectra using different excitation wavelengths is done from individual cells with rates of hundreds and thousands of events per sec [1, 2]. Moreover, SFC analysis makes possible additional differentiation of heterogeneous algal mixtures by size and granularity in a manner similar to conventional flow cytometry (FCM) [1]. The emission spectrum information for every single cell could be combined with light scattering data Natasha S. Barteneva and Ivan A. Vorobjev (eds.), Spectral and Imaging Cytometry: Methods and Protocols, Methods in Molecular Biology, vol. 2635, https://doi.org/10.1007/978-1-0716-3020-4_2, © The Author(s) 2023 23
- 40. through sequential gating on combinations of standard dot plots and histograms. The populations could now be separated not only by using conventional fluorescent conjugated antibodies but by also using the autofluorescent signal from unstained cells [3]. 24 Natasha S. Barteneva et al. Since the spectral unmixing algorithm is based on the record of single stained probes [4], it cannot be directly applied to the natural algal probes having bright autofluorescence from different sources, and another approach has to be considered. In 2019 we developed a novel “virtual filtering” approach (SFC-VF) based on the spectral flow cytometry analysis and use of variable regions of algal autofluorescence spectra in combination with light scattering-related separation of algal populations based on algae cellular size and granularity [5]. We applied SFC-VF to differentiate and characterize microalgae taxa in binary and multi- component mixtures as well as natural environmental microalgae assemblages and were able: (1) to differentiate microalgal cells from different phytoplankton taxa with a similar combination of pig- ments; and (2) to remove fluorescence signal from contaminating sources using light scatter gating. Moreover, unlike FCM, SFC makes it possible to separate individual algal cells presented in heterogenous algal populations (such as cryptophytes) based on their unique spectral data. The SFC-VF method relies on identifying of the most variable regions of the spectra of the mixtures of algal strains analyzed pairwise and on creating a matrix of SFC fluorescent channels corresponding to those regions. Spectral differences between single algal strains (morphology—Fig. 1, left column) were captured by both spectral flow cytometer Sony SP6800 (Sony Biotechnology Inc., USA, 405 nm and 488 nm excitation) and spectrofluorimeter (Fig. 1, right column). However, the spectrofluorimeter provided an averaged signal from the population of algal cells, debris, and fluorescent organic matter. The separation of algal mixtures based on the conventional FCM approach and a filter combination used for algal analysis (such as phycoerythrin (PE) bandpass 575/25 nm) versus allophycocyanin (APC) bandpass (660/20 nm) was complicated by the heterogeneity of algal populations. In the SFC-VF approach, the sensitivity of chlorophyll- associated channels (CH 24–30) captured on the SP6800 was switched to the minimal level. Then, the non-chlorophyll-based spectral differences (from accessory pigments) in the 420–650 nm wavelength range became prominent, enabling better discrimina- tion of algal strains (Fig. 2). Further SFC analysis of algal cultures was continued with the reduced intensity of chlorophyll-associated channels. Mixtures of algal cultures were analyzed in a pairwise manner generating different algal combinations. Initially, several variants of
- 41. Spectral Flow Cytometry of Microalgae 25 Fig. 1 Light microscopy and spectrofluorometric data of algal cell cultures. (i) Aphanizomenon sp., (ii) Cryptomonas pyrenoidifera, (iii) Dinobryon divergens, (iv) Cyclotella sp., (v) Chlorella sp. First column: light microscopy image of algal cultures; second column: spectrofluorometric data of corresponding culture obtained with 407 nm (solid line) and 488 nm (dashed line) excitation. Scale bar 5 μm. Notice significant differences in the relative intensities at the peaks for Aphanizomenon sp. and D. divergens
- 42. 26 Natasha S. Barteneva et al. Fig. 2 Spectral analysis of algal culture mixtures D. divergens and C. pyrenoidifera (a), Cyclotella sp. and Aphanizomenon. (b), and Aphanizomenon and Chlorella sp. (c). Spectral data of all cells in the mixture were
- 43. ä a matrix of fluorescent channels corresponding to virtual filters capturing the algal spectra variability regions were created (Fig. 3). Spectral Flow Cytometry of Microalgae 27 We then selected a combination of fluorescent channels (virtual filter) that provides the best separation of two cell populations by a single dot plot. The spectra of the discriminated populations were further validated with the spectra of single algal culture controls. Furthermore, all five algal strains were mixed together and analyzed using the spectral flow cytometry analyzer. To discriminate all algal taxa, individual plot was not sufficient; instead, we used sequential gating and a combination of fluorescent channels based on virtual filters, previously selected for pairwise culture analysis (Fig. 4). Using the above mentioned approach, we tested whether a particular microalgae type or species can be traced in the mixture of environmental microalgae populations based on its spectral pro- file. Different quantities (from 50% to 0.5%) of Aphanizomenon Fig. 3 Virtual filtering analysis algorithm for a mixture of microalgae cells. The mixture of microalgae cultures is analyzed using the spectral analyzer SP6800, and the obtained total spectrum of the mixture is examined for the most variable and elongated regions. A matrix of several virtual filters corresponding to the variable spectral regions is then created, and the combination of the filters providing the best separation of populations was selected (Step 1). The spectra of discriminated and gated populations are validated with the spectra in the algal spectral database (control spectra) (Step 2). In the environmental sample, virtual filters are applied, and a population different from the major one using an appropriate virtual filter could be analyzed and attributed to the cultured microalgae accordingly (Step 3) Fig. 2 (continued) obtained under 488 nm laser excitation and 405 nm laser excitation spectrum charts. Based on the most variable spectral regions, combination of virtual filters corresponding to spectrum regions in channels 15–20 (488 nm excitation) and channel 32 (488 nm excitation), channels 31–32 (488 nm excitation) and channels V1-CH9 (405 nm excitation), and in channel 32 (488 nm excitation) and channels 4–15 (405 nm excitation) were selected to achieve the best discrimination of the two cell populations. Spectra of gated populations were then plotted to confirm the identity of discriminated populations
- 44. sp. culture were mixed with environmental samples and analyzed using SFC-VF. A combination of the virtual filters CH 22 (405 nm excitation) and V1-2 (405 nm excitation) enabled the best separa- tion of Aphanizomenon sp. population in the 1:1 mixture of Apha- nizomenon sp. and environmental sample (50% Aphanizomenon cells: 50% pond sample) and was used for the analysis of other volume ratios. Spectra of Aphanizomenon sp. cells could be traced in the mixture containing as little as 0.5% proportion relative to the total volume (see Note 1). 28 Natasha S. Barteneva et al. Fig. 4 Spectral analysis of five algal cultures Aphanizomenon sp., C. pyrenoidifera, D. divergens, Cyclotella sp. and Chlorella sp. Mixed together. C. pyrenoidifera and Cyclotella sp. populations were separated within the mixture based on CH 12–14 and CH 32 (488 nm excitation) filters (Step 1). Then unseparated part of the mixture (marked Unknown 1) was gated and projected onto CH 4–15 (405 nm excitation) versus CH 32 (488 nm excitation) dot plot to discriminate the cell population of Aphanizomenon sp. (Step 2). Conse- quently, the unidentified population (Unknown 2) was gated and visualized on a combination of CH 24–28 and CH 30 (488 nm excitation) filters to detach the last two populations of D. divergens and Chlorella sp. with very similar spectral profiles (Step 3) In conventional cytometry, optical bandpass filters are used to separate fluorescent signals during instrument detection. Optimi- zation of fluorescence detection and decreasing the acquisition of signal coming from a region with a high level of autofluorescence (e.g., GFP signal from cellular autofluorescence in a green-range region) require the replacement of a standard optical filter with a modified one [6]. The SFC-VF approach allows the creation of “virtual bandpass filters” with no hardware modification and with- out spectral unmixing. As a result, it was possible to narrow or widen the spectral signal that is taken into consideration from ~10 to ~300 nm bandwidth (for the SP6800 instrument) and to achieve significant discrimination of algal populations.
- 45. Spectral Flow Cytometry of Microalgae 29 Initially, we analyzed representatives of five major groups of microalgae, namely (1) Cyclotella sp. from phylum Bacillariophyta (diatoms); (2) Cryptomonas pyrenoidifera from phylum Cryptista (cryptophytes; cryptomonades); (3) Aphanizomenon sp. from phy- lum Cyanobacteria (“blue-green algae”, cyanoprokaryotes); (4) Chlorella sp. from phylum Chlorophyta (“green algae”, chlor- ophytes); (5) Dinobryon divergens from phylum Ochrophyta (“golden algae”; chrysophytes) as model microalgal species with a spectral flow cytometer SP6800 (Sony Biotechnology Inc., USA). The data presented show the potential of our approach in the identification and quantitative evaluation of algal mixtures and experimental samples. In our study, we used fresh cultures; how- ever, it is anticipated that different preservation protocols (fixation in paraformaldehyde and freezing in liquid nitrogen) may have a smoothing effect on the shape of emission spectra as it happens for the absorption spectral region related to phycobilins. A recently introduced ID7000 instrument (Sony Biotechnol- ogy Inc., USA) is a significantly improved spectral flow cytometer compared to its predecessor Sony SP6800. It has a larger dynamic range of PMTs and an increased number of lasers (up to 7). These features make the discrimination of the algae species even simpler and more robust. Since the dynamic range of PMTs in this cytometer is large enough, it was possible to use the standard voltage for chlorophyll channels along with other channels. The absolute amount of chlo- rophyll in varied species could be significantly different. Thus, to discriminate algae, a chlorophyll signal can be used. To test the capability of ID7000 in the separation of autofluor- escent spectra from different algae, we first recorded individual spectra for all three species used (Fig. 5) and denoted regions of interest there. Next, after recording the algal mixture, we applied two regions around Chl a maximum channel (Fig. 6) representing each species and compared spectra obtained from these subpopulations with the original ones (Fig. 6d). The spectra obtained from the groups selected by these regions were nearly identical to what was measured in every single sample, proving that such selection allows good discrimination between two species. 2 Materials 2.1 Instrumentation and Accessories 1. Varioscan Flash spectral scanning multimode reader (ThermoScientific, USA). 2. The spectral flow cytometer (spectral FCM) analyzer SP6800 (Sony Biotechnology Inc., USA) was equipped with 40 mW blue 488 nm, 60 mW violet 405 nm, and 60 mW red 638 nm
- 46. 30 Natasha S. Barteneva et al. Fig. 5 Spectral analysis of algal cultures Chlorella sp., Acutodesmus obliquus, Porphyridium sordidum with ID7000 spectral flow cytometer (Sony Biotechnology Inc., USA). For further analysis, regions of interest (ROI) were created in the Chl a channels (shown in black in 488 nm spectra) lasers and a 32-channel linear array photomultiplier (500–800 nm range for 488 nm excitation and 420–800 nm range for 405/638 lasers combination), and acquisition and analysis software 3. The spectral flow cytometer (spectral FCM) analyzer ID7000 (Sony Biotechnology Inc., USA) was equipped with 20 mW deep UV 320 nm, 50 mW UV 355 nm, 100 mW violet 405 nm, 150 mW blue 488, 100 mW yellow-green 561 nm, 140 mW red 637 nm lasers and 150 mW far red 808 nm, 186 detectors: 184 fluorescence channels, one forward scatter, one side scatter, and equipped with ID7000 acquisition and analysis software (Sony Biotechnology Inc., USA). 4. Algae growth and harvesting chamber Percival model AL-30L2 (Percival Scientific Inc., USA) for algal culture incu- bation (with controlled temperature, light, and humidity conditions). 5. Brightfield microscope Axiovert with a color camera (Carl Zeiss Inc., Germany) (see Note 2).
- 47. Spectral Flow Cytometry of Microalgae 31 Fig. 6 Spectral analysis of mixed algal cultures Chlorella sp. and Acutodesmus obliquus, with ID7000 spectral flow cytometer. (a) All spectra, mixture in a ratio 1:7. (b) Enlargement of the spectra excited from 488 nm laser. ROI used for the selection of each species are shown as black rectangles. (c) All spectra, algal mixture in a ratio 1:50. The same ROI were applied for the species selection. (d) Comparison of the spectra obtained from pure samples and by selection using ROI
- 48. 3.2 Spectrocyto- fluorimetric 32 Natasha S. Barteneva et al. 2.2 List of Microalgae Cell Cultures Microalgae cell cultures from major microalgae taxa, including Cyclotella sp. CCMP334, Chlorella sp. CCMP251, Dinobryon divergens CCMP3055, Cryptomonas pyrenoidifera CCMP1177, Aphanizomenon sp. CCMP2764, Acutodesmus obliquus SAG 276-1, and Porphyridium sordidum SAG 114.79 were obtained from the National Center for Marine Algae and Microbiota (NCMA; Bigelow Laboratory for Ocean Sciences, USA) and Göt- tingen University’s collection of algal cultures (Germany). 2.3 Reagents 1. Microalgae cell culture media: (1) DY-V medium; (2) L1 medium; (3) L1 derivative, L1–11 psu medium. 2. Eight peak beads (Sony Biotechnology Inc., USA). 3. Align Check beads (Sony Biotechnology Inc., USA). 4. 12 × 75 mm round-bottom Falcon polystyrene tubes. 3 Methods 3.1 Cultivation of Algae Cultures Freshwater cultures D. divergens, Aphanizomenon sp., and C. pyrenoidifera were maintained in DY-V medium (modified from Lehman and co-authors [7]) at 14 °C and 20 °C, respectively, under 150 μmoles/m2 /s light irradiance and 12/12 L/D cycle. Chlorella sp. and Cyclotella sp were maintained in L1 medium and L1 derivative, L1–11 psu medium, respectively, at 14 °C under 150 μmoles/m2 /s light irradiance and 12/12 L/D cycle. 1. Two or more phytoplankton cell cultures (e.g., Chlorella sp. CCMP1177, Acutodesmus obliquus SAG-276-1 and Porphyr- idium sordidum SAG 114.79) were used for experiments with ID7000 spectral flow cytometer. 1. Prior to the analysis, spin down each microalgae culture and resuspend it in a small volume. Count algal cells (microscope). For spectral cytometry analysis, 1000 μL volume of each cul- ture should be used to analyze single culture controls. Acquisition of Microalgal Samples 2. Spectral analysis of algal cell cultures for ID 6800: 3. Prepare mixtures using 500 μL volume of each culture to analyze 10 pairwise culture mixtures and 200 μL volume of each culture to analyze a mixture of all five cultures together (ratio 1:1:1:1:1). Alternatively, mix Chlorella sp. and Acutodes- mus obliquus cultures with relatively equal cell densities in 1:1, 1:10 volume ratio making up to 250 μL sample. The next steps are accommodated for the use of the ID7000 spectral system (Sony Biotechnlogy Inc., USA). 4. Turn on the spectral flow cytometer and run autocalibration using calibration beads. Use Ultra Rainbow calibration beads (Spherotech, USA or Sony Biotechnology Inc., USA) for auto- matic calibration.
- 49. Spectral Flow Cytometry of Microalgae 33 5. Open the Acquisition window in the ID7000 software. 6. Prime the fluidics lines by flushing with sheath fluid. 7. Dissolve two drops of Align Check beads in 450 μL water. 8. Dissolve two drops of eight-peak beads in 450 μL of water. 9. Run the Daily and Performance QC. 10. Spectral analysis of single algal cell cultures. For SP6800 spec- tral cytometer: Adjust the laser power for 488 nm and for 405 nm lasers; reduce gain for channels 24–32 to the minimum and adjust gain for other channels. Record emission spectra of single cells in the range 420–800 nm using excitation at 405/407 nm and in the range 500–800 nm using excitation at 488 nm for SP6800. 11. Record mixed samples. 3.3 Spectral Analysis of Algal Cell Cultures for ID 7000 1. Choose Template—24 Tube Rack in the Experiment tab. 2. Load adjusted for different microalgae ID7000 settings (FSC to—16, SSC gain to 30, the threshold value to 11%, and fluorescence PMT voltage from 40% to 70%). 3. Set the sample flow rate to 1 under the “Flow Control” tab to keep the intermediate flow velocity. 4. Set the stopping condition to 50,000. 5. Create FSC_A vs. SSC_A dot plot and ribbon plot for all lasers. (see Note 3). 6. Place the round-bottom tube with the Chlorella sp., Acutodes- mus obliquus, and two mixed cultures at different ratios in 24 Tube rack. 7. Place the rack in the multi-well plate holder and click “Load”. 8. Highlight sample positions as a “Target” and move all samples to sample group 1. 9. Choose Set current position in the first sample tube by right- clicking, and then click “Preview.” 10. Once the sample is being processed, observe if any parameters from the “Detector & Threshold,” e.g., fluorescence PMT voltage and/or FSC/SSC gain, need to be tuned. 11. After tuning, click “Auto Acquire” to record the samples (see Notes 4–7, Fig. 5). 12. Record mixed samples with ID 7000 spectral flow cytometer (Fig. 6).
- 50. Another Random Scribd Document with Unrelated Content
- 51. C. L. S. C. NOTES ON REQUIRED READINGS FOR JUNE. PICTURES FROM ENGLISH HISTORY. P. 141.—“Erpingham.” An English general, distinguished for personal courage, a chief excellence in feudal times. “Truncheon,” trŭnˈ shun. A baton or military staff, employed in directing the movements of troops. P. 143.—“Three French Dukes.” Since the fourteenth century the eldest son of the king of France, and heir apparent to the crown, is surnamed Dauphin. “Count” (from which comes companion) is one of the imperial court, a nobleman in rank, about equal to an English earl. Dukes (from dux, leader, or duco to lead) were princes in peace, and leaders of clans in war. P. 145.—“Jack Cade.” A man of low condition; Irish by birth; once an exile because of his crimes, but having returned to England he became the successful leader in riotous demonstrations of most disastrous consequences. He had great power of control over a turbulent crowd, but the rioters became insubordinate, and the injuries were such that a price was offered for the leader’s head, and Jack was assassinated. “Cheapside.” Part of a principal thoroughfare in London, north of the Thames, and nearly parallel with it. If the name, as is supposed, at first marked the locality where shop-keepers, content with small profits, sold their goods cheap, it is less appropriate now. As the city extended new names were given to the same street passing through
- 52. the successive additions to the city. Going west on Cheapside the avenue widens, and is in succession called New Gate, Holborn Viaduct, New Oxford, Uxbridge and High Street. P. 146.—“Duke of Somerset,” sŭmˈ ūr-sĕt. Edward Seymour, Lord Protector of England, was uncle to Edward VI, during whose minority he acted as regent of the realm—a most powerful nobleman. His brilliant victory over the Scots at Pinkey greatly strengthened his influence. There was much in his administration to be commended, but the execution of his own brother, and that of the accomplished Earl of Surrey, left a stain on his otherwise fair record. Through the machinations of his rival, he was deprived of his high office, and perished, on Tower Hill in 1552. “Earl of Warwick,” wŏrˈ ick. Richard Neville, a powerful chief at that time, and a cousin of King Edward IV. He was a most remarkable man, and his character and methods are a study. A powerful antagonist, and brave in battle, he was also a shrewd politician, and was much concerned with the affairs of the government. He does not seem to have coveted civic honors for himself, or to have had any aspirations for regal authority. His ambition was rather to make kings, and to unmake them when their character or policy did not suit. By marriage he succeeded to the earldom, and the vast estates of Warwick. He fell at the battle of Barnet. P. 149.—“Margaret of Anjou,” ănˈ joo. Daughter of a French count, and Queen of England—a woman of fine talents, well educated, and full of energy. She became unpopular with the English and was forced to flee from the country. She may have lacked womanly delicacy, but did not deserve the adverse criticism received. Her circumstances justified many of her seeming improprieties. P. 150.—“Towton,” often written Touton. The scene of the bloodiest battle of English history. A hundred thousand were engaged, and the carnage was terrible.
- 53. “Vimeira,” ve-miˈ rä. A town in Portugal where, during the same campaign, the French were again repulsed with great loss. “Talavera,” tä-läˈ va-rä. In the province of Toledo, Spain. The battle referred to took place in 1809, when Sir Arthur Wellesley defeated the French. “Albuera,” ăl-boo-āˈ rä. A small town in the province of Estremadura, Spain, where the English were victorious in 1811. This victory cost them nearly four fifths of the men engaged. “Salamanca,” sal-â-mancˈ â. The capital of a province of the same name in Spain, on the river Tormes, 120 miles northwest from Madrid. Wellington defeated the French here in 1812—a victory which put southern Spain into England’s power. “Vittorea,” ve-toˈ re-ä. On the road from Bayonne to Madrid, where Wellesley defeated Joseph Bonaparte, in 1813, capturing 150 guns and $5,000,000 of military and other stores, the accumulations of five years’ occupation of the place. P. 152.—“Montagu,” mŏnˌ ta-gūˈ . The orthography is not uniform. He was of the powerful family of Nevilles, and brother of the Earl of Warwick. They fell together on the bloody field at Barnet. “Gloucester,” glŏsˈ ter. This was Richard, brother of the king. “Coniers,” konˈ i-ers. P. 153.—“Cognizance,” kŏgˈ nĭ-zans. A badge to indicate a person of distinction, or the party to which he belongs. Flags are used for the same purpose on modern battlefields. P. 154.—“D’Eyncourt,” dāˌ in-courˈ . “Cromwell.” Not Oliver, of course, but one of his ancestors, probably Thomas, who afterward became widely known as a statesman and politician in the service of Henry VIII. P. 155.—“Redoubted.” Regarded with fear, dreaded.
- 54. P. 156.—“Exeter,” Earl of. The Earl was brother-in-law to Edward, and fought with the Lancastrians in the civil war. P. 157.—“The Destrier’s Breast,” dāsˌ tre-āˈ . A French word meaning charger or war horse. P. 158.—“Victorious Touton.” On the bloody field of Towton, or Touton, at a crisis in the battle, Warwick had killed his favorite steed in the sight of his soldiers, kissing and swearing by the cross on the hilt of his sword to share with them a common fate, whether of life or death. He was victorious then. P. 160.—“Casque,” cäsk. A piece of defensive armor to protect the head and neck in battle. P. 162.—“Tewksbury,” tukesˈ bĕr-e. A town in Gloucestershire, on the Avon and Severn. Edward there defeated the Lancastrians. “Mirwall Abbey.” A quiet retreat not far from Leicester, north- northwest from London. P. 163.—“Fleshed,” flesht. Used murderously on human flesh, especially for the first time. “Harquebuse,” härˈ kwe-bŭse. An old-fashioned gun resembling a musket, and supported, when in use, upon a forked stick. “Morris pike.” An obsolete expression for a Moorish pike. P. 164.—“Frushed,” frusht. Trimmed, adjusted. P. 166.—“Tournay,” toorˌ nāˈ . A city of some historic importance in Belgium, on the river Scheldt, near the French border. It was the birthplace of Perkin Warbeck. P. 169.—“Beaulieu,” bū-lĭ. A secluded place, sought for refuge. P. 171.—“Ardres,” ārdr; “Francois,” frŏnˈ swäˌ .
- 55. “St. Michael,” mīˈ kāl. Jews, Mahomedans, and Romanists reverence St. Michael as their guardian angel. A favorite symbol of protection was an image of the saint, with drawn sword in hand, conquering the dragon. P. 172.—“Duprat,” du-präˈ . A French minister of state, and a diplomat of ability. “Louise of Savoy,” savˈ oy or sa-voiˈ . Once a sovereign duchy, since a department of France, south of Switzerland, and west of Italy. P. 173.—“Sieur de Fleuranges,” sēˈ urˌ deh fluhˈ rŏngˌ . P. 174.—“Guisnes,” gheen. In France, not far from Ardres. P. 175.—“Almoner.” An officer connected with religious houses, intrusted principally with the distribution of alms, and also serving as chaplain to the sick, or those condemned to die. P. 181.—“Prebendary,” prebˈ end-a-ry. A clergyman attached to a collegiate or cathedral church, who has his prebend or maintenance in consideration of his officiating at stated times in the church services. “Caermarthen,” kar-marˈ then. The chief town in Caermarthenshire, South Wales, a beautifully situated parliamentary borough, on the river Towy, a few miles from the bay. Caermarthen was the scene of the final struggle for Welsh independence under Llewellyn, the last of the princes. P. 187. “Babington conspiracy.” Anthony Babington, a gentleman of ancient and opulent family, when young became a leader of a band of zealous Catholics who were smarting under the persecutions to which the members of that communion were exposed in the days of Elizabeth. Their primary object was to promote the Catholic cause. When Mary, Queen of Scots, was forced to flee to England as a suppliant, Babington and his associates became interested in her. They conspired to rescue Mary and assassinate Elizabeth. The conspirators, when arrested, rather gloried in the undertaking; as to
- 56. the fate intended for Elizabeth, Babington declared it no crime, in his estimation, to take the life of a sovereign “who had stript him and his brethren of all their political rights and reduced them to the condition of helots in the land of their fathers.” They were sentenced and executed. P. 192.—“In manus, Domine tuas, commendo animam meam,” Into thy hands, O Lord, I commit my spirit. P. 193.—“Fotheringay.” A town in Northamptonshire. Its famous castle was the birthplace of Richard III. Here Mary, Queen of Scots, was imprisoned and executed. The Dukes of York, Richard and Edward, are buried at Fotheringay. P. 194.—“The Lizard.” The extreme southern point of land in England, on the British Channel. “Looe.” A town of the Cornish mining region in the southern part of Cornwall. P. 195.—“Drake,” Sir Francis. A most daring and efficient naval officer, and one of the founders of the naval greatness of England. In 1587 he was sent in command of a fleet to Cadiz, where, by a bold dash, he destroyed one hundred ships destined for the invasion of England, and the next year he commanded as vice-admiral in the victory obtained over the Spanish Armada. “Frobisher,” frŏbˈ ish-er, Sir Martin. An English navigator of the fifteenth century, who made many discoveries in the arctic regions, and was the first explorer for a northwest passage. He had a command in the great sea fight against the Spaniards in 1588. “Hawkins,” Sir John. He was previously associated with Drake in several important expeditions, and served as rear-admiral in the fight that, together with the elements, destroyed the Armada. “Weathergage.” The position of a ship to the windward of another. Hence a favorable position for making an attack with sailing vessels.
- 57. “Medina Sidonia,” ma-deˈ nä se-doˈ ne-ä. Shortly before the time fixed for the sailing of the fleet and army for the invasion of England, owing to the death of the admiral Santa Cruz, and also his rear- admiral, the Duke of Medina Sidonia, the extreme southern province of Spain, a man unacquainted with naval matters, was made captain-general of the fleet. He had, however, for his rear-admiral, Martinez Recalde, an expert seaman. “Recalde,” rā-kälˈ dä. P. 196.—“Oquendo,” o-kānˈ do; “Pedro de Valdez,” peˈ dro da väldĕth ˈ . “Andalusian,” anˌ da-luˈ shi-an. The southern part of Spain. It was formerly called Vandalusia, because of the Vandals who settled there. It is a delightful country, having a mild climate, and generally a fertile soil. Cadiz is the principal seaport and commercial city. P. 197.—“Guipuzcoan,” ge poosˈ ko-an. The smallest but most densely populated of what are known as the Basque provinces; three Spanish provinces distinguished from all other divisions, in the character, language, and manners of the people. They have few of the characteristics of Spaniards, and acquired political privileges not enjoyed by others, and a form of government nearly republican. P. 198.—“Gravelines,” grävˈ lēnˌ . A small fortified and seaport town of France, in a marshy region at the mouth of the river Aa. “Galleons.” Ships of three or four decks, used by the Spaniards both for war and commerce. “Galleasses.” A kind of combination of the galleon and the galley; propelled both by sails and oars. “Sir Henry Palmer;” “Sir William Winter.” English officers who were active in the attack on the Spanish fleet. P. 199.—“Alonzo de Leyra,” a-lonˈ zo dā leiˈ rä; “Diego Flores de Valdez,” de-āˈ go floˈ reth dā välˈ deth; “Bertendona,” bĕrˈ tān-doˌ nä; “Don
- 58. Francisco de Toledo,” don fran-chesˈ ko dā to-lāˈ do; “Pimental,” pe- manˈ täl; “Telles Enriquez,” telˈ leth än-reˈ keth. “Luzon,” loo-thonˈ ; “Garibay,” gä-re-biˈ . P. 200.—“Borlase,” bor-lazˈ . A captain in the fleet of Van der Does. “Admiral Van der Does,” doos. A Hollander. P. 201.—“Ribadavia,” re-bä-däˈ ve-ä. A kind of Spanish wine. “Lepanto.” A seaport town of Greece, on the Gulf of Lepanto. In 1571 it was the scene of one of the greatest and most important naval battles ever fought. The Turkish sultan, Selim, with two hundred and fifty royal galleys and many smaller vessels, engaged the allied forces of Spain, Italy and the Venetian Republic, and was defeated with loss in killed and prisoners of thirty thousand men. The decline of the Turkish empire dates from the battle of Lepanto. P. 203.—“Essex.” (1567-1601.) Essex’s career had been a romantic one. From his first appearance at court at 17, he captivated Elizabeth. He was present at the battle of Zutphen, and joined an expedition against Portugal in 1596. His position as court favorite caused many intrigues to be formed against him, but he kept the queen’s favor, although often offending her. Elizabeth had ordered him imprisoned after the Ireland expedition, more to correct than to destroy him, but upon being dismissed he attempted to compel the queen to dismiss his enemies by raising a force against her. This led to his execution. P. 207.—“Walter Raleigh.” (1552-1618.) Navigator, author, courtier and commander. His first public services were his explorations in North America, during which he occupied the region named Virginia. Having given up his patent for exploration in the New World, he became interested in a project for the conquest of El Dorado. In pursuit of this he sailed in 1595 to South America, but soon returned. He assisted at the capture of Cadiz in 1596. After the death of Elizabeth he lost favor with the throne and was accused of
- 59. treason and convicted. For thirteen years he was confined in the Tower, where he wrote his “History of the World.” In 1615 he obtained his release to open a gold mine in Guinea. The search was unsuccessful. Having encountered in battle at St. Thomas a party of Spaniards, on his return the Spanish court demanded that he be punished, and the king, James I., resolved to execute the sentence passed on him fifteen years before. “Coke,” kŏōk. (1549-1634.) An eminent English judge and jurist. At the trial of Raleigh in 1603 his position was that of attorney- general. During the trial he showed the greatest insolence to Raleigh. “Yelverton,” yĕlˈ ver-ton. (1566-1630.) An English statesman and jurist. P. 208.—“Distich,” dĭsˈ tik. A couple of verses or poetic lines making complete sense. P. 209.—“St. Giles.” A favorite saint in France, England and Scotland. Many localities and public places were named from the saints. The reference here is to a drinking place named in honor of St. Giles. It was situated near Tyburn, which, until 1783, was the chief place of execution in London. Since that date Old Bailey, or Newgate, has been the place of execution. “Oldys,” ōlˈ dis. (1687-1761.) An English biographer and bibliographer. He wrote a life of Sir Walter Raleigh, prefixed to Raleigh’s “History of the World.” P. 210.—“Arundel,” arˈ un-del. (1540?-1639.) The first Lord Arundel. He had served in the war against the Turks under the German emperor, and from him had received the title of Count of the Roman Empire. P. 211.—“Naunton,” naunˈ ton. An English statesman, who died in 1635. He was secretary of state under James I., and the author of an account of the court of Queen Elizabeth.
- 60. “Paul’s Walk,” Bond Street, London, was known as St. Paul’s, before the commonwealth. Here crowds of loungers used to collect to gossip. They soon became known as Paul’s Walkers; now they are called Bond Street Loungers. “Mantle.” According to this old story, as the queen was going from the royal barge to the palace she came to a spot where the ground was so wet that she stopped. Raleigh immediately covered the spot with his rich cloak, on which she stepped. For his gallantry he is said to have received his knighthood and a grant of 12,000 acres of forfeited land in Ireland. P. 212.—“Spanish Main.” The circular bank of islands forming the northern and eastern boundaries of the Caribbean Sea. It is not the sea that is meant, but the bank of islands. P. 213.—“Roundheads.” The Puritans, so called because they wore their hair short, while the Royalists wore long hair covering their shoulders. “Cavaliers.” The adherents of Charles I. were members of the royal party, knights or gentlemen, to whom the name cavaliers was ordinarily applied. P. 214.—“Janizaries,” jănˈ i-za-ries. A Turkish word. “A soldier of a privileged military class which formed the nucleus of the Turkish infantry, but was suppressed in 1826.” P. 215.—“Turenne,” tū-rĕnˈ . (1611-1675.) A famous general and marshal of France, who during his whole life was actively engaged in the French wars. “Counterscarp,” counˈ ter-scärp. The exterior slope of a ditch, made for preventing an approach to a town or fortress. P. 216.—“Pelagian.” Holding the doctrines of Pelagius, who denied the received tenets in regard to free will, original sin, grace, and the merit of good works.
- 61. “Bulstrode,” bulˈ strode. (1588-1659.) An English jurist. P. 217.—“Sidney.” (1622-1683.) An eminent English patriot. He belonged to the army of parliament, but held no office under Cromwell. When Charles II. was restored he was on the continent, where he remained. In 1666 he solicited Louis XIV. to aid him in establishing a republic in England, and having returned to England he joined the leaders of the popular party. In 1683 he was tried as an accomplice in the Rye House plot, and executed. “Ludlow.” (1620-1693.) A republican general who assisted in founding the English republic, but was opposed to Cromwell’s ambition. He had been commander of the army, but his opposition to Cromwell lost him the position. On Oliver’s death he was replaced, but at the Restoration escaped to France, where he spent the remainder of his life. P. 227.—“O. S.” Dates reckoned according to the calendar of Julius Cæsar, who first attempted to make the calendar year coincide with the motions of the sun, are said to be Old Style as contrasted with the dates of the Gregorian calendar. This latter corrected the mistake of the former, and was adopted by Catholic countries about 1582, but Protestant England did not accept it until 1752. P. 228.—“Shomberg,” shomˈ berg. (1616-1690.) P. 233.—“Jeffreys.” (1648-1689.) A lawyer of great ferocity. In 1685 he caused 320 of Monmouth’s adherents to be hung, and 841 to be sold as slaves. P. 234.—“South Sea Bubble.” This scheme was proposed in 1711, by the Earl of Oxford, in order to provide for the national debt. The debt was taken by prominent merchants, to whom the government agreed to pay for a certain time six per cent. interest, and to whom they gave a monopoly of the trade of the South Seas. From 1711 to 1718 the scheme was honestly carried out, but after that time all scruples were thrown aside, and the rage of speculation here described followed.
- 62. P. 235.—“The Rue Quincampoix.” A street of Paris where John Law developed his South Sea Bubble. He was a Scottish financier (1671-1729), who had won a place in London society, and supported himself by gaming. In 1715 he persuaded the Regent of France to favor his schemes, obtained a charter for a bank, and in connection with it formed this company, which had the exclusive right of trade between France and Louisiana, China, India, etc. The stock rose to twenty times its original value. He was appointed minister of finance in 1720, but confidence was soon lost in his plan, and notes on his bank rapidly fell. Law was obliged to leave France, and finally died poor. P. 236.—“Scire Facias.” Cause it to be known. P. 237.—“Walpole.” (1676-1745.) Walpole had been prominent in politics since the accession of George I., and in 1715 was made first lord of the treasury. P. 241.—“Lord Mahon.” The fifth Earl of Stanhope. He was prominent in public affairs during his life, but his fame rests upon his historical works, of which he published several. “A History of England, from the Peace of Utrecht to the Peace of Versailles,” is the best known. “Maxima rerum Roma.” Rome greatest of all things. P. 242.—“Newcastle.” (1693-1768.) An English Whig. P. 243.—“Pelham.” (1694-1754.) A brother of the above, who in 1742 succeeded Walpole as chancellor of the exchequer. He was one of the chief ministers of state 1743-1744. “Godolphin,” go-dolˈ phin. An eminent English statesman, in the service of Charles II., afterward retained in office under James II., and made first lord of the treasury under William and Mary. Under Queen Anne he was again put in this position, from which he had been removed in 1697, and retained it until 1710. He died in 1712.
- 63. P. 244.—“Aix,” āks; “Rochefort,” rotchˈ fort, or roshˈ for; “St. Malos,” or St. Malo, mäˈ loˌ ; “Cherbourg,” sherˈ burg, or sherˈ boorˌ . See map of France in The Chautauquan for March. “Kensington.” A palace at Kensington, a western suburb of London, the birthplace of Queen Victoria. “Grand Alliance.” An alliance formed in 1689 by England, Germany, the States-General, and afterward by Spain and Savoy, to prevent the union of Spain and France. “Goree,” goˈ rāˌ . An island on the west coast of Africa belonging to France. “Guadaloupe,” gwăd-loop. The most important island of the French West Indies. “Toulon,” tooˈ lōnˌ . A seaport of southern France, at the head of a bay of the Mediterranean. It is the largest fort on the Sea, covering 240 acres. “Boscawen,” bosˈ ca-wen. (1711-1761.) An English admiral. “Lagos,” lâˈ goce. On the coast of Portugal. P. 245.—“Conflans,” kon-flon. (1690-1777.) At this time marshal of France. “Hawke,” hawk. (1715-1781.) An English admiral. In 1765 he became first lord of the admiralty, and in 1776 was raised to the peerage. “Chandernagore,” chanˌ der-na-gōreˈ ; “Pondicherry,” ponˈ de-shĕrˌ ree. “Clive.” The founder of the British empire in India. “Coote.” A British general who distinguished himself in wars of India.
- 64. “Bengal,” ben-galˈ ; “Bahar,” ba-harˈ ; “Orissa,” o-risˈ sa; “Carnatic,” car-natˈ ic. Divisions of India at the time of the struggle of the English for possession. “Acbar,” ac-barˈ ; “Aurungzebe,” ōˈ rŭng-zābˌ . Emperors of Hindoostan. P. 247.—“Guildhall,” guildˈ hall. A public building of London which serves as a town hall. All important public meetings, elections and city feasts are held here. Monuments of several statesmen adorn the hall. P. 248.—“Sackville.” The offense referred to was this: At the battle of Minden, in 1759, Lord Sackville commanded the British troops under Prince Ferdinand of Brunswick, but refused to obey orders. On return to England he was tried for this and dismissed from service. P. 251.—“Mecklenburg Strelitz,” meckˈ len-burg strelˈ itz. The eastern division of the two parts into which the territory of Mecklenburg is divided. P. 254.—“Landgravine,” lăndˈ gra-vïne. The wife of a landgrave, a German nobleman holding about the rank of an English earl or French count. “Hesse Homburg,” hess homˈ burg. A former German landgraviate now belonging to Prussia. P. 255.—“Les Miserables,” the poor. A popular novel by Victor Hugo. “Austerlitz,” ausˈ ter-lits. A town of Moravia, where in 1805 Napoleon had gained a brilliant victory over the Prussian and Russian forces. “Waterloo.” A village of Belgium, about eight miles southeast of Brussels.
- 65. “Blucher,” blooˈ ker. (1742-1819.) A Prussian field-marshal, sent to the aid of Wellington. P. 256.—“Nivelles,” neˈ vĕlˌ . A road running to Nivelles, a town about seventeen miles south of Brussels. “Genappe,” jāˈ näpˌ ; “Ohaine,” ōˌ hānˈ ; “Braine l’Alleud,” brān läl-leuˈ . “Mont St. Jean.” A village near Waterloo. “Hougomont,” ooˌ gō-mŏnˈ . A château and wood. “Reille,” räl. (1775-1860.) A French general, who was at this time an aid-de-camp of Napoleon. In 1847 he was made marshal of France. “La Belle Alliance,” lä bĕl älˈ leˌ ŏnsˌ . A farm near Waterloo. “La Haye Sainte,” lä ai sānt. A farm house. P. 258.—“Milhaud,” milˌ hōˈ . “Lefebvre Desnouettes,” lĕhˈ fāvrˌdāˌ noo-ĕtˈ . (1773-1822.) A French general. “Gendarme,” zhŏng-därmˈ . An obsolete name for heavy cavalry. “Chasseurs,” shăsˈ sûr. Light cavalry. “Veillons au Sainte,” etc. Guard the welfare of the empire. “Ney,” nā. (1769-1815.) One of the most prominent of Napoleon’s generals. After Napoleon’s abdication Ney joined Louis XVIII., but on the return of Napoleon, rejoined him. After the battle of Waterloo he was arrested, condemned, and shot. P. 259.—“Moskova,” mos-koˈ va. A river of Russia, on which the French defeated the Russians. “Hippanthropist,” hip-panˈ thro-pist. A fabulous animal whose body was partly like a man and partly like a horse.
- 66. P. 262.—“Pibrock,” pīˈ brock. Bagpipe. P. 263.—“Chevau-legers.” The French for light cavalry. “Badajoz,” bad-a-hōsˈ . A fortified town, capital of a province of the same name in Spain. Wellington carried it by assault in 1812, and sacked the city. P. 264.—“Alava,” äˈ lä-vä, (1771-1843.) A Spanish general and statesman. “Frischemont,” freshˈ ā-mŏnˌ . “Grouchy,” grooˌ sheˈ . (1766-1847.) A French general and marshal. P. 265.—“Denouement,” de-nōōˈ mong. The discovery of the end of a story, the catastrophe of a drama or romance. “Friant,” freˈ ōngˌ ; “Michel,” meˈ shĕlˌ ; “Roguet,” rōˌ guāˈ ; “Mallet,” mäˌ la ˈ ; “Pont de Morvan,” pon deh morˈ vonˌ . P. 266.—“Sauve qui peut.” Let each save himself. “Vive l’Empereur.” Long live the emperor. “Drouet d’Erlon,” droˌ āˈ dĕrˈ lōnˈ . (1765-1844.) Marshal of France and governor-general of Algeria. P. 267.—“Guyot,” gēˌ oˈ ; “Ziethen,” tseeˈ ten. A Prussian general. P. 268.—“Menschikoff,” menˈ shiˌ koff. (1789-1869.) “Raglan,” (1788-1855.) Served in the Peninsula War under Wellington, and lost his arm at Waterloo; was afterward Wellington’s military secretary. He commanded the British army in the Crimean War, and died in camp in 1855. P. 271.—“Tumbril,” tŭmˈ bril. A two-wheeled cart which accompanies artillery, for carrying tools, etc. P. 272.—“Punctilio,” punc-tĭlˈ yo. Exactness in forms or ceremony.
- 67. “Ouglitz,” ougˈ litz; “Kourgané,” kour-gä-nāˈ .
- 68. NOTES ON REQUIRED READINGS IN “THE CHAUTAUQUAN.” READINGS FROM ROMAN HISTORY. P. 497, c. 1.—“Cisalpine.” On the hither side of the Alps, with reference to Rome, that is, on the south side of the Alps, opposed to transalpine. “Doria Baltea,” doˈ ri-a bal-teˈ a. Formerly called the Duria. It is a river which rises in the south of the Alps, and flows through the country to the Salassi, into the Po. It is said to bring gold dust with it. “Salassians,” sa-lasˈ si-ans. A brave, fierce people, formerly living at the foot of the Pennine Alps. P. 497, c. 2.—“Insubrians,” in-suˈ bri-ans. A Gallic people who had crossed the Alps and settled in the north of Italy. They had become one of the most powerful and warlike of the Gallic tribes in Cisalpine Gaul. “Leptis,” lepˈ tis. An important place on the coast of northern Africa, now in ruins. “Adrumetum,” or Hadrumetum, adˈ ri-mēˌ tum. A large city founded by the Phœnicians in northern Africa. It is now called Hammeim. “Polybius,” po-lybˈ i-us. A Greek historian, born about 206 B. C. P. 498, c. 1.—“Masinissa,” mas-i-nisˈ sa. The Numidians were divided into two tribes, of the easternmost of which the father of
- 69. Masinissa was king. He was an ally of the Carthagenians, and for many years warred with them against Syphax, the king of the other Numidian tribe. Masinissa remained friendly to the Carthagenians until Hasdrubal, who had betrothed his daughter to him, broke his promise, marrying her to Syphax. Masinissa then joined the Romans, to whom he rendered valuable service both before and at this battle. He was rewarded with much territory, which he ruled in peace until the breaking out of war between him and Carthage in 150. This outbreak led to the Third Punic War. Masinissa died, however, soon after the beginning of the trouble. “Lælius,” læˈ lĭ-us. Sometimes called Sapiens (the wise). Was an intimate friend of Scipio Africanus, the younger, while his father had been the companion of the elder Scipio. Polybius was his friend, and probably gained much help from him in writing his history. Lælius had a fine reputation as a philosopher and statesman, and it was Seneca’s advice to a friend “to live like Lælius.” “Maniples,” manˈ i-ples. Literally a handful, from the Latin words for hand and full. A name given to a small company of Roman soldiers. “Ligurians,” li-guˈ ri-ans. Inhabitants of Liguria. A name given to a district of Italy which at that time lay south of the river Po. P. 498, c. 2.—“Metaurus,” me-tauˈ rus. A small river of northern Italy flowing into the Adriatic Sea, made memorable by the defeat and death of Hannibal on its banks in 207 B. C. “Euboic.” Pertaining to Eubœa. An island east of Greece, the largest of the archipelago, lying in the Ægean Sea. SUNDAY READINGS. P. 500, c. 1.—“Savonarola,” sä-vo-nä-roˈ lä. (1452-1468.) A celebrated Italian reformer. In his early ministry he effected important reforms and gained great political influence. Being sent to
- 70. Florence he became the leader of the liberal party which succeeded the expulsion of the Medici. Having refused to submit to papal authority he was excommunicated, and popular favor leaving him he was executed. Savonarola published several works in Latin and Italian, among which was the one here quoted from, De Simplicitate Christianæ Vitæ, “On the Simplicity of the Christian Life.” READINGS IN ART. P. 500, c. 2.—“St. Bees.” A college in the village of Cumberland. St. Bees was so called from a nunnery founded here in 650, and dedicated to the Irish saint, Bega. “Ship Court.” A part of the district known as Old Bailey, near Ludgate Hill, in London. The house in which Hogarth was born was torn down in 1862. P. 501, c. 1.—“Hudibras.” See page 306 of The Chautauquan, note on Samuel Butler. “Thornhill.” (1676-1734.) He was a historical painter of some celebrity. His chief productions are the cupola of St. Paul’s cathedral, which Queen Anne commissioned him to paint, and the decoration of several palaces. He was the first English artist to be knighted, and he sat in Parliament several years. No doubt his greatest honor was to be Hogarth’s father-in-law. “Watteau,” vätˌ tōˈ . (1684-1721.) A French painter of much original power, who holds about the same place in the French schools as Hogarth in the English. His subjects were usually landscapes, with gay court scenes, balls, masquerades, and the like, in the foreground. The brilliancy of his coloring and the grace of his figures are particularly fine. “Chardin,” sharˈ dănˌ . (1701-1779.) An eminent French painter. His pictures were mainly domestic scenes, executed with beauty and
- 71. truth. “Walpole,” Horace. (1717-1797.) A famous literary gossip and wit of Hogarth’s time. Although highly educated and given an opportunity for a political career, he preferred his pictures, books, and curiosities. Among his many works were “A Catalogue of Royal and Noble Authors,” and “Anecdotes of Painting in England.” Walpole was no admirer of Hogarth, for he says of him: “As a painter he has slender merit.” “Churchill.” Called “The Great Churchill.” (1731-1764.) A popular English poet and satirist. In youth he was fitted for a curate’s place, but after ordination and two years of the profession he abandoned his position and began his career as a writer, producing several popular poems and satires. He was accused of profligacy, but Macaulay says: “His vices were not so great as his virtues.” “Wilkes,” John. (1727-1797) A friend of the former, and a celebrated English politician. Well educated, clever, bold and unscrupulous. In his second term in Parliament he was obliged to resign from his indiscreet attack on Lord Bute, in a journal which he had founded. The next year he accused the king of an “infamous fallacy,” which so enraged the administration that Wilkes was finally outlawed. Returning to England he was elected to Parliament, but arrested. He was repeatedly expelled from the House, a persecution which secured the favor of the people. In 1774 he was made lord mayor of London, and was afterward a member of Parliament for many years. “Sigismunda.” Daughter of Tancred, prince of Salerno. She fell in love with a page, to whom she was secretly married. Tancred discovering this put Guiscardo, the husband, to death, and sent his heart in a golden cup to his daughter. “Pinegas,” pinˈ e-gas. “Zuccarelli,” dzook-ä-rĕlˈ ee. (1702-1788.) An eminent landscape painter of Tuscany. His scenery is pleasing and pictures well finished.