Going into Lengths and Widths, and Depths? ?
Microscopic Cytomics Quantifying Cell Function and
A.O.H. Gerstner,1A. T? arnok2*
ALTHOUGH microscopy has always been an integral part of
cytometry and especially of flow cytometry, this basis has long
been forgotten. The first quantitative measurements, per-
formed by Casperson et al. (1) on a microscope in the early
1950’s, ultimately led to the development of a commercially
available slide-based cytometer in the 1990’s by Kamentsky
et al. (2). The rapid distribution of this instrument among
scientists, cell biologists, and some clinicians, has made this
fundamental basis of cytometry more visible again: ‘‘Cyto-
metry is coming home.’’ The various characteristic capacities
of slide-based versus flow cytometry have been outlined in
part previously. In recent times, two major issues attract our
1. The specimen on the slide can be evaluated repeatedly
and the information obtained can be assigned to the cells on a
single-cell basis, i.e. ‘‘individually.’’ This opens the door to an
n-parametric analysis as has been outlined by Mittag et al.
2. The cells can be kept within their natural environment
i.e. the tissue and their connection with the neighborhood can
Schubert (4) was the first to exploit this feature of the
slide-based design and presented the concept of toponomics:
similarly as it is important not only to what amount a protein
is expressed but also its exact location in the cell. Also of sub-
stantial impact is the identification of what kind of neighbor-
ing cells surround a given cell of interest.
Together with these theoretical concepts, delicate hard-
ware support has been developed. Whereas Clatch et al. (5)
have originally described an immunophenotyping method
using a simple glass slide modified by adhesive strips and later
applications took plain glass for cell immobilization, recently
several authors have presented elaborate solid supports for
repeated single cell analysis. A novel approach to the immense
complexity of the cellular immune system is developed by
Hennig et al. (6). Iterative restaining on a slide-based platform
is successfully used to measure an almost unlimited set of mar-
kers of cell differentiation and cell function in living cells as
was previously outlined theoretically (7). This assay takes
commercially available slides and yields very detailed data on
single cells with instrumentation that is rather inexpensive. It
needs minimal sample size and shows excellent correlation
with flow cytometry.
Tajiri et al. (8) use a specifically produced microwell chip
for combining a functional assay (cytokine secretion) with the
analysis of intracellular proteins and surface markers. The mi-
crowell design offers the possibility to cob the cells in a liquid
environment allowing short time culture and time-lapse anal-
ysis. If analyses had included more than one fluorochrome,
even more detailed data could have been obtained. However,
depending on the condition of the cells the illumination
needed for fluorophore bleaching could also have a directly
modulating effect on cell function and could therefore inter-
fere with functional analyses.
1Department of Otorhinolaryngology / Head and Neck Surgery,
University of Bonn, Bonn, Germany
2Department of Pediatric Cardiology, Cardiac Centre, University of
Leipzig, Leipzig, Germany
*Correspondence to: Prof. Attila T? arnok, Department of Pediatric
Cardiology, Cardiac Centre, University Leipzig, Str€ umpellstr. 39,
04289 Leipzig, Germany.
Published online in Wiley InterScience
© 2009 International Society for Advancement of Cytometry
Cytometry Part A • 75A:279?281, 2009
Tracking the same cells by time-lapse analysis is also an
issue concerning the matter of neurite growth and branching.
First steps for machine-based tracing of individual neurites
were achieved by Meijering et al. 2004 (9) by developing Neu-
ronJ. However, obtaining quantitative data requires time-con-
suming post-acquisition data manipulation. To improve this,
Popko et al. (10) introduce the automated analysis software
XL-Calculations allowing batch analysis to yield quantitative
data on the growth of neurites at a level of detail that has not
been previously reported. Yu et al. (11) chose another way to
obtain data on neurite outgrowth and introduce NeuronCyto.
Using dynamic watershed lines for keeping topological de-
pendence, their software yields quantitative data on neurite
growth on a single-cell basis. It can be applied even in dense
cell cultures using cellular images. Both approaches, XL-Cal-
culations and NeuronCyto, determine length, branching com-
plexity, and number of neurites. The authors hope for applica-
tions in neuroregeneration.
A further step deeper into the analysis of cell-interaction
in natural conditions is modeled by H€ andel et al. (12). They
apply their model system to mouse colon tissue and investi-
gate the direct neighboring of regulatory T-cell characterized
by Foxp3-expression and proliferating cells detected by ki67.
Although it does not give a definitive diagnosis and is not
intended to do so, this approach for quantitative tissue analy-
sis or tissomics (13) could yield therapeutically relevant data
that so far are available only on a subjective basis if at all.
Another ‘‘close-to-clinical’’ application is presented by Good-
ale et al. (14) who characterize tumor cell dissemination
applying a combination of flow and slide-based cytometry.
Both platforms yield comparative data in a preclinical setting.
Their quantitative analysis of tumor spread might represent
the kinetics relevant in patients as well.
Quantitative analysis in cytometry however is not limited
to static expression of markers or production of stimulated
cytokines but can also be applied to movement as shown by
Kamgou? e et al. (15). The ability to analyze the shortening of
single sacromeres in cardiomyocytes impressively outlines the
capacities of slide-based assays as they can follow contraction
waves traveling along the cell. It allows quantifying the cardio-
myocyte contraction without the need of fluorescent calcium
probes and yields an excellent spatiotemporal resolution.
High-throughput single-cell based analysis is applied by
Matula et al. (16) to quantify viral infection in an automated
manner. They make use of a novel gradient-based thresholding
scheme for cell nucleus segmentation which turned out to be
of particular use in densely packed cell clusters. The capacity
of this image-based approach is underlined by the ability to
quantify viral replication in transfected cells on siRNA
cell arrays. This is an excellent model for industry scale high-
The 3D-organization of a cell is of particular relevance in
cell biology, and its quantitative or even automated analysis is
a demanding task. Pinidiyaarachchi et al. (17) developed a
method for detecting and localizing fluorescent signals
generated by molecule complexes and were able to accurately
measure the distance of these complexes to the nuclear mem-
brane after stimulation. It transfers the concept of toponomics
into live-cell analysis and allows one to spot ligand-induced
A true masterpiece of cell analysis is performed by Du
and Wasser (18) who took the challenge of 3D imaging of
Drosophila muscles. They developed a fully-automated algo-
rithm for the acquisition of 3D-image stacks of single living
muscles and to compensate for non-linear movement due to
periodic contractions and developmental changes. It was used
to show single muscles undergoing apoptosis in early stages of
In conclusion, this issue of Cytometry Part A collects the
latest achievements in quantitative microscopic analysis of
biological specimens. The articles impressively highlight
the capacities of current quantitative cell-based assays. Since
the parameters are analyzed in their cellular context they open
the door to a cytome-based understanding of diseases, leading
back to Virchow’s Cellularpathologie (19) centered on the cell
as the ultimate unit of life. Instead of analyzing a cell-shake
that has lost any information about the histological and cyto-
logical topology as in standard proteomic assays (20,21) these
cytomic techniques allow us to perform locational proteomics
in its truest sense. This background raises the hope that these
assays will make their way to clinical applications. This devel-
opment gets more and more visible for the last two years; dur-
ing that time several special issues of Cytometry Part A have
focused on high-content cytometry and systems biology
(22,23) to which this issue serves as an update.
1. Caspersson T, Lomakka G, Svensson G, S€ afstr€ om R. A versatile ultramicrospectro-
graph for multiple-line and surface scanning high resolution measurements employ-
ing automatized data analysis. Exp Cell Res 1955;3:40–51.
2. Kamentsky LA, Kamentsky LD. Microscope-based multiparameter laser scanning
cytometer yielding data comparable to flow cytometry data. Cytometry 1991;12:381–
3. Mittag A, Lenz D, Gerstner AOH, T? arnok A. Hyperchromatic cytometry principles
for cytomics using slide based cytometry. Cytometry Part A 2006;69A:691–703.
4. Schubert W. Topological proteomics, toponomics. MELK-technology. Adv Biochem
Eng Biotechnol 2003;83:189–209.
5. Clatch RJ, Foreman JR, Walloch JL. Simplified immunophenotypic analysis by laser
scanning cytometry. Cytometry 1998;34:3–16.
6. Hennig C, Adams N, Hansen G. A versatile platform for comprehensive chip-based
explorative cytometry. Cytometry Part A 2009;75A:362–370 (this issue).
7. Laffers W, Mittag A, Lenz D, T? arnok A, Gerstner AOH. Iterative restaining as a piv-
otal tool for n-color immunophenotyping by slide-based cytometry. Cytometry Part
8. Tajiri K, Kishi H, Ozawa T, Sugiyama T, Muraguchi A. SFMAC: A novel method for
analyzing multiple parameters on lymphocytes with a single fluorophore in cell-
microarray system. Cytometry Part A 2009;75A:282–288 (this issue).
9. Meijering E, Jacob M, Sarria JCF, Steiner P, Hirling H, Unser M. Design and valida-
tion of a tool for neurite tracing and analysis in fluorescence microscopy images.
Cytometry Part A 2004;58A:167–176.
10. Popko J, Fernandes A, Brites D, Lanier LM. Automated analysis of NeuronJ tracing
data. Cytometry Part A 2009;75A:371–376 (this issue).
11. Yu W, Lee HK, Hariharan S, Bu WY, Ahmed S. Quantitative neurite outgrowth mea-
surement based on image segmentation with topological dependence. Cytometry
Part A 2009;75A:289–297 (this issue).
12. H€ andel N, Brockel A, Heindl M, Klein E, Uhlig HH. Cell-cell-neighborhood relations
in tissue sections—A quantitative model for tissue cytometry. Cytometry Part A
2009;75A:356–361 (this issue).
13. Ecker RC, T? arnok A. Cytomics goes 3D: Toward tissomics. Cytometry Part A
14. Goodale D, Phay C, Postenka CO, Keeney M, Allan AL. Characterization of tumor
cell dissemination patterns in preclinical models of cancer metastasis using flow cyto-
metry and laser scanning cytometry. Cytometry Part A 2009;75A:344–355 (this issue).
15. Kamgou? e A, Ohayon J, Usson Y, Riou L, Tracqui P. Quantification of cardiomyocyte Download full-text
contraction based on image correlation analysis. Cytometry Part A 2009;75A:298–308
16. Matula P, Kumar A, W€ orz I, Erfle H, Bartenschlager R, Eils R, Rohr K. Single-cell-
based image analysis of high-throughput cell array screens for quantification of viral
infection. Cytometry Part A 2009;75A:309–318 (this issue).
17. Pinidiyaarachchi A, Zieba A, Allalou A, Pardali K, W€ ahlby C. A detailed analysis of
3D subcellular signal localization. Cytometry Part A 2009;75A:319–328 (this issue).
18. Du T, Wasser M. 3D image stack reconstruction in live cell microscopy of Drosophila
muscles and its validation. Cytometry Part A 2009;75A:329–343 (this issue).
19. Virchow R.Cellularpathologie in ihrer Begr€ undung auf physiologische und patholo-
gische Gewebelehre—20 Vorlesungen. Berlin: August Hirschwald, 1858.
20. Barboro P, Rubagotti A, Orecchia P, Spina B, Truini M, Repaci E, Carmignani G,
Romagnoli A, Introini C, Boccardo F, Carnemolla B, Balbi C. Differential proteomic
analysis of nuclear matrix in muscle-invasive bladder cancer: Potential to improve
diagnosis and prognosis. Cell Oncol 2008;30:13–26.
21. Jansen C, Hebeda KM, Linkels M, Grefte JMM, Raemaekers JMM, van Krieken
JHJM, Groenen PJTA. Protein profiling of B-cell lymphomas using tissue biop-
sies: A potential tool for small samples in pathology. Cell Oncol 2008;30:27–
22. T? arnok A. A focus on high-content cytometry. Cytometry Part A 2008;73A:
23. Sz€ oll€ osi J, Nolan J, T? arnok A. New trends in cytometry in the era of systems biology.
Cytometry Part A 2008;73A:267–269.
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