Cytomics for Discovering Drugs
Attila T? arnok*
THERE are two major fields of applications for the cytomics
approach: Predictive medicine for preventative medicine and
drug discovery. Drug discovery demands that thousands of
compounds are rapidly tested for their biological activity in
order to detect those rare events that may bear the potential of
becoming useful pharmaceutics somewhere in the future (1,2).
This requires the combination of appropriate cell systems for
allowing for high-content screening (mostly imaging systems)
as well as fast and stable automated image and data analysis
systems enabling unbiased data scrutiny. Ideally, such cytomic
oriented detection work flow should help to reduce the fre-
quency of false positive hits (requiring substantial effort and
costs for further testing) and false negative hits (leading to loss
of potentially precious compounds).
Evensen et al. (3) established a unique co-culture assay of
endothelial cells and vascular smooth muscle cells for screen-
ing of anti-angiogenic drugs for cancer therapy. Their system
enables investigation of drug efficacy on immature, VEGF-
dependent, and growing endothelium as well as on mature,
nonproliferating endothelium. The authors tested in a pilot
study several anti-angiogenic drugs using a microtiter-plate
imaging system. They found that tube total length is the most
informative parameter derived from the images and they
discovered by their quantitative assay some unexpected
Automated high-throughput imaging combined with
quantitative analysis of the actin cytoskeleton opens the
opportunity for rapid standardized determination of the
effects of treatment or infection on cell structure. An image
analysis tool for automated measurement of cytoskeleton
modifications in a high-throughput imaging system is demon-
strated by Weichsel et al. (4). They introduce the parameter
image coherency and show that it is suitable to detect
accurately global alterations in the cytoskeleton organization.
De Vos et al. (5) focus their interest on the fully auto-
mated multivariate phenotypic classification of individual cell
nuclei and subnuclear spots by automated classification and
supervised machine learning. These authors recently devel-
oped controlled light exposure microscopy, a novel technology
that strongly reduces photodamage by limiting excitation in
parts of the image where full exposure is not needed (6). In
their present publication, they applied fluorescence mosaic
microscopy for image acquisition and ImageJ for image analy-
sis of DAPI and histone gamma-H2AX (7) labeled cells. The
authors developed a comprehensive work flow for high-
content screening of the nuclear architecture of cell lines trea-
ted with a genotoxic agent without or in combination with
ionizing or UV irradiation. This approach is suitable for high-
throughput screening in drug discovery.
Image quality is crucial in image-based screening and
quantitation. Low quality images may render unreliable results
and need to be further processed or discarded. Now Zeder
et al. (8) developed an automated, artificial neural network
based, quality assessment of autonomously acquired micro-
scopic images for fluorescently stained bacteria. Their software
proves to have a slightly higher success rate than human obser-
vers in classifying image qualities correctly. More importantly,
it relieves the experimenter from monotonous work.
In general, high-throughput high-content imaging sys-
tems acquire 2D images. However, intracellular structures are
clearly located in a 3D environment that is a prerequisite for
exerting their spatio-temporal function. Allalou et al. (9)
developed a robust signal detection algorithm for 3D fluores-
cence microscopy consisting of a detector and a verifier to
detect point-like signals in 3D images. The authors state that
their approach is superior over hitherto used image analysis
methods for 3D recognition.
Finally, time-lapse microscopy of cell proliferation and
gene expression is of importance in detecting long-term effects
Department of Pediatric Cardiology, Heart Centre Leipzig, University
of Leipzig, Leipzig, Germany
Received 30 November 2009; Accepted 10 December 2009
*Correspondence to: Attila T? arnok; Department of Pediatric
Cardiology, Heart Centre Leipzig, University Leipzig, Str€ umpellstr,
39, 04289 Leipzig, Germany.
Published online in Wiley InterScience
© 2009 International Society for Advancement of Cytometry
Cytometry Part A • 77A: 1?2, 2010
of drugs on cellular systems. However, the analysis of such
serial images is tedious. Now Wang et al. (10) developed ro-
bust automated methods for image segmentation and
dynamic lineage analysis for the single-cell fluorescence micro-
scope. These tools are portable and applicable for diverse
detection modalities and for various species.
Standardization is essential in unifying data, for perform-
ing interlaboratory comparisons and for using different
instruments to perform analysis on the same platform. These
demands lead in the past to the development of the Flow
Cytometry Data File Standard (FCS) with the latest upgrade
being FCS 3.0. Now, The international society for the advance-
ment of cytometry data standard task force (ISAC DSTF) pre-
sents the most recent FCS upgrade: FCS 3.1 (11) with several
improvements and simplifications.
In conclusion, this first issue of the year shows the broad
range of developments in cytometry providing to improve
drug discovery and to get a better hold on the heterogeneity of
responses that unknown drugs may evoke in respective cellular
test systems (12).
I thank Dr. Jozsef Bocsi, Heart Center Leipzig, for his
help with this manuscript.
1. Herrera G, Diaz L, Martinez-Romero A, Gomes A, Villam? on E, Callaghan RC,
O’Connor JE. Cytomics: A multiparametric, dynamic approach to cell research. Toxi-
col In Vitro 2007;21:176–182.
2. Barten MJ, Tarnok A, Garbade J, Bittner HB, Dhein S, Mohr FW, Gummert JF. Phar-
macodynamics of T-cell function for monitoring immunosuppression. Cell Prolif
3. Evensen L, Micklem DR, Link W, Lorens JB. A novel imaging-based high-through-
put-screening approach to antiangiogenic drug discovery. Cytometry Part A 2010;
77A:41–51 (this issue).
4. Weichsel J, Herold N, Lehmann MJ, Kr€ ausslich H-G, Schwarz US. A quantitative
measure for alterations in the actin cytoskeleton investigated with automated high-
throughput microscopy. Cytometry Part A 2010;77A:52–63 (this issue).
5. De Vos WH, Van Neste L, Dieriks B, Joss GH, Van Oostveldt P. High content image
cytometry in the context of subnuclear organization. Cytometry Part A 2010;77A:
64–75 (this issue).
6. De Vos WH, Hoebe RA, Joss GH, Haffmans W, Baatout S, Van Oostveldt P, Manders
EM. Controlled light exposure microscopy reveals dynamic telomere microterritories
throughout the cell cycle. Cytometry Part A 2009;75A:428–439.
7. Zhao H, Traganos F, Darzynkiewicz Z. Kinetics of histone H2AX phosphorylation
and Chk2 activation in A549 cells treated with topotecan and mitoxantrone in rela-
tion to the cell cycle phase. Cytometry Part A 2008;73A:480–489.
8. Zeder M, Kohler E, Pernthaler J. Automated quality assessment of autonomously
acquired microscopic images of fluorescently stained bacteria. Cytometry Part A
2010;77A:76–85 (this issue).
9. Allalou A, Pinidiyaarachchi A, W€ ahlby C. Robust signal detection in 3D fluorescence
microscopy. Cytometry Part A 2010;77A:86–96 (this issue).
10. Wang Q, Niemi J, Tan C-M, You L, West M. Image segmentation and dynamic line-
age analysis in single-cell fluorescence microscopy. Cytometry Part A 2010;77A:101–
110 (this issue).
11. Spidlen J, Moore W, Parks D, Goldberg M, Bray C, Bierre P, Gorombey P, Hyun B,
Hubbard M, Lange S, Lefebvre R, Leif R, Novo D, Ostruszka L, Treister A, Wood J,
Murphy RF, Roederer M, Sudar D, Zigon R, Brinkman RR. Data file standard for
flow cytometry, Version FCS 3.1. Cytometry Part A 2010;77A:97–199 (this issue).
12. Slack MD, Martinez ED, Wu LF, Altschuler SJ. Characterizing heterogeneous cellular
responses to perturbations. Proc Natl Acad Sci USA 2008;105:19306–19311.