Beyond the Flat World

Attila T�arnok*

CELL analysis becomes increasingly complex, the more the

former high-end flow cytometry technologies become com-

monplace in research and routine clinical laboratories. Stand-

ard instruments nowadays have multiple laser colors as excita-

tion light sources combined with automated preanalytics,

multiple light detectors, or spectral analyzers to unravel the

different color labels assigned to a plethora of surface or intra-

cellular antigens or functional parameters. These instruments

are, in combination with new assays and protocols (1,2), able

to unravel new, hitherto unrecognized cell populations, cell

signaling cascades, and so on.

However, these massive and dramatic advances in meas-

uring and staining technology are still followed by the (ar-

chaic) way of manually analyzing different cell states and phe-

notypes, at least traditionally. But when we try to identify and

to characterize cells that have been carefully measured in a 10

or even higher dimensional space (with respect to colors and

scatter properties) we are still using the stone age tools of

expert knowledge (which is important) and a cascade of

(sometimes erratic) combination of one- and two-dimen-

sional plots. On the one hand, this approach that we use for

nearly three decades now makes sense, as we ‘‘know,’’ which

cell populations exist and which we want to identify. On the

other hand, we are biased by our own prejudice of preset

expectations and knowledge about cell phenotypes we are

looking for. Thus, the manual way to analyze complex date

bears two major problems. From the aspect of research and

discovery, the investigator is chained to his expectations and

not free to discover the unexpected. This is not investigatory

research! Regarding standardized and unbiased analysis, as it

is required for clinical diagnosis and quality control for GLP

and GMP (D’Alessio et al., this issue, page 14), high standards

are required that can be only warranted for complex data by

highly experienced experts.

As a consequence, highly reliable automated tools are

needed that are able to perform an interpretation that gener-

ates an unbiased identification and analysis of complex data

beyond the 2D flat world of dot-plots. Over the last few dec-

ades, many more or less successful approaches have been

made to overcome this bias, and tools have been developed for

automated data analysis and reduction of complex data to its

essential components. In this January issue, Aghaeepour and

colleagues (this issue, page 6) present a novel approach named

flowMeans. They compare their approach for the analysis of

complex FCM data not only with manual but also with alter-

native automated analysis tools. As also commented on by

Luta (this issue, page 3), this approach proves favorable over

manual scoring and alternative automated approaches. This

indicates that we may be entering a new era of reliable auto-

mated flow data analysis.

Such an automated approach will apply not only for mul-

tiplexed analysis of stem cells as reported by D’Alessio and col-

leagues (this issue, page 14) but also for the unbiased analysis of

complex imaging data as shown by Nandakumar and colleagues

(this issue, page 25). The latter authors applied single cell 3D

computing tomography to identify preneoplastic cells. This

analysis is comprised of a multitude (thus multidimensionality)

of phenotypic and morphometric data, including nuclear size,

elevated nuclear content, and chromatin texture among others.

Nitric oxide (NO) is a critical second messenger that not

only induces and modulates neuronal signal transduction but

also effectively mediates cell killing of Leishmania parasites by

monocytes. Kumar and coworkers (3) critically evaluated the

use of probes for NO detection. In the present issue, Sarkar

and colleagues (this issue, page 35) applied NO monitoring

within Leishmania parasites and the affected macrophages.

The authors report that decreased levels of NO indicate a re-

version of the completion of treatment.

Department of Pediatric Cardiology, Heart Centre, University ofLeipzig, Germany

Received 20 November 2010; Accepted 3 December 2010

*Correspondence to: Prof. Attila T�arnok, Department of PediatricCardiology, Heart Centre Leipzig, University of Leipzig,Str€umpellstr. 39, 04289 Leipzig, Germany.

E-mail: tarnok@medizin.uni-leipzig.de

Published online in Wiley Online Library(wileyonlinelibrary.com)

DOI: 10.1002/cyto.a.21013

© 2010 International Society for Advancement of Cytometry

EDITORIAL

Cytometry Part A • 79A: 1�2, 2011

Regarding the move from the flat to the unknown world

of new cells and cell properties, trogocytosis is an interesting

and possibly immunologically relevant phenomenon. I have

briefly reviewed trogocytosis recently in an editorial (4) with

respect to a manuscript by Daubeauf et al. (5) that was the first

manuscript on this topic published in Cytometry Part A. Now,

Iwasaki and colleagues (this issue, page 46) report that translo-

cation of CD8ab from T-lymphocytes to monocytes is happen-

ing in peripheral blood. This occurs by cell membrane transfer

as the CD8ab expressing monocytes do not express these mole-

cules on the RNA level. As already outlined above, trogocytosis

is a newly discovered phenomenon with immunological rele-

vance that has escaped the attention for very many years. This

is again an argument for automated and unbiased data analysis

that could help to get beyond our routine trails.

New dyes increase the way to make the cell analysis not

only more complex but also more precise (1). The combina-

tion of several organic fluorophores with excitation in the UV

to visible light spectrum has its natural limitations because of

spectral cross-talk that makes data analysis very complex and

demands substantial care when setting up an experiment. Flu-

orescent semiconductors or quantum dots (QDs) that came

up many years ago provided a new class of labels that helped

to expand the complexity (6). Now, Crow and colleagues (this

issue, page 57) developed a new marker for flow cytometry,

immunolabeled nanorods, of �100 nm length that relies on

plasmonic scattering. These particles have several advantages

such as tunability, high scattering intensity, biocompatibility,

and plasmonic scattering also in the IR region. Thereby, these

nanorods could further expand the multiplexing options in

flow cytometry. However, as Zarkowsky and colleagues

demonstrate in their technical note (this issue, page 84),

there are some critical issues that need to be taken into consid-

eration when working with nanoparticles. As the authors

show, fluorescence of QDs can be irreversibly and completely

eliminated when they get in contact with even traces of heavy

metals such as copper, zinc or iron cations. These may be con-

taminants of formalin solutions, among others. Elimination of

such heavy metal contaminations for example by cation chela-

tors is therefore critical. This is only one of a few specific pre-

cautions that have to be taken into account when using nano-

particles in flow and image cytometry.

Whereas increasing the multiplexity of cell population

analysis by combining a plethora of colors is the domain of

polychromatic flow cytometry, single-cell visualization and

single-cell tracking are that of imaging and image cytometry.

In the recent past, the combination of the virtues of flow cyto-

metry and imaging has been solved by new elaborate instru-

mentation, leading to the discovery of hitherto unknown cell

populations (7), for example, cell tracking for the measure-

ment of transient signaling events is performed by fluores-

cence microscopy (Negraes et al.; this issue, page 77). The

disadvantages of measuring such responses in flow are that

cells are only measured once and then they are lost and that

fast (second or subsecond) responses may be hidden by the lag

time the stimulated cells need to get to the laser focus. There-

fore, (1) only data on population responses but not that of

individual cells are detected, and (2) special tricks are needed

to reduce the lag time (8). In the past, several instrument con-

cepts for flow cytometry have been proposed to overcome this

obstacle and to allow measuring the identical cell multiple

times. Sitton and Srienc (this issue, page 66) developed a flow

cytometry apparatus that allows single cell tracking for many

hours in suspension. In their device, the cells are moved in a

capillary by a pump through a laser focus and then are sucked

back. This movement is repeated in both directions up to sev-

eral hundred times with hundreds to thousands of cells. Indi-

vidual cells are identified based on the time when they cross

the laser and based on cell specific properties such as size. This

new technology could become an alternative to imaging, in

particular for cells in suspension such as leukocytes that

render problematic when measured in the microscope as they

normally would not attach to the solid surface of a microscope

slide.

In summary, this new year’s issue of Cytometry Part A

compiles the broad versatility of scientific questions and appli-

cations with the virtue of the technologically innovative power

of the quantitative cell analysis community.

ACKNOWLEDGMENT

I thank Dr. Jozsef Bocsi for his support and help with this

editorial.

LITERATURE CITED

1. Roederer M, T�arnok A. OMIPs—Orchestrating multiplexity in polychromaticscience. Cytometry A 2010;77A:811–812.

2. Krutzik PO, Crane JM, Clutter MR, Nolan GP. High-content single-cell drug screen-ing with phosphospecific flow cytometry. Nat Chem Biol 2008;4:132–142.

3. Kumar S, Patel S, Jyoti A, Keshari RS, Verma A, Barthwal MK, Dikshit M. Nitric ox-ide-mediated augmentation of neutrophil reactive oxygen and nitrogen species for-mation: Critical use of probes. Cytometry A 2010;77A:1038–1048.

4. T�arnok A. Patch bandits. Cytometry A 2009;75A:377–379.

5. Daubeuf S, Bordier C, Hudrisier D, Joly E. Suitability of various membrane lipophilicprobes for the detection of trogocytosis by flow cytometry. Cytometry A2009;75A:380–389.

6. Tarnok A. Quantum of dots. Cytometry A 2010;77A:905–906

7. Zuba-Surma EK, Kucia M, Ratajczak J, Ratajczak MZ. ‘‘Small stem cells’’ in adult tis-sues: Very small embryonic-like stem cells stand up! Cytometry A 2009;75A:4–13.

8. Vines A, McBean GJ, Blanco-Fern�andez A. A flow-cytometric method for continuousmeasurement of intracellular Ca(21) concentration. Cytometry A 2010;77A:1091–1097.

EDITORIAL

2 Editorial