Editorial
A Focus on High-Content Cytometry Attila Tarnok*
IN biological systems, cells and their environment form intricate and extremely complex networks among each other. In the last few years it became obvious that for the deeper understanding of these cellular networks systems biology (1) and cytomics (2–4) are the key. To get a hold on the heterogeneity and its maturation or disease related alterations, the techniques of measuring cell systems is required. The expression ‘‘high-content screening (HCS)’’ (5) has recently been described: ‘‘High content screening can be defined as an automated imaging approach to understanding compound activities in cellular assays where, in each well of a microplate, you can measure spatial distribution of targets in cells, individual cell and organelle morphology, and complex phenotypes. It provides the flexibility to measure cell subpopulations and to combine multiple measurements per cell, while simultaneously eliminating unwanted cells and artifacts.’’ (6). High Content screening is also known as chemical biology, cell based screening, chemical genetics, phenotypic screening, visual screening, cytomics, and it has a major place in pharmaceutical company drug discovery (7–9). There are two eminent fields of application that immediately will profit from these systematic ways to analyze cellular networks: clinical diagnosis and drug discovery. In clinical diagnosis an improved quality of disease recognition is expected, which was termed predictive medicine (4,10). Predictive medicine, a concept that aims at the early and individualized prediction of disease progression even before clinical signs become manifest (4,10), is of a clear importance for the future. Present diagnostics are prognostic and allow risk assessment on a group basis (i.e. high versus low risk or high versus low responders to a therapy). However, they cannot foretell on an individual basis which individual
will progress or respond in exactly what manner (9). Regarding the steadily increasing costs of health care in the northwestern hemisphere, predictive medicine carries the promise that unnecessary treatments will be reduced. Treatments which have benefits but also serious side-effects can be avoided for the benefit of the patient. In drug discovery, cell-based assays are required that allow(by high-throughput analysis(to recognize the biological effect of a great number of agents. Ideally, these assays should detect simultaneously different cellular reactions (e.g. modulation of proliferation, death, gene expression) (7,8). Automated image based screening permits the identification of small compounds altering cellular phenotypes and is of interest for the discovery of new pharmaceuticals and new cell biological tools for modifying cell function (7,8). The selection of molecules based on a cellular phenotype does not require a priori knowledge of the biochemical targets that are affected by compounds and while this may be a benefit for compound discovery, the biochemical target itself must be subsequently identified. Given the increase in the use of phenotypic/visual screening as a cell biological tool, methods are required that permit systematic biochemical target identification if these molecules are to be of broad use (11). Target identification has been defined as the rate limiting step in chemical genetics/ high-content screening (12). Even though cell based imaging is by definition the foundation of HCS, flow cytometry (FCM) played from the beginning a key role (13,14). As recently outlined by Peluso et al., novel developments will make FCM simpler, easier to use, and automated to make it streamline for HCS (15). This is in particular based on the technological advantage that FCM is by nature a high-throughput technology dedicated to measuring
Department Pediatric Cardiology, Cardiac Centre, University Leipzig, Germany
Published online in Wiley InterScience (www.interscience.wiley.com)
*Correspondence to: Prof. Attila Tarnok, Dept. Pediatric Cardiology, Cardiac Centre, University Leipzig, Str€umpellstr. 39, 04289 Leipzig, Germany.
DOI: 10.1002/cyto.a.20571
E-mail:
[email protected]
Cytometry Part A 73A: 381 383, 2008
© 2008 International Society for Advancement of Cytometry
EDITORIAL simultaneously a multitude of cellular properties by polychromatic staining and analysis (16). Slide-based cytometry (SBC), is in principle a closely related screening approach (17), can fulfill the same or even higher level of HCS analysis on separated individual cells (18) and on cells in their histological context (19–21). After launching the newly formatted journal (22), we started to edit issues wherein manuscripts were focused around a specific topic. These were until now automated recognition of cells and their compounds (23) and cell proliferation and death (24). This issue has a collection of manuscripts loosely attached to the systemic HC investigations that may be termed as ‘‘High-Content Cytometry’’. Several manuscripts in this issue deal with experimental approaches to establish expression screening assays. Dennis et al. (25) report of their newly developed Multifunctional Androgen Receptor Screening Analysis that combines a GFP expression system with a rapid micro scale FCM analysis. This method can be used to test for drugs affecting androgen receptor expression that is of eminent impact in diagnosis and drug discovery (25,26). However, it may be relatively easily expanded to test for drug effects on other cellular products, epigenetic modulators of gene expression (27) or mutations (28), among others. In two companion studies, McLaughlin et al. (29,30) report the development of a 9-color FCM panel and their experience with cross-platform comparison. Using this HC approach is essential in order to unequivocally phenotype the cellular subset of interest and then to screen for intracellular expression of multiple cytokines (31). One important conclusion of the authors is that the setup of a nine-color panel has to be performed empirically as theoretical considerations may be inappropriate for the analysis under such complex experimental conditions. The second conclusion is that even under strictly controlled conditions results may vary between laboratories. Although these approaches seem still far away from routine applications high level routine clinical laboratories begin to use polychromatic cytometry with six or more colors as routine procedures (32,33). Presently, it seems to be far in the future that polychromatic FCM with up to 17 colors becomes routine in clinical diagnosis or HCS (31). This may change quickly, due to progress in instrument and dye development and bioinformatics (reviewed in Ref. 34). Indeed, the communication (35) asks the right question: ‘‘How many events is enough?’’ to tell that under polychromatic conditions a unique subset of cells is really present or just an artifact. Essentially, HCS is still the domain of automated imaging. Whereas analysis by FCM provides a platform for highthroughput imaging, it is generally an order of magnitude lower in throughput. Thus, hitherto HC analysis by imaging is a tool for the analysis of a limited number of cells (i.e. a few hundred). High-speed confocal based instruments analyze up to 200 cells per second (36); laser scanning cytometers up to 400 cells per second (37). This means that they are not well suited to analysis of hundred-thousands to millions of cells as relevant in cellular immunodiagnostics. This makes HCS so 382
far an ideal tool for drug discovery and cell based research where mainly relatively homogeneous populations of cultured cells are the objects of interest. This may change in future: Paar et al. (38) (Commentary by Nagy (39)) introduces a high-throughput method to monitor protein function on the plasma membrane by total internal reflection fluorescent microscopy (TIRFM). This method is not only more rapid than traditional confocal analysis (with up to 1,000 cells per second it gets close to the performance of bench top FCMs) but has also an over 4-fold increase in detection sensitivity. A major challenge for the application of HC analysis in FCM is the lack of automated data analysis systems that are suitable for routine applications to parallel high-throughput technological advancements. This lack has hindered highthroughput FCM from reaching its full potential. Two manuscripts (40,41) address this issue. Boedigheimer and Ferbas (40) developed a mixture modeling (MM) method, and Rogers et al. (40) an automated tool termed Cytometric Fingerprinting (CF). MM is a new and alternative assay to automated clustering and multidimensional classification (42,43). It is based on the analysis of a mixture of Gaussian distributions in the FCM data. CF, by contrast, relies on multivariate probability distribution functions. Both approaches were able to automatically analyze FCM data and detect e.g. spiked cells in mixed populations. When automated data analysis can be included into standard operating procedures it has to become routine in the future. Automation can provide an un-biased, operator independent, approach for multiplexed-data analysis. Thus, it will become an important tool for discovery of novel cell populations, expression patterns, among others for the unraveling of the cytome. Automated, rapid, and reliable image segmentation was for a long time (44) and is still a major issue in image cytometry (36,45). Gudla et al. (46) developed a high-throughput image segmentation system to segment nuclei from microscopic images. The authors provide evidence that their system is robust and accurate for segmenting automatically nuclei from images with a high yield. The loosely HCS related manuscripts in this issue intend to further underline the importance of cytometry in this highly evolving and competitive field. Present and future developments will show which type of technology will survive in the drug development and the clinical diagnostics fields. Still a substantial input is required to unify the instrumentational and analytical tools for HC cell-based analyses. Careful cross-platform comparisons and adaptations of quality control and standardization procedures are still needed in order to develop unified standard analytical procedures. This will be a persistent, tedious, and long road. Part of it has already been done in FCM by some unification of instrumentation and mostly unified data formats. This level of comparability is still a long-term challenge for image cytometers as well as automated high-content data analysis software. However, it is crucial if not mandatory to cope with the future challenges of systems biology and cytomics. We may just have started to see the tip of this iceberg. A Focus on High-Content Cytometry
EDITORIAL
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