Introduction A: Recent Advances in Cytometry Instrumentation, Probes, and Methods—Review
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CHAPTER 1 Introduction A: Recent Advances in Cytometry Instrumentation, Probes, and Methods—Review y y y Arkadiusz Pierzchalski,*, Anja Mittag*, and Attila Tarnok*, *Department of Pediatric Cardiology, Heart Centre, University of Leipzig, Germany y Translational Centre for Regenerative Medicine (TRM), University of Leipzig, Germany Abstract I. Preface II. Image Cytometry A. Seeing Is Believing B. Image Cytometry Applications III. New Instrumentations A. Multiparametric Capabilities of Image Cytometry B. The Merge of Systems C. Modifications of the Well-Known – The Microcytometers D. Better – Easier – Affordable E. Off the Beaten Track – Non-fluorescent Analyses IV. New Probes, Components, and Methods A. Let There Be Light B. More Colorful World C. Revealing Cell Fates V. New Strategies for Data Analysis VI. Perspective References Abstract Cytometric techniques are continually being improved, refined, and adapted to new applications. This chapter briefly outlines recent advances in the field of cytometry with the main focus on new instrumentations in flow and image cytometry as well as new probes suitable for multiparametric analyses. There is a remarkable METHODS IN CELL BIOLOGY, VOL 102 0091-679X/10 $35.00 Copyright 2011, Elsevier Inc. All rights reserved. 1 DOI 10.1016/B978-0-12-374912-3.00001-8 2 Arkadiusz Pierzchalski et al. trend for miniaturizing cytometers, developing label-free and fluorescence-free analytical approaches, and designing ‘‘intelligent’’ probes. Furthermore, new meth- ods for analyzing complex data for extracting relevant information are reviewed. I. Preface Cytometry is the art and science of measuring phenotypical and functional characteristics of thousands to millions of cells in complex cell systems. Just a few decades ago, it became evident in cellular sciences that the scientific and diagnostic value of analyzing single-cell constituents that may be genes or gene products reached its limits. Cellular systems rely on a multitude of pathways reacting on external or internal stimuli and perturbations. This cognition gave rise to new disciplines in biomedical science with the ‘‘wholistic’’ approach of determining system-wide pattern alterations, termed ‘‘omics’’. The first omics approach was genomics soon followed by proteomics, cytomics, lipidomics, etc. Since the entire pattern of cell features changes in response to particular stimuli, the observation of the system in its totality (the ‘‘omics’’ approach), whether it is genome, proteome, etc., is closer to reality than the investigation of individual parameters alone. Investigation of complex cell systems by the ‘‘bulk’’ techniques such as Western immunoblotting not allowing for the distinction between properties of their individ- ual (cellular) members runs into the pitfall of overlaying specific signals of single highly relevant cells with that of an overbearing background (Szaniszlo et al., 2006). Furthermore, the information on heterogeneity of cell populations, which is critical in many situations (e.g., to identify individual cells that are drug-resistant), is not available. This means that the system-wide determination also needs to recognize and analyze individual cells. Techniques that allow for obtaining information for cytomics or single-cell genomics and proteomics of hundreds to millions of indi- vidual cells would be advantageous. This perspective received particular attention by the progress in stem cell research, which opened new vistas to revolutionize in near future cellular therapy and regenerative medicine. The potential of applications of stem cells in clinical medicine, in particular, distinctly exemplifies why there is a need for multiplexed and high-speed single- cell analysis. Each organ appears to have its own specialized stem cells type essential for its regeneration. However, these cells are extremely rare and can only be unequiv- ocally identified by the characteristic expression pattern of a multitude of markers (Tarnok et al., 2010b). Nowadays, stem cell characterization covers practically all possible progenitor cells from many tissues, for example, liver, cornea cells, hemato- poietic cells, endothelial cells, very small embryonic stem cells, vascular progenitors from adipose tissue, and others (Adams et al., 2009; Challen et al., 2009; Mobius-€ Winkler et al., 2009; Porretti et al., 2010; Takacs et al., 2009; Zimmerlin et al., 2010; Zuba-Surma et al., 2008). Although presently not yet uniformly accepted in the whole scientific community, even tumors seem to have their own stem cells (Fabi an et al., 2009), which may evoke new therapeutic strategies for curing cancer. 1. Recent Advances in Cytometry Instrumentation, Probes, and Methods 3 Cytometry is the technology and science of choice for precisely identifying rare cells and describing the heterogeneity of cell populations in mixed systems. With all its different facets like flow cytometry (FCM), image cytometry, or chip-based technology, it quantitatively scrutinizes individual cells. This is based on binding of or reacting with a plethora of specific detecting molecules but is also realized by technologies that rely on physical properties such as electrical impedance or Raman light scattering. Although the foundations of cytometry date back to the mid-1960s, ongoing technological advances make a regular upgrade of the state-of-the-art technologies, new assays with all their advances, and consequently novel perspec- tives in cell analysis necessary. Single-cell and multiplexed analyses are presently the shooting stars of biotech- nology and they will alter our view on many mechanisms of biological processes, enforce completely innovative ways for diagnosis and treatment, and will improve the development of new drugs. This will be briefly outlined in the following and detailed in specific sections within this and the following chapters of this book. II. Image Cytometry Image cytometry, also termed slide-based cytometry or laser scanning cytometry (LSC) or image-assisted cytometry, is a high-content screening method. It is char- acterized by high reproducibility, capability of high-throughput analysis, and it can be standardized similar to FCM (Mittag and Tarnok, 2009). Image cytometry was used for many different applications and a wide range of biological, preclinical, and clinical materials (Gerstner et al., 2009; Harnett, 2007; Pozarowski et al., 2006; Rew et al., 2006). While FCM is unsurpassed in routine analysis of blood specimens, the analysis of solid tissue possesses unique challenges for which this technology is less suited. Most important in tissue analysis is to investigate cells in their spatial and topo- logical context. Most often there is only limited amount of sample material available for the detailed functional and/or phenotypic analysis of specific cell subsets. In this context, image cytometry is a valuable tool for clinical analysis. It is feasible to perform diagnosis even from extremely small and/or hypocellular specimens such as body fluids and fine-needle aspiration biopsies (Gerstner et al., 2002; Mocellin et al., 2001, 2003; Pozarowski et al., 2006). Cells or cell consti- tuents of interest are generally tagged and identified by fluorescence labels. Measurement is comparable to FCM and fluorescence microscopy. This is making obtained data and its analysis familiar for users of these instruments. It is also possible to automatically image whole slides in multiple colors (Varga et al., 2009). Also chromatically stained tissue, more familiar in pathology and immu- nohistochemistry (IHC), can be quantitatively analyzed by image cytometry. Advanced image analysis was also applied for automated classification of inflam- mation in histological sections (Ficsor et al., 2008). LSC has been shown to be a reliable and efficient, relatively high-throughput, and high-content automated 4 Arkadiusz Pierzchalski et al. technology to quantify morphological endpoints in IHC labeled and nonfluores- cent tissue samples (Peterson et al., 2008). A. Seeing Is Believing Data analysis based on images allows for unambiguous identification of cells, cell aggregates, or biological constituents of interest based on morphology or fluores- cence labeling. Data seem to be more reliable if one can verify results by eye as ‘‘a picture is worth a thousand dots’’ (Bisha and Brehm-Stecher, 2009). Morphometric image analysis allows for extracting a list of numerical parameters. Identified objects can be described in rates for shape, texture, size, intensity, etc. It is possible to train classification algorithms to discriminate between cell phe- notypes (Pepperkok and Ellenberg, 2006) with high accuracy. However, these algo- rithms are limited in recognizing new phenotypes. Suitable for that purpose are ‘‘intelligent’’ classification systems that automatically learn and define new classes with similar characteristics (Pepperkok and Ellenberg, 2006). It is a valuable tool in location proteomics, for quantitative classification of intracellular structures (Huh et al., 2009; Newberg et al., 2009; Shariff et al., 2010). Also live cells can be imaged and monitored over time. Cell motility complicates direct retrieval of cell informa- tion from single captured images, but improved cell tracking algorithms allow for connecting objects in time, tracking of object splitting (cell division), or merging (cell fusion). Analysis of time-lapsed data sets provides information of individual cell cycle progression (Chen et al., 2006), cell migration (Brown et al., 2010; Degerman et al.,