Advanced Optical Flow Cytometry E-Book

• Herausgeber: Wiley-VCH
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1379

Veröffentlichungsjahr: 2011

Beschreibung

A detailed look at the latest research in non-invasive in vivo cytometry and its applications, with particular emphasis on novel biophotonic methods, disease diagnosis, and monitoring of disease treatment at single cell level in stationary and flow conditions.
This book thus covers the spectrum ranging from fundamental interactions between light, cells, vascular tissue, and cell labeling particles, to strategies and opportunities for preclinical and clinical research. General topics include light scattering by cells, fast video microscopy, polarization, laser-scanning, fluorescence, Raman, multi-photon, photothermal, and photoacoustic methods for cellular diagnostics and monitoring of disease treatment in living organisms. Also presented are discussions of advanced methods and techniques of classical flow cytometry.

Leseprobe

Table of Contents

Related Titles

Title Page

Copyright

Preface

List of Contributors

Chapter 1: Perspectives in Cytometry

1.1 Background

1.2 Basics of Cytometry

1.3 Cytomics

1.4 Cytometry—State of the Art

1.5 Perspectives

1.6 Conclusion

References

Chapter 2: Novel Concepts and Requirements in Cytometry

2.1 Introduction

2.2 Fluorescence Microscopy

2.3 Fluorescence Reader Systems

2.4 Microfluidics Based on Optical Tweezers

2.5 Conclusion

Acknowledgment

References

Chapter 3: Optical Imaging of Cells with Gold Nanoparticle Clusters as Light Scattering Contrast Agents: A Finite-Difference Time-Domain Approach to the Modeling of Flow Cytometry Configurations

3.1 Introduction

3.2 Fundamentals of the FDTD Method

3.3 FDTD Simulation Results of Light Scattering Patterns from Single Cells

3.4 FDTD OPCM Nanobioimaging Simulation Results

3.5 Conclusion

Acknowledgment

References

Chapter 4: Optics of White Blood Cells: Optical Models, Simulations, and Experiments

4.1 Introduction

4.2 Optical Models of White Blood Cells

4.3 Direct and Inverse Light-Scattering Problems for White Blood Cells

4.4 Experimental Measurement of Light Scattering by White Blood Cells

4.5 Conclusion

Acknowledgments

References

Chapter 5: Optical Properties of Flowing Blood Cells

5.1 Introduction

5.2 Blood Physiology

5.3 Complex Refractive Index of Hemoglobin

5.4 Light Propagation in Turbid Media

5.5 Method for the Determination of Optical Properties of Turbid Media

5.6 Optical Properties of Red Blood Cells

5.7 Optical Properties of Plasma

5.8 Optical Properties of Platelets

5.9 Comparison of Optical Influences Induced by Physiological Blood Parameters

5.10 Summary

Acknowledgments

References

Chapter 6: Laser Diffraction by the Erythrocytes and Deformability Measurements

6.1 Introduction

6.2 Parameters of the Erythrocytes

6.3 Parameters of the Ektacytometer

6.4 Light Scattering by a Large Optically Soft Particle

6.5 Fraunhofer Diffraction

6.6 Light Scattering by a Transparent Elliptical Disc

6.7 Light Scattering by an Elliptical Disc with Arbitrary Coordinates of the Disc Center

6.8 Light Diffraction by an Ensemble of Particles

6.9 Light Diffraction by Particles with Random Coordinates

6.10 Light Scattering by Particles with Regular Coordinates

6.11 Description of the Experimental Setup

6.12 Sample Preparation Procedure

6.13 Examples of Experimental Assessment of Erythrocyte Deformability in Norm and Pathology

6.14 Conclusion

References

Chapter 7: Characterization of Red Blood Cells' Rheological and Physiological State Using Optical Flicker Spectroscopy

7.1 Introduction

7.2 Cell State-Dependent Mechanical Properties of Red Blood Cells

7.3 Flicker in Erythrocytes

7.4 Experimental Techniques for Flicker Measurement in Blood Cells

7.5 The Measured Quantities in Flicker Spectroscopy and the Cell Parameters Monitored

7.6 Flicker Spectrum Influence by Factors of Various Nature

7.7 Membrane Flicker and Erythrocyte Functioning

7.8 Flicker in Other Cells

7.9 Conclusions

References

Chapter 8: Digital Holographic Microscopy for Quantitative Live Cell Imaging and Cytometry

8.1 Introduction, Motivation, and Background

8.2 Principle of DHM

8.3 DHM in Cell Analysis

8.4 Conclusion

Acknowledgment

References

Chapter 9: Comparison of Immunophenotyping and Rare Cell Detection by Slide-Based Imaging Cytometry and Flow Cytometry

9.1 Introduction

9.2 Comparison of Four-Color CD4/CD8 Leukocyte Analysis by SFM and FCM Using Qdot Staining

9.3 Comparison of Leukocyte Subtyping by Multiparametric Analysis with LSC and FCM

9.4 Absolute and Relative Tumor Cell Frequency Determinations

9.5 Analysis of Drug-Induced Apoptosis in Leukocytes by Propidium Iodide

9.6 Conclusion

Acknowledgment

References

Chapter 10: Microfluidic Flow Cytometry: Advancements toward Compact, Integrated Systems

10.1 Introduction

10.2 On-Chip Flow Confinement

10.3 Optical Detection System

10.4 On-Chip Sorting

10.5 Conclusion

Acknowledgments

References

Chapter 11: Label-Free Cell Classification with Diffraction Imaging Flow Cytometer

11.1 Introduction

11.2 Modeling of Scattered Light

11.3 FDTD Simulation with 3D Cellular Structures

11.4 Simulation and Measurement of Diffraction Images

11.5 Summary

Acknowledgments

References

Chapter 12: An Integrative Approach for Immune Monitoring of Human Health and Disease by Advanced Flow Cytometry Methods

12.1 Introduction

12.2 Optimized Protocols for Advanced Flow Cytometric Analysis of Human Samples

12.3 Reagents for Advanced Flow Cytometric Analysis of Human Samples

12.4 Conclusion: The Future of Advanced Flow Cytometry in Human Research

Acknowledgments

Abbreviations

References

Chapter 13: Optical Tweezers and Cytometry

13.1 Introduction

13.2 Optical Tweezers: Manipulating Cells with Light

13.3 Use of Optical Tweezers for the Measurement of Viscoelastic Parameters of Cells

13.4 Cytometry with Raman Optical Tweezers

13.5 Cell Sorting

13.6 Summary

References

Chapter 14: In vivo Image Flow Cytometry

14.1 Introduction

14.2 State of the Art of Intravital Microscopy

14.3 In vivo Lymph Flow Cytometry

14.4 High-Resolution Single-Cell Imaging in Lymphatics

14.5 In vivo Blood Flow Cytometry

14.6 Conclusion

Acknowledgments

References

Chapter 15: Instrumentation for In vivo Flow Cytometry – a Sickle Cell Anemia Case Study

15.1 Introduction

15.2 Clinical Need

15.3 Instrumentation

15.4 Image Processing

15.5 Modeling

15.6 Device Design – Sickle Cell Anemia Imaging System

15.7 Imaging Results – Sickle Cell Anemia Imaging System

15.8 Discussion and Future Directions

References

Chapter 16: Advances in Fluorescence-Based In vivo Flow Cytometry for Cancer Applications

16.1 Introduction

16.2 Background: Cancer Metastasis

16.3 Clinical Relevance: Role of CTCs in Cancer Development and Response to Treatment

16.4 Current Methods

16.5 In vivo Flow Cytometry (IVFC)

16.6 Single-Photon IVFC (SPIVFC)

16.7 Multiphoton IVFC (MPIVFC)

16.8 Summary and Future Directions

Acknowledgments

References

Chapter 17: In vivo Photothermal and Photoacoustic Flow Cytometry

17.1 Introduction

17.2 Photothermal and Photoacoustic Effects at Single-Cell Level

17.3 PT Technique

17.4 Integrated PTFC for In vivo Studies

17.5 Integrated PAFC for In vivo Studies

17.6 In vivo Lymph Flow Cytometery

17.7 In vivo Mapping of Sentinel Lymph Nodes (SLNs)

17.8 Concluding Remarks and Discussion

Acknowledgments

References

Chapter 18: Optical Instrumentation for the Measurement of Blood Perfusion, Concentration, and Oxygenation in Living Microcirculation

18.1 Introduction

18.2 Xe Clearance

18.3 Nailfold Capillaroscopy

18.4 LDPM/LDPI

18.5 Laser Speckle Perfusion Imaging (LSPI)

18.6 TiVi

18.7 Comparison of TiVi, LSPI, and LDPI

18.8 Pulse Oximetry

18.9 Conclusions

Acknowledgments

References

Chapter 19: Blood Flow Cytometry and Cell Aggregation Study with Laser Speckle

19.1 Introduction

19.2 Laser Speckle Contrast Imaging

19.3 Investigation of Optimum Imaging Conditions with Numerical Simulation

19.4 Spatio-Temporal Laser Speckle Contrast Analysis

19.5 Fast Blood Flow Visualization Using GPU

19.6 Detecting Aggregation of Red Blood Cells or Platelets Using Laser Speckle

19.7 Conclusion

Acknowledgments

References

Chapter 20: Modifications of Optical Properties of Blood during Photodynamic Reactions In vitro and In vivo

20.1 Introduction

20.2 Description and Brief History of PDT

20.3 PDT Mechanisms

20.4 Blood and PDT

20.5 Properties of Blood, Blood Cells, and Photosensitizers: Before Photodynamic Reaction

20.6 Photodynamic Reactions in Blood and Blood Cells, Blood Components, and Cells

20.7 Types of Photodynamic Reactions in Blood: In vitro versus In vivo

20.8 Blood Sample In vitro as a Model Studying Photodynamic Reaction

20.9 Monitoring of Oxygen Consumption and Photobleaching in Blood during PDT In vivo

20.10 Photodynamic Disinfection of Blood

20.11 Photodynamic Therapy of Blood Cell Cancer

20.12 Summary

Acknowledgments

Glossary

References

Index

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The Editor

Prof. Valery V. Tuchin

Saratov State University

Optics and Biophotonics

Saratov, Russian Feeration

[email protected]

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Preface

Flow cytometry was invented in the late 1960s, and since then the flow cytometer has become an indispensable tool in modern research and clinical laboratories [1–7]. Beyond the routine usage, new trends can be observed in the development of flow cytometric techniques. The main technological improvements include high-speed sorting, phase-sensitive flow cytometry, multicolor flow cytometry, high-throughput multiplex bead assays, and spectral detection, and it provides the basis for extensive data collection.

Classical flow cytometry (FC) uses an instrument system for making, processing, and displaying one or more measurements on individual cells in flowing cell suspension [1–7]. Cells may be stained with one or more fluorescent dyes specific to cell components of interest, for example, DNA, and fluorescence of each cell is measured as cells one by one rapidly transverse the excitation beam (laser or mercury arc lamp). Fluorescence provides a quantitative measure of various biochemical and biophysical properties of the cell. Other measurable optical parameters, which are applicable to the measurement of cell size, shape, density, granularity, and stain uptake, include light absorption, light scattering, and polarization degree.

Numerous clinical and research applications, especially in anatomic pathology for detection and study of malignant lesions, use the so-called image cytometry. This technique encompasses morphometry and densitometry as measuring techniques, and neural networks and expert systems for processing of collected data.

Another cytometric technique is the microscope-based laser scanning cytometry, which allows one to make fluorescence measurements and topographic analysis on individual cells. Laser-induced fluorescence of labeled cellular specimens is detected using multiple discrete wavelengths, and the spatially resolved data are processed to quantify cell proliferation, apoptosis, gene expression, protein transport, and other cellular processes. For instance, confocal microscopy and two-photon imaging techniques are able to detect fluorescently labeled cells not only in vitro but also in vivo [8].

Improvements in image cytometric techniques speeded up in the last two decades, applying more and more sensitive detectors and introducing nonlinear optics (two photon excitation) for lifetime measurements (fluorescence lifetime imaging microscopy, FLIM). The latest developments were able to break the diffraction limit as in the scanning near-field optical microscopy (SNOM), total internal reflection microscopy (TIRFM), fluorescence resonance energy transfer (FRET), stimulated emission depletion (STED), and 4Pi and multiobjective microscopy.

Conventional FC is currently the method of choice for rapid quantification of cells, but it requires invasive extraction of cells from a living organism and associated procedures (e.g., fluorescence labeling and sorting), which may lead to unpredictable artifacts, and prevents long-term cell monitoring in the native biological environment. Among in vivo techniques, both nonoptical (e.g., PET and MRI) and optical (e.g., scattering, fluorescence, confocal, and multiphoton) techniques can be used for visualizing only single static or slowly migrating cells [8–10]. To detect fast moving cells in blood and lymph flows, a number of methods providing in vivo FC have been developed [11–21]. In particular, the principle of FC has been adapted to the in vivo monitoring of labeled cells in ear blood vessels, and a few modifications of in vivo flow cytometers that are capable of real-time confocal detection of fluorescently labeled cells in both the arterial and venous circulation of small animals, have been built [12–14].

The alternative photothermal (PT) and photoacoustic (PA) techniques for in vivo blood FC, which do not require cell labeling and are not sensitive to light scattering and autofluorescence background, have also been recently suggested [15–17]. These techniques have potential application in the study of normal and abnormal cells in their native condition in blood or lymph flows in vivo, including molecular imaging, studying the metabolism and pathogenesis of diseases at a cellular level, and monitoring and quantifying metastatic and apoptotic cells and/or their responses to therapeutic interventions (e.g., drug or radiation).

Video microscopy and particle tracking methods adapted and integrated with an ultrahigh-speed imaging camera were used to measure lymph velocities throughout the entire lymphatic contraction cycle in the rat mesentery [18–23]. In vivo, label-free, high-speed (up to 10 000 with the potential for 40 000 fps), high-resolution (up to 300 nm) optical imaging of circulating individual erythrocytes, leukocytes, and platelets in fast blood flow has been developed [22]. Different potential applications of in vivo digital video microscopy include visualization of circulating cells and their deformability in lymph and blood flows and the study of the kinetics of platelets and leukocyte rolling, with high sensitivity and resolution.

Multiphoton fluorescence flow cytometry and its confocal and fiber-optic modifications hold a great promise for in vivo monitoring of multiple circulating cell populations in blood and lymph flows by exciting and detecting the emission from multiple fluorophores, such as fluorescent proteins and exogenous chromophores, important for multilabeling of cells of interest [24, 25].

There are many books on cytometry published since the 1980s (see, for example, the list on the web site [1]). They could be classified as books on general flow cytometry and cell sorting, clinical cytometry, and microscopic and imaging cytometry. The most recent and comprehensive are Refs [2, 3, 5, 8–10, 26–30]. The book by Shapiro [5] is the fourth edition on classic flow cytometry. This is one of the best textbooks, and covers well the field of practical flow cytometry prior to 2003 well. Recently, two books on flow cytometry and cellular diagnostics have been published in German and French [26, 27]. Both books were written and edited by well-known experts in the field. Karger has published the English translation of the German book edited by Ulrich Sack, Attila Tárnok, and Gregor Rothe, which is a bestseller in German [28]. Practical cytometry protocols have been given in the third edition of the book edited by Michael G. Ormerod [29]. The recent second edition of the book by Wojciech Gorczyca is more clinically and practically oriented [30]. Michael G. Ormerod has also designed an introductory book “to give that knowledge, aiming at people coming to flow cytometry for the first time,” in which all the major applications in mammalian biology are covered [31].

While the above-mentioned books describe clinical diagnostic methods and receipts of their applications, the current book is more research oriented and opens new perspectives in the development of flow cytometry for in vivo studies. It contains novel results of basic research on light scattering by different types of cells, which are very important for the improvement of already existing technologies and for designing new technologies in optical cytometry. The recently invented and fast-moving-to-practice methods of in vivo flow cytometry, based on ultrafast video and phase intra-vital microscopy and light scattering, diffraction, speckle, fluorescence, multiphoton, Raman, photothermal, and photoacoustic phenomena, are presented in the book.

In Chapter 1, Perspectives in Cytometry by Anja Mittag and Attila Tárnok, in addition to definitions, historical aspects, and the importance of cytometry in the development of biology and medicine, its prospective application as a science and diagnostic tool are discussed – in particular, for comprehensive analyses, on the basis of the simultaneous detection of several parameters, of up to millions of individual cells in one sample.

Chapter 2 by Herbert Schneckenburger et al., Novel Concepts and Requirements in Cytometry, presents slide-based cytometry techniques and the concepts of high content screening (HCS) where detailed information is accumulated from a single cell or examination of multicellular spheroids where 3D detection methods are required. These techniques include microscopic setups, fluorescence reader systems, and microfluidic devices with micromanipulation, for example, cell sorting.

In Chapter 3 by Stoyan Tanev et al., Optical Imaging of Cells with Gold Nanoparticle Clusters as Light Scattering Contrast Agents: A Finite-Difference Time-Domain Approach to the Modeling of Flow Cytometry Configurations, a brief summary of different formulations of the finite-difference time-domain (FDTD) approach is presented in the framework of its strengths, for cytometry in general and for potential applications in in vivo flow cytometry based on light scattering, including nanoscale targets. This chapter focuses on comparison of light scattering by a single biological cell alone under controlled refractive index matching conditions and by cells labeled using gold nanoparticle clusters. The optical phase contrast microscopy (OPCM) is analyzed as a prospective modality for in vivo flow cytometry.

In Chapter 4 by Valeri P. Maltsev et al., Optics of White Blood Cells: Optical Models, Simulations, Experiments, a state-of-the-art summary of analytical and numerical simulating methods and experimental approaches for precise description and detection of elastic light scattering from white blood cells (WBCs) are presented. The discussion of the instrumental tools for measurement of light scattering lays emphasis on scanning flow cytometry. This chapter gives some basis for understanding the methods, techniques, and experimental results presented in the following chapters, as it presents solutions for the inverse light-scattering problem to obtain cellular characteristics from light scattering data.

The optical properties of blood are discussed in Chapter 5 by Martina Meinke et al., Optical Properties of Flowing Blood Cells. Authors use the transport theory accounting for the multiplicity of light scattering events, where the optical properties of blood are described by the absorption and scattering coefficients and the anisotropy factor. The double integrating sphere measurement technique combined with inverse Monte Carlo simulation is applied for extraction of the optical parameters of undiluted blood. It is shown that the influence of the shear rate and osmolarity have to be taken into account when the blood is prepared ex vivo and the physiological environment cannot be ensured.

In Chapter 6, Laser Diffraction on RBC and Deformability Measurement by Alexander V. Priezzhev et al., RBC shape variability and deformability as intrinsic properties, their strong relation to RBC aggregation and blood rheology, and the determination of the general hemorheologic status of human organism are considered. It is shown that laser diffraction can be efficiently applied to quantitatively assess the deformability properties of RBC in the blood of a particular individual. The theoretical basis of diffractometry and implementation of particular experimental techniques to experimental and clinical measurements, as well as potentialities and pitfalls of the technique are discussed.

The principles and fundamentals of flicker spectroscopy as a quantitative tool to measure static and dynamic mechanical properties of composite cell membrane are presented in Chapter 7, Characterization of Red Blood Cells Rheological and Physiological State Using Optical Flicker Spectroscopy by Vadim L. Kononenko. These mechanical properties are associated with cell membrane and cytoplasm molecular organization and composition and cell metabolic activity and could be characteristic not only for RBC, but for other blood cell types as well. Microscope-based flicker spectroscopy technique in combination with quantitative phase imaging and fluorescence microscopy can be easy integrated into the slide-based cytometry arrangement. As the author states, the approximate models developed are good, but not enough to reconstruct precisely the details of erythrocyte cell membrane mechanical properties; thus a more advanced theory of flicker spectroscopy is needed.

Chapter 8, Digital Holographic Microscopy for Quantitative Live Cell Imaging and Cytometry, by Björn Kemper and Jürgen Schnekenburger, demonstrates the principles and applications of quantitative cell imaging using digital holographic microscopy (DHM). The quantitative phase contrast imaging, cell thickness determination, multifocus imaging, and 2D cell tracking provided by DHM show that it is a suitable method for the label-free characterization of dynamic live cell processes involving morphological alterations and migration and for the analysis of cells in 3D environments. Examples and illustrations of applicability of the technique in tumor cell biology and for the development of improved systems for drug and toxicity testing are presented.

Chapter 9 by József Bocsi et al., Comparison of Immunophenotyping and Rare Cell Detection by Slide-Based Imaging Cytometry and by Flow Cytometry, allows one to get answers on the following questions: are flow cytometry (FC) and slide-based cytometry (SBC) comparable? What is the type of cytometer and analysis technique that should be chosen for the given biological problem to be solved? Answers are illustrated by applying scanning fluorescence microscope (SFM) to determine of CD4/CD8 T cell ratio, laser scanning cytometer (LSC) to multiparametric leukocyte phenotyping and apoptosis analysis on the basis of DNA content measurements, and SFM and LSC to rare and frequent tumor cell detection.

A brief  overview and discussion of recent progress in microfluidic flow cytometry, including the main components of full scale flow cytometers, containing systems for fluidic control, optical detection and cell sorting, each of which are being developed into on-chip microfluidic platforms, are given by Shawn O. Meade et al. in Chapter 10, Microfluidic Flow Cytometry: Advancements Toward Compact, Integrated Systems.

In Chapter 11 by Xin-Hua Hu and Jun Q. Lu, Label-Free Cell Classification with a Diffraction Imaging Flow Cytometer, aiming at the accurate modeling of light scattering from biological cells with realistic cell structures and the development of a high contrast diffraction imaging flow cytometer for experimental study, the authors are focused on the application of the FDTD method for modeling of coherent light scattering from cells. Numerical and experimental results are presented and their implications to future improvement of the flow cytometry are discussed.

In Chapter 12 by Rabindra Tirouvanziam et al., An Integrative Approach for Immune Monitoring of Human Health and Disease by Advanced Flow Cytometry Methods, the authors show key steps to move past the current limitations and truly enable the use of advanced flow cytometry tools for human research, promoting simplified, low-cost, and better standardized methods for sample collection, highlighting the enormous opportunities for research on reagents available for advanced flow cytometry analysis of human samples, and novel insights into relations of human immunity with age, gender, ethnicity, environmental exposure, health conditions, and therapies.

R. Dasgupta and P.K. Gupta, in Chapter 13, Optical Tweezers and Cytometry, give a brief introduction to optical tweezers and an overview of their use in cytometric applications, including measurements of viscoelastic properties of cells, in particular RBC, and Raman spectroscopic studies at single cell level. A few examples illustrating the potential of this approach for cytometric applications are also presented.

Chapter 14 by Valery V. Tuchin et al., In vivo Image Flow Cytometry, presents one of the novel approaches in flow cytometry – in vivo video imaging digital flow cytometry. The fundamentals and instrumentation of video imaging flow cytometry, as well as spatial and temporal resolution of the method, are discussed. Experimental animal models, data on imaging and detection of individual cells in lymph and blood flows, and cell velocity measurements in lymph and blood vessels are presented and discussed. Intravessel RBC deformability measurement, monitoring of intralymphatic cell aggregation, and many other cell interaction phenomena are demonstrated and quantified. Perspectives of the technique for disease diagnostics and monitoring and cell flow response on drugs, pollutions, and toxins are shown.

Chapter 15 by Stephen P. Morgan and Ian M. Stockford, Instrumentation for In Vivo Flow Cytometry: A Sickle Cell Anemia Case Study, discusses label-free monitoring of the properties of circulating blood cells for the in vivo monitoring of sickle cell anemia. For discriminating sickled RBCs in a background of normal cells, absorption measurements associated with sickle cell lower oxygen saturation and polarization measurements to identify their polymerization ability via cell adhesion to the vascular walls and, thus, more slow flow, are used. Illumination methods overcoming surface reflections, such as orthogonal polarization spectral imaging, dark field epi-illumination, and sidestream dark field illumination, are analyzed. In humans blood cell imaging has been performed either on the lower lip or under the tongue where the superficial mucosal tissue above the microcirculation is thinner than at other sites on the body. All steps of clinical instrumentation design, starting from discussion of the clinical needs for the measurements, the illumination and detection requirements, image processing methods for correction of image distortions, and a Monte Carlo model of the image formation process, up to engineering of the clinical prototype and presentation of clinical results are highlighted by the authors.

Accounting for the clinical importance of detection and quantification of circulating tumor cells (CTCs) for cancer diagnosis, staging, and treatment, in Chapter 16, Advances in Fluorescence-Based In Vivo Flow Cytometry for Cancer Applications, Cherry Greiner and Irene Georgakoudi, review the principles and instrumentation designs of fluorescence-based in vivo flow cytometry (IVFC) and present data on the in vivo quantification of CTCs. The confocal and multiphoton microscopic techniques and systems adapted for the detection of fluorescently labeled CTCs in blood vessels are described. The noninvasive nature of IVFC systems and their capability to provide sensitive, continuous and dynamic monitoring of CTCs in blood flow are proved.

Chapter 17, In Vivo Photothermal and Photoacoustic Flow Cytometry by Valery V. Tuchin et al., is devoted to presentation of the prospective approaches of IVFC that use laser-induced photothermal (PT) and photoacoustic (PA) effects. The authors analyze in detail integrated IVFC techniques combining a few different methods, such as PT imaging conjugated with thermo-lens and PA imaging, transmittance digital microscopy, and phase-sensitive and fluorescence imaging. The unique capabilities of the PT/PAFC (photoacoustic flow cytometry) technique for IVFC are illustrated in many examples of in vivo and ex vivo studies within lymph and blood vessels of animal models. Data on cell velocity measurements, detection, and real-time monitoring of circulating blood and lymph cells, bacteria, CTCs, contrast agents, and nanoparticles, and on quantification of cell interactions are presented. Perspectives of PT/PAFC technique for early diagnostics of cancer are discussed.

Martin J. Leahy and Jim O'Doherty, in Chapter 18, Optical Instrumentation for the Measurement of Blood Perfusion, Concentration, and Oxygenation in Living Microcirculation, compare the operation of an established microcirculation imaging technique, such as laser Doppler perfusion imaging (LDPI), which, for example, has been shown to accurately assess burn depth, with laser speckle perfusion imaging (LSPI) and tissue viability imaging (TiVi) in human skin tissue using the occlusion and reactive hyperaemia response. On the basis of the presented experimental data they conclude that LSPI and TiVi are both welcome tools in the study of the microcirculation, but care must be taken in the interpretation of the images since blood flow velocity and blood concentration in tissue are essentially different parameters.

In Chapter 19, Blood Flow Cytometry and Cell Aggregation Study with Laser Speckle by Qingming Luo et al., the fundamentals and instrumentation of laser speckle contrast imaging (LSCI) are analyzed with a discussion of important imaging parameters for the optimal imaging conditions. Some recent advancements, such as spatio-temporal algorithm for laser speckle contrast analysis and fast blood flow visualization using GPU (Graphics Processing Unit), are overviewed. The application of LSCI for investigation of RBC aggregation is discussed.

Chapter 20, Modifications of Blood Optical Properties during Photodynamic Reactions In Vitro and In Vivo, by Alexandre Douplik et al., describes photodynamic reactions where blood cells are involved. Change in blood properties on light delivery and modification of photosensitizer (PS) optical properties on interaction with blood at photodynamic therapy (PDT) are considered. The blood cell uptake of PSs and modification of blood optical properties caused by PDT reactions in vitro and in vivo are analyzed. The authors of the chapter believe that IVFC can be applied to study PDT-induced processes related to blood and blood cells.

As it follows from the above, this book is focused on state-of-the-art research in a novel field, noninvasive in vivo cytometry, and its applications, with particular emphasis on the novel biophotonic methods, disease diagnosis, and monitoring of disease treatment at single cell level in stationary and flow conditions. However, discussions of advanced methods and techniques of classical flow cytometry are also presented. The use of photonic technologies in medicine is a rapidly emerging and potentially powerful approach for disease detection and treatment. This book seeks to advance scholarly research that spans from fundamental interactions between light, cells, vascular tissue, and cell labeling particles, to strategies and opportunities for preclinical and clinical research. General topics include light scattering by cells, fast video microscopy, polarization, laser scanning, fluorescence, Raman, multiphoton, photothermal, and photoacoustic methods for cellular diagnostics and monitoring of disease treatment in living organisms. Specific topics include optics of erythrocytes, leukocytes, platelets, and lymphocytes; novel photonic cytometry techniques; in vivo studies using animal models; in vivo cytometry techniques used in humans (mucosal, nail bed, and skin); detection of metastatic cancer cells and labeling of nanoparticles in blood and lymph microvessels; immune monitoring of human health; cytomics in regenerative/predictive medicine; comparison of laser scanning slide-based and flow cytometry, and so on.

The book is for research workers, practitioners, and professionals in the field of cytometry. Advanced students (MS and Ph.D.) as well as undergraduate students specialized in biomedical physics and engineering, biomedical optics and biophotonics, and medical science may use this book as a comprehensive tutorial helpful in the preparation of their research work and diploma. Scientists or professionals and students in other disciplines, such as laser and optical engineering and technology, spectroscopy, tomography, developmental and other directions of biology, tissue engineering, and different specialties in medicine, are also potential readership.

This book represents a valuable contribution by well-known experts in the field of photonic cytometry with their particular interest in a variety of advanced cytometry problems, including in vivo flow cytometry. The contributors are drawn from Canada, China, Denmark, Germany, India, Ireland, Russia, The Netherlands, UK, and the USA. I greatly appreciate the cooperation and contributions of all authors in the book, who have done great work in the preparation of their chapters.

It should be mentioned that this book presents results of international collaborations and exchanges of ideas among many research groups participating in the book project. This book project was supported by many international grants, which are credited in the particular chapters. Here, I would like to mention only a few, PHOTONICS 4 LIFE, which is a consortium of a well-balanced pan-European dimension. The inclusion of the members of this consortium in this book is of great significance, encompassing five chapters. My own work on the book was supported by grants 2.1.1/4989 and 2.2.1.1/2950 of RF Program on the Development of High School Potential and RF Governmental contracts 02.740.11.0484, 02.740.11.0770, and 02.740.11.0879 of The Program “Scientific and Pedagogical Personnel of Innovative Russia.”

I am grateful to Valerie Moliere for her suggestion to publish this book and help on project activation and to Ulrike Werner for the technical editing of the book and communication with the contributors.

I would like to thank all those authors and publishers who freely granted permissions to reproduce their copyrighted works.

I greatly appreciate the cooperation, contributions, and support of all my colleagues from the Optics and Biophotonics Chair and Research-Educational Institute of Optics and Biophotonics of the Physics Department of Saratov State University and the Institute of Precise Mechanics and Control of the Russian Academy of Science.

I express my gratitude to my family, especially to my wife Natalia and grandchildren Dasha, Zhenya, Stepa, and Serafim for their indispensable support, understanding, and patience during my writing and editing of this book.

August 2010 Saratov

References

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10. Masters, B.R. and So, T.P.C. (2004) Handbook of Multiphoton Excitation Microscopy and other Nonlinear Microscopies, Oxford University Press, New York.

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12. Georgakoudi, I., Solban, N., Novak, J., Rice, W., Wei, X., Hasan, T., and Lin, C.P. (2004) In vivo flow cytometry: a new method for enumerating circulating cancer cells. Cancer Res., 64, 5044–5047.

13. Sipkins, D.A., Wei, X., Wu, J.W., Runnels, J.M., Côté, D., Means, T.K., Luster, A.D., Scadden, D.T., and Lin, C.P. (2005) In vivo imaging of specialized bone marrow endothelial microdomains for tumour engraftment. Nature (London), 435, 969–973.

14. Boutrus, S., Greiner, C., Hwu, D., Chan, M., Kuperwasser, C., Lin, C.P., and Georgakoudia, I. (2007) Portable two-color in vivo flow cytometer for real-time detection of fluorescently-labeled circulating cells. J. Biomed. Opt., 12 (2), 020507-1–020507-3.

15. Zharov, V.P., Galanzha, E.I., and Tuchin, V.V. (2005) Photothermal image flow cytometry in vivo. Opt. Let., 30 (6), 628–630.

16. Zharov, V.P., Galanzha, E.I., and Tuchin, V.V. (2006) In vivo photothermal flow cytometry: imaging and detection of individual cells in blood and lymph flow. J. Cell Biochem., 97 (5), 916–930.

17. Zharov, V.P., Galanzha, E.I., Shashkov, E.V., Khlebtsov, N.G., and Tuchin, V.V. (2006) In vivo photoacoustic flow cytometry for monitoring of circulating single cancer cells and contrast agents. Opt. Lett., 31, 3623–3625.

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19. Galanzha, E.I., Tuchin, V.V., and Zharov, V.P. (2005) In vivo integrated flow image cytometry and lymph/blood vessels dynamic microscopy. J. Biomed. Opt., 10, 54018-1–54018-8.

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23. Galanzha, E.I., Tuchin, V.V., and Zharov, V.P. (2007) Advances in small animal mesentery models for in vivo flow cytometry, dynamic microscopy, and drug screening (invited review). World J. Gastroenterol., 13 (2), 198–224.

24. Tkaczyk, E.R., Zhong, C.F., Ye, J.Y. et al. (2008) In-vivo monitoring of multiple circulating cell populations using two-photon flow cytometry. Opt. Commun., 281, 888–894.

25. Chang, Y.-C., Yong Ye, J., Thomas, T.P., Cao, Z., Kotlyar, A., Tkaczyk, E.R., Baker, J.R. Jr., and Norris, T.B. (2010) Fiber-optic multiphoton flow cytometry in whole blood and in vivo. J. Biomed. Opt., 15, 047004.

26. Sack, U., Tárnok, A., and Rothe, G. (eds) (2006) Zelluläre Diagnostik: Grundlagen, Methoden und klinische Anwendungen der Durchflusszytometrie, Karger.

27. Ronot, X., Grunwald, D., Mayol, J.F., and Boutannat, J. (eds) (2006) La Cytométrie en Flux, TEC & DOC, EM Inter.

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31. Ormerod, M.G. (2008) Flow Cytometry – A Basic Introduction, De Novo software, the electronic version of the book (with colour figures), free for you to read, http://flowbook.denovosoftware.com/, page last modified 08:42, 12 Aug 2010 by Michael Ormerod, February 21, 2011.

List of Contributors

József Bocsi

University of Leipzig

Department of Paediatric

Cardiology

Heart Centre

Strümpellstr. 39, 04275

Leipzig

Germany

and

University of Leipzig

LIFE - Leipzig Research Center

for Civilization Diseases

Faculty of Medicine

Philipp-Rosenthal-Str. 27, 04103

Leipzig

Germany

Thomas Bruns

Hochschule Aalen

Institut für Angewandte

Forschung

Beethovenstr. 1

73431 Aalen

Germany

Chun-Hao Chen

University of California

San Diego

Department of Bioengineering

9500 Gilman Drive

MC 0407

La Jolla, CA 92093

USA

Sung Hwan Cho

University of California

San Diego

Department of Materials Science

and Engineering

9500 Gilman Drive

MC 0407

La Jolla, CA 92093

USA

Monique Crubezy

Stanford University School of

Medicine

Beckman Center

279 Campus Drive

Stanford, CA 94305-5318

USA

Raktim Dasgupta

Laser Biomedical Applications

and Instrumentation Division

Raja Ramanna Centre for

Advanced Technology

P.O: CAT

Indore 452013

India

Daisy Diaz

Stanford University

School of Medicine

Beckman Center

279 Campus Drive

Stanford, CA 94305-5318

USA

Alexandre Douplik

Clinical Photonics Lab at

Erlangen Graduate School in

Advanced Optical

Technologies (SAOT)

Friedrich-Alexander Universität

Erlangen-Nürnberg

Erlangen

Germany

and

Medical Photonics Engineering at

Chair of Photonics Technologies

Friedrich-Alexander Universität

Erlangen-Nürnberg

Erlangen

Germany

Moritz Friebel

Laser- und Medizin-Technologie

GmbH

Fabeckstrasse 60–62

14195 Berlin

Germany

Ekaterina I. Galanzha

Saratov State University

Optics and Biophotonics

Department and Research-

Educational Institute of Optics

and Biophotonics

83 Astrakhanskaya St.

Saratov 410012

Russia

and

University of Arkansas

for Medical Sciences

Philips Classic Laser Laboratories

4301 W. Markham St.

Little Rock, AR 72205 7199

USA

Irene Georgakoudi

Tufts University

Biomedical Engineering

Department

4 Colby Street

Medford, MA 02155

USA

Yael Gernez

Stanford University

School of Medicine

Beckman Center

279 Campus Drive

Stanford, CA 94305-5318

USA

Jessica Godin

University of California

San Diego

Department of Electrical and

Computer Engineering

9500 Gilman Drive

MC 0407

La Jolla, CA 92093

USA

Cherry Greiner

Tufts University

Biomedical Engineering

Department

4 Colby Street

Medford, MA 02155

USA

Pradeep Kumar Gupta

Laser Biomedical Applications

and Instrumentation Division

Raja Ramanna Centre for

Advanced Technology

P.O: CAT

Indore 452013

India

Jürgen Helfmann

Laser- und Medizin-Technologie

GmbH

Fabeckstrasse 60–62

14195 Berlin

Germany

Alfons G. Hoekstra

University of Amsterdam

Faculty of Science

Computational Science

Science Park 904

1098 XH Amsterdam

The Netherlands

Xin-Hua Hu

East Carolina University

Department of Physics

MS 563

Greenville, North Carolina 27858

USA

Björn Kemper

University of Muenster

Center for Biomedical

Optics and Photonics

Robert-Koch-Str. 45

48149 Muenster

Germany

Vadim L. Kononenko

Russian Academy of Sciences

N.M. Emanuel Institute of

Biochemical Physics

Kosygin Str. 4

Moscow, 119334

Russia

Julie Laval

Stanford University

School of Medicine

Beckman Center

279 Campus Drive

Stanford, CA 94305-5318

USA

Martin J. Leahy

University of Limerick

Department of Physics

Castletroy

Plassey Park Rd.

Limerick

Ireland

and

Royal College of Surgeons in Ireland

123. St., Stephen's Green

Dublin

Ireland

Pengcheng Li

Huazhong University of

Science and Technology

Britton Chance Center for

Biomedical Photonics

Wuhan National Laboratory for

Optoelectronics

1037 Luoyu Road

Wuhan 430074

China

Yu-Hwa Lo

University of California

San Diego

Department of Electrical and

Computer Engineering

9500 Gilman Drive

MC 0407

La Jolla, CA 92093

USA

Viktor Loshchenov

General Physics Institute

Russian Academy of Science

Moscow

Russia

Andrei E. Lugovtsov

Lomonosov Moscow

State University

Department of Physics

International Laser Center

Moscow 119991

Russia

Jun Q. Lu

East Carolina University

Department of Physics

MS 563

Greenville, North Carolina 27858

USA

Qingming Luo

Huazhong University of

Science and Technology

Britton Chance Center for

Biomedical Photonics

Wuhan National Laboratory for

Optoelectronics

1037 Luoyu Road

Wuhan 430074

China

Megha Makam

Stanford University

School of Medicine

Beckman Center

279 Campus Drive

Stanford, CA 94305-5318

USA

Valeri P. Maltsev

Institute of Chemical Kinetics

and Combustion

Laboratory of Cytometry and

Biokinetics

Institutskaya 3

Novosibirsk 630090

Russia

and

Novosibirsk State University

Physics Department

Pirogova 2

Novosibirsk 630090

Russia

Shawn O. Meade

University of California

San Diego

Department of Electrical and

Computer Engineering

9500 Gilman Drive

MC 0407

La Jolla, CA 92093

USA

Martina C. Meinke

Charité – Universitätsmedizin

Berlin

Klinik für Dermatologie

Venerologie und Allergologie

Charité Campus Mitte

Charitéplatz 1

10117 Berlin

Germany

Anja Mittag

University of Leipzig

Department of Paediatric

Cardiology

Heart Centre

Strümpellstr. 39, 04275 Leipzig

Germany

and

University of Leipzig

Translational Centre for

Regenerative Medicine (TRM)

Leipzig

Philipp-Rosenthal-Str. 55, 04103

Leipzig

Germany

Stephen P. Morgan

University of Nottingham

Electrical Systems and Optics

Research Division

Faculty of Engineering

University Park

Nottingham, NG7 2RD

UK

Sergei Yu. Nikitin

Lomonosov Moscow

State University

Department of Physics

International Laser Center

Moscow 119991

Russia

Jim O'Doherty

Royal Surrey County Hospital

Department of Medical Physics

Egerton Road

Guildford GU2 7XX

UK

James Pond

Lumerical Solutions

201-1290 Homer Street

Vancouver, BC V6B 2Y5

Canada

Alexander V. Priezzhev

Lomonosov Moscow

State University

Department of Physics

International Laser Center

Moscow 119991

Russia

Wen Qiao

University of California

San Diego

Department of Electrical and

Computer Engineering

9500 Gilman Drive

MC 0407

La Jolla, CA 92093

USA

Jianjun Qiu

Huazhong University of

Science and Technology

Britton Chance Center for

Biomedical Photonics

Wuhan National Laboratory for

Optoelectronics

1037 Luoyu Road

Wuhan 430074

China

Herbert Schneckenburger

Hochschule Aalen

Institut für Angewandte

Forschung

Beethovenstr. 1

73431 Aalen

Germany

and

Universität Ulm

Institut für Lasertechnologien in

der Medizin und Messtechnik

Helmholtzstr. 12

89081 Ulm

Germany

Jürgen Schnekenburger

University of Muenster

Department of Medicine B

Gastroenterological Molecular

Cell Biology

Domagkstr. 3A

48149 Muenster

Germany

Ian M. Stockford

University of Nottingham

Electrical Systems and Optics

Research Division

Faculty of Engineering

University Park

Nottingham NG7 2RD

UK

Alexander Stratonnikov

General Physics Institute

Russian Academy of Science

Moscow

Russia

Wenbo Sun

Science Systems and Applications

Inc.

10210 Greenbelt Road

Lanham, MD 20706

USA

Stoyan Tanev

University of Southern Denmark

Department of Technology and

Innovation

Integrative Innovation

Management Unit

Niels Bohrs Alle 1

DK-5230 Odense M

Denmark

Attila Tárnok

University of Leipzig

Department of Paediatric

Cardiology

Heart Centre

Strümpellstr. 39, 04275

Leipzig

Germany

and

University of Leipzig

LIFE - Leipzig Research Center

for Civilization Diseases

Faculty of Medicine

Philipp-Rosenthal-Str. 27, 04103

Leipzig

Germany

Rabindra Tirouvanziam

Stanford University School of

Medicine

Beckman Center

279 Campus Drive

Stanford, CA 94305-5318

USA

Frank S. Tsai

University of California

San Diego

Department of Electrical and

Computer Engineering

9500 Gilman Drive

MC 0407

La Jolla, CA 92093

USA

Valery V. Tuchin

Saratov State University

Optics and Biophotonics

Department and Research-

Educational Institute of Optics

and Biophotonics

83 Astrakhanskaya St.

Saratov 410012

Russia

and

Laboratory on Laser Diagnostics

of Technical and Living Systems

Institute of Precise Mechanics

and Control of RAS

24, Rabochaya st.

Saratov 410028

Russia

Michael Wagner

Hochschule Aalen

Institut für Angewandte

Forschung

Beethovenstr. 1

73431 Aalen

Germany

Petra Weber

Hochschule Aalen

Institut für Angewandte

Forschung

Beethovenstr. 1

73431 Aalen

Germany

Maxim A. Yurkin

Institute of Chemical Kinetics

and Combustion

Laboratory of Cytometry and

Biokinetics

Institutskaya 3

Novosibirsk 630090

Russia

and

Novosibirsk State University

Physics Department

Pirogova 2

Novosibirsk 630090

Russia

Vladimir P. Zharov

University of Arkansas

for Medical Sciences

Philips Classic Laser Laboratories

4301 W. Markham St.

Little Rock

AR 72205 7199

USA

Olga Zhernovaya

International

Research-Educational Center of

Optical Technologies for Industry

and Medicine “Photonics”

Saratov State University

Saratov

Russia

1

Perspectives in Cytometry

Anja Mittag and Attila Tárnok

1.1 Background

Cytometry is the general term for quantitative single cell analyses. Without cytometric analyses, work in modern life sciences would be unthinkable. Since its introduction, cytometry has been influencing and promoting development in biology and medicine. A high number of molecular parameters are analyzable within heterogeneous cell systems by cytometry. If the normality of a heterogeneous cell system is known, changes can be identified. Hence, biological alterations induced by malignancies, infections, and so on, are diagnosable. Such phenotypic changes allow for understanding disease-related (or induced) alterations of molecule expression patterns and hence, the functionality of the whole biological system. This interest to unravel molecular properties of single cells of healthy and diseased organisms (and to compare them) led to the development of the first cytophotometric instruments in the middle of the last century [1].

Analyses in those days were usually based on different light absorption capabilities of cell constituents of cells fixed on microscopic slides, with or without staining (e.g., Feulgen). Since these analyses were very time consumptive (5–10 min per nucleus or cytoplasm region), measurements of high cell numbers were simply not possible [2]. This technology was followed by instruments for blood cell counting with a higher throughput where cell concentrations were enumerated by counting electrical voltage pulse during cell transit [3].

Application of fluorescence dyes opened the way for obtaining more information per cell. In 1961, the first use of fluorescence for quantitation was reported [4]. Since then, development of new instruments was focused toward fluorescence analysis. In 1969, the first impulse cytophotometer (ICP-11) (Phywe GmbH, Göttingen [5]) was commercially launched where the fluorescence (resulting from mercury arc lamp excitation) of several thousand cells per second was measured by photomultiplier tubes (PMTs). Later, lasers were employed as stable light sources for excitation of fluorescence dyes. The first flow cytometer equipped with two lasers was available in 1976 [6]. Several fluorescence dyes could now be measured simultaneously. The basic principle of this technology is still applied in modern flow cytometers: cells are separated by sheath fluid, (hydrodynamically) focused, and excited by (laser) light in flow. The scattered and emitted or absorbed light is measured.

The demand for comprehensive analyses and with it the simultaneous detection of several parameters on many (thousands to millions of) individual cells in one sample led to further developments in the field of cytometric analyses. More lasers as well as detectors were included to be able to perform three- or four-color measurements (plus information of scattered light) routinely.

This was sufficient for many applications, or at least, it had to be. Detailed cellular subtyping, coexpression of specific markers, cytokine analyses of certain cell types, cell–cell interaction, and so on, are, in the majority of cases, not possible by using only four fluorescence parameters. The list of applications is long where multiparametric analyses are essential.

1.2 Basics of Cytometry

The beauty of labeling specific markers or cellular functions with fluorescence dyes lies in its multicolor approach and therewith the feasibility of simultaneous analysis of many parameters. If cells are stained with different “colors,” each single color can be distinguished from each other and multiple information can be obtained for single cells. Admittedly, differentiation of more than three colors by the eye is almost impossible, but light detectors in cytometers (PMTs or camera), in combination with appropriate bandpass filters, are able to detect wavelength ranges (i.e., specific “colors”) of interest. Discrimination of fluorescence dyes is hereby possible by defining certain wavelength ranges, suitable for specific fluorescence dyes. However, the usually very broad emission spectra of fluorescence dyes make it sometimes hard to differentiate between dyes in one detection filter owing to spectral overlap. This problem is known for fluorescence dyes such as fluorescein isothiocyanate (FITC) and phycoerythrin (PE) but can be mathematically solved by compensation, that is, “purification” of specific fluorescence from unwanted signals. Another novel possibility to overcome the problem of spectral overlap is multispectral analyses (known from microscopy) although it is rarely used in flow cytometry (FCM) [7].

In general, there are two different types of cytometric analyses named by the analytical technique: FCM and slide-based cytometry (SBC). As previously mentioned, cytometry has its roots in the analysis of cells on a slide. Owing to the higher throughput, development moved to the FCM, although now, with higher computing and storage capacity of workstations, cytometry by microscopy has been revitalized. Nevertheless, both methods are quite similar and identical in many details.

1.2.1 Flow Cytometry

It is apparent from the name cytometry that cells are analyzed in flow. Generally, cells in suspension are sucked or pressed into the cytometer by overpressure or mechanical pumps. Covered with sheath fluid, cells are separated (like pearls on a string) and move actively to the place of analysis. Lasers (or also light emitting diodes nowadays) excite the cell (i.e., the fluorescence dye on it) and the emitted fluorescence is detected by PMTs. On the basis of specific characteristics (mainly fluorescence of a certain label but also light scattering properties), separation of wanted cell types and its concentration and purification can be accomplished. However, fluorescence-activated cell sorting (FACS) is necessary for that, which can be time consumptive. In “normal” FCM, the sample is usually lost after analysis. Up to 50 000 cells can be measured per second, although the normal throughput is usually around 1000−10 000 cells per second.

Fluorescence information of the cells can be displayed as histograms and dotplots. Clever experimental setup (marker selection, fluorescence combination) and smart gating strategies allow for extraction of multiple information out of a three- or four-color staining. Nevertheless, detailed subtyping or functional information (e.g., activation) of specific cellular subtypes can hardly be obtained by such low-color analyses [8].

Although the main principle in FCM has not dramatically changed since its beginning, there are of course some new developments besides increasing number of detectors and lasers. There is not only hydrodynamic focusing of cells (with the need for utilization of sheath fluid) but also focusing derived from acoustic radiation pressure forces [9] or the utilization of photodetectors for sensing the position of particles in the sample stream without sheath fluid [10]. Without sheath fluid (but with a unique flow cell design), even usage of FCM in space is conceivable [11]. Another development is the implementation of imaging in FCM. There are flow cytometers available that are able to capture images of analyzed cells in flow for morphological analysis [12].

Cellular analyses in FCM, however, are restricted to cell suspensions. Solid tissue or adherent cells cannot be analyzed, that is, not without prior trypsinization or disintegration of tissue. For these types of specimens, SBC was developed.

1.2.2 Slide-Based Cytometry

There are two major types of SBC systems: camera-based detection in combination with lamp illumination (e.g., [13]) and laser excitation and fluorescence detection via PMTs (e.g., [14]). However, mixed systems, for example, lasers and camera, are available, too. No matter which modality is used for excitation and detection, the core of these instruments is a fluorescence microscope. But that does not mean that every fluorescence microscope is capable of cytometric analysis. Cytometric analysis means quantitative analysis of the whole cell, that is, it requires optics with a relatively low numerical aperture. Analysis of single slices of a cell, as, for example, in confocal microscopy, is not cytometric. Moreover, analyses using microscopes with lamps or diodes as excitation source and no corrections (optical or software solutions) for light excitation intensity, that is, stability of the excitation light, are also not cytometric. Owing to unstable excitation intensity, one cannot be certain that the resulting fluorescence intensity of cells in different fields of view (or different samples, irrespective of the same acquisition setups) will provide the same fluorescence intensity. Qualitative statements about existing fluorescences are possible but no quantitative conclusion can be made about cell activation or other marker expression of cells. Prerequisites for cytometric analyses with microscopes are stable excitation power, even illumination of the sample, and a steady and sensitive detection of the emitted fluorescence.

Even though cytometric analyses were slide based at the beginning and the modern concept of SBC was presented in the 1980s, the first type of such instruments, the laser scanning cytometer (LSC), became commercially available a decade later [15]. The reason for this was probably the time needed for image analyses in the past. However, higher processing power and storage capacities of modern computers promoted development in this field.

Unlike FCM, samples in SBC analyses are fixed on a slide or plate. Although mainly developed for tissue analysis, LSC was used for many different applications, for example, cell cycle studies [16–18], apoptosis [19, 20], immunophenotyping [21–24], tumor analysis in solid tissue [25], fine-needle aspirate biopsies [26], circulating tumor cell analysis [27], stem cell analysis [28, 29], or study of the effects of drugs [30, 31].

The principle of LSC analysis is comparable to FCM. Fluorescence dyes on (or in) cells are excited by laser light and the emitted fluorescence is split into certain wavelength bands by optical filters and detected by PMTs. The deviation from FCM is that cells remain on the slide and can be further analyzed or even cultured.

LSC allows for studying growth and the variety of expression of specific markers during development of cells in their “natural” environment [31]. Possible effects of cell preparation, for example, stress or activation caused by detachment of cells from the surface (like for FCM analyses), can be avoided. Moreover, interactions between single cells can hardly be observed on detached cells. This applies for cell cultures as well as tissue sections. Another advantage is that cells can be traced and analyzed during culturing [29].

1.3 Cytomics

Since the complexity of biological systems is very high, a multiplicity of different information from cells, their interaction, and triggered reactions (e.g., by external stimuli or diseases) is necessary to understand such systems. For this purpose, different concepts were and still are under development to get a better insight into biological processes in organisms. Cytomics is one of these concepts. Its aim is to characterize single cells in cell systems and to unravel the interactions of cells within these systems [32]. Another concept is systems biology. The aim of systems biology is similar to that of cytomics, but it focuses more on understanding intracellular behavior, that is, the interaction of single cellular constituents such as genes, proteins, metabolites, and organelles and in silico modeling [33]. Interconnection of different analyses is very important for this purpose, that is, to obtain all needed information and combine them appropriately. In contrast to other concepts like genomics (characterization of genome [34]), proteomics (analysis of proteome [35]), lipidomics (cellular lipid constituents [36]), or other -omics, where only certain components of cells are in the focus of interest, cytomics and systems biology focus on interaction of cells and cellular constituents.

Always, biological conditions are the result of the interaction of all components of a complex system. Therefore, such a system must be analyzed as a unit to unravel its secrets. For example, different developmental stages of an organism have the same genome but are different (also phenotypically) in their protein composition [37]. Cytomics and systems biology start there and go even further. Not only single components are under investigation but also the relations and interactions between different components. Therewith, changes in cell systems can be understood – from work flows within the cell (systems biology) to interactions of the whole system (cytomics). If these actions are known, alterations (even before clinical manifestation) can be classified and can lead to predictive and preventive individualized medicine [38, 39]. Cells are the elementary building units of an organism and hence, their analysis is the easiest way to identify diseases or reasons for diseases. Alterations to healthy conditions can be found by differential screening, that is, examining a multiplicity of cell types for phenotype, activation, or cytokine production (multiparameter analyses), and extracting important and relevant cell types and marker combinations for a further diagnostic panel. However, it is clear that the mass of information obtained from multiparametric cytometry must be analyzed appropriately to find causal connections. Bioinformatics tools, that is, algorithms for cluster analyses, can be applied [40–42].

1.4 Cytometry—State of the Art

Routine are still fluorescence analyses with a relatively low amount of measured parameters (sample stained with three to four colors). However, developments of new fluorescence dyes with promising spectral characteristics, for example, UV excitable dyes for protein labeling (e.g., Quantum dots [43]) and, of course, instruments capable for multiparametric analyses, led to the increased usage of multiparametric, that is, multicolor, analyses in laboratories worldwide. Cellular analyses with 6–12 colors (polychromatic cytometry) simultaneously are no longer a rarity (e.g., [8, 22, 44]).

Multicolor analyses allow for detailed understanding of complex cellular structures, cell subtypes, and cell–cell interactions. All fluorescence information, that is, the tagged biological components, generate a network from where information of interest can be extracted. Admittedly, there are attempts to perform it with three- or four-parameter analyses. To this end, as an example, in FCM analyses many aliquots of the same sample (e.g., blood) are stained for different antibodies. All these single tubes are measured and afterward, information obtained from each tube is combined, for example, for cellular subtyping of the sample (but not on a single cell level). The presence of main cellular subtypes can certainly be detected but it is not possible to get details. For example, a staining for CD3, CD4, and CD8 in one tube yields information on the amount and distribution of T helper and cytotoxic T cells as well as double positive and double negative T cells. Conclusions on further subtypes, for example, existence of αβ-TCR (T cell receptor) or γδ-TCR or expression of CD16 or CD56 in NKT cells, are not possible. If cells are stained for CD3, CD19, and CD16/CD56 one can get an overview of lymphocyte subtypes (i.e., T cells, B cells, NK cells, and NKT cells) but merging this information with one of the other tubes and drawing a conclusion on, for example, the presence of αβ-TCR+CD4+CD8+ NKT cells is impossible. Therefore, one needs this information on a single cell basis, for example, a sample with CD3, CD4, CD8, CD16/CD56 (or even better, CD16 and CD56), and αβ-TCR. Only then, the required information can be extracted from the analysis.

Admittedly, some recent developments promise to combine several (three to four color) FCM measurements of a sample into one metadata file (i.e., a virtual multiparameter analysis by data merging) on the basis of a leading antibody [45] but this might not be feasible with every desired cellular subtype and, of course, needs verification with a true polychromatic analysis. For answering complex questions, multiparametric analyses are indispensable [8]. Another advantage of multiparametric analyses is the fact that information density increases with each parameter added to the existing setting. The resulting network provides the opportunity to find interactions of cells, coexpression of markers, and so on, that were never be thought of before. Moreover, polychromatic cytometry reduces costs of analysis. Although a complex pattern of fluorescence dyes means also the use of “unusual” dyes or fluorescence marker combinations (mostly more expensive than the commonly used ones), the combination of markers in one analysis prevents the repetitive use of markers in several tubes for identifying main populations.

1.4.1 Multiparametric Analyses

As an example for the complexity of multicolor analyses, an eight-color measurement on LSC [22] is described here.

For precise analysis and correct interpretation of data, a careful selection of antibodies and their corresponding fluorescence dyes is essential [46]. Only optimal experiment settings allow for unequivocal identification of cell types, clear discrimination between positive and negative cells, and doubt-free analysis of data. This means that in most of the cases different antibody fluorescence combinations need to be tested before selecting the final experiment settings. For the eight-color experiment on LSC, the following panel was selected for a simultaneous staining of several surface markers in a sample of human peripheral blood leukocytes: CD14-FITC, CD4-PE, CD56-PECy5, CD16-PECy7, CD45-APC (Allophycocyanin), CD8-APCCy5.5, and CD3-APCCy7. This mixture allowed for identification of the required lymphocyte cell subtypes (focus on NK and NKT cells) by an appropriate gating strategy. In Figure 1.1, analysis including gating strategy is shown. Since LSC lacks side scatter, which is usually used in FCM to discriminate between leukocyte subsets (neutrophils, monocytes, lymphocytes), fluorescence signals were used for this purpose. Nevertheless, leukocytes could be subdivided because of their different expression of CD45 (pan-leukocyte marker) and CD14 (LPS-Receptor) into the subsets: monocytes, lymphocytes, granulocytes (neutrophilic and eosinophilic), and basophilic granulocytes after excluding cell debris, artifacts, and aggregates based on their forward scatter maximum pixel and area values (for details, see [22]). The lack of a CD14 signal on basophils enables their differentiation from neutrophils and eosinophils (both CD14weak) and monocytes (CD14bright). Within the monocytes there is a small but prominent population of CD16+ cells with a slightly reduced CD14 expression: proinflammatory monocytes. CD16weak cells within the granulocyte population are eosinophils. Thereby, the main leukocyte populations were discriminated.

Figure 1.1 Eight-color analysis by LSC. EDTA anticoagulated blood was stained for CD3, CD4, CD8, CD14, CD16, CD19, CD45, and CD56. Artifacts were excluded by FSC (forward light scatter) MaxPixel versus area for further analysis (top left) and events of nonleukocyte origin by the lack of CD45 expression (top center). Leukocytes were further subdivided by their different antigen expression.

Figure was published earlier in: Mittag et al. [22].