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Chapter 11.12 : Flow Cytometry

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Flow Cytometry, Page 1 of 2

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Citation: Leber A. 2016. Flow Cytometry, p 11.12.1.1-11.12.2.14. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.12
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Figures

Image of Figure 11.12.1–1
Figure 11.12.1–1

Graphical overview of flow cytometer systems integration. Cells are directed to the laser interrogation point via the fluidics system. The optics system encompasses the excitation and capture of fluorescent signals as each cell passes the laser. Electronics will digitize and process samples, as well as apply compensation to raw data signals.

Citation: Leber A. 2016. Flow Cytometry, p 11.12.1.1-11.12.2.14. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.12
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Image of Figure 11.12.1–2
Figure 11.12.1–2

Major populations in human peripheral blood. The left-hand plot depicts FSC versus SSC. As both measure of intrinsic physical characteristics of cells, no fluorescent staining of cells is required in this case. The right-hand plot depicts CD45 staining ( axis) plotted against SSC ( axis). This plot proves useful for better discrimination of cell populations in some cases.

Citation: Leber A. 2016. Flow Cytometry, p 11.12.1.1-11.12.2.14. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.12
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Image of Figure 11.12.2–1
Figure 11.12.2–1

Graphic representation of the SI. The SI is a measure of resolution sensitivity, or the ability to resolve a positive signal from existing background. Higher SI values indicate better resolution capability. D, distance between the median fluorescence signal (MFI) of the positive and negative peaks; W, width of the negative peak.

Citation: Leber A. 2016. Flow Cytometry, p 11.12.1.1-11.12.2.14. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.12
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Image of Figure 11.12.2–2
Figure 11.12.2–2

Human peripheral blood was stained with different CCR5 clones conjugated to Alexa Fluor 488. Note the variation in staining intensity for each clone. Histograms are gated on the lymphocyte population. Each histogram represents staining with a different clone.

Citation: Leber A. 2016. Flow Cytometry, p 11.12.1.1-11.12.2.14. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.12
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Image of Figure 11.12.2–3
Figure 11.12.2–3

Human peripheral blood was stained with CD3 Brilliant Violet 421 and CD4 PerCP/Cy5.5. Dot plots are gated on lymphocyte population. Note the shift in fluorescence (arrows) of PerCP/Cy5.5 and CD3 Brilliant Violet 421 negative populations when samples were stained with both CD3 and CD4.

Citation: Leber A. 2016. Flow Cytometry, p 11.12.1.1-11.12.2.14. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.12
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Image of Figure 11.12.2–4
Figure 11.12.2–4

Comparison of medians of positive and negative populations aids in correct compensation of single-color controls. Human peripheral blood lymphocytes were stained with CD45RA FITC and acquired on a BD Biosciences LSRII flow cytometer. Dot plots are gated on the lymphocyte population. When the PE MFIs of both the positive and negatively stained populations are similar, then the sample is correctly compensated (graph 3). Solid bar = PE MFI.

Citation: Leber A. 2016. Flow Cytometry, p 11.12.1.1-11.12.2.14. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.12
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Image of Figure 11.12.2–5
Figure 11.12.2–5

PMT voltage significantly affects compensation. Compensation beads were stained with PE-labeled anti-human CD8. (Top) PMT settings for FITC and PE were significantly different, leading to almost similar detections of PE in the FITC channel and to a compensation value of 88.7%. (Bottom) When the PE and FITC PMTs are more balanced, the spillover into the FITC channel is lower, leading to a lower compensation value.

Citation: Leber A. 2016. Flow Cytometry, p 11.12.1.1-11.12.2.14. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.12
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References

/content/book/10.1128/9781555818814.chap11.12
1. Verwer B. 2002. BD FACSDiva Option White Paper. Becton, Dickinson and Company, San Jose, CA.
2. Roederer M. 2001. Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. Cytometry 45:194205.
1. Shapiro HM. 2003. Practical Flow Cytometry, 4th ed. John Wiley and Sons Inc, Hoboken, NJ.
2. Maecker H, Trotter J. 2012. BD Biosciences Application Note: Selecting Reagents for Multicolor Flow Cytometry. Becton, Dickinson and Company, San Jose, CA.
3. Baumgarth N, Roederer M. 2000. A practical approach to multicolor flow cytometry for immunophenotyping. J Immunol Methods 243:7797.
4. Ng AAP, Lee BTK, Teo TSY, Poidinger M, Connolly JE. 2012. Optimal cellular preservation for high dimensional flow cytometric analysis of multicentre trials. J Immunol Methods 385:7989.
5. Kidd PG, Nicholson JKA. 1997. Immunophenotyping by flow cytometry, p 229–244. In Rose NR, De Marcario EC, Lane HC, Folds JD, Nakamura RM (ed), Manual of Clinical Laboratory Immunology, 5th ed. ASM Press, Washington, DC.
6. Renzi P, Ginns LC. 1987. Analysis of T-cell subsets in normal adults. Comparison of whole blood lysis technique to Ficoll-Hypaque separation by flow cytometry. J Immunol Methods 98:5356.
7. Schwartz A, Marti GE, Poon R, Gratama JW, Fernandez-Repollet E. 1998. Standardizing flow cytometry: a classification system of fluorescence standards used for flow cytometry. Cytometry 33:106114.
8. Wood B. 2006. 9-color and 10-color flow cytometry in the clinical laboratory. Arch Pathol Lab Med 130:680690.
9. Perfetto SP, Ambrozak D, Nguyen R, Chattopadhyay PK, Roederer M. 2012. Quality assurance for polychromatic flow cytometry using a suite of calibration beads. Nat Protoc 7:20672079.
10. Roederer M. 2001. Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. Cytometry 45:194205.
11. Owens MA, Vall HG, Hurley AA, Wormsley SB. 2000. Validation and quality control of immunophenotyping in clinical flow cytometry. J Immunol Methods 243:3350.
12. Maecker HT, McCoy JP, Nussenblatt R. 2012. Standardizing immunophenotyping for the Human Immunology Project. Nat Rev Immunol 12:191200.
13. Hulspas R, O’Gorman MR, Wood BL, Gratama JW, Sutherland DR. 2009. Considerations for the control of background fluorescence in clinical flow cytometry. Cytometry B Clin Cytom 76:355364.
14. O’Gorman MRG, Thomas J. 1999. Isotype controls—time to let go? Cytometry 38:7880.
15. Herzenberg LA, Tung J, Moore WA, Herzenberg LA, Parks DA. 2006. Interpreting flow cytometry data: a guide for the perplexed. Nat Immunol 7:681685.
16. Maecker HT, McCoy JP, FOCIS Human Immunophenotyping Consortium. 2010. A model for harmonizing flow cytometry in clinical trials. Nat Immunol 11:975977.
17. Aghaeepour N, Finak G, The FlowCAP Consortium, The DREAM Consortium, Hoos H, Mosmann T, Brinkman R, Gottardo R, Scheuermann RH. 2013. Critical assessment of automated flow cytometry data analysis techniques. Nat Methods 10:228238.

Tables

Generic image for table
Table 11.12.1–1

Available flow cytometry instrumentation

Citation: Leber A. 2016. Flow Cytometry, p 11.12.1.1-11.12.2.14. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.12
Generic image for table
Table 11.12.2–1

Some common fluorochromes and their characteristics

Citation: Leber A. 2016. Flow Cytometry, p 11.12.1.1-11.12.2.14. In Clinical Microbiology Procedures Handbook, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818814.ch11.12

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