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How To Guide

10 Tips to Optimize Clinical Flow Cytometry

Rectangle Image
How To Guide

10 Tips to Optimize Clinical Flow Cytometry

Flow cytometry has transformed the clinical immunodiagnostics landscape in recent decades.1 This sophisticated and powerful technique uses fluorophore-tagged antibodies to fluorescently label cells for various biomarkers, which are then analyzed to reveal the distribution pattern of biomarkers within the cell population at a single-cell resolution.2 One common application of clinical flow cytometry is for immunophenotyping, which identifies immune cells to aid in the diagnosis of hematologic malignancies, especially leukemias and lymphomas.3-5 It is also employed to quantify DNA cell content,6 as in childhood lymphoblastic leukemia,7,8 which correlates with patient outcome. Additionally, it is useful for analyzing response to therapy and for detecting residual malignant cells post-treatment.9

Immunological disease treatment is another clinical application of flow cytometry, where it is used to identify CD4+ and CD8+ T-cell subsets following HIV infection10 or to diagnose primary immune deficiencies.11 It can reveal the presence of certain immune cell subsets following allogeneic bone marrow or hematopoietic stem cell transplantation.12,13 An emerging application of clinical flow cytometry is using fluorescence-activated cell sorting (FACS) to collect cells post-analysis satisfying certain phenotypic criteria, isolating stem cells for transplantation or cell-based therapies.1

Clinical flow cytometry applications are performed with validated protocols, which have been carefully designed to consider the optimal fluorophore combinations to minimize spectral overlap and for sufficient biomarkers to identify the population of interest. In addition, a balance between rare cell populations with fluorophore brightness ensures that the population of interest will be discernible. Flow cytometry boasts several advantages, such as: (i) single-cell resolution capability that enables identification of rare cellular subpopulations within a heterogeneous population, which may have important ramifications for disease diagnosis,2 (ii) multiplexed, simultaneous detection of several biomarkers,14 (iii) complex but robust instrumentation and software processing package for consistent performance and ease of data analysis, (iv) rapid sample processing to inform treatment course sooner.

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Calibration is critical

It is important to maintain the flow cytometer in excellent working condition. This means that daily instrument calibration and standardization are necessary to ensure consistency between data collected from different days and to verify the machine is in working order.2,15 This can be accomplished by running a calibration check against reference beads, carefully prepared within narrow tolerance limits with little bead-to-bead variation, according to the manufacturer’s instructions, for all the colors that will be employed. This process tests the instrument’s sensitivity and linearity and ensures that the beads’ mean fluorescence intensity falls within the correct target range for each laser in the instrument. This process is automated in most modern flow cytometers and the instrument produces a readout of all tested parameters.

Color compensation

The fluorophores which are conjugated to the antibodies used in flow cytometry have relatively broad excitation and emission profiles (Figure 1). This results in “cross-talk” between fluorophores where laser excitation intended to excite a particular fluorophore “A” unintentionally results in some signal for “A” in the channel for “B”.2 Color compensation is a process to eliminate or minimize this cross-talk and should be performed with beads conjugated to each of the fluorophores the assay will employ plus a negative control according to the manufacturer’s instructions.


Figure 1. Illustration of spectral overlap between the emission profiles for fluorescein isothiocyanate (FITC) and phycoerythrin (PE). The 488 nm laser, which is used to excite FITC, leads to FITC emission that spills over into the bandpass (BP) filter range (indicated by blue arrow) for PE, contributing to signal in the PE channel.

Consider controls

The beads’ optical parameters differ to immune cells’ optical parameters. Therefore, once QC is performed, it is desirable to run assay-specific controls using the settings from the previous time the assay was run. At this stage, adjustments may be made to the photomultiplier tube voltages (PMTV) for the forward scatter (FSC) and side scatter (SSC) to bring lymphocytes on scale. Next, controls should be performed using: (i) unstained negative cells, which should fall below a previously established threshold for positive cells in all fluorescence channels, (ii) stained positive controls for each fluorophore employed in the assay, which should fall within the scale above threshold, and (iii) isotype antibody controls, which should fall below the threshold. Adjustments to PMTV for each fluorophore color can be applied to refine the placement of the negative and positive controls. 

Sample accession and processing

Correct sample processing entails properly logging the sample into the laboratory’s information system, noting which anticoagulant was used since it might affect flow analysis, and selecting a method to lyse red blood cells.2 A live-dead dye is important to discern live cells from dead ones, thus limiting the analysis to live cells and excluding dead cells from the analysis, which may autofluoresce and cause background noise.15 If only staining for cell surface receptors, fresh cells may be used and a membrane integrity dye may be employed to discriminate live cells from dead ones.2 If also staining for intracellular proteins, staining for surface receptors needs to be performed prior to fixation and permeabilization for intracellular staining. Additionally, pretreatment with fixable viability dyes will be necessary for live-dead discrimination since the fixation process permeabilizes cells and thus precludes the use of membrane integrity dyes.16

Consistency is key

Flow cytometry is a sensitive technique and to get reproducible results from experiment to experiment it is important to limit variation, even among the subtler experimental parameters, e.g., temperature cells are kept at, antibody staining procedure and the exposure of antibody-conjugated fluorophores to light.

Reagent validation

Although reagents are prepared to exacting standards, batch-to-batch variation can still occur. Additionally, storage conditions and shipping and handling procedures may affect sensitive reagents such as antibodies. To ensure that reagents are behaving as expected, reagent validation may be performed by: (i) direct analysis by comparison to reference materials, (ii) parallel testing of old versus new reagents, such as overlap testing when opening new reagent bottles/tubes, and (iii) comparison with controls, for example by using commercial QC cell samples.2 Antibody concentration for new batches may be verified by an antibody titration into cells, e.g., lymphocytes, to confirm the antibody concentration to be employed in assays. In addition, good record-keeping, such as registering expiration date and lot number can also help mitigate variation in reagents induced by storage and time.

Data analysis

There are many ways to perform analysis of data collected by flow cytometry and several formats to display the data in. Careful consideration to data integrity and the rationale for a particular method of analysis needs to be accomplished to ensure it meets the needs of the clinical test. Once a method of data analysis is established, it is important to analyze the results in the same manner for the same type of clinical flow test, e.g., immunophenotyping for precursor B-cell lymphoblastic leukemia, in order to obtain consistent results. This includes: (i) the gating procedure, a process that decides which fluorescence channel the analysis considers first,2 (ii) correct labeling of populations (e.g., which cells are the precursor B-cells), (iii) annotation of populations as a percentage of total events or as a percentage of the population it was gated from, and (iv) the type of scale used to plot the results, e.g., logarithmic versus bi-exponential (HyperLog or “Logicle”).2,15,17,18

Perceive the pattern

It is important to become adept at recognizing the pattern of cell population distribution in healthy samples so that samples from patients with a disease are discernable and stand out. Familiarity with the anticipated pattern distribution of cells also helps perceive whether a problem has occurred within the instrument since its calibration and standardization earlier that day, such as turbulence within the flow cell caused by debris, which may impact the results.

Protect patient identity

Due to patient privacy, it is important to ensure that samples and data are confidential and secure. Password protection and encryption measures can facilitate this. In addition, data integrity and backup to protect against data loss are also essential safeguards. Record-Keeping Remembers Newer flow cytometers can store the QC report after calibration, making QC history directly accessible to observe trends over time. Alternatively, Levey-Jennings plots can be utilized to analyze the evolution of QC data.2 QC values that persistently fall outside a range or consistently above or below the mean may indicate an instrument malfunction or drift. 

Disclaimer: We are not in any way instructing how clinical flow should be performed, but merely providing an educational guide. 

References

  1. Jaye, D. L.; Bray, R. A.; Gebel, H. M.; Harris, W. A.; Waller, E. K. Translational applications of flow cytometry in clinical practice. J Immunol 2012, 188 (10), 4715. 
  2. Donnenberg, A. D.; Donnenberg, V. S. In Handbook of Human Immunology; 2nd ed.; O’Gorman, M. R. G.;Dannenberg, A. D., Eds.; Taylor & Francis: Boca Raton, London, New York, 2008. 
  3. Davis, B. H.; Holden, J. T.; Bene, M. C.; Borowitz, M. J.; Braylan, R. C.; Cornfield, D.; Gorczyca, W.; Lee, R.; Maiese, R.; Orfao, A.et al. 2006 Bethesda International Consensus recommendations on the flow cytometric immunophenotypic analysis of hematolymphoid neoplasia: medical indications. Cytometry B Clin Cytom 2007, 72 Suppl 1, S5. 
  4. Craig, F. E.; Foon, K. A. Flow cytometric immunophenotyping for hematologic neoplasms. Blood 2008, 111 (8), 3941. 
  5. Jain, N.; Lamb, A. V.; O’Brien, S.; Ravandi, F.; Konopleva, M.; Jabbour, E.; Zuo, Z.; Jorgensen, J.; Lin, P.; Pierce, S.et al. Early T-cell precursor acute lymphoblastic leukemia/lymphoma (ETP-ALL/LBL) in adolescents and adults: a high-risk subtype. Blood 2016, 127 (15), 1863. 
  6. Barlogie, B.; Raber, M. N.; Schumann, J.; Johnson, T. S.; Drewinko, B.; Swartzendruber, D. E.; Gohde, W.; Andreeff, M.; Freireich, E. J. Flow cytometry in clinical cancer research. Cancer Res 1983, 43 (9), 3982. 
  7. Stary, J.; Hrodek, O.; Hausner, P.; Petrakova, A.; Goetz, P.; Kreuger, A. The importance of blast cell DNA content for prognosis of childhood acute lymphoblastic leukemia. Neoplasma 1990, 37 (3), 293.
  8. Varma, N.; Naseem, S. Application of flow cytometry in pediatric hematology-oncology. Pediatr Blood Cancer 2011, 57 (1), 18. 
  9. Moreton, P.; Kennedy, B.; Lucas, G.; Leach, M.; Rassam, S. M.; Haynes, A.; Tighe, J.; Oscier, D.; Fegan, C.; Rawstron, A.et al. Eradication of minimal residual disease in B-cell chronic lymphocytic leukemia after alemtuzumab therapy is associated with prolonged survival. J Clin Oncol 2005, 23 (13), 2971. 
  10. Barnett, D.; Walker, B.; Landay, A.; Denny, T. N. CD4 immunophenotyping in HIV infection. Nat Rev Microbiol 2008, 6 (11 Suppl), S7. 
  11. Oliveira, J. B.; Notarangelo, L. D.; Fleisher, T. A. Applications of flow cytometry for the study of primary immune deficiencies. Curr Opin Allergy Clin Immunol 2008, 8 (6), 499. 
  12. Waller, E. K.; Rosenthal, H.; Jones, T. W.; Peel, J.; Lonial, S.; Langston, A.; Redei, I.; Jurickova, I.; Boyer, M. W. Larger numbers of CD4(bright) dendritic cells in donor bone marrow are associated with increased relapse after allogeneic bone marrow transplantation. Blood 2001, 97 (10), 2948. 
  13. Reddy, V.; Winer, A. G.; Eksioglu, E.; Meier-Kriesche, H. U.; Schold, J. D.; Wingard, J. R. Interleukin 12 is associated with reduced relapse without increased incidence of graft-versus-host disease after allogeneic hematopoietic stem cell transplantation. Biol Blood Marrow Transplant 2005, 11 (12), 1014. 
  14. Baumgarth, N.; Roederer, M. A practical approach to multicolor flow cytometry for immunophenotyping. J Immunol Methods 2000, 243 (1-2), 77. 
  15. Tung, J. W.; Heydari, K.; Tirouvanziam, R.; Sahaf, B.; Parks, D. R.; Herzenberg, L. A. Modern flow cytometry: a practical approach. Clin Lab Med 2007, 27 (3), 453. 
  16. Perfetto, S. P.; Chattopadhyay, P. K.; Lamoreaux, L.; Nguyen, R.; Ambrozak, D.; Koup, R. A.; Roederer, M. Amine-reactive dyes for dead cell discrimination in fixed samples. Curr Protoc Cytom 2010, Chapter 9, Unit 9.34. 
  17. Parks, D. R.; Roederer, M.; Moore, W. A. A new “Logicle” display method avoids deceptive effects of logarithmic scaling for low signals and compensated data. Cytometry A 2006, 69 (6), 541. 
  18. Bagwell, C. B. Hyperlog-a flexible log-like transform for negative, zero, and positive valued data. Cytometry A 2005, 64 (1), 34.

Meet The Author
Masha Savelieff, PhD
Masha Savelieff, PhD
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