The Value and Versatility of Clinical Flow Cytometry
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What is flow cytometry and how does it work?
Flow cytometry (FCM) is a scientific technique used to measure the physical and biochemical characteristics of cells.1 The sample is injected into the flow cytometer instrument, where it is typically focused to flow one cell at a time past light sources and detectors. Tens of thousands of cells can be examined in seconds to determine their morphology, granularity, scattering and transmission of light, or fluorescence of biomarkers, depending on the variation of FCM used.
The first conventional fluorescence-based flow cytometer was developed and commercialized in the late ‘60s/early ’70s in Germany.2 Over the last five decades, FCM has developed rapidly in terms of the number of its applications and the quantity and dimensionality of the data it generates.1,3 Dr. Minh Doan, formerly of the Imaging Platform of the Broad Institute (USA) and now head of Bioimaging Analytics at GlaxoSmithKline in the USA, states, “There have been significant advances in all three V’s of flow cytometry data: velocity (throughput/speed of data acquisition), volume (data content), and variety (sample types and signal acquisition technology).”
Michael Parsons, manager of the Flow Cytometry Core of the Lunenfeld-Tanenbaum Research Institute in Toronto, Canada, agrees. “The two biggest trends in flow cytometry are high content data and the merging of technologies from separate disciplines. For example, the last five years or so have seen the emergence of mass cytometry, which merges the disciplines of flow cytometry and mass spectrometry. In its latest iteration, an image cytometry module has been incorporated to generate unprecedented amounts of content (number of measured parameters) from relatively small amounts of patient tissue. Spectral flow cytometry has also established itself as an important emerging technology.” Indeed, mass cytometry can now measure up to 50 features on a single cell simultaneously using antibodies tagged with rare earth metals,4 and imaging flow cytometry allows for 1000’s of morphological features and multiple fluorescence markers to be analyzed per cell.3
Flow cytometry, therefore, has inarguable potential as a clinical tool for disease diagnosis, prognosis, and therapeutic monitoring. However, some challenges remain in translating the full promise of FCM into clinical practice. Here, some of the current clinical applications of FCM will be discussed, as well as some of the compelling new applications being researched.
Cancer detection, classification, and clinical management
Flow cytometers were originally developed to provide a rapid screening method for cellular aneuploidy (abnormal number of chromosomes) and to measure cell cycle distribution, both of which are important for tumor prognosis and treatment.5 However, it has since been realized that for many cancers, especially in the early stages, the accompanying changes in DNA content are not detectable.1
Despite this initial setback, FCM is now routinely used in basic research and clinical practice, particularly in oncology.1,3 Immunophenotyping has become one of the most common FCM techniques and is used to characterize cell subpopulations based on the presence of different surface antigens, which can be detected with FCM using fluorescently labeled antibodies.1
For example, leukemias and lymphomas express a specific set of cell surface markers depending on their stage and differentiation pathway.1 Therefore, using immunophenotyping, FCM is frequently applied to the clinical diagnosis and sub-classification of these cancers, such as Dr. Michele Paessler’s work at the Immunology Laboratory at the Children’s Hospital of Philadelphia, USA.6 FCM is also used to identify and sort hematopoietic stem cells (stem cells that can differentiate into blood cells) from the peripheral blood following intensive chemotherapy for blood cancers.1 These stem cells can then be used to repopulate patients’ depleted bone marrow.
Other oncology applications include minimal residual disease (MRD) detection, such as by Prof. Michael Borowitz’s team at the Johns Hopkins Hospital in Baltimore, USA,7 and the detection of apoptosis (cell death) for determining the efficacy of cancer treatments.1 FCM can detect very low levels of disease (as few as 1 malignant cell among 10,000 normal cells), which can be important in the clinical management of cancer.
Similarly, FCM of “liquid biopsies” could be used to detect circulating tumor cells in the bloodstream.3 These cells are extremely rare, and with its high sensitivity, FCM is perfectly poised to make a significant impact in this area. This approach has potential for the clinical detection of early-stage cancer as well as the detection of circulating metastatic or drug-resistant cancer cells. For example, a study published earlier this year described label-free liquid biopsy with very high throughput (> 1 million cells/second) for drug-susceptibility testing during leukemia treatment.8
In addition to the numerous oncology applications of FCM, it is also a highly versatile tool for clinical immunology.
Prior to an organ transplant, FCM can be used to crossmatch the patient's serum with donor lymphocytes to detect antibodies that could result in organ rejection.1 Postoperatively, the analysis of various cell markers on the peripheral blood lymphocytes can indicate early transplant rejection, detect bone marrow toxicity arising from immunosuppressive therapies, and help differentiate infections from organ rejection. For blood transfusions, FCM can be used to detect contamination of blood with residual white blood cells, which can have adverse effects such as pulmonary edema.9
Groups such as Dr. Roshini Abraham’s at Nationwide Children’s Hospital in Ohio, USA, are using FCM to diagnose primary immunodeficiency disorders with the use of immunophenotyping and functional assays.10 These disorders are caused by genetic mutations that result in defects in the immune system, such as X-linked (Bruton’s) agammaglobulinemia and X-linked hyper-IgM syndrome. Over 300 of these disorders have been identified thus far, and the causative mutations lower immune defense against the attack of infections.
HIV is, of course, an example of a secondary (acquired) immunodeficiency disorder. FCM analysis of CD4 and other markers on lymphocytes in the peripheral blood is used to monitor the treatment of HIV patients, and a CD4 count <200 cells/mL together with a positive antibody test for HIV is used as a diagnostic for AIDS.1 Secondary immunodeficiencies can also be caused by e.g., substance abuse, malnutrition, other medical conditions, and certain medical treatments. FCM of a panel of markers can be used to confirm suspected cases.1
In pregnancy, when a Rhesus blood group D-negative mother carries a D-positive fetus, fetal-maternal bleeding can sensitize the mother to the D-positive blood cells from the fetus and this can be fatal to subsequent D-positive newborns.11 FCM is used to measure the degree of fetal-maternal hemorrhage to determine the correct dose of prophylactics to be administered shortly after delivery.
In addition to oncology and immunology applications, FCM is also used to diagnose a variety of rare hematologic disorders12 as well as autoimmune/autoinflammatory disorders such as spondylarthritis (arthritis of the spine).13 Another area of research that is likely to give rise to increasing clinical applications in the future is that of platelet activity, which is important in many clinical conditions.1,14
The future of clinical flow cytometry
The latest developments in FCM are allowing ever more refined gating of target subpopulations, and thousands of parameters can now potentially be measured per cell to generate unique “fingerprints.”3 However, the current approach in advanced FCM is typically to capture all the data first and then manually (and subjectively) select only a few, specific features for further analysis. Thus, much information of interest may be lost, preventing powerful comparisons of cell subpopulations or disease phenotypes that may never otherwise be visible to the researcher. Therefore, there is a pressing need for objective, automated, and robust data analysis tools, and, to gain widespread use, user-friendly software interfaces. Arguably, for this reason, advanced FCM remains primarily a research rather than a clinical tool.
Experts suggest that it may be possible to overcome this data analysis hurdle by applying machine learning approaches coupled with further standardization of FCM workflows.3,15 “The most exciting applications of high content data revolve around the use of machine learning, in particular, deep learning, to extract relevant meaning from large data sets. Machine learning, coupled with big data, has the potential for driving diagnosis and treatment options tailored to the patient’s disease in a timely manner,” says Dr. Parsons. In addition, Prof. Sadao Ota of RCAST at the University of Tokyo, Japan, points out, “We still need to figure out how to design a workflow that convincingly validates diagnostic results, especially if the diagnosis employs the power of machine learning.” Such developments are necessary before the rich information content of advanced FCM technology can be fully applied in the clinic.
In terms of other future advances in the field, Prof. Ota specifically makes mention of the potential of cell sorters combined with FCM.16 “There are exciting and unique applications of sorters in fields such as cell therapy and regenerative medicine. Also, creating key applications of imaging cell sorters in pharmaceutical fields may accelerate global drug discovery.” Dr. Doan concurs, “Disease heterogeneity makes it hard to validate findings. Perhaps the use of flow cytometry with sorting capability can help such validation, where events-of-interest collected by flow cytometry can be validated with other downstream assays.” Finally, as Dr. Doan notes, “With multiple layers of data(types) incorporated altogether, there are now possibilities to do more with less, i.e., label-free sample measurement, which could lead to more direct, faster, and smarter diagnoses. Rare events (e.g., metastatic cancer cells) may soon be detected better than before.”
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