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A Semi-automated Approach to Preparing Antibody Cocktails for Immunophenotypic Analysis of Human Peripheral Blood
Published on: February 8, 2016
Strategies for improving the reporting of human immunophenotypes by flow cytometry
Michael P Gustafson1, Yi Lin2, Mabel Ryder3
1Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 1st St. SW, Rochester, MN 55905, USA.
Insights
Measuring immune cell phenotypes as cell counts in whole blood samples improves data consistency. This approach facilitates accurate comparisons across studies, overcoming limitations of percentage-based reporting and sample manipulation.
Area of Science:
- Immunology
- Biotechnology
- Clinical Research
Background:
- Flow cytometry is essential for immune cell analysis, with technological advancements enabling more complex immunophenotyping.
- Inconsistent reporting of immunophenotype data in human studies hinders understanding of disease, immunotherapy response, and immune homeostasis.
- Barriers to cross-comparing flow cytometry data from human studies and clinical trials are examined.
Purpose of the Study:
- To identify and address barriers in reporting flow cytometry data for human studies.
- To propose standardized methods for improved data comparability and interpretation.
Main Methods:
- Analysis of immunophenotype reporting methods, including percentage-based versus cell count enumeration.
- Evaluation of the impact of sample processing techniques, such as density gradient centrifugation, on immunophenotype data.
- Comparison of data derived from minimally manipulated samples (whole blood) versus processed samples.
Main Results:
- Reporting phenotypes as percentages of a parent population without providing parent data can lead to misleading conclusions.
- Enumerating phenotypes as cell counts (cells/μl) allows for more accurate comparisons of relationships among different cell populations.
- Density gradient centrifugation can alter surface marker expression and immunophenotype distribution by preventing cell count measurements.
Conclusions:
- Measuring immunophenotypes as cell counts from minimally manipulated samples, such as whole blood, enhances flow cytometry data reporting.
- Standardized reporting using cell counts will facilitate more direct and reliable comparisons of data across diverse human studies and clinical trials.
Background:
Flow cytometry is the gold standard for phenotyping and quantifying immune cells. New technologies have greatly increased our capacity to measure both routine and complex immunophenotypes. The reporting of immunophenotype data is not consistent in human studies yet it is quite critical for understanding disease specific changes, responses to immunotherapies, and normal immune homeostasis. Here we examine the barriers that hinder cross comparisons of flow cytometry data collected from human studies and clinical trials.
Findings:
We demonstrate that phenotypes reported as percentages within a cell compartment (i.e. myeloid derived suppressor cells as a percent of mononuclear cells) without providing data on the parent population may contribute to misleading conclusions. The enumeration of phenotypes as cell counts (cells/μl) provides a basis to more accurately compare the relationships among phenotypes. Finally, we provide evidence that density gradient centrifugation, which eliminates the ability to measure phenotypes as cell counts, can affect the expression of surface markers and consequently alter the distribution of particular immunophenotypes.
Conclusions:
We propose that by measuring immunophenotypes as cell counts from minimally manipulated samples (whole blood) will improve the reporting of flow data and facilitate more direct comparisons of data across human studies.
