Flow cytometry methodology and data interpretation is the most significant source of intra-lab and lab-to-lab variability in stem cell research and cell therapy field. Even with use of identical methods and reagents, manual data analysis alone can result in 17-44% variation between labs. Standardization of flow cytometry, as a potential solution for variability problem, is especially important in clinical laboratory and for comparison results of clinical trials. At last three consortia were created in the last few years to address flow cytometry standardization – the Euroflow, the Human Immune Phenotyping Consortium (HIPC) and the FlowCAP. This week, HIPC published results of their attempt to standardize immuphenotyping of human blood.
The authors coordinate anlysis of 5 panels of human blood cells (T cells, Treg, Th1/2/17, B cells, and NK/DC/monocytes), stained with 8-color antibody cocktails, across 9 laboratories. They used a number of different standardization tools:
- defined procedure (SOP)
- lyophilized reagent cocktails in 96-well plates
- reference material – lyophilized PBMC control (CytoTrol)
- cryopreserved PBMC from the same vendor
- similar cytometer (Fortessa or LSR)
- central data analysis
- automated gating (OpenCyto and flowDensity)
I’d highly encourage you to read results of this study in details, but the most important conclusion is that HIPC approach to standardization works well.
The within-site coefficients of variability for the different cell populations and panels were reduced by between 94% and 43% (mean 73%, IQR 18%) compared to the between-site CVs for the same panels and populations.
Central data analysis and automated analysis allowed to reduce variability in comparison to “individual center” analysis. Automated gating can reproduce manual gating without significant bias. For some panels, automated analysis allowed to reduce variability, compared to manual analysis. The impact of using standardized plates:
Using the standardized lyoplates combined with a unified gating strategy utilizing automated methods it was possible to resolve biological variation between samples for the T-cell, B-cell, and T-regulatory panels, while the technical variability in the DC/Mono/NK panel was too large to reliably resolve biological differences between samples.
Another important message from the study is that standardization cannot replace good laboratory practices:
While standardization of reagents (via lyoplates) and harmonization of analysis pipelines can sometimes address data quality issues caused by differences in protocol adherence between centers or inadequate quality control and compensation issues), such problems are still best addressed through detailed SOPs, quality control, and proficiency testing.
Finally, this study is a good example of open science:
All materials, including primary data files, processed data, workspaces and analysis code, are made freely available using existing data standards and providing a valuable resource to the experimental and computational communities.
HIPC study is one of few good examples of standardization impact on such variable method as flow cytometry. It works! I’d like to see similar studies for stem cell panels from different tissues. In cell therapy, such things as automated gating and standard lyoplates could be used for product characteriazation and release testing.