Standardization of Workflow and Flow Cytometry Panels for Quantitative Expression Profiling of Surface Antigens on Blood Leukocyte Subsets: An HCDM CDMaps Initiative

Abstract

BackgroundThe Human Cell Differentiation Molecules (HCDM) organizes Human Leukocyte Differentiation Antigen (HLDA) workshops to test and name clusters of antibodies that react with a specific antigen. These cluster of differentiation (CD) markers have provided the scientific community with validated antibody clones, consistent naming of targets and reproducible identification of leukocyte subsets. Still, quantitative CD marker expression profiles and benchmarking of reagents at the single-cell level are currently lacking.ObjectiveTo develop a flow cytometric procedure for quantitative expression profiling of surface antigens on blood leukocyte subsets that is standardized across multiple research laboratories.MethodsA high content framework to evaluate the titration and reactivity of Phycoerythrin (PE)-conjugated monoclonal antibodies (mAbs) was created. Two flow cytometry panels were designed: an innate cell tube for granulocytes, dendritic cells, monocytes, NK cells and innate lymphoid cells (12-color) and an adaptive lymphocyte tube for naive and memory B and T cells, including TCRγδ+, regulatory-T and follicular helper T cells (11-color). The potential of these 2 panels was demonstrated via expression profiling of selected CD markers detected by PE-conjugated antibodies and evaluated using 561 nm excitation.ResultsUsing automated data annotation and dried backbone reagents, we reached a robust workflow amenable to processing hundreds of measurements in each experiment in a 96-well plate format. The immunophenotyping panels enabled discrimination of 27 leukocyte subsets and quantitative detection of the expression of PE-conjugated CD markers of interest that could quantify protein expression above 400 units of antibody binding capacity. Expression profiling of 4 selected CD markers (CD11b, CD31, CD38, CD40) showed high reproducibility across centers, as well as the capacity to benchmark unique clones directed toward the same CD3 antigen.ConclusionWe optimized a procedure for quantitative expression profiling of surface antigens on blood leukocyte subsets. The workflow, bioinformatics pipeline and optimized flow panels enable the following: 1) mapping the expression patterns of HLDA-approved mAb clones to CD markers; 2) benchmarking new antibody clones to established CD markers; 3) defining new clusters of differentiation in future HLDA workshops.

Precision and Accuracy of Receptor Quantification on Synthetic and Biological Surfaces Using DNA-PAINT

“…This leads to high sensitivity and opens the door to detect low levels of expression due to the high specificity of the method. This is of significant importance to future studies since it is a clear advantage compared to the benchmark techniques employed to measure receptor densities, such as flow cytometry, 42 − 44 immunohistochemistry, 45 − 47 ELISA, 48 , 49 or fluorescence in situ hybridization. 50 , 51 The impact of the ability to quantify low expression levels has already been demonstrated for cancer diagnosis 19 but is likely to be relevant for a wide range of diseases.…”

Mass cytometry for the multiplexed quantification and characterization of target expression on circulating cells in whole blood

“…For example, CD28 ABC on memory CD4+ T cells was 5000 ± 300 in this study versus 9400 ± 7000 in [19], or CD64 ABC on monocytes was 35,000 ± 6000 in this study versus 17,000 ± 9000 in [19] (the ABC listed here as mean ± SD of samples from the examined donors). A possible reason for the discrepancies in the ABC reported by different groups was discussed in [29], as follows: since the quantification of Ab binding intensity was conducted only on the cell populations displaying a signal above background, the latter is an arbitrary threshold determined using fluorescence‐minus one (FMO) or metal minus many (MMM) samples, the gating on the positive subsets might have been set differently by the analysts, resulting in varying median ABC in the positive gate.…”

Design, optimisation and standardisation of a high‐dimensional spectral flow cytometry workflow assessing T‐cell immunophenotype in patients with melanoma

“…Furthermore, these visualisation tools must be interpreted in light of the known biological significance of marker expression level, which may be continuously related to expression for some markers while displaying threshold effects for others. 45,46 High-dimensional evaluation of large datasets by flow cytometry has previously been hampered by technical variation between experiments, an issue termed 'batch effects'. 47,48 Previous studies assessing batch effects have typically focused on shorter time periods, fewer batches or utilised CyTOF, 41,[49][50][51] making this study notable for its exploration of longer term batch effects as they apply to spectral flow cytometry.…”

“…Manual validation of high‐dimensional analyses remains important, as common data visualisation tools such as heatmap expression plots are affected by the choice of scaling, 44 and median expression levels may not accurately represent marker expression profiles. Furthermore, these visualisation tools must be interpreted in light of the known biological significance of marker expression level, which may be continuously related to expression for some markers while displaying threshold effects for others 45,46 …”