CD Maps—Dynamic Profiling of CD1–CD100 Surface Expression on Human Leukocyte and Lymphocyte Subsets

Abstract

CD molecules are surface molecules expressed on cells of the immune system that play key roles in immune cell-cell communication and sensing the microenvironment. These molecules are essential markers for the identification and isolation of leukocytes and lymphocyte subsets. Here, we present the results of the first phase of the CD Maps study, mapping the expression of CD1–CD100 (n = 110) on 47 immune cell subsets from blood, thymus, and tonsil using an eight-color standardized EuroFlow approach and quantification of expression. The resulting dataset included median antibody binding capacities (ABCs) and percentage of positivity for all markers on all subsets and was developed into an interactive CD Maps web resource. Using the resource, we examined differentially expressed proteins between granulocyte, monocyte, and dendritic cell subsets, and profiled dynamic expression of markers during thymocyte differentiation, T-cell maturation, and between functionally distinct B-cell subset clusters. The CD Maps resource will serve as a benchmark of antibody reactivities ensuring improved reproducibility of flow cytometry-based research. Moreover, it will provide a full picture of the surfaceome of human immune cells and serves as a useful platform to increase our understanding of leukocyte biology, as well as to facilitate the identification of new biomarkers and therapeutic targets of immunological and hematological diseases.

Analytical performance of a 25‐marker spectral cytometry immune monitoring assay in peripheral blood

“…Variability in large panel preparation can be mitigated with robotic pipetting platforms or lyophilized samples. Meanwhile, interactive web resources have become available to address the quality of antibody reagents and evaluate the expression profiles of a multitude of immune biomarkers [15,25].…”

The Role of PARP1 in Monocyte and Macrophage Commitment and Specification: Future Perspectives and Limitations for the Treatment of Monocyte and Macrophage Relevant Diseases with PARP Inhibitors

“…The first group of functionally linked membrane proteins is mainly represented by clusters of differentiation, also known as classification determinants (CDs). These molecules include receptors, adhesion molecules, membrane-bound enzymes, and glycans that play multiple roles in development, activation, and differentiation [ 52 ]. Monocytes express various antigens depending on their stage of development, such as CD4, CD11b, CD13, CD14, CD15, CD18, CD34, CD36, CD45, and CD64 as well as the human leukocyte antigen DR isotype (HLA-DR) [ 53 ].…”

Infinity Flow: High-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning

“…Critical to the MPC approach, the Backbone panel should be designed with at least one empty fluorescence channel (usually PE or APC) and optimized to minimize spectral overlap into this open channel. The sample is then aliquoted across w wells (typically w ≈ 300) (Amir et al 2019; Collier et al 2017; Dutertre et al 2019; Graessel et al 2015; Koh et al 2016; Uezumi et al 2016; Kalina et al 2019) (Figure 1b). Each well contains a distinct antibody clone conjugated to the fluorophore not used for the Backbone.…”

“…The Infinity Flow computational pipeline is available as an open-source R package, and can run on standard FCS files generated from massively-parallel cytometry experiments. Others have published large scale antibody screens using MPC experiments, but the analysis of these datasets has been limited to manual or automated analysis of the Backbone data (Amir et al 2019; Collier et al 2017; Graessel et al 2015; Koh et al 2016; Uezumi et al 2016; Kalina et al 2019). In contrast, Infinity Flow uses supervised machine learning (specifically multivariate non-linear regression algorithms) to aggregate expression data from all the well-specific FCS files into a single dense matrix of imputed expression.…”