bioRxivImmunologyPreprint
Type 1-polarized DC immunotherapeutic contains heterogeneous populations with IL-12p70 production restricted to a rare subset
Allison E. DePuytiD, Peter E. J. ShoucairiD, Holly A. BilbeniD, Jeremy James MartinsoniD, Bernard J. C. MacatangayiD, Charles R. RinaldoiD, Paweł KalińskiiD, Robbie B. MailliardiD
Abstract
Monocyte-derived DC therapies programmed for robust IL-12p70 production have been associated with favorable outcomes in cancer clinical trials. However, clinical responses remain inconsistent even under standardized protocols, and the cellular basis for this variability is unknown. We leveraged single-cell multiomics to characterize two widely used DC platforms, the high-IL-12p70-producing alpha-Type-1-polarized DC (DC1) and the IL-12p70-deficient DC induced in the presence of PGE2 (PGE2-DC), at baseline and following rhCD40L activation. DC generated from 7 healthy participants representing the spectrum of rhCD40L-induced IL-12p70 production were profiled by transcriptome analysis with concurrent 42-plex surface proteomics, multiplex ELISA, and ELISpot quantification of IL-12p70-producing cells. While DC1 and PGE2-DC distinctly responded to rhCD40L, DC1 alone unexpectedly comprised 3 transcriptionally and phenotypically distinct subpopulations in resting and stimulated states. Only a limited fraction of DC1 coexpressed IL12A and IL12B (IL-12p70 producers), which was confirmed by ELISpot at the protein level. The distribution of DC1 subclusters varied markedly between individuals and correlated with bulk cytokine and chemokine secretion profiles. Heterogeneity within DC1 preparations may underlie inconsistent clinical trial outcomes, and identification of associated surface proteins provides a prospective strategy for subcluster enrichment to enhance DC release criteria and patient stratification for optimized therapeutic efficacy.
- organism: human
- assay: scRNA-seq
- assay: proteomics
- research focus: cell type annotation
- research focus: clustering
- approach: deep learning
- approach: statistical model
- Citations
- 0
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