bioRxivImmunologyPreprint
Stimulation strategy matters: A B-cell focused single-cell transcriptomic study on in vitro stimulated immune cells
Tine D'hamersiD, Jana NeirinckiD, Malicorne BuysseiD, Ciel De VriendtiD, Tessa C. C. KerreiD, Filomeen HaerynckiD, Mattias HofmansiD, Carolien BonroyiD, Celine EveraertiD
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Abstract
Distinct in vitro B-cell stimulation strategies, defined by stimulation condition and incubation duration, have been described in diverse studies. Often, these approaches are considered interchangeable due to comparable flow-cytometric outputs concerning fluorescence intensities and B-cell subset distributions. However, whether these phenotypic similarities also reflect comparable underlying transcriptional states have not been systematically evaluated yet. In this study, we provide an unbiased comparison of B-cell activation strategies on healthy individuals, resulting in a single-cell transcriptomic atlas (4 donors; ~2,029 cells per sample; 81,160 cells in total with 56.3% annotated as B cells). Two T-cell independent stimulators targeting toll-like receptors (ODN2006 and IFN-{gamma}/R-848), were assessed next to two T-cell dependent stimulators (anti-CD40 and anti-CD3/CD28 antibodies), all incubated for eight and 30 hours. Observed overlap for differential abundance, differential gene expression and cell-cell interactions could directly be linked to T-cell (in)dependent working mechanism. Strategy-specific results uncovered time-point specific transcriptional changes, revealed the largest set of differentially expressed genes for IFN-{gamma}/R-848 and ODN2006, and showed that IFN-{gamma}/R-848 and anti-CD3/CD28 induced the majority of the cell-cell interactions. Ultimately, our reference atlas emphasizes the importance and facilitates the rational selection of optimal B-cell stimulation strategies, particularly for studies aiming to interrogate B-cell dysregulation in immune-mediated diseases.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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