Transcriptomics

Dataset Information

0

Single cell resolution landscape of equine peripheral blood mononuclear cells reveals diverse immune cell subtypes including T-bet+ B cells


ABSTRACT: Traditional laboratory model organisms represent a small fraction of the diversity of multicellular life, and findings in any given experimental model often do not translate to other species. Immunology research in non-traditional model organisms presents multiple challenges, many stemming from an incomplete understanding of potentially species-specific immune cell types, frequencies and phenotypes. Identifying and characterizing immune cells in such organisms is frequently limited by the availability of species-reactive immunophenotyping reagents for flow cytometry, and insufficient prior knowledge of cell type-defining markers. Here, we demonstrate the utility of single cell RNA sequencing (scRNA-Seq) to characterize immune cells for which traditional experimental tools are limiting. Specifically, we used scRNA-Seq to comprehensively define the cellular diversity of equine peripheral blood mononuclear cells (PBMCs) from healthy horses across different breeds, ages, and sexes. We identified 30 cell type clusters partitioned into five major populations: Monocytes/Dendritic Cells, B cells, CD3+PRF1+ lymphocytes, CD3+PRF1- lymphocytes, and Basophils. Comparative analyses revealed many analogous cell populations in human PBMC, including transcriptionally heterogeneous monocytes and distinct dendritic cell subsets (cDC1, cDC2, plasmacytoid DC). Unexpectedly, we found that a majority of the equine peripheral B cell compartment is comprised of T-bet+ B cells; an immune cell subpopulation typically associated with chronic infection and inflammation in human and mouse. Taken together, our results demonstrate the potential of scRNA-Seq for cellular analyses in non-traditional model organisms, and form the basis for an immune cell atlas of horse peripheral blood.

ORGANISM(S): Equus caballus

PROVIDER: GSE148416 | GEO | 2021/01/25

REPOSITORIES: GEO

Similar Datasets

2016-02-18 | GSE64385 | GEO
2023-09-11 | GSE225275 | GEO
2016-02-18 | E-GEOD-64385 | biostudies-arrayexpress
2022-08-19 | GSE196388 | GEO
2020-12-08 | GSE156467 | GEO
2024-03-07 | GSE247126 | GEO
2005-07-13 | GSE2657 | GEO
| phs001618 | dbGaP
2018-01-10 | GSE105784 | GEO
2014-08-28 | E-GEOD-60341 | biostudies-arrayexpress