Transcriptomics

Dataset Information

0

Strategies for optimizing CITE-seq for human islets and other tissues


ABSTRACT: Defining the immunological landscape of human tissue is an important area of research, but challenges include the impact of tissue disaggregation on cell phenotypes and the low abundance of immune cells in many tissues. Here, we describe methods to troubleshoot and standardize Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) for studies involving enzymatic digestion of human tissue. We tested epitope susceptibility of 92 antibodies commonly used to differentiate immune lineages and cell states on human peripheral blood mononuclear cells following treatment with an enzymatic digestion cocktail used to isolate islets. We observed CD4, CD8a, CD25, CD27, CD120b, CCR4, CCR6, and PD1 display significant sensitivity to enzymatic treatment, effects that often could not be overcome with alternate antibodies. Comparison of flow cytometry-based CITE-seq antibody titrations and sequencing data supports that for the majority of antibodies, flow cytometry accurately predicts optimal antibody concentrations for CITE-seq. Comparison by CITE-seq of immune cells in enzymatically digested islet tissue and donor- matched spleen not treated with enzymes revealed little digestion-induced epitope cleavage, suggesting increased sensitivity of CITEseq and/or that the islet structure may protect resident immune cells from enzymes. Within islets, CITEseq identified immune cells difficult to identify by transcriptional signatures alone, such as distinct tissue-resident T cell subsets, mast cells, and innate lymphoid cells (ILCs). Collectively this study identifies strategies for the rational design and testing of CITE-seq antibodies for single-cell studies of immune cells within islets and other tissues.

ORGANISM(S): Homo sapiens

PROVIDER: GSE224767 | GEO | 2023/02/12

REPOSITORIES: GEO

Dataset's files

Source:
Action DRS
Other
Items per page:
1 - 1 of 1

Similar Datasets

2023-01-03 | GSE221787 | GEO
2009-12-01 | E-MEXP-1140 | biostudies-arrayexpress
2022-09-18 | GSE213282 | GEO
2022-12-19 | GSE215802 | GEO
| EGAS00001005849 | EGA
2018-12-07 | GSE123476 | GEO
2021-03-26 | PXD024400 | Pride
2020-10-26 | GSE143796 | GEO
2022-01-10 | GSE172261 | GEO
2005-01-01 | MODEL1302180002 | BioModels