Caron, D. P. | Search bioRxiv

bioRxiv2024

Multimodal profiling reveals tissue-directed signatures of human immune cells altered with age

The immune system comprises multiple cell lineages and heterogeneous subsets found in blood and tissues throughout the body. While human immune responses differ between sites and over age, the underlying sources of variation remain unclear as most studies are limited to peripheral blood. Here, we took a systems approach to comprehensively profile RNA and surface protein expression of over 1.25 million immune cells isolated from blood, lymphoid organs, and mucosal tissues of 24 organ donors aged 20-75 years. We applied a multimodal classifier to annotate the major immune cell lineages (T cells, B cells, innate lymphoid cells, and myeloid cells) and their corresponding subsets across the body, leveraging probabilistic modeling to define bases for immune variations across donors, tissue, and age. We identified dominant tissue-specific effects on immune cell composition and function across lineages for lymphoid sites, intestines, and blood-rich tissues. Age-associated effects were intrinsic to both lineage and site as manifested by macrophages in mucosal sites, B cells in lymphoid organs, and T and NK cells in blood-rich sites. Our results reveal tissue-specific signatures of immune homeostasis throughout the body and across different ages. This information provides a basis for defining the transcriptional underpinnings of immune variation and potential associations with disease-associated immune pathologies across the human lifespan.

immunology↗

bioRxiv2023

Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues

Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and dissecting transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins, as in cells of the immune system. Cellular Indexing of Transcriptomes and Epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell type annotation requires a classifier that integrates multimodal data. Here, we describe MultiModal Classifier Hierarchy (MMoCHi), a marker-based approach for classification, reconciling gene and protein expression without reliance on reference atlases. We benchmark MMoCHi using sorted T lymphocyte subsets and annotate a cross-tissue human immune cell dataset. MMoCHi outperforms leading transcriptome-based classifiers and multimodal unsupervised clustering in its ability to identify immune cell subsets that are not readily resolved and to reveal novel subset markers. MMoCHi is designed for adaptability and can integrate annotation of cell types and developmental states across diverse lineages, samples, or modalities.

genomics↗