Biology subjects

Specht, W. L.

Publications and source records attributed to Specht, W. L..

2 recordsLinked to original sources

bioRxiv2025

Tissue signatures of human macrophages during homeostasis and activation

Human macrophages (M{Phi}s) reside in tissues and develop tissue-specific identities. While studies in mice have identified molecular signatures for site-specific M{Phi} differentiation, we know less about the transcriptional profiles of human M{Phi}s in distinct sites, including mucosal tissues and lymphoid organs during homeostasis and activation. Here, we use multimodal single-cell sequencing and ex vivo stimulation assays to define tissue signatures for populations of human M{Phi}s isolated from lungs, small intestine, spleen, bone marrow, and lymph nodes obtained from individual organ donors. Our results reveal distinct tissue-adapted gene and protein profiles of metabolic, adhesion, and immune interaction pathways, which are specific to M{Phi}s and not monocytes isolated from the same sites. These signatures exhibit homology to murine M{Phi}s from the same sites. Tissue-adapted M{Phi}s remained responsive to polarizing cytokine stimuli ex vivo, with upregulation of expected transcripts and secreted proteins, while retaining tissue-specific profiles. Together, our findings show how human M{Phi} identity is coupled to their site of residence for mucosal and lymphoid organs and is intrinsically maintained during activation and polarization.

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↗