Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage

By D. Aran, Agnieszka P. Looney, Leqian Liu, Esther Wu, Valerie Fong, Austin Hsu, S. Chak, R. Naikawadi, P. Wolters, Adam R. Abate and 2 more. Nature Immunology, 2019

4,038 citations recorded by the source corpus.

Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here, we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1+SiglecF+ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologs of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury. Using scRNA-seq analysis, Bhattacharya and colleagues identify a subset of profibrotic lung macrophages that have a gene expression signature intermediate between those of monocytes and alveolar macrophages.

Subfields: Molecular Biology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/a4d99547655d02c1cfa5d8c627a10248507b96f2

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Citations in the corpus

Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data (2022, methodology): “Another group of supervised methods, such as CaSTLe 45 , ACTINN 46 , SingleR 48 and CHETAH 49 , utilize reference bulk or single-cell transcriptomic data for cell-type predictions, and therefore require comprehensive, manually-annotated and high-quality reference datasets for training; furthermore,…”
The role of macrophages in the resolution of inflammation. (2019, positive): “These findings were confirmed in the same mouse model using single-cell RNASeq (99).”
Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment (2020, positive): “Data-driven cell-type classification (Aran et al., 2019) and cluster-specific marker gene expression identified all cell”
scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network (2021, methodology): “Several methods including SingleR (8), CHETAH (9), scMap (10), scID (11), scPred (12), ACTINN (13), CellAssign (14), Garnett (15), SCINA (16), singleCellNet (17) and support vector machine (SVM) belong to this category as described in the recent review (18).”
Potent Neutralizing Antibodies against SARS-CoV-2 Identified by High-Throughput Single-Cell Sequencing of Convalescent Patients’ B Cells (2020, neutral): “Cell types were identified using SingleR (Aran et al., 2019) against a human immune reference dataset (Monaco et al.”
A comparison of automatic cell identification methods for single-cell RNA sequencing data (2019, background): “2 R Correlation to training set No No [26]”
High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue (2020, positive): “, 2019) to perform automated cell type annotations (Figure 6E) with SingleR (Aran et al., 2019).”