Biology subjects

Unterman, A.

Publications and source records attributed to Unterman, A..

5 recordsLinked to original sources

bioRxiv2025

Spatial and Single-cell Transcriptomics Reveal Programs Governing Fibroblastic Foci Fibroblasts

RationaleFibroblastic foci (FF) are central for fibrosis propagation in idiopathic pulmonary fibrosis (IPF), yet their resident fibroblast subpopulation remains poorly defined. ObjectiveTo deeply characterize FF-fibroblasts and identify programs governing their differentiation and activation. MethodsGeoMx spatial transcriptomics was applied to IPF lung regions, comparing carefully delineated FF (excluding overlying epithelium) with areas of established fibrosis, to derive an FF-specific signature. This signature was projected onto a large single-cell RNA-sequencing (scRNA-seq) dataset to identify an FF-specific fibroblast cluster, facilitating its deep characterization. Measurements and Main ResultsGeoMx revealed 272 increased genes in FF; this FF-signature was validated against several independent spatial transcriptomics and proteomics datasets. FF-signature genes were highly enriched in a specific cluster of fibroblasts in the IPF scRNA-seq dataset. MMP11 was the most specific gene of the FF-fibroblast cluster, and was validated using in situ RNA hybridization. Furthermore, higher MMP11 bronchoalveolar lavage protein levels were found in progressive pulmonary fibrosis and were associated with worse outcome in Cox regression (HR 4.54, 95% CI 1.24-15.8). Cell-cell interaction analysis with fibroblasts and adjacent aberrant basaloid cells, detected signals that may induce the FF-fibroblast signature, including from TGF-{beta} superfamily ligands. Using trajectory and transcription factor (TF) analysis we show that the trajectory from non-FF to FF-specific fibroblasts is correlated with activation of EMT-associated TFs and deactivation of AP-1 family TFs. ConclusionsWe identified transcriptional programs governing FF-fibroblasts, for which MMP11 serves as a novel highly-specific biomarker with prognostic significance. We highlight potential regulators whose targeting may curb pulmonary fibrosis progression.

genomics↗

bioRxiv2023

Mast-Cell Expressed Membrane Protein-1 (MCEMP1) is expressed in classical monocytes and alveolar macrophages in Idiopathic Pulmonary Fibrosis and regulates cell chemotaxis, adhesion, and migration in a TGFβ dependent manner.

BackgroundMast-Cell Expressed Membrane Protein-1 (MCEMP1) is higher in Idiopathic Pulmonary Fibrosis (IPF) patients with increased risk of death and poor outcomes. Here we seek to establish the mechanistic role of MCEMP1 in pulmonary fibrosis. MethodsMCEMP1 expression was analyzed by single-cell RNA sequencing, immunofluorescence in Peripheral Blood Mononuclear Cells (PBMC) as well as in lung tissues from IPF patients and controls. Chromatin Immunoprecipitation (ChiP) and Proximity Ligation Assay (PLA) were used to study the transcriptional regulation of MCEMP1. Transient RNA interference and lentivirus transduction were used to knockdown and knock-in MCEMP1 in THP-1 cells to study chemotaxis, adhesion, and migration. Bulk RNA sequencing was used to identify the mechanisms by which MCEMP1 participates in monocyte function. Active RHO pull-down assay was used to validate bulk RNA sequencing results. ResultsWe identified increased MCEMP1 expression in classical monocytes and alveolar macrophages in IPF compared to controls. MCEMP1 was upregulated by TGF{beta} at the mRNA and protein levels in THP-1. TGF{beta}-mediated MCEMP1 upregulation results from the cooperation of SMAD3 and SP1 via concomitant binding to SMAD3/SP1 cis-regulatory elements within the MCEMP1 promoter. In terms of its function, we found that MCEMP1 regulates TGF{beta}-mediated monocyte chemotaxis, adhesion, and migration. 400 differentially expressed genes were found to increase after TGF{beta} stimulation of THP-1, further increased in MCEMP1 knock-in cells treated with TGF{beta} and decreased in MCEMP1 knockdown cells treated with TGF{beta}. GO annotation analysis of these genes showed enrichment for positive regulation of RHO GTPase activity and signal transduction. While TGF{beta} enhanced RHO GTPase activity in THP-1 cells, this effect was attenuated following MCEMP1 knockdown. ConclusionMCEMP1 is highly expressed in circulating classical monocytes and alveolar macrophages in IPF. MCEMP1 is regulated by TGF{beta} and participates in the chemotaxis, adhesion, and migration of circulating monocytes by modulating the effect of TGF{beta} in RHO activity. Our results suggest that MCEMP1 may regulate the migration and transition of monocytes to monocyte-derived alveolar macrophages during pulmonary fibrosis development and progression.

immunology↗

bioRxiv2023

Peripheral Blood Single-Cell Sequencing Uncovers Common and Specific Immune Aberrations in Fibrotic Lung Diseases

Rationale and ObjectivesThe extent and commonality of peripheral blood immune aberrations in fibrotic interstitial lung diseases are not well characterized. In this study, we aimed to identify common and distinct immune aberrations in patients with idiopathic pulmonary fibrosis (IPF) and fibrotic hypersensitivity pneumonitis (FHP) using cutting-edge single-cell profiling technologies. MethodsSingle-cell RNA sequencing was performed on patients and healthy controls peripheral blood and bronchoalveolar lavage samples using 10X Genomics 5 gene expression and V(D)J profiling. Cell type composition, transcriptional profiles, cellular trajectories and signaling, and T and B cell receptor repertoires were studied. The standard Seurat R pipeline was followed for cell type composition and differential gene expression analyses. Transcription factor activity was imputed using the DoRothEA-VIPER algorithm. Pseudotime analyses were conducted using Monocle3, while RNA velocity analyses were performed with Velocyto, scVelo, and CellRank. Cell-cell connectomics were assessed using the Connectome R package. V(D)J analyses were conducted using CellRanger and Immcantation frameworks. Across all analyses, disease group differences were assessed using the Wilcoxon rank-sum test. Measurements and Main Results327,990 cells from 83 samples were profiled. Overall, changes in monocytes were common to IPF and FHP, whereas lymphocytes exhibited disease-specific aberrations. Both diseases displayed enrichment of CCL3hi/CCL4hi CD14+ monocytes (p<2.2e-16) and S100Ahi CD14+ monocytes (p<2.2e-16) versus controls. Trajectory and RNA velocity analysis suggested that pro-fibrotic macrophages observed in BAL originated from peripheral blood monocytes. Lymphocytes exhibited disease-specific aberrations, with CD8+ GZMKhi T cells and activated B cells primarily enriched in FHP patients. V(D)J analyses revealed unique T and B cell receptor complementarity-determining region 3 (CDR3) amino acid compositions (p<0.05) in FHP and significant IgA enrichment in IPF (p<5.2e-7). ConclusionsWe identified common and disease-specific immune mechanisms in IPF and FHP; S100Ahi monocytes and SPP1hi macrophages are common to IPF and FHP, whereas GMZKhi T lymphocytes and T and B cell receptor repertoires were unique in FHP. Our findings open novel strategies for the diagnosis and treatment of IPF and FHP.

genomics↗

bioRxiv2021

ProtAnno, an Automated Cell Type Annotation Tool for Single Cell Proteomics Data that Integrates Information from Multiple Reference Sources

Compared with sequencing-based global genomic profiling, cytometry labels targeted surface markers on millions of cells in parallel either by conjugated rare earth metal particles or Unique Molecular Identifier (UMI) barcodes. Correct annotation of these cells to specific cell types is a key step in the analysis of these data. However, there is no computational tool that automatically annotates single cell proteomics data for cell type inference. In this manuscript, we propose an automated single cell proteomics data annotation approach called ProtAnno to facilitate cell type assignments without laborious manual gating. ProtAnno is designed to incorporate information from annotated single cell RNA-seq (scRNA-seq), CITE-seq, and prior data knowledge (which can be imprecise) on biomarkers for different cell types. We have performed extensive simulations to demonstrate the accuracy and robustness of ProtAnno. For several single cell proteomics datasets that have been manually labeled, ProtAnno was able to correctly label most single cells. In summary, ProtAnno offers an accurate and robust tool to automate cell type annotations for large single cell proteomics datasets, and the analysis of such annotated cell types can offer valuable biological insights.

bioinformatics↗

bioRxiv2020

Type I Interferon Transcriptional Network Regulates Expression of Coinhibitory Receptors in Human T cells

While inhibition of T cell co-inhibitory receptors has revolutionized cancer therapy, the mechanisms governing their expression on human T cells have not been elucidated. Type 1 interferon (IFN-I) modulates T cell immunity in viral infection, autoimmunity, and cancer, and may facilitate induction of T cell exhaustion in chronic viral infection1,2. Here we show that IFN-I regulates co-inhibitory receptors expression on human T cells, inducing PD-1/TIM-3/LAG-3 while surprisingly inhibiting TIGIT expression. High-temporal-resolution mRNA profiling of IFN-I responses enabled the construction of dynamic transcriptional regulatory networks uncovering three temporal transcriptional waves. Perturbation of key transcription factors on human primary T cells revealed both canonical and non-canonical IFN-I transcriptional regulators, and identified unique regulators that control expression of co-inhibitory receptors. To provide direct in vivo evidence for the role of IFN-I on co-inhibitory receptors, we then performed single cell RNA-sequencing in subjects infected with SARS-CoV-2, where viral load was strongly associated with T cell IFN-I signatures. We found that the dynamic IFN-I response in vitro closely mirrored T cell features with acute IFN-I linked viral infection, with high LAG3 and decreased TIGIT expression. Finally, our gene regulatory network identified SP140 as a key regulator for differential LAG3 and TIGIT expression. The construction of co-inhibitory regulatory networks induced by IFN-I with identification of unique transcription factors controlling their expression may provide targets for enhancement of immunotherapy in cancer, infectious diseases, and autoimmunity.

immunology↗