Spatial and phenotypic heterogeneity of resident and monocyte-derived macrophages during inflammatory exacerbations leading to pulmonary fibrosis.

Introduction: Genetic mutations in critical nodes of pulmonary epithelial function are linked to the pathogenesis of pulmonary fibrosis (PF) and other interstitial lung diseases. The slow progression of these pathologies is often intermitted and accelerated by acute exacerbations, complex non-resolving cycles of inflammation and parenchymal damage, resulting in lung function decline and death. Excess monocyte mobilization during the initial phase of an acute exacerbation, and their long-term persistence in the lung, is linked to poor disease outcome. Methods: The present work leverages a clinical idiopathic PF dataset and a murine model of acute inflammatory exacerbations triggered by mutation in the alveolar type-2 cell-restricted Surfactant Protein-C [SP-C] gene to spatially and phenotypically define monocyte/macrophage changes in the fibrosing lung. Results: SP-C mutation triggered heterogeneous CD68+ macrophage activation, with highly active peri-injured cells relative to those sampled from fully remodeled and healthy regions. Ingenuity pathway analysis of sorted CD11b-SigF+CD11c+ alveolar macrophages defined asynchronous activation of extracellular matrix re-organization, cellular mobilization, and Apolipoprotein E (Apoe) signaling in the fibrosing lung. Cell-cell communication analysis of single cell sequencing datasets predicted pro-fibrogenic signaling (fibronectin/Fn1, osteopontin/Spp1, and Tgfb1) emanating from Trem2/TREM2 + interstitial macrophages. These cells also produced a distinct lipid signature from alveolar macrophages and monocytes, characterized by Apoe expression. Mono- and di-allelic genetic deletion of ApoE in SP-C mutant mice had limited impact on inflammation and mortality up to 42 day after injury. Discussion: Together, these results provide a detailed spatio-temporal picture of resident, interstitial, and monocyte-derived macrophages during SP-C induced inflammatory exacerbations and end-stage clinical PF, and propose ApoE as a biomarker to identify activated macrophages involved in tissue remodeling.

Single Cell Analysis of Peripheral TB-Associated Granulomatous Lymphadenitis.

We successfully employed a single cell RNA sequencing (scRNA-seq) approach to describe the cells and the communication networks characterizing granulomatous lymph nodes of TB patients. When mapping cells from individual patient samples, clustered based on their transcriptome similarities, we uniformly identify several cell types that known to characterize human and non-human primate granulomas. Whether high or low Mtb burden, we find the T cell cluster to be one of the most abundant. Many cells expressing T cell markers are clearly quantifiable within this CD3 expressing cluster. Other cell clusters that are uniformly detected, but that vary dramatically in abundance amongst the individual patient samples, are the B cell, plasma cell and macrophage/dendrocyte and NK cell clusters. When we combine all our scRNA-seq data from our current 23 patients (in order to add power to cell cluster identification in patient samples with fewer cells), we distinguish T, macrophage, dendrocyte and plasma cell subclusters, each with distinct signaling activities. The sizes of these subclusters also varies dramatically amongst the individual patients. In comparing FNA composition we noted trends in which T cell populations and macrophage/dendrocyte populations were negatively correlated with NK cell populations. In addition, we also discovered that the scRNA-seq pipeline, designed for quantification of human cell mRNA, also detects Mtb RNA transcripts and associates them with their host cell's transcriptome, thus identifying individual infected cells. We hypothesize that the number of detected bacterial transcript reads provides a measure of Mtb burden, as does the number of Mtb-infected cells. The number of infected cells also varies dramatically in abundance amongst the patient samples. CellChat analysis identified predominating signaling pathways amongst the cells comprising the various granulomas, including many interactions between stromal or endothelial cells and the other component cells, such as Collagen, FN1 and Laminin,. In addition, other more selective communications pathways, including MIF, MHC-1, MHC-2, APP, CD 22, CD45, and others, are identified as originating or being received by individual immune cell components.

A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing data.

Genetic and gene expression heterogeneity is an essential hallmark of many tumors, allowing the cancer to evolve and to develop resistance to treatment. Currently, the most commonly used data types for studying such heterogeneity are bulk tumor/normal whole-genome or whole-exome sequencing (WGS, WES); and single-cell RNA sequencing (scRNA-seq), respectively. However, tools are currently lacking to link genomic tumor subclonality with transcriptomic heterogeneity by integrating genomic and single-cell transcriptomic data collected from the same tumor. To address this gap, we developed scBayes, a Bayesian probabilistic framework that uses tumor subclonal structure inferred from bulk DNA sequencing data to determine the subclonal identity of cells from single-cell gene expression (scRNA-seq) measurements. Grouping together cells representing the same genetically defined tumor subclones allows comparison of gene expression across different subclones, or investigation of gene expression changes within the same subclone across time (i.e., progression, treatment response, or relapse) or space (i.e., at multiple metastatic sites and organs). We used simulated data sets, in silico synthetic data sets, as well as biological data sets generated from cancer samples to extensively characterize and validate the performance of our method, as well as to show improvements over existing methods. We show the validity and utility of our approach by applying it to published data sets and recapitulating the findings, as well as arriving at novel insights into cancer subclonal expression behavior in our own data sets. We further show that our method is applicable to a wide range of single-cell sequencing technologies including single-cell DNA sequencing as well as Smart-seq and 10x Genomics scRNA-seq protocols.