A Web Interface for Quantifying the Immune Cell Composition of Tissue Transcriptomes

We developed a web tool, SaVanTv2.0, that leverages cell type signatures, or genes highly expressed in cell types, for characterizing the underlying cell populations of gene expression data. SaVanTv2.0 offers an accessible web interface, diverse exploratory, inferential, and predictive analytics, and interpretable visualizations to distinguish cells across disease categories. We demonstrated SaVanTv2.0’s biomedical applications by (1) suggesting higher expression of neutrophils and lymphocytes and lower expression of monocytes in early-stage symptomatic compared with asymptomatic influenza patients, (2) distinguishing higher expression of B cells, monocytes, and neutrophils, and lower expression of natural killer cells and plasmacytoid dendritic cells in active compared with latent tuberculosis patients for a non-invasive diagnosis, and (3) contributing to the discovery of a T cell independent response that drives lepromatous leprosy. SaVanTv2.0 thereby carries out cell decomposition for the accessible and interpretable discovery of biomarkers and elucidation of immune responses in diseased samples

Dual RNA-Seq of Human Leprosy Lesions Identifies Bacterial Determinants Linked to Host Immune Response

Summary: To understand how the interaction between an intracellular bacterium and the host immune system contributes to outcome at the site of infection, we studied leprosy, a disease that forms a clinical spectrum, in which progressive infection by the intracellular bacterium Mycobacterium leprae is characterized by the production of type I IFNs and antibody production. Dual RNA-seq on patient lesions identifies two independent molecular measures of M. leprae, each of which correlates with distinct aspects of the host immune response. The fraction of bacterial transcripts, reflecting bacterial burden, correlates with a host type I IFN gene signature, known to inhibit antimicrobial responses. Second, the bacterial mRNA:rRNA ratio, reflecting bacterial viability, links bacterial heat shock proteins with the BAFF-BCMA host antibody response pathway. Our findings provide a platform for the interrogation of host and pathogen transcriptomes at the site of infection, allowing insight into mechanisms of inflammation in human disease. : Montoya et al. utilizes dual RNA-seq to define the bacterial burden and transcriptional state within leprosy skin lesions. These molecular measures vary across patients and provide separate but informative metrics about the pathogenesis of mycobacterial disease, revealing that the immune system responds to bacterial states and not just their abundance. Keywords: mycobacteria, immunology, systems immunology, microbiology, plasma cell, host-pathogen, tuberculosis, heat shock, humoral, translational, transcriptome, sequencing, bioinformatics, computational biolog

HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes

© 2020 National Academy of Sciences. All rights reserved. Single-cell quantification of RNAs is important for understanding cellular heterogeneity and gene regulation, yet current approaches suffer from low sensitivity for individual transcripts, limiting their utility for many applications. Here we present Hybridization of Probes to RNA for sequencing (HyPR-seq), a method to sensitively quantify the expression of hundreds of chosen genes in single cells. HyPR-seq involves hybridizing DNA probes to RNA, distributing cells into nanoliter droplets, amplifying the probes with PCR, and sequencing the amplicons to quantify the expression of chosen genes. HyPR-seq achieves high sensitivity for individual transcripts, detects nonpolyadenylated and low-abundance transcripts, and can profile more than 100,000 single cells. We demonstrate how HyPR-seq can profile the effects of CRISPR perturbations in pooled screens, detect time-resolved changes in gene expression via measurements of gene introns, and detect rare transcripts and quantify cell-type frequencies in tissue using low-abundance marker genes. By directing sequencing power to genes of interest and sensitively quantifying individual transcripts, HyPR-seq reduces costs by up to 100-fold compared to whole-transcriptome single-cell RNA-sequencing, making HyPR-seq a powerful method for targeted RNA profiling in single cells

An atlas of healthy and injured cell states and niches in the human kidney.

Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods1. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations