Recent advances in single cell genomics technologies have facilitated studies on the developing immune system at unprecedented scale and resolution. However, these studies have focused on one or a few organs and were thus limited in understanding the developing immune system as a distributed network across tissues. Here, we profiled prenatal haematopoietic organs, lymphoid organs and non-lymphoid tissues using a combination of single-cell RNA sequencing, paired antigen-receptor sequencing and spatial transcriptomics to reconstruct the developing human immune system. Our analysis revealed the acquisition of immune effector transcriptome profiles in macrophages, mast cells and NK cells from the second trimester, and the transcriptomic changes accompanying the late-stage maturation of developing monocytes and T cells that extended from their organ of origin to peripheral tissues. We uncovered system-wide blood and immune cell development beyond the conventional primary haematopoietic organs. We further identified, extensively characterised and functionally validated the human prenatal B1 cells. Finally, we provide evidence for thymocyte-thymocyte selection origin for αβTCR- expressing unconventional T cells based on TCR gene usage and an in vitro artificial thymic organoid culture model. Our comprehensive atlas of the developing human immune system provides both valuable data resources and biological insights that will facilitate cell engineering, regenerative medicine and disease understanding. One-Sentence Summary By performing a comprehensive single-cell RNA sequencing atlas of human developing immune system together with antigen-receptor sequencing and spatial transcriptomics, we explored the cross-gestation and cross-organ variability in immune cells, discovered system-wide blood and immune cell development, identified, characterised and functionally validated the properties of human prenatal B1 cells and the origin of unconventional T cells.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612819

Mapping the developing human immune system across organs

http://www.cell.com/article/S0092867422004627/pdf

Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution

Pathogenic variants in genes that cause dilated cardiomyopathy (DCM) and arrhythmogenic cardiomyopathy (ACM) convey high risks for the development of heart failure through unknown mechanisms. Using single-nucleus RNA sequencing, we characterized the transcriptome of 880,000 nuclei from 18 control and 61 failing, nonischemic human hearts with pathogenic variants in DCM and ACM genes or idiopathic disease. We performed genotype-stratified analyses of the ventricular cell lineages and transcriptional states. The resultant DCM and ACM ventricular cell atlas demonstrated distinct right and left ventricular responses, highlighting genotype-associated pathways, intercellular interactions, and differential gene expression at single-cell resolution. Together, these data illuminate both shared and distinct cellular and molecular architectures of human heart failure and suggest candidate therapeutic targets. Description A close-up look at cardiomyopathies Cardiomyopathies are diseases of the heart muscle that interfere with its ability to pump blood effectively and can result in heart failure. They are divided into different categories based on the clinical presentation. Reichart et al. performed single-nucleus RNA sequencing on heart samples from patients with cardiomyopathies with or without known genetic causes, as well as samples from controls without structural heart disease. The authors identified key cell types and their locations in the heart, cellular interactions, and biological signaling pathways, offering insights into the biology of the heart in healthy and diseased states. —YN A single-cell atlas of the heart identifies cellular interactions and pathways involved in two types of cardiomyopathy. INTRODUCTION Human heart failure is a highly morbid condition that affects 23 million individuals worldwide. It emerges in the setting of an array of different cardiovascular disorders, which has propelled the notion that diverse stimuli converge on a common final pathway. Consistent with this, initiating etiologies do not direct heart failure treatments, which are often inadequate and necessitate mechanical interventions and cardiac transplantation. The recent application of single-nucleus RNA sequencing (snRNAseq) transcriptional analyses to characterize the cellular composition and molecular states in the healthy adult human heart provides an emerging benchmark by which disease-related changes can be assessed. Moreover, the discovery of human pathogenic variants that cause dilated cardiomyopathy (DCM) and arrhythmogenic cardiomyopathy (ACM), disorders associated with high rates of heart failure, provides direct opportunities to evaluate whether genotype influences heart failure pathways. RATIONALE A systematic identification of shared and distinct molecules and pathways involved in heart failure is lacking, and knowledge of these fundamental data could propel the development of more effective treatments. To enable these discoveries, we performed snRNAseq of explanted ventricular tissues from 18 healthy donors and 61 heart failure patients. By focusing analyses on multiple samples with pathogenic variants in DCM genes (LMNA, RBM20, and TTN), ACM genes (PKP2), or pathogenic variant–negative (PV negative) samples, we characterized genotype-stratified and common heart failure responses. RESULTS From 881,081 nuclei isolated from left and right diseased and healthy ventricles, we identified 10 major cell types and 71 distinct transcriptional states. DCM and ACM tissues showed significant depletion of cardiomyocytes and increased endothelial and immune cells. Fibrosis was expanded in disease hearts, but, unexpectedly, fibroblasts were not increased, and instead showed altered transcriptional states that indicated activated remodeling of the extracellular matrix. Genotype-stratified analyses identified multiple transcriptional changes shared only among the hearts harboring pathogenic variants or distinctive for individual and subsets of DCM and ACM genotypes. We validated many of these by single-molecule fluorescent in situ hybridization. Through analyses of receptor and ligand expression across all cells, we observed changes in intercellular signaling and communications, such as increased endothelin signaling in LMNA hearts, tumor necrosis factor in PKP2 hearts, and others. We also identified specific cardiac cell lineages expressing genes with common polymorphisms that were identified in validated association studies of DCM. Because our findings indicated genotype-enriched transcripts and cell states, we harnessed machine learning to develop a graph attention network for the multinomial classification of genotypes. This network showed remarkably high prediction of the genotypes for each cardiac sample, thereby reinforcing our conclusion that genotypes activate very specific heart failure pathways. CONCLUSION snRNAseq of human ventricular samples illuminated cell types and states, molecular signals, and intercellular communications that characterize DCM and ACM. The cellular and molecular architectures that induce heart failure are both shared and distinct across genotypes. These data provide candidate therapeutic targets for future research and interventional opportunities to improve and personalize treatments for cardiomyopathies and heart failure. Genotype-stratified analyses of heart failure at the single-nuclei level. The transcriptomes of 881,081 nuclei from 61 heart failure patients were profiled and compared with the transcriptional signatures of 18 healthy controls. Genotype-stratified analyses of cell types and cell state compositions, differential gene expression, cell-cell interactions, and machine learning illuminated the shared and distinct transcriptional signatures resulting from pathogenic variants in DCM and ACM.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9528698

Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies

We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.

RNA velocity unraveled

Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools ( https://scvi-tools.org ). 

DestVI identifies continuums of cell types in spatial transcriptomics data

/pdf/a-python-library-for-probabilistic-analysis-of-single-cell-3ne7av02.pdf

Michael Jayasuriya

Papers

A Python library for probabilistic analysis of single-cell omics data