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The stromal compartment of the tumor microenvironment consists of a heterogeneous set of tissue-resident and tumor-infiltrating cells, which are profoundly moulded by cancer cells. An outstanding question is to what extent this heterogeneity is similar between cancers affecting different organs. Here, we profile 233,591 single cells from patients with lung, colorectal, ovary and breast cancer ( n  = 36) and construct a pan-cancer blueprint of stromal cell heterogeneity using different single-cell RNA and protein-based technologies. We identify 68 stromal cell populations, of which 46 are shared between cancer types and 22 are unique. We also characterise each population phenotypically by highlighting its marker genes, transcription factors, metabolic activities and tissue-specific expression differences. Resident cell types are characterised by substantial tissue specificity, while tumor-infiltrating cell types are largely shared across cancer types. Finally, by applying the blueprint to melanoma tumors treated with checkpoint immunotherapy and identifying a naïve CD4 + T-cell phenotype predictive of response to checkpoint immunotherapy, we illustrate how it can serve as a guide to interpret scRNA-seq data. In conclusion, by providing a comprehensive blueprint through an interactive web server, we generate the first panoramic view on the shared complexity of stromal cells in different cancers.

Cardiovascular disease is the leading cause of death worldwide. Advanced insights into disease mechanisms and therapeutic strategies require a deeper understanding of the molecular processes involved in the healthy heart. Knowledge of the full repertoire of cardiac cells and their gene expression profiles is a fundamental first step in this endeavour. Here, using state-of-the-art analyses of large-scale single-cell and single-nucleus transcriptomes, we characterize six anatomical adult heart regions. Our results highlight the cellular heterogeneity of cardiomyocytes, pericytes and fibroblasts, and reveal distinct atrial and ventricular subsets of cells with diverse developmental origins and specialized properties. We define the complexity of the cardiac vasculature and its changes along the arterio-venous axis. In the immune compartment, we identify cardiac-resident macrophages with inflammatory and protective transcriptional signatures. Furthermore, analyses of cell-to-cell interactions highlight different networks of macrophages, fibroblasts and cardiomyocytes between atria and ventricles that are distinct from those of skeletal muscle. Our human cardiac cell atlas improves our understanding of the human heart and provides a valuable reference for future studies. Single-cell and single-nucleus RNA sequencing are used to construct a cellular atlas of the human heart that will aid further research into cardiac physiology and disease.

scGESTALT enables large-scale characterization of cell types and lineage relationships during vertebrate brain development. The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method, GESTALT, used CRISPR–Cas9 barcode editing for large-scale lineage tracing, but was restricted to early development and did not identify cell types. Here we present scGESTALT, which combines the lineage recording capabilities of GESTALT with cell-type identification by single-cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points, capturing lineage information from later stages of development. Sequencing of ∼60,000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data, we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types, brain regions, and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease.

A single-cell method for probing genome-wide chromatin accessibility has been developed; the results provide insight into the relationship between cell-to-cell variation associated with specific trans -factors and cis -elements, as well insights into the relationship between chromatin accessibility and three-dimensional genome organization. Chromatin mapped in individual cells Technological advances for interrogating single cells are allowing a more detailed understanding of cell-to-cell variation in gene expression. Here, William Greenleaf and colleagues describe a single-cell transposase-based method, termed single-cell ATAC-seq (scATAC-seq), for probing DNA accessibility genome-wide. They generate chromatin accessibility maps in several types of mammalian cells, and although analysis of cellular variation at individual regulatory elements is not yet feasible, they can assess variation in accessibility across sets of genomic features and find particular transcription factors associated with increased accessibility variation. Cell-to-cell variation is a universal feature of life that affects a wide range of biological phenomena, from developmental plasticity 1 , 2 to tumour heterogeneity 3 . Although recent advances have improved our ability to document cellular phenotypic variation 4 , 5 , 6 , 7 , 8 , the fundamental mechanisms that generate variability from identical DNA sequences remain elusive. Here we reveal the landscape and principles of mammalian DNA regulatory variation by developing a robust method for mapping the accessible genome of individual cells by assay for transposase-accessible chromatin using sequencing (ATAC-seq) 9 integrated into a programmable microfluidics platform. Single-cell ATAC-seq (scATAC-seq) maps from hundreds of single cells in aggregate closely resemble accessibility profiles from tens of millions of cells and provide insights into cell-to-cell variation. Accessibility variance is systematically associated with specific trans -factors and cis -elements, and we discover combinations of trans -factors associated with either induction or suppression of cell-to-cell variability. We further identify sets of trans -factors associated with cell-type-specific accessibility variance across eight cell types. Targeted perturbations of cell cycle or transcription factor signalling evoke stimulus-specific changes in this observed variability. The pattern of accessibility variation in cis across the genome recapitulates chromosome compartments 10 de novo, linking single-cell accessibility variation to three-dimensional genome organization. Single-cell analysis of DNA accessibility provides new insight into cellular variation of the ‘regulome’.

The high selectivity of the human blood-brain barrier (BBB) restricts delivery of many pharmaceuticals and therapeutic antibodies to the central nervous system. Here, we describe an in vitro microfluidic organ-on-a-chip BBB model lined by induced pluripotent stem cell-derived human brain microvascular endothelium interfaced with primary human brain astrocytes and pericytes that recapitulates the high level of barrier function of the in vivo human BBB for at least one week in culture. The endothelium expresses high levels of tight junction proteins and functional efflux pumps, and it displays selective transcytosis of peptides and antibodies previously observed in vivo. Increased barrier functionality was accomplished using a developmentally-inspired induction protocol that includes a period of differentiation under hypoxic conditions. This enhanced BBB Chip may therefore represent a new in vitro tool for development and validation of delivery systems that transport drugs and therapeutic antibodies across the human BBB. In vitro blood-brain barrier (BBB) models do not fully recapitulate the in vivo barrier function. Here the authors develop an organ-on-a-chip BBB model using iPS-derived human brain endothelial cells differentiated under hypoxia, primary human pericytes and astrocytes, which maintains in vivo-like BBB barrier and shuttling functions for a week.

Broad-spectrum antibiotics for suspected early-onset neonatal sepsis (sEONS) may have pronounced effects on gut microbiome development and selection of antimicrobial resistance when administered in the first week of life, during the assembly phase of the neonatal microbiome. Here, 147 infants born at ≥36 weeks of gestational age, requiring broad-spectrum antibiotics for treatment of sEONS in their first week of life were randomized 1:1:1 to receive three commonly prescribed intravenous antibiotic combinations, namely penicillin + gentamicin, co-amoxiclav + gentamicin or amoxicillin + cefotaxime (ZEBRA study, Trial Register NL4882). Average antibiotic treatment duration was 48 hours. A subset of 80 non-antibiotic treated infants from a healthy birth cohort served as controls (MUIS study, Trial Register NL3821). Rectal swabs and/or faeces were collected before and immediately after treatment, and at 1, 4 and 12 months of life. Microbiota were characterized by 16S rRNA-based sequencing and a panel of 31 antimicrobial resistance genes was tested using targeted qPCR. Confirmatory shotgun metagenomic sequencing was executed on a subset of samples. The overall gut microbial community composition and antimicrobial resistance gene profile majorly shift directly following treatment (R 2  = 9.5%, adjusted p -value = 0.001 and R 2  = 7.5%, adjusted p -value = 0.001, respectively) and normalize over 12 months (R 2  = 1.1%, adjusted p -value = 0.03 and R 2  = 0.6%, adjusted p -value = 0.23, respectively). We find a decreased abundance of Bifidobacterium spp. and increased abundance of Klebsiella and Enterococcus spp. in the antibiotic treated infants compared to controls. Amoxicillin + cefotaxime shows the largest effects on both microbial community composition and antimicrobial resistance gene profile, whereas penicillin + gentamicin exhibits the least effects. These data suggest that the choice of empirical antibiotics is relevant for adverse ecological side-effects. Here, in a randomized trial of 147 infants receiving distinct antibiotic regimens for early-onset neonatal sepsis, Reyman et al. characterize the gut microbiome and resistance profiles, finding differential effects of antibiotic combinations on microbial community composition and antimicrobial resistance genes.

An algorithm uncovers transcriptome dynamics during differentiation by ordering RNA-Seq data from single cells. Defining the transcriptional dynamics of a temporal process such as cell differentiation is challenging owing to the high variability in gene expression between individual cells. Time-series gene expression analyses of bulk cells have difficulty distinguishing early and late phases of a transcriptional cascade or identifying rare subpopulations of cells, and single-cell proteomic methods rely on a priori knowledge of key distinguishing markers 1 . Here we describe Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points. Applied to the differentiation of primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation. We validated some of these predicted regulators in a loss-of function screen. Monocle can in principle be used to recover single-cell gene expression kinetics from a wide array of cellular processes, including differentiation, proliferation and oncogenic transformation.

Single-cell analysis of the adult human brain is facilitated by improved methods for RNA-seq and hypersensitive-site mapping. Detailed characterization of the cell types in the human brain requires scalable experimental approaches to examine multiple aspects of the molecular state of individual cells, as well as computational integration of the data to produce unified cell-state annotations. Here we report improved high-throughput methods for single-nucleus droplet-based sequencing (snDrop-seq) and single-cell transposome hypersensitive site sequencing (scTHS-seq). We used each method to acquire nuclear transcriptomic and DNA accessibility maps for >60,000 single cells from human adult visual cortex, frontal cortex, and cerebellum. Integration of these data revealed regulatory elements and transcription factors that underlie cell-type distinctions, providing a basis for the study of complex processes in the brain, such as genetic programs that coordinate adult remyelination. We also mapped disease-associated risk variants to specific cellular populations, which provided insights into normal and pathogenic cellular processes in the human brain. This integrative multi-omics approach permits more detailed single-cell interrogation of complex organs and tissues.

The accurate and timely diagnosis of disease is a prerequisite for efficient therapeutic intervention and epidemiological surveillance. Diagnostics based on the detection of nucleic acids are among the most sensitive and specific, yet most such assays require costly equipment and trained personnel. Recent developments in diagnostic technologies, in particular those leveraging clustered regularly interspaced short palindromic repeats (CRISPR), aim to enable accurate testing at home, at the point of care and in the field. In this Review, we provide a rundown of the rapidly expanding toolbox for CRISPR-based diagnostics, in particular the various assays, preamplification strategies and readouts, and highlight their main applications in the sensing of a wide range of molecular targets relevant to human health. This Review summarizes the rapidly expanding toolbox of assays for CRISPR-based diagnostics and highlights their point-of-care applications.

The proximal tubule is a key regulator of kidney function and glucose metabolism. Diabetic kidney disease leads to proximal tubule injury and changes in chromatin accessibility that modify the activity of transcription factors involved in glucose metabolism and inflammation. Here we use single nucleus RNA and ATAC sequencing to show that diabetic kidney disease leads to reduced accessibility of glucocorticoid receptor binding sites and an injury-associated expression signature in the proximal tubule. We hypothesize that chromatin accessibility is regulated by genetic background and closely-intertwined with metabolic memory, which pre-programs the proximal tubule to respond differently to external stimuli. Glucocorticoid excess has long been known to increase risk for type 2 diabetes, which raises the possibility that glucocorticoid receptor inhibition may mitigate the adverse metabolic effects of diabetic kidney disease. Diabetic kidney disease leads to changes in glucose metabolism and inflammation. Here the authors use multimodal single cell sequencing to show that this disease leads to reduced accessibility of glucocorticoid receptor binding sites in the proximal tubule and increased gluconeogenesis.