Explore the vast range of titles available.

ChIPmentation combines chromatin immunoprecipitation with on-bead tagmentation for rapid and highly robust ChIP-seq library preparation. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is widely used to map histone marks and transcription factor binding throughout the genome. Here we present ChIPmentation, a method that combines chromatin immunoprecipitation with sequencing library preparation by Tn5 transposase ('tagmentation'). ChIPmentation introduces sequencing-compatible adaptors in a single-step reaction directly on bead-bound chromatin, which reduces time, cost and input requirements, thus providing a convenient and broadly useful alternative to existing ChIP-seq protocols.

Recent studies have provided insights into the pathology of and immune response to COVID-19 1 – 8 . However, a thorough investigation of the interplay between infected cells and the immune system at sites of infection has been lacking. Here we use high-parameter imaging mass cytometry 9 that targets the expression of 36 proteins to investigate the cellular composition and spatial architecture of acute lung injury in humans (including injuries derived from SARS-CoV-2 infection) at single-cell resolution. These spatially resolved single-cell data unravel the disordered structure of the infected and injured lung, alongside the distribution of extensive immune infiltration. Neutrophil and macrophage infiltration are hallmarks of bacterial pneumonia and COVID-19, respectively. We provide evidence that SARS-CoV-2 infects predominantly alveolar epithelial cells and induces a localized hyperinflammatory cell state that is associated with lung damage. We leverage the temporal range of fatal outcomes of COVID-19 in relation to the onset of symptoms, which reveals increased macrophage extravasation and increased numbers of mesenchymal cells and fibroblasts concomitant with increased proximity between these cell types as the disease progresses—possibly as a result of attempts to repair the damaged lung tissue. Our data enable us to develop a biologically interpretable landscape of lung pathology from a structural, immunological and clinical standpoint. We use this landscape to characterize the pathophysiology of the human lung from its macroscopic presentation to the single-cell level, which provides an important basis for understanding COVID-19 and lung pathology in general. Imaging mass cytometry of the human lung reveals the cellular composition and spatial architecture during COVID-19 and other acute injuries, enabling the characterization of lung pathophysiology from structural, immunological and clinical perspectives.

Cell atlas projects and high-throughput perturbation screens require single-cell sequencing at a scale that is challenging with current technology. To enable cost-effective single-cell sequencing for millions of individual cells, we developed ‘single-cell combinatorial fluidic indexing’ (scifi). The scifi-RNA-seq assay combines one-step combinatorial preindexing of entire transcriptomes inside permeabilized cells with subsequent single-cell RNA-seq using microfluidics. Preindexing allows us to load several cells per droplet and computationally demultiplex their individual expression profiles. Thereby, scifi-RNA-seq massively increases the throughput of droplet-based single-cell RNA-seq, and provides a straightforward way of multiplexing thousands of samples in a single experiment. Compared with multiround combinatorial indexing, scifi-RNA-seq provides an easy and efficient workflow. Compared to cell hashing methods, which flag and discard droplets containing more than one cell, scifi-RNA-seq resolves and retains individual transcriptomes from overloaded droplets. We benchmarked scifi-RNA-seq on various human and mouse cell lines, validated it for primary human T cells and applied it in a highly multiplexed CRISPR screen with single-cell transcriptome readout of T cell receptor activation.Combining whole-transcriptome preindexing with standard droplet microfluidics, scifi-RNA-seq enables single-cell RNA-seq with massive throughput and built-in sample multiplexing.

Spatial omics technologies enable a deeper understanding of cellular organizations and interactions within a tissue of interest. These assays can identify specific compartments or regions in a tissue with differential transcript or protein abundance, delineate their interactions, and complement other methods in defining cellular phenotypes. A variety of spatial methodologies are being developed and commercialized; however, these techniques differ in spatial resolution, multiplexing capability, scale/throughput, and coverage. Here, we review the current and prospective landscape of single cell to subcellular resolution spatial omics technologies and analysis tools to provide a comprehensive picture for both research and clinical applications.

Multiplexed imaging and spatial transcriptomics enable highly resolved spatial characterization of cellular phenotypes, but still largely depend on laborious manual annotation to understand higher-order patterns of tissue organization. As a result, higher-order patterns of tissue organization are poorly understood and not systematically connected to disease pathology or clinical outcomes. To address this gap, we developed an approach called UTAG to identify and quantify microanatomical tissue structures in multiplexed images without human intervention. Our method combines information on cellular phenotypes with the physical proximity of cells to accurately identify organ-specific microanatomical domains in healthy and diseased tissue. We apply our method to various types of images across healthy and disease states to show that it can consistently detect higher-level architectures in human tissues, quantify structural differences between healthy and diseased tissue, and reveal tissue organization patterns at the organ scale. Unsupervised discovery of tissue architecture with graphs (UTAG) combines information on cellular morphology and protein expression with the physical proximity of cells to identify architectural domains from highly multiplexed imaging data.

Respiratory failure is the leading cause of death in patients with severe SARS-CoV-2 infection 1 , 2 , but the host response at the lung tissue level is poorly understood. Here we performed single-nucleus RNA sequencing of about 116,000 nuclei from the lungs of nineteen individuals who died of COVID-19 and underwent rapid autopsy and seven control individuals. Integrated analyses identified substantial alterations in cellular composition, transcriptional cell states, and cell-to-cell interactions, thereby providing insight into the biology of lethal COVID-19. The lungs from individuals with COVID-19 were highly inflamed, with dense infiltration of aberrantly activated monocyte-derived macrophages and alveolar macrophages, but had impaired T cell responses. Monocyte/macrophage-derived interleukin-1β and epithelial cell-derived interleukin-6 were unique features of SARS-CoV-2 infection compared to other viral and bacterial causes of pneumonia. Alveolar type 2 cells adopted an inflammation-associated transient progenitor cell state and failed to undergo full transition into alveolar type 1 cells, resulting in impaired lung regeneration. Furthermore, we identified expansion of recently described CTHRC1 + pathological fibroblasts 3 contributing to rapidly ensuing pulmonary fibrosis in COVID-19. Inference of protein activity and ligand–receptor interactions identified putative drug targets to disrupt deleterious circuits. This atlas enables the dissection of lethal COVID-19, may inform our understanding of long-term complications of COVID-19 survivors, and provides an important resource for therapeutic development. Lung samples collected soon after death from COVID-19 are used to provide a single-cell atlas of SARS-CoV-2 infection and the ensuing molecular changes.

Biological data visualization is challenged by the growing complexity of datasets. Traditional single-data plots or simple juxtapositions often fail to fully capture dataset intricacies and interrelations. To address this, we introduce “cross-layout,” a novel visualization paradigm that integrates multiple plot types in a cross-like structure, with a central main plot surrounded by secondary plots for enhanced contextualization and interrelation insights. We also introduce “Marsilea,” a Python-based implementation of cross-layout visualizations, available in both programmatic and web-based interfaces to support users of all experience levels. This paradigm and its implementation offer a customizable, intuitive approach to advance biological data visualization.

Imaging-based methods are widely used for studying the subcellular localization of proteins in living cells. While routine for individual proteins, global monitoring of protein dynamics following perturbation typically relies on arrayed panels of fluorescently tagged cell lines, limiting throughput and scalability. Here, we describe a strategy that combines high-throughput microscopy, computer vision and machine learning to detect perturbation-induced changes in multicolour tagged visual proteomics cell (vpCell) pools. We use genome-wide and cancer-focused intron-targeting sgRNA libraries to generate vpCell pools and a large, arrayed collection of clones each expressing two different endogenously tagged fluorescent proteins. Individual clones can be identified in vpCell pools by image analysis using the localization patterns and expression level of the tagged proteins as visual barcodes, enabling simultaneous live-cell monitoring of large sets of proteins. To demonstrate broad applicability and scale, we test the effects of antiproliferative compounds on a pool with cancer-related proteins, on which we identify widespread protein localization changes and new inhibitors of the nuclear import/export machinery. The time-resolved characterization of changes in subcellular localization and abundance of proteins upon perturbation in a pooled format highlights the power of the vpCell approach for drug discovery and mechanism-of-action studies. Reicher, Reiniš et al. report a method for multicolour tagging using genome-scale intron-targeting sgRNA libraries that, in combination with computer vision, enables the systematic detection of protein localization changes.

Chronic lymphocytic leukaemia (CLL) is characterized by substantial clinical heterogeneity, despite relatively few genetic alterations. To provide a basis for studying epigenome deregulation in CLL, here we present genome-wide chromatin accessibility maps for 88 CLL samples from 55 patients measured by the ATAC-seq assay. We also performed ChIPmentation and RNA-seq profiling for ten representative samples. Based on the resulting data set, we devised and applied a bioinformatic method that links chromatin profiles to clinical annotations. Our analysis identified sample-specific variation on top of a shared core of CLL regulatory regions. IGHV mutation status—which distinguishes the two major subtypes of CLL—was accurately predicted by the chromatin profiles and gene regulatory networks inferred for IGHV -mutated versus IGHV -unmutated samples identified characteristic differences between these two disease subtypes. In summary, we discovered widespread heterogeneity in the chromatin landscape of CLL, established a community resource for studying epigenome deregulation in leukaemia and demonstrated the feasibility of large-scale chromatin accessibility mapping in cancer cohorts and clinical research. Chronic lymphocytic leukemia (CLL) is characterized by substantial clinical heterogeneity. Here, the authors report the genome-wide chromatin accessibility maps for 88 CLL samples from 55 patients using ATAC-seq, and 10 matched RNA-seq datasets, providing a resource for studying epigenome deregulation in CLL.

Aberrations in genes coding for subunits of the BRG1/BRM associated factor (BAF) chromatin remodeling complexes are highly abundant in human cancers. Currently, it is not understood how these mostly loss-of-function mutations contribute to cancer development and how they can be targeted therapeutically. The cancer-type-specific occurrence patterns of certain subunit mutations suggest subunit-specific effects on BAF complex function, possibly by the formation of aberrant residual complexes. Here, we systematically characterize the effects of individual subunit loss on complex composition, chromatin accessibility and gene expression in a panel of knockout cell lines deficient for 22 BAF subunits. We observe strong, specific and sometimes discordant alterations dependent on the targeted subunit and show that these explain intracomplex codependencies, including the synthetic lethal interactions SMARCA4–ARID2, SMARCA4–ACTB and SMARCC1–SMARCC2. These data provide insights into the role of different BAF subcomplexes in genome-wide chromatin organization and suggest approaches to therapeutically target BAF-mutant cancers. The authors generate cell lines deficient for 22 BAF subunits, studying effects on complex composition, chromatin accessibility and gene expression. They identify synthetic lethal interactions between SMARCA4–ARID2, SMARCA4–ACTB and SMARCC1–SMARCC2.