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Large-scale multiplexed mosaic CRISPR perturbation in the whole organism.
Here, we report inducible mosaic animal for perturbation (iMAP), a transgenic platform enabling in situ CRISPR targeting of at least 100 genes in parallel throughout the mouse body. iMAP combines Cre-loxP and CRISPR-Cas9 technologies and utilizes a germline-transmitted transgene carrying a large array of individually floxed, tandemly linked gRNA-coding units. Cre-mediated recombination triggers expression of all the gRNAs in the array but only one of them per cell, converting the mice to mosaic organisms suitable for phenotypic characterization and also for high-throughput derivation of conventional single-gene perturbation lines via breeding. Using gRNA representation as a readout, we mapped a miniature Perturb-Atlas cataloging the perturbations of 90 genes across 39 tissues, which yields rich insights into context-dependent gene functions and provides a glimpse of the potential of iMAP in genome decoding.
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A resource for whole-body gene expression map of human tissues based on integration of single cell and bulk transcriptomics.
New technologies enable single-cell transcriptome analysis, mapping genome-wide expression across the human body. Here, we present an extended analysis of protein-coding genes in all major human tissues and organs, combining single-cell and bulk transcriptomics. To enhance transcriptome depth, 31 tissues were analyzed using a pooling method, identifying 557 unique cell clusters, manually annotated by marker gene expression. Genes were classified by body-wide expression and validated through antibody-based profiling. All results are available in the updated open-access Single Cell Type section of the Human Protein Atlas for genome-wide exploration of genes, proteins, and their spatial distribution in cells.
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AnnSQL: a Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational resources.
SUMMARY: As single-cell genomics technologies continue to accelerate biological discovery, software tools that use elegant syntax and minimal computational resources to analyze atlas-scale datasets are increasingly needed. Here, we introduce AnnSQL, a Python package that constructs an AnnData-inspired database using the in-process DuckDb engine, enabling orders-of-magnitude performance enhancements for parsing single-cell genomics datasets with the ease of SQL. We highlight AnnSQL functionality and demonstrate transformative runtime improvements by comparing AnnData or AnnSQL operations on a 4.4 million cell single-nucleus RNA-seq dataset: AnnSQL-based operations were executed in minutes on a laptop for which equivalent operations in AnnData or Seurat largely failed (or were ∼700× slower) on a high-performance computing cluster. AnnSQL lowers computational barriers for large-scale single-cell/nucleus RNA-seq analysis on a personal computer, while demonstrating a promising computational infrastructure extendable for complete single-cell workflows across various genome-wide measurements. AVAILABILITY AND IMPLEMENTATION: AnnSQL is a pip installable package that can be found at https://github.com/ArpiarSaundersLab/annsql along with documentation at https://docs.annsql.com.
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DNA methylation atlas of the mouse brain at single-cell resolution.
Mammalian brain cells show remarkable diversity in gene expression, anatomy and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. Here we carry out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single-nucleus DNA methylation sequencing1,2 to profile 103,982 nuclei (including 95,815 neurons and 8,167 non-neuronal cells) from 45 regions of the mouse cortex, hippocampus, striatum, pallidum and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, we constructed an artificial neural network model that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data3 enabled prediction of high-confidence enhancer-gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments4. By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse cerebrum.
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Unveiling cell subpopulations in T1D mouse islets using single-cell RNA sequencing.
Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of beta cells by immune cells. The interactions among cells within the islets may be closely linked to the pathogenesis of T1D. In this study, we used single-cell RNA sequencing (scRNA-Seq) to analyze the cellular heterogeneity within the islets of a T1D mouse model. We established a T1D mouse model induced by streptozotocin and identified cell subpopulations using scRNA-Seq technology. Our results revealed 11 major cell types in the pancreatic islets of T1D mice, with heterogeneity observed in the alpha and beta cell subgroups, which may play a crucial role in the progression of T1D. Flow cytometry further confirmed a mature alpha and beta cell reduction in T1D mice. Overall, our scRNA-Seq analysis provided insights into the cellular heterogeneity of T1D islet tissue and highlighted the potential importance of alpha and beta cells in developing T1D.NEW & NOTEWORTHY In this study, we created a comprehensive single-cell atlas of pancreatic islets in a T1D mouse model using scRNA-Seq and identified 11 major cell types in the islets, highlighting the role of alpha and beta cells in T1D. This study revealed a significant reduction in the maturity alpha and beta cells in T1D mice through flow cytometry. It also demonstrated the heterogeneity of alpha and beta cells, potentially crucial for T1D progression. Overall, our scRNA-Seq analysis provided new insights for understanding and treating T1D by studying cell subtype changes and functions.
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Maintenance of human haematopoietic stem and progenitor cells in vitro using a chemical cocktail.
Identification of effective culture conditions to maintain and possibly expand human HSPCs in vitro is an important goal. Recent advances highlight the efficacy of chemicals in maintaining and converting cell fates. We screened 186 chemicals and found that a combination of CHIR-99021, Forskolin and OAC1 (CFO) maintained human CD34-positive cells in vitro. Efficiency of the culture system was characterized using flow cytometry for CD34-positive cells, a colony-forming assay and xeno-transplants. We found that human CD34-positive cells treated with this combination had enhanced expression of human HSPC markers and increased haematopoietic re-populating ability in immune-deficient mice. Single-cell RNA-seq analyses showed that the in vitro cultured human CD34-positive cells were heterogeneous. We found that CFO supports maintenance of human CD34-positive cells by activating HOXA9, GATA2 and AKT-cAMP signaling pathway. These data have implications in therapies requiring maintenance and/or expansion of human HSPCs.
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Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space.
Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., single-cell RNA sequencing, scRNA-seq, single-cell assay for transposase-accessible chromatin use sequencing, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX's advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build upon previous scientific insights.
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Longitudinal single cell atlas identifies complex temporal relationship between type I interferon response and COVID-19 severity.
Due to the paucity of longitudinal molecular studies of COVID-19, particularly those covering the early stages of infection (Days 1-8 symptom onset), our understanding of host response over the disease course is limited. We perform longitudinal single cell RNA-seq on 286 blood samples from 108 age- and sex-matched COVID-19 patients, including 73 with early samples. We examine discrete cell subtypes and continuous cell states longitudinally, and we identify upregulation of type I IFN-stimulated genes (ISGs) as the predominant early signature of subsequent worsening of symptoms, which we validate in an independent cohort and corroborate by plasma markers. However, ISG expression is dynamic in progressors, spiking early and then rapidly receding to the level of severity-matched non-progressors. In contrast, cross-sectional analysis shows that ISG expression is deficient and IFN suppressors such as SOCS3 are upregulated in severe and critical COVID-19. We validate the latter in four independent cohorts, and SOCS3 inhibition reduces SARS-CoV-2 replication in vitro. In summary, we identify complexity in type I IFN response to COVID-19, as well as a potential avenue for host-directed therapy.
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A tumor microenvironment integrated (TMI) staging system for pancreatic ductal adenocarcinoma based on cancer-associated fibroblast and molecular consensus signatures.
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive solid tumor with poor prognosis. Existing prognostication systems rely on clinical staging, but systems of tumor classification through molecular signatures and the association between cancer-associated fibroblast (CAF) subtypes and clinical outcomes suggest that prognostic systems could be improved by integrating these data. METHODS: We clustered a discovery cohort (n = 320) of PDAC into basal and nonbasal tumor subtypes by applying a molecular signature previously defined by O’Kane et al. We performed deconvolution using data of annotated cells from separate single-cell RNA-seq studies to estimate the fractions of different CAFs in each case in the discovery cohort and further generated a fraction threshold for myofibroblastic CAFs (myCAFs) enrichment delineating poor prognosis. We integrated these molecular classifications and metastasis status to generate a tumor microenvironment integrated (TMI) staging system, which we subsequently compared against the 7th American Joint Committee on Cancer (AJCC) clinical staging system using an external validation cohort (n = 156). We finally explored the signaling pathways within the single-cell dataset to identify key signaling pathways associated with myCAFs. RESULTS: TMI staging outperformed AJCC staging in predicting overall survival within both the discovery cohort (Harrell’s C-index: 0.558 vs. 0.547) and validation cohort (Harrell’s C-index: 0.535 vs. 0.500). In terms of differences, TMI was better able to account for overall survival rate variations, while AJCC staging remained stronger in hazard consistency, a surrogate of the similarity of survival rates within subgroups of each defined stage. We further identified fibronectin 1 (FN1) as a key ECM signaling protein within PDAC tumors, both in myCAF autocrine signaling as well as between myCAFs and ductal epithelial cells (including tumor cells), that are enriched in both basal (TMI-Poor) and nonbasal, myCAF-enriched (TMI-Intermediate) cases, and that delineate a poorer prognosis. CONCLUSION: The integration of tumor subtyping and stromal myCAF fractions with clinical staging improves prognostication in PDAC. Among the ECM signaling proteins, FN1 shows promise in explaining the relationship between basal subtypes and myCAF-enrichment, with poor prognosis.
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Microglia and Myeloid Cell Populations of the Developing Mouse Retina.
Microglia make important contributions to central nervous system (CNS) development, but the breadth of their distinct developmental functions remains poorly understood. The mouse retina has been a key model system for understanding fundamental mechanisms controlling the assembly of the CNS. To gain insight into where and how microglia might influence retinal development, here we identified molecularly unique myeloid cell populations that are selectively present during development and characterized their anatomical locations. Development-specific transcriptional states were identified using single-cell (sc) and single-nucleus RNA-sequencing (RNA-seq) across multiple timepoints. Transcriptional states were validated in vivo by histological staining for key RNA and/or protein markers. Several of these development-specific myeloid populations have been described before in brain scRNA-seq atlases but not validated in vivo, while others are unique to our retinal dataset. We identify two closely related microglial populations, labeled by the Spp1 and Hmox1 genes, that are distinguished mainly by transcriptional targets of the NRF2 transcription factor. Both types are present selectively within the developing retinal nerve fiber layer where they engulf neurons and astrocytes undergoing developmental cell death. Hmox1+ microglia were also localized selectively at the wavefront of developing vasculature during retinal angiogenesis, suggesting that developmental events associated with angiogenesis modulate NRF2 activity and thereby induce microglia to switch between the Spp1+ and Hmox1+ states. Overall, our results identify transcriptional profiles that define specific populations of retinal microglia, opening the way to future investigations of how these programs support microglial functions during development.