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ATLAS-seq: a microfluidic single-cell TCR screen for antigen-reactive TCRs.
Discovering antigen-reactive T cell receptors (TCRs) is central to developing effective engineered T cell immunotherapies. However, the conventional technologies for isolating antigen-reactive TCRs (i.e., major histocompatibility complex (MHC) multimer staining) focus on high-affinity interactions between the TCR and MHC-antigen complex, and may fail to identify TCRs with high efficacy for activating T cells. Here, we develop a microfluidic single-cell screening method for antigen-reactive T cells named ATLAS-seq (Aptamer-based T Lymphocyte Activity Screening and SEQuencing). This technology isolates and characterizes activated T cells via an aptamer-based fluorescent molecular sensor, which monitors the cytotoxic cytokine IFNγ secretion from single T cells upon antigen stimulation, followed by single-cell RNA and single-cell TCR sequencing. We use ATLAS-seq to screen TCRs reactive to cytomegalovirus (CMV) or prostate specific antigen (PSA) from peripheral blood mononuclear cells (PBMCs). ATLAS-seq identifies distinct TCR clonotype populations with higher T cell activation levels compared to TCRs recovered by MHC multimer staining. Select TCR clonotypes from ATLAS-seq are more efficient in target cell killing than those from MHC multimer staining. Collectively, ATLAS-seq provides an efficient and broadly applicable technology to screen antigen-reactive TCRs for engineered T cell immunotherapy.
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Single cells tell it all.
A new single-cell atlas of gene expression provides insights into the patterning of the neural plate of mice.
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Predicting gene regulatory networks from cell atlases.
Recent single-cell RNA-sequencing atlases have surveyed and identified major cell types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from three major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences, including sampled tissues, sequencing depth, and author assigned cell type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell types from specialised cell type-specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wild-type and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.
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An atlas for hemocytes in an insect.
Single-cell RNA sequencing has revealed distinct subpopulations of hemocytes in fruit fly larvae.
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Spatial transcriptomics in neuroscience.
The brain is one of the most complex living tissue types and is composed of an exceptional diversity of cell types displaying unique functional connectivity. Single-cell RNA sequencing (scRNA-seq) can be used to efficiently map the molecular identities of the various cell types in the brain by providing the transcriptomic profiles of individual cells isolated from the tissue. However, the lack of spatial context in scRNA-seq prevents a comprehensive understanding of how different configurations of cell types give rise to specific functions in individual brain regions and how each distinct cell is connected to form a functional unit. To understand how the various cell types contribute to specific brain functions, it is crucial to correlate the identities of individual cells obtained through scRNA-seq with their spatial information in intact tissue. Spatial transcriptomics (ST) can resolve the complex spatial organization of cell types in the brain and their connectivity. Various ST tools developed during the past decade based on imaging and sequencing technology have permitted the creation of functional atlases of the brain and have pulled the properties of neural circuits into ever-sharper focus. In this review, we present a summary of several ST tools and their applications in neuroscience and discuss the unprecedented insights these tools have made possible.
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Intra- and extra-cranial BCOR-ITD tumours are separate entities within the BCOR-rearranged family.
BCOR-ITD tumours form an emerging family of aggressive entities with an internal tandem duplication (ITD) in the last exon of the BCOR gene. The family includes cerebral tumours, termed central nervous system BCOR-ITD (CNS BCOR-ITD), and sarcomatous types described in the kidney as clear cell sarcoma of the kidney (CCSK), in the endometrium as high-grade endometrial stromal sarcoma, and in the bone and soft tissue as undifferentiated round cell sarcoma or primitive myxoid mesenchymal tumour of infancy. Based on a series of 33 retrospective cases, including 10 CNS BCOR-ITD and 23 BCOR-ITD sarcomas, we interrogated the homogeneity of the entity regarding clinical, radiological, and histopathological findings, and molecular signatures. Whole-transcriptomic sequencing and DNA methylation profiling were used for unsupervised clustering. BCOR-ITD tumours mostly affected young children with a median age at diagnosis of 2.1 years (range 0-62.4). Median overall survival was 3.9 years and progression-free survival was 1.4 years. This dismal prognosis is shared among tumours in all locations except CCSK. Histopathological review revealed marked differences between CNS BCOR-ITD and BCOR-ITD sarcomas. These two groups were consistently segregated by unsupervised clustering of expression (n = 22) and DNA methylation (n = 21) data. Proximity between the two groups may result from common somatic changes within key pathways directly related to the novel activity of the ITD itself. Conversely, comparison of gene signatures with single-cell RNA-Seq atlases suggests that the distinction between BCOR-ITD sarcomas and CNS BCOR-ITD may result from differences in cells of origin.
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Clump sequencing exposes the spatial expression programs of intestinal secretory cells.
Single-cell RNA sequencing combined with spatial information on landmark genes enables reconstruction of spatially-resolved tissue cell atlases. However, such approaches are challenging for rare cell types, since their mRNA contents are diluted in the spatial transcriptomics bulk measurements used for landmark gene detection. In the small intestine, enterocytes, the most common cell type, exhibit zonated expression programs along the crypt-villus axis, but zonation patterns of rare cell types such as goblet and tuft cells remain uncharacterized. Here, we present ClumpSeq, an approach for sequencing small clumps of attached cells. By inferring the crypt-villus location of each clump from enterocyte landmark genes, we establish spatial atlases for all epithelial cell types in the small intestine. We identify elevated expression of immune-modulatory genes in villus tip goblet and tuft cells and heterogeneous migration patterns of enteroendocrine cells. ClumpSeq can be applied for reconstructing spatial atlases of rare cell types in other tissues and tumors.
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Tabula Sapiens: An Atlas of Single-Cell Gene Expression.
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Quantification of escape from X chromosome inactivation with single-cell omics data reveals heterogeneity across cell types and tissues.
Several X-linked genes escape from X chromosome inactivation (XCI), while differences in escape across cell types and tissues are still poorly characterized. Here, we developed scLinaX for directly quantifying relative gene expression from the inactivated X chromosome with droplet-based single-cell RNA sequencing (scRNA-seq) data. The scLinaX and differentially expressed gene analyses with large-scale blood scRNA-seq datasets consistently identified the stronger escape in lymphocytes than in myeloid cells. An extension of scLinaX to a 10x multiome dataset (scLinaX-multi) suggested a stronger escape in lymphocytes than in myeloid cells at the chromatin-accessibility level. The scLinaX analysis of human multiple-organ scRNA-seq datasets also identified the relatively strong degree of escape from XCI in lymphoid tissues and lymphocytes. Finally, effect size comparisons of genome-wide association studies between sexes suggested the underlying impact of escape on the genotype-phenotype association. Overall, scLinaX and the quantified escape catalog identified the heterogeneity of escape across cell types and tissues.
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LiverSCA: A comprehensive and user-friendly cell atlas in human hepatocellular carcinoma.
We developed a cell atlas named LiverSCA on human liver cancer single-cell RNA sequencing data. It has a user-friendly web interface and comprehensive functionalities aiming to help researchers to make easy access to cellular and molecular landscapes of the tumor microenvironment in liver cancer. LiverSCA includes a complete analytical pipeline that allow mechanistic exploration on a wide variety of functionalities, such as cell clustering, cell annotation, identification of differentially expressed genes, functional enrichment analysis, analysis of cellular crosstalk, and pseudo-time trajectory analysis. Notably, our intuitive web interface allows users, particularly wet-lab researchers, to easily explore and undertake data discovery, without the need to handle any of the raw data.