Multimodal profiling of human postnatal thymocytes using CITE-seq
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PROVIDER: PRJNA1130891 | ENA |
REPOSITORIES: ENA
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Project description:T cell development in the human thymus has primarily been studied using antibody-based approaches, such as flow cytometry, which can create difficulties in translating phenotypic findings to scRNA-seq data. In order to bridge this gap and obtain paired surface protein and RNA information for individual cells, we carried out CITE-seq with a 143-plex customised antibody panel on human postnatal thymocytes. This was further combined with TCR-seq for TRA and TRB loci to gain insights into the V(D)J recombination progress in developing cells. Using information from all three modalities, we annotated over 30 different stages of human T cell development, which align with known surface marker profiles. This data was utilised in the context of the human thymus spatial atlas for high-resolution spatial mapping of developing T cells, which revealed differences in the migration and maturation kinetics of CD4 and CD8 lineage single positive thymocytes.
2024-07-08 | GSE271304 | GEO
Project description:Human Early Postnatal Thymus [CITE-seq]
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Project description:Single-cell RNA sequencing (scRNA-seq) is an invaluable tool for profiling cells in complex tissues and dissecting activation states that lack well-defined surface protein expression. For immune cells, the transcriptomic profile captured by scRNA- seq cannot always identify cell states and subsets defined by conventional flow cytometry. Emerging technologies have enabled multimodal sequencing of single cells, such as paired sequencing of the transcriptome and surface proteome by CITE-seq, but integrating these high dimensional modalities for accurate cell type annotation remains a challenge in the field. Here, we describe a machine learning tool called MultiModal Classifier Hierarchy (MMoCHi) for the cell-type annotation of CITE-seq data. Our classifier involves several steps: 1) we use landmark registration to remove batch-related staining artifacts in CITE-Seq protein expression, 2) the user defines a hierarchy of classifications based on cell type similarity and ontology and provides markers (protein or gene expression) for the identification of ground truth populations within the dataset by threshold gating, 3) progressing through this user-defined hierarchy, we train a random forest classifier using all available modalities (surface proteome and transcriptome data), and 4) we use these forests to predict cell types across the entire dataset. Applying MMoCHi to CITE-seq data of immune cells isolated from eight distinct tissue sites of two human organ donors yields high-purity cell type annotations encompassing the broad array of immune cell states in the dataset. This includes T and B cell memory subsets, macrophages and monocytes, and natural killer cells, as well as rare populations of plasmacytoid dendritic cells, innate T cells, and innate lymphoid cell subsets. We validate the use of feature importances extracted from the classifier hierarchy to select robust genes for improved identification of T cell memory subsets by scRNA-seq. Together, MMoCHi provides a comprehensive system of tools for the batch-correction and cell- type annotation of CITE-seq data. Moreover, this tool provides flexibility in classification hierarchy design allowing for cell type annotations to reflect a researcher’s specific experimental design. This flexibility also renders MMoCHi readily extendable beyond immune cell annotation, and potentially adaptable to other sequencing modalities.
2023-08-08 | GSE229791 | GEO
Project description:Atherosclerotic plaques are complex tissues composed of a heterogeneous mixture of cells. However, our understanding of the comprehensive transcriptional and phenotypical landscape of the cells within these lesions is limited. To characterize the landscape of human carotid atherosclerosis in greater detail, we combined cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-cell RNA sequencing (scRNA-seq) to classify all cell types within lesions (n=21; 13 symptomatic) to achieve a comprehensive multimodal understanding of the cellular identities of atherosclerosis and their association with clinical pathophysiology. We identified 25 cell populations, each with a unique multi-omic signature, including macrophages, T cells, NK cells, mast cells, B cells, plasma cells, neutrophils, dendritic cells, endothelial cells, fibroblasts, and smooth muscle cells (SMCs). Among the macrophages, we identified 2 proinflammatory subsets enriched in IL1B or C1Q expression, 2 TREM2 positive foam cells (one expressing inflammatory genes), and subpopulations with a proliferative gene signature and SMC-specific gene signature with fibrotic pathways upregulated. Further characterization revealed various subsets of SMCs and fibroblasts, including SMC-derived foam cells. These foamy SMCs were localized in the deep intima of coronary atherosclerotic lesions. Utilizing CITE-seq data, we developed a flow cytometry panel, using cell surface proteins CD29, CD142, and CD90, to isolate SMC-derived cells from lesions. Lastly, we observed reduced proportions of efferocytotic macrophages, classically activated endothelial cells, and contractile and modulated SMC-derived cells, while inflammatory SMCs were enriched in plaques of clinically symptomatic vs asymptomatic patients. Our multimodal atlas of cell populations within atherosclerosis provides novel insights into the diversity, phenotype, location, isolation, and clinical relevance of the unique cellular composition of human carotid atherosclerosis. These findings facilitate both the mapping of cardiovascular disease susceptibility loci to specific cell types as well as the identification of novel molecular and cellular therapeutic targets for the treatment of the disease.
2024-04-12 | GSE253902 | GEO
Project description:Within the thymus, regulation of the cellular cross-talk directing T cell development is dependent on spatial interactions within specialized niches. To create a holistic, spatially defined map of tissue niches guiding postnatal T cell development we employed the multidimensional imaging platform CO-detection by indEXing (CODEX), as well as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) and Assay for Transposable-Accessible Chromatin (ATAC-seq). We generated age-matched 4–5-month-old postnatal thymus datasets for both male and female donors, and describe signaling and cell-cell interaction networks in sequential thymocyte developmental niches. Together, these data represent a unique age-matched spatial multiomic resource to investigate how sex-based differences in thymus regulation and T cell development arise, and provide an essential resource to understand the mechanisms underlying immune function and dysfunction in males and females.
2024-08-30 | GSE264587 | GEO
Project description:To prevent autoimmunity, thymocytes expressing self-reactive T cell receptors (TCRs) are negatively selected, however, divergence into tolerogenic, agonist-selected lineages represent an alternative fate. As thymocyte development, selection, and lineage choices are dependent on spatial context and cell-to-cell interactions, we have performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and spatial transcriptomics on paediatric human thymus. Thymocytes expressing markers of strong TCR signalling diverged from the conventional developmental trajectory prior to CD4+ lineage commitment, while markers of different agonist cell populations (CD8αα(I), CD8αα(II), T(agonist), Treg(diff) and Treg) exhibited variable timing of induction. Expression profiles of chemokines and co-stimulatory molecules, together with spatial localization, supported that dendritic cells, B cells, and stromal cells contribute to agonist selection, with different subsets influencing thymocytes at specific developmental stages within distinct spatial niches. Understanding factors influencing agonist T cells is needed to benefit from their immunoregulatory effects in clinical use.
2023-02-01 | GSE207204 | GEO
Project description:ATACseq of 11 subsets of human postnatal developing thymocytes
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Project description:RNAseq of 11 subsets of developing human postnatal thymocytes
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Project description:Multimodal hierarchical classification allows for efficient annotation of CITE-seq data
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Project description:High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis [CITE-seq]
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