Multimodal profiling of human postnatal thymocytes using CITE-seq
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1130891 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR29680711_1.fastq.gz | Fastqsanger.gz | |||
| SRR29680711_2.fastq.gz | Fastqsanger.gz | |||
| SRR29680712_1.fastq.gz | Fastqsanger.gz | |||
| SRR29680712_2.fastq.gz | Fastqsanger.gz | |||
| SRR29680713_1.fastq.gz | Fastqsanger.gz |
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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.
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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.
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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.
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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.
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