Single-Cell Multiomics Reveals Distinct Cell States at the Top of the Human Hematopoietic Hierarchy [CITE-seq]
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PROVIDER: PRJNA723753 | ENA |
REPOSITORIES: ENA
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Project description:Single-Cell Multiomics Reveals Distinct Cell States at the Top of the Human Hematopoietic Hierarchy
| PRJNA723746 | ENA
Project description:Single-Cell Multiomics Reveals Distinct Cell States at the Top of the Human Hematopoietic Hierarchy [scATAC-seq]
| PRJNA723752 | ENA
Project description:The advent of single cell (Sc) genomics has challenged the dogma of haematopoiesis as a tree-like structure of stepwise lineage commitment through distinct and increasingly restricted progenitor populations. Instead, analysis of ScRNA-seq has proposed that the earliest events in human hematopoietic stem cell (HSC) differentiation are characterized by only subtle molecular changes, with hematopoietic stem and progenitor cells (HSPCs) existing as a continuum of low-primed cell-states that gradually transition into a specific lineage (CLOUD-HSPCs). Here, we combine ScRNA-seq, ScATAC-seq and cell surface proteomics to dissect the heterogeneity of CLOUD-HSPCs at different stages of human life. Within CLOUD-HSPCs, pseudotime ordering of both mRNA and chromatin data revealed a bifurcation of megakaryocyte/erythroid and lympho/myeloid trajectories immediately downstream a subpopulation with an HSC-specific enhancer signature. Importantly, both HSCs and lineage-restricted progenitor populations could be prospectively isolated based on correlation of their molecular signatures with CD35 and CD11A expression, respectively. Moreover, we describe the changes that occur in this heterogeneity as hematopoiesis develops from neonatal to aged bone marrow, including an increase of HSCs and depletion of lympho-myeloid biased MPPs. Thus, this study dissects the heterogeneity of human CLOUD-HSPCs revealing distinct HSPC-states of relevance in homeostatic settings such as ageing.
2023-09-27 | GSE173076 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2023-09-27 | GSE173077 | GEO
Project description:The advent of single cell (Sc) genomics has challenged the dogma of haematopoiesis as a tree-like structure of stepwise lineage commitment through distinct and increasingly restricted progenitor populations. Instead, analysis of ScRNA-seq has proposed that the earliest events in human hematopoietic stem cell (HSC) differentiation are characterized by only subtle molecular changes, with hematopoietic stem and progenitor cells (HSPCs) existing as a continuum of low-primed cell-states that gradually transition into a specific lineage (CLOUD-HSPCs). Here, we combine ScRNA-seq, ScATAC-seq and cell surface proteomics to dissect the heterogeneity of CLOUD-HSPCs at different stages of human life. Within CLOUD-HSPCs, pseudotime ordering of both mRNA and chromatin data revealed a bifurcation of megakaryocyte/erythroid and lympho/myeloid trajectories immediately downstream a subpopulation with an HSC-specific enhancer signature. Importantly, both HSCs and lineage-restricted progenitor populations could be prospectively isolated based on correlation of their molecular signatures with CD35 and CD11A expression, respectively. Moreover, we describe the changes that occur in this heterogeneity as hematopoiesis develops from neonatal to aged bone marrow, including an increase of HSCs and depletion of lympho-myeloid biased MPPs. Thus, this study dissects the heterogeneity of human CLOUD-HSPCs revealing distinct HSPC-states of relevance in homeostatic settings such as ageing.
2023-09-27 | GSE173075 | GEO
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:Single cells from human colorectal cancer and normal adjacent colon of 16 patients were used for single-cell RNA-seq, TCR-seq, CITE-seq and Cell hashing. In brief, single cells were incubated for 3h with or without PMA/Ionomycin, and were treated with Cell hashing and CITE-seq antibodies to distinguish samples, stimulation/non-stimulation, and cell surface proteins. Sorted viable CD3+TCRαβ+ single cells were loaded into 10x genomics ChromiumTM controller to make nanoliter-scale droplets with uniquely barcoded 5’ gel beads called GEMs. After GEM-RT and the following some cDNA amplification steps, cDNAs derived from cellular mRNA were pooled for downstream processing and library preparation according to the manufacturer’s instructions. The 5’ transcript library was sequenced with Illumina Novaseq. The single cell TCR enriched library was sequenced with Illumina Miseq using 150 paired-end reads. HTO/ADTs from Cell hashing or CITE-seq were amplified using specific primers that append P5 and P7 sequences for illumina sequencing (Miseq or Nextseq). All fastq files were demultiplexed. Cell hashing and CITE-seq barcodes are available in attached text files. Fastq files from RNA-seq and TCR-seq can be processed through cellranger and vdjranger by 10xgenomics. The datasets include the data of independent experiments at May 29, June 16, June 23, and Aug 13, 2019. Details are available in Masuda et al., bioRxiv, 2020, The functional and phenotypic diversity of single T-cell infiltrates in human colorectal cancer as correlated with clinical outcome.
2022-03-02 | E-MTAB-9455 | biostudies-arrayexpress
Project description:Multiomics lymphocytes single-cell assay on normal human versus immunodeficiency patients
| PRJEB46204 | ENA
Project description:Proteomics has exposed a plethora of post-translational modifications, but demonstrating functional relevance requires new approaches. Top-down proteomics can characterize co-occurring modifications in terms of localization, abundance and hierarchy. Here, we present a top-down MS analysis workflow for the discovery and quantification of proteoforms. Confident fragment assignment allows for localization of modification sites and quantification of all proteoforms, including positional isomers, as validated by investigating synthetic isoforms of ubiquitin and hyper-phosphorylated Bora.
2021-03-18 | PXD012084 | Pride