Dataset Information

An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors.

ABSTRACT: Analysis of the human hematopoietic progenitor compartment is being transformed by single-cell multimodal approaches. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables coupled surface protein and transcriptome profiling, thereby revealing genomic programs underlying progenitor states. To perform CITE-seq systematically on primary human bone marrow cells, we used titrations with 266 CITE-seq antibodies (antibody-derived tags) and machine learning to optimize a panel of 132 antibodies. Multimodal analysis resolved >80 stem, progenitor, immune, stromal and transitional cells defined by distinctive surface markers and transcriptomes. This dataset enables flow cytometry solutions for in silico-predicted cell states and identifies dozens of cell surface markers consistently detected across donors spanning race and sex. Finally, aligning annotations from this atlas, we nominate normal marrow equivalents for acute myeloid leukemia stem cell populations that differ in clinical response. This atlas serves as an advanced digital resource for hematopoietic progenitor analyses in human health and disease.

SUBMITTER: Zhang X

PROVIDER: S-EPMC11003869 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors.

Zhang Xuan X Song Baobao B Carlino Maximillian J MJ Li Guangyuan G Ferchen Kyle K Chen Mi M Thompson Evrett N EN Kain Bailee N BN Schnell Dan D Thakkar Kairavee K Kouril Michal M Jin Kang K Hay Stuart B SB Sen Sidharth S Bernardicius David D Ma Siyuan S Bennett Sierra N SN Croteau Josh J Salvatori Ornella O Lye Melvin H MH Gillen Austin E AE Jordan Craig T CT Singh Harinder H Krause Diane S DS Salomonis Nathan N Grimes H Leighton HL

Nature immunology 20240321 4

Analysis of the human hematopoietic progenitor compartment is being transformed by single-cell multimodal approaches. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables coupled surface protein and transcriptome profiling, thereby revealing genomic programs underlying progenitor states. To perform CITE-seq systematically on primary human bone marrow cells, we used titrations with 266 CITE-seq antibodies (antibody-derived tags) and machine learning to optimize a pane ...[more]

PMID: 38514887

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Project description:Understanding the cellular heterogeneity and lineage potential of human bone marrow hematopoietic progenitors is required to understand normal differentiation and the mechanisms of hematological disease. Central to this objective are protocols that enable the delineation, isolation and functional characterization or known and novel cell populations, which are traditionally empowered by flow cytometry. Recognizing the need for tackling cellular heterogeneity, we have designed a use case for compiling next-generation processing methods via existing and highly curated single-cell interrogation technologies. Multi-omics approaches, such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), enable concurrent transcriptomic and immunophenotyping at a single-cell level but necessitate precision antibody titration mixes that can be directly integrated with parallel flow cytometry strategies. To bridge this gap, we performed a molecular titration of 266 antibodies using primary human bone marrow cells spanning race and sex to approximate diversity. Through machine learning and user curation, we created an optimized titrated bone marrow CITE-seq panel of 132 antibodies. Multimodal integration of these data using a game-theory driven approach, improved our ability to resolve new stable stem, progenitor, and transitional cell populations, readily defined by unique RNA and ADT markers. Leveraging complexity-matching high-dimensional flow cytometry (pyInfinityFlow), we consolidated the findings from CITE-seq and subsequently developed novel isolation strategies for maturing granulocytes, late-stage erythroid progenitors and improved purification of megakaryocyte/erythrocyte progenitors (MEP). Finally, we integrated CITE-seq with flow cytometry data from matching donors, successfully identified 37 cell surface markers consistently expressed across donors and technologies. The compiled bone marrow cell atlas serves as an advanced digital analytical resource for progenitor analyses in health and disease.

Dataset Information

An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors.

Publications

An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors.

Similar Datasets

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