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Record W3214653528 · doi:10.1182/blood-2021-151090

Single Cell Analysis Elucidates the Maturation of Human Stem and Progenitor Cell Function from Fetal through Adult Hematopoiesis

2021· article· en· W3214653528 on OpenAlexaff
Hojun Li, Jideofor Ezike, Anton Afanassiev, Laura Greenstreet, Stephen X. Zhang, Jennifer Whangbo, Vincent L. Butty, Enrico Moiso, Guinevere G. Connelly, Vivian Morris, Dahai Wang, George Q. Daley, Salil Garg, Stella T. Chou, Aviv Regev, Edroaldo Lummertz da Rocha, Geoffrey Schiebinger, R. Grant Rowe

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHaematopoiesisBiologyProgenitor cellStem cellBone marrowMyeloidImmunologyPopulationBlood cellLymphopoiesisEmbryonic stem cellHematopoietic stem cellCell biologyGeneticsGeneMedicine

Abstract

fetched live from OpenAlex

Abstract Hematopoiesis continually replenishes the supply of circulating blood cells from embryonic development through the entirety of human lifespan. Although all hematopoietic lineages are produced throughout life, biases in lineage output occur at various stages, including lymphoid bias in childhood and myeloid bias in adulthood. Furthermore, many blood disorders demonstrate marked biases in age of onset, such as bone marrow failure disorders, clonal hematopoiesis of indeterminate potential, and numerous hematologic malignancies. A lack of insight into the normal physiologic changes occurring in the hematopoietic stem and progenitor cell (HSPC) compartments during development and maturation fundamentally limits our understanding of how age biased blood disorders arise. Two major unresolved questions are: (1) what changes in the molecular regulation of hematopoietic lineage commitment occur over the course of human life? And (2) are certain HSPC states present only during specific ages of life, and if so, do age-specific HSPCs have distinct biology? To address these questions we performed single cell RNA sequencing (scRNAseq) on human HSPCs from first and second trimester fetal liver hematopoiesis, and bone marrow hematopoiesis spanning childhood into mature adulthood. In total, HSPC samples were obtained from 14 distinct human donors. Dimensionality reduction and marker gene analysis identified uncommitted hematopoietic stem cells (HSCs) and the developmental trajectories of each lineage emanating from multipotent HSCs. We then identified the genes activated upon commitment to each hematopoietic lineage during fetal, childhood, and mature adult hematopoiesis using the Population Balance Analysis and Stationary Optimal Transport algorithms, followed by Elastic Net gene regression. For each lineage we determined the putative transcription factor network that is consistently active in driving commitment to that lineage throughout life, but surprisingly also found the existence of adjunctive transcription factor networks that only drove lineage commitment at specific ages. We next used unbiased clustering of scRNAseq data to identify 21 distinct subtypes in the HSPC compartment across human life. Using marker gene analysis and singleCellNet algorithm comparisons to an existing human adult bone marrow scRNAseq data set, we hierarchically ordered and annotated these HSPC subtypes ranging from uncommitted HSCs to lineage committed progenitor cells. We found that cellular distribution within the HSPC compartment amongst these subtypes varied markedly throughout human lifetime, with higher representation of HSCs in fetal life, predominance of lymphoid progenitors in childhood, and higher representation of myeloid progenitors in adulthood. Focusing on the distribution of cells among HSC subtypes over human life, we identified an HSC subtype exclusive to mid-gestation that was not present in early fetal or postnatal timepoints. This HSC subtype had a characteristic immunophenotype and was enriched for expression of early response transcription factors and mRNA decay factors. We functionally validated that this mid-gestation-specific HSC subtype was phenotypically unique using colony formation assays and xenotransplantation. Mid-gestation-specific HSCs were more clonogenic with a greater number of multi-lineage outcomes, and also demonstrated increased multilineage engraftment capacity compared to other HSC subtypes Our findings reveal that the intrinsic biology of hematopoietic lineage commitment fundamentally changes over the course of the human lifetime, and define and validate age-specific HSPC subtypes. In particular, the biology of the mid-gestation-specific HSC we identified has potential applications for improving engraftment and multi-lineage reconstitution in hematopoietic cell transplantation. Disclosures Regev: Genentech: Current Employment; Celsius Therapeutics: Current equity holder in publicly-traded company, Other: Co-founder; Immunitas: Current equity holder in publicly-traded company; ThermoFisher Scientific: Membership on an entity's Board of Directors or advisory committees; Syros Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Neogene Therapeutics: Membership on an entity's Board of Directors or advisory committees; Asimov: Membership on an entity's Board of Directors or advisory committees.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.199
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
Has abstractyes

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