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Record W2557550388 · doi:10.1182/blood.v118.21.907.907

Molecular and Functional Characterization of Early Lineage Commitment of Human Hematopoietic Stem Cells

2011· article· en· W2557550388 on OpenAlexaff
Elisa Laurenti, Sergei Doulatov, Sasan Zandi, Jing Chen, Craig April, Monica Doedens, Jian‐Bing Fan, John E. Dick

Bibliographic record

VenueBlood · 2011
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsBiologyProgenitor cellHaematopoiesisStem cellMyeloidCell biologyImmunologyCellular differentiationLineage markersGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Abstract 907 The hematopoietic system is a highly regulated cellular hierarchy, responsible for the day-to-day production of mature blood cells which can be divided in two major lineages, myeloid and lymphoid. Hematopoietic stem cells (HSCs) have the unique ability to give rise to all hematopoietic cell types, by first generating lineage-commited progenitors which in turn will produce terminally differentiated cells. HSCs are characterized by their extensive self-renewal and differentiation capacities. While in mice the mechanisms underlying early HSC differentiation and lineage determination are well understood at the molecular level, very few transcription factors regulating lineage decisions have been identified in human hematopoiesis. Our group has recently established a novel cell sorting strategy for human HSCs and early lineage committed progenitors (Doulatov et al., Nature Immunology, 2010; Notta et al., Science, 2011) which uncovered the existence of a novel human multilymphoid progenitor (MLP). MLPs give rise to all lymphoid cell types, as well as dendritic cells and monocytic cells. Here we report a comprehensive analysis of gene expression at each developmental stage of the early human hematopoietic hierarchy, ranging from the long-term repopulating stem cells to lineage-restricted progenitors through multipotent progenitors such as MLP, CMP (common myeloid progenitor), GMP (granulocyte-monocyte progenitor) and MEP (megakaryocyte-erythroid progenitor). We show that hematopoietic specification is defined by a small number of global gene expression clusters that correspond to major biological lineages and that lineage programs in committed progenitors are paired with HSC-shared priming programs. HSCs display most extensive priming along the lympho-myeloid branch (MLP). In contrast early progenitors of the megakaryocytic/erythrocytic lineage form a distinct cluster, highly enriched for cell cycle genes. To identify regulators of each major developmental transition, we computationally extracted population-specific gene-sets (“signatures”). We then integrated transcription factor expression data and enrichment of transcription factors binding sites in the promoters of each “signature” to obtain a map of transcriptional regulators in the context of the developmental hierarchy. Based on this model, we selected more than 15 candidate genes for functional validation. We chose genes predicted to act either on lymphoid (MLP), myeloid (MLP, CMP) or erythroid (MEP) commitment. Among these, we investigated the function of BCL11a, a C2H2 zinc finger transcriptional repressor, which expression is primed in HSCs then peaks in the newly discovered MLP population, indicating a putative role in lymphocyte specification. Consistent with this hypothesis, BCL11a has been implicated in the development of B cell progenitors in mouse. When BCL11a was knocked down in cord blood derived hematopoietic stem cells and early progenitors, we observed reduced formation of cells committed to the B cell fate both in vitro and in an in vivo xenograft assay. BCL11a knock-down resulted in a partial block of B cell maturation at the proB to preB cell transition, that was accompanied by a decrease in the key B cell maturation transcription factor, Pax5. These preliminary results suggest that BCL11a directs B cell specification in human and that our genome-wide strategy not only provides a valuable resource for the hematology community but also allows identification of key regulators of early human lineage commitment. Disclosures: No relevant conflicts of interest to declare.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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)0.0020.001

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 head0.028
GPT teacher head0.231
Teacher spread0.203 · 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 designObservational
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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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