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Record W2952842332 · doi:10.1016/j.celrep.2018.11.056

Heterogeneous Responses of Hematopoietic Stem Cells to Inflammatory Stimuli Are Altered with Age

2018· article· en· W2952842332 on OpenAlexfundno aff
Mati Mann, Arnav Mehta, Carl G. de Boer, Monika S. Kowalczyk, Kevin Lee, Pearce Haldeman, Noga Rogel, Abigail Knecht, Daneyal Farouq, Aviv Regev, David Baltimore

Bibliographic record

VenueCell Reports · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsnot available
FundersNational Cancer InstituteBank of AmericaNational Human Genome Research InstituteCanadian Institutes of Health ResearchFrontiers FoundationCharles A. King TrustSimeon J. Fortin Charitable FoundationNational Institute of Allergy and Infectious DiseasesHoward Hughes Medical InstituteCalifornia Institute of TechnologyBroad InstituteRaymond and Beverly Sackler Foundation
KeywordsHaematopoiesisMyeloidStem cellBiologyImmunologyHematopoietic stem cellInflammationPopulationImmune systemCancer researchCell biologyMedicine

Abstract

fetched live from OpenAlex

Long-term hematopoietic stem cells (LT-HSCs) maintain hematopoietic output throughout an animal's lifespan. However, with age, the balance is disrupted, and LT-HSCs produce a myeloid-biased output, resulting in poor immune responses to infectious challenge and the development of myeloid leukemias. Here, we show that young and aged LT-HSCs respond differently to inflammatory stress, such that aged LT-HSCs produce a cell-intrinsic, myeloid-biased expression program. Using single-cell RNA sequencing (scRNA-seq), we identify a myeloid-biased subset within the LT-HSC population (mLT-HSCs) that is prevalent among aged LT-HSCs. We identify CD61 as a marker of mLT-HSCs and show that CD61-high LT-HSCs are uniquely primed to respond to acute inflammatory challenge. We predict that several transcription factors regulate the mLT-HSCs gene program and show that Klf5, Ikzf1, and Stat3 play an important role in age-related inflammatory myeloid bias. We have therefore identified and isolated an LT-HSC subset that regulates myeloid versus lymphoid balance under inflammatory challenge and with age.

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 score0.001
Threshold uncertainty score0.002

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 head0.017
GPT teacher head0.232
Teacher spread0.215 · 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

Citations196
Published2018
Admission routes1
Has abstractyes

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