Evidence map›Paper›PMID 32614947›Full record

ArticleBlood2020

Single-cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs.

Antonella Fidanza, Patrick S Stumpf, Prakash Ramachandran, Sara Tamagno, Ann Babtie, Martha Lopez-Yrigoyen, A Helen Taylor, Jennifer Easterbrook, Beth E P Henderson, Richard Axton and 5 more

Open access · bronzeAbstract read
In one paragraph

Article in Blood, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed
3.9field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

45 citing papers in PubMed, 66 citations in OpenAlex.

  1. Article
  2. Review
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  4. Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026
    Review
  5. Article
  6. Review
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  8. Article
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  18. Deep Learning in Hematology: From Molecules to Patients.Clinical hematology international · 2024
    Review
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors at 5 institutions in 2 countries.

Antonella FidanzaCentre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom.
Patrick S StumpfJoint Research Center for Computational Biomedicine, Uniklinik Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Aachen, Germany.
Prakash RamachandranCentre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom.
Sara TamagnoCentre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom.
Ann BabtieCentre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, United Kingdom; and.
Martha Lopez-YrigoyenCentre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom.
A Helen TaylorCentre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom.
Jennifer EasterbrookCentre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom.
Beth E P HendersonCentre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom.
Richard AxtonCentre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom.
Neil C HendersonCentre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom.
Alexander MedvinskyCentre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom.
Katrin OttersbachCentre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom.
Nicola RomanòCentre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
Lesley M ForresterCentre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom.
MRC Centre for Regenerative Medicine · GBCentre for Inflammation Research · GBImperial College London · GBRWTH Aachen University · DEUniversity of Edinburgh · GB

Funding

Biotechnology and Biological Sciences Research Council BB/J004243/1Biotechnology and Biological Sciences Research Council BB/N011597/1Biotechnology and Biological Sciences Research Council S002219/1Chief Scientist Office ETM/432Medical Research Council MC_PC_15078Medical Research Council MR/K001744/1Medical Research Council MR/K017047/1Medical Research Council MR/L018160/1Medical Research Council MR/N008340/1Medical Research Council MR/R018081/1
6 · The paper itself

Abstract

Hematopoietic stem and progenitor cells (HSPCs) develop in distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) recapitulates some of these processes; however, it has proven difficult to generate functional hematopoietic stem cells (HSCs). To define the dynamics and heterogeneity of HSPCs that can be generated in vitro from hPSCs, we explored single-cell RNA sequencing (scRNAseq) in combination with single-cell protein expression analysis. Bioinformatics analyses and functional validation defined the transcriptomes of naïve progenitors and erythroid-, megakaryocyte-, and leukocyte-committed progenitors, and we identified CD44, CD326, ICAM2/CD9, and CD18, respectively, as markers of these progenitors. Using an artificial neural network that we trained on scRNAseq derived from human fetal liver, we identified a wide range of hPSC-derived HSPCs phenotypes, including a small group classified as HSCs. This transient HSC-like population decreased as differentiation proceeded, and was completely missing in the data set that had been generated using cells selected on the basis of CD43 expression. By comparing the single-cell transcriptome of in vitro-generated HSC-like cells with those generated within the fetal liver, we identified transcription factors and molecular pathways that can be explored in the future to improve the in vitro production of HSCs.

Indexed as

Antigens, DifferentiationHematopoietic Stem CellsMachine LearningPluripotent Stem CellsRNA-SeqSingle-Cell AnalysisFetusGene Expression RegulationHumansLiverAntigens, Differentiation

Identifiers

PMID32614947
PMCPMC7862875
OpenAlexW3040669613

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.