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Cited by 77Single-cell and spatial transcriptomicsDevelopmental Biology and Gene RegulationGene Regulatory Network Analysis

Coordinated changes in gene expression kinetics underlie both mouse and human erythroid maturation

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Barile, Melania, Imaz-Rosshandler, Ivan, Inzani, Isabella, Ghazanfar, Shila, Nichols, Jennifer, Marioni, John C., Guibentif, Carolina, Göttgens, Berthold·2021·Genome Biology
DOI 10.1186/s13059-021-02414-yPMC PMC8258993

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Abstract

Background: Single-cell technologies are transforming biomedical research, including the recent demonstration that unspliced pre-mRNA present in single-cell RNA-Seq permits prediction of future expression states. Here we apply this RNA velocity concept to an extended timecourse dataset covering mouse gastrulation and early organogenesis. Results: Intriguingly, RNA velocity correctly identifies epiblast cells as the starting point, but several trajectory predictions at later stages are inconsistent with both real-time ordering and existing knowledge. The most striking discrepancy concerns red blood cell maturation, with velocity-inferred trajectories opposing the true differentiation path. Investigating the underlying causes reveals a group of genes with a coordinated step-change in transcription, thus violating the assumptions behind current velocity analysis suites, which do not accommodate time-dependent changes in expression dynamics. Using scRNA-Seq analysis of chimeric mouse embryos lacking the major erythroid regulator Gata1, we show that genes with the step-changes in expression dynamics during erythroid differentiation fail to be upregulated in the mutant cells, thus underscoring the coordination of modulating transcription rate along a differentiation trajectory. In addition to the expected block in erythroid maturation, the Gata1-chimera dataset reveals induction of PU.1 and expansion of megakaryocyte progenitors. Finally, we show that erythropoiesis in human fetal liver is similarly characterized by a coordinated step-change in gene expression. Conclusions: By identifying a limitation of the current velocity framework coupled with in vivo analysis of mutant cells, we reveal a coordinated step-change in gene expression kinetics during erythropoiesis, with likely implications for many other differentiation processes.

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  • Ninety-six multiple rate kinetics (MURK) genes possess coordinated step-change in transcription and hence violate the assumptions behind scVelo.

    “Ninety-six multiple rate kinetics (MURK) genes previously shown to possess coordinated step-change in transcription and hence violate the assumptions behind scVelo were removed82.”

    From Single-cell multi-omics identifies chronic inflammation as a driver of TP53-mutant leukemic evolution (2023) · cites this paper as [82]

    The Results section, “Analysis of unspliced reads reveals complex expression kinetics,” links MURK genes’ increased transcription rates to incorrect scVelo velocity predictions, and the Discussion says removing them can improve predictions. But the Methods section, “MURK gene identification,” reports 89 mouse genes and 97 human genes, not 96; the Discussion also notes that MURK genes can occur where scVelo predictions are correct. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

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References 60 · Cited by 77

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  1. [1]Akunuru S Geiger H Aging, clonality, and rejuvenation of hematopoietic stem cells Trends Mol Med. 2016 22 8 701 712 10.1016/j.molmed.2016.06.003 27380967 PMC4969095 · DOI
  2. [2]Schultz MB Sinclair DA When stem cells grow old: phenotypes and mechanisms of stem cell aging Development. 2016 143 1 3 14 10.1242/dev.130633 26732838 PMC4725211 · DOI
  3. [3]Mahadevaiah SK Sangrithi MN Hirota T Turner JMA A single-cell transcriptome atlas of marsupial embryogenesis and X inactivation Nature. 2020 586 7830 612 617 10.1038/s41586-020-2629-6 32814901 PMC7116401 · DOI
  4. [4]Gerber T, Murawala P, Knapp D, Masselink W, Schuez M, Hermann S, et al. Single-cell analysis uncovers convergence of cell identities during axolotl limb regeneration. Science. 2018;362(6413):eaaq0681. PMC6669047 30262634
  5. [5]Wagner DE Weinreb C Collins ZM Briggs JA Megason SG Klein AM Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo Science. 2018 360 6392 981 987 10.1126/science.aar4362 29700229 PMC6083445 · DOI · Paper page
  6. [6]Ton MN Guibentif C Gottgens B Single cell genomics and developmental biology: moving beyond the generation of cell type catalogues Curr Opin Genet Dev. 2020 64 66 71 10.1016/j.gde.2020.05.033 32629366 · DOI
  7. [7]Borrett MJ Innes BT Jeong D Tahmasian N Storer MA Bader GD Kaplan DR Miller FD Single-cell profiling shows murine forebrain neural stem cells reacquire a developmental state when activated for adult neurogenesis Cell Rep. 2020 32 6 108022 10.1016/j.celrep.2020.108022 32783944 · DOI
  8. [9]Dahlin JS Hamey FK Pijuan-Sala B Shepherd M Lau WWY Nestorowa S Weinreb C Wolock S Hannah R Diamanti E Kent DG Göttgens B Wilson NK A single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice Blood. 2018 131 21 e1 e11 10.1182/blood-2017-12-821413 29588278 PMC5969381 · DOI · Paper page
  9. [10]La Manno G Soldatov R Zeisel A Braun E Hochgerner H Petukhov V RNA velocity of single cells Nature. 2018 560 7719 494 498 10.1038/s41586-018-0414-6 30089906 PMC6130801 · DOI · Paper page
  10. [12]Zhou W Yui MA Williams BA Yun J Wold BJ Cai L Rothenberg EV Single-cell analysis reveals regulatory gene expression dynamics leading to lineage commitment in early T cell development Cell Syst. 2019 9 4 321 337 10.1016/j.cels.2019.09.008 31629685 PMC6932747 · DOI
  11. [13]Kanton S Boyle MJ He Z Santel M Weigert A Sanchis-Calleja F Organoid single-cell genomic atlas uncovers human-specific features of brain development Nature. 2019 574 7778 418 422 10.1038/s41586-019-1654-9 31619793 · DOI
  12. [14]Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat Biotechnol. 2020. 32747759
  13. [15]Grosveld F van Assendelft GB Greaves DR Kollias G Position-independent, high-level expression of the human beta-globin gene in transgenic mice Cell. 1987 51 6 975 985 10.1016/0092-8674(87)90584-8 3690667 · DOI
  14. [16]Higgs DR Wood WG Jarman AP Sharpe J Lida J Pretorius IM Ayyub H A major positive regulatory region located far upstream of the human alpha-globin gene locus Genes Dev. 1990 4 9 1588 1601 10.1101/gad.4.9.1588 2253879 · DOI
  15. [17]Mettananda S Gibbons RJ Higgs DR Understanding alpha-globin gene regulation and implications for the treatment of beta-thalassemia Ann N Y Acad Sci. 2016 1368 1 16 24 10.1111/nyas.12988 26695885 · DOI
  16. [18]Zhang P Behre G Pan J Iwama A Wara-Aswapati N Radomska HS Negative cross-talk between hematopoietic regulators: GATA proteins repress PU.1 Proc Natl Acad Sci U S A. 1999 96 15 8705 8710 10.1073/pnas.96.15.8705 10411939 PMC17580 · DOI · Paper page
  17. [19]McGrath K Palis J Ontogeny of erythropoiesis in the mammalian embryo Curr Top Dev Biol. 2008 82 1 22 10.1016/S0070-2153(07)00001-4 18282515 · DOI
  18. [20]Pevny L Simon MC Robertson E Klein WH Tsai SF D'Agati V Orkin SH Costantini F Erythroid differentiation in chimaeric mice blocked by a targeted mutation in the gene for transcription factor GATA-1 Nature. 1991 349 6306 257 260 10.1038/349257a0 1987478 · DOI · Paper page
  19. [21]Pevny L Lin CS D'Agati V Simon MC Orkin SH Costantini F Development of hematopoietic cells lacking transcription factor GATA-1 Development. 1995 121 1 163 172 10.1242/dev.121.1.163 7867497 · DOI
  20. [22]Gutierrez L Tsukamoto S Suzuki M Yamamoto-Mukai H Yamamoto M Philipsen S Ablation of Gata1 in adult mice results in aplastic crisis, revealing its essential role in steady-state and stress erythropoiesis Blood. 2008 111 8 4375 4385 10.1182/blood-2007-09-115121 18258797 · DOI
  21. [23]Fujiwara Y Browne CP Cunniff K Goff SC Orkin SH Arrested development of embryonic red cell precursors in mouse embryos lacking transcription factor GATA-1 Proc Natl Acad Sci U S A. 1996 93 22 12355 12358 10.1073/pnas.93.22.12355 8901585 PMC37995 · DOI · Paper page
  22. [24]Shivdasani RA Fujiwara Y McDevitt MA Orkin SH A lineage-selective knockout establishes the critical role of transcription factor GATA-1 in megakaryocyte growth and platelet development EMBO J. 1997 16 13 3965 3973 10.1093/emboj/16.13.3965 9233806 PMC1170020 · DOI
  23. [25]Pijuan-Sala B Griffiths JA Guibentif C Hiscock TW Jawaid W Calero-Nieto FJ Mulas C Ibarra-Soria X Tyser RCV Ho DLL Reik W Srinivas S Simons BD Nichols J Marioni JC Göttgens B A single-cell molecular map of mouse gastrulation and early organogenesis Nature. 2019 566 7745 490 495 10.1038/s41586-019-0933-9 30787436 PMC6522369 · DOI · Paper page
  24. [26]Argelaguet R Arnol D Bredikhin D Deloro Y Velten B Marioni JC Stegle O MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data Genome Biol. 2020 21 1 111 10.1186/s13059-020-02015-1 32393329 PMC7212577 · DOI · Paper page
  25. [27]Storry JR Joud M Christophersen MK Thuresson B Akerstrom B Sojka BN Homozygosity for a null allele of SMIM1 defines the Vel-negative blood group phenotype Nat Genet. 2013 45 5 537 541 10.1038/ng.2600 23563606 · DOI
  26. [29]Tsang AP Visvader JE Turner CA Fujiwara Y Yu C Weiss MJ Crossley M Orkin SH FOG, a multitype zinc finger protein, acts as a cofactor for transcription factor GATA-1 in erythroid and megakaryocytic differentiation Cell. 1997 90 1 109 119 10.1016/S0092-8674(00)80318-9 9230307 · DOI · Paper page
  27. [30]Weiss MJ Yu C Orkin SH Erythroid-cell-specific properties of transcription factor GATA-1 revealed by phenotypic rescue of a gene-targeted cell line Mol Cell Biol. 1997 17 3 1642 1651 10.1128/MCB.17.3.1642 9032291 PMC231889 · DOI
  28. [31]Guibentif C Griffiths JA Imaz-Rosshandler I Ghazanfar S Nichols J Wilson V Göttgens B Marioni JC Diverse routes toward early somites in the mouse embryo Dev Cell. 2021 56 1 141 153 10.1016/j.devcel.2020.11.013 33308481 PMC7808755 · DOI
  29. [32]Vyas P Ault K Jackson CW Orkin SH Shivdasani RA Consequences of GATA-1 deficiency in megakaryocytes and platelets Blood. 1999 93 9 2867 2875 10.1182/blood.V93.9.2867.409k24_2867_2875 10216081 · DOI · Paper page
  30. [33]Monteiro R Pouget C Patient R The gata1/pu.1 lineage fate paradigm varies between blood populations and is modulated by tif1gamma EMBO J. 2011 30 6 1093 1103 10.1038/emboj.2011.34 21336259 PMC3061038 · DOI
  31. [34]Pina C May G Soneji S Hong D Enver T MLLT3 regulates early human erythroid and megakaryocytic cell fate Cell Stem Cell. 2008 2 3 264 273 10.1016/j.stem.2008.01.013 18371451 · DOI
  32. [35]Palis J Hematopoietic stem cell-independent hematopoiesis: emergence of erythroid, megakaryocyte, and myeloid potential in the mammalian embryo FEBS Lett. 2016 590 22 3965 3974 10.1002/1873-3468.12459 27790707 · DOI
  33. [36]Kondo A Fujiwara T Okitsu Y Fukuhara N Onishi Y Nakamura Y Sawada K Harigae H Identification of a novel putative mitochondrial protein FAM210B associated with erythroid differentiation Int J Hematol. 2016 103 4 387 395 10.1007/s12185-016-1968-4 26968549 · DOI
  34. [37]Popescu DM Botting RA Stephenson E Green K Webb S Jardine L Calderbank EF Polanski K Goh I Efremova M Acres M Maunder D Vegh P Gitton Y Park JE Vento-Tormo R Miao Z Dixon D Rowell R McDonald D Fletcher J Poyner E Reynolds G Mather M Moldovan C Mamanova L Greig F Young MD Meyer KB Lisgo S Bacardit J Fuller A Millar B Innes B Lindsay S Stubbington MJT Kowalczyk MS Li B Ashenberg O Tabaka M Dionne D Tickle TL Slyper M Rozenblatt-Rosen O Filby A Carey P Villani AC Roy A Regev A Chédotal A Roberts I Göttgens B Behjati S Laurenti E Teichmann SA Haniffa M Decoding human fetal liver haematopoiesis Nature. 2019 574 7778 365 371 10.1038/s41586-019-1652-y 31597962 PMC6861135 · DOI · Paper page
  35. [38]Ezer D Moignard V Gottgens B Adryan B Determining physical mechanisms of gene expression regulation from single cell gene expression data PLoS Comput Biol. 2016 12 8 e1005072 10.1371/journal.pcbi.1005072 27551778 PMC4995004 · DOI
  36. [39]Kim JK Marioni JC Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data Genome Biol. 2013 14 1 R7 10.1186/gb-2013-14-1-r7 23360624 PMC3663116 · DOI
  37. [40]Liu X Chen Y Zhang Y Liu Y Liu N Botten GA Cao H Orkin SH Zhang MQ Xu J Multiplexed capture of spatial configuration and temporal dynamics of locus-specific 3D chromatin by biotinylated dCas9 Genome Biol. 2020 21 1 59 10.1186/s13059-020-01973-w 32138752 PMC7059722 · DOI
  38. [41]Hoppe PS Schwarzfischer M Loeffler D Kokkaliaris KD Hilsenbeck O Moritz N Endele M Filipczyk A Gambardella A Ahmed N Etzrodt M Coutu DL Rieger MA Marr C Strasser MK Schauberger B Burtscher I Ermakova O Bürger A Lickert H Nerlov C Theis FJ Schroeder T Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios Nature. 2016 535 7611 299 302 10.1038/nature18320 27411635 · DOI
  39. [42]Choe KS Ujhelly O Wontakal SN Skoultchi AI PU.1 directly regulates cdk6 gene expression, linking the cell proliferation and differentiation programs in erythroid cells J Biol Chem. 2010 285 5 3044 3052 10.1074/jbc.M109.077727 19955566 PMC2823399 · DOI
  40. [43]Carotta S Wu L Nutt SL Surprising new roles for PU.1 in the adaptive immune response Immunol Rev. 2010 238 1 63 75 10.1111/j.1600-065X.2010.00955.x 20969585 · DOI · Paper page
  41. [44]Roberts I, Alford K, Hall G, Juban G, Richmond H, Norton A, et al. GATA1-mutant clones are frequent and often unsuspected in babies with Down syndrome: identification of a population at risk of leukemia. Blood. 2013;122(24):3908–17. 10.1182/blood-2013-07-515148. PMC3995281 24021668 · DOI
  42. [45]Bhatnagar N Nizery L Tunstall O Vyas P Roberts I Transient abnormal myelopoiesis and AML in Down syndrome: an update Curr Hematol Malig Rep. 2016 11 5 333 341 10.1007/s11899-016-0338-x 27510823 PMC5031718 · DOI
  43. [46]Li Z Godinho FJ Klusmann JH Garriga-Canut M Yu C Orkin SH Developmental stage-selective effect of somatically mutated leukemogenic transcription factor GATA1 Nat Genet. 2005 37 6 613 619 10.1038/ng1566 15895080 · DOI
  44. [47]Edwards CR Ritchie W Wong JJ Schmitz U Middleton R An X A dynamic intron retention program in the mammalian megakaryocyte and erythrocyte lineages Blood. 2016 127 17 e24 e34 10.1182/blood-2016-01-692764 26962124 PMC4850870 · DOI
  45. [48]Pimentel H Parra M Gee SL Mohandas N Pachter L Conboy JG A dynamic intron retention program enriched in RNA processing genes regulates gene expression during terminal erythropoiesis Nucleic Acids Res. 2016 44 2 838 851 10.1093/nar/gkv1168 26531823 PMC4737145 · DOI
  46. [49]Wan Y Larson DR Splicing heterogeneity: separating signal from noise Genome Biol. 2018 19 1 86 10.1186/s13059-018-1467-4 29986741 PMC6036703 · DOI
  47. [51]Lun AT McCarthy DJ Marioni JC A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor F1000Res 2016 5 2122 27909575 PMC5112579
  48. [52]Ashburner M Ball CA Blake JA Botstein D Butler H Cherry JM Davis AP Dolinski K Dwight SS Eppig JT Harris MA Hill DP Issel-Tarver L Kasarskis A Lewis S Matese JC Richardson JE Ringwald M Rubin GM Sherlock G Gene ontology: tool for the unification of biology. The Gene Ontology Consortium Nat Genet. 2000 25 1 25 29 10.1038/75556 10802651 PMC3037419 · DOI · Paper page
  49. [53]The Gene Ontology C The Gene Ontology Resource: 20 years and still GOing strong Nucleic Acids Res. 2019 47 D1 D330 D3D8 10.1093/nar/gky1055 30395331 PMC6323945 · DOI · Paper page
  50. [55]Wray J Kalkan T Gomez-Lopez S Eckardt D Cook A Kemler R Smith A Inhibition of glycogen synthase kinase-3 alleviates Tcf3 repression of the pluripotency network and increases embryonic stem cell resistance to differentiation Nat Cell Biol. 2011 13 7 838 845 10.1038/ncb2267 21685889 PMC3160487 · DOI
  51. [56]Ran FA Hsu PD Wright J Agarwala V Scott DA Zhang F Genome engineering using the CRISPR-Cas9 system Nat Protoc. 2013 8 11 2281 2308 10.1038/nprot.2013.143 24157548 PMC3969860 · DOI · Paper page
  52. [57]Lun ATL, Riesenfeld S, Andrews T, Dao TP, Gomes T, participants in the 1st Human Cell Atlas J, et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 2019;20(1):63. PMC6431044 30902100
  53. [58]Wolf FA Angerer P Theis FJ SCANPY: large-scale single-cell gene expression data analysis Genome Biol. 2018 19 1 15 10.1186/s13059-017-1382-0 29409532 PMC5802054 · DOI · Paper page
  54. [59]Barile M, Imaz-Rosshandler I, Inzani I, Ghazanfar S, Nichols J, Marioni JC, Guibentif C, Göttgens B. Coordinated changes in gene expression kinetics underlie both mouse and human erythroid maturation. GSE167576. Gene Expression Omnibus. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE167576 (2021). Accessed 15 June 2021. PMC8258993 34225769
  55. [60]Barile M, Imaz-Rosshandler I, Inzani I, Ghazanfar S, Nichols J, Marioni JC, Guibentif C, Göttgens B. Coordinated changes in gene expression kinetics underlie both mouse and human erythroid maturation. Zenodo. doi:10.5281/zenodo.4954417 (2021). PMC8258993 34225769 · DOI
  56. [61]Barile M, Imaz-Rosshandler I, Inzani I, Ghazanfar S, Nichols J, Marioni JC, Guibentif C, Göttgens B. Coordinated changes in gene expression kinetics underlie both mouse and human erythroid maturation. Github. https://github.com/mebarile/Gata1_Erythroid_kinetics (2021). Accessed 9 June 2021. PMC8258993 34225769

Authors
Barile, M., Imaz-Rosshandler, I., Inzani, I., Ghazanfar, S., Nichols, J., Marioni, J. C., Guibentif, C., & Göttgens, B.
Affiliations
  • Wellcome/MRC Cambridge Stem Cell Institute (GB)
  • University of Cambridge (GB)
  • European Bioinformatics Institute (GB)
Corresponding author
  • Guibentif, Carolina
  • Göttgens, Berthold
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2021-07-05
Volume
22
Issue
1
Pages
197-197
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-021-02414-y
PMC
PMC8258993
OpenAlex
W3182034091
Cited by
77
References
60
Citations by year
2026: 20 · 2025: 12 · 2024: 8 · 2023: 21 · 2022: 11 · 2021: 5
FWCI
3.51
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Apollo (University of Cambridge) (submittedVersion)
  • Apollo
  • Apollo (cc-by)
Funders
  • Wellcome Trust (105031/D/14/Z, 097922/Z/11/Z, 206328/Z/17/Z)
  • Vetenskapsrådet (2017-06278)
  • Royal Society (NIF\R1\181950)
  • Blood Cancer UK (18002)
  • Medical Research Council (MR/M008975/1, MR/S036113/1)
  • British Heart Foundation (FS/18/56/35177)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
RNA velocity, erythropoiesis, erythroid maturation, gene expression kinetics, differentiation trajectories, GATA-1, PU.1, megakaryocyte progenitors, epiblast, gastrulation, single-cell RNA sequencing
MeSH terms
Proto-Oncogene Protein Spi-1, Animals, Cell Differentiation, Embryo, Mammalian, Erythropoiesis, Fetus, Gastrula, Humans, Kinetics, Liver, Proto-Oncogene Proteins, Transcriptional Activation, Trans-Activators, Gene Expression Regulation, Developmental, Organogenesis, Erythroid Cells, GATA1 Transcription Factor, Mice, Single-Cell Analysis, Datasets as Topic

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