Differentiation is accompanied by a progressive loss in transcriptional memory.
Cell differentiation
Gene expression variability
Sister cells
Transcriptional memory
Transcriptome
Journal
BMC biology
ISSN: 1741-7007
Titre abrégé: BMC Biol
Pays: England
ID NLM: 101190720
Informations de publication
Date de publication:
12 Mar 2024
12 Mar 2024
Historique:
received:
05
04
2023
accepted:
13
02
2024
medline:
12
3
2024
pubmed:
12
3
2024
entrez:
12
3
2024
Statut:
epublish
Résumé
Cell differentiation requires the integration of two opposite processes, a stabilizing cellular memory, especially at the transcriptional scale, and a burst of gene expression variability which follows the differentiation induction. Therefore, the actual capacity of a cell to undergo phenotypic change during a differentiation process relies upon a modification in this balance which favors change-inducing gene expression variability. However, there are no experimental data providing insight on how fast the transcriptomes of identical cells would diverge on the scale of the very first two cell divisions during the differentiation process. In order to quantitatively address this question, we developed different experimental methods to recover the transcriptomes of related cells, after one and two divisions, while preserving the information about their lineage at the scale of a single cell division. We analyzed the transcriptomes of related cells from two differentiation biological systems (human CD34+ cells and T2EC chicken primary erythrocytic progenitors) using two different single-cell transcriptomics technologies (scRT-qPCR and scRNA-seq). We identified that the gene transcription profiles of differentiating sister cells are more similar to each other than to those of non-related cells of the same type, sharing the same environment and undergoing similar biological processes. More importantly, we observed greater discrepancies between differentiating sister cells than between self-renewing sister cells. Furthermore, a progressive increase in this divergence from first generation to second generation was observed when comparing differentiating cousin cells to self renewing cousin cells. Our results are in favor of a gradual erasure of transcriptional memory during the differentiation process.
Sections du résumé
BACKGROUND
BACKGROUND
Cell differentiation requires the integration of two opposite processes, a stabilizing cellular memory, especially at the transcriptional scale, and a burst of gene expression variability which follows the differentiation induction. Therefore, the actual capacity of a cell to undergo phenotypic change during a differentiation process relies upon a modification in this balance which favors change-inducing gene expression variability. However, there are no experimental data providing insight on how fast the transcriptomes of identical cells would diverge on the scale of the very first two cell divisions during the differentiation process.
RESULTS
RESULTS
In order to quantitatively address this question, we developed different experimental methods to recover the transcriptomes of related cells, after one and two divisions, while preserving the information about their lineage at the scale of a single cell division. We analyzed the transcriptomes of related cells from two differentiation biological systems (human CD34+ cells and T2EC chicken primary erythrocytic progenitors) using two different single-cell transcriptomics technologies (scRT-qPCR and scRNA-seq).
CONCLUSIONS
CONCLUSIONS
We identified that the gene transcription profiles of differentiating sister cells are more similar to each other than to those of non-related cells of the same type, sharing the same environment and undergoing similar biological processes. More importantly, we observed greater discrepancies between differentiating sister cells than between self-renewing sister cells. Furthermore, a progressive increase in this divergence from first generation to second generation was observed when comparing differentiating cousin cells to self renewing cousin cells. Our results are in favor of a gradual erasure of transcriptional memory during the differentiation process.
Identifiants
pubmed: 38468285
doi: 10.1186/s12915-024-01846-9
pii: 10.1186/s12915-024-01846-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
58Subventions
Organisme : Agence Nationale de la Recherche
ID : ANR-17-CE12-0031
Informations de copyright
© 2024. The Author(s).
Références
Miura H, Hiratani I. Cell cycle dynamics and developmental dynamics of the 3D genome: toward linking the two timescales. Curr Opin Genet Dev. 2022;73:101898.
pubmed: 35026526
doi: 10.1016/j.gde.2021.101898
Sigal A, Milo R, Cohen A, Geva-Zatorsky N, Klein Y, Liron Y, et al. Variability and memory of protein levels in human cells. Nature. 2006;444(7119):643–6.
pubmed: 17122776
doi: 10.1038/nature05316
Schwanhäusser B, Wolf J, Selbach M, Busse D. Synthesis and degradation jointly determine the responsiveness of the cellular proteome: Insights & Perspectives. BioEssays. 2013;35(7):597–601.
pubmed: 23696377
doi: 10.1002/bies.201300017
Corre G, Stockholm D, Arnaud O, Kaneko G, Viñuelas J, Yamagata Y, et al. Stochastic fluctuations and distributed control of gene expression impact cellular memory. PLoS ONE. 2014;9(12):e115574.
pubmed: 25531401
pmcid: 4274012
doi: 10.1371/journal.pone.0115574
Kimmerling RJ, Lee Szeto G, Li JW, Genshaft AS, Kazer SW, Payer KR, et al. A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages. Nat Commun. 2016;7:10220.
pubmed: 26732280
pmcid: 4729820
doi: 10.1038/ncomms10220
Phillips NE, Mandic A, Omidi S, Naef F, Suter DM. Memory and relatedness of transcriptional activity in mammalian cell lineages. Nat Commun. 2019;10(1):1208.
pubmed: 30872573
pmcid: 6418128
doi: 10.1038/s41467-019-09189-8
Muramoto T, Muller I, Thomas G, Melvin A, Chubb JR. Methylation of H3K4 Is required for inheritance of active transcriptional states. Curr Biol. 2010;20(5):397–406.
pubmed: 20188556
doi: 10.1016/j.cub.2010.01.017
Shaffer SM, Emert BL, Reyes Hueros RA, Cote C, Harmange G, Schaff DL, et al. Memory sequencing reveals heritable single-cell gene expression programs associated with distinct cellular behaviors. Cell. 2020;182(4):947–959.e17.
pubmed: 32735851
pmcid: 7496637
doi: 10.1016/j.cell.2020.07.003
Chang HH, Hemberg M, Barahona M, Ingber DE, Huang S. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature. 2008;453(7194):544–7.
pubmed: 18497826
pmcid: 5546414
doi: 10.1038/nature06965
Kalmar T, Lim C, Hayward P, Munoz-Descalzo S, Nichols J, Garcia-Ojalvo J, et al. Regulated fluctuations in NANOG expression mediate cell fate decisions in embryonic stem cells. PLoS Biol. 2009;7(7):e1000149.
pubmed: 19582141
pmcid: 2700273
doi: 10.1371/journal.pbio.1000149
Hu M, Krause D, Greaves M, Sharkis S, Dexter M, Heyworth C, et al. Multilineage gene expression precedes commitment in the hemopoietic system. Genes Dev. 1997;11(6):774–85.
pubmed: 9087431
doi: 10.1101/gad.11.6.774
Pina C, Fugazza C, Tipping AJ, Brown J, Soneji S, Teles J, et al. Inferring rules of lineage commitment in haematopoiesis. Nat Cell Biol. 2012;14(3):287–94.
pubmed: 22344032
doi: 10.1038/ncb2442
Mojtahedi M, Skupin A, Zhou J, Castaño IG, Leong-Quong RYY, Chang H, et al. Cell fate decision as high-dimensional critical state transition. PLoS Biol. 2016;14(12):e2000640. Number: 12.
Richard A, Boullu L, Herbach U, Bonnafoux A, Morin V, Vallin E, et al. Single-cell-based analysis highlights a surge in cell-to-cell molecular variability preceding irreversible commitment in a differentiation process. PLoS Biol. 2016;14(12):e1002585.
pubmed: 28027290
pmcid: 5191835
doi: 10.1371/journal.pbio.1002585
Moussy A, Cosette J, Parmentier R, da Silva C, Corre G, Richard A, et al. Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment. PLoS Biol. 2017;15(7):e2001867.
pubmed: 28749943
pmcid: 5531424
doi: 10.1371/journal.pbio.2001867
Gao M, Ling M, Tang X, Wang S, Xiao X, Qiao Y, et al. Comparison of high-throughput single-cell RNA sequencing data processing pipelines. Brief Bioinforma. 2021;22(3):bbaa116.
doi: 10.1093/bib/bbaa116
Moris N, Edri S, Seyres D, Kulkarni R, Domingues AF, Balayo T, et al. Histone acetyltransferase KAT2A stabilizes pluripotency with control of transcriptional heterogeneity. Stem Cells. 2018;36(12):1828–38.
pubmed: 30270482
doi: 10.1002/stem.2919
Guillemin A, Duchesne R, Crauste F, Gonin-Giraud S, Gandrillon O. Drugs modulating stochastic gene expression affect the erythroid differentiation process. PLoS ONE. 2019;14(11):e0225166.
pubmed: 31751364
pmcid: 6872177
doi: 10.1371/journal.pone.0225166
Stumpf PS, Smith RCG, Lenz M, Schuppert A, Müller FJ, Babtie A, et al. Stem cell differentiation as a non-Markov stochastic process. Cell Syst. 2017;5:268–82.
pubmed: 28957659
pmcid: 5624514
doi: 10.1016/j.cels.2017.08.009
Racine L, Parmentier R, Niphadkar S, Chhun J, Martignoles JA, Delhommeau F, et al. Metabolic adaptation pilots the differentiation of human hematopoietic cells. bioRxiv. 2023. https://doi.org/10.1101/2023.09.17.558120 . (preprint)
Dussiau C, Boussaroque A, Gaillard M, Bravetti C, Zaroili L, Knosp C, et al. Hematopoietic differentiation is characterized by a transient peak of entropy at a single-cell level. BMC Biol. 2022;20(1):60.
pubmed: 35260165
pmcid: 8905725
doi: 10.1186/s12915-022-01264-9
Toh K, Saunders D, Verd B, Steventon B. Zebrafish neuromesodermal progenitors undergo a critical state transition in vivo. iScience. 2022;25(10):105216.
pubmed: 36274939
pmcid: 9579027
doi: 10.1016/j.isci.2022.105216
Zreika S, Fourneaux C, Vallin E, Modolo L, Seraphin R, Moussy A, et al. Evidence for close molecular proximity between reverting and undifferentiated cells. BMC Biol. 2022;20(1):155.
pubmed: 35794592
pmcid: 9258043
doi: 10.1186/s12915-022-01363-7
Teschendorff AE, Feinberg AP. Statistical mechanics meets single-cell biology. Nat Rev Genet. 2021. https://doi.org/10.1038/s41576-021-00341-z .
Gao NP, Gandrillon O, Páldi A, Herbach U, Gunawan R. Single-cell transcriptional uncertainty landscape of cell differentiation. Available from: https://f1000research.com/articles/12-426 .
Wehling A, Loeffler D, Zhang Y, Kull T, Donato C, Szczerba B, et al. Combining single-cell tracking and omics improves blood stem cell fate regulator identification. Blood. 2022;140(13):1482–95.
pubmed: 35820055
pmcid: 9523371
doi: 10.1182/blood.2022016880
Weinreb C, Rodriguez-Fraticelli A, Camargo FD, Klein AM. Lineage tracing on transcriptional landscapes links state to fate during differentiation. Science. 2020;367(6479).
Gandrillon O, Schmidt U, Beug H, Samarut J. TGF-Beta cooperates with TGF-Alpha to induce the self-renewal of normal erythrocytic progenitors: evidence for an autocrine mechanism. EMBO J. 1999;18(10):2764–81.
pubmed: 10329623
pmcid: 1171358
doi: 10.1093/emboj/18.10.2764
Gandrillon O, Samarut J. Role of the different RAR isoforms in controlling the erythrocytic differentiation sequence. Interference with the v-erbA and p135gag-myb-ets nuclear oncogenes. Oncogene. 1998;16(5):563–74.
pubmed: 9482102
doi: 10.1038/sj.onc.1201550
Scrucca L, Fop M, Murphy TB, Raftery AE. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R Journal. 2016;8(1):289–317.
pubmed: 27818791
doi: 10.32614/RJ-2016-021
Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, et al. Massively parallel single-cell RNA-Seq for marker-free decomposition of tissues into cell types. Science. 2014;343(6172):776–9.
pubmed: 24531970
pmcid: 4412462
doi: 10.1126/science.1247651
Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017;35(4):316–9.
pubmed: 28398311
doi: 10.1038/nbt.3820
R Core Team. R: a language and environment for statistical computing. Vienna, Austria.
Cole MB, Risso D, Wagner A, DeTomaso D, Ngai J, Purdom E, et al. Performance assessment and selection of normalization procedures for single-cell RNA-Seq. Cell Syst. 2019;8(4):315–328.e8.
pubmed: 31022373
pmcid: 6544759
doi: 10.1016/j.cels.2019.03.010
Breda J, Zavolan M, van Nimwegen E. Bayesian inference of gene expression states from single-cell RNA-seq data. Nat Biotechnol. 2021;39(8):1008–16.
pubmed: 33927416
doi: 10.1038/s41587-021-00875-x
Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 2019;20(1):296.
pubmed: 31870423
pmcid: 6927181
doi: 10.1186/s13059-019-1874-1
Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. 2019;37(1):38–44.
doi: 10.1038/nbt.4314
Parmentier R, Racine L, Moussy A, Chantalat S, Sudharshan R, Papili Gao N, et al. Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells. PLoS Biol. 2022;20(10):e3001849.
pubmed: 36288293
pmcid: 9604949
doi: 10.1371/journal.pbio.3001849
Terrén I, Orrantia A, Vitallé J, Zenarruzabeitia O, Borrego F. CFSE dilution to study human T and NK cell proliferation in vitro. Methods Enzymol. 2020;631:239–55. Elsevier.
Parish CR. Fluorescent dyes for lymphocyte migration and proliferation studies. Immunol Cell Biol. 1999;77(6):499–508.
pubmed: 10571670
doi: 10.1046/j.1440-1711.1999.00877.x
Kim W, Klarmann KD, Keller JR. Gfi-1 regulates the erythroid transcription factor network through Id2 repression in murine hematopoietic progenitor cells. Blood. 2014;124(10):1586–96.
pubmed: 25051963
pmcid: 4155270
doi: 10.1182/blood-2014-02-556522
Da Cunha AF, Brugnerotto AF, Duarte ADSS, Lanaro C, Costa GGL, Saad STO, et al. Global gene expression reveals a set of new genes involved in the modification of cells during erythroid differentiation: modification of cells during erythroid differentiation. Cell Prolif. 2010;43(3):297–309.
pubmed: 20546246
pmcid: 6496675
doi: 10.1111/j.1365-2184.2010.00679.x
Aggarwal CC, Hinneburg A, Keim DA. On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche J, Vianu V, editors. Database Theory - ICDT 2001. Berlin: Springer Berlin Heidelberg; 2001. pp. 420–34.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol). 1995;57(1):289–300.
Richard A, Vallin E, Romestaing C, Roussel D, Gandrillon O, Gonin-Giraud S. Erythroid differentiation displays a peak of energy consumption concomitant with glycolytic metabolism rearrangements. PLoS ONE. 2019;14(9):e0221472.
pubmed: 31483850
pmcid: 6726194
doi: 10.1371/journal.pone.0221472
Bonnaffoux A, Herbach U, Richard A, Guillemin A, Gonin-Giraud S, Gros PA, et al. WASABI: a dynamic iterative framework for gene regulatory network inference. BMC Bioinformatics. 2019;20(1):220.
pubmed: 31046682
pmcid: 6498543
doi: 10.1186/s12859-019-2798-1
Aslan Kamil M, Fourneaux C, Yilmaz A, Stavros S, Parmentier R, Paldi A, et al. An image-guided microfluidic system for single-cell lineage tracking. PloS ONE. 2023;18(8):e0288655.
pubmed: 37527253
pmcid: 10393162
doi: 10.1371/journal.pone.0288655
Wang F, Higgins JM. Histone modifications and mitosis: countermarks, landmarks, and bookmarks. Trends Cell Biol. 2013;23(4):175–84.
pubmed: 23246430
doi: 10.1016/j.tcb.2012.11.005
Golloshi R, Sanders JT, McCord RP. Genome organization during the cell cycle: unity in division. Wiley Interdiscip Rev Syst Biol Med. 2017;9(5).
Palozola KC, Donahue G, Zaret KS. EU-RNA-seq for in vivo labeling and high throughput sequencing of nascent transcripts. STAR Protoc. 2021;2(3):100651.
pubmed: 34485932
pmcid: 8403648
doi: 10.1016/j.xpro.2021.100651
Kadauke S, Udugama MI, Pawlicki JM, Achtman JC, Jain DP, Cheng Y, et al. Tissue-specific mitotic bookmarking by hematopoietic transcription factor GATA1. Cell. 2012;150(4):725–37.
pubmed: 22901805
pmcid: 3425057
doi: 10.1016/j.cell.2012.06.038
Suter DM, Molina N, Gatfield D, Schneider K, Schibler U, Naef F. Mammalian genes are transcribed with widely different bursting kinetics. Science. 2011;332(6028):472–4.
pubmed: 21415320
doi: 10.1126/science.1198817
Tunnacliffe E, Chubb JR. What is a transcriptional burst? Trends Genet. 2020;36(4):288–97.
pubmed: 32035656
doi: 10.1016/j.tig.2020.01.003
Rodriguez J, Larson DR. Transcription in living cells: molecular mechanisms of bursting. Annu Rev Biochem. 2020;89:189–212.
pubmed: 32208766
doi: 10.1146/annurev-biochem-011520-105250
Pedraza JM, van Oudenaarden A. Noise propagation in gene networks. Science. 2005;307(5717):1965–9.
pubmed: 15790857
doi: 10.1126/science.1109090
Kim S, Shendure J. Mechanisms of interplay between transcription factors and the 3D genome. Mol Cell. 2019;76(2):306–19.
pubmed: 31521504
doi: 10.1016/j.molcel.2019.08.010
Martin-Martin N, Carracedo A, Torrano V. Metabolism and transcription in cancer: merging two classic tales. Front Cell Dev Biol. 2017;5:119.
pubmed: 29354634
doi: 10.3389/fcell.2017.00119
Differentiation is accompagnied by a progressive loss in transcriptional memory. NCBI Bioproject accession: PRJNA882056. 2022. https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA882056 .
Sister Cells Supporting data. 2022. https://gitbio.ens-lyon.fr/LBMC/sbdm/sister-cells .