Differentiation proceeds along a continuum of increasingly fate-restricted intermediates, referred to as canalization1,2. Canalization is essential for stabilizing cell fate, but the mechanisms that underlie robust canalization are unclear. Here we show that the BRG1/BRM-associated factor (BAF) chromatin-remodelling complex ATPase gene Brm safeguards cell identity during directed cardiogenesis of mouse embryonic stem cells. Despite the establishment of a well-differentiated precardiac mesoderm, Brm−/− cells predominantly became neural precursors, violating germ layer assignment. Trajectory inference showed a sudden acquisition of a non-mesodermal identity in Brm−/− cells. Mechanistically, the loss of Brm prevented de novo accessibility of primed cardiac enhancers while increasing the expression of neurogenic factor POU3F1, preventing the binding of the neural suppressor REST and shifting the composition of BRG1 complexes. The identity switch caused by the Brm mutation was overcome by increasing BMP4 levels during mesoderm induction. Mathematical modelling supports these observations and demonstrates that Brm deletion affects cell fate trajectory by modifying saddle–node bifurcations2. In the mouse embryo, Brm deletion exacerbated mesoderm-deleted Brg1-mutant phenotypes, severely compromising cardiogenesis, and reveals an in vivo role for Brm. Our results show that Brm is a compensable safeguard of the fidelity of mesoderm chromatin states, and support a model in which developmental canalization is not a rigid irreversible path, but a highly plastic trajectory.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection
Genome Biology Open Access 24 May 2022
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
Open source GitHub repository codes are provided for single-cell data analysis (https://github.com/swhota/Brm-scripts), mathematical modelling of Brahma with definitions of all model state variables, parameters, parameter values and interactions (https://github.com/mkm1712/Brahma_model), logic-based differential equations generation using Netflux (https://github.com/saucermanlab/Netflux), peaks from ChIP–seq and ATAC-seq datasets (https://github.com/gladstone-institutes/Hota_et_al_2021_Brm_safeguards_canalization_cardiac_diff) and the pipeline for automated ChIP–seq and ATAC-seq data processing (https://github.com/gladstone-institutes/MonkeyPipeline).
Waddington, C. H. The Strategy of the Genes, a Discussion of Some Aspects of Theoretical Biology 20 (George Allen & Unwin Ltd, 1957).
Ferrell, J. E., Jr Bistability, bifurcations, and Waddington’s epigenetic landscape. Curr. Biol. 22, R458–R466 (2012).
Reyes, J. C. et al. Altered control of cellular proliferation in the absence of mammalian Brahma (SNF2α). EMBO J. 17, 6979–6991 (1998).
Van Houdt, J. K. J. et al. Heterozygous missense mutations in SMARCA2 cause Nicolaides-Baraitser syndrome. Nat. Genet. 44, 445–449 (2012).
Tsurusaki, Y. et al. Mutations affecting components of the SWI/SNF complex cause Coffin-Siris syndrome. Nat. Genet. 44, 376–378 (2012).
Kadoch, C. Diverse compositions and functions of chromatin remodeling machines in cancer. Sci. Transl. Med. 11, eaay1018 (2019).
Hoffman, G. R. et al. Functional epigenetics approach identifies BRM/SMARCA2 as a critical synthetic lethal target in BRG1-deficient cancers. Proc. Natl Acad. Sci. USA 111, 3128–3133 (2014).
Smith-Roe, S. L. & Bultman, S. J. Combined gene dosage requirement for SWI/SNF catalytic subunits during early mammalian development. Mamm. Genome 24, 21–29 (2013).
Bultman, S. J. et al. BRG1 and BRM SWI/SNF ATPases redundantly maintain cardiomyocyte homeostasis by regulating cardiomyocyte mitophagy and mitochondrial dynamics in vivo. Cardiovasc. Pathol. 25, 258–269 (2016).
Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 59–59 (2019).
Farrell, J. A. et al. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science 360, eaar3131 (2018).
Gouti, M. et al. A gene regulatory network balances neural and mesoderm specification during vertebrate trunk development. Dev. Cell 41, 243–261 (2017).
Thomson, M. et al. Pluripotency factors in embryonic stem cells regulate differentiation into germ layers. Cell 145, 875–889 (2011).
Hota, S. K. et al. Dynamic BAF chromatin remodeling complex subunit inclusion promotes temporally distinct gene expression programs in cardiogenesis. Development 146, dev174086 (2019).
Takeuchi, J. K. et al. Chromatin remodelling complex dosage modulates transcription factor function in heart development. Nat. Commun. 2, 187 (2011).
Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).
Weber, C. M. et al. mSWI/SNF promotes polycomb repression both directly and through genome-wide redistribution. Nat. Struct. Mol. Biol. 28, 501–511 (2021).
Zhu, Q. et al. The transcription factor Pou3f1 promotes neural fate commitment via activation of neural lineage genes and inhibition of external signaling pathways. eLife 3, e02224 (2014).
Battaglioli, E. et al. REST repression of neuronal genes requires components of the hSWI.SNF complex. J. Biol. Chem. 277, 41038–41045 (2002).
Kattman, S. J. et al. Stage-specific optimization of activin/nodal and BMP signaling promotes cardiac differentiation of mouse and human pluripotent stem cell lines. Cell Stem Cell 8, 228–240 (2011).
Paulsen, M., Legewie, S., Eils, R., Karaulanov, E. & Niehrs, C. Negative feedback in the bone morphogenetic protein 4 (BMP4) synexpression group governs its dynamic signaling range and canalizes development. Proc. Natl Acad. Sci. USA 108, 10202–10207 (2011).
Arias, A. M. & Hayward, P. Filtering transcriptional noise during development: concepts and mechanisms. Nat. Rev. Genet. 7, 34–44 (2006).
Bier, E. & De Robertis, E. M. BMP gradients: a paradigm for morphogen-mediated developmental patterning. Science 348, aaa5838 (2015).
Kraeutler, M. J., Soltis, A. R. & Saucerman, J. J. Modeling cardiac β-adrenergic signaling with normalized-Hill differential equations: comparison with a biochemical model. BMC Syst. Biol. 4, 157–12 (2010).
Lessard, J. et al. An essential switch in subunit composition of a chromatin remodeling complex during neural development. Neuron 55, 201–215 (2007).
Lamba, D. A., Hayes, S., Karl, M. O. & Reh, T. Baf60c is a component of the neural progenitor-specific BAF complex in developing retina. Dev. Dyn. 237, 3016–3023 (2008).
Zuryn, S. et al. Sequential histone-modifying activities determine the robustness of transdifferentiation. Science 345, 826–829 (2014).
Molina-García, L. et al. Direct glia-to-neuron transdifferentiation gives rise to a pair of male-specific neurons that ensure nimble male mating. eLife 9, e48361 (2020).
Jiang, Z. et al. Knockdown of Brm and Baf170, components of chromatin remodeling complex, facilitates reprogramming of somatic cells. Stem Cells Dev. 24, 2328–2336 (2015).
Treutlein, B. et al. Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq. Nature 534, 391–395 (2016).
Wamstad, J. A. et al. Dynamic and coordinated epigenetic regulation of developmental transitions in the cardiac lineage. Cell 151, 206–220 (2012).
Alexander, J. M. et al. Brg1 modulates enhancer activation in mesoderm lineage commitment. Development 142, 1418–1430 (2015).
Ho, L. et al. An embryonic stem cell chromatin remodeling complex, esBAF, is essential for embryonic stem cell self-renewal and pluripotency. Proc. Natl Acad. Sci. USA 106, 5181–5186 (2009).
Conti, L. et al. Niche-independent symmetrical self-renewal of a mammalian tissue stem cell. PLoS Biol. 3, e283 (2005).
Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).
Nora, E. P. et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944 (2017).
Abmayr, S. M., Yao, T., Parmely, T. & Workman, J. L. Preparation of nuclear and cytoplasmic extracts from mammalian cells. Curr. Protoc. Pharmacol. 75, 12.1.1–12.1.10 (2006).
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Zambon, A. C. et al. GO-Elite: a flexible solution for pathway and ontology over-representation. Bioinformatics 28, 2209–2210 (2012).
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).
McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).
Lambiotte, R., Delvenne, J. C. & Barahona, M. Laplacian dynamics and multiscale modular structure in networks. Preprint at https://arxiv.org/abs/0812.1770 (2008).
Teschendorff, A. E. & Enver, T. Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome. Nat. Commun. 8, 15599 (2017).
Eling, N., Richard, A. C., Richardson, S., Marioni, J. C. & Vallejos, C. A. Correcting the mean-variance dependency for differential variability testing using single-cell RNA sequencing data. Cell Syst. 7, 284–294.e12 (2018).
Wang, J., Zhang, K., Xu, L. & Wang, E. Quantifying the Waddington landscape and biological paths for development and differentiation. Proc. Natl Acad. Sci. USA 108, 8257–8262 (2011).
Waddington, C. H. Canalization of development and the inheritance of acquired characters. Nature 150, 563–565 (1942).
Bhattacharya, S., Zhang, Q. & Andersen, M. E. A deterministic map of Waddington’s epigenetic landscape for cell fate specification. BMC Syst. Biol. 5, 85–12 (2011).
O’Geen, H., Echipare, L. & Farnham, P. J. Using ChIP-seq technology to generate high-resolution profiles of histone modifications. Methods Mol. Biol. 791, 265–286 (2011).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137–R139 (2008).
Xing, H., Mo, Y., Liao, W. & Zhang, M. Q. Genome-wide localization of protein-DNA binding and histone modification by a bayesian change-point method with ChIP-seq data. PLoS Comput. Biol. 8, e1002613 (2012).
Neph, S. et al. BEDOPS: high-performance genomic feature operations. Bioinformatics 28, 1919–1920 (2012).
McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 46, W537–W544 (2018).
McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
Choi, M. et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30, 2524–2526 (2014).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S: Statistics and Computing 4th edn (2002).
Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2018).
Rhee, H. S. et al. Expression of terminal effector genes in mammalian neurons is maintained by a dynamic relay of transient enhancers. Neuron 92, 1252–1265 (2016).
We thank N. Carli, Y. Hao, M. Bernardi and J. McGuire (Gladstone Genomics Core) for RNA-seq and 10x Genomics library preparation; staff at the UCSF Center for Applied Technologies for sequencing; E. Nora for help with Brm-AID strain construction; J. Zhang (Gladstone Transgenic Core) for mouse knockout generation; staff at the Gladstone Stem Cell Core for cell culture; R. Wang for ChIP–seq; K. Choudhary for ATAC-seq analysis; K. Claiborn for editorial assistance; and G. Maki for graphics. This work was supported by grants from the NIH/NHLBI (P01HL089707 and P01HL146366 to B.G.B. and N.J.K.; Bench to Bassinet Program UM1HL098179; and R01HL114948 to B.G.B. and R01HL137755 to J.J.S.); and postdoctoral fellowships from the American Heart Association (13POST17290043), Tobacco Related Disease Research Program (22FT-0079), NIH training grant (2T32-HL007731-26) and career development award (861914) from the American Heart Association to S.K.H. I.S.K. was supported by funds from the Society for Pediatric Anesthesia, Hellman Family Fund, UCSF REAC Award and the UCSF Department of Anesthesia and Perioperative Care. This work was also supported by an NIH/NCRR grant (C06 RR018928) to the J. David Gladstone Institutes, The Roddenberry Foundation and The Younger Family Fund (to B.G.B.).
B.G.B. is a co-founder and shareholder of Tenaya Therapeutics and consults for and has equity in Silvercreek Pharmaceuticals. The work presented here is not related to the interests of Tenaya Therapeutics or Silvercreek Pharmaceuticals. The Krogan laboratory has received research support from Vir Biotechnology and F. Hoffmann-La Roche. N.K. has consulting agreements with the Icahn School of Medicine at Mount Sinai, New York, Maze Therapeutics and Interline Therapeutics, is a shareholder of Tenaya Therapeutics and has received stocks from Maze Therapeutics and Interline Therapeutics. The other authors declare no competing interests.
Peer review information
Nature thanks Gerald Crabtree, Brian Hendrich and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Loss of BRM leads to expression of neural genes in cardiac differentiation and has minimal effect in neural differentiation.
a, Brm mRNA expression during cardiac differentiation from Wamstad et al.31. b, Violin plots of Brm expression of single-cell data from this study. c, Western blot of WT and BRM KO cells at D10 of cardiac differentiation. d, Bulk RNA-seq analysis of WT and BRM KO cells at D4, D5.3 and D10 stages of differentiation. Counts per million (CPM) average of three biological replicates were plotted as a ratio of KO over WT. Gene Ontology (GO) biological process enrichment was determined by GOElite. e, Dots plots showing expression of indicated genes from D10 WT and Brm−/− scRNA-seq data. f, Scheme of neural precursor differentiation from ES cells and TUBB3 immunostaining of WT and Brm−/− cells differentiated to neural precursor (D13) cells. Scale bars are 200 μm.
Extended Data Fig. 2 BRM prevents acquisition of neural fate after pre-cardiac mesoderm formation.
a–d, scRNA-seq data of cardiac differentiation projected on UMAP space showing gene expression feature plots (a), dot plots of quantitative bulk changes in gene expression between WT and Brm−/− cells for early developmental, cardiac mesoderm, cardiac precursors, cardiomyocytes, genes enriched in Brm−/− cells, and a select set of genes involved in neuroectoderm development (b), PAGA connectivity lines for WT (c) or Brm−/− (d) at D4, D6 and D10 stages of differentiation. e, Feature plots of developmental trajectory analysis using URD for selected cardiac and neural genes. f, g, Pluripotency is unaffected in BRM KO cells. f, Immunostaining of WT and Brm−/− ES cells with indicated pluripotency markers. Scale bars are 2 μm, magnification 63x. g, scRNA-seq of WT and Brm−/− cells in ES cell cluster together. h, Integration of scRNA-seq data from D0 ES cells with D4, D6 and D10 scRNA-seq datasets.
Extended Data Fig. 3 Loss of BRG1 early in differentiation leads to formation of non-cardiac cell types.
a, Comparison of Brg1 and Brm expression during cardiac differentiation31. b, Scheme of cardiac differentiation showing timing of induction with 4-hydroxy tamoxifen (4-OHT) or the control tetrahydrofuran (THF) and scRNA-seq. THF or 4-OHT was treated for 2 days to achieve complete Brg1 deletion14. c–e, UMAPs of scRNA-seq data at D4 and D10 of differentiation of WT and conditional BRG1 KO genotypes (c), clusters with inferred cell types (d) and feature plots of expression of indicated genes (e). f, Dot plots comparing gene expression quantification of WT and conditional BRG1 KO at D4 and D10 of differentiation. g, Cardiac troponin T (cTnT) and TUBB3 immunostaining at D10 for WT and BRG1 cKO cells deleted at D4 of differentiation. Scale bars are 200 μm. h, Integration of scRNA-seq data of Brg1 cKO and Brm KO at D10 stage of differentiation.
Extended Data Fig. 4 BRM is required during cardiac mesoderm formation.
a, Mean difference plots of ATAC-seq data plotting average log fold change between WT and Brm−/− cells and average log CPM (3 biological replicates each) at D0 and D2 of differentiation. Statistically significant (FDR <0.05) peaks showing log2 fold change >1, unchanged, and <1 are shown in red, black and blue respectively. b–c, ATAC-seq browser tracks showing WT and BRM KO chromatin accessibility at D4, D6 and D10 of cardiac differentiation along with H3K27ac active enhancer marks near cardiac genes (b) and indicated neural gene loci, along with neural precursor H3K27ac marks65 (c). d–e, BRM-mediated open and closed chromatin regions compared with cardiac and neural progenitor enhancers. Closed and open chromatin in Brm−/− at D6 (d) and at D10 (e) are compared with respective cardiac and neural progenitor enhancers. f, Motifs enriched at the open chromatin regions in WT and BRM KO cells at D4, D6, D10 differentiation stages. BRM activity is essential before D4 of differentiation. g, Auxin inducible degron mouse ES strain of BRM (Brm-AID) differentiated to cardiomyocytes at D10 and treated without (lane 1) or with auxin analog indole acetic acid (IAA) for indicated period of time shows rapid BRM degradation by western blot. h–i, Schematic of cardiac differentiation showing time of IAA treatment and beating at D10. Cells treated with IAA for indicated length of time (h) or a period of two days at a time (i) were analysed by immunostaining of cardiac troponin T at D10. Scale bars are 200 μm.
Extended Data Fig. 5 BRM loss leads to reduced H3K27ac marks near cardiac genes and increased H3K27ac marks near neural genes.
a, Differential enrichment of H3K27me3 marks in WT and Brm−/− cells during cardiac differentiation displayed in the form of a heat map. b, Clusters b, c, and d were re-clustered and shown in a separate heat map (right). GREAT analysis of significant (Benjamini-Hochberg adjusted p-value (FDR) <0.01) GO biological processes (within 1Mb) enrichment for the clusters are on the right with representative genes shown. c, Heat map of significantly affected (FDR < 0.05, fold change 2) H3K27ac peaks due to loss of BRM at D4, D6 and D10 of differentiation. GREAT GO biological processes enriched (within 1mb) are shown to the right of the clusters. d, Number of regions significantly affected in Brm−/− cells at D4, D6 and D10 of differentiation are plotted over WT. e–g, GO biological processes enriched for genes (within 1mb) near sites that gained (upper panels) or reduced (lower panels) H3K27ac marks in Brm−/− cells at D4 (e), D6 (f) and D10 (g) of differentiation. h–j, Motifs enriched at the differentially enriched sites in Brm−/− cells are shown at D4 (h), D6 (i) and D10 (j) stages of cardiac differentiation respectively. k, Western blot of indicated proteins in WT or BRM KO cells during D0, D2, D4, D6 and D10 of cardiac differentiation.
Extended Data Fig. 6 BRM regulates REST binding during cardiac differentiation.
a–d, Genome browser (IGV) tracks showing BRM-3xFLAG ChIP–seq over indicated loci (a) and heat maps of BRM-3xFLAG ChIP–seq over identified BRM binding sites at D4 (b), D6 (c) and D10 (d) of differentiation. e–f, GO biological processes enriched (within 100kb) (e) and motifs enriched (f) in BRM binding sites at the indicated differentiation stages. g, Western blot of REST expression in WT or BRM KO cells during D0, D2, D4, D6 and D10 of cardiac differentiation h–i, Genome browser (IGV) tracks of Brm-3x FLAG ChIP–seq near neural related genes over indicated genomic loci and REST ChIP–seq in WT and Brm−/− cells at D4 (h) and D6 (i) of cardiac differentiation.
Extended Data Fig. 7 BMP4 restores WT-like chromatin accessibility in Brm−/− cells.
a, Scheme of cardiac differentiation showing timing of IAA and BMP4 addition. Cardiac troponin T (cTnT) immunostaining of an auxin inducible degron strain of BRM (Brm-AID) at D10 of differentiation induced with two different BMP4 concentrations with or without IAA present throughout the differentiation. b, Immunostaining with cTnT shows that Brg1 loss is not rescued by addition of increasing the amount of BMP4. Scale bars are 200 μm. c–e, Heat maps showing differential enrichment of ATAC-seq peaks of WT and BRM KO cells at D4 (c), D6(d) and D10 (e) of cardiac differentiation with normal (1x) and high (4x) BMP4 concentrations. Boxed regions show restoration of WT-like chromatin in KO cells at high BMP4 condition. Vertical lanes show replicate data. f–g, Browser tracks show chromatin accessibility in WT and Brm−/− cells along with H3K27ac marks in cardiomyocytes and neural precursor cells (purple track) near indicated cardiac genes (f) and neural genes (g).
Extended Data Fig. 8 BMP4 restore WT-like gene expression in Brm−/− cells and increases gene expression noise in D4 cells.
a, Dot plots showing quantitative changes in gene expression between WT and Brm−/− cells induced with normal (1x) or high (4x) BMP4 concentrations at D4, D6 and D10 stages of differentiation for early developmental, cardiac mesoderm, precursor, and myocyte genes enriched in BRM KO cells. b–d, Transcriptional trajectory analysis of WT and BRM KO cells showing the genotype representation in normal BMP4 concentration (b), normal BMP4 for WT and 4x BMP4 concentration for BRM KO cells (c) and URD feature plots of expression of Nkx2–5, and Actc1 (d). e, Western blots showing BMP receptor, Smad1 and phospho-SMAD expression during D0 to D4 of cardiac differentiation, f–g, Scatter plots of scRNA-seq data showing mean gene expression and variance from mean gene expression at D4 stage of differentiation for WT (f) and Brm−/− cells (g) in low and high BMP4 conditions. h–i, Signalling entropy calculated similarly for WT (h) and Brm−/− cells (i) with low and high BMP4 conditions.
Extended Data Fig. 9 Computational model using logic-based differential equations supports BRM’s role in cardiac and neural cell fate.
a, The model interaction graph including signalling components and transcription factors critical for cardiac differentiation. b–d, The model outputs determine the cell fate (b) and temporal variations in fractional cell population during cardiac differentiation for WT (c) and Brm−/− (d) cells. e–h, Model-predicted fractional activities of cardiac and neural transcription factors GATA4 (e), and FGF8 (f), as well as mediators of BRM POU3F1 (g) and REST (h) during cardiac differentiation. i–j, Model-predicted variations of quasi-potential landscape and subsequent path of WT (i) and Brm−/− (j) cells induced with different levels of BMP4 from normal (3.2 ng ml−1) to high (12.8 ng ml−1) during cardiac differentiation. k, Model simulation shows that Brm−/− cells (solid line) induced with high BMP4 at D3 (dotted line) would follow a path similar to that induced with D2 (dashed line) as computed from the GATA4 (red) and FGF8 (black) fractional activities, forming cardiomyocytes. Green line show fate variables with neural fate at 1 and cardiac fate at 0 and predicts D4 as the time of fate divergence. l, Phase portrait plots of bifurcation analysis of WT (upper panels) and BRM KO (lower panels) during indicated differentiation days. As differentiation progresses, WT cells undergo two sequential saddle-node bifurcations (V-> VRV* and VRV*-> V*) completing a hysteresis, while BRM KO cells undergo a saddle node bifurcation (V->VRV*) that reverses with a delay in differentiation timing (VRV*->V) with a dampened hysteresis. V = valley, R = ridge and V* = valley different from V.
Extended Data Fig. 10 BRG1 compensates for BRM loss in vivo.
a, Anti-FLAG affinity purification of BRG1- complex followed by mass spectrometry. BRG1 (bait protein) normalized peptide intensity ratios of Brm−/− (Brg1-3xFLAG;Brm−/−) over WT (Brg1-3x FLAG) are plotted at five different stages of differentiation (left panel) and Brm−/− cells at high BMP4 over normal BMP4 at MES, cardiac precursor (CP) and cardiomyocyte (CM) stages of differentiation (right panel). b, The exon–intron organization of Smarca2 (encodes BRM) and the site of guide RNA that targets exon 3. The mouse strain from this transfection had a 4 bp deletion leading to premature stop codon. c, Western blot with anti-BRM antibody showing loss of BRM protein in Brm−/− mouse brain whole cell extract. α-tubulin is used as a loading control. d, Heterozygous Brm mouse mating resulted in pups and embryos at expected mendelian ratios. e–f, Western blot with antibody against BRG1 shows partial BRG1 compensation in absence of BRM in adult mouse brain (upper panel) and heart (lower panel) with quantifications shown to the right (e), but no compensation in the in vitro cardiac differentiation system (f) g, E 8.5 mouse embryos stained with MEF2c or cardiac troponin T (cTnT) for the indicated genotypes. Scale bars are 200 μm.
Supplementary Fig. 1: raw immunoblots of Fig. 3, Extended Data Figs. 1, 4–6, 8 and 10. Supplementary Fig. 2: the gating strategy for FACS analysis using isotype control IgG or cardiac troponin T.
Supplementary Table 1
Differential gene expression between WT and homozygous Brm-KO cells at D4 of cardiac differentiation.
Supplementary Table 2
Marker genes enriched in the individual clusters of Fig. 1j consisting of both WT and Brm-KO cells at D4, D6 and D10 of cardiac differentiation.
Supplementary Table 3
Differential gene expression between WT and homozygous Brm-KO embryonic stem cells.
Supplementary Table 4
Differentially enriched ATAC-seq peaks between WT and homozygous Brm-KO cells at D4, D6 and D10 of cardiac differentiation.
Supplementary Table 5
BRM–3×Flag tag ChIP–seq peaks at D4, D6 and D10 of cardiac differentiation.
Supplementary Table 6
Marker genes enriched in the individual clusters of Fig. 3c (bottom) consisting of both WT and homozygous Brm-KO cells induced with normal and high BMP4 concentrations at D4, D6 and D10 of cardiac differentiation.
Supplementary Table 7
Quality control parameters for scRNA-seq data processing pipelines showing various quality control and cut-off parameters.
Supplementary Video 1
Video of WT cells showing beating CMs at D10 of differentiation.
Supplementary Video 2
Video of homozygous Brm-KO clone 1 cells at D10 of differentiation.
Supplementary Video 3
Video of homozygous Brm-KO clone 2 cells at D10 of differentiation.
Supplementary Video 4
Video of homozygous Brm-KO clone 3 cells at D10 of differentiation.
Supplementary Video 5
Video of heterozygous Brm-KO clone 1 cells at D10 of differentiation showing beating CMs.
Supplementary Video 6
Video of heterozygous Brm-KO clone 2 cells at D10 of differentiation showing beating CMs.
Supplementary Video 7
Quasi-Waddington diagram video of WT cells at normal BMP4 concentration from D0 to D10 of cardiac differentiation.
Supplementary Video 8
Quasi-Waddington diagram video of homozygous Brm-KO cells at normal BMP4 concentration from D0 to D10 of cardiac differentiation.
Supplementary Video 9
Quasi-Waddington diagram video of WT cells at high BMP4 concentration from D0 to D10 of cardiac differentiation.
Supplementary Video 10
Quasi-Waddington diagram video of homozygous Brm-KO cells at high BMP4 concentration from D0 to D10 of cardiac differentiation.
Supplementary Video 11
Bifurcation analysis of the model showing cell phase portraits from D0 to D10 for WT cells at normal BMP4 concentration.
Supplementary Video 12
Bifurcation analysis of the model showing cell phase portraits from D0 to D10 for homozygous Brm-KO cells at normal BMP4 concentration.
Rights and permissions
About this article
Cite this article
Hota, S.K., Rao, K.S., Blair, A.P. et al. Brahma safeguards canalization of cardiac mesoderm differentiation. Nature 602, 129–134 (2022). https://doi.org/10.1038/s41586-021-04336-y
This article is cited by
Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection
Genome Biology (2022)
Generating specificity in genome regulation through transcription factor sensitivity to chromatin
Nature Reviews Genetics (2022)
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.