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A fast chemical reprogramming system promotes cell identity transition through a diapause-like state

Nature Cell Biology volume 25, pages 1146–1156 (2023)Cite this article

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Abstract

Cellular reprogramming by only small molecules holds enormous potentials for regenerative medicine. However, chemical reprogramming remains a slow process and labour intensive, hindering its broad applications and the investigation of underlying molecular mechanisms. Here, through screening of over 21,000 conditions, we develop a fast chemical reprogramming (FCR) system, which significantly improves the kinetics of cell identity rewiring. We find that FCR rapidly goes through an interesting route for pluripotent reprogramming, uniquely transitioning through a developmentally diapause-like state. Furthermore, FCR critically enables comprehensive characterizations using multi-omics technologies, and has revealed unexpected important features including key regulatory factors and epigenetic dynamics. Particularly, activation of pluripotency-related endogenous retroviruses via inhibition of heterochromatin significantly enhances reprogramming. Our studies provide critical insights into how only environmental cues are sufficient to rapidly reinstate pluripotency in somatic cells, and make notable technical and conceptual advances for solving the puzzle of regeneration.

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Fig. 1: Large-scale small molecule screens for CR.
Fig. 2: Establish an FCR platform.
Fig. 3: FCR is transcriptionally dynamic and goes through a diapause-like state.
Fig. 4: scRNA-seq characterization and examination of diapause-like state in FCR.
Fig. 5: Genome-wide characterization of epigenetic dynamics during FCR.
Fig. 6: Epigenetic remodelling on iPS cell-related ERVs.

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Data availability

All sequencing data generated during this study are available from the Gene Expression Omnibus (GEO) under the accession ID GSE188834. It contains bulk RNA-seq data (GSE188830 and GSE220554), ATAC-seq data (GSE188824), CUT&Tag data (GSE188826), RRBS data (GSE188831) and scRNA-seq data (GSE218855). Previously published TFR RNA-seq datasets were downloaded from GEO: GSE102518 and GSE125644. The accession numbers of two CR RNA-seq datasets are GSE73631 and GSE110264. The accession number of the diapause ICM RNA-seq dataset is GSE143494. The TFR ATAC-seq dataset was downloaded from GEO: GSE101905. The mouse genome used in this study: mm10 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.20/) and GRCm39 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.27/). The screen data have been deposited on figshare (https://doi.org/10.6084/m9.figshare.21959552). Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank members of Zhu lab for helpful discussions. This work was supported by the grants from National Natural Science Foundation of China (nos. 32270781 and 31970818), the National Key Research and Development Program of China (no. 2016YFC1305300) and the Outstanding Youth Fund of Zhejiang Province (no. R17C120002).

Author information

Author notes

  1. These authors contributed equally: Xi Chen, Yunkun Lu.

Authors and Affiliations

  1. The Second Affiliated Hospital and Life Sciences Institute and School of Medicine, The MOE Key Laboratory of Biosystems Homeostasis and Protection and Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Zhejiang University, Hangzhou, China

    Xi Chen, Yunkun Lu, Xiaojie Ma, Jiaqi Pu, Lianyu Lin, Qian Deng, Yuhan Li, Yan Jin, Zhensheng Hu, Ziyu Zhou, Guo Chen, Liling Jiang & Saiyong Zhu

  2. State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Leyun Wang & Wei Li

  3. Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China

    Leyun Wang & Wei Li

  4. Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Childhealth, Hangzhou, China

    Jiaqi Pu & Junfen Fu

  5. Institute of Regenerative Medicine and Orthopedics, Institutes of Health Central Plain, Xinxiang Medical University, Xinxiang, China

    Weiyun Wang

  6. Hangzhou Women’s Hospital, Prenatal Diagnosis Center, Hangzhou, China

    Hao Wang

  7. State Key Laboratory of Organ Failure Research, Department of Developmental Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China

    Xiaoyang Zhao

  8. Life Sciences Institute, Zhejiang University, Hangzhou, China

    Xiangwei He

  9. Department of Pharmacology and Therapeutics, School of Medicine, University of Florida, Gainesville, FL, USA

    Holger A. Russ

  10. Diabetes Institute, School of Medicine, University of Florida, Gainesville, FL, USA

    Holger A. Russ

Authors
  1. Xi Chen
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Contributions

S.Z. and X.C. conceived and designed the project. X.C. performed the experiments. Y. Lu, X.C. and L.L. analysed the sequencing data. L.W. and W.L. performed the germline transmission experiment. X.M., J.P., Q.D., Y. Li, W.W., Y.J., Z.H., Z.Z., G.C., L.J., H.W., X.Z. and J.F. provided reagents and helped with experiments. X.H. and H.A.R. helped with discussing and editing the paper. S.Z., X.C. and Y. Lu wrote the paper with comments and contributions from all authors.

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Correspondence to Saiyong Zhu.

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Extended data

Extended Data Fig. 1 Concentration tests.

a–f, Concentration test results of each small molecule and growth factor in D0-2 (a), D2-4 (b), D4-6 (c), D6-8 (d), D8-10 (e), D10-12 (f). Data are mean ± SD, n = 3 biological replicates.

Source data

Extended Data Fig. 2 Characterization of FCR-generated iPSCs.

a, Histogram showing the targets of small molecules. b, iPSC colony number of samples that medium was fused. Data are mean ± SD, n = 3 biological replicates. **P < 0.01; ***P < 0.001 (two-tailed Student’s t test). P values were 0.0026, 0.0025, 0.00042 from left to right. c, Expression of Oct4, Sox2, Rex1 and Nanog. Data are mean ± SD, n = 3 biological replicates. d, Volcano plots of differentially expressed genes. Red, up-regulated genes in iPSC; blue, down-regulated genes in iPSC; R, Pearson correlation coefficient. e, DNA methylation pattern in promotor regions of Oct4 and Nanog. f, Karyotype analysis of iPSC (passage 8). g, k, o, r, Representative fields from the indicated cells. Scale bars, 200 μm. h, l, p, s, Representative fields from FCR day 12 of the indicated cells. Scale bars, 200 μm. i, m, Expression of Oct4. Scale bars, 200 μm. j, n, q, t, Morphologies and pluripotency gene expression of iPSC lines. Scale bars, 200 μm. u, Volcano plot of differentially expressed genes. Red, up-regulated genes in 129XOG2 iPSC; blue, down-regulated genes in 129XOG2 iPSC; R, Pearson correlation coefficient. v, Summary of reprogramming efficiencies. w, iPSC colony number of samples that each FCR small molecule was added in mouse TFR. C, CHIR99021; 6, 616452; F, Forskolin; A, AM580; E, EPZ004777; SR, SR11237; CD, CD3254; V, VPA; CCT, CCT129202; R, R406; D, 5-aza-dC; S, SGC0946; DMH, DMH1; BML, BML-277; AZD, AZD9291 mesylate; CBP, SGC-CBP30; Cit, Citarinostat; TMP, TMP269; WS, WS6; PD, PD0325901. Data are mean ± SD, n = 3 biological replicates. x, iPSC colony number of samples that each FCR small molecule was added in human TFR. Data are mean ± SD, n = 3 biological replicates.

Source data

Extended Data Fig. 3 Profile of FCR process.

a, Read coverage histograms representing the abundance of RNA, ATAC, H3K27ac, H3K4me3, H3K18la, H3K27me3, and H3K9me3 at the genomic loci of Acta2, Gata4, and Oct4. b, Spearman correlations between replicated sequencing samples (heatmaps of correlation matrices), including RNA-seq, ATAC-seq and CUT&Tag (H3K27ac, H3K4me3, H3K18la, H3K9me3 and H3K27me3). Each sequencing sample has 2 biological replicates.

Extended Data Fig. 4 Reprogramming trajectory of FCR.

a, Expression heatmap of 7,201 differentially expressed genes (DEGs, FC > 4 and maximal FPKM > 5), these genes were classified by K-means clustering and fell into 5 stages (left). Typical GO terms and representative genes of each stage (right). P values were calculated by one-tailed hypergeometric test. b, Line charts of representative pluripotency gene expression. c, Expression of Foxa2, Sox17, and Epcam on FCR day 4 as detected by immunofluorescence. Scale bars, 100 μm. d, PCA of normalized gene expression from all FCR samples and CR samples. PC1 and PC2 (left), PC1 and PC3 (right). Reprogramming trajectories were similar but not identical. e, Gene set enrichment analysis (GSEA) of transcriptomes from adjacent stages showed enrichment of the top 10 KEGG pathways (ranked by adjusted P values). P values were calculated by one-tailed hypergeometric test.

Source data

Extended Data Fig. 5 Diapause-like state is a feature unique to FCR.

a, Scatter plots showing gene expression of GO terms and KEGG pathways that significantly changed both in the comparison Diapause ICM/control ICM (x axis) and in D8/D4 (y axis). The gray dots represent all protein-coding genes. The blue dots represent genes in down-regulated terms (DNA replication-related genes: n = 212; Chromosome segregation-related genes: n = 273; Spliceosomal complex-related genes: n = 158; rRNA processing-related genes: n = 176; mRNA processing-related genes: n = 361; Ribosome biogenesis-related genes: n = 247) and the red dots represent genes in up-regulated terms (Lysosome-related genes: n = 120). The boxplots at the margins show the distributions of fold changes. b, Scatter plots showing gene expression of GO terms and KEGG pathways that significantly changed both in the comparison Diapause ICM/control ICM (x axis) and in D8/D4_CF1 (y axis). The gray dots represent all protein-coding genes. The blue dots represent genes in down-regulated terms (Cell cycle-related genes: n = 1,295; DNA replication-related genes: n = 212; Chromosome segregation-related genes: n = 273; RNA splicing-related genes: n = 302; Spliceosomal complex-related genes: n = 158; mRNA processing-related genes: n = 361; rRNA processing-related genes: n = 176; Translation-related genes: n = 489; Ribosome biogenesis-related genes: n = 247) and the red dots represent genes in up-regulated terms (Signaling receptor activity-related genes: n = 230; Lysosome-related genes: n = 120). The boxplots at the margins show the distributions of fold changes. c, Transcription heatmaps of genes involved in DNA replication, chromosome segregation, cell cycle, spliceosomal complex, RNA splicing, mRNA processing, rRNA processing, translation, and ribosome biogenesis. Heatmaps were generated by intra-group normalization of gene expression. In boxplots, boxes represent the median (bold black lines), first and third quartiles, whiskers represent ± 1.5 × interquartile range.

Extended Data Fig. 6 Cell fate transitions during FCR.

a, UMAP visualization of cell clustering of FCR scRNA-seq profiles. b, UMAP visualization of marker gene expression. Blue to grey gradients represent the normalized FPKM of each gene. c, Dot plot showing the expression pattern of fibroblast, mesenchymal cell, epithelial cell, extraembryonic endoderm (XEN), trophectoderm (TE), and pluripotency-related genes in FCR. d, Bar diagrams showing the average regulon activities of representative regulons in different cell types. n = 2 biological replicates. e, Pseudotime order of marker genes along FCR successful trajectory. The black lines represent the mean expression curve of each gene. f, PCA of D12 minus-one RNA-seq data. g-i, Bar diagrams of Oct4, Cdx2 and Sox7 expression in each minus-one RNA-seq datasets, n = 2 biological replicates. j, Expression on heatmap showing the K-means clustering of differentially expressed genes in each minus-one dataset (left), and top KEGG terms of each cluster (right), P value of each KEGG term was calculated by one-tailed hypergeometric test. k, Statistic analysis of FACS results representing EdU incorporation efficiency with the removal of PD0325901. Data are mean ± SD, n = 3 biological replicates. ***P < 0.001 (two-tailed Student’s t test). P value was 0.00058. l, Schematic diagram represents the roles of each small molecule in influencing cell fate determination at the late stage of FCR.

Source data

Extended Data Fig. 7 Dynamic changes of chromatin accessibility, histone modifications, and DNA methylation across FCR.

a, Bar diagrams showing the average ATAC, H3K27ac, H3K4me3, H3K18la, H3K27me3, and H3K9me3 peak abundance along FCR. n = 2 biological replicates. b, Bar diagrams showing the average H3K18la abundance at gained sites in D4 vs D0 (left). GO terms of genes related to these H3K18la_gain sites (right). n = 2 biological replicates. c, Bar diagrams showing the average H3K27me3 abundance at lost sites in D6 vs D0 (left). GO terms of genes related to these H3K27me3_loss sites (right). n = 2 biological replicates. d, Bar diagrams showing the average H3K4me3 abundance at gained sites in iPSC vs D8 (left). GO terms of genes related to these H3K4me3_gain sites (right). n = 2 biological replicates. e, Distribution of low (< 2%), middle (2% - 90%), and high (> 90%) methylation sites on promoters (2-kb around the TSS). Line chart represents average methylation levels. f, Bar diagrams showing the average methylation levels of demethylated promoters in D12 vs D4 (left). GO terms of genes related to these demethylated promoters (right). n = 2 biological replicates. In GO enrichment analysis, P values were calculated by one-tailed hypergeometric test.

Extended Data Fig. 8 Global analysis of chromatin accessibility and histone modifications across FCR.

a, Histograms of ATAC dynamic peak numbers along FCR (left) and TFR (right). Blue: open-to-close loci; red: close-to-open loci. b, Stacked histograms showing the numbers of H3K27ac, H3K4me3, H3K18la, H3K27me3 and H3K9me3 differential peaks and differentially methylated sites (DMSs) along FCR. Blue: down-regulated peaks or DMSs; red: up-regulated peaks or DMSs. c, Venn diagrams of active histone modification peaks along FCR. d, Venn diagrams of repressive histone modification peaks along FCR.

Extended Data Fig. 9 Distributions of histone modifications.

a, Circos (from outer to inner) displays chromosomes (grey), the distributions of protein-coding genes (green), the distributions of H3K9me3 (red), and the distributions of LTR (blue), respectively. Yellow boxes show the representative regions. b, d, f, h, Density plots showing the distance between H3K27ac, H3K18la, H3K4me3, H3K27me3 peaks, and protein-coding genes or LTRs. c, e, g, i, Bar diagrams representing the average distance between H3K27ac, H3K18la, H3K4me3, H3K27me3 peaks and protein-coding genes or LTRs. n = 2 biological replicates. j, Bar diagrams showing the average H3K27ac, H3K18la, H3K4me3 and H3K27me3 abundance in regions of LTR, LINE and SINE.

Extended Data Fig. 10 Schematic diagram.

The development of FCR. In this study, we carried out large-scale chemical screenings, and identified a series of small molecules that can effectively promote chemical reprogramming, and these improvements together amount to the FCR system. Furthermore, we applied genome-wide technologies to profile the FCR process in details, and comprehensively investigated reprogramming trajectory, molecular events, and epigenetic dynamics during FCR, providing rich knowledge about the cell-fate transition.

Supplementary information

Supplementary Information

Supplementary Fig. 1. An example of FACS gating strategy, relative to Fig. 3i.

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Supplementary Tables

Supplementary Table 1: Key resources. Supplementary Table 2: Small molecule screening data. Supplementary Table 3: Small molecule screening data. Supplementary Table 4: Summary of oligonucleotides. Supplementary Table 5: Summary of shRNAs and sgRNAs.

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Chen, X., Lu, Y., Wang, L. et al. A fast chemical reprogramming system promotes cell identity transition through a diapause-like state. Nat Cell Biol 25, 1146–1156 (2023). https://doi.org/10.1038/s41556-023-01193-x

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