Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics

Nature Methods volume 20, pages 1368–1378 (2023)Cite this article

Subjects

Abstract

Gene regulatory networks (GRNs) are key determinants of cell function and identity and are dynamically rewired during development and disease. Despite decades of advancement, challenges remain in GRN inference, including dynamic rewiring, causal inference, feedback loop modeling and context specificity. To address these challenges, we develop Dictys, a dynamic GRN inference and analysis method that leverages multiomic single-cell assays of chromatin accessibility and gene expression, context-specific transcription factor footprinting, stochastic process network and efficient probabilistic modeling of single-cell RNA-sequencing read counts. Dictys improves GRN reconstruction accuracy and reproducibility and enables the inference and comparative analysis of context-specific and dynamic GRNs across developmental contexts. Dictys’ network analyses recover unique insights in human blood and mouse skin development with cell-type-specific and dynamic GRNs. Its dynamic network visualizations enable time-resolved discovery and investigation of developmental driver transcription factors and their regulated targets. Dictys is available as a free, open-source and user-friendly Python package.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: An overview of Dictys to reconstruct and analyze context-specific and dynamic GRNs from single-cell transcriptome and chromatin accessibility profiles.
Fig. 2: Dictys uncovers cell-type-specific GRNs, novel and established TFs and regulation in human hematopoiesis.
Fig. 3: Dictys refines GRN inference with multiomic data from mouse skin.
Fig. 4: Five quantitative evaluations demonstrated superior performance from Dictys over existing methods.
Fig. 5: Dynamic GRN reconstruction, TF and regulation discovery and time-resolved investigation using Dictys.

Similar content being viewed by others

Data availability

This study used the following databases and data files: ENCODE blacklist genome regions for hg19 (https://www.encodeproject.org/annotations/ENCSR636HFF/), the Cistrome database (http://cistrome.org), the HOCOMOCO motif database (https://hocomoco11.autosome.org), KnockTF (http://www.licpathway.net/KnockTF/browse.php), GTEx (https://www.gtexportal.org) and the UK Biobank GWAS results (http://www.nealelab.is/uk-biobank). This study used the following published datasets: human blood scRNA-seq and scATAC-seq (GSE139369), mouse skin SHARE-seq (GSE140203), HiC in erythroid cells (https://osf.io/u8tzp/) and Cebpa-WT and Cebpa-KO mouse datasets (GSE89767 and supplementary information from Theilgaard-Mönch et al.66). The reconstructed cell-type-specific and dynamic GRNs and the tutorial data for Dictys are available at Wang et al.87 and the Dictys homepage (https://github.com/pinellolab/dictys). Source data are provided with this paper.

Code availability

Dictys is publicly available at https://github.com/pinellolab/dictys. All the analyses presented in this manuscript were produced with Dictys versions 0.1.0 (initial submission) and 0.1.1 (revisions), both deposited in Zenodo88,89.

References

  1. Barabási, A.-L. & Oltvai, Z. N. Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5, 101–113 (2004).

    Article  PubMed  Google Scholar 

  2. Friedman, N. Inferring cellular networks using probabilistic graphical models. Science 303, 799–805 (2004).

    Article  CAS  PubMed  Google Scholar 

  3. Karlebach, G. & Shamir, R. Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9, 770–780 (2008).

    Article  CAS  PubMed  Google Scholar 

  4. Sanguinetti, G. & Huynh-Thu, V. A. (eds) Gene Regulatory Networks (Springer New York, 2019).

  5. Schlitt, T. & Brazma, A. Current approaches to gene regulatory network modelling. BMC Bioinformatics 8, S9 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Levine, M. & Davidson, E. H. Gene regulatory networks for development. Proc. Natl. Acad. Sci. USA. 102, 4936–4942 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Basso, K. et al. Reverse engineering of regulatory networks in human B cells. Nat. Genet. 37, 382–390 (2005).

    Article  CAS  PubMed  Google Scholar 

  8. Scutari, M. Learning Bayesian networks with thebnlearnRPackage. J. Stat. Softw. 35, 1–22 (2010).

  9. Huynh-Thu, V. A., Irrthum, A., Wehenkel, L. & Geurts, P. Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5, e12776 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Zhu, J. et al. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat. Genet. 40, 854–861 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wang, L. & Michoel, T. Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data. PLoS Comput. Biol. 13, e1005703 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Goode, D. K. et al. Dynamic gene regulatory networks drive hematopoietic specification and differentiation. Dev. Cell 36, 572–587 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Trasanidis, N. et al. Systems medicine dissection of chr1q-amp reveals a novel PBX1–FOXM1 axis for targeted therapy in multiple myeloma. Blood 139, 1939–1953 (2022).

    Article  CAS  PubMed  Google Scholar 

  14. Thompson, D., Regev, A. & Roy, S. Comparative analysis of gene regulatory networks: from network reconstruction to evolution. Annu. Rev. Cell Dev. Biol. 31, 399–428 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Ramirez, R. N. et al. Dynamic gene regulatory networks of human myeloid differentiation. Cell Syst. 4, 416–429 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kamimoto, K. et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kartha, V. K. et al. Functional inference of gene regulation using single-cell multi-omics. Cell Genom. 2, 100166 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Fleck, J. S. et al. Inferring and perturbing cell fate regulomes in human brain organoids. Nature https://doi.org/10.1038/s41586-022-05279-8 (2022).

  20. Jiang, Y. et al. Nonparametric single-cell multiomic characterization of trio relationships between transcription factors, target genes, and cis-regulatory regions. Cell Syst. 13, 737–751 (2022).

    Article  CAS  PubMed  Google Scholar 

  21. Cao, Y., Kitanovski, S., Küppers, R. & Hoffmann, D. UMI or not UMI, that is the question for scRNA-seq zero-inflation. Nature Biotechnol. 39, 158–159 (2021).

    Article  CAS  Google Scholar 

  22. Pearl, J. Causality (Cambridge University Press, 2009).

  23. Wang, L. Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr. Nat. Commun. 12, 6395 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183, 1103–1116 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhu, C. et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat. Struct. Mol. Biol. 26, 1063–1070 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Single Cell Multiome ATAC + Gene Expression. 10x Genomics https://www.10xgenomics.com/products/single-cell-multiome-atac-plus-gene-expression (accessed Dec 2022).

  27. Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Piper, J. et al. Wellington: a novel method for the accurate identification of digital genomic footprints from DNase-seq data. Nucleic Acids Res. 41, e201 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Gusmao, E. G., Allhoff, M., Zenke, M. & Costa, I. G. Analysis of computational footprinting methods for DNase sequencing experiments. Nat. Methods 13, 303–309 (2016).

    Article  PubMed  Google Scholar 

  30. Sung, M.-H., Baek, S. & Hager, G. L. Genome-wide footprinting: ready for prime time? Nat. Methods 13, 222–228 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Vierstra, J. & Stamatoyannopoulos, J. A. Genomic footprinting. Nat. Methods 13, 213–221 (2016).

    Article  CAS  PubMed  Google Scholar 

  32. Svensson, V. Droplet scRNA-seq is not zero-inflated. Nat. Biotechnol. 38, 147–150 (2020).

    Article  CAS  PubMed  Google Scholar 

  33. Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yokomizo, T. et al. Hlf marks the developmental pathway for hematopoietic stem cells but not for erythro-myeloid progenitors. J. Exp. Med. 216, 1599–1614 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Wickrema, A. & Crispino, J. D. Erythroid and megakaryocytic transformation. Oncogene 26, 6803–6815 (2007).

    Article  CAS  PubMed  Google Scholar 

  36. Wu, Y. et al. Highly efficient therapeutic gene editing of human hematopoietic stem cells. Nat. Med. 25, 776–783 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ma, O., Hong, S., Guo, H., Ghiaur, G. & Friedman, A. D. Granulopoiesis requires increased C/EBPα compared to monopoiesis, correlated with elevated CEBPA in immature G-CSF receptor versus M-CSF receptor expressing cells. PLoS ONE 9, e95784 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Friedman, A. D. C/EBPα in normal and malignant myelopoiesis. Int. J. Hematol. 101, 330–341 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Pridans, C. et al. Identification of PAX5 target genes in early B cell differentiation. J. Immunol. 180, 1719–1728 (2008).

    Article  CAS  PubMed  Google Scholar 

  40. Holmes, M. L., Pridans, C. & Nutt, S. L. The regulation of the B-cell gene expression programme by PAX5. Immunol. Cell Biol. 86, 47–53 (2008).

    Article  CAS  PubMed  Google Scholar 

  41. Walker, P. S. & Reid, M. E. The Gerbich blood group system: a review. Immunohematology 26, 60–65 (2010).

    Article  CAS  PubMed  Google Scholar 

  42. Oldenborg, P. A. et al. Role of CD47 as a marker of self on red blood cells. Science 288, 2051–2054 (2000).

    Article  CAS  PubMed  Google Scholar 

  43. Laurenti, E. et al. Hematopoietic stem cell function and survival depend on c-Myc and N-Myc activity. Cell Stem Cell 3, 611–624 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Park, C. S., Lewis, A., Chen, T. & Lacorazza, D. Concise review: regulation of self-renewal in normal and malignant hematopoietic stem cells by Krüppel-like factor 4. Stem Cells Transl. Med. 8, 568–574 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Chen, R.-L., Chou, Y.-C., Lan, Y.-J., Huang, T.-S. & Shen, C.-K. J. Developmental silencing of human ζ-globin gene expression is mediated by the transcriptional repressor RREB1. J. Biol. Chem. 285, 10189–10197 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Cheng, C. K. et al. Helicase-like transcription factor is a RUNX1 target whose downregulation promotes genomic instability and correlates with complex cytogenetic features in acute myeloid leukemia. Haematologica 101, 448–457 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Javierre, B. M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Huang, J. et al. Dynamic control of enhancer repertoires drives lineage and stage-specific transcription during hematopoiesis. Dev. Cell 36, 9–23 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wu, S. et al. Single-cell transcriptomics reveals lineage trajectory of human scalp hair follicle and informs mechanisms of hair graying. Cell Discov. 8, 49 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Wu, B. et al. R164C mutation in FOXQ1 H3 domain affects formation of the hair medulla. Exp. Dermatol. 22, 234–236 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ecoeur, F. et al. Antagonizing retinoic acid-related-orphan receptor γ activity blocks the T helper 17/interleukin-17 pathway leading to attenuated pro-inflammatory human keratinocyte and skin responses. Front. Immunol. 10, 577 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wells, J. et al. Ovol2 suppresses cell cycling and terminal differentiation of keratinocytes by directly repressing c-Myc and Notch1. J. Biol. Chem. 284, 29125–29135 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Dai, X. et al. STAT5a/PPARγ pathway regulates involucrin expression in keratinocyte differentiation. J. Invest. Dermatol. 127, 1728–1735 (2007).

    Article  CAS  PubMed  Google Scholar 

  54. Ambler, C. A. & Watt, F. M. Expression of Notch pathway genes in mammalian epidermis and modulation by β-catenin. Dev. Dyn. 236, 1595–1601 (2007).

    Article  CAS  PubMed  Google Scholar 

  55. Mehic, D., Bakiri, L., Ghannadan, M., Wagner, E. F. & Tschachler, E. Fos and Jun proteins are specifically expressed during differentiation of human keratinocytes. J. Invest. Dermatol. 124, 212–220 (2005).

    Article  CAS  PubMed  Google Scholar 

  56. Contreras-Jurado, C., Lorz, C., García-Serrano, L., Paramio, J. M. & Aranda, A. Thyroid hormone signaling controls hair follicle stem cell function. Mol. Biol. Cell 26, 1263–1272 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Vidal, V. P. I. et al. Sox9 is essential for outer root sheath differentiation and the formation of the hair stem cell compartment. Curr. Biol. 15, 1340–1351 (2005).

    Article  CAS  PubMed  Google Scholar 

  58. Chang, C.-Y. et al. NFIB is a governor of epithelial–melanocyte stem cell behaviour in a shared niche. Nature 495, 98–102 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Chen, H. et al. Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nat. Commun. 10, 1903 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Kawaida, R. et al. Jun dimerization protein 2 (JDP2), a member of the AP-1 family of transcription factor, mediates osteoclast differentiation induced by RANKL. J. Exp. Med. 197, 1029–1035 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Kurotaki, D., Sasaki, H. & Tamura, T. Transcriptional control of monocyte and macrophage development. Int. Immunol 29, 97–107 (2017).

    Article  CAS  PubMed  Google Scholar 

  63. Chung, S. et al. Distinct role of FoxO1 in M-CSF- and GM-CSF-differentiated macrophages contributes LPS-mediated IL-10: implication in hyperglycemia. J. Leukoc. Biol. 97, 327–339 (2015).

    Article  PubMed  Google Scholar 

  64. Kurotaki, D. et al. Essential role of the IRF8–KLF4 transcription factor cascade in murine monocyte differentiation. Blood 121, 1839–1849 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. AbuSamra, D. B. et al. Not just a marker: CD34 on human hematopoietic stem/progenitor cells dominates vascular selectin binding along with CD44. Blood Adv. 1, 2799–2816 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Theilgaard-Mönch, K. et al. Transcription factor-driven coordination of cell cycle exit and lineage-specification in vivo during granulocytic differentiation. Nat. Commun. 13, 3595 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Pundhir, S. et al. Enhancer and transcription factor dynamics during myeloid differentiation reveal an early differentiation block in Cebpa null progenitors. Cell Rep. 23, 2744–2757 (2018).

    Article  CAS  PubMed  Google Scholar 

  68. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Ruuth, M. et al. USF1 deficiency alleviates inflammation, enhances cholesterol efflux and prevents cholesterol accumulation in macrophages. Lipids Health Dis. 17, 285 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Dengler, H. S. et al. Distinct functions for the transcription factor Foxo1 at various stages of B cell differentiation. Nat. Immunol. 9, 1388–1398 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Piper, J. et al. Wellington-bootstrap: differential DNase-seq footprinting identifies cell-type determining transcription factors. BMC Genomics 16, 1000 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Uhlenbeck, G. E. & Ornstein, L. S. On the theory of the Brownian motion. Phys. Rev. 36, 823–841 (1930).

    Article  CAS  Google Scholar 

  74. Risken, H. in The Fokker–Planck Equation 63–95 (Springer Berlin Heidelberg, 1996).

  75. Bingham, E. et al. Pyro: deep universal probabilistic programming. J. Mach. Learn. Res. 20, 1–6 (2019).

    Google Scholar 

  76. Zheng, R. et al. Cistrome Data Browser: expanded datasets and new tools for gene regulatory analysis. Nucleic Acids Res. 47, D729–D735 (2019).

    Article  CAS  PubMed  Google Scholar 

  77. Kulakovskiy, I. V. et al. HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP–seq analysis. Nucleic Acids Res. 46, D252–D259 (2018).

    Article  CAS  PubMed  Google Scholar 

  78. Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71, 858–871 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Van Rijsbergen, C. J. Information Retrieval. (Butterworths, 1979).

  81. Dodd, L. E. & Pepe, M. S. Partial AUC estimation and regression. Biometrics 59, 614–623 (2003).

    Article  PubMed  Google Scholar 

  82. Cappellato, M., Baruzzo, G. & Di Camillo, B. Investigating differential abundance methods in microbiome data: a benchmark study. PLoS Comput. Biol. 18, e1010467 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A. & Murali, T. M. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat. Methods 17, 147–154 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796–804 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Feng, C. et al. KnockTF: a comprehensive human gene expression profile database with knockdown/knockout of transcription factors. Nucleic Acids Res. 48, D93–D100 (2020).

    Article  CAS  PubMed  Google Scholar 

  86. The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  PubMed Central  Google Scholar 

  87. Wang, L., Trasanidis, N. & Pinello, L. Tutorial datasets for Dictys. Zenodo https://doi.org/10.5281/zenodo.6787658 (2022).

  88. Wang, L. pinellolab/dictys: 0.1.0. Zenodo https://doi.org/10.5281/zenodo.7072041 (2022).

  89. Wang, L. pinellolab/dictys: v0.1.1. Zenodo https://doi.org/10.5281/ZENODO.7659460 (2023).

Download references

Acknowledgements

We wish to thank J. Buenrostro, H. Chen, Y. Hu, V. Kartha and Q. Qin for helpful discussions. This study was supported by US NIH R35 HG010717 (L.P.) and the European Hematology Association Research mobility grant award WIIH.NEP018 (N.T.). We thank the members of the Pinello lab for helpful discussions.

Author information

Author notes

  1. These authors contributed equally: Lingfei Wang, Nikolaos Trasanidis.

Authors and Affiliations

  1. Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA

    Lingfei Wang, Nikolaos Trasanidis, Guanlan Dong, Michael Hu & Luca Pinello

  2. Gene Regulation Observatory, The Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Lingfei Wang, Daniel E. Bauer & Luca Pinello

  3. Hugh and Josseline Langmuir Centre for Myeloma Research, Centre for Haematology, Department of Immunology and Inflammation, Imperial College London, London, UK

    Nikolaos Trasanidis

  4. Division of Hematology/Oncology, Boston Children’s Hospital, Department of Pediatric Oncology, Dana–Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, MA, USA

    Ting Wu & Daniel E. Bauer

  5. Division of Genetics and Genomics, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Bioinformatics and Integrative Genomics PhD Program, Harvard Medical School, Boston, MA, USA

    Guanlan Dong

Authors
  1. Lingfei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nikolaos Trasanidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ting Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guanlan Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Daniel E. Bauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Luca Pinello
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.W., N.T. and L.P. conceived the study. N.T. developed TF binding network functions. L.W. developed the stochastic process model, network analysis and dynamic network. G.D. developed the CellOracle benchmarking interface. M.H. developed the dynamic network layout. N.T., L.W. and T.W. analyzed and interpreted biological data. L.W. and N.T. performed benchmarking. L.P. and D.E.B. supervised the study and provided funding. L.W., N.T. and L.P. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Luca Pinello.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Ivan Costa and Tim Stuart for their contribution to the peer review of this work. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Notes and Figs. 1–23.

Reporting Summary

Peer Review File

Supplementary Video 1

Movie visualization and analysis of dynamic GRNs for the monocyte lineage. Top left, dynamic cell tracking highlighting those used for GRN reconstruction. Top right, dynamic differential analysis (as per Fig. 2d). Other panels (left to right) show the dynamic expression level, dynamic regulatory activity, dynamic regulation strength and dynamic GRN subnetwork (as per Fig. 2f). The rows show TFs coarsely grouped into different waves of regulation (one per row) across the developmental continuum.

Supplementary Video 2

Movie visualization and analysis of dynamic GRNs for the erythroid lineage. Top left, dynamic cell tracking highlighting those used for GRN reconstruction. Top right, dynamic differential analysis (as per Fig. 2d). Other panels (left to right) show the dynamic expression level, dynamic regulatory activity, dynamic regulation strength and dynamic GRN subnetwork (as per Fig. 2f). The rows show TFs coarsely grouped into different waves of regulation (one per row) across the developmental continuum.

Supplementary Video 3

Movie visualization and analysis of dynamic GRNs for the B cell lineage. Top left, dynamic cell tracking highlighting those used for GRN reconstruction. Top right, dynamic differential analysis (as per Fig. 2d). Other panels (left to right) show the dynamic expression level, dynamic regulatory activity, dynamic regulation strength and dynamic GRN subnetwork (as per Fig. 2f). The rows show TFs coarsely grouped into different waves of regulation (one per row) across the developmental continuum.

Supplementary Table 1

Supplementary Tables 1–4.

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, L., Trasanidis, N., Wu, T. et al. Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics. Nat Methods 20, 1368–1378 (2023). https://doi.org/10.1038/s41592-023-01971-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-023-01971-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing