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Cited by 136Bioinformatics and Genomic NetworksSingle-cell and spatial transcriptomicsGene Regulatory Network Analysis

CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data

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Hu, Yuxuan, Peng, Tao, Gao, Lin, Tan, Kai·2021·Science Advances
DOI 10.1126/sciadv.abf1356PMC PMC8046375

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Abstract

Abstract Single-cell technology has opened the door for studying signal transduction in a complex tissue at unprecedented resolution. However, there is a lack of analytical methods for de novo construction of signal transduction pathways using single-cell omics data. Here we present CytoTalk, a computational method for de novo constructing cell type-specific signal transduction networks using single-cell RNA-Seq data. CytoTalk first constructs intracellular and intercellular gene-gene interaction networks using an information-theoretic measure between two cell types. Candidate signal transduction pathways in the integrated network are identified using the prize-collecting Steiner forest algorithm. We applied CytoTalk to a single-cell RNA-Seq data set on mouse visual cortex and evaluated predictions using high-throughput spatial transcriptomics data generated from the same tissue. Compared to published methods, genes in our inferred signaling pathways have significantly higher spatial expression correlation only in cells that are spatially closer to each other, suggesting improved accuracy of CytoTalk. Furthermore, using single-cell RNA-Seq data with receptor gene perturbation, we found that predicted pathways are enriched for differentially expressed genes between the receptor knockout and wild type cells, further validating the accuracy of CytoTalk. In summary, CytoTalk enables de novo construction of signal transduction pathways and facilitates comparative analysis of these pathways across tissues and conditions.

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  • Single-cell RNA sequencing data enables cell type-specific trans-regulation inference through co-expression analysis.

    “Single-cell RNA sequencing (scRNA-seq) data enables cell type-specific trans-regulation inference through co-expression analysis such as PIDC and SCENIC21–30.”

    From Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data (2025) · cites this paper as [26]

    The Materials and Methods section (“Construction of intracellular functional gene interaction network”; page unavailable) says CytoTalk builds a coexpression network for each cell type from single-cell data. But the paper uses those networks to infer signal-transduction pathways between cell types; the cited passages do not establish cell type-specific trans-regulatory inference or describe PIDC or SCENIC. (LLM, conf=0.83)

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  1. [1]D. A. Skelly, G. T. Squiers, M. A. McLellan, M. T. Bolisetty, P. Robson, N. A. Rosenthal, A. R. Pinto, Single-cell transcriptional profiling reveals cellular diversity and intercommunication in the mouse heart. Cell Rep. 22, 600– 610 (2018). 29346760
  2. [2]M. P. Kumar, J. Du, G. Lagoudas, Y. Jiao, A. Sawyer, D. C. Drummond, D. A. Lauffenburger, A. Raue, Analysis of single-cell RNA-seq identifies cell-cell communication associated with tumor characteristics. Cell Rep. 25, 1458– 1468.e4 (2018). 30404002 PMC7009724
  3. [4]R. Vento-Tormo, M. Efremova, R. A. Botting, M. Y. Turco, M. Vento-Tormo, K. B. Meyer, J.-E. Park, E. Stephenson, K. Polanski, A. Goncalves, L. Gardner, S. Holmqvist, J. Henriksson, A. Zou, A. M. Sharkey, B. Millar, B. Innes, L. Wood, A. Wilbrey-Clark, R. P. Payne, M. A. Ivarsson, S. Lisgo, A. Filby, D. H. Rowitch, J. N. Bulmer, G. J. Wright, M. J. T. Stubbington, M. Haniffa, A. Moffett, S. A. Teichmann, Single-cell reconstruction of the early maternal-fetal interface in humans. Nature 563, 347– 353 (2018). 30429548 PMC7612850
  4. [5]Y. E. Antebi, N. Nandagopal, M. B. Elowitz, An operational view of intercellular signaling pathways. Curr. Opin. Syst. Biol. 1, 16– 24 (2017). 29104946 PMC5665397
  5. [6]M. Billmann, V. Chaudhary, M. F. ElMaghraby, B. Fischer, M. Boutros, Widespread rewiring of genetic networks upon cancer signaling pathway activation. Cell Syst. 6, 52– 64.e4 (2018). 29199019 PMC5791663
  6. [7]S. Wang, M. Karikomi, A. L. MacLean, Q. Nie, Cell lineage and communication network inference via optimization for single-cell transcriptomics. Nucleic Acids Res. 47, e66 (2019). 30923815 PMC6582411
  7. [8]R. Browaeys, W. Saelens, Y. Saeys, NicheNet: Modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159– 162 (2019). 31819264
  8. [9]L. H. Gallo, K. N. Nelson, A. N. Meyer, D. J. Donoghue, Functions of fibroblast growth factor receptors in cancer defined by novel translocations and mutations. Cytokine Growth Factor Rev. 26, 425– 449 (2015). 26003532
  9. [10]X. Zhang, D. Martinez, Z. Koledova, G. Qiao, C. H. Streuli, P. Lu, FGF ligands of the postnatal mammary stroma regulate distinct aspects of epithelial morphogenesis. Development 141, 3352– 3362 (2014). 25078648 PMC4199126
  10. [11]K. Polyak, R. Kalluri, The role of the microenvironment in mammary gland development and cancer. Cold Spring Harb. Perspect. Biol. 2, a003244 (2010). 20591988 PMC2964182
  11. [12]Y.-C. Hsu, L. Li, E. Fuchs, Emerging interactions between skin stem cells and their niches. Nat. Med. 20, 847– 856 (2014). 25100530 PMC4358898
  12. [14]L. Huminiecki, A. T. Lloyd, K. H. Wolfe, Congruence of tissue expression profiles from Gene Expression Atlas, SAGEmap and TissueInfo databases. BMC Genomics 4, 31 (2003). 12885301 PMC183867
  13. [15]N. Kryuchkova-Mostacci, M. Robinson-Rechavi, A benchmark of gene expression tissue-specificity metrics. Brief. Bioinform. 18, 205– 214 (2017). 26891983 PMC5444245
  14. [16]B. Jassal, L. Matthews, G. Viteri, C. Gong, P. Lorente, A. Fabregat, K. Sidiropoulos, J. Cook, M. Gillespie, R. Haw, F. Loney, B. May, M. Milacic, K. Rothfels, C. Sevilla, V. Shamovsky, S. Shorser, T. Varusai, J. Weiser, G. Wu, L. Stein, H. Hermjakob, P. D’Eustachio, The reactome pathway knowledgebase. Nucleic Acids Res. 48, D498– D503 (2019). PMC7145712 31691815
  15. [17]A. H. Bild, G. Yao, J. T. Chang, Q. Wang, A. Potti, D. Chasse, M.-B. Joshi, D. Harpole, J. M. Lancaster, A. Berchuck, J. A. Olson Jr., J. R. Marks, H. K. Dressman, M. West, J. R. Nevins, Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439, 353– 357 (2006). 16273092
  16. [18]N. Tuncbag, A. Braunstein, A. Pagnani, S.-S. C. Huang, J. Chayes, C. Borgs, R. Zecchina, E. Fraenkel, Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem. J. Comput. Biol. 20, 124– 136 (2013). 23383998 PMC3576906
  17. [19]A. Gupta, J. Könemann, S. Leonardi, R. Ravi, G. Schäfer, An efficient cost-sharing mechanism for the prize-collecting Steiner forest problem, in SODA ‘07 Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA, 2007), pp. 1153–1162.
  18. [22]A.-Q. Chen, Z. Fang, X.-L. Chen, S. Yang, Y.-F. Zhou, L. Mao, Y.-P. Xia, H.-J. Jin, Y.-N. Li, M.-F. You, X.-X. Wang, H. Lei, Q.-W. He, B. Hu, Microglia-derived TNF-α mediates endothelial necroptosis aggravating blood brain–barrier disruption after ischemic stroke. Cell Death Dis. 10, 487 (2019). 31221990 PMC6586814
  19. [26]J. A. Stogsdill, J. Ramirez, D. Liu, Y. H. Kim, K. T. Baldwin, E. Enustun, T. Ejikeme, R.-R. Ji, C. Eroglu, Astrocytic neuroligins control astrocyte morphogenesis and synaptogenesis. Nature 551, 192– 197 (2017). 29120426 PMC5796651
  20. [27]M. Segarra, M. R. Aburto, J. Hefendehl, A. Acker-Palmer, Neurovascular interactions in the nervous system. Annu. Rev. Cell Dev. Biol. 35, 615– 635 (2019). 31590587
  21. [28]B. Pósfai, C. Cserép, B. Orsolits, Á. Dénes, New insights into microglia–neuron interactions: A neuron’s perspective. Neuroscience 405, 103– 117 (2019). 29753862
  22. [30]P. Sherchan, L. Huang, O. Akyol, C. Reis, J. Tang, J. H. Zhang, Recombinant Slit2 reduces surgical brain injury induced blood brain barrier disruption via Robo4 dependent Rac1 activation in a rodent model. Sci. Rep. 7, 746 (2017). 28389649 PMC5429690
  23. [31]A. T. Argaw, L. Asp, J. Zhang, K. Navrazhina, T. Pham, J. N. Mariani, S. Mahase, D. J. Dutta, J. Seto, E. G. Kramer, N. Ferrara, M. V. Sofroniew, G. R. John, Astrocyte-derived VEGF-A drives blood-brain barrier disruption in CNS inflammatory disease. J. Clin. Invest. 122, 2454– 2468 (2012). 22653056 PMC3386814
  24. [32]C.-H. L. Eng, M. Lawson, Q. Zhu, R. Dries, N. Koulena, Y. Takei, J. Yun, C. Cronin, C. Karp, G.-C. Yuan, L. Cai, Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568, 235– 239 (2019). 30911168 PMC6544023
  25. [33]M. Kanehisa, M. Furumichi, M. Tanabe, Y. Sato, K. Morishima, KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353– D361 (2017). 27899662 PMC5210567
  26. [34]M. Cohen, A. Giladi, A.-D. Gorki, D. G. Solodkin, M. Zada, A. Hladik, A. Miklosi, T.-M. Salame, K. B. Halpern, E. David, S. Itzkovitz, T. Harkany, S. Knapp, I. Amit, Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting. Cell 175, 1031– 1044.e18 (2018). 30318149
  27. [35]G. E. Hernandez, M. L. Iruela-Arispe, The many flavors of monocyte/macrophage--endothelial cell interactions. Curr. Opin. Hematol. 27, 181– 189 (2020). 32167947 PMC7717488
  28. [36]J. Kalucka, L. Bierhansl, B. Wielockx, P. Carmeliet, G. Eelen, Interaction of endothelial cells with macrophages—Linking molecular and metabolic signaling. Pflügers Arch. 469, 473– 483 (2017). 28236120
  29. [38]A. E. Teschendorff, P. Sollich, R. Kuehn, Signalling entropy: A novel network-theoretical framework for systems analysis and interpretation of functional omic data. Methods 67, 282– 293 (2014). 24675401
  30. [39]M. Cardoso-Moreira, J. Halbert, D. Valloton, B. Velten, C. Chen, Y. Shao, A. Liechti, K. Ascenção, C. Rummel, S. Ovchinnikova, P. V. Mazin, I. Xenarios, K. Harshman, M. Mort, D. N. Cooper, C. Sandi, M. J. Soares, P. G. Ferreira, S. Afonso, M. Carneiro, J. M. A. Turner, J. L. Vande Berg, A. Fallahshahroudi, P. Jensen, R. Behr, S. Lisgo, S. Lindsay, P. Khaitovich, W. Huber, J. Baker, S. Anders, Y. E. Zhang, H. Kaessmann, Gene expression across mammalian organ development. Nature 571, 505– 509 (2019). 31243369 PMC6658352
  31. [40]E. E. Winter, L. Goodstadt, C. P. Ponting, Elevated rates of protein secretion, evolution, and disease among tissue-specific genes. Genome Res. 14, 54– 61 (2004). 14707169 PMC314278
  32. [41]M. Ryaboshapkina, M. Hammar, Tissue-specific genes as an underutilized resource in drug discovery. Sci. Rep. 9, 7233 (2019). 31076736 PMC6510781
  33. [42]X. Sun, H. Zeng, A. Kumar, J. A. Belser, T. R. Maines, T. M. Tumpey, Constitutively expressed IFITM3 protein in human endothelial cells poses an early infection block to human influenza viruses. J. Virol. 90, 11157– 11167 (2016). 27707929 PMC5126373
  34. [43]A. Bruchez, K. Sha, J. Johnson, L. Chen, C. Stefani, H. McConnell, L. Gaucherand, R. Prins, K. A. Matreyek, A. J. Hume, E. Mühlberger, E. V. Schmidt, G. G. Olinger, L. M. Stuart, A. Lacy-Hulbert, MHC class II transactivator CIITA induces cell resistance to Ebola virus and SARS-like coronaviruses. Science 370, 241– 247 (2020). 32855215 PMC7665841
  35. [44]I. Thevarajan, T. H. O. Nguyen, M. Koutsakos, J. Druce, L. Caly, C. E. van de Sandt, X. Jia, S. Nicholson, M. Catton, B. Cowie, S. Y. C. Tong, S. R. Lewin, K. Kedzierska, Breadth of concomitant immune responses prior to patient recovery: A case report of non-severe COVID-19. Nat. Med. 26, 453– 455 (2020). 32284614 PMC7095036
  36. [45]Y. Zhang, L. Qin, Y. Zhao, P. Zhang, B. Xu, K. Li, L. Liang, C. Zhang, Y. Dai, Y. Feng, J. Sun, Z. Hu, H. Xiang, J. C. Knight, T. Dong, R. Jin, Interferon-induced transmembrane protein 3 genetic variant rs12252-C associated with disease severity in coronavirus disease 2019. J. Infect. Dis. 222, 34– 37 (2020). 32348495 PMC7197559
  37. [46]V. Marx, A dream of single-cell proteomics. Nat. Methods 16, 809– 812 (2019). 31406385
  38. [47]P. E. Meyer, Information-theoretic variable selection and network inference from microarray data, thesis, Université Libre de Bruxelles (2008).
  39. [48]A. A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky, R. Dalla Favera, A. Califano, in BMC Bioinformatics (BioMed Central, 2006), vol. 7, pp. S7. PMC1810318 16723010
  40. [49]G. Sales, C. Romualdi, parmigene—A parallel R package for mutual information estimation and gene network reconstruction. Bioinformatics 27, 1876– 1877 (2011). 21531770
  41. [50]B. A. Doganer, L. K. Q. Yan, H. Youk, Autocrine signaling and quorum sensing: Extreme ends of a common spectrum. Trends Cell Biol. 26, 262– 271 (2016). 26671200
  42. [53]S. H. Baumeister, G. J. Freeman, G. Dranoff, A. H. Sharpe, Coinhibitory pathways in immunotherapy for cancer. Annu. Rev. Immunol. 34, 539– 573 (2016). 26927206
  43. [54]F. A. Schildberg, S. R. Klein, G. J. Freeman, A. H. Sharpe, Coinhibitory pathways in the B7-CD28 ligand-receptor family. Immunity 44, 955– 972 (2016). 27192563 PMC4905708
  44. [55]A. C. Anderson, N. Joller, V. K. Kuchroo, Lag-3, Tim-3, and TIGIT: Co-inhibitory receptors with specialized functions in immune regulation. Immunity 44, 989– 1004 (2016). 27192565 PMC4942846
  45. [56]J. Wang, M. F. Sanmamed, I. Datar, T. T. Su, L. Ji, J. Sun, L. Chen, Y. Chen, G. Zhu, W. Yin, L. Zheng, T. Zhou, T. Badri, S. Yao, S. Zhu, A. Boto, M. Sznol, I. Melero, D. A. A. Vignali, K. Schalper, L. Chen, Fibrinogen-like protein 1 is a major immune inhibitory ligand of LAG-3. Cell 176, 334– 347.e12 (2019). 30580966 PMC6365968
  46. [58]M. X. Goemans, D. P. Williamson, in Approximation Algorithms for NP-Hard Problems, D. S. Hochbaum, Ed. (PWS publishing company, 1997), pp. 144–191.
  47. [59]L. Pirhaji, P. Milani, M. Leidl, T. Curran, J. Avila-Pacheco, C. B. Clish, F. M. White, A. Saghatelian, E. Fraenkel, Revealing disease-associated pathways by network integration of untargeted metabolomics. Nat. Methods 13, 770– 776 (2016). 27479327 PMC5209295
  48. [60]C. Hegde, P. Indyk, L. Schmidt, in International Conference on Machine Learning (2015), pp. 928–937.
  49. [61]A. Frankish, M. Diekhans, A.-M. Ferreira, R. Johnson, I. Jungreis, J. Loveland, J. M. Mudge, C. Sisu, J. Wright, J. Armstrong, I. Barnes, A. Berry, A. Bignell, S. C. Sala, J. Chrast, F. Cunningham, T. D. Domenico, S. Donaldson, I. T. Fiddes, C. G. Girón, J. M. Gonzalez, T. Grego, M. Hardy, T. Hourlier, T. Hunt, O. G. Izuogu, J. Lagarde, F. J. Martin, L. Martínez, S. Mohanan, P. Muir, F. C. P. Navarro, A. Parker, B. Pei, F. Pozo, M. Ruffier, B. M. Schmitt, E. Stapleton, M.-M. Suner, I. Sycheva, B. Uszczynska-Ratajczak, J. Xu, A. Yates, D. Zerbino, Y. Zhang, B. Aken, J. S. Choudhary, M. Gerstein, R. Guigó, T. J. P. Hubbard, M. Kellis, B. Paten, A. Reymond, M. L. Tress, P. Flicek, GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, D766– D773 (2018). PMC6323946 30357393
  50. [62]D. Risso, F. Perraudeau, S. Gribkova, S. Dudoit, J.-P. Vert, A general and flexible method for signal extraction from single-cell RNA-seq data. Nat. Commun. 9, 284 (2018). 29348443 PMC5773593
  51. [63]M. I. Love, W. Huber, S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014). 25516281 PMC4302049
  52. [65]I. Yanai, H. Benjamin, M. Shmoish, V. Chalifa-Caspi, M. Shklar, R. Ophir, A. Bar-Even, S. Horn-Saban, M. Safran, E. Domany, D. Lancet, O. Shmueli, Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. Bioinformatics 21, 650– 659 (2005). 15388519
  53. [67]U. Raudvere, L. Kolberg, I. Kuzmin, T. Arak, P. Adler, H. Peterson, J. Vilo, g: Profiler: A web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191– W198 (2019). 31066453 PMC6602461
  54. [68]F. Supek, M. Bošnjak, N. Škunca, T. Šmuc, REVIGO summarizes and visualizes long lists of gene ontology terms. PLOS ONE 6, e21800 (2011). 21789182 PMC3138752

Cited by

Sorted by citations

Authors
Hu, Y., Peng, T., Gao, L., & Tan, K.
Affiliations
  • Xidian University (CN)
  • Children's Hospital of Philadelphia (US)
Corresponding author
Tan, Kai
ORCID
Venue
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Published
2021-04-16
Volume
7
Issue
16
ISSN
2375-2548 (ISSN-L)
DOI
10.1126/sciadv.abf1356
PMC
PMC8046375
OpenAlex
W3155546248
Cited by
136
References
12
Citations by year
2026: 12 · 2025: 30 · 2024: 44 · 2023: 22 · 2022: 20 · 2021: 7 · 2020: 1
FWCI
7.30
Type
journal article
Language
en
Open access
Licence
Locations
  • Science Advances (publishedVersion, cc-by-nc)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
  • National Heart, Lung, and Blood Institute (HL156090)
  • National Cancer Institute (CA233285, CA243072)
  • Alex’s Lemonade Stand Foundation for Childhood Cancer
  • Leona M. and Harry B. Helmsley Charitable Trust (2008-040602)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
signaling networks, single-cell transcriptomics, spatial transcriptomics, cell type-specific signaling, macrophages, endothelial cells

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