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Cancer Systems and Integrative Biology pp 85–94Cite as

Integration of Single-Cell RNA-Sequencing and Network Analysis to Investigate Mechanisms of Drug Resistance

Part of the Methods in Molecular Biology book series (MIMB,volume 2660)

Abstract

Innate resistance and therapeutic-driven development of resistance to anticancer drugs is a common complication of cancer therapy. Understanding mechanisms of drug resistance can lead to development of alternative therapies. One strategy is to subject drug-sensitive and drug-resistant variants to single-cell RNA-seq (scRNA-seq) and to subject the scRNA-seq data to network analysis to identify pathways associated with drug resistance. This protocol describes a computational analysis pipeline to study drug resistance by subjecting scRNA-seq expression data to Passing Attributes between Networks for Data Assimilation (PANDA), an integrative network analysis tool that incorporates protein–protein interactions (PPI) and transcription factor (TF)-binding motifs.

Key words

  • Single-cell RNA-sequencing
  • Drug resistance network
  • Data integration
  • Protein–protein interactions
  • Transcription factor-binding motifs
  • Passing Attributes between Networks for Data Assimilation
  • Gene set enrichment analysis
  • Connectivity map analysis

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References

  1. Schnepp PM, Ahmed A, Escara-Wilke J et al (2021) Transcription factor network analysis based on single cell RNA-seq identifies that Trichostatin-a reverses docetaxel resistance in prostate cancer. BMC Cancer 21:1316. https://doi.org/10.1186/s12885-021-09048-0

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  2. Glass K, Huttenhower C, Quackenbush J, Yuan GC (2013) Passing messages between biological networks to refine predicted interactions. PLoS One 8(5):e64832. https://doi.org/10.1371/journal.pone.0064832

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  3. Glass K, Quackenbush J, Spentzos D et al (2015) A network model for angiogenesis in ovarian cancer. BMC Bioinform 16:115. https://doi.org/10.1186/s12859-015-0551-y

    CrossRef  Google Scholar 

  4. Sonawane AR (2017) Understanding tissue-specific gene regulation. Cell Rep 21(4):1077–1088. https://doi.org/10.1016/j.celrep.2017.10.001

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  5. R Core Team (2022) R: a language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

    Google Scholar 

  6. Hao Y, Hao S, Andersen-Nissen E et al (2021) Integrated analysis of multimodal single-cell data. Cell 184(13):3573–3587.e29. https://doi.org/10.1016/j.cell.2021.04.048

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  7. Dawei L, Guan-tin C (2018) wordcloud2: create word cloud by ‘htmlwidget’. R package version 0.2.1. https://CRAN.R-project.org/package=wordcloud2

  8. Grant CE, Bailey TL, Noble WS (2011) FIMO: scanning for occurrences of a given motif. Bioinformatics 27(7):1017–1018. https://doi.org/10.1093/bioinformatics/btr064

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  9. Subramaniana A, Tamayoa P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102:15545–15550. https://doi.org/10.1073/pnas.0506580102

    CrossRef  CAS  Google Scholar 

  10. Mootha V, Lindgren C, Erikssonp KF et al (2003) PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34:267–273. https://doi.org/10.1038/ng1180

    CrossRef  CAS  PubMed  Google Scholar 

  11. Schnepp PM, Shelley G, Dai J et al (2020) Single-cell transcriptomics analysis identifies nuclear protein 1 as a regulator of docetaxel resistance in prostate cancer cells. Mol Cancer Res 18(9):1290–1301. https://doi.org/10.1158/1541-7786

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lamb J, Crawford ED, Peck D et al (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313(5795):1929–1935. https://doi.org/10.1126/science.1132939

    CrossRef  CAS  PubMed  Google Scholar 

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Acknowledgments

This work was supported by National Cancer Institute awards P01-CA093900 and P30-CA046592 by the use of the following Cancer Center Shared Resources: the Single Cell Spatial Analysis Shared Resource, the Cancer Data Science Shared Resource, and the Single Cell Spatial Analysis Program.

Author information

Authors and Affiliations

  1. Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA

    Stephanie The, Patricia M. Schnepp, Greg Shelley, Jill M. Keller & Evan T. Keller

  2. Single Cell Spatial Analysis Program, University of Michigan, Ann Arbor, MI, USA

    Arvind Rao & Evan T. Keller

  3. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA

    Stephanie The & Arvind Rao

  4. Department of Urology, School of Medicine, University of Michigan, Ann Arbor, MI, USA

    Patricia M. Schnepp, Greg Shelley, Jill M. Keller & Evan T. Keller

  5. Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA

    Jill M. Keller & Evan T. Keller

  6. Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI, USA

    Arvind Rao & Evan T. Keller

Authors
  1. Stephanie The
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  2. Patricia M. Schnepp
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  3. Greg Shelley
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  4. Jill M. Keller
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  5. Arvind Rao
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  6. Evan T. Keller
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Corresponding author

Correspondence to Evan T. Keller .

Editor information

Editors and Affiliations

  1. Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA

    Usha N. Kasid

  2. The Hormel Institute, University of Minnesota, Austin, MN, USA

    Robert Clarke

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© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Cite this protocol

The, S., Schnepp, P.M., Shelley, G., Keller, J.M., Rao, A., Keller, E.T. (2023). Integration of Single-Cell RNA-Sequencing and Network Analysis to Investigate Mechanisms of Drug Resistance. In: Kasid, U.N., Clarke, R. (eds) Cancer Systems and Integrative Biology. Methods in Molecular Biology, vol 2660. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3163-8_7

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  • DOI: https://doi.org/10.1007/978-1-0716-3163-8_7

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3162-1

  • Online ISBN: 978-1-0716-3163-8

  • eBook Packages: Springer Protocols

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