A multiplex single-cell RNA-Seq pharmacotranscriptomics pipeline for drug discovery

被引:0
|
作者
Alice Dini [1 ]
Harlan Barker [1 ]
Emilia Piki [2 ]
Subodh Sharma [1 ]
Juuli Raivola [1 ]
Astrid Murumägi [3 ]
Daniela Ungureanu [4 ]
机构
[1] University of Oulu,Disease Networks Unit, Faculty of Biochemistry and Molecular Medicine
[2] Tampere University,Tampere University Hospital and Faculty of Medicine and Health Technology
[3] University of Helsinki,Applied Tumor Genomics, Research Program Unit, Faculty of Medicine
[4] University of Helsinki,Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE)
关键词
D O I
10.1038/s41589-024-01761-8
中图分类号
学科分类号
摘要
The gene-regulatory dynamics governing drug responses in cancer are yet to be fully understood. Here, we report a pipeline capable of producing high-throughput pharmacotranscriptomic profiling through live-cell barcoding using antibody–oligonucleotide conjugates. This pipeline combines drug screening with 96-plex single-cell RNA sequencing. We show the potential of this approach by exploring the heterogeneous transcriptional landscape of primary high-grade serous ovarian cancer (HGSOC) cells after treatment with 45 drugs, with 13 distinct classes of mechanisms of action. A subset of phosphatidylinositol 3-OH kinase (PI3K), protein kinase B (AKT) and mammalian target of rapamycin (mTOR) inhibitors induced the activation of receptor tyrosine kinases, such as the epithelial growth factor receptor (EGFR), and this was mediated by the upregulation of caveolin 1 (CAV1). This drug resistance feedback loop could be mitigated by the synergistic action of agents targeting PI3K–AKT–mTOR and EGFR for HGSOC with CAV1 and EGFR expression. Using this workflow could enable the personalized testing of patient-derived tumor samples at single-cell resolution.
引用
收藏
页码:432 / 442
页数:10
相关论文
共 50 条
  • [21] Single-cell RNA-seq: advances and future challenges
    Saliba, Antoine-Emmanuel
    Westermann, Alexander J.
    Gorski, Stanislaw A.
    Vogel, Joerg
    NUCLEIC ACIDS RESEARCH, 2014, 42 (14) : 8845 - 8860
  • [22] Practical Compass of Single-Cell RNA-Seq Analysis
    Okada, Hiroyuki
    Chung, Ung-il
    Hojo, Hironori
    CURRENT OSTEOPOROSIS REPORTS, 2023, 22 (5) : 433 - 440
  • [23] Embracing the dropouts in single-cell RNA-seq analysis
    Peng Qiu
    Nature Communications, 11
  • [24] From single-cell RNA-seq to transcriptional regulation
    Gioele La Manno
    Nature Biotechnology, 2019, 37 : 1421 - 1422
  • [25] Guidelines for reporting single-cell RNA-seq experiments
    Anja Füllgrabe
    Nancy George
    Matthew Green
    Parisa Nejad
    Bruce Aronow
    Silvie Korena Fexova
    Clay Fischer
    Mallory Ann Freeberg
    Laura Huerta
    Norman Morrison
    Richard H. Scheuermann
    Deanne Taylor
    Nicole Vasilevsky
    Laura Clarke
    Nils Gehlenborg
    Jim Kent
    John Marioni
    Sarah Teichmann
    Alvis Brazma
    Irene Papatheodorou
    Nature Biotechnology, 2020, 38 : 1384 - 1386
  • [26] How deep is enough in single-cell RNA-seq?
    Aaron M Streets
    Yanyi Huang
    Nature Biotechnology, 2014, 32 : 1005 - 1006
  • [27] Single-cell RNA-Seq unveils tumor microenvironment
    Lee, Hae-Ock
    Park, Woong-Yang
    BMB REPORTS, 2017, 50 (06) : 283 - 284
  • [28] How deep is enough in single-cell RNA-seq?
    Streets, Aaron M.
    Huang, Yanyi
    NATURE BIOTECHNOLOGY, 2014, 32 (10) : 1005 - 1006
  • [29] Single-cell RNA-seq keeps cells alive
    Eleni Kotsiliti
    Nature Biotechnology, 2022, 40 : 1432 - 1432
  • [30] Comparison of transformations for single-cell RNA-seq data
    Constantin Ahlmann-Eltze
    Wolfgang Huber
    Nature Methods, 2023, 20 : 665 - 672