IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-37897-9.html
   My bibliography  Save this article

Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics

Author

Listed:
  • Aqib Hasnain

    (University of California Santa Barbara)

  • Shara Balakrishnan

    (University of California Santa Barbara)

  • Dennis M. Joshy

    (University of California Santa Barbara)

  • Jen Smith

    (University of California Santa Barbara)

  • Steven B. Haase

    (Duke University)

  • Enoch Yeung

    (University of California Santa Barbara)

Abstract

A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the library’s ability to be useful outside the lab by detecting malathion in the environment. The engineered host cell, a living malathion sensor, can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.

Suggested Citation

  • Aqib Hasnain & Shara Balakrishnan & Dennis M. Joshy & Jen Smith & Steven B. Haase & Enoch Yeung, 2023. "Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37897-9
    DOI: 10.1038/s41467-023-37897-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-37897-9
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-37897-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chloé Grazon & R C. Baer & Uroš Kuzmanović & Thuy Nguyen & Mingfu Chen & Marjon Zamani & Margaret Chern & Patricia Aquino & Xiaoman Zhang & Sébastien Lecommandoux & Andy Fan & Mario Cabodi & Catherine, 2020. "A progesterone biosensor derived from microbial screening," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    2. Atte Aalto & Lauri Viitasaari & Pauliina Ilmonen & Laurent Mombaerts & Jorge Gonçalves, 2020. "Gene regulatory network inference from sparsely sampled noisy data," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    3. Jake P Taylor-King & Asbjørn N Riseth & Will Macnair & Manfred Claassen, 2020. "Dynamic distribution decomposition for single-cell snapshot time series identifies subpopulations and trajectories during iPSC reprogramming," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-21, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dan Xiao & Wen Zhang & Xiaoting Guo & Yidong Liu & Chunxia Hu & Shiting Guo & Zhaoqi Kang & Xianzhi Xu & Cuiqing Ma & Chao Gao & Ping Xu, 2021. "A d-2-hydroxyglutarate biosensor based on specific transcriptional regulator DhdR," Nature Communications, Nature, vol. 12(1), pages 1-14, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37897-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.