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Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns

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  • Hannah M. Schlüter

    (Massachusetts Institute of Technology
    Broad Institute of MIT and Harvard)

  • Caroline Uhler

    (Massachusetts Institute of Technology
    Broad Institute of MIT and Harvard)

Abstract

Recent barcoding technologies allow reconstructing lineage trees while capturing paired single-cell RNA-sequencing (scRNA-seq) data. Such datasets provide opportunities to compare gene expression memory maintenance through lineage branching and pinpoint critical genes in these processes. Here we develop Permutation, Optimization, and Representation learning based single Cell gene Expression and Lineage ANalysis (PORCELAN) to identify lineage-informative genes or subtrees where lineage and expression are tightly coupled. We validate our method using synthetic data and apply it to recent paired lineage and scRNA-seq data of lung cancer in a mouse model and embryogenesis of mouse and C. elegans. Our method pinpoints subtrees giving rise to metastases or new cell states, and genes identified as most informative about lineage overlap with known pathways involved in lung cancer progression. Furthermore, our method highlights differences in how gene expression memory is maintained through divisions in cancer and embryogenesis, thereby providing a tool for studying cell state memory through divisions across biological systems.

Suggested Citation

  • Hannah M. Schlüter & Caroline Uhler, 2025. "Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56388-7
    DOI: 10.1038/s41467-025-56388-7
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    References listed on IDEAS

    as
    1. Karren Dai Yang & Karthik Damodaran & Saradha Venkatachalapathy & Ali C Soylemezoglu & G V Shivashankar & Caroline Uhler, 2020. "Predicting cell lineages using autoencoders and optimal transport," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-20, April.
    2. Karren Dai Yang & Anastasiya Belyaeva & Saradha Venkatachalapathy & Karthik Damodaran & Abigail Katcoff & Adityanarayanan Radhakrishnan & G. V. Shivashankar & Caroline Uhler, 2021. "Multi-domain translation between single-cell imaging and sequencing data using autoencoders," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    3. Xinhai Pan & Hechen Li & Pranav Putta & Xiuwei Zhang, 2023. "LinRace: cell division history reconstruction of single cells using paired lineage barcode and gene expression data," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    4. Michelle M. Chan & Zachary D. Smith & Stefanie Grosswendt & Helene Kretzmer & Thomas M. Norman & Britt Adamson & Marco Jost & Jeffrey J. Quinn & Dian Yang & Matthew G. Jones & Alex Khodaverdian & Nir , 2019. "Molecular recording of mammalian embryogenesis," Nature, Nature, vol. 570(7759), pages 77-82, June.
    5. Hamim Zafar & Chieh Lin & Ziv Bar-Joseph, 2020. "Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    6. Aden Forrow & Geoffrey Schiebinger, 2021. "LineageOT is a unified framework for lineage tracing and trajectory inference," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    7. Chen Weng & Fulong Yu & Dian Yang & Michael Poeschla & L. Alexander Liggett & Matthew G. Jones & Xiaojie Qiu & Lara Wahlster & Alexis Caulier & Jeffrey A. Hussmann & Alexandra Schnell & Kathryn E. Yos, 2024. "Deciphering cell states and genealogies of human haematopoiesis," Nature, Nature, vol. 627(8003), pages 389-398, March.
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