IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-023-44503-5.html
   My bibliography  Save this article

Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data

Author

Listed:
  • Ethan Bahl

    (University of Iowa
    University of Iowa)

  • Snehajyoti Chatterjee

    (University of Iowa
    University of Iowa)

  • Utsav Mukherjee

    (University of Iowa
    University of Iowa
    University of Iowa)

  • Muhammad Elsadany

    (University of Iowa
    University of Iowa)

  • Yann Vanrobaeys

    (University of Iowa
    University of Iowa)

  • Li-Chun Lin

    (University of Iowa
    University of Iowa)

  • Miriam McDonough

    (University of Iowa
    University of Iowa)

  • Jon Resch

    (University of Iowa)

  • K. Peter Giese

    (King’s College London)

  • Ted Abel

    (University of Iowa
    University of Iowa)

  • Jacob J. Michaelson

    (University of Iowa
    University of Iowa
    University of Iowa
    University of Iowa)

Abstract

Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing and allow for the interrogation of activity-dependent transcription at cellular resolution. Here, we present NEUROeSTIMator, a deep learning model that integrates transcriptomic signals to estimate neuronal activation in a way that we demonstrate is associated with Patch-seq electrophysiological features and that is robust against differences in species, cell type, and brain region. We demonstrate this method’s ability to accurately detect neuronal activity in previously published studies of single cell activity-induced gene expression. Further, we applied our model in a spatial transcriptomic study to identify unique patterns of learning-induced activity across different brain regions in male mice. Altogether, our findings establish NEUROeSTIMator as a powerful and broadly applicable tool for measuring neuronal activation, whether as a critical covariate or a primary readout of interest.

Suggested Citation

  • Ethan Bahl & Snehajyoti Chatterjee & Utsav Mukherjee & Muhammad Elsadany & Yann Vanrobaeys & Li-Chun Lin & Miriam McDonough & Jon Resch & K. Peter Giese & Ted Abel & Jacob J. Michaelson, 2024. "Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44503-5
    DOI: 10.1038/s41467-023-44503-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-44503-5
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-44503-5?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. Gökcen Eraslan & Lukas M. Simon & Maria Mircea & Nikola S. Mueller & Fabian J. Theis, 2019. "Single-cell RNA-seq denoising using a deep count autoencoder," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    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. Ziqi Zhang & Xinye Zhao & Mehak Bindra & Peng Qiu & Xiuwei Zhang, 2024. "scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Nicolae Sapoval & Amirali Aghazadeh & Michael G. Nute & Dinler A. Antunes & Advait Balaji & Richard Baraniuk & C. J. Barberan & Ruth Dannenfelser & Chen Dun & Mohammadamin Edrisi & R. A. Leo Elworth &, 2022. "Current progress and open challenges for applying deep learning across the biosciences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Lingfei Wang, 2021. "Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    4. Songming Tang & Xuejian Cui & Rongxiang Wang & Sijie Li & Siyu Li & Xin Huang & Shengquan Chen, 2024. "scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    5. Ian Covert & Rohan Gala & Tim Wang & Karel Svoboda & Uygar Sümbül & Su-In Lee, 2023. "Predictive and robust gene selection for spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    6. Jing Qi & Yang Zhou & Zicen Zhao & Shuilin Jin, 2021. "SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-20, June.
    7. Zhijian Li & Christoph Kuppe & Susanne Ziegler & Mingbo Cheng & Nazanin Kabgani & Sylvia Menzel & Martin Zenke & Rafael Kramann & Ivan G. Costa, 2021. "Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    8. Zhenchao Tang & Guanxing Chen & Shouzhi Chen & Jianhua Yao & Linlin You & Calvin Yu-Chian Chen, 2024. "Modal-nexus auto-encoder for multi-modality cellular data integration and imputation," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    9. Xin Tang & Jiawei Zhang & Yichun He & Xinhe Zhang & Zuwan Lin & Sebastian Partarrieu & Emma Bou Hanna & Zhaolin Ren & Hao Shen & Yuhong Yang & Xiao Wang & Na Li & Jie Ding & Jia Liu, 2023. "Explainable multi-task learning for multi-modality biological data analysis," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    10. George C. Linderman & Jun Zhao & Manolis Roulis & Piotr Bielecki & Richard A. Flavell & Boaz Nadler & Yuval Kluger, 2022. "Zero-preserving imputation of single-cell RNA-seq data," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    11. Lulu Shang & Xiang Zhou, 2022. "Spatially aware dimension reduction for spatial transcriptomics," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    12. Lucy Xia & Christy Lee & Jingyi Jessica Li, 2024. "Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    13. Md Tauhidul Islam & Jen-Yeu Wang & Hongyi Ren & Xiaomeng Li & Masoud Badiei Khuzani & Shengtian Sang & Lequan Yu & Liyue Shen & Wei Zhao & Lei Xing, 2022. "Leveraging data-driven self-consistency for high-fidelity gene expression recovery," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    14. Vidhya M. Ravi & Nicolas Neidert & Paulina Will & Kevin Joseph & Julian P. Maier & Jan Kückelhaus & Lea Vollmer & Jonathan M. Goeldner & Simon P. Behringer & Florian Scherer & Melanie Boerries & Marie, 2022. "T-cell dysfunction in the glioblastoma microenvironment is mediated by myeloid cells releasing interleukin-10," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    15. Andrea Riba & Attila Oravecz & Matej Durik & Sara Jiménez & Violaine Alunni & Marie Cerciat & Matthieu Jung & Céline Keime & William M. Keyes & Nacho Molina, 2022. "Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    16. Yasa Baig & Helena R. Ma & Helen Xu & Lingchong You, 2023. "Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    17. Yuge Wang & Hongyu Zhao, 2022. "Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders," PLOS Computational Biology, Public Library of Science, vol. 18(4), pages 1-31, April.
    18. Xinyi Zhang & Xiao Wang & G. V. Shivashankar & Caroline Uhler, 2022. "Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    19. Jinhee Park & Hyerin Kim & Jaekwang Kim & Mookyung Cheon, 2020. "A practical application of generative adversarial networks for RNA-seq analysis to predict the molecular progress of Alzheimer's disease," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-20, July.
    20. Xiang Lin & Tian Tian & Zhi Wei & Hakon Hakonarson, 2022. "Clustering of single-cell multi-omics data with a multimodal deep learning method," Nature Communications, Nature, vol. 13(1), pages 1-18, 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:15:y:2024:i:1:d:10.1038_s41467-023-44503-5. 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.