Deconvolution of cell types and states in spatial multiomics utilizing TACIT.

Journal: Nature communications
PMID: 40258827

Abstract

Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning increasingly plays a role, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we develop TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data. TACIT uses unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000 cells; 51 cell types) from three niches (brain, intestine, gland), TACIT outperforms existing unsupervised methods in accuracy and scalability. Integrating TACIT-identified cell types reveals new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discover under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.

Authors

  • Khoa L A Huynh
    Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
  • Katarzyna M Tyc
    Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
  • Bruno F Matuck
    Department of Oral and Craniofacial Molecular Biology, Philips Institute for Oral Health Research, Virginia Commonwealth University, Richmond, VA, USA.
  • Quinn T Easter
    Department of Oral and Craniofacial Molecular Biology, Philips Institute for Oral Health Research, Virginia Commonwealth University, Richmond, VA, USA.
  • Aditya Pratapa
    Broad Institute of MIT and Harvard, United States.
  • Nikhil V Kumar
    Adams School of Dentistry, University of North Carolina, Chapel Hill, USA.
  • Paola Pérez
    Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA.
  • Rachel J Kulchar
    Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA.
  • Thomas J F Pranzatelli
    Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA.
  • Deiziane de Souza
    Department of Pathology, Medicine School of University of Sao Paulo, SP, BR, Sao Paulo, Brazil.
  • Theresa M Weaver
    Department of Oral and Craniofacial Molecular Biology, Philips Institute for Oral Health Research, Virginia Commonwealth University, Richmond, VA, USA.
  • Xufeng Qu
    Department of Computer Science, University of Kentucky, Lexington, KY.
  • Luiz Alberto Valente Soares Junior
    Division of Dentistry of Hospital das Clinicas of University of Sao Paulo, SP, BR, Sao Paulo, Brazil.
  • Marisa Dolhnokoff
    Department of Pathology, Medicine School of University of Sao Paulo, SP, BR, Sao Paulo, Brazil.
  • David E Kleiner
    Laboratory of Pathology National Cancer Institute, National Institutes of Health Bethesda MD.
  • Stephen M Hewitt
    Journal of Histochemistry & Cytochemistry, Truchas, New Mexico.
  • Luiz Fernando Ferraz da Silva
    Department of Pathology, Medicine School of University of Sao Paulo, SP, BR, Sao Paulo, Brazil.
  • Vanderson Geraldo Rocha
    Department of Hematology, Transfusion and Cell Therapy Service, University of Sao Paulo, Sao Paulo, Brazil.
  • Blake M Warner
    Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA.
  • Kevin M Byrd
    Department of Oral and Craniofacial Molecular Biology, Philips Institute for Oral Health Research, Virginia Commonwealth University, Richmond, VA, USA. kevinmbyrd@gmail.com.
  • Jinze Liu

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