Deconvolution of cell types and states in spatial multiomics utilizing TACIT

Khoa L. A. Huynh, Katarzyna M. Tyc, Bruno F. Matuck, Quinn T. Easter, Aditya Pratapa, Nikhil V. Kumar, Paola Pérez, Rachel J. Kulchar, Thomas J. F. Pranzatelli, Deiziane Souza, Theresa M. Weaver, Xufeng Qu, Luiz Alberto Valente Soares Junior, Marisa Dolhnokoff, David E. Kleiner, Stephen M. Hewitt, Luiz Fernando Ferraz Silva, Vanderson Geraldo Rocha, Blake M. Warner, Kevin M. Byrd () and Jinze Liu ()
Additional contact information
Khoa L. A. Huynh: Virginia Commonwealth University
Katarzyna M. Tyc: Virginia Commonwealth University
Bruno F. Matuck: Virginia Commonwealth University
Quinn T. Easter: Virginia Commonwealth University
Aditya Pratapa: Duke University
Nikhil V. Kumar: University of North Carolina
Paola Pérez: National Institutes of Health
Rachel J. Kulchar: National Institutes of Health
Thomas J. F. Pranzatelli: National Institutes of Health
Deiziane Souza: BR
Theresa M. Weaver: Virginia Commonwealth University
Xufeng Qu: Massey Cancer Center
Luiz Alberto Valente Soares Junior: BR
Marisa Dolhnokoff: BR
David E. Kleiner: National Institutes of Health
Stephen M. Hewitt: National Institutes of Health
Luiz Fernando Ferraz Silva: BR
Vanderson Geraldo Rocha: University of Sao Paulo
Blake M. Warner: National Institutes of Health
Kevin M. Byrd: Virginia Commonwealth University
Jinze Liu: Virginia Commonwealth University

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-58874-4

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