Deconvolution of cell types and states in spatial multiomics utilizing TACIT
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
- Quinn T. Easter (ORCID: https://orcid.org/0000-0002-8018-0405)
- Vanderson Rocha (ORCID: https://orcid.org/0000-0003-0094-619X)
- Theresa Weaver
- Aditya Pratapa (ORCID: https://orcid.org/0000-0002-3323-6250)
- Thomas Pranzatelli (ORCID: https://orcid.org/0000-0002-5763-1036)
- Stephen M. Hewitt (ORCID: https://orcid.org/0000-0001-8283-1788)
- David E. Kleiner (ORCID: https://orcid.org/0000-0003-3442-4453)
- Rachel J. Kulchar (ORCID: https://orcid.org/0000-0002-2904-7107)
- Katarzyna M. Tyc (ORCID: https://orcid.org/0000-0002-3850-6934)
- Paola Pérez (ORCID: https://orcid.org/0000-0002-1695-5763)
- Xufeng Qu
- Blake M. Warner (ORCID: https://orcid.org/0000-0002-4961-018X)
- Khoa Huynh (ORCID: https://orcid.org/0000-0001-5374-6111)
- Kevin M. Byrd (ORCID: https://orcid.org/0000-0002-5565-0524)
- Luiz Alberto Valente Soares (ORCID: https://orcid.org/0000-0003-0717-7354)
- Luiz Fernando Ferraz da Silva (ORCID: https://orcid.org/0000-0002-0181-6357)
- Bruno Fernandes Matuck (ORCID: https://orcid.org/0000-0002-2132-3402)
- Marisa Dolhnokoff
- Jinze Liu (ORCID: https://orcid.org/0000-0003-0555-9412)
- Deiziane de Souza
- Nikhil V. Kumar
Institutions
- University of North Carolina at Chapel Hill (US)
- National Institutes of Health (US)
- Duke University (US)
- Universidade de São Paulo (BR)
- Virginia Commonwealth University (US)
- National Institute of Dental and Craniofacial Research (US)
- Virginia Cancer Institute (US)
- National Cancer Institute (US)
- Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (BR)
- Center for Cancer Research (US)
- VCU Massey Comprehensive Cancer Center (US)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1038/s41467-025-58874-4
- Citations
- 11
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 8.87
Funders
- Georgia Clinical and Translational Science Alliance
- National Institutes of Health
- National Cancer Institute