1. Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT.
- Author
-
Huynh KLA, Tyc KM, Matuck BF, Easter QT, Pratapa A, Kumar NV, Pérez P, Kulchar RJ, Pranzatelli TJF, de Souza D, Weaver TM, Qu X, Soares Junior LAV, Dolhnokoff M, Kleiner DE, Hewitt SM, Ferraz da Silva LF, Rocha VG, Warner BM, Byrd KM, and Liu J
- Abstract
Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed 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 outperformed existing unsupervised methods in accuracy and scalability. Integrating TACIT-identified cell types with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discovered under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications., Competing Interests: Conflict of interest: The authors had access to the study data and reviewed and approved the final manuscript. Although the authors view each of these as noncompeting financial interests, KMB, QTE, BFM, and BMW are all active members of the Human Cell Atlas; furthermore, KMB is a scientific advisor at Arcato Laboratories. All other authors declare no competing interests.
- Published
- 2024
- Full Text
- View/download PDF