Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT.

Autor:	Huynh KLA; Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA., Tyc KM; Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.; Massey Cancer Center, Richmond VA, USA., Matuck BF; Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA., Easter QT; Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA., Pratapa A; Department of Cell Biology, Duke University, Durham, NC, USA., Kumar NV; Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA., Pérez P; Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA., Kulchar RJ; Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA., Pranzatelli TJF; Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA., de Souza D; Department of Pathology, Medicine School of University of Sao Paulo, SP, BR., Weaver TM; Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA., Qu X; Massey Cancer Center, Richmond VA, USA., Soares Junior LAV; Division of Dentistry of Hospital das Clinicas of University of Sao Paulo, SP, BR., Dolhnokoff M; Department of Pathology, Medicine School of University of Sao Paulo, SP, BR., Kleiner DE; Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Hewitt SM; Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA., Ferraz da Silva LF; Department of Pathology, Medicine School of University of Sao Paulo, SP, BR., Rocha VG; Department of Hematology, Transfusion and Cell Therapy Service, University of Sao Paulo, Sao Paulo, Brazil., Warner BM; Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA., Byrd KM; Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA.; Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA.; Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA., Liu J; Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.; Massey Cancer Center, Richmond VA, USA.
Jazyk:	angličtina
Zdroj:	Research square [Res Sq] 2024 Jun 27. Date of Electronic Publication: 2024 Jun 27.
DOI:	10.21203/rs.3.rs-4536158/v1
Abstrakt:	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.
Databáze:	MEDLINE
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