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Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT.
Huynh, Khoa L A; Tyc, Katarzyna M; Matuck, Bruno F; Easter, Quinn T; Pratapa, Aditya; Kumar, Nikhil V; Pérez, Paola; Kulchar, Rachel; Pranzatelli, Thomas; de Souza, Deiziane; Weaver, Theresa M; Qu, Xufeng; Valente Soares, Luiz Alberto; Dolhnokoff, Marisa; Kleiner, David E; Hewitt, Stephen M; da Silva, Luiz Fernando Ferraz; Rocha, Vanderson Geraldo; Warner, Blake M; Byrd, Kevin M; Liu, Jinze.
Afiliação
  • Huynh KLA; Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
  • Tyc KM; Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.
  • Matuck BF; Massey Cancer Center, Richmond VA, USA.
  • Easter QT; Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA.
  • Pratapa A; Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA.
  • Kumar NV; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
  • Pérez P; Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA.
  • Kulchar R; Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA.
  • Pranzatelli T; Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA.
  • de Souza D; Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA.
  • Weaver TM; Department of Pathology, Medicine School of University of Sao Paulo, SP, BR.
  • Qu X; Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA.
  • Valente Soares LA; Massey Cancer Center, Richmond VA, USA.
  • Dolhnokoff M; Division of Dentistry of Hospital das Clinicas of University of Sao Paulo, SP, BR.
  • Kleiner DE; Department of Pathology, Medicine School of University of Sao Paulo, SP, BR.
  • Hewitt SM; Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • da Silva LFF; Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • Rocha VG; Department of Pathology, Medicine School of University of Sao Paulo, SP, BR.
  • Warner BM; Department of Hematology, Transfusion and Cell Therapy Service, University of Sao Paulo, Sao Paulo, Brazil.
  • Byrd KM; Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA.
  • Liu J; Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation & Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA.
bioRxiv ; 2024 Jun 03.
Article em En | MEDLINE | ID: mdl-38895230
ABSTRACT
Identifying cell types and states remains a time-consuming and 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, using 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. Integration of TACIT-identified cell with a novel Shiny app revealed 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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: BioRxiv Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: BioRxiv Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos