Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT Article Swipe
Khoa Huynh
,
Katarzyna M. Tyc
,
Bruno Fernandes Matuck
,
Quinn T. Easter
,
Aditya Pratapa
,
Nikhil V. Kumar
,
Paola Pérez
,
Rachel Kulchar
,
Thomas Pranzatelli
,
Deiziane de Souza
,
Theresa Weaver
,
Xufeng Qu
,
Luiz Alberto Valente Soares
,
Marisa Dolhnokoff
,
David E. Kleiner
,
Stephen M. Hewitt
,
Luiz Fernando Ferraz da Silva
,
Vanderson Rocha
,
Blake M. Warner
,
Kevin M. Byrd
,
Jinze Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.05.31.596861
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.05.31.596861
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.05.31.596861
- https://www.biorxiv.org/content/biorxiv/early/2024/06/03/2024.05.31.596861.full.pdf
- OA Status
- green
- Cited By
- 7
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399303883
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399303883Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.05.31.596861Digital Object Identifier
- Title
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Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACITWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-03Full publication date if available
- Authors
-
Khoa Huynh, Katarzyna M. Tyc, Bruno Fernandes Matuck, Quinn T. Easter, Aditya Pratapa, Nikhil V. Kumar, Paola Pérez, Rachel Kulchar, Thomas Pranzatelli, Deiziane de Souza, Theresa Weaver, Xufeng Qu, Luiz Alberto Valente Soares, Marisa Dolhnokoff, David E. Kleiner, Stephen M. Hewitt, Luiz Fernando Ferraz da Silva, Vanderson Rocha, Blake M. Warner, Kevin M. Byrd, Jinze LiuList of authors in order
- Landing page
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https://doi.org/10.1101/2024.05.31.596861Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2024/06/03/2024.05.31.596861.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2024/06/03/2024.05.31.596861.full.pdfDirect OA link when available
- Concepts
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Deconvolution, Scale (ratio), Tacit knowledge, Geography, Computer science, Cartography, Knowledge management, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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45Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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