GZMK+CD8+ T cells Target a Specific Acinar Cell Type in Sjögren’s Disease

Warner, Blake; Pranzatelli, Thomas; Perez, Paola; Ku, Anson; Matuck, Bruno Fernandes; Huynh, Khoa; Sakai, Shunsuke; Abed, Mehdi; Jang, Shyh-Ing; Yamada, Eiko; Dominick, Kalie; Ahmed, Zara; Oliver, Amanda; Wasikowski, Rachael; Easter, Quinn; Magone, M. Teresa; Baer, Alan; Pelayo, Eileen; Khavandgar, Zohreh; Gupta, Sarthak; Kleiner, David; Lessard, Christopher; Farris, A; Martin, Daniel; Morell, Robert; Zheng, Changyu; Rachmaninoff, Nicholas; Maldonado-Ortiz, Jose; Qu, Xufeng; Aure, Marit; Dezfulian, Mohammad; Lake, Ross; Teichmann, Sarah; Barber, Daniel; Tsoi, Lam; Sowalsky, Adam; Tyc, Katarzyna; Gudjonsson, Johann; Byrd, Kevin; Johnson, Philip; Liu, Jinze; Chiorini, John

doi:10.21203/rs.3.rs-3601404/v1

Cited by 3 publications

(1 citation statement)

References 64 publications

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“…We compared TACIT against two annotation approaches: Seurat with label transfer from scRNA-seq data (Seurat transfer), and Louvain 29,31 . Signature lists for TACIT were created from the top five most enriched genes in each annotated cluster in the Seurat transfer result 32 . While the UMAP plot shows overall consistency in cell type annotation across the three methods, TACIT’s annotation excels in clear distinctions among three subtypes of acinar cells (Fig.…”

Section: Resultsmentioning

confidence: 99%

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

Huynh,

Tyc,

Matuck

et al. 2024

Preprint

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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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Section: Resultsmentioning

confidence: 99%

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

Huynh,

Tyc,

Matuck

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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Update on the pathophysiology and treatment of primary Sjögren syndrome

Baldini,

Fulvio,

La Rocca

et al. 2024

Nat Rev Rheumatol

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Spatial Transcriptomics Unravel the Tissue Complexity of Oral Pathogenesis

Haller,

Abedi,

Hafedi

et al. 2024

J Dent Res

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Spatial transcriptomics (ST) is a cutting-edge methodology that enables the simultaneous profiling of global gene expression and spatial information within histological tissue sections. Traditional transcriptomic methods lack the spatial resolution required to sufficiently examine the complex interrelationships between cellular regions in diseased and healthy tissue states. We review the general workflows for ST, from specimen processing to ST data analysis and interpretations of the ST dataset using visualizations and cell deconvolution approaches. We show how recent studies used ST to explore the development or pathogenesis of specific craniofacial regions, including the cranium, palate, salivary glands, tongue, floor of mouth, oropharynx, and periodontium. Analyses of cranial suture patency and palatal fusion during development using ST identified spatial patterns of bone morphogenetic protein in sutures and osteogenic differentiation pathways in the palate, in addition to the discovery of several genes expressed at critical locations during craniofacial development. ST of salivary glands from patients with Sjögren’s disease revealed co-localization of autoimmune antigens with ductal cells and a subpopulation of acinar cells that was specifically depleted by the dysregulated autoimmune response. ST of head and neck lesions, such as premalignant leukoplakia progressing to established oral squamous cell carcinomas, oral cancers with perineural invasions, and oropharyngeal lesions associated with HPV infection spatially profiled the complex tumor microenvironment, showing functionally important gene signatures of tumor cell differentiation, invasion, and nontumor cell dysregulation within patient biopsies. ST also enabled the localization of periodontal disease–associated gene expression signatures within gingival tissues, including genes involved in inflammation, and the discovery of a fibroblast subtype mediating the transition between innate and adaptive immune responses in periodontitis. The increased use of ST, especially in conjunction with single-cell analyses, promises to improve our understandings of craniofacial development and pathogenesis at unprecedented tissue-level resolution in both space and time.

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GZMK+CD8+ T cells Target a Specific Acinar Cell Type in Sjögren’s Disease

Cited by 3 publications

References 64 publications

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

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

Update on the pathophysiology and treatment of primary Sjögren syndrome

Spatial Transcriptomics Unravel the Tissue Complexity of Oral Pathogenesis

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