Results 11 to 20 of about 68,356 (275)

Spatial transcriptomics in neuroscience

Experimental and Molecular Medicine, 2023
The brain is one of the most complex living tissue types and is composed of an exceptional diversity of cell types displaying unique functional connectivity.
Namyoung Jung, Tae-Kyung Kim
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Unsupervised spatially embedded deep representation of spatial transcriptomics

Genome Medicine
Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications.
Hang Xu, Huazhu Fu, Yahui Long, Kok Siong Ang, Raman Sethi, Kelvin Chong, Mengwei Li, Rom Uddamvathanak, Hong Kai Lee, Jingjing Ling, Ao Chen, Ling Shao, Longqi Liu, Jinmiao Chen   +13 more
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Museum of Spatial Transcriptomics [PDF]

Nature Methods, 2020
AbstractThe function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors depends on the spatial organization of their cells. In the past decade high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression ...
Lambda Moses, Lior Pachter
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Spatially Aware Dimension Reduction for Spatial Transcriptomics [PDF]

Nature Communications, 2022
AbstractSpatial transcriptomics are a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing spatial transcriptomic data is computationally challenging, as the data collected from various spatial transcriptomic technologies are often noisy and display substantial spatial
Lulu Shang, Xiang Zhou
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Spatial Transcriptomic Technologies

Cells, 2023
Spatial transcriptomic technologies enable measurement of expression levels of genes systematically throughout tissue space, deepening our understanding of cellular organizations and interactions within tissues as well as illuminating biological insights in neuroscience, developmental biology and a range of diseases, including cancer.
Tsai-Ying Chen, Li You, Jose Angelito U. Hardillo, Miao-Ping Chien   +3 more
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Clustering spatial transcriptomics data

Bioinformatics, 2021
AbstractMotivationRecent advancements in fluorescence in situ hybridization (FISH) techniques enable them to concurrently obtain information on the location and gene expression of single cells. A key question in the initial analysis of such spatial transcriptomics data is the assignment of cell types.
Haotian Teng, Ye Yuan, Ziv Bar-Joseph
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Computational solutions for spatial transcriptomics

Computational and Structural Biotechnology Journal, 2022
Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information.
Iivari Kleino, Paulina Frolovaitė, Tomi Suomi, Laura L. Elo   +3 more
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Spatially informed cell-type deconvolution for spatial transcriptomics

Nature Biotechnology, 2022
Many spatially resolved transcriptomic technologies do not have single-cell resolution but measure the average gene expression for each spot from a mixture of cells of potentially heterogeneous cell types. Here, we introduce a deconvolution method, conditional autoregressive-based deconvolution (CARD), that combines cell-type-specific expression ...
Ying Ma, Xiang Zhou
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Spatial transcriptomics

The American Journal of Pathology
Dataset of spatial transcriptomics of endometrium and ...
Pierre Isnard, Benjamin D. Humphreys
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Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

Nature Communications, 2023
Advances in spatial transcriptomics technologies have enabled the gene expression profiling of tissues while retaining spatial context. Here the authors present GraphST, a graph self-supervised contrastive learning method that learns informative and ...
Yahui Long, Kok Siong Ang, Mengwei Li, Kian Long Kelvin Chong, Raman Sethi, Chengwei Zhong, Hang Xu, Zhiwei Ong, Karishma Sachaphibulkij, Ao Chen, Li Zeng, Huazhu Fu, Min Wu, Lina Hsiu Kim Lim, Longqi Liu, Jinmiao Chen   +15 more
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