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Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research

Advanced Science, 2023
Spatial transcriptomics is a newly emerging field that enables high‐throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems.
Han‐Eol Park, Song Hyun Jo, Rosalind H. Lee, Christian P. Macks, Taeyun Ku, Jihwan Park, Chung Whan Lee, Junho K. Hur, Chang Ho Sohn   +8 more
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Spatial Transcriptomics for Tumor Heterogeneity Analysis

Frontiers in Genetics, 2022
The molecular heterogeneity of cancer is one of the major causes of drug resistance that leads to treatment failure. Thus, better understanding the heterogeneity of cancer will contribute to more precise diagnosis and improved patient outcomes.
Qiongyu Li, Xinya Zhang, Rongqin Ke
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Detection of differentially expressed genes in spatial transcriptomics data by spatial analysis of spatial transcriptomics: A novel method based on spatial statistics

Frontiers in Neuroscience, 2022
BackgroundSpatial transcriptomics (STs) simultaneously obtains the location and amount of gene expression within a tissue section. However, current methods like FindMarkers calculated the differentially expressed genes (DEGs) based on the classical ...
Zhihua Qiu, Zhihua Qiu, Shaojun Li, Ming Luo, Shuanggen Zhu, Yongjun Jiang, Zhijian Wang   +6 more
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Modeling zero inflation is not necessary for spatial transcriptomics

Genome Biology, 2022
Background Spatial transcriptomics are a set of new technologies that profile gene expression on tissues with spatial localization information. With technological advances, recent spatial transcriptomics data are often in the form of sparse counts with ...
Peiyao Zhao, Jiaqiang Zhu, Ying Ma, Xiang Zhou   +3 more
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Single-cell spatial explorer: easy exploration of spatial and multimodal transcriptomics

BMC Bioinformatics, 2023
Background: The development of single-cell technologies yields large datasets of information as diverse and multimodal as transcriptomes, immunophenotypes, and spatial position from tissue sections in the so-called ’spatial transcriptomics’.
Frédéric Pont, Juan Pablo Cerapio, Pauline Gravelle, Laetitia Ligat, Carine Valle, Emeline Sarot, Marion Perrier, Frédéric Lopez, Camille Laurent, Jean Jacques Fournié, Marie Tosolini   +10 more
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Opportunities and challenges in the application of single-cell and spatial transcriptomics in plants

Frontiers in Plant Science, 2023
Single-cell and spatial transcriptomics have diverted researchers’ attention from the multicellular level to the single-cell level and spatial information.
Ce Chen, Yining Ge, Lingli Lu, Lingli Lu
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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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Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

Nature Communications, 2023
Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them
Wei Liu, Xu Liao, Ziye Luo, Yi Yang, Mai Chan Lau, Yuling Jiao, Xingjie Shi, Weiwei Zhai, Hongkai Ji, Joe Yeong, Jin Liu   +10 more
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Spatial transcriptomics deconvolution at single-cell resolution using Redeconve

Nature Communications, 2023
Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution.
Zixiang Zhou, Yunshan Zhong, Zemin Zhang, Xianwen Ren   +3 more
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Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data

Genome Biology, 2023
Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells.
Agnieszka Geras, Shadi Darvish Shafighi, Kacper Domżał, Igor Filipiuk, Alicja Rączkowska, Paulina Szymczak, Hosein Toosi, Leszek Kaczmarek, Łukasz Koperski, Jens Lagergren, Dominika Nowis, Ewa Szczurek   +11 more
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