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Search · four archives
12 papers · ranked by Valyu relevance
Vladyslav Honcharuk, Keiko Takemoto, Diego Diez, Shinpei Kawaoka + 1 more
The 10x Genomics Xenium platform enables high-resolution spatial transcriptomics at single-cell and subcellular scales, but effective reuse of public Xenium datasets is hindered by large data sizes and heterogeneous file formats. We previously developed DeepSpaceDB, a spatial transcriptomics database designed for…
Claudia Arnedo-Pac, Jo Heffer, Marina Golotiuk, Ania M. Piskorz + 1 more
Spatially resolved transcriptomics was named Method of the Year 2020^1^ and has continued to evolve rapidly since then, providing novel insights in development, physiology, and disease processes. Many approaches now offer excellent performance in formalin-fixed paraffin embedded tissue, opening up a wealth of archived…
Md Ishtyaq Mahmud, Veena Kochat, Suresh Satpati, Jagan Mohan Reddy Dwarampudi + 3 more
High-resolution spatial transcriptomics platforms, such as Xenium, generate single-cell images that capture both molecular and spatial context, but their extremely high dimensionality poses major challenges for representation learning and clustering. In this study, we analyze data from the Xenium platform, which…
Anthony Baptista, Rosamond Nuamah, Ciro Chiappini, Anita Grigoriadis
Spatial transcriptomics (ST) has revolutionised transcriptomics analysis by preserving tissue architecture, allowing researchers to study gene expression in its native spatial context. However, despite its potential, ST still faces significant technical challenges. Two major issues include: (1) the integration of raw…
Yiyang Zhang, Bokai Zhao, Xiaoru Zhang, Zongchang Du + 2 more
Single-cell-resolution spatial transcriptomics profiles gene expression at cellular locations in native tissues, yet accurate cell-type annotation remains challenging: imaging-based platforms are constrained by targeted gene panels, whereas sequencing-based platforms often suffer from sparse molecular capture and…
Lixia Chen Wu, Xinyu Hu, Fengwei Zhan, Chuhanwen Sun + 6 more
Sequencing-based spatial transcriptomics technologies, including Visium HD and Stereo-seq, now enable transcriptome-wide profiling at subcellular resolution. However, these platforms generate measurements over spatially barcoded units rather than biologically segmented cells, creating a fundamental bottleneck for…
Esra Busra Isik, Yusuf Hakan Usta, Haozhe Liu, Maryam Riazi + 4 more
Recent developments in spatial omics technologies have enabled the generation of high dimensional molecular data, such as transcriptomes, proteomes, and epigenomes, within their spatial tissue context, either through cocprofiling on the same slice or through serial tissue sections. These datasets, which are often…
Yang Xu, Callum J. Sargeant, Yue You, Yupei You + 5 more
Spatial transcriptomics technology has developed rapidly in recent years, with various sequencing-based platforms such as 10x Visium, Slide-seq and Stereo-seq becoming widely used by researchers. Each platform brings its own set of protocols and customised data analysis pipelines which presents challenges when the goal…
Yuhang Yang, Yonggan Bu, Shengyuan Zhou, Yiming Luo + 1 more
Spatial Transcriptomics (ST) measures gene expression while preserving spatial context, but its high cost and low throughput leave public datasets small. Inferring expression directly from widely available Hematoxylin and Eosin (H&E) stained histology offers a cost-effective alternative. However, existing approaches…
Angelo Anacleto, Weiqiu Cheng, Qianlu Feng, Chun-Seok Cho + 15 more
Sequencing-based spatial transcriptomics (sST) enables transcriptome-wide gene expression mapping but falls short of reaching the optical resolution (200–300 nm) of imaging-based methods. Here, we present Seq-Scope-X (Seq-Scope-eXpanded), which empowers submicrometer-resolution Seq-Scope with tissue expansion to…
Xun Ding, Kendall Hoff, Radha Swaminathan, Mikaela Koutrouli + 13 more
Spatial transcriptomics enables analysis of gene expression that is spatially resolved within a tissue section, making it possible to elucidate the relationship between individual cells within the context of the tissue. This transformative technology enables researchers to better understand gene function within the…
Bencong Zhu, Guanyu Hu, Yang Xie, Lin Xu + 2 more
Department of Statistics, The Chinese University of Hong Kong Guanyu Hu Department of Biostatistics and Data Science and Center for Spatial Temporal Modeling for Applications in Population Sciences, The University of Texas Health Science Center at Houston Yang Xie Peter O'Donnell Jr. School of Public Health, The…