11 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…
Amanda Janesick, Robert Shelansky, Andrew Gottscho, Florian Wagner + 10 more
Single cell and spatial technologies that profile gene expression across a whole tissue are revolutionizing the resolution of molecular states in clinical tissue samples. Commercially available methods that characterize either single cell or spatial gene expression are currently limited by low sample throughput and/or…
Ruth O Allen, Farhan Ameen, Emily Duchini, Thomas Ashhurst + 7 more
Spatial imaging technologies provide an expansive view of tissue microenvironments through high-plex profiling of protein and molecular targets in situ. Imaging mass cytometry (IMC; Standard BioTools) is a trusted method for defining immune phenotypes based on up to 40 protein targets, whilst Xenium in situ spatial…
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…
Lingyu Li, Tianjie Wang, Zhuo Liang, Huajian Yu + 3 more
Spatial transcriptomics (ST) has emerged as a powerful tool for analyzing cell-cell communication (CCC) across various biological processes, ranging from embryonic development to cancer progression. However, its limited resolution and high data sparsity hinder the detailed characterization of CCC patterns within…
Yiming Li, Cenfu Wei, Wenjing Yang, Hai Wang + 16 more
Using over 100 intestinal tissue sections from non-diseased controls and patients with ulcerative colitis or Crohn’s disease across multiple inflammatory bowel disease consortia, we construct a spatially resolved atlas containing over three million cells and systematically evaluate two imaging-based spatial…
Lindsey Lammlin, Huong X Tran, Aanya Mohan, Michael D Newton + 6 more
Successful generation of high-quality spatial transcriptomics data from murine musculoskeletal tissues has been impeded by the challenge of preserving RNA integrity through the harsh tissue processing steps required for histological sectioning. In particular, the need to thoroughly fix and decalcify mineralized tissues…
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…
Quanlei Liu, Chunhao Shen, Yang Dai, Ting Tang + 9 more
Background Temporal lobe epilepsy (TLE) is among the most common types of epilepsy and often leads to cognitive, emotional, and psychiatric issues due to the frequent seizures. A notable pathological change related to TLE is hippocampal sclerosis (HS), which is characterized by neuronal loss, gliosis, and an increased…
Sergi Cervilla, Daniela Grases, Elena Perez, Francisco X. Real + 4 more
Spatial biology experiments integrate the molecular and histological landscape of tissues to provide a previously inaccessible view of tissue biology, unlocking the architecture of complex multicellular tissues. Within spatial biology, spatial transcriptomics platforms are among the most advanced, allowing researchers…
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As time-resolved x-ray absorption spectroscopy experiments become more prevalent, new tools are required to process and analyze the large amounts of data measured efficiently. To address this growing demand, we developed autoXAS: a python package for easy, fast, and reproducible processing and analysis of in-situ and…