27 papers · ranked by Valyu relevance
Helena L. Crowell, Yixing Dong, Ilaria Billato, Peiying Cai + 30 more
Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows…
Junchao Zhu, Ruining Deng, Junlin Guo, Tianyuan Yao + 13 more
Spatial transcriptomics (ST) enables the simultaneous measurement of gene expression and spatial localization within tissue sections, providing unprecedented opportunities to dissect tissue architecture and functional organization. As a relatively new omics technology, bioinformatics has driven much of the innovation…
Peilei Deng, Jiaruo Huang, Wencan He, Zhiyuan Li + 6 more
Spatiotemporal heterogeneity is recognized as a key driver of functional diversity in tissues. Spatial transcriptomics, which integrates high-throughput transcriptomics with high-resolution tissue imaging, enables the precise mapping of gene expression patterns at the tissue section level. This technology overcomes the…
Vladyslav Honcharuk, Afeefa Zainab, Yoshiya Horimoto, Keiko Takemoto + 3 more
Spatial transcriptomics enables detailed mapping of gene expression within tissues, revealing spatial organization of cellular and molecular processes. However, generating such data is costly and technically challenging, and analysis requires advanced bioinformatics skills. Although public datasets are growing…
Kalen Clifton, Vivien Jiang, Rafael dos Santos Peixoto, Srujan Singh + 3 more
Comparative analysis of spatial transcriptomics (ST) data is needed to identify genes that spatially change in their expression patterns between conditions, such as in diseased versus healthy tissues. Existing methods, including those developed for and adapted from non-spatial transcriptomics, generally focus on…
Shuo Shuo Liu, Shikun Wang, Yuxuan Chen, Anil K. Rustgi + 2 more
Background Spatial transcriptomics have emerged as a powerful tool in biomedical research because of its ability to capture both the spatial contexts and abundance of the complete RNA transcript profile in organs of interest. However, limitations of the technology such as the relatively low resolution and comparatively…
Mohammad Faiz Iqbal Faiz, Elliot Jokl, Rachel Jennings, Karen Piper Hanley + 3 more
Spatial transcriptomics is rapidly advancing toward single cell level resolution, revealing complex tissue architectures organized across continuous anatomical gradients. However, accurate identification of spatial domains remains a central computational challenge, as many existing clustering approaches blur anatomical…
Andrea Sottosanti, Davide Risso, Francesco Denti
Spatial transcriptomics measures the expression of thousands of genes in a tissue sample while preserving its spatial structure. This class of technologies has enabled the investigation of the spatial variation of gene expressions and their impact on specific biological processes. Identifying genes with similar…
Jordan J. Smith, Xi Wang, Matthew McPheeters, Made Airanthi Widjaja-Adhi + 4 more
Spatial transcriptomics enables high-resolution mapping of gene expression in intact tissues but remains challenging due to complex computational workflows that limit accessibility and reproducibility. Here, we present a Model Context Protocol (MCP) framework enabling natural language-driven spatial transcriptomics…
Jeremy W. Prokop, Stephanie M. Bilinovich, Ember Tokarski, Sangeetha Vishweswaraiah + 27 more
Although single-cell RNA sequencing advances cellular discovery, spatial RNA sequencing promises to refine our tissue physiology and disease knowledge (189, 190). The complex anatomy of cells within any biological tissue has long been appreciated through histology, and dating back to 1987, groups have advanced the…
Florica Constantine, Zoltan Laszik, Sandrine Dudoit, Elizabeth Purdom
Spatial transcriptomics allows the unprecedented examination of gene expression levels at the resolution of spatially-situated single cells in a high-throughput manner. As the technology is adopted more broadly, studies frequently collect data from multiple tissue samples, which leads to unique challenges that…
Jiasen Zhang, Xi Qiao, Liangliang Zhang, Weihong Guo
Spatial transcriptomics allows researchers to visualize and analyze gene expression within the precise location of tissues or cells. It provides spatially resolved gene expression data but often lacks cellular resolution, necessitating cell type deconvolution to infer cellular composition at each spatial location. In…
Chen Yang, Xianyang Zhang, Jun Chen
Identifying spatially variable genes in spatial transcriptomics requires methods that are accurate, well calibrated and scalable, yet current approaches trade expressive kernels for tractable computation. We present FLASHS, which moves spatial testing to the frequency domain: Random Fourier Features and sparse…
Yanni Cao, Kangcheng Xu, Xiaohui Li, Junyuan Zhang + 3 more
Title: Simple Summary While traditional gene sequencing technologies can identify cell types and gene activity, they cannot tell us where these cells live or how they are organized within tumor tissue. Spatial transcriptomics, an emerging technology, can simultaneously record gene expression information at each…
Wenhao Li, Yuan Zhou, Andrés Moya
Title: Simple Summary Spatial transcriptomics allows researchers to measure gene expression while preserving the positions of cells and molecules within tissues. Imaging-based spatial transcriptomics methods do this by directly visualizing RNA molecules or amplified signals in tissue samples, often at single-cell or…
Macrina Lobo, Ziqi Zhang, Xiuwei Zhang
Spot-based spatial transcriptomics (ST) captures aggregated transcriptomic profiles at spatial locations (spots) in tissue slices. Cell type deconvolution methods decode each spot and estimate the proportion of every cell type in the spot, necessary for uncovering spatial cell type distributions for further downstream…
Pritam Dey, Rajarshi Guhaniyogi, Yang Ni, Bani K. Mallick
Spatially resolved transcriptomics is a fast-developing set of technologies that enables the measurement of localized gene expression across spatial locations in a sample. Detecting spatially varying genes is critical for analyzing such data, yet existing methods often fail to account for inter-gene correlations…
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…
Hongyi Yu, Yaoyu Fang, Jiahe Qian, Xinkun Wang + 2 more
Purpose: Spatial transcriptomics (ST) enables gene expression measurements within the tissue context. However, these measurements are often noisy, low-resolution, and sparsely sampled, which limits the recovery of fine spatial structure. Deep neural networks have become powerful tools for expression imputation from…
Siyuan Zhao, Nafiul Nipu, Hossein Fathollahian, Olga Karginova + 3 more
We present Loom, a spatial transcriptomics (ST) visual computing system to support the analysis of pseudo-temporal trajectories, comparative investigation across samples and regions of interest, and the examination of spatially structured processes within local microenvironments. ST is a molecular profiling technology…
Alvin Sheng, Sandra E. Safo, Thierry Chekouo
Recent advances in spatial transcriptomics have enabled researchers to profile gene expression at the single-cell spatial resolution, often for multiple tissue samples in a single study. This high-dimensional molecular profile for each cell can be used to sort cells into cell types with distinct functions, or segment…
Puneet Velidi, Zhengxiao Wei, Farouk S. Nathoo
Gaussian process models underlie many spatial transcriptomics tools but typically assume stationary covariance. Covariance non-stationarity has long been recognized in spatial statistics as an important feature of spatial data, yet it has received little attention in spatial transcriptomics. We show that this omission…
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…
Authors not listed
Understanding how plants respond to dynamic and spatially variable stimuli is a key goal in plant sciences. Traditional imaging methods often involve a trade-off between environmental control and spatial resolution, limiting their ability to capture real-time responses in high resolution. Microfluidic technology…
Authors not listed
The WRN helicase has recently emerged as a promising therapeutic target for microsatellite instability (MSI)-high cancers. Here, we report LXW-P1, a potent WRN degrader derived from marine bromotyrosine alkaloids. Its molecular target was identified using an AI-guided, pathway-informed perturbation transcriptomics…
Authors not listed
Molecular mechanisms governing initiation steps of the assembly of thousands of endogenous multi-protein complexes (EMCs) remain incompletely understood. Here, multiple lines of observations are reported reflecting the biological functions-aligned initiation sequence of hybrid assembly pathways (HAPs) of EMCs. HAPs…
Authors not listed
Genetically encoded covalent warheads at the protein-protein interface, enable the locking of interaction from the transient complex into a stable covalent adduct, which could improve the binding affinity, selectivity, and kinetics between a protein binder and its target. Given the diverse chemical microenvironments…