25 papers · ranked by Valyu relevance
Linlin Zhang, Dongsheng Chen, Dongli Song, Xiaoxia Liu + 3 more
'Xun Xu' 'Xiangdong Wang'] The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with molecular alterations, defining in situ intercellular molecular communications and knowledge on spatiotemporal…
Jun Du, Yu-Chen Yang, Zhi-Jie An, Ming-Hui Zhang + 4 more
'Zou-Fang Huang' 'Ye Yuan' 'Jian Hou'] Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot…
Zhiwei Fan, Yangyang Luo, Huifen Lu, Tiangang Wang + 4 more
'Weiling Zhao' 'Pora Kim' 'Xiaobo Zhou'] Title: Abstract In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location…
Boxiang Liu, Yanjun Li, Liang Zhang
Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation…
Dario Righelli, Lukas M Weber, Helena L Crowell, Brenda Pardo + 6 more
Spatially-resolved transcriptomics (SRT) refers to a new set of high-throughput technologies, which measure up to transcriptome-wide gene expression along with the spatial coordinates of the measurements. Technological platforms differ in terms of the number of measured genes (from hundreds to full transcriptome) and…
Jan Kueckelhaus, Jasmin von Ehr, Vidhya M. Ravi, Paulina Will + 6 more
Spatial transcriptomic is a technology to provide deep transcriptomic profiling by preserving the spatial organization. Here, we present a framework for SPAtial Transcriptomic Analysis (SPATA, https://themilolab.github.io/SPATA), to provide a comprehensive characterization of spatially resolved gene expression…
Yiming Li, Saya Dennis, Meghan R. Hutch, Yanyi Ding + 10 more
Spatial transcriptomics provides researchers with a better understanding of gene expression within the tissue context. Although large volumes of spatial transcriptomics data have been generated, the lack of systematic curation and analysis makes data reuse challenging. Herein, we present Spatial transcriptOmics…
Chase Holdener, Shaowen Jiang, Danica M. Sutherland, Kira A. Griswold + 3 more
The morbidity and mortality associated with viral diseases in plants, animals, and humans are significant concerns. Understanding how viruses cause disease and identifying the viral and host factors that determine the outcome of infection are essential to develop new antiviral therapeutics and strategies to induce…
Oscar E. Ospina, Christopher M. Wilson, Alex C. Soupir, Anders Berglund + 3 more
Spatially-resolved transcriptomic technologies promise to increase our understanding of the tumor microenvironment, which will lead to better cancer prognosis and therapies. Several spatial transcriptomics technologies have been developed, as well as a few methods for data analysis. Nonetheless, analytical pipelines…
Jade Xiaoqing Wang, Xiang Zhou
Spatial transcriptomic technologies are becoming increasingly high-resolution, enabling precise measurement of gene expression at the subcellular level. Here, we introduce a computational method called subcellular expression localization analysis (ELLA), for modeling the subcellular localization of mRNAs and detecting…
Xiaoxia Liu, Yujia Jiang, Dongli Song, Linlin Zhang + 12 more
'Rui Hou' 'Yong Zhang' 'Jian Chen' 'Yunfeng Cheng' 'Longqi Liu' 'Xun Xu' 'Gang Chen' 'Duojiao Wu' 'Tianxiang Chen' 'Ao Chen' 'Xiangdong Wang'] Title: Abstract Spatial transcriptomics is considered as an important part of spatiotemporal molecular images to bridge molecular information with clinical images. Of those…
Xianghao Zhan, Jingyu Xu, Yuanning Zheng, Zinaida Good + 1 more
Spatial transcriptomics enables spatial gene expression profiling, motivating computational models that capture spatially conditioned regulatory relationships. We introduce SAGE-FM, a lightweight spatial transcriptomics foundation model based on graph convolutional networks (GCN) trained with a masked-central-spot…
Matthew N. Bernstein, Zijian Ni, Aman Prasad, Jared Brown + 4 more
Recent advances in spatially resolved transcriptomics technologies enable both the measurement of genome-wide gene expression profiles and their mapping to spatial locations within a tissue. A first step in spatial transcriptomics data analysis is identifying genes with expression that varies spatially, and robust…
Boxiang Liu, Yanjun Li, Liang Zhang
Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation…
Linbu Liao, Esha Madan, António M. Palma, Hyobin Kim + 10 more
'Praveen Bhoopathi' 'Robert Winn' 'Jose Trevino' 'Paul Fisher' 'Cord Herbert Brakebusch' 'Gahyun Kim' 'Junil Kim' 'Rajan Gogna' 'Kyoung Jae Won'] Background Understanding cellular heterogeneity within tissues hinges on knowledge of their spatial context. However, it is still challenging to accurately map cells to their…
Uthsav Chitra, Brian J. Arnold, Hirak Sarkar, Cong Ma + 3 more
Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving a topographic map of a tissue…
Zhiwei Fan, Tiangang Wang, Kexin Huang, Binwu Ying + 1 more
Recent advances in spatial omics technologies have revolutionized our ability to study biological systems with unprecedented resolution. By preserving the spatial context of molecular measurements, these methods enable comprehensive mapping of cellular heterogeneity, tissue architecture, and dynamic biological…
Qi Liu, Chih-Yuan Hsu, Yu Shyr
The expeditious growth in spatial omics technologies enable profiling genome-wide molecular events at molecular and single-cell resolution, highlighting a need for fast and reliable methods to characterize spatial patterns. We developed SpaGene, a model-free method to discover any spatial patterns rapidly in large…
Xiaoyu Li, Fangfang Zhu, Wenwen Min
using scRNA-seq Authors: ['Xiaoyu Li' 'Fangfang Zhu' 'Wenwen Min'] The rapid development of spatial transcriptomics (ST) technologies is revolutionizing our understanding of the spatial organization of biological tissues. Current ST methods, categorized into next-generation sequencing-based (seq-based) and fluorescence…
Guanao Yan, Shuo Harper Hua, Jingyi Jessica Li
In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 31 state-of-the-art methods, categorizing SVGs into three types: overall…
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…
Katharina Limbeck, Bastian Rieck
Persistence Diagrams Authors: ['Katharina Limbeck' 'Bastian Rieck'] Evaluating spatial patterns in data is an integral task across various domains, including geostatistics, astronomy, and spatial tissue biology. The analysis of transcriptomics data in particular relies on methods for detecting spatially-dependent…
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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…
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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…
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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…