17 papers · ranked by Valyu relevance
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…
Emir Radkevich, Darwin D’Souza, Raphael Mattiuz, Raphael Merand + 22 more
Visium is a spatial sequencing technology that utilizes messenger RNA (mRNA) to spatially map gene expression within tissues. Despite its potential, research utilizing deconvolution tools and exploring microenvironment dynamics remains challenging. We address this gap by benchmarking deconvolution tools across diverse…
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…
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…
Yingying Lu, Qin Chen, Lingling An
The advent of spatial transcriptomics technology has allowed for the acquisition of gene expression profiles with multi-cellular resolution in a spatially resolved manner, presenting a new milestone in the field of genomics. However, the aggregate gene expression from heterogeneous cell types obtained by these…
Souvik Seal, Benjamin G. Bitler, Debashis Ghosh
In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and…
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…
Fatoumata Mangane, Pierre Bost
The rapid advancement of spatial transcriptomic technologies, particularly in situ hybridization based methods, has enabled the profiling of gene expression at sub cellular resolution across large tissue sections. Commercial platforms such as Xenium and CosMx now routinely generate high-quality datasets of increasing…
Shicheng Zhang, Koichi Saeki, Hiroshi Haeno
Spatial transcriptomics captures neighborhood-dependent gene expression, but existing workflows do not always fully account for measurement scale and often treat space implicitly. We present geneSCOPE, a framework that integrates ecology-inspired statistics with network analysis. Molecules are binned on a grid whose…
Chihao Zhang, Kangning Dong, Kazuyuki Aihara, Luonan Chen + 1 more
Spatial transcriptomics characterizes gene expression profiles while retaining the information of the spatial context, providing an unprecedented opportunity to understand cellular systems. One of the essential tasks in such data analysis is to determine spatially variable genes (SVGs), which demonstrate spatial…
Felicita Pia Masone, Francesco Napolitano
Spatial Transcriptomics assays allow to study gene expression as a function of the spatial position of cells across a tissue sample. Although several methods have been proposed to identify spatially variable genes, they do not take into account the position of the injection site in the case of treated samples. In this…
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…
Lei Zhang, Ying Zhu, Shuqin Zhang
Spatially resolved transcriptomics (SRT) has transformed biomedical research by enabling gene expression profiling at near- or sub-cellular resolution while preserving spatial context. However, interpreting SRT data to understand cellular and gene organization remains challenging. Current methods focus on identifying…
Jiawen Chen, Caiwei Xiong, Quan Sun, Geoffery W. Wang + 4 more
Spatial omics technologies revolutionize our view of biological processes within tissues. However, existing methods fail to capture localized, sharp changes characteristic of critical events (e.g. tumor development). Here, we present StarTrail, a novel gradient based method that powerfully defines rapidly changing…
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…
Mengwei Hu, Yi Cui, Qianhui Huang, Khoi Chu + 18 more
Current spatial CRISPR screening technologies are limited by targeted readouts and high costs, restricting the scope of biological discovery. Here we present SPAtial Cell Exploration (SPACE), a spatial CRISPR screening platform that integrates whole-transcriptome profiling (∼18,000 genes), multiplexed protein detection…
Artür Manukyan, Ella Bahry, Emanuel Wyler, Erik Becher + 12 more
The growing number of spatial omic technologies have created a demand for computational tools capable of managing, storing, and analyzing spatial datasets with multiple modalities and spatial resolutions. Meanwhile, computer vision is becoming an integral part of processing spatial data readouts where image…