16 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…
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…
Jahanzeb Saqib, Junil Kim
Spatial transcriptomics technologies have significantly enhanced the analysis of gene expression profiles by retaining the spatial information of intact tissue sections and enabling the possibility of a more profound comprehension of tissue structures and cellular relationships. Despite this, most platforms have…
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…
Haoran Lu, Luyang Fang, Orlando Zeng, Ping Ma + 2 more
Spatial transcriptomics analysis is a powerful approach for dissecting the structure of tissue microenvironment and uncovering the mechanism of cell-cell communications. However, existing technologies are limited by either spatial resolution or gene coverage. Most single-cell resolution technologies target only a few…
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…
Sankarasubramanian Jagadesan, Chittibabu Guda, Jan Aerts
Spatial transcriptomics has transformed our ability to study gene expression within the intact tissue architecture by allowing for a deeper understanding of complex tissues in healthy and disease states. By retaining spatial relationships, the effects of gene expression can be studied at the cellular microenvironments…
Lei Zhang, Ying Zhu, Shuqin Zhang
Spatially resolved transcriptomics (SRT) profiles gene expressions at near- or sub-cellular resolution while preserving their spatial context, yet interpreting SRT data to understand spatial cellular and molecular organization remains challenging. Most existing computational methods focus on global spatial domains but…
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…
Yijun Li, Stefan Stanojevic, Bing He, Zheng Jing + 4 more
Spatial omics technologies are one of the breakthroughs in science in the last several years (, , , ). Such technologies are able to systematically measure transcriptome information in the tissue space, thereby preserving the spatial context of gene expression. The addition of spatial information allows researchers to…
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…
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…
Christina Huan Shi, Juan C. Piña-Crespo, Kevin Y. Yip, Timothy Y. Huang
Alzheimer’s disease (AD) is a complex neurodegenerative disorder that is associated with cognitive decline in the elderly. While β-amyloid (Aβ) plaques and neurofibrillary tau tangles have been used to define and stage AD onset in human brain, how these pathologies can affect various cell types nearby remains a subject…
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…
Shicheng Zhang, Koichi Saeki, Hiroshi Haeno
Spatial transcriptomics captures context-dependent gene expression; however, existing workflows do not consistently account for measurement scale and often rely on a user-defined spatial neighbor graph, making results sensitive to this choice and limiting cross-study comparability. We present geneSCOPE (gene Spatial…