24 papers · ranked by Valyu relevance
An Wang, Donald Geman, Uthsav Chitra, Laurent Younes
Spatial transcriptomics (ST) technologies measure gene expression at thousands of locations within a two-dimensional tissue slice, enabling the study of spatial gene expression patterns. Spatial variation in gene expression is characterized by spatial gradients, or the collection of vector fields describing the…
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…
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…
Chichun Tan, Ying Ma
The rapid advancement of spatially resolved transcriptomics (SRT) technology enables gene expression profiling across tissue locations while preserving spatial context. Gene co-expression analysis in SRT data provides critical insights into how genes function together within the tissue microenvironment. However…
Koushul Ramjattun, Alyson Wang, Hannah Lee, Shilpi Giri + 6 more
The advent of spatial omics has revolutionized our understanding of tissue biology; however, these technologies remain largely descriptive and do not capture how changes in gene regulation propagate across spatial neighborhoods. While in-silico perturbation methods and foundation models aim to model the impact of…
Jimena Garcia-Guillen, Mahla Ahmadi, Theophilus Frimpong, Iris Seaman + 6 more
How spatial patterns arise during embryonic development is classically explained by the French Flag model, in which cells acquire positional identities by interpreting morphogen concentration thresholds. However, in many developmental systems, spatial patterns instead emerge progressively through temporal programs of…
Xinyan Chen, Chenzhi Lai, Tian He, Zong Chen + 1 more
Background Craniosynostosis is a congenital disorder characterized by premature suture fusion and aberrant skull morphogenesis. The cellular dynamics and regulatory mechanisms of suture mesenchymal stem cells (SuSCs) in this disease remain poorly defined. Methods We integrated single-cell RNA sequencing and…
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…
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…
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…
Keunho Byeon, Jin Tae Kwak
Spatial transcriptomics offers spatially resolved gene expression profiling within tissue sections, but its cost and limited throughput hinder large-scale deployment. To extend this capability to routine practice, recent computational methods aim to infer spatial gene expression directly from ubiquitous hematoxylin and…
Wei Wang, Quoc-Toan Ly, Chong Yu, Jun Bai
—Spatial transcriptomics (ST) enables transcriptomewide profiling while preserving the spatial context of tissues, offering unprecedented opportunities to study tissue organization and cell–cell interactions in situ. Despite recent advances, existing methods often lack effective integration of histological morphology…
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…
Yuan Zhang, Ziyan Sun, Zhixin Shi, Mengdi Nan + 4 more
Spatial transcriptomics is transforming our multidimensional understanding of cellular spatial organization and its functional mechanisms in processes such as development and disease by systematically resolving the spatial heterogeneity of gene expression within tissues. To delve deeper into the dynamic processes…
Tori Millsteed, Robert J. Henry
Spatiotemporal gene expression is the underlying determinant of how living things grow and function, including the processes of seed development and germination. Understanding how these regulatory pathways work in space and time is critical to harnessing the functional diversity of the components of the seed and…
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…
Hongyan Cao, Gaiqin Liu, Jingyi Xia, Runle Chen + 8 more
Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we…
Xin Li, Xiaofei Dong, Zhenke Duan, Lulu Shang + 4 more
Spatial domain identification requires jointly modeling molecular signatures and physical coordinates, yet current tools frequently over-smooth biological boundaries, require user-specified cluster numbers, and lack principled multimodal integration. We introduce BaySC, an integrative Bayesian spatial clustering…
Jiayu Su, Jun Hou Fung, Haoyu Wang, Dian Yang + 3 more
Program for Mathematical Genomics, Columbia University, New York, NY, USA Department of Systems Biology, Columbia University, New York, NY, USA New York Genome Center, New York, NY, USA Department of Molecular Pharmacology and Therapeutics, Columbia University, New York, NY, USA Department of Computer Science, Columbia…
Alexander Hindeleh, Wei Xiong, Charles Wang, Xingguang Luo + 2 more
Recent advances in single-cell RNA sequencing (scRNA-seq) have transformed neuroscience research by enabling the identification of genes, cell types, and molecular pathways involved in brain development and function. However, scRNA-seq lacks spatial information regarding the anatomic location of gene expression.…
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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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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…
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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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The capacity to pattern biomolecules within microfluidic devices expands the scope of microfluidic technologies. In such patterned systems, surface-bound components remained localized, while the microfluidic network supplies reagents and removes waste products. This approach has enabled continuous protein expression…