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Spatial biology’s next era is here with MERFISH 2.0. From Surface Structures to Subcellular Depths Detect more transcripts, more cells, and more insights.

Spatial transcriptomics is an essential application for investigating cellular structures and interactions and requires multimodal information to precisely study spatial domains. Here, we propose STAIG, a deep-learning model that integrates gene expression, spatial coordinates, and…

Imaging-based spatial transcriptomics (iST), such as MERFISH, CosMx SMI, and Xenium, quantify gene expression level across cells in space, but more importantly, they directly reveal the subcellular distribution of RNA transcripts at the single-molecule resolution….

One fundamental yet open question is how eukaryotic chromosomes fold into segregated territories, 14 a process essential for gene transcription and cell fate. Through analyzing Hi-C and chromatin15 tracing DNA-FISH data, we identify long-range chromo…

Spatial transcriptomics has emerged as a pivotal technology for profiling gene expression of cells within their spatial context. The rapid growth of publicly available spatial data presents an opportunity to further our understanding of microenvironments…

Cellular organization is central to tissue function and homeostasis, influencing development, disease progression, and therapeutic outcomes. The emergence of spatial omics technologies, including spatial transcriptomics and proteomics, has enabled the integration of molecular and histological…

Clustering can identify the natural structure that is inherent to measured data. For single-cell omics, clustering finds cells with similar molecular phenotype after which cell types are annotated. Leiden clustering is one of the algorithms…

Spatially resolved transcriptomics has made it possible to study the subcellular organization of mRNA, a critical aspect of cellular function. However, there is a dearth of analytical tools to identify and interpret the functional significance…

Spatial transcriptomics (ST) technologies have revolutionized our understanding of cellular ecosystems. However, these technologies face challenges such as sparse gene signals and limited gene detection capacities, which hinder their ability to fully capture comprehensive spatial…

Spatially resolved transcriptomics (SRT) provides an invaluable avenue for examining cell-cell interactions within native tissue environments. The development and evaluation of analytical tools for SRT data necessitate tools for generating synthetic datasets with known ground…

Single-cell spatial transcriptomics reveals how cells organize in healthy and diseased tissues. From these data, tissue segmentation analysis defines discrete compartments that organize cells into functional multicellular units. Existing methods for automated tissue segmentation rely…

Spatial transcriptomics (ST) data, by providing spatial information, enables simultaneous analysis of gene expression distributions and their spatial patterns within tissue. Clustering or spatial domain detection represents an essential methodology for ST data, facilitating the…

Single-omics approaches often provide a limited view of complex biological systems, whereas multiomics integration offers a more comprehensive understanding by combining diverse data views. However, integrating heterogeneous data types and interpreting the intricate relationships between…

Recent advances in spatial transcriptomics have enabled simultaneous preservation of high-throughput gene expression profiles and the spatial context, enabling high-resolution exploration of distinct regional characterization in tissue. To effectively understand the underlying biological mechanisms within…

Cis-acting regulatory enhancer elements are valuable tools for gaining cell type-specific genetic access. Leveraging large chromatin accessibility atlases, putative enhancer sequences can be identified and deployed in adeno-associated virus (AAV) delivery platforms. However, a significant…

Recent advances in single-cell spatial transcriptomics (scST) have enabled the analysis of gene transcription levels in individual cells while preserving their spatial positions. Cell-type mapping and annotation are crucial in understanding the complex interactions between…

Spatial transcriptomics data analysis integrates gene expression profiles with their corresponding spatial locations to identify spatial domains, infer cell-type dynamics, and detect gene expression patterns within tissues. However, the current spatial transcriptomics analysis neglects the…