Journal: bioRxiv

Article Title: A Foundational Generative Model for Cross-platform Unified Enhancement of Spatial Transcriptomics

doi: 10.64898/2025.12.23.696267

Figure Lengend Snippet: a , Model overview. FOCUS is a diffusion-based generative model that leverages ST and H&E encoders , pretrained on large-scale, cross-tissue data to extract robust multimodal features. It integrates multimodal conditions as inputs, including under-refined ST maps, paired H&E images with cell segmentation masks, scRNA-seq references, and spatial gene co-expression matrices. Each challenge is addressed through tailored modules, with a cross-challenge coordination strategy enabling module interaction for coherent improvement across challenges. b , Large-scale, cross-platform multimodal dataset. In total, we assemble 6,876 paired ST-H&E images (corresponding to over 1.7 million patches) with matched cell segmentation masks, referenced scRNA-seq from public resources (over 5.8 million scRNA-seq cell profiles; Table S1), and precomputed spatial gene co-expression matrices. The data collection spans ten ST platforms, including eight sequencing-based (Visium (probe-based and polyA-based), Visium Cytassist, VisiumHD, Spatial Transcriptomics, Stereo-seq, BMK S1000, and Open-ST) and two imaging-based (Xenium and CosMx) platforms, and two species (human and mouse), comprising 17 normal and 17 cancer tissues, with whole-transcriptome profiles available for both species. c, Benchmarking and validation across challenges and downstream tasks, including spatial domain characterization, cell-cell communications, and cell-cell co-localization.

Article Snippet: These include sequencing-based: Visium (probe-based and polyA-based) , Visium CytAssist , and VisiumHD (all Visium platforms are products of 10X Genomics), Spatial Transcriptomics (Spatial Transcriptomics AB), Stereo-seq (BGI), BMK S1000 (BMKGENE), and Open-ST , and image-based platforms: Xenium (10X Genomics) and CosMx (Nanostring).

Techniques: Diffusion-based Assay, Expressing, Sequencing, Imaging, Biomarker Discovery

Journal: bioRxiv

Article Title: A Foundational Generative Model for Cross-platform Unified Enhancement of Spatial Transcriptomics

doi: 10.64898/2025.12.23.696267

Figure Lengend Snippet: a , Cosine similarity between cell-cell correlation matrices derived from H&E and ST (from the native Stereo-seq platform, FOCUS and competing methods) across low, intermediate, and high complexity regions. FOCUS achieves the greatest improvement in cosine similarity, especially in high-complexity regions. b , Spatial maps of complexity and H&E-ST feature consistency, with pixels classified by joint categories of complexity and feature consistency; FOCUS yields the largest extent of high-complexity regions with high H&E-ST feature consistency. c-d , Model robustness analysis. c , Cosine similarity (defined in a ) in high-complexity regions determined by various factors of cell density, size, shape, or texture. FOCUS consistently maintains the highest similarity compared to the native platform and competing methods, demonstrating its robust profiling performance. d , Illustration of spatial distribution of factor-specific complexity in a human colon cancer tissue section on Stereo -seq. (e-f) , Joint enhancement of challenges in sensitivity and complexity, tested on Stereo-seq ovarian cancer. e, The challenge coordination score reveals co-occurring challenges sensitivity and complexity, triggering joint optimization in FOCUS. f, Left: H&E image with the corresponding cellular complexity map, where low sensitivity (quantified by UMIs per cell) and poor H&E-ST feature consistency co-occur. Right: performance comparison with single-task baselines, i.e., scDOT for sensitivity and PatchNCE for complexity. FOCUS performs best on both tasks, demonstrating the benefit of joint optimization.

Article Snippet: These include sequencing-based: Visium (probe-based and polyA-based) , Visium CytAssist , and VisiumHD (all Visium platforms are products of 10X Genomics), Spatial Transcriptomics (Spatial Transcriptomics AB), Stereo-seq (BGI), BMK S1000 (BMKGENE), and Open-ST , and image-based platforms: Xenium (10X Genomics) and CosMx (Nanostring).

Techniques: Derivative Assay, Comparison

Journal: The Journal of Experimental Medicine

Article Title: Rediscovering the human thymus through cutting-edge technologies

doi: 10.1084/jem.20230892

Figure Lengend Snippet: Single-cell and spatial omics methods

Article Snippet: , Tissue section , Microarray-based sequencing , , , 10X Genomics Visium , Spatial, mRNA (55 μm) , Unbiased, probes include 18,000 unique genes , Low capture efficiency, low resolution.

Techniques: Sequencing, Multiplex Assay, Expressing, Microarray, Imaging, Biomarker Discovery

Journal: Cell Reports Methods

Article Title: Assessment of spatial transcriptomics for oncology discovery

doi: 10.1016/j.crmeth.2022.100340

Figure Lengend Snippet:

Article Snippet: An alternative to the FFPE-polyA-ST protocol is a surrogate measure of gene expression that uses gene-specific probe pairs to detect transcripts (Visium Spatial Gene Expression for FFPE, 10x Genomics).

Techniques: Recombinant, Gene Expression, Sequencing, Software, Microscopy

Journal: Nature Reviews. Genetics

Article Title: Methods and applications for single-cell and spatial multi-omics

doi: 10.1038/s41576-023-00580-2

Figure Lengend Snippet: Spatial multi-ome profiling of tissue samples can be achieved by applying spatial mono-omics assays separately on adjacent or serial tissue sections (part a ) or in a combined way on the same tissue section (parts b – e ). a , Serial fresh-frozen or formalin-fixed paraffin-embedded (FFPE) tissue sections can be analysed using different spatial mono-omic assays, potentially also combining with morphological stainings and annotations on the same or adjacent sections, followed by computational data integration. b , Microfluidic deterministic barcoding strategies in tissue allow next-generation sequencing (NGS)-based spatial multi-omics profiling of transcriptome-plus-proteins, as in DBiT-seq and Spatial-CITE-seq , and epigenome-plus-transcriptome, as in ATAC&RNA-seq and CUT&Tag-RNAseq . Using dual microfluidic chip-based spatial barcoding of poly(A) RNAs together with proteins or epigenome information at the crossroads of chip channels, a spatially barcoded 2D pixel map of the tissue is created. c , Advanced fluorescence in situ hybridization (FISH)-based methods, including MERFISH , , and seqFISH+ , , , allow microscopy-based identification of thousands of transcripts together with genomic loci in single cells, in addition to being compatible with limited protein readouts using fluorescent or DNA-conjugated antibody readout strategies. These high-resolution imaging methods leverage predefined optical barcoding schemes and complex encoding and readout probe designs. d , Array-based assays, including Spatial Transcriptomics (ST) and 10x Genomics Visium , make use of slides with arrayed oligo-dT spots for capturing and spatial barcoding of poly(A) RNAs followed by NGS profiling. This can be combined with upfront haematoxylin and eosin (H&E) staining or limited protein antibody staining and tissue imaging for spatial mapping. In SM-Omics and SPOTS , these technologies have also been shown to be compatible with antibody-derived tag (ADT)-conjugated antibody-based co-profiling of a larger number of proteins. e , NanoString GeoMx digital spatial profiling (DSP) , , allows quantification of RNAs and proteins in specific regions of interest (ROIs) by counting uniquely barcoded oligonucleotides that are covalently linked through a UV-photocleavable linker with probes or antibodies. Tissue marker staining, imaging, ROI selection and illumination by directed UV light causes disintegration of the photocleavable linkers that are collected and profiled by NGS, followed by spatial mapping to the ROIs. cDNA, complementary DNA; gDNA, genomic DNA; OCT, optimal cutting temperature compound; UMI, unique molecular identifier.

Article Snippet: These high-resolution imaging methods leverage predefined optical barcoding schemes and complex encoding and readout probe designs. d , Array-based assays, including Spatial Transcriptomics (ST) and 10x Genomics Visium , make use of slides with arrayed oligo-dT spots for capturing and spatial barcoding of poly(A) RNAs followed by NGS profiling.

Techniques: Formalin-fixed Paraffin-Embedded, Next-Generation Sequencing, Biomarker Discovery, RNA Sequencing, Fluorescence, In Situ Hybridization, Microscopy, Imaging, Staining, Derivative Assay, Marker, Selection