Insights on Spatial Transcriptomics

Standard spatial transcriptomics platforms can analyze tissue samples up to about 25 square millimeters. But what if you need to study an entire tumor or organ section? The context: Spatial transcriptomics has emerged as a powerful tool for understanding how gene expression varies across tissue space, providing insights into cell-cell interactions, tissue organization, and disease mechanisms. However, current commercial platforms face significant constraints: high costs, lengthy processing times, limited gene coverage, and crucially, small capture areas that restrict analysis to tissue fragments rather than whole organs or large anatomical structures. This size limitation is particularly problematic for studying complex diseases like multiple sclerosis, where pathological changes occur across vast brain regions with heterogeneous patterns that can't be captured in small tissue sections. Amelia Schroeder et al. developed iSCALE (inferring Spatially resolved Cellular Architectures in Large-sized tissue Environments), a computational framework that addresses this scale problem by leveraging the relationship between gene expression patterns and histological features visible in standard H&E stained slides. Key technical approach: - "Daughter capture" integration: Combines multiple small spatial transcriptomics data from different tissue regions - H&E-guided prediction: Uses machine learning to predict gene expression patterns across entire large tissue sections based on histological features - Semi-automatic alignment: Develops methods to computationally stitch together data from different tissue sections - Cellular-resolution inference: Predicts super-resolution gene expression at near single-cell level Validation and applications: The method was tested on multiple sclerosis brain samples, where iSCALE uncovered lesion-associated cellular characteristics that were undetectable by conventional ST experiments. The approach enables analysis of tissue areas far exceeding the physical constraints of current platforms while maintaining spatial resolution. Broader implications: This work demonstrates a shift from hardware-limited to computationally-enabled spatial transcriptomics. Rather than requiring ever-larger capture arrays, iSCALE shows how intelligent integration of limited experimental data with computational inference can overcome physical platform constraints. The approach could enable spatial transcriptomics studies of whole organs, developmental processes requiring large-scale analysis, and disease contexts where pathology spans anatomical regions. How might computational approaches like this change the scale of questions we can address in spatial biology? paper: https://lnkd.in/e2fmn7UC code: https://lnkd.in/eiGsh2HD #SpatialTranscriptomics #ComputationalBiology #MachineLearning #Neuroscience #MultipleSclerosis #DigitalPathology

🟥 Spatially Resolved Single-Cell Atlas of Organ Development and Regeneration Understanding the mechanisms of organ development and regeneration requires a deep understanding of the spatial and cellular architecture of tissues over time. Recent advances in spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) have enabled the construction of high-resolution atlases that map gene expression within intact tissues at the cellular level. These atlases provide an unprecedented view of the molecular architecture and dynamic changes of organs during embryogenesis, postnatal development, and injury-induced regeneration. Spatially resolved single-cell atlases combine positional information with transcriptomic lineage information, allowing researchers to identify specific cell types, track their developmental trajectories, and understand their interactions in tissue microenvironments. For example, during the development of the heart, liver, and kidney, these atlases reveal transient progenitor populations, regionalized signaling gradients, and lineage differentiation that regulate tissue morphogenesis. During regeneration, such as in liver or skin injury models, spatial single-cell analysis can reveal cellular plasticity, reactivation of developmental programs, and microenvironmental remodeling processes that guide tissue repair. Importantly, spatial single-cell atlases can also facilitate the identification of signaling hubs and intercellular communication networks by capturing ligand-receptor interactions in situ. This spatial context is critical for understanding how stem cells are maintained, how differentiation is spatially regulated, and how inflammatory or fibrotic responses are initiated during regeneration. In addition, by comparing normal development with regeneration and pathological states, researchers can uncover deviations in cell fate decisions or signaling that lead to disease. Overall, spatially resolved single-cell atlases are foundational resources for developmental biology, regenerative medicine, and disease modeling. They provide a blueprint for designing targeted interventions that promote regeneration or prevent fibrosis and tissue degeneration. As the technical resolution and scalability improve, these atlases will play an increasingly important role in guiding stem cell engineering, organoid design, and precision medicine strategies. Reference [1] Jie Liao et al., Trends in Biotechnology 2021 (DOI: 10.1016/j.tibtech.2020.05.006) #SpatialTranscriptomics #SingleCellAtlas #OrganDevelopment #RegenerativeMedicine #StemCells #TissueEngineering #PrecisionMedicine #Bioinformatics #DevelopmentalBiology #BiomedicalResearch #CSTEAMBiotech

Excited to share our latest spatial transcriptomics tool "STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks"! 🔬 Spatially resolved transcriptomics is revolutionizing our understanding of cellular organization by integrating high-throughput transcriptome measurements with spatial data. However, many current technologies fall short of achieving single-cell resolution. Enter STdGCN, our innovative graph model that leverages single-cell RNA sequencing (scRNA-seq) as a reference to deconvolute cell types in spatial transcriptomic (ST) data. 💡 What makes STdGCN unique? - It integrates expression profiles from scRNA-seq with spatial localization from ST data. - Extensive benchmarking across multiple datasets shows STdGCN outperforming 17 state-of-the-art models. - In a human breast cancer Visium dataset, STdGCN effectively delineates stroma, lymphocytes, and cancer cells, providing deeper insights into the tumor microenvironment. - In human heart ST data, STdGCN identifies critical changes in endothelial-cardiomyocyte communications during tissue development. Our findings demonstrate the power of STdGCN in enhancing the resolution and accuracy of cell-type deconvolution, paving the way for more precise and insightful spatial transcriptomic analyses. Kudos to Dr. Yawei Li on pushing the boundaries of what's possible in the field of spatial transcriptomics! Paper (no pay wall): https://lnkd.in/gR49yjR6 Code: https://lnkd.in/gSrpUNHN #Research #Innovation #SpatialTranscriptomics #GraphConvolutionalNetworks #scRNAseq #Bioinformatics #CancerResearch #HeartDevelopment #DataScience #AIinHealthcare #STdGCN

To unlock the full potential of spatial omics in understanding disease, we need to integrate multiple molecular layers such as RNA, protein, and metabolites across tissue sections that rarely align perfectly. Our team, led by @Aditya Pratapa, Rohit Singh, and Purushothama Rao Tata developed SAME, or Spatial Alignment of Multimodal Expression, a method that flexibly aligns spatial data even when sections are distorted by tears, folds, or biological variation. This enables more accurate and interpretable maps of how cells behave and interact in complex environments like tumors and mucosa. Read the preprint: https://lnkd.in/eUQGAXFM Code available at: https://lnkd.in/e9H9njCd Most existing alignment tools assume tissue sections are structurally preserved, which often is not the case. SAME introduces a new concept called space-tearing transforms, a controlled mathematical approach that accommodates local disruptions while preserving overall tissue layout. This makes it possible to integrate diverse spatial modalities with high fidelity, revealing biologically meaningful patterns that are often missed by single-modality or rigid alignment methods. Key innovations: --Controlled topological flexibility for real tissue architectures Phenotype-based cross-modal matching, independent of raw feature correlation --Support for diverse modalities including spatial RNA (Vizgen MERSCOPE, 10x Genomics Xenium), protein (Akoya Biosciences, Inc. PhenoCycler), and metabolite (MALDI-MSI/Bruker Spatial Biology) data What this enables: --Identification of cryptic immune niches in healthy and diseased tissues (e.g., functionally distinct T cell programs in oral mucosa and lung adenocarcinoma) --Discovery of spatially localized metabolic crosstalk, such as mevalonic acid enrichment within tumor-macrophage microenvironments --Integration-ready experimental designs, allowing each tissue section to be optimized for its specific assay Grateful to contribute work with an outstanding team from Duke University, Virginia Commonwealth University/VCU School of Dentistry, the @Max Planck Institute, @German Center for Lung Research, Thoraxklinik am Universitätsklinikum Heidelberg, and Justus Liebig University Giessen. #spatialbiology #multiomics #computationalbiology #bioinformatics #cancerresearch #tissuearchitecture #StraticaBiosciences