Project description:Spatial transcriptomics and proteomics provide complementary information that independently transformed our understanding of complex biological processes. However, experimental integrations of these modalities are limited. To overcome this, we developed Spatial PrOtein and Transcriptome Sequencing (SPOTS) for high-throughput simultaneous integration of spatial transcriptomics and protein profiling. Compared to unimodal measurements, SPOTS substantially improves signal resolution and cell clustering and enhances the discovery power in differential gene expression analysis across tissue regions.
Project description:Tissue function relies on the precise spatial organization of cells characterized by distinct molecular profiles. Single-cell RNA-Seq captures molecular profiles but not spatial organization. Conversely, spatial profiling assays to date have lacked global transcriptome information, throughput or single-cell resolution. Here, we develop High-Density Spatial Transcriptomics (HDST), a method for RNA-Seq at high spatial resolution. Spatially barcoded reverse transcription oligonucleotides are coupled to beads that are randomly deposited into tightly packed individual microsized wells on a slide. The position of each bead is decoded with sequential hybridization using complementary oligonucleotides providing a unique bead-specific spatial address. We then capture, and spatially in situ barcode, RNA from the histological tissue sections placed on the HDST array. HDST recovers hundreds of thousands of transcript-coupled spatial barcodes per experiment at 2 μm resolution. We demonstrate HDST in the mouse brain, use it to resolve spatial expression patterns and cell types, and show how to combine it with histological stains to relate expression patterns to tissue architecture and anatomy. HDST opens the way to spatial analysis of tissues at high resolution.
Project description:Spatial transcriptomics enables deep exploration of cellular gene expression, making it possible to elucidate the relationship between individual cells and tissues, thus enabling researchers to better understand development and disease. In this study, we present spatial transcriptomics data using a novel high-resolution DNA chip with a capture region size of 6.5 x 6.5 mm containing 2 x 2 µm features for spatial barcoding with no gaps between them, thereby maximizing the capture area. These chips are manufactured at wafer scale using photolithography and are transferred to hydrogels, making them compatible with existing workflows for fresh frozen or paraffin-embedded samples. Herein, we examined a fresh frozen sample from an adult mouse liver. Using a bin size of 10, representing a 20 µm x 20 µm capture area, and at 68.78%, sequencing saturation, we obtained over 1.3 billion unique mapped reads, with a median of 16,967 unique reads per region, indicating the potential for more unique reads with deeper sequencing. This high-resolution mapping of liver cell types and the visualization of gene expression patterns illustrate significant advancements in spatial sequencing technology.
Project description:Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease characterized by repetitive alveolar injuries with excessive deposition of extracellular matrix (ECM) proteins. A crucial need in understanding IPF pathogenesis is identifying cell types associated with histopathological regions, particularly local fibrosis centers known as fibroblast foci. To address this, we integrated published spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) transcriptomics and adopted the Query method and the Overlap method to determine cell type enrichments in histopathological regions. Distinct fibroblast cell types are highly associated with fibroblast foci, and transitional alveolar type 2 and aberrant KRT5-/KRT17+ epithelial cells are associated with morphologically normal alveoli in human IPF lungs. Furthermore, we employed laser capture microdissection directed mass spectrometry to profile proteins. By comparing with another published similar dataset, common differentially expressed proteins and enriched pathways related to ECM structure organization and collagen processing were identified in fibroblast foci. Importantly, cell type enrichment results from innovative spatial proteomics and scRNA-seq data integration accord with those from spatial transcriptomics and scRNA-seq data integration, supporting the capability and versatility of the entire approach. In summary, we integrated spatial multi-omics with scRNA-seq data to identify disease-associated cell types and potential targets for novel therapies in IPF intervention. The approach can be further applied to other disease areas characterized by spatial heterogeneity.
Project description:Spatial transcriptomics (ST) technologies enable the mapping of gene expression to specific regions within tissues. However, current ST platforms present inherent trade-offs between resolution and throughput, which cannot be entirely addressed by a single technology. To unravel the spatiotemporal landscape of gene expression and cellular interactions during kidney injury and repair, we employed an integrated approach combining Xenium's high-resolution in situ sequencing with Visium's whole-transcriptome spatial profiling.
Project description:Spatial transcriptomics (ST) technologies enable the mapping of gene expression to specific regions within tissues. However, current ST platforms present inherent trade-offs between resolution and throughput, which cannot be entirely addressed by a single technology. To unravel the spatiotemporal landscape of gene expression and cellular interactions during kidney injury and repair, we employed an integrated approach combining Xenium's high-resolution in situ sequencing with Visium's whole-transcriptome spatial profiling.
Project description:Understanding the heterogeneity of Rheumatoid Arthritis (RA) and identifying therapeutic targets remain challenging using traditional bulk transcriptomics alone, as it lacks the spatial and protein-level resolution needed to fully capture disease and tissue complexities. In this study, we applied Laser Capture Microdissection (LCM) coupled with mass spectrometry-based proteomics to analyze histopathological niches of the RA synovium, enabling the identification of protein expression profiles of the diseased synovial lining and sublining microenvironments compared to their healthy counterparts. In this respect, key pathogenetic RA proteins like membrane proteins (TYROBP, AOC3, SLC16A3, TCIRG1 and NCEH1), and extracellular matrix (ECM) proteins (PLOD2, OGN and LUM) showed different expression patterns in diseased synovium compartments. To enhance our understanding of cellular dynamics within the dissected regions, we further integrated the proteomic dataset with single-cell RNA sequencing (scRNA-seq) and deduced cell type enrichment including T cells, fibroblasts, NK cells, myeloid cells, B cells and synovial endothelial cells. By combining high-resolution spatial proteomics and transcriptomic analyses, we provide novel insights into the molecular mechanisms driving RA and highlight potential protein targets for therapeutic intervention. This integrative approach offers a more comprehensive view of RA synovial pathology and mitigates the limitations of traditional bulk transcriptomics in target discovery.
Project description:Spatial transcriptomics workflows using barcoded capture arrays are commonly used for resolving gene expression in tissues. However, existing techniques are either limited by capture array density or are cost prohibitive for large scale atlasing. We present Nova-ST, a dense nano-patterned spatial transcriptomics technique derived from randomly barcoded Illumina sequencing flow cells. Nova-ST enables customized, low cost, flexible, and high-resolution spatial profiling of large tissue sections. Benchmarking on mouse brain sections demonstrates significantly higher sensitivity compared to existing methods, at reduced cost.