Project description:Expression profiling in spatially defined regions is crucial for systematically understanding tissue complexity. Here, we report a method of photo-irradiation for in-situ barcoding hybridization and ligation sequencing, named PBHL-seq, which allows targeted expression profiling from the photo-irradiated region of interest in intact fresh frozen and FFPE tissue samples. PBHL-seq uses photo-caged oligodeoxynucleotides for in situ reverse transcription followed by spatially targeted barcoding of cDNAs to create spatially indexed transcriptomes of photo-illuminated regions. We recover thousands of differentially enriched transcripts from different regions by applying PBHL-seq to OCT-embedded tissue (E14.5 mouse embryo and mouse brain) and FFPE mouse embryo (E15.5). We also apply PBHL-seq to the subcellular microstructures (cytoplasm and nucleus, respectively) and detect thousands of differential expression genes. Thus, PBHL-seq provides an accessible workflow for expression profiles from the region of interest in frozen and FFPE tissue at subcellular resolution with areas expandable to centimeter scale, while preserving the sample intact for downstream analysis.
Project description:Here, we present a protocol for using spatial transcriptomics in bone and multi-tissue musculoskeletal formalin-fixed paraffin-embedded (FFPE) samples from mice. We describe steps for tissue harvesting, sample preparation, paraffin embedding, and FFPE sample selection. We detail procedures for sectioning and placement on spatial slides prior to imaging, decrosslinking, library preparation, and final analyses of the sequencing data. The complete protocol takes ca. 18 days for mouse femora with adjacent muscle; of this time, >50% is required for mineralized tissue decalcification. For complete details on the use and execution of this protocol, please refer to Wehrle et al.1 and Mathavan et al.2.
Project description:Spatial transcriptomics and multiplexed imaging are complementary methods for studying tissue biology. Here we describe a simple method for transcriptional profiling of formalin fixed histology specimens based on mechanical isolation of tissue micro-regions containing 5-20 cells. Sequencing micro-regions from an archival melanoma specimen having multiple distinct histologies reveals significant differences in transcriptional programs associated with tumor invasion, proliferation, and immunoediting and parallel imaging confirms changes in immuno-phenotypes and cancer cell states.
Project description:Spatial transcriptomics and multiplexed imaging are complementary methods for studying tissue biology. Here we describe a simple method for transcriptional profiling of formalin fixed histology specimens based on mechanical isolation of tissue micro-regions containing 5-20 cells. Sequencing micro-regions from an archival melanoma specimen having multiple distinct histologies reveals significant differences in transcriptional programs associated with tumor invasion, proliferation, and immunoediting and parallel imaging confirms changes in immuno-phenotypes and cancer cell states.
Project description:Spatial transcriptomics and multiplexed imaging are complementary methods for studying tissue biology. Here we describe a simple method for transcriptional profiling of formalin fixed histology specimens based on mechanical isolation of tissue micro-regions containing 5-20 cells. Sequencing micro-regions from an archival melanoma specimen having multiple distinct histologies reveals significant differences in transcriptional programs associated with tumor invasion, proliferation, and immunoediting and parallel imaging confirms changes in immuno-phenotypes and cancer cell states.
Project description:Spatially resolved transcriptomics is a relatively new technique that maps transcriptional information within a tissue. Analysis of these datasets is challenging because gene expression values are highly sparse due to dropout events, and there is a lack of tools to facilitate in silico detection and annotation of regions based on their molecular content. Therefore, we develop a computational tool for detecting molecular regions and region-based Missing value Imputation for Spatially Transcriptomics (MIST). We validate MIST-identified regions across multiple datasets produced by 10x Visium Spatial Transcriptomics, using manually annotated histological images as references. We benchmark MIST against a spatial k-nearest neighboring baseline and other imputation methods designed for single-cell RNA sequencing. We use holdout experiments to demonstrate that MIST accurately recovers spatial transcriptomics missing values. MIST facilitates identifying intra-tissue heterogeneity and recovering spatial gene-gene co-expression signals. Using MIST before downstream analysis thus provides unbiased region detections to facilitate annotations with the associated functional analyses and produces accurately denoised spatial gene expression profiles.
Project description:Spatially resolved transcriptomics of tissue sections enables advances in fundamental and applied biomedical research. Here, we present Multiplexed Deterministic Barcoding in Tissue (xDBiT) to acquire spatially resolved transcriptomes of nine tissue sections in parallel. New microfluidic chips were developed to spatially encode mRNAs over a total tissue area of 1.17 cm2 with a 50 µm resolution. Optimization of the biochemical protocol increased read and gene counts per spot by one order of magnitude compared to previous reports. Furthermore, the introduction of alignment markers allowed seamless registration of images and spatial transcriptomic spots. Together with technological advances, we provide an open-source computational pipeline to prepare raw sequencing data for downstream analysis. The functionality of xDBiT was demonstrated by acquiring 16 spatially resolved transcriptomic datasets from five different murine organs, including the cerebellum, liver, kidney, spleen, and heart. Factor analysis and deconvolution of spatial transcriptomes allowed for in-depth characterization of the murine kidney.
Project description:The rapid development of spatial transcriptomics (ST) techniques has allowed the measurement of transcriptional levels across many genes together with the spatial positions of cells. This has led to an explosion of interest in computational methods and techniques for harnessing both spatial and transcriptional information in analysis of ST datasets. The wide diversity of approaches in aim, methodology and technology for ST provides great challenges in dissecting cellular functions in spatial contexts. Here, we synthesize and review the key problems in analysis of ST data and methods that are currently applied, while also expanding on open questions and areas of future development.