Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2.
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ABSTRACT:
SUBMITTER: Stickels RR
PROVIDER: S-EPMC8606189 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:High-resolution spatial transcriptomics enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remains unexplored. We applied Slide-seqV2 to mouse and human kidneys, in healthy and distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in tissue from nine distinct human kidneys, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially restricted kidney filter and blood-flow-regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially restricted subpopulation of epithelial cells, we discovered perturbations in 77 genes associated with the unfolded protein response. Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods.
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Project description:MotivationRecent rapid developments in spatial transcriptomic techniques at cellular resolution have gained increasing attention. However, the unique characteristics of large-scale cellular resolution spatial transcriptomic datasets, such as the limited number of transcripts captured per spot and the vast number of spots, pose significant challenges to current cell-type deconvolution methods.ResultsIn this study, we introduce stVAE, a method based on the variational autoencoder framework to deconvolve the cell-type composition of cellular resolution spatial transcriptomic datasets. To assess the performance of stVAE, we apply it to five datasets across three different biological tissues. In the Stereo-seq and Slide-seqV2 datasets of the mouse brain, stVAE accurately reconstructs the laminar structure of the pyramidal cell layers in the cortex, which are mainly organized by the subtypes of telencephalon projecting excitatory neurons. In the Stereo-seq dataset of the E12.5 mouse embryo, stVAE resolves the complex spatial patterns of osteoblast subtypes, which are supported by their marker genes. In Stereo-seq and Pixel-seq datasets of the mouse olfactory bulb, stVAE accurately delineates the spatial distributions of known cell types. In summary, stVAE can accurately identify spatial patterns of cell types and their relative proportions across spots for cellular resolution spatial transcriptomic data. It is instrumental in understanding the heterogeneity of cell populations and their interactions within tissues.Availability and implementationstVAE is available in GitHub (https://github.com/lichen2018/stVAE) and Figshare (https://figshare.com/articles/software/stVAE/23254538).
| S-EPMC10612402 | biostudies-literature
Project description:High resolution spatial transcriptomics is a transformative technology that enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remain largely unexplored. Here we applied Slide-seqV2 to mouse and human kidneys, in healthy and in distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in human kidney by analyzing tissue from 9 distinct donors, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially-restricted kidney filter and blood flow regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially-restricted subpopulation of epithelial cells, we also found perturbations in 77 genes associated with the unfolded protein response (UPR), including Tmed9. Treatment with a TMED9-targeting compound showed efficient removal of toxic mutant proteins and reversal of the UPR. Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods and actionable targets.
2022-01-28 | GSE190094 | GEO
Project description:Recent spatial gene expression technologies enable comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing analysis methods do not address the limited resolution of the technology or use the spatial information efficiently. Here, we introduce BayesSpace, a fully Bayesian statistical method that uses the information from spatial neighborhoods for resolution enhancement of spatial transcriptomic data and for clustering analysis. We benchmark BayesSpace against current methods for spatial and non-spatial clustering and show that it improves identification of distinct intra-tissue transcriptional profiles from samples of the brain, melanoma, invasive ductal carcinoma and ovarian adenocarcinoma. Using immunohistochemistry and an in silico dataset constructed from scRNA-seq data, we show that BayesSpace resolves tissue structure that is not detectable at the original resolution and identifies transcriptional heterogeneity inaccessible to histological analysis. Our results illustrate BayesSpace's utility in facilitating the discovery of biological insights from spatial transcriptomic datasets.
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Project description:Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1-10 cells) spots per patient sampled in a honeycomb pattern, co-registered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x-y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural networks to predict spatial RNA patterns at the Visium spots under the hypothesis that the transformer- and graph-based approaches better capture relevant spatial tissue architecture. We further analyzed the model's ability to recapitulate spatial autocorrelation statistics using SPARK and SpatialDE. Overall, the results indicate that the transformer- and graph-based approaches were unable to outperform the convolutional neural network architecture, though they exhibited optimal performance for relevant disease-associated genes. Initial findings suggest that different neural networks that operate on different scales are relevant for capturing distinct disease pathways (e.g., epithelial to mesenchymal transition). We add further evidence that deep learning models can accurately predict gene expression in whole slide images and comment on understudied factors which may increase its external applicability (e.g., tissue context). Our preliminary work will motivate further investigation of inference for molecular patterns from whole slide images as metastasis predictors and in other applications.
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Project description:Single-cell decisions made in complex environments underlie many bacterial phenomena. Image-based, transcriptomics approaches offer an avenue to study such behaviors, yet these approaches have been hindered by the massive density of bacterial mRNA. To overcome this challenge, we combine 1000-fold volumetric expansion with multiplexed error robust fluorescence in situ hybridization (MERFISH) to create bacterial-MERFISH, a method enabling high-throughput, spatially resolved profiling of thousands of operons within individual bacteria. Using bacterial-MERFISH, we dissect the response of E. coli to carbon starvation, systematically map subcellular RNA organization, and chart the adaptation of B. thetaiotaomicron to micron-scale niches in the mammalian colon. We envision bacterial-MERFISH could prove useful in the study of bacterial single-cell decisions made in diverse, spatially structured, and native environments.
2024-11-12 | GSE268480 | GEO
Project description:Spatial positions of cells in tissues strongly influence function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. We developed Slide-seq, a method for transferring RNA from tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the locations of the RNA to be inferred by sequencing. Using Slide-seq, we localized cell types identified by single-cell RNA sequencing datasets within the cerebellum and hippocampus, characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, and defined the temporal evolution of cell type-specific responses in a mouse model of traumatic brain injury. These studies highlight how Slide-seq provides a scalable method for obtaining spatially resolved gene expression data at resolutions comparable to the sizes of individual cells.
| S-EPMC6927209 | biostudies-literature
Project description:Spatially resolved transcriptomics (SRT) techniques have revolutionized the characterization of molecular profiles while preserving spatial and morphological context. However, most next-generation sequencing-based SRT techniques are limited to measuring gene expression in a confined array of spots, capturing only a fraction of the spatial domain. Typically, these spots encompass gene expression from a few to hundreds of cells, underscoring a critical need for more detailed, single-cell resolution SRT data to enhance our understanding of biological functions within the tissue context. Addressing this challenge, we introduce BayesDeep, a novel Bayesian hierarchical model that leverages cellular morphological data from histology images, commonly paired with SRT data, to reconstruct SRT data at the single-cell resolution. BayesDeep effectively model count data from SRT studies via a negative binomial regression model. This model incorporates explanatory variables such as cell types and nuclei-shape information for each cell extracted from the paired histology image. A feature selection scheme is integrated to examine the association between the morphological and molecular profiles, thereby improving the model robustness. We applied BayesDeep to two real SRT datasets, successfully demonstrating its capability to reconstruct SRT data at the single-cell resolution. This advancement not only yields new biological insights but also significantly enhances various downstream analyses, such as pseudotime and cell-cell communication.
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Project description:Spatial resolution is a key parameter in imaging mass spectrometry (IMS). Aside from being a primary determinant in overall image quality, spatial resolution has important consequences on the acquisition time of the IMS experiment and the resulting file size. Hardware and software modifications during instrumentation development can dramatically affect the spatial resolution achievable using a given imaging mass spectrometer. As such, an accurate and objective method to determine the working spatial resolution is needed to guide instrument development and ensure quality IMS results. We have used lithographic and self-assembly techniques to fabricate a pattern of crystal violet as a standard reticle slide for assessing spatial resolution in matrix-assisted laser desorption/ionization (MALDI) IMS experiments. The reticle is used to evaluate spatial resolution under user-defined instrumental conditions. Edgespread analysis measures the beam diameter for a Gaussian profile and line scans measure an "effective" spatial resolution that is a convolution of beam optics and sampling frequency. The patterned crystal violet reticle was also used to diagnose issues with IMS instrumentation such as intermittent losses of pixel data.
| S-EPMC5672908 | biostudies-literature
Project description:<p><strong>BACKGROUND:</strong> Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features -- a reliable feature detection is mandatory.</p><p><strong>RESULTS:</strong> We developed a new feature detection algorithm centWave for high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity.</p><p><strong>CONCLUSION:</strong> The new feature detection algorithm meets the requirements of current metabolomics experiments. centWave can detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, matchedFilter (the original algorithm of XCMS) and the centroidPicker from MZmine. The centWave algorithm was integrated into the Bioconductor R-package XCMS and is available from (<a href='http://www.bioconductor.org/' rel='noopener noreferrer' target='_blank'>http://www.bioconductor.org/</a>).</p>
2020-01-15 | MTBLS1430 | MetaboLights