Project description:Purpose: Here, we describe a method, Array-seq, to repurpose classical oligonucleotide microarrays for spatial transcriptomics profiling. We demonstrate that Array-seq yields spatial transcriptomes with high detection sensitivity and localization specificity using histological sections from mouse tissues as test systems. Moreover, we show that the large surface area of Array-seq slides enables the generation of spatial transcriptomes at high throughput by profiling multi-organ sections, in three dimensions by processing serial sections from one sample, and across whole human organs using spleen sections. Experimental Methods: To generate Array-seq slides, we first obtain microarrays carrying custom-design probes that contain common sequences flanking unique barcodes at known coordinates. Second, we perform a simple, two-step reaction that produces mRNA capture probes across all spots on the microarray and thereby creates Array-seq slides set for spatial transcriptomics. Following in-situ mRNA capture, reverse transcription, libaray preparation, Array-seq libraries are sequenced using the Illumina sequencing-by-synthesis (SBS) platform.
Project description:Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. We conduct chimeric experiments to evaluate the spot swapping effect in 10x Visium spatial transcriptomics protocol. We propose SpotClean to adjust for spot swapping and, in doing so, to increase the sensitivity and precision with which downstream analyses are conducted.
Project description:Using microfluidics, well-defined barcodes were generated on the slide surface by cross-amplification, followed by high-throughput sequencing using Novaseq to detect spatial transcriptomic information in the mouse brain.
Project description:Spatial transcriptomics technologies that can quantify gene expression in space are transforming contemporary biology research. Some of such methods use spatially barcoded bead arrays that are optically sequenced by a microscopy setup to detect bead barcodes in space which can be consecutively matched to cell barcodes from the respective single cell sequencing experiment. To have good quality barcodes and a high number of barcode matches in space, robust and efficient computational pipelines are needed to process raw microscopy images and call the bases of bead barcodes accurately. Here, we present Optocoder, a computational pipeline that takes raw optical sequencing microscopy images as input and outputs bead barcodes in space. Optocoder efficiently aligns images, detects beads, and corrects for confounding factors of the fluorescence signal such as crosstalk and phasing before base calling. Furthermore, we implement a machine learning pipeline that is trained using the signal from the beads that match to illumina barcodes in order to predict non-matching bead barcodes which can boost up the number of barcode matches. We benchmark Optocoder using data from an in-house spatial transcriptomics platform as well as data from the Slide-seq method and we show that it can efficiently process both datasets with minimal modification.
2022-06-24 | GSE193472 | GEO
Project description:Spatial resolved transcriptomics of healthy and Alzheimer's hippocampal tissue slides
Project description:Spatial transcriptomics was performed on mouse kidneys 2 datys and 4 days after systemic MRSA infection. Two consecutive slides were prepared for each time point.
Project description:Induced pluripotent stem cell (iPSC) derived organoid systems provide models to study human organ development. Single-cell transcriptome sequencing enables highly-resolved descriptions of cell state heterogeneity within these systems and computational methods can reconstruct developmental trajectories. However, new approaches are needed to directly measure lineage relationships in these systems. Here we establish an inducible dual channel lineage recorder, iTracer, that couples reporter barcodes, inducible CRISPR/Cas9 scarring, and single-cell transcriptomics to analyze state and lineage relationships in iPSC-derived systems. This data set include the spatial iTracer data of three slices of one cerebral organoid measured by 10x Visium.
Project description:We used Visium technology (10X Genomics) to infer cell-to-cell communication in ovarian and uterine tissue based on spatial proximity. Organs from 3-month mice in diestrus and 18-month old mice were collected and frozen in OCT. 10 µm thick tissue slices were placed on Visium Spatial Gene Expression Slides (10X Genomics) and stained with Hematoxylin and Eosin (H&E). Libraries were prepared by manufacturer’s recommendations and sequenced on NovaSeq6000. For samples that were sequenced in two runs, both sequencing runs were merged when running spaceranger (10X Genomics). Original nd2 microscopy images and results of scRNA-seq (linked datasets) and spatial transcriptomics analysis are available at Biostudies (S-BIAD482 and S-BSST852).
Project description:Cellular barcoding using heritable synthetic barcodes coupled to high throughput sequencing is a powerful technique for the accurate tracing of clonal lineages in a wide variety of biological contexts. Recent studies have integrated cellular barcoding with a single-cell transcriptomics readout, extending the capabilities of these lineage tracing methods to the single-cell level. However there remains a lack of scalable and standardised open-source tools to pre-process and visualise both bulk and single-cell level cellular barcoding datasets. Here, we describe bartools, an open-source R-based toolkit that streamlines the pre-processing, analysis and visualisation of synthetic cellular barcoding datasets. In addition, we developed BARtab, a portable and scalable Nextflow pipeline that automates upstream barcode extraction, quality control, filtering and enumeration from high throughput sequencing data. In addition to population-level cellular barcoding datasets, BARtab and bartools contain methods for the extraction, annotation, and visualisation of transcribed barcodes from single-cell RNA-seq and spatial transcriptomics experiments, thus extending the analytical toolbox to also support novel expressed cellular barcoding methodologies. We showcase the integrated BARtab and bartools workflow through the analysis of bulk, single-cell, and spatial transcriptomics cellular barcoding datasets.
Project description:The spatial transcriptomic analysis of barley embryos in response to 75 µM ABA was investigated using Visium Spatial Transcriptomics (10×Genomics) technology. Embryo sections were collected from wild-type 'Sebastian' and a double mutant hvcbp20.ab/hvcbp80.b impaired in both subunits of the Cap-Binding Complex (CBC): CBP20 and CBP80 at 1 day after imbibition (DAI) under control and 75 µM ABA conditions. Cryosectioned tissues were stained, mounted on Visium slides, and processed for spatial gene expression profiling. Sequencing was performed on an Illumina NovaSeq 6000 platform (paired-end, 151 bp). The data were aligned to the barley reference genome (cv. MorexV3) and used to identify differentially expressed genes (DEGs) across six distinguished embryonic regions: coleoptile, cotyledon, mesocotyl, plumule, scutellum, and radicle. The dataset provides insights into the spatial regulation of ABA-responsive genes in embryos during barley seed germination.