ZipSeq : Barcoding for Real-time Mapping of Single Cell Transcriptomes
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE145502 | GEO | 2020/06/01
REPOSITORIES: GEO
Similar Datasets
Project description: Not available
| S-EPMC7891292 | biostudies-literature
Project description:We present a new approach, Light-Seq, for multiplexed spatial indexing of intact biological samples using light-directed DNA barcoding in fixed cells and tissues followed by ex situ sequencing. Light-Seq combines spatially-targeted, rapid photocrosslinking of DNA barcodes onto cDNAs in situ with a novel one-step DNA stitching reaction to create pooled, spatially-indexed sequencing libraries. This light-directed barcoding enables in situ selection of multiple cell populations in intact fixed tissue samples for full transcriptome sequencing based on location, morphology, or protein stains, without cellular dissociation. Applying Light-Seq to mouse retinal sections, we recovered thousands of differentially enriched transcripts from three adjacent cellular layers and discovered new biomarkers for a very rare neuronal subtype, dopaminergic amacrine cells, from only 4-8 individual cells per section. Light-Seq provides an accessible workflow to combine in situ imaging and protein staining with next-generation sequencing of the same cells, leaving the sample intact sample for further analysis post-sequencing.
2022-10-10 | GSE208650 | GEO
Project description:In this study, we present a multiplexed version of 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 spots of 50 µm×50 µm. Optimization of the biochemical protocol increased read and gene counts per spot by one order of magnitude compared with previous reports. Furthermore, the introduction of alignment markers allows seamless registration of images and spatial transcriptomic spot coordinates. Together with technological advances, we provide an open-source computational pipeline to transform raw sequencing data from xDBiT experiments into the AnnData format. The functionality of xDBiT was demonstrated by the acquisition of 16 spatially resolved transcriptomic datasets from five different murine organs, including cerebellum, liver, kidney, spleen, and heart. Factor analysis and deconvolution of xDBiT spatial transcriptomes allowed for in-depth characterization of the murine kidney.
2023-02-09 | GSE207843 | GEO
Project description:Scientific knowledge on the subjects: Injurious mechanical ventilation amplifies acute lung injury in a heterogeneous and regional fashion but the molecular mechanisms underlying regional lung injury and the protective effects of prone positioning are unclear. Regionally injurious ventilation is associated with discrete differential lung transcriptomic changes. Ventilating in the prone, compared with the supine position abrogates regional injury by depressing MKP-1. Adult rats were ventilated with high (18 mL/Kg, PEEP 0) tidal volume (Vt) in supine or prone position. Non ventilated rats were used as controls. Dorsal-caudal lung mRNA was analyzed by microarray.
2011-08-26 | E-GEOD-31678 | biostudies-arrayexpress
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.
2019-09-09 | GSE130682 | GEO
Project description:We describe the development of a high-sensitivity protein quantification system called HaloTag protein barcoding assay. The assay involves target protein linking to a unique molecule-counting oligonucleotide by click chemistry.
2019-11-04 | GSE122542 | GEO
Project description:ZipSeq : Barcoding for Real-time Mapping of Single Cell Transcriptomes
| PRJNA607323 | ENA
Project description:Scientific knowledge on the subjects: Injurious mechanical ventilation amplifies acute lung injury in a heterogeneous and regional fashion but the molecular mechanisms underlying regional lung injury and the protective effects of prone positioning are unclear. Regionally injurious ventilation is associated with discrete differential lung transcriptomic changes. Ventilating in the prone, compared with the supine position abrogates regional injury by depressing MKP-1.
2011-08-27 | GSE31678 | GEO
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:Intra-tumoral heterogeneity can wreak havoc on current precision medicine strategies due to challenges in sufficient sampling of geographically separated areas of biodiversity distributed across centimeter-scale tumor distances. In particular, modern tissue profiling approaches are still largely designed to only interrogate small tumor fragments; which may constitute a minute and non-representative fraction of the overall neoplasm. To address this gap, we developed a pipeline that leverages deep learning to define topographic histomorphologic fingerprints of tissue and create Histomic Atlases of Variation Of Cancers (HAVOC). Importantly, using a number of spatially-resolved readouts, including mass-spectrometry-based proteomics and immunohistochemisy, we demonstrate that these personalized atlases of histomic variation can define regional cancer boundaries with distinct biological programs. Using larger tumor specimens, we show that HAVOC can map spatial organization of cancer biodiversity spanning tissue coordinates separated by multiple centimeters. By applying this tool to guide profiling of 19 distinct geographic partitions from 6 high-grade gliomas, HAVOC revealed that distinct states of differentiation can often co-exist and be regionally distributed across individual tumors. Finally,to highlight generalizability, we further benchmark HAVOC on additional tumor types and experimental models of heterogeneity. Together, we establish HAVOC as a versatile and accessible tool to generate small-scale maps of tissue heterogeneity and guide regional deployment of molecular resources to relevant and biodiverse tumor niches.
2023-08-23 | PXD037548 | Pride