Optocoder: computational decoding of spatially indexed bead arrays
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA796322 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR17556938_1.fastq.gz | Fastqsanger.gz | |||
| SRR17556938_2.fastq.gz | Fastqsanger.gz | |||
| SRR17556939_1.fastq.gz | Fastqsanger.gz | |||
| SRR17556939_2.fastq.gz | Fastqsanger.gz | |||
| SRR17556940_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 8 |
Similar Datasets
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:Advancing technologies that quantify gene expression in space are transforming contemporary biology research. A class of spatial transcriptomics methods uses barcoded bead arrays that are optically decoded via microscopy and are later matched to sequenced data from the respective libraries. To obtain a detailed representation of the tissue in space, robust and efficient computational pipelines are required to process microscopy images and accurately basecall the bead barcodes. Optocoder is a computational framework that processes microscopy images to decode bead barcodes in space. It efficiently aligns images, detects beads, and corrects for confounding factors of the fluorescence signal, such as crosstalk and phasing. Furthermore, Optocoder employs supervised machine learning to strongly increase the number of matches between optically decoded and sequenced barcodes. We benchmark Optocoder using data from an in-house spatial transcriptomics platform, as well as from Slide-Seq(V2), and we show that it efficiently processes all datasets without modification. Optocoder is publicly available, open-source and provided as a stand-alone Python package on GitHub: https://github.com/rajewsky-lab/optocoder.
| S-EPMC9172073 | biostudies-literature
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Florencia Pauli mailto:fpauli@hudsonalpha.org). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track is produced as part of the ENCODE project. The track displays the methylation status of specific CpG dinucleotides in the given cell types as identified by the Illumina Infinium Human Methylation 450 Bead Array platform (http://www.illumina.com/products/methylation_450_beadchip_kits.ilmn). In general, methylation of CpG sites within a promoter causes silencing of the gene associated with that promoter. The Infinium Human Methylation 450 platform uses bisulfite treated genomic DNA to assay the methylation status of more than 450,000 CpG sites covering all designatable RefSeq genes, including promoter, 5' and 3' regions, without bias against those lacking CpG islands. Additionally, the assay includes CpG islands and shores, CpG sites outside of CpG islands, non-CpG methylated sites identified in human stem cells, differentially methylated sites identified in tumor versus normal (multiple forms of cancer) and across several tissue types, CpG islands outside of coding regions, miRNA promoter regions, and disease-associated regions identified through GWAS. Detailed information for the CpG targets is in an CSV formatted spreadsheet in the supplemental directory (http://hgdownload-test.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeHaibMethyl450/supplemental/). For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/cell). Genomic DNA was isolated from each cell line with the QIAGEN DNeasy Blood & Tissue Kit according to the instructions provided by the manufacturer. DNA concentrations and a level of quality of each preparation was determined by fluorescence with the Qubit Fluorometer (Invitrogen). Genomic DNA was treated with sodium bisulfite, converting unmethylated cytosines of CpG dinucleotides into uracils; methylated cytosines did not get converted. After bisulfite treatment, the methylation status of a site was assayed by single base-pair extension with a Cy3 or Cy5 labeled nucleotide on oligo-beads specific for the methylated or unmethylated state. The bisulfite conversion reaction was done using the Zymo Research EZ-96 DNA Methylation Kit (http://www.zymoresearch.com/product/ez-96-dna-methylation-kit-d5003). One step of the protocol was modified. During the incubation, a 30 second 95oC denaturing step every hour was included to increase reaction efficiency as recommended by the Illumina Infinium Human Methylation27 protocol. The bead arrays were run according to the protocol provided by Illumina (http://hgdownload-test.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeHaibMethyl450/supplemental/wgEncodeHaibMethyl450IlluminaProtocol.pdf). A beta value was calculated for each CpG target with Illumina's Bead Studio software with the Methylation Module v3.2. Beta-value = intensity value from the methylated bead type/(intensity values from the methylated + intensity value from unmethylated bead types + 100). The data was then quality-filtered using p-values. Beta values with p-value greater than 0.01 are considered to fall below the minimum intensity and threshold are displayed as "NA". Any beta value equal to or greater than 0.6 was considered fully methylated. Any beta value equal to or less than 0.2 was considered to be fully unmethylated. Beta values between 0.2 and 0.6 were considered to be partially methylated. Score in the bed files is beta value x 1000
2012-09-07 | E-GEOD-40699 | biostudies-arrayexpress
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Florencia Pauli mailto:fpauli@hudsonalpha.org). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track is produced as part of the ENCODE project. The track displays the methylation status of specific CpG dinucleotides in the given cell types as identified by the Illumina Infinium HumanMethylation27 BeadArray platform (http://www.illumina.com/pages.ilmn?ID=243). In general, methylation of CpG sites within a promoter causes silencing of the gene associated with that promoter. Detailed information for the CpG targets is in an XLS formatted spreadsheet on the Myers' lab protocols website (http://hudsonalpha.org/myers-lab/protocols). For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/cell). Genomic DNA was isolated from each cell line with the QIAGEN DNeasy Blood & Tissue Kit according to the instructions provided by the manufacturer. DNA concentrations and a level of quality of each preparation was determined by fluorescence with the Qubit Fluorometer (Invitrogen). The Methyl27K platform uses bisulfite treated genomic DNA to assay the methylation status of 27,578 CpG sites within more than 14,000 genes. When genomic DNA is treated with sodium bisulfite, unmethylated cytosine of CpG dinucleotides are converted into uracils; methylated cytosines do not get converted. After bisulfite treatment, the methylation status of a site is assayed by single base-pair extension with a Cy3 or Cy5 labeled nucleotide on oligo-beads specific for the methylated or unmethylated state. A beta value is calculated by Illumina's Bead Studio software for each CpG target. This value represents the intensity value from the methylated bead type divided by the sum of the intensity values from the methylated and unmethylated bead types for any given CpG target. Bisulfite conversion reaction was done using the Zymo Research EZ-96 DNA Methylation Kit (http://www.zymoresearch.com/epigenetics/dna-methylation/ez-96-dna-methylation-kit). One step of the protocol was modified. During the incubation, a 30 sec 95oC denaturing step every hour was included to increase reaction efficiency as recommended by the Illumina Infinium Human Methylation27 protocol. The bead arrays were run according to the protocol provided by Illumina (http://www.illumina.com/pagesnrn.ilmn?ID=275). The intensity data from the BeadArray was processed using Illumina's BeadStudio software with the Methylation Module v3.2. The data was then quality-filtered using p-values. Any beta value equal to or greater than 0.6 is considered fully methylated. Any beta value equal to or less than 0.2 is considered to be fully unmethylated. Beta values between 0.2 and 0.6 are considered to be partially methylated. Beta-values are quality filtered and spots that fall below the minimum intensity threshold are displayed as "NA". Score in the bed files is beta value x 1000
2012-09-07 | E-GEOD-40700 | biostudies-arrayexpress
Project description:Here, we have developed a novel methodology called IRIS (Imaging Reconstruction using Indexed Sequencing) that enables cost-effective spatial transcriptomics profiling without relying on optical imaging. Through neighborhood interaction-based reconstruction, IRIS allows extensive analysis of large tissue sections and many replicates with adjustable mapping resolution at only a fraction of the cost of other commercial platforms. With the IRIS platform, we reconstructed a large area spatial area with two whole mouse brain coronal sections. Moreover, we also created a spatially resolved transcriptome atlas of the mouse brain and identified aging-associated changes in gene expression and spatial organization across various brain cell types. Further analysis of cell-cell interaction changes identified aging-associated foci in white matter regions enriched with inflammatory subtypes of microglia and oligodendrocytes. Overall, the IRIS methodology cost-effective and ease-of-use approach makes it broadly applicable to the studies of spatial gene expression changes in various systems.
2024-09-12 | GSE270383 | GEO
Project description:We introduce single cell combinatorial indexed cytometry by sequencing (SCITO-seq), a single cell proteomics workflow that combines split-pool indexing and droplet-based sequencing
2021-03-31 | GSE147808 | GEO
Project description:Decoding murine cytomegalovirus
| PRJNA874807 | ENA
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: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:Computational Genomics of Preeclampsia
| PRJNA809817 | ENA