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Status |
Public on Feb 09, 2023 |
Title |
Cross-contamination experiment: Liver, mouse 3, replicate 4 |
Sample type |
SRA |
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Source name |
Liver
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Organism |
Mus musculus |
Characteristics |
tissue: Liver age: 22 months strain: C57BL/6J Sex: male genotype: wild-type
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Extracted molecule |
polyA RNA |
Extraction protocol |
The amplified cDNA was purified using SPRIselectTM beads (Beckman Coulter) following a left sided size selection with a bead-to-sample ratio of 0.8X. In brief, 160 µL of sample were mixed with 128 µL of resuspended SPRIselect beads and incubated for 5 min at rt. Beads were washed two times in 85% ethanol and air-dried for 3 min. The cDNA was eluted in 20 µL ultrapure water by incubation at 37 °C for 10 min. The supernatants were transferred to a fresh tube resulting in 9 tubes of purified cDNA. The quality of the cDNA was analyzed using the Bioanalyzer High Sensitivity DNA chip (Agilent) and samples were stored at -20 °C. The concentration of the cDNA was determined using a Qubit 1X dsDNA assay (Invitrogen) and the sequencing library was generated using the Nextera XT DNA Library Preparation Kit (Illumina). The quality of the library was assessed using the Bioanalyzer High Sensitivity DNA chip (Agilent). Samples were sequenced on a NovaSeq 6000 system (Illumina) at a sequencing depth of minimum 50,000 reads per spot using a 100 cycles kit in paired-end mode. Following read length configurations were used: R1: 7 cycles, i7: 6 cycles, R2: 58 cycles.
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Library strategy |
OTHER |
Library source |
transcriptomic |
Library selection |
other |
Instrument model |
Illumina NovaSeq 6000 |
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Data processing |
ReadsToCounts pipeline: Concerns all samples that are not marked as "cross-contamination experiment" in the title. This script takes two FASTQ files (Read 1 and Read 2) and barcode-coordinate information as input and processes them as follows: Read 1 sequences are trimmed and filtered using cutadapt v3.7 (Martin, 2011) and mapped against the mm10 (GRCm38.p6) mouse genome using STAR-2.7.4a (Dobin et al., 2013). Unique molecular identifiers (UMIs) and spatial barcodes are extracted from Read 2 using the Drop-seq tool TagBamWithReadSequenceExtended and a custom Python 3 pipeline assigns coordinates using barcode information provided in a CSV file. The DigitalExpression function is used to collaps the UMIs and generate a spot-count matrix. RNA metrics are calculated using CollectRnaSeqMetrics. CountsToAnndata pipeline: Concerns all samples that are not marked as "cross-contamination experiment" in the title. In this stage the spot-count matrix and imaging data are aligned and integrated. In brief, the positions of the alignment marker vertices are extracted semi-automatically from the alignment images using napari (Sofroniew et al., 2022) and Squidpy (Palla et al., 2022). The coordinates of the vertices are used to register alignment image and xDBiT spots by performing an affine transformation using OpenCV (Bradski, 2000). In order to align the high-resolution images of the first imaging round with the xDBiT spots, the SIFT algorithm (Lowe, 2004) is used to extract common features between the alignment DAPI image and the high-resolution DAPI image. Based on the coordinates of these features an affine transformation matrix is determined which is used to align the xDBiT spots to the high-resolution image. The dataset is saved in the AnnData format (Virshup et al., 2021). In this study we included intronic reads (Supp. Fig. 2 C) into the analysis. Cross-contamination experiment: This data processing description concerns only the samples which are marked with "Cross-contamination experiment" in their title (MUC28220-MUC28227). To calculate the percentage of spilled over reads, only read 2, containing the RevT barcodes and spatial barcodes was analyzed. For the analysis the ReadsToCounts script was modified to disregard read 1 and instead only run up to the barcode filtering steps to retrieve counts of the spatial barcodes and RevT primer barcodes. This script can be invoked using the `--spatial_only` flag. Further, to be able to catch information about reads from other wells, information about all 36 barcodes used in the experiment was added to the barcode legend file. Count values of the found barcodes were stored in the `recording_dictionary.json` file in the `rna_out` folder. This information was read, analyzed and plotted using Python 3.8. Assembly: mm10 (GRCm38.p6) Supplementary files format and content: gzipped tar archive containing gene expression matrix in txt.gz format and metadata file from ReadsToCounts pipeline Supplementary files format and content: h5ad file containing anndata object with xDBiT spatial transcriptomic data and aligned images Library strategy: xDBiT
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Submission date |
Jan 13, 2023 |
Last update date |
Feb 10, 2023 |
Contact name |
Johannes Wirth |
E-mail(s) |
johannes.wirth@helmholtz-muenchen.de
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Organization name |
Helmholtz Zentrum Munich
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Street address |
Ingolstädter Landstraße 1
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City |
Neuherberg |
ZIP/Postal code |
85764 |
Country |
Germany |
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Platform ID |
GPL24247 |
Series (1) |
GSE207843 |
Spatial Transcriptomics Using Multiplexed Deterministic Barcoding in Tissue |
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Relations |
BioSample |
SAMN32730529 |
SRA |
SRX19028554 |
Supplementary file |
Size |
Download |
File type/resource |
GSM6934369_Sample_MUC28223_out.tar.gz |
47.2 Kb |
(ftp)(http) |
TAR |
SRA Run Selector |
Raw data are available in SRA |
Processed data provided as supplementary file |
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