|
| Status |
Public on Feb 28, 2024 |
| Title |
Pancreas, CIP injected, 7 days, rep3 |
| Sample type |
SRA |
| |
|
| Source name |
Pancreas
|
| Organism |
Mus musculus |
| Characteristics |
tissue: Pancreas
strain: C57BL/6
treatment: CIP_7d
|
| Treatment protocol |
Pancreatitis was induced in C57BL/6 mice at 8-9 weeks of age using intraperitoneal caerulein injections on two consecutive days once every hour for 8 hours. Formalin-fixed paraffin-embedded microarray tissue sections (5 µm) were used for Nanostring's GeoMX Whole Transcriptome Atlas Digital Spatial Profiling.
|
| Extracted molecule |
total RNA |
| Extraction protocol |
For GeoMx WTA profiling, we generated two 12-core tissue blocks containing each time point. Formalin-fixed paraffin-embedded tissue sections (5 μm) were processed according to the Nanostring Manual Slide Preparation guide (MAN-10150-01). A total of 57 regions of interest (ROIs, 650 μm x 650 μm) were profiled, with 2-4 ROIs selected from each tissue sample.
The GeoMX Digital Spatial Profiler instrument selectively illuminated each ROI with UV light, permitting the photocleavage of a 66 bp-long DNA barcode from each oligonucleotide probe that was hybridized to an mRNA target within the ROI. The DNA barcodes from each ROI were deposited in a collection plate, and PCR was used to uniquely index each ROI’s barcodes with specific Illumina i5/i7 dual indexing primers. Amplified, indexed libraries were then pooled, purified with SPRI beads (Beckman Coulter) and the library quality assessed by capillary electrophoresis using a Fragment Analyzer (Agilent). Paired-end sequencing was performed on a NovaSeq6000 (Illumina), targeting the Nanostring recommended read depth of 2.5 billion read-pairs (calculated using the sum of each ROI’s area).
|
| |
|
| Library strategy |
OTHER |
| Library source |
transcriptomic |
| Library selection |
other |
| Instrument model |
Illumina NovaSeq 6000 |
| |
|
| Description |
TMA1_026
QC_BQC_raw_matrixonly.xlsx
|
| Data processing |
*library strategy: spatial transcriptomics
FASTQ read data was then processed through the GeoMx NGS Pipeline software to generate digital count conversion files (.dcc files), which were uploaded back to the GeoMx Digital Spatial Profiler instrument for subsequent processing and analysis.
Initial steps of data processing were performed in the GeoMx Digital Spatial Profiler Analysis Suite (v2.3.3.10). Counts were deduplicated based on UMIs and ROIs and filtered for those containing greater than 5% of genes detected (~1000 gene targets). Negative probes were filtered using the Grubbs outlier test to remove outliers, with 119/120 probes passing filter. Gene probes were filtered for those detected 2% above the limit of quantification (LOQ), with LOQ defined as two standard deviations greater than the geometric mean of the negative probes. Upper quartile (Q3) normalization was performed using all targets. Scaling was not performed.
Downstream analysis was performed in python (v3.10) using rpy2 (v3.5.1) to integrate R tools. The batch effect of slide was regressed out using limma’s (v3.56.1) ‘removeBatchEffect’19 and ‘voom’ normalization was performed prior to PCA visualization. Differentially expressed genes (DEGs) were determined using a linear mixed model with slide as a covariable and Benjamini/Hochberg correction for multiple hypothesis testing.
Assembly: mm10
Supplementary files format and content: segmentSummary_readTypes.xlsx: Tab-delimited spreadsheet containing segment information
Supplementary files format and content: TargetProperties_raw.xlsx: Tab-delimited spreadsheet containing target properties information
Supplementary files format and content: BioProbeInformation.xlsx: Tab-delimited spreadsheet containing bioprobe information
Supplementary files format and content: QC_BQC_raw_matrixonly.xlsx: Tab-delimited spreadsheet containing raw gene-counts matrix post filtering
Supplementary files format and content: QC_BQC_Q3Norm_matrixonly.xlsx: Tab-delimited spreadsheet containing Q3 normalized gene-counts matrix
|
| |
|
| Submission date |
Jun 26, 2023 |
| Last update date |
Feb 28, 2024 |
| Contact name |
Sahar Nissim |
| E-mail(s) |
snissimlab@gmail.com
|
| Organization name |
Brigham and Women's Hospital
|
| Department |
Genetics
|
| Lab |
Nissim
|
| Street address |
77 Avenue Louis Pasteur
|
| City |
Boston |
| State/province |
MA |
| ZIP/Postal code |
02115 |
| Country |
USA |
| |
|
| Platform ID |
GPL24247 |
| Series (1) |
| GSE235874 |
A Novel Approach for Pancreas Transcriptomics Reveals the Cellular Landscape in Homeostasis and Acute Pancreatitis |
|
| Relations |
| BioSample |
SAMN35992261 |
| SRA |
SRX20786207 |
