Dataset summary
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Original publication DOI: https://doi.org/10.1038/s41467-023-36071-5
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Specific data link: https://data.mendeley.com/datasets/4w6krnywhn/1https://data.mendeley.com/datasets/442mhsrpcm/1
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Original abstract: Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins.
Summary statistics
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Slides: 8
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Patients: 2
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Spots: 34583
How to use
# Import spared library
import spared
from spared import datasets
# Dataset
dataset = datasets.get_dataset("mirzazadeh_mouse_brain", visualize=True)
