Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples: Supplementary Data 1
Supplementary data for the study "Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples" by Reza Mirzazadeh, Zaneta Andrusivova and Ludvig Larsson et al. Spatially resolved transcriptomics (SRT) has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of unbiased SRT methods targeting the polyA tail of mRNA, relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available SRT 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 the RNA-Rescue Spatial Transcriptomics (RRST) workflow to improve mRNA recovery and robustness for fresh frozen (FF) specimens with moderate-to-low RNA quality. First, we benchmarked RRST against the standard Visium spatial gene expression protocol on high RNA quality samples. Then, we demonstrate the RRST to recover and rescue data from challenging samples including; human lung, colon, small intestine, childhood brain tumor, mouse brain and mouse bone/cartilage. Our results demonstrate RRST as a versatile, powerful, and reproducible protocol for FF specimens of different qualities and origins. Available data: 1. gene annotation files are available in the genes/ folder. 2. R objects containing spot annotations are available in the R_objects/ folder. 3. Sheets in .xlsx or .csv format containing sample meta data, annotations of mouse bone tissue and Visium FFPE probe set information are available in the sheets/ folder. 4. Tar archives containing spaceranger output files are available in the spaceranger_output/ folder. 5. High resolution H&E images are available in the images/ folder for colon, lung, mousebone, mousebrain, pediatric braun tumor and prostate cancer samples. Small intestine H&E images can be found in Supplementary Data 2 (see paper).
Steps to reproduce
R notebooks and a docker container are available at https://github.com/ludvigla/RRST.