Wound image and transcriptome datasets of swine acute wounds
Data files
Aug 25, 2025 version files 84.76 GB
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README.md
5.52 KB
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RNAseq_dataset-20250825.zip
14.19 MB
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Wound_photo_cropped.zip
355.18 MB
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WoundPhotos-Day_0-Pig_1323.zip
2.99 GB
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WoundPhotos-Day_0-Pig_1324.zip
3.14 GB
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WoundPhotos-Day_0-Pig_1325.zip
3.10 GB
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WoundPhotos-Day_0-Pig_1326.zip
3.08 GB
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WoundPhotos-Day_0-Pig_1327.zip
3.10 GB
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WoundPhotos-Day_0-Pig_1328.zip
3.23 GB
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WoundPhotos-Day_1.zip
9.14 GB
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WoundPhotos-Day_11.zip
3.32 GB
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WoundPhotos-Day_13.zip
3.18 GB
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WoundPhotos-Day_15.zip
1.50 GB
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WoundPhotos-Day_16.zip
1.41 GB
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WoundPhotos-Day_19.zip
714.20 MB
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WoundPhotos-Day_2.zip
8.98 GB
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WoundPhotos-Day_21.zip
688.66 MB
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WoundPhotos-Day_3.zip
7.40 GB
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WoundPhotos-Day_4.zip
7.91 GB
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WoundPhotos-Day_5.zip
5.95 GB
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WoundPhotos-Day_6.zip
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WoundPhotos-Day_7.zip
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WoundPhotos-Day_9.zip
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Abstract
Wound healing progresses through overlapping phases: hemostasis, inflammation, proliferation, and remodeling. Continuous characterization of these transitions remains limited. Here, we employed a swine excisional wound model to monitor cellular dynamics across the healing timeline. Both non-invasive imaging and wound biopsy samples from the wound edge and center were acquired. Wound photographs were analyzed using advanced artificial intelligence methods. Wound biopsy samples were subject to RNA sequencing to generate gene expression profiles for the course of healing. By combining the image and the gene expression analyses, we were able to create the comprehensive data for wound healing, which can serve as ground truth for building wound diagnostic and treatment algorithms.
Dataset DOI: 10.5061/dryad.0rxwdbsbr
Description of the data and file structure
The dataset contains observations from an acute wound healing experiment in pigs. Wound photographs and RNAseq data were collected at multiple time points from day 0 to day 21 post-wound onset. Tissue samples for RNAseq were taken from both wound edges and wound centers.
Experiment description:
Six domestic pigs (Yorkshire-mix breed, females, 45-50 Kg, pig ID: 1323, 1324, 1325, 1326, 1327, and 1328) were utilized. Twelve full-thickness, circular excision wounds (2 cm in diameter, wound ID: A, B, C, D, E, F, G, H, I, J, K, and L) were created in each animal. Baseline (Day 0) wound images and the excised skin tissue were collected.
During the post-operative period, wound images and biopsy samples from the wound edge and wound center were collected on post-operative days 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15, 16, 19, and 21. The biopsy samples were sent for RNA sequencing analysis.
Wound images are provided as .JPG files. For some images, both the standard compressed .JPG file and the corresponding source .CR2 file are available. The .CR2 files are Canon RAW images, which are uncompressed and may be useful for users requiring the highest image quality for re-analysis. Files with the same name but different extensions (.JPG and .CR2) represent the same image. The .CR2 format can be opened with specialized image processing software such as Adobe Photoshop, ImageJ (with the appropriate plugins), or other RAW image converters.
Files and variables
Data and files structure:
RNAseq_dataset-20250825.zip:
- RNAseq Codes.csv: Sample labels.
- PigWoundRNAData.xlsx: Combined gene expression and sample data file.
Columns represent samples, rows represent genes.
Ensemble ID is provided in the last column.
Sample information (see last 4 rows):
Day: Day from wound onset.
Pig: 6 animals with the following IDs: 1323, 1324, 1325, 1326, 1327, 1328.
Wound: Wound ID. Each animal had 12 wounds, labeled by letters A to L. Wound ID is not specified for day 0 samples, which are labeled as "U" (undefined).
Edge/Center: Location of the sample in the wound. "e" indicates the edge, and "c" indicates the center. Sample location is not specified for day 0 samples, which are labeled as "n".
Wound_photo_cropped.zip:
- Cropped image codes.csv: A list of images with the following metadata: day, pig ID, wound ID, image filename, and folder names.
- Cropped Wound images: Pre-processed images cropped into circles containing the whole wound.
WoundPhotos-Day_0-Pig_1323.zip: Original images of wound A to L from pig 1323 on day 0. A wound scale and ruler are included.
WoundPhotos-Day_0-Pig_1324.zip: Original images of wound A to L from pig 1324 on day 0.
WoundPhotos-Day_0-Pig_1325.zip: Original images of wound A to L from pig 1325 on day 0.
WoundPhotos-Day_0-Pig_1326.zip: Original images of wound A to L from pig 1326 on day 0.
WoundPhotos-Day_0-Pig_1327.zip: Original images of wound A to L from pig 1327 on day 0.
WoundPhotos-Day_0-Pig_1328.zip: Original images of wound A to L from pig 1328 on day 0.
WoundPhotos-Day_1.zip: Original images of wounds on day 1.
WoundPhotos-Day_2.zip: Original images of wounds on day 2.
WoundPhotos-Day_3.zip: Original images of wounds on day 3.
WoundPhotos-Day_4.zip: Original images of wounds on day 4.
WoundPhotos-Day_5.zip: Original images of wounds on day 5.
WoundPhotos-Day_6.zip: Original images of wounds on day 6.
WoundPhotos-Day_7.zip: Original images of wounds on day 7.
WoundPhotos-Day_9.zip: Original images of wounds on day 9.
WoundPhotos-Day_11.zip: Original images of wounds on day 11.
WoundPhotos-Day_13.zip: Original images of wounds on day 13.
WoundPhotos-Day_15.zip: Original images of wounds on day 15.
WoundPhotos-Day_16.zip: Original images of wounds on day 16.
WoundPhotos-Day_19.zip: Original images of wounds on day 19.
WoundPhotos-Day_21.zip: Original images of wounds on day 21.
Code/software
This dataset is linked to an online gene time series plotting tool: https://geneexpressionsearch.pythonanywhere.com/
The code for the tool is provided in a GitHub repository: https://github.com/mkesapra/GeneExpressionSearch
Access information
Raw gene sequencing data, NCBI Sequence Read Archive:
https://www.ncbi.nlm.nih.gov/sra/?term=SRP591687
https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1276066
Original and normalized gene transcript counts, NCBI Gene Expression Omnibus:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE305817
Related publication:
Zlobina, K., Yang, H., Kesapragada, M., Lu, F., Gallegos, A., Villa-Martinez, G., Alhamo, M.A., Zhu, K., Recendez, C., Collins, C., Rolandi, M., Soulika, A., Aslankoohi, E., Zhao, M., Gomez, M., & Isseroff, R.R. (2025). A high-resolution temporal transcriptomic and imaging dataset of porcine wound healing. Scientific Data.
Six domestic pigs (Yorkshire-mix breed, females, 45-50 Kg) were utilized and divided to 2 groups (3 animals/group) for wound biopsy collection in a rotation order (group 1 biopsy on post-op day 1, 3, 5, 7, 11, 15, and 21/endpoint; group 2 biopsy on day 2, 4, 6, 9, 13, 16, and 19/endpoint). Twelve full-thickness, excisional wounds at 2cm in diameter were created bilaterally on each side of the dorsum of each animal after shaving, depilation, and skin preparation. Wound images (day 0-21) were captured with a DSLR camera at a set distance of 1 foot above the wounds, and a photo scale (Medline NE1 Wound Assessment Tool) was used for calibration in each image. To determine the wound healing timeline, open wound areas were analyzed by manual tracking in ImageJ or automatically cropped and analyzed by algorithms.
RNA samples (a total of 150 samples of 72 paired samples from the wound edge and center, and 6 healthy skin samples as controls) were extracted from the wound biopsies with Qiagen RNeasy Fibrous Tissue Mini Kit (Qiagen, German). Around 200ng mRNA/sample were sent to Novogene (Beijiang, China) for RNAseq analysis, including RNA sample quality control, library preparation with poly-A enrichment, sequencing by Illumina sequencing system NovaSeq X Plus (PE150) for >20 millions paired reads, generation of raw data in the FASTQ format, and aligned for gene counts.
The transcriptome data is represented by the table of gene expression counts "gene_count.xlsx", where each row represents a gene and each column represents a sample. The column names are SampleID labels. The encoding of sample labels is explained in the table "CodesDaVinci.xlsx". This table contains the pig ID, wound number, day of collection from the wound onset, and wound location label (center or edge). The expression time series plots of other genes can be found through an online tool at https://geneexpressionsearch.pythonanywhere.com.