RNAseq analysis of gene counts and expression levels in diabetic foot ulcers
Data files
Aug 30, 2025 version files 80.88 MB
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counts.csv
15.81 MB
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expression.csv
65.05 MB
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Patients.xlsx
11.50 KB
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README.md
3.92 KB
Abstract
In this study we examine the temporal changes in the transcriptome of chronic wounds to identify dynamic differences between healing and non-healing wounds. Wound tissue samples were collected over a 12-week period from human subjects with chronic, diabetic foot ulcers. Some healed during the observation period, and some remained unhealed. Bulk RNAseq analysis was performed to find differences in dynamic patterns.
https://doi.org/10.5061/dryad.2v6wwpzzc
Principle Investigator Contact Information:
Name: Rivkah Isseroff
Institution: University of California, Davis
Email: rrisseroff@ucdavis.edu
Dataset Overview
Temporal wound tissue was collected from 17 patients with a diabetic foot ulcer at Northern California VA Medical Center (Mather, CA) between 2021 and 2022. Wounds were monitored over 12 weeks to classify them as "healers" or "non-healers" (please see "Patients.xlsx" for the sample information.). Tissue from wound margins was collected during the debridement of wound care, and preserved in RNALater (Life Tech). Tissue samples were homogenized, total RNA was extracted for RNAseq. To map to the human genome, The Cogent NGS analysis pipeline (CogentAP) from Takara Bio (v2.0) was used to de-multiplex and analyze each fastq file. The analysis process involved the "cogent analyze" wrapper function, which carried out the following tasks: (1) trimmed reads using cutadapt (version 3.2, doi:https://doi.org/10.14806/ej.17.1.200), (2) aligned genomes to the Homo sapiens genome GRCh38 using STAR (version 2.7.2a, doi: 10.1093/bioinformatics/bts635), and (3) performed read counting for exonic, genomic, and mitochondrial regions in Homo sapiens genes from ENSEMBL gene annotation version 103 (https://www.ensembl.org/Homo_sapiens/Info/Index) using featureCounts (version 2.0.1, doi: 10.1093/bioinformatics/btt656). Normalization was performed to adjust for differences in sequencing depth between samples, and the size factors were estimated by computing the median ratio of counts for each gene across all samples. The resulting dataset comprises 117 sample points, each representing 58,735 genes.
Funding
This project was supported by the BETR program from DARPA (award number: D20AC00003-12), and a VA merit award (award number: I01 CX001503).
Ethics Approval
The tissue collected was conducted under an approved, exempt VA IRB protocol for the discarded tissue during the standard of care.
Related Data Sources
The raw data of the RNAseq is available at: https://www.ncbi.nlm.nih.gov/sra/PRJNA1200081
Recommended Citation
Ksenia Zlobina, Manasa Kesapragada, Hsin-Ya Yang, Mirabel Dafinone, Rawlings Lyle, Elham Aslankoohi, Marco Rolandi, Athena Soulika, Sara Dahle, Rivkah Isseroff, Marcella Gomez. In Review. A Dynamical Systems Approach Reveals Cycling Transcriptomic Patterns in Non-healing Chronic Wounds. Nature Communications.
Files and variables
File: Patients.xlsx
Description: Sample key of the patient number, week of collection, and identification of healing or non-healing wound.
File: counts.csv
Description: Gene counts in each tissue sample.
Variables
- Please see "Patients.xlsx" for the sample information.
File: expression.csv
Description: Normalized expression of each tissue sample.
Variables
- Please see "Patients.xlsx" for the sample information.
Code/software
N/A
Access information
Data was derived from the following sources:
Human subjects data
In the approved IRB protocol, no patient consent was needed for the discarded tissue as these are non-consent requiring procedures routinely performed in the clinics by both nurses and physicians.
The discarded tissue was de-identified during the collecting process and kept in numbered tubes without PHI information for lab analysis.
Seventeen patients, 18+ years from Northern California VA Medical Center (Mather, CA) with a diabetic foot ulcer that had not improved > 50% for 4+ weeks, were enrolled in the study from 2021 to 2022. Wounds were monitored over 12 weeks to classify them as "healers" or "non-healers". Tissue from wound margins was collected during the standard of care and debridement, and preserved in RNALater (Life Tech). Tissue samples were homogenized, total RNA was extracted with the Quick-RNA Mag Bead kit (Zymo Research), and the integrity of the RNA was evaluated using an Agilent Tape Station (Agilent). For sequencing, indexed libraries were constructed using the SMARTer Stranded Total RNA-Seq Kit v3 (Takara Bio). Both the quantity and quality of these libraries were appraised by a Qubit fluorometer and an Agilent 2100 bioanalyzer. Molar concentrations of the libraries were confirmed by qPCR prior to pooling. The sequencing process was conducted on the Illumina NovaSeq 6000 platform, utilizing PE150 chemistry (Illumina).
The Cogent NGS analysis pipeline (CogentAP) from Takara Bio (v2.0) was used to de-multiplex and analyze each fastq file. The analysis process involved the "cogent analyze" wrapper function, which carried out the following tasks: (1) trimmed reads using cutadapt (version 3.2, doi:https://doi.org/10.14806/ej.17.1.200), (2) aligned genomes to the Homo sapiens genome GRCh38 using STAR (version 2.7.2a, doi: 10.1093/bioinformatics/bts635), and (3) performed read counting for exonic, genomic, and mitochondrial regions in Homo sapiens genes from ENSEMBL gene annotation version 103 (https://www.ensembl.org/Homo_sapiens/Info/Index) using featureCounts (version 2.0.1, doi: 10.1093/bioinformatics/btt656). Normalization was performed to adjust for differences in sequencing depth between samples, and the size factors were estimated by computing the median ratio of counts for each gene across all samples. The resulting dataset comprises 117 sample points, each representing 58,735 genes.