Background: Wound transcriptomic analysis can be used to quantify wound healing stages and identify leverage points for wound healing intervention. However, individual gene signatures corresponding to wound healing stages vary from one experiment to another and are highly dependent on both experimental setup and bioinformatics methods.
Methods: We develop a systematic approach to informatively compare time series from publicly available wound transcriptomic datasets, including mouse and human wounds, and identify consistent gene expression patterns.
Results: We reveal the limitations of gene expression data collection, interpretation, and comparison. For example, the sample rate of wound transcriptomic sample collection must be higher than the rate of changes in the wound healing processes, otherwise, important changes in gene expression may be missed. This may lead to mis finding the most significant genes, as peaks of expression for highly differentially expressed genes are lost. Nevertheless, we derived a short list of genes highly differentially expressed in all datasets under consideration. After clustering and normalization, these genes clearly demonstrate similarly changing dynamics of expression between the wounds and may be used for wound healing stage detection.
Conclusions: A list of genes that may be used for transcriptomics-based wound healing stage detection is provided. In addition, we suggest experimental approaches that could help researchers to extract more meaningful results.