Endometriosis, the growth of endometrial tissue outside the uterine cavity, is associated with chronic pelvic pain, subfertility and an increased risk of ovarian cancer. Current treatments include the surgical removal of the lesions or the induction of a hypoestrogenic state. However, a reappearance of the lesion after surgery is common and a hypoestrogenic state is less than optimal for women of reproductive age. Additional approaches are required. Endometriosis lesions exist in a unique microenvironment characterized by increased concentrations of hormones, inflammation, oxidative stress and iron. This environment influences cell survival through the binding of membrane receptors and a subsequent cascading activation of intracellular kinases that stimulate a cellular response. Many of these kinase signalling pathways are constitutively activated in endometriosis. These pathways are being investigated as therapeutic targets in other diseases and thus may also represent a target for endometriosis treatment. To identify relevant English language studies published up to 2015 on kinase signalling pathways in endometriosis, we searched the Pubmed database using the following search terms in various combinations; ‘endometriosis’, ‘inflammation’, ‘oxidative stress’, ‘iron’, ‘kinase’, ‘NF kappa’, ‘mTOR’, ‘MAPK’ ‘p38’, ‘JNK’, ‘ERK’ ‘estrogen’ and progesterone’. Further citing references were identified using the Scopus database and finally current clinical trials were searched on the clinicaltrials.gov trial registry. The current literature on intracellular kinases activated by the endometriotic environment can be summarized into three main pathways that could be targeted for treatments: the canonical IKKβ/NFκB pathway, the MAPK pathways (ERK1/2, p38 and JNK) and the PI3K/AKT/mTOR pathway. A number of pharmaceutical compounds that target these pathways have been successfully trialled in in vitro and animal models of endometriosis, although they have not yet proceeded to clinical trials. The current generation of kinase inhibitors carry a potential for adverse side effects. Kinase signalling pathways represent viable targets for endometriosis treatment. At present, however, further improvements in clinical efficacy and the profile of adverse effects are required before these compounds can be useful for long-term endometriosis treatment. A better understanding of the molecular activity of these kinases, including the specific extracellular compounds that lead to their activation in endometriotic cells specifically should facilitate their improvement and could potentially lead to new, non-hormonal treatments of endometriosis.

Endometriosis affects approximately 15% of reproductive aged women and is associated with chronic pelvic pain and infertility. However, the molecular mechanisms by which endometriosis impacts fertility are poorly understood. The developmentally regulated, imprinted H19 long noncoding RNA (lncRNA) functions to reduce the bioavailability of microRNA let‐7 by acting as a molecular sponge. Here we report that H19 expression is significantly decreased in the eutopic endometrium of women with endometriosis as compared to normal controls. We show that decreased H19 increases let‐7 activity, which in turn inhibits Igf1r expression at the post‐transcriptional level, thereby contributing to reduced proliferation of endometrial stromal cells. We propose that perturbation of this newly identified H19/Let‐7/IGF1R regulatory pathway may contribute to impaired endometrial preparation and receptivity for pregnancy in women with endometriosis. Our finding represents the first example of a lncRNA‐based mechanism in endometriosis and its associated infertility, thus holding potential in the development of novel therapeutics for women with endometriosis and infertility.

A variety of methods have been developed to demultiplex pooled samples in a single cell RNA sequencing (scRNA-seq) experiment which either require hashtag barcodes or sample genotypes prior to pooling. We introduce scSplit which utilizes genetic differences inferred from scRNA-seq data alone to demultiplex pooled samples. scSplit also enables mapping clusters to original samples. Using simulated, merged, and pooled multi-individual datasets, we show that scSplit prediction is highly concordant with demuxlet predictions and is highly consistent with the known truth in cell-hashing dataset. scSplit is ideally suited to samples without external genotype information and is available at: https://github.com/jon-xu/scSplit