Quantitative Identification of Implantation Receptivity by Single Cell Transcriptome Analysis

Non-invasive Transcriptomic Signature at the Single Cell Level in Endometrial Fluid as a Novel Diagnostic Test of Human Endometrial Receptivity

This project aims to study the endometrial transcriptome at the single cell level in both endometrial native tissue (biopsies) and endometrial fluid. Basically, the goals are: 1) To use the resolution provided by single-cell RNA-seq to deconvolute real time dynamics in endometrium transcriptome throughout the menstrual cycle from artifacts produced by known population heterogeneity, and 2) To use endometrium fluid to replace endometrium biopsies as signal source to develop a new diagnostic platform to determine the endometrium receptive state which is high resolution and minimally invasive. This study will provide an unprecedented high-resolution characterization of the endometrium cellular hierarchy via whole transcriptome analysis throughout the menstrual stages. These results will lead to a robust definition of stage-defining gene expression signatures and resolve the long-standing inconsistencies originating from bulk tissue studies. The dataset will be further explored via informatics tools to provide biological insights into the dynamics of endometrium and initiate new anchor points for functional studies.

Successful embryo implantations for in vitro fertilization require both healthy embryos and a receptive endometrium. The window of implantation (WOI), during which endometrium reaches receptive state, varies among individuals. Displacements of WOI have been reported to be a major cause of repeated implantation failures. However, a reliable diagnostic metric to evaluate endometrial receptive status is lacking. The propose of this study is to use single-cell RNA seq and bioinformatics tools to develop a high-resolution platform to characterize endometrium activities and to measure endometrial receptive state in a non-invasive manner.

First objective is to develop an experimental pipeline to generate high quality single-cell RNA-seq data from endometrial biopsies and endometrial fluid from healthy patients throughout the menstrual cycle at each of these stages: early proliferative (EP; days 0-8), late proliferative (LP; days 9-14), early secretory (ES; days 15-18), mid-secretory or receptive (MS; days 19-23), and late secretory (LS; days 24-30). These tissues will be dissociated mechanically and enzymatically and subjected to microfluidic sorting, single cell transcriptome amplification and analysis. These data will provide the first transcriptome-wide single cell data various cell types of the human endometrium.

Secondly, this study look at establish algorithmic models to distinguish epithelial and stroma populations informatically, without the need to first purify populations of interest. This will enable transcriptome analysis from mixed populations of cells.

Finally evaluate the use of endometrial fluid as an alternative source for receptivity diagnosis analyzing the transcriptome profile of single cells from Endometrial fluid collected from human patients at different menstrual stages. These signatures will also be compared and correlated with signatures obtained from biopsies, which will help us better understand the biological events that lead to the occurrence of these cells in the endometrial fluid and evaluate the potential of extending their use for diagnosis of other endometrium conditions.

Inclusion Criteria:

• Healthy women in natural cycle;
• Normal karyotype;
• Negative serologic tests for HIV, HBV, HCV, RPR;
• BMI: 18 - 30 Kg/m2 (both included);
• Women with regular menstrual cycle (3-4/28-30 days).

Exclusion Criteria:

• Patients who had carried an intrauterine device in the previous 3 months;
• Patients who have taken hormonal contraceptives in the previous 2 months;
• Adnexal or uterine pathologies;
• Polycystic ovary;
• Any unstable disease or medical condition that could interfere with the study or put in risk the health of the patient (evaluated by the principal researcher of the research team);
• Any illness or medical unstable condition.