Noninvasive sexing of human preimplantation embryos using RT-PCR in the spent culture media: A proof-of-concept study

An Update on Non-invasive Approaches for Genetic Testing of the Preimplantation Embryo

Preimplantation Genetic Testing (PGT) aims to reduce the chance of an affected pregnancy or improve success in an assisted reproduction cycle. Since the first established pregnancies in 1990, methodological approaches have greatly evolved, combined with significant advances in the embryological laboratory. The application of preimplantation testing has expanded, while the accuracy and reliability of monogenic and chromosomal analysis have improved. The procedure traditionally employs an invasive approach to assess the nucleic acid content of embryos. All biopsy procedures require high technical skill, and costly equipment, and may impact both the accuracy of genetic testing and embryo viability. To overcome these limitations, many researchers have focused on the analysis of cell-free DNA (cfDNA) at the preimplantation stage, sampled either from the blastocoel or embryo culture media, to determine the genetic status of the embryo non-invasively. Studies have assessed the origin of cfDNA and its application in non-invasive testing for monogenic disease and chromosomal aneuploidies. Herein, we discuss the state-of-the-art for modern non-invasive embryonic genetic material assessment in the context of PGT. The results are difficult to integrate due to numerous methodological differences between the studies, while further work is required to assess the suitability of cfDNA analysis for clinical application.

Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform

Extracellular vesicles (EVs) are double-layered phospholipid membrane vesicles that are released by most cells and can mediate intercellular communication through their RNA cargo. In this study, we tested if the NanoString nCounter platform can be used for the analysis of EV-mRNA. We developed and optimized a methodology for EV enrichment, EV-RNA extraction and nCounter analysis. Then, we demonstrated the validity of our workflow by analyzing EV-RNA profiles from the plasma of 19 cancer patients and 10 controls and developing a gene signature to differentiate cancer versus control samples. TRI reagent outperformed automated RNA extraction and, although lower plasma input is feasible, 500 μL provided highest total counts and number of transcripts detected. A 10-cycle pre-amplification followed by DNase treatment yielded reproducible mRNA target detection. However, appropriate probe design to prevent genomic DNA binding is preferred. A gene signature, created using a bioinformatic algorithm, was able to distinguish between control and cancer EV-mRNA profiles with an area under the ROC curve of 0.99. Hence, the nCounter platform can be used to detect mRNA targets and develop gene signatures from plasma-derived EVs.