Bioinformatic identification of euploid and aneuploid embryo secretome signatures in IVF culture media based on MALDI-ToF mass spectrometry

Quantitative Proteomics and Metabolomics of Culture Medium from Single Human Embryo Reveal Embryo Quality-Related Multiomics Biomarkers

An effective tool to assess embryo quality in the assisted reproduction clinical practice will enhance successful implantation rates and mitigate high risks of multiple pregnancies. Potential biomarkers secreted into culture medium (CM) during embryo development enable rapid and noninvasive methods of assessing embryo quality. However, small volumes, low biomolecule concentrations, and impurity interference collectively preclude the identification of quality-related biomarkers in single blastocyst CM. Here, we developed a noninvasive trace multiomics approach to screen for potential markers in individual human blastocyst CM. We collected 84 CM samples and divided them into high-quality (HQ) and low-quality (LQ) groups. We evaluated the differentially expressed proteins (DEPs) and metabolites (DEMs) in HQ and LQ CM. A total of 504 proteins and 189 metabolites were detected in individual blastocyst CM. Moreover, 9 DEPs and 32 DEMs were identified in different quality embryo CM. We also categorized HQ embryos into positive implantation (PI) and negative implantation (NI) groups based on ultrasound findings on day 28. We identified 41 DEPs and 4 DEMs associated with clinical implantation outcomes in morphologically HQ embryos using a multiomics analysis approach. This study provides a noninvasive multiomics analysis technique and identifies potential biomarkers for clinical embryo developmental quality assessment.

Metabolic imaging of human embryos is predictive of ploidy status but is not associated with clinical pregnancy outcomes: a pilot trial

STUDY QUESTION

Does fluorescence lifetime imaging microscopy (FLIM)-based metabolic imaging assessment of human blastocysts prior to frozen transfer correlate with pregnancy outcomes?

SUMMARY ANSWER

FLIM failed to distinguish consistent patterns in mitochondrial metabolism between blastocysts leading to pregnancy compared to those that did not.

WHAT IS KNOWN ALREADY

FLIM measurements provide quantitative information on NAD(P)H and flavin adenine dinucleotide (FAD+) concentrations. The metabolism of embryos has long been linked to their viability, suggesting the potential utility of metabolic measurements to aid in selection.

STUDY DESIGN, SIZE, DURATION

This was a pilot trial enrolling 121 IVF couples who consented to have their frozen blastocyst measured using non-invasive metabolic imaging. After being warmed, 105 couples' good-quality blastocysts underwent a 6-min scan in a controlled temperature and gas environment. FLIM-assessed blastocysts were then transferred without any intervention in management.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Eight metabolic parameters were obtained from each blastocyst (4 for NAD(P)H and 4 for FAD): short and long fluorescence lifetime, fluorescence intensity, and fraction of the molecule engaged with enzyme. The redox ratio (intensity of NAD(P)H)/(intensity of FAD) was also calculated. FLIM data were combined with known metadata and analyzed to quantify the ability of metabolic imaging to differentiate embryos that resulted in pregnancy from embryos that did not. De-identified discarded aneuploid human embryos (n = 158) were also measured to quantify correlations with ploidy status and other factors. Statistical comparisons were performed using logistic regression and receiver operating characteristic (ROC) curves with 5-fold cross-validation averaged over 100 repeats with random sampling. AUC values were used to quantify the ability to distinguish between classes.

MAIN RESULTS AND THE ROLE OF CHANCE

No metabolic imaging parameters showed significant differences between good-quality blastocysts resulting in pregnancy versus those that did not. A logistic regression using metabolic data and metadata produced an ROC AUC of 0.58. In contrast, robust AUCs were obtained when classifying other factors such as comparison of Day 5 (n = 64) versus Day 6 (n = 41) blastocysts (AUC = 0.78), inner cell mass versus trophectoderm (n = 105: AUC = 0.88) and aneuploid (n = 158) versus euploid and positive pregnancy embryos (n = 108) (AUC = 0.82).

LIMITATIONS, REASONS FOR CAUTION

The study protocol did not select which embryo to transfer and the cohort of 105 included blastocysts were all high quality. The study was also limited in number of participants and study sites. Increased power and performing the trial in more sites may have provided a stronger conclusion regarding the merits of the use of FLIM clinically.

WIDER IMPLICATIONS OF THE FINDINGS

FLIM failed to distinguish consistent patterns in mitochondrial metabolism between good-quality blastocysts leading to pregnancy compared to those that did not. Blastocyst ploidy status was, however, highly distinguishable. In addition, embryo regions and embryo day were consistently revealed by FLIM. While metabolic imaging detects mitochondrial metabolic features in human blastocysts, this pilot trial indicates it does not have the potential to serve as an effective embryo viability detection tool. This may be because mitochondrial metabolism plays an alternative role post-implantation.

STUDY FUNDING/COMPETING INTEREST(S)

This study was sponsored by Optiva Fertility, Inc. Boston IVF contributed to the clinical site and services. Becker Hickl, GmbH, provided the FLIM system on loan. T.S. was the founder and held stock in Optiva Fertility, Inc., and D.S. and E.S. had options with Optiva Fertility, Inc., during this study.

TRIAL REGISTRATION NUMBER

The study was approved by WCG Connexus IRB (Study Number 1298156).

Making and selecting the best embryo in the laboratory

Over the past 4 decades our ability to maintain a viable human embryo in vitro has improved dramatically, leading to higher implantation rates. This has led to a notable shift to single blastocyst transfer and the ensuing elimination of high order multiple gestations. Future improvements to embryo culture systems will not only come from new improved innovative media formulations (such as the inclusion of antioxidants), but plausibly by moving away from static culture to more dynamic perfusion-based systems now made a reality owing to the breakthroughs in three-dimensional printing technology and micro fabrication. Such an approach has already made it feasible to create high resolution devices for intracytoplasmic sperm injection, culture, and cryopreservation, paving the way not only for improvements in outcomes but also automation of assisted reproductive technology. Although improvements in culture systems can lead to further increases in pregnancy outcomes, the ability to quantitate biomarkers of embryo health and viability will reduce time to pregnancy and decrease pregnancy loss. Currently artificial intelligence is being used to assess embryo development through image analysis, but we predict its power will be realized through the creation of selection algorithms based on the integration of information related to metabolic functions, cell-free DNA, and morphokinetics, thereby using vast amounts of different data types obtained for each embryo to predict outcomes. All of this will not only make assisted reproductive technology more effective, but it will also make it more cost effective, thereby increasing patient access to infertility treatment worldwide.

Prospective on Imaging Mass Spectrometry in Clinical Diagnostics

Imaging mass spectrometry (IMS) is a molecular technology utilized for spatially driven research, providing molecular maps from tissue sections. This article reviews matrix-assisted laser desorption ionization (MALDI) IMS and its progress as a primary tool in the clinical laboratory. MALDI mass spectrometry has been used to classify bacteria and perform other bulk analyses for plate-based assays for many years. However, the clinical application of spatial data within a tissue biopsy for diagnoses and prognoses is still an emerging opportunity in molecular diagnostics. This work considers spatially driven mass spectrometry approaches for clinical diagnostics and addresses aspects of new imaging-based assays that include analyte selection, quality control/assurance metrics, data reproducibility, data classification, and data scoring. It is necessary to implement these tasks for the rigorous translation of IMS to the clinical laboratory; however, this requires detailed standardized protocols for introducing IMS into the clinical laboratory to deliver reliable and reproducible results that inform and guide patient care.

Non-invasive evaluation of embryo quality for the selection of transferable embryos in human in vitro fertilization-embryo transfer

The ultimate goal of human assisted reproductive technology is to achieve a healthy pregnancy and birth, ideally from the selection and transfer of a single competent embryo. Recently, techniques for efficiently evaluating the state and quality of preimplantation embryos using time-lapse imaging systems have been applied. Artificial intelligence programs based on deep learning technology and big data analysis of time-lapse monitoring system during in vitro culture of preimplantation embryos have also been rapidly developed. In addition, several molecular markers of the secretome have been successfully analyzed in spent embryo culture media, which could easily be obtained during in vitro embryo culture. It is also possible to analyze small amounts of cell-free nucleic acids, mitochondrial nucleic acids, miRNA, and long non-coding RNA derived from embryos using real-time polymerase chain reaction (PCR) or digital PCR, as well as next-generation sequencing. Various efforts are being made to use non-invasive evaluation of embryo quality (NiEEQ) to select the embryo with the best developmental competence. However, each NiEEQ method has some limitations that should be evaluated case by case. Therefore, an integrated analysis strategy fusing several NiEEQ methods should be urgently developed and confirmed by proper clinical trials.

Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups

Clinical bioinformatics is a newly emerging field that applies bioinformatics techniques for facilitating the identification of diseases, discovery of biomarkers, and therapy decision. Mathematical modelling is part of bioinformatics analysis pipelines and a fundamental step to extract clinical insights from genomes, transcriptomes and proteomes of patients. Often, the chosen modelling techniques relies on either statistical, machine learning or deterministic approaches. Research that combines bioinformatics with modelling techniques have been generating innovative biomedical technology, algorithms and models with biotech applications, attracting private investment to develop new business; however, startups that emerge from these technologies have been facing difficulties to implement clinical bioinformatics pipelines, protect their technology and generate profit. In this commentary, we discuss the main concepts that startups should know for enabling a successful application of predictive modelling in clinical bioinformatics. Here we will focus on key modelling concepts, provide some successful examples and briefly discuss the modelling framework choice. We also highlight some aspects to be taken into account for a successful implementation of cost-effective bioinformatics from a business perspective.

A Rapid and Affordable Screening Tool for Early-Stage Ovarian Cancer Detection Based on MALDI-ToF MS of Blood Serum

Ovarian cancer is a worldwide health issue that grows at a rate of almost 250,000 new cases every year. Its early detection is key for a good prognosis and even curative surgery. However, current medical examination methods and tests have been inefficient in detecting ovarian cancer at the early stage, leading to preventable death. So far, new screening tests based on molecular biomarker analysis techniques have not resulted in any substantial improvement in early-stage diagnosis and increased survival. Thus, whilst there remains clear potential to improve outcomes through early detection, novel approaches are needed. Here, we postulated that MALDI-ToF-mass-spectrometry-based tests can be a solution for effective screening of ovarian cancer. In this retrospective cohort study, we generated and analyzed the mass spectra of 181 serum samples of women with and without ovarian cancer. Using bioinformatics pipelines for analysis, including predictive modeling and machine learning, we found distinct mass spectral patterns composed of 9–20 key combinations of peak intensity or peak enrichment features for each stage of ovarian cancer. Based on a scoring algorithm and obtained patterns, the optimal sensitivity for detecting each stage of cancer was 95–97% with a specificity of 97%. Scoring all algorithms simultaneously could detect all stages of ovarian cancer at 99% sensitivity and 92% specificity. The results further demonstrate that the matrix and mass range analyzed played a key role in improving the mass spectral data quality and diagnostic power. Altogether, with the results reported here and increasing evidence of the MS assay’s diagnostic accuracy and instrument robustness, it has become imminent to consider MS in the clinical application for ovarian cancer screening.

Metabolic state of human blastocysts measured by fluorescence lifetime imaging microscopy

STUDY QUESTION

Can non-invasive metabolic imaging via fluorescence lifetime imaging microscopy (FLIM) detect variations in metabolic profiles between discarded human blastocysts?

SUMMARY ANSWER

FLIM revealed extensive variations in the metabolic state of discarded human blastocysts associated with blastocyst development over 36 h, the day after fertilization and blastocyst developmental stage, as well as metabolic heterogeneity within individual blastocysts.

WHAT IS KNOWN ALREADY

Mammalian embryos undergo large changes in metabolism over the course of preimplantation development. Embryo metabolism has long been linked to embryo viability, suggesting its potential utility in ART to aid in selecting high quality embryos. However, the metabolism of human embryos remains poorly characterized due to a lack of non-invasive methods to measure their metabolic state.

STUDY DESIGN, SIZE, DURATION

We conducted a prospective observational study. We used 215 morphologically normal human embryos from 137 patients that were discarded and donated for research under an approved institutional review board protocol. These embryos were imaged using metabolic imaging via FLIM to measure the autofluorescence of two central coenzymes, nicotinamide adenine (phosphate) dinucleotide (NAD(P)H) and flavine adenine dinucleotide (FAD+), which are essential for cellular respiration and glycolysis.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Here, we used non-invasive FLIM to measure the metabolic state of human blastocysts. We first studied spatial patterns in the metabolic state within human blastocysts and the association of the metabolic state of the whole blastocysts with stage of expansion, day of development since fertilization and morphology. We explored the sensitivity of this technique in detecting metabolic variations between blastocysts from the same patient and between patients. Next, we explored whether FLIM can quantitatively measure metabolic changes through human blastocyst expansion and hatching via time-lapse imaging. For all test conditions, the level of significance was set at P < 0.05 after correction for multiple comparisons using Benjamini-Hochberg's false discovery rate.

MAIN RESULTS AND THE ROLE OF CHANCE

We found that FLIM is sensitive enough to detect significant metabolic differences between blastocysts. We found that metabolic variations between blastocyst are partially explained by both the time since fertilization and their developmental expansion stage (P < 0.05), but not their morphological grade. Substantial metabolic variations between blastocysts from the same patients remain, even after controlling for these factors. We also observe significant metabolic heterogeneity within individual blastocysts, including between the inner cell mass and the trophectoderm, and between the portions of hatching blastocysts within and without the zona pellucida (P < 0.05). And finally, we observed that the metabolic state of human blastocysts continuously varies over time.

LIMITATIONS, REASONS FOR CAUTION

Although we observed significant variations in metabolic parameters, our data are taken from human blastocysts that were discarded and donated for research and we do not know their clinical outcome. Moreover, the embryos used in this study are a mixture of aneuploid, euploid and embryos of unknown ploidy.

WIDER IMPLICATIONS OF THE FINDINGS

This work reveals novel aspects of the metabolism of human blastocysts and suggests that FLIM is a promising approach to assess embryo viability through non-invasive, quantitative measurements of their metabolism. These results further demonstrate that FLIM can provide biologically relevant information that may be valuable for the assessment of embryo quality.

STUDY FUNDING/COMPETING INTEREST(S)

Supported by the Blavatnik Biomedical Accelerator Grant at Harvard University. Becker and Hickl GmbH and Boston Electronics sponsored research with the loaning of equipment for FLIM. D.J.N. is an inventor on patent US20170039415A1.

TRIAL REGISTRATION NUMBER

N/A.

Current Advancements in Noninvasive Profiling of the Embryo Culture Media Secretome

There have been over 8 million babies born through in vitro fertilization (IVF) and this number continues to grow. There is a global trend to perform elective single embryo transfers, avoiding risks associated with multiple pregnancies. It is therefore important to understand where current research of noninvasive testing for embryos stands, and what are the most promising techniques currently used. Furthermore, it is important to identify the potential to translate research and development into clinically applicable methods that ultimately improve live birth and reduce time to pregnancy. The current focus in the field of human reproductive medicine is to develop a more rapid, quantitative, and noninvasive test. Some of the most promising fields of research for noninvasive assays comprise cell-free DNA analysis, microscopy techniques coupled with artificial intelligence (AI) and omics analysis of the spent blastocyst media. High-throughput proteomics and metabolomics technologies are valuable tools for noninvasive embryo analysis. The biggest advantages of such technology are that it can differentiate between the embryos that appear morphologically identical and has the potential to identify the ploidy status noninvasively prior to transfer in a fresh cycle or before vitrification for a later frozen embryo transfer.