Raman profiling of embryo culture medium to identify aneuploid and euploid embryos

Raman spectroscopy: A promising analytical tool used in human reproductive medicine

Over the past few years, there has been growing interest in developing new methods of embryo quality assessment to improve the outcomes of assisted reproductive technologies in the medical field. Raman microscopy as an increasingly promising analytical tool has been widely used in life sciences, biomedicine and "omics" to study molecular, biochemical components, living cells and tissues due to the label-free and non-destructive nature of the imaging technique. This paper reviews the analytical capability of Raman microscopy and applications of Raman spectroscopy technology mainly in reproductive medicine. The purpose of this review is to introduce the Raman spectroscopy technology, application and underlying principles of the method, to provide an intact picture of its uses in biomedical science and reproductive medicine, to offer ideas for its future application, verification and validation. The focus is on the application of Raman spectroscopy in the reproductive medicine field, including the application in gametes, embryos and spent embryo culture media.

Can time-lapse culture combined with artificial intelligence improve ongoing pregnancy rates in fresh transfer cycles of single cleavage stage embryos?

Purpose

With the rapid advancement of time-lapse culture and artificial intelligence (AI) technologies for embryo screening, pregnancy rates in assisted reproductive technology (ART) have significantly improved. However, clinical pregnancy rates in fresh cycles remain dependent on the number and type of embryos transferred. The selection of embryos with the highest implantation potential is critical for embryologists and influences transfer strategies in fertility centers. The superiority of AI over traditional morphological scoring for ranking cleavage-stage embryos based on their implantation potential remains controversial.

Methods

This retrospective study analyzed 105 fresh embryo transfer cycles at the Centre for Reproductive Medicine from August 2023 to March 2024, following IVF/ICSI treatment at the cleavage stage. All embryos were cultured using time-lapse technology and scored using an automated AI model (iDAScore V2.0). Embryos were categorized into three groups based on the iDAScore V2.0: Group A (8 cells, iDA: 1.0-5.7); Group B (8 cells, iDA: 5.8-8.0); and Group C (>8 cells, iDA: 5.8-8.0). Clinical treatment outcomes, embryonic development, and pregnancy outcomes were analyzed and compared across the groups.

Results

Baseline characteristics such as patient age, AMH levels, AFC, and basal sex hormones showed no significant differences among the three groups (p > 0.05). The iDAscores were significantly higher in Group C (7.3 ± 0.5) compared to Group B (6.7 ± 0.5) and the iDAscores were significantly higher in Group B (6.7 ± 0.5) compared to Group A (4.8 ± 1.0) (p < 0.001).The mean number of high-quality embryos was highest in Group C (4.7 ± 3.0), followed by Group B (3.6 ± 1.7) and Group A (2.1 ± 1.2) (p < 0.001). There was no statistical difference (p = 0.392) in the ongoing pregnancy rate for single cleavage-stage transfers between Group B (54.5%, 30/55) and Group A (38.1%, 8/21), although there was a tendency for Group B to be higher.

Conclusion

Combining time-lapse culture with AI scoring may enhance ongoing pregnancy rates in single cleavage-stage fresh transfer cycles.

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.

Agarose amplification based sequencing characterization cell-free RNA in preimplantation spent embryo medium

BACKGROUND

The cell-free RNA (cf-RNA) of spent embryo medium (SEM) has aroused a concern of academic and clinical researchers for its potential use in non-invasive embryo screening. However, comprehensive characterization of cf-RNA from SEM still presents significant technical challenges, primarily due to the limited volume of SEM. Hence, there is urgently need to a small input liquid volume and ultralow amount of cf-RNA library preparation method to unbiased cf-RNA sequencing from SEM. (75) RESULT: Here, we report a high sensitivity agarose amplification-based cf-RNA sequencing method (SEM-Acf) for human preimplantation SEM cf-RNA analysis. It is a cf-RNA sequencing library preparation method by adding agarose amplification. The agarose amplification sensitivity (0.005 pg) and efficiency (105.35 %) were increased than that of without agarose addition (0.45 pg and 96.06 %) by ∼ 90 fold and 9.29 %, respectively. Compared with SMART sequencing (SMART-seq), the correlation of gene expression was stronger in different SEM samples by using SEM-Acf. The cf-RNA number of detected and coverage uniformity of 3' end were significantly increased. The proportion of 5' end adenine, alternative splicing events and short fragments (<400 bp) were increased. It is also found that 4-mer end motifs of cf-RNA fragments was significantly differences between different embryonic stage by day3 spent cleavage medium and day5/6 spent blastocyst medium. (141) SIGNIFICANCE: This study established an efficient SEM amplification and library preparation method. Additionally, we successfully described the characterizations of SEM cf-RNA in preimplantation embryo using SEM-Acf, including expression features and fragment lengths. SEM-Acf facilitates the exploration of cf-RNA as a noninvasive embryo screening biomarker, and opens up potential clinical utilities of small input liquid volume and ultralow amount cf-RNA sequencing. (59).

Application of artificial intelligence in gametes and embryos selection

Gamete and embryo quality are critical to the success rate of Assisted Reproductive Technology (ART) cycles, but there remains a lack of methods to accurately measure the quality of sperm, oocytes and embryos. The ability of Artificial Intelligence (AI) technology to analyze large amounts of data, especially video and images, is particularly useful in gamete and embryo assessment and selection. The well-trained model has fast calculation speed and high accuracy, which can help embryologists to perform more objective gamete and embryo selection. Various artificial intelligence models have been developed for gamete and embryo assessment, some of which exhibit good performance. In this review, we summarize the latest applications of AI technology in semen analysis, as well as selection for sperm, oocyte and embryo, and discuss the existing problems and development directions of artificial intelligence in this field.

Rapid and non-invasive diagnostic techniques for embryonic developmental potential: a metabolomic analysis based on Raman spectroscopy to identify the pregnancy outcomes of IVF-ET

The non-invasive and rapid assessment of the developmental potential of embryos is of great clinical importance in assisted reproductive technology (ART). In this retrospective study, we analyzed the metabolomics of 107 samples provided by volunteers and utilized Raman spectroscopy to detect the substance composition in the discarded culture medium of 53 embryos resulting in successful pregnancies and 54 embryos that did not result in pregnancy after implantation. The culture medium from D3 cleavage-stage embryos was collected after transplantation and a total of 535 (107 × 5) original Raman spectra were obtained. By combining several machine learning methods, we predicted the developmental potential of embryos, and the principal component analysis-convolutional neural network (PCA-CNN) model achieved an accuracy rate of 71.5%. Furthermore, the chemometric algorithm was used to analyze seven amino acid metabolites in the culture medium, and the data showed significant differences in tyrosine, tryptophan, and serine between the pregnancy and non-pregnancy groups. The results suggest that Raman spectroscopy, as a non-invasive and rapid molecular fingerprint detection technology, shows potential for clinical application in assisted reproduction.

Automation in ART: Paving the Way for the Future of Infertility Treatment

In vitro fertilisation (IVF) is estimated to account for the birth of more than nine million babies worldwide, perhaps making it one of the most intriguing as well as commoditised and industrialised modern medical interventions. Nevertheless, most IVF procedures are currently limited by accessibility, affordability and most importantly multistep, labour-intensive, technically challenging processes undertaken by skilled professionals. Therefore, in order to sustain the exponential demand for IVF on one hand, and streamline existing processes on the other, innovation is essential. This may not only effectively manage clinical time but also reduce cost, thereby increasing accessibility, affordability and efficiency. Recent years have seen a diverse range of technologies, some integrated with artificial intelligence, throughout the IVF pathway, which promise personalisation and, at least, partial automation in the not-so-distant future. This review aims to summarise the rapidly evolving state of these innovations in automation, with or without the integration of artificial intelligence, encompassing the patient treatment pathway, gamete/embryo selection, endometrial evaluation and cryopreservation of gametes/embryos. Additionally, it shall highlight the resulting prospective change in the role of IVF professionals and challenges of implementation of some of these technologies, thereby aiming to motivate continued research in this field.

Non-Coding RNAs as Biomarkers for Embryo Quality and Pregnancy Outcomes: A Systematic Review and Meta-Analysis

Despite advances in in vitro fertilization (IVF), there is still a lack of non-invasive and reliable biomarkers for selecting embryos with the highest developmental and implantation potential. Recently, small non-coding RNAs (sncRNAs) have been identified in biological fluids, and extracellular sncRNAs are explored as diagnostic biomarkers in the prediction of IVF outcomes. To determine the predictive role of sncRNAs in embryo quality and IVF outcomes, a systematic review and meta-analysis was performed. Articles were retrieved from PubMed, EMBASE, and Web of Science from 1990 to 31 July 2022. Eighteen studies that met the selection criteria were analyzed. In total, 22 and 47 different sncRNAs were found to be dysregulated in follicular fluid (FF) and embryo spent culture medium (SCM), respectively. MiR-663b, miR-454 and miR-320a in FF and miR-20a in SCM showed consistent dysregulation in two different studies. The meta-analysis indicated the potential predictive performance of sncRNAs as non-invasive biomarkers, with a pooled area under curve (AUC) value of 0.81 (95% CI 0.78, 0.844), a sensitivity of 0.79 (95% CI 0.72, 0.85), a specificity of 0.67 (95% CI 0.52, 0.79) and a diagnostic odds ratio (DOR) of 8 (95% CI 5, 12). Significant heterogeneity was identified among studies in sensitivity (I2  =  46.11%) and specificity (I2  =  89.73%). This study demonstrates that sncRNAs may distinguish embryos with higher developmental and implantation potentials. They can be promising non-invasive biomarkers for embryo selection in ART. However, the significant heterogeneity among studies highlights the demand for prospective multicenter studies with optimized methods and adequate sample sizes in the future.

Contributions of artificial intelligence reported in Obstetrics and Gynecology journals: a systematic review

Background

The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others.

Objective

The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals.

Methods

The PubMed database was searched for citations indexed with “artificial intelligence” and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: “obstetrics”; “gynecology”; “reproductive techniques, assisted”; or “pregnancy.” All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process.

Results

The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals.

Conclusions

In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered.

Chromosome Segregation in the Oocyte: What Goes Wrong during Aging

Human female fertility and reproductive lifespan decrease significantly with age, resulting in an extended post-reproductive period. The central dogma in human female reproduction contains two important aspects. One is the pool of oocytes in the human ovary (the ovarian reserve; approximately 10⁶ at birth), which diminishes throughout life until menopause around the age of 50 (approximately 10³ oocytes) in women. The second is the quality of oocytes, including the correctness of meiotic divisions, among other factors. Notably, the increased rate of sub- and infertility, aneuploidy, miscarriages, and birth defects are associated with advanced maternal age, especially in women above 35 years of age. This postponement is also relevant for human evolution; decades ago, the female aging-related fertility drop was not as important as it is today because women were having their children at a younger age. Spindle assembly is crucial for chromosome segregation during each cell division and oocyte maturation, making it an important event for euploidy. Consequently, aberrations in this segregation process, especially during the first meiotic division in human eggs, can lead to implantation failure or spontaneous abortion. Today, human reproductive medicine is also facing a high prevalence of aneuploidy, even in young females. However, the shift in the reproductive phase of humans and the strong increase in errors make the problem much more dramatic at later stages of the female reproductive phase. Aneuploidy in human eggs could be the result of the non-disjunction of entire chromosomes or sister chromatids during oocyte meiosis, but partial or segmental aneuploidies are also relevant. In this review, we intend to describe the relevance of the spindle apparatus during oocyte maturation for proper chromosome segregation in the context of maternal aging and the female reproductive lifespan.

LC-MS Analysis Revealed the Significantly Different Metabolic Profiles in Spent Culture Media of Human Embryos with Distinct Morphology, Karyotype and Implantation Outcomes

In this study we evaluated possible differences in metabolomic profiles of spent embryo culture media (SECM) of human embryos with distinct morphology, karyotype, and implantation outcomes. A total of 153 samples from embryos of patients undergoing in vitro fertilization (IVF) programs were collected and analyzed by HPLC-MS. Metabolomic profiling and statistical analysis revealed clear clustering of day five SECM from embryos with different morphological classes and karyotype. Profiling of day five SECM from embryos with different implantation outcomes showed 241 significantly changed molecular ions in SECM of successfully implanted embryos. Separate analysis of paired SECM samples on days three and five revealed 46 and 29 molecular signatures respectively, significantly differing in culture media of embryos with a successful outcome. Pathway enrichment analysis suggests certain amino acids, vitamins, and lipid metabolic pathways to be crucial for embryo implantation. Differences between embryos with distinct implantation potential are detectable on the third and fifth day of cultivation that may allow the application of culture medium analysis in different transfer protocols for both fresh and cryopreserved embryos. A combination of traditional morphological criteria with metabolic profiling of SECM may increase implantation rates in assisted reproductive technology programs as well as improve our knowledge of the human embryo metabolism in the early stages of development.

Evaluation of Laser Confocal Raman Spectroscopy as a Non-Invasive Method for Detecting Sperm DNA Contents

RESEARCH QUESTION

Is Raman spectroscopy an efficient and accurate method to detect sperm chromosome balance state by DNA content differences?

DESIGN

Semen samples were provided by diploid healthy men, and the analysis parameters met the current World Health Organization standards. The DNA content was assessed by analysis of the corresponding spectra obtained from a laser confocal Raman spectroscope. The sperm sex chromosome information was obtained by fluorescence in situ hybridization (FISH). Comparative analysis was performed between FISH results and Raman spectral analysis results.

RESULTS

Different parts of the sperm head showed different spectral signal intensities, which indicated that there were different chemical components. Standard principal component analysis (PCA) can preliminarily classify sperm with different DNA contents into two groups. Further analysis showed that there were significant differences in the 785 DNA backbone peaks and 714-1,162 cm^-1 DNA skeleton regions among sperm with different DNA contents. The peak and regional peak of the DNA skeleton of X sperm were significantly higher than those of Y sperm (X vs. Y, p < 0.05). The above sperm types were confirmed by FISH. ROC curve analysis shows that there is a correlation between the Raman spectrum data and FISH results.

CONCLUSION

Raman spectroscopy can identify X and Y sperms by analyzing the DNA content difference. However, the accuracy of the detection still needs to be improved. Nevertheless, Raman spectroscopy has a potential application value in the field of sperm aneuploidy detection and may even be used as a non-invasive predictor of sperm aneuploid state in preimplantation genetic testing (PGT-A).

Glutamine as a Potential Noninvasive Biomarker for Human Embryo Selection

To determine whether glutamine consumption is associated with embryo quality and aneuploidy, a retrospective study was conducted in an in vitro fertilization center. Spent embryo culture media from patients undergoing assisted reproduction treatment and preimplantation genetic testing (PGT) were obtained on day 3 of in vitro culture. Embryo quality was assessed for cell number and fragmentation rate. PGT for aneuploidy was performed using whole genome amplification and DNA sequencing. Glutamine levels in spent embryo culture media were analyzed by gas chromatography–mass spectrometry. The results demonstrated that glutamine was a primary contributor to the classification of the good-quality and poor-quality embryos based on the orthogonal partial least-squares discriminant analysis model. Glutamine consumption in the poor-quality embryos was significantly higher than that in the good-quality embryos (P < 0.05). A significant increase in glutamine consumption was observed from aneuploid embryos compared with that from euploid embryos (P < 0.01). The Pearson correlation coefficients between embryo quality and glutamine consumption, and between aneuploidy and glutamine consumption, were 0.430 and 0.757, respectively. The area under the ROC curve was 0.938 (95% CI: 0.902–0.975) for identifying aneuploidy. Animal experiments demonstrate that increased glutamine consumption may be a compensatory mechanism to mitigate oxidative stress. Our data suggest that glutamine consumption is associated with embryo quality and aneuploidy. Glutamine may serve as a molecular indicator for embryo assessment and aneuploidy testing.

Raman Spectrum of Follicular Fluid: A Potential Biomarker for Oocyte Developmental Competence in Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder in reproductive women where abnormal folliculogenesis is considered as a common characteristic. Our aim is to evaluate the potential of follicular fluid (FF) Raman spectra to predict embryo development and pregnancy outcome, so as to prioritize the best promising embryo for implantation, reducing both physiological and economical burdens of PCOS patients. In addition, the altered metabolic profiles will be identified to explore the aetiology and pathobiology of PCOS. In this study, follicular fluid samples obtained from 150 PCOS and 150 non-PCOS women were measured with Raman spectroscopy. Individual Raman spectrum was analyzed to find biologic components contributing to the occurrence of PCOS. More importantly, the Raman spectra of follicular fluid from the 150 PCOS patients were analyzed via machine-learning algorithms to evaluate their predictive value for oocyte development potential and clinical pregnancy. Mean-centered Raman spectra and principal component analysis (PCA) showed global differences in the footprints of follicular fluid between PCOS and non-PCOS women. Two Raman zones (993-1,165 cm^-1 and 1,439-1,678 cm^-1) were identified for describing the largest variances between the two groups, with the former higher and the latter lower in PCOS FF. The tentative assignments of corresponding Raman bands included phenylalanine and β -carotene. Moreover, it was found that FF, in which oocytes would develop into high-quality blastocysts and obtain high clinical pregnancy rate, were detected with lower quantification of the integration at 993-1,165 cm^-1 and higher quantification of the integration at 1,439-1,678 cm^-1 in PCOS. In addition, based on Raman spectra of PCOS FF, the machine-learning algorithms via the fully connected artificial neural network (ANN) achieved the overall accuracies of 90 and 74% in correctly assigning oocyte developmental potential and clinical pregnancy, respectively. The study suggests that the PCOS displays unique metabolic profiles in follicular fluid which could be detected by Raman spectroscopy. Specific bands in Raman spectra have the biomarker potential to predict the embryo development and pregnancy outcome for PCOS patients. Importantly, these data may provide some valuable biochemical information and metabolic signatures that will help us to understand the abnormal follicular development in PCOS.

Non-invasive Metabolomic Profiling of Embryo Culture Medium Using Raman Spectroscopy With Deep Learning Model Predicts the Blastocyst Development Potential of Embryos

Purpose: This study aimed to establish a non-invasive predicting model via Raman spectroscopy for evaluating the blastocyst development potential of day 3 high-quality cleavage stage embryos. Methods: Raman spectroscopy was used to detect the metabolic spectrum of spent day 3 (D3) embryo culture medium, and a classification model based on deep learning was established to differentiate between embryos that could develop into blastocysts (blastula) and that could not (non-blastula). The full-spectrum data for 80 blastula and 48 non-blastula samples with known blastocyst development potential from 34 patients were collected for this study. Results: The accuracy of the predicting method was 73.53% and the main different Raman shifts between blastula and non-blastula groups were 863.5, 959.5, 1,008, 1,104, 1,200, 1,360, 1,408, and 1,632 cm-1 from 80 blastula and 48 non-blastula samples by the linear discriminant method. Conclusion: This study demonstrated that the developing potential of D3 cleavage stage embryos to the blastocyst stage could be predicted with spent D3 embryo culture medium using Raman spectroscopy with deep learning classification models, and the overall accuracy reached at 73.53%. In the Raman spectroscopy, ribose vibration specific to RNA were found, indicating that the difference between the blastula and non-blastula samples could be due to materials that have similar structure with RNA. This result could be used as a guide for biomarker development of embryo quality assessment in the future.

A robust metabolomics approach for the evaluation of human embryos from in vitro fertilization

The identification of the most competent embryos for transfer to the uterus constitutes the main challenge of in vitro fertilization (IVF). We established a metabolomic-based approach by applying Fourier transform infrared (FTIR) spectroscopy on 130 samples of 3-day embryo culture supernatants from 26 embryos that implanted and 104 embryos that failed. On examining the internal structure of the data by unsupervised multivariate analysis, we found that the supernatant spectra of nonimplanted embryos constituted a highly heterogeneous group. Whereas ∼40% of these supernatants were spectroscopically indistinguishable from those of successfully implanted embryos, ∼60% exhibited diverse, heterogeneous metabolic fingerprints. This observation proved to be the direct result of pregnancy's multifactorial nature, involving both intrinsic embryonic traits and external characteristics. Our data analysis strategy thus involved one-class modelling techniques employing soft independent modelling of class analogy that identified deviant fingerprints as unsuitable for implantation. From these findings, we could develop a noninvasive Fourier-transform-infrared-spectroscopy-based approach that represents a shift in the fundamental paradigm for data modelling applied in assisted-fertilization technologies.

Why Has Metabolomics So Far Not Managed to Efficiently Contribute to the Improvement of Assisted Reproduction Outcomes? The Answer through a Review of the Best Available Current Evidence

Metabolomics emerged to give clinicians the necessary information on the competence, in terms of physiology and function, of gametes, embryos, and the endometrium towards a targeted infertility treatment, namely, assisted reproduction techniques (ART). Our minireview aims to investigate the current status of the use of metabolomics in assisted reproduction, the potential flaws in its use, and to propose specific solutions towards the improvement of ART outcomes through the use of the intervention. We used published reports assessing the role of metabolomic investigation of the endometrium, oocytes, and embryos in improving clinical outcomes in women undergoing ART. We initially found that there is no evidence to support that fertility outcomes can be improved through metabolomics profiling. In contrast, it may be helpful for understanding and appraising the nutritional environment of oocytes and embryos. The causes include the different infertility populations, the difference between animals and humans, technical limitations, and the great heterogeneity in the variables employed. Suggested steps include the standardization of variables of the method itself, the universal creation of a panel where all biomarkers are stored concerning specific infertile populations with different phenotypes or etiologies, specific bioinformatics contribution, significant computing power for data processing, and importantly, properly conducted trials.

An overview of the current and emerging platforms for preimplantation genetic testing for aneuploidies (PGT-A) in in vitro fertilization programs

Preimplantation genetic testing for aneuploidies (PGT-A) and PGT for monogenic disorders (PGT-M) have currently been used widely, aiming to improve IVF outcomes. Although with many years of unsatisfactory results, PGT-A has been revived because new technologies have been adopted, such as platforms to examine all 24 types of chromosomes in blastocysts. This report compiles current knowledge regarding the available PGT platforms, including quantitative PCR, array CGH, and next-generation sequencing. The diagnostic capabilities of are compared and respective advantages/disadvantages outlined. We also address the limitations of current technologies, such as assignment of embryos with balanced translocation. We also discuss the emerging novel PGT technologies that likely will change our future practice, such as non-invasive PGT examining spent culture medium. Current literature suggest that most platforms can effectively reach concordant results regarding whole-chromosome ploidy status of all 24 types of chromosomes. However, different platforms have different resolutions and experimental complexities; leading to different turnaround time, throughput and differential capabilities of detecting mosaicism, segmental mutations, unbalanced translocations, concurrent PGT-A and PGT-M etc. Based on these information, IVF staff can more appropriately interpret PGT data and counsel patients, and select suitable platforms to meet personalized needs. The present report also concisely discusses some crucial clinical outcomes by PGT, which can clarify the role of applying PGT in daily IVF programs. Finally the up-to-date information about the novel use of current technologies and the newly emerging technologies will also help identify the focus areas for the design of new platforms for PGT in the future.

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.

Raman spectroscopy of follicular fluid and plasma with machine-learning algorithms for polycystic ovary syndrome screening

Polycystic ovary syndrome (PCOS) is the main cause of anovulatory infertility and affects women throughout their lives. The specific diagnostic method is still under investigation. In the present study, we aimed to identify the metabolic tracks of the follicular fluid and plasma samples from women with PCOS by performing Raman spectroscopy with principal component analysis and spectral classification models. Follicular fluid and plasma samples obtained from 50 healthy (non-PCOS) and 50 PCOS women were collected and measured by Raman spectroscopy. Multivariate statistical methods and different machine-learning algorithms based on the Raman spectra were established to analyze the results. The principal component analysis of the Raman spectra showed differences in the follicular fluid between the non-PCOS and PCOS groups. The stacking classification models based on the k-nearest-neighbor, random forests and extreme gradient boosting algorithms yielded a higher accuracy of 89.32% by using follicular fluid than the accuracy of 74.78% obtained with plasma samples in classifying the spectra from the two groups. In this regard, PCOS may lead to the changes of metabolic profiles that can be detected by Raman spectroscopy. As a novel, rapid and affordable method, Raman spectroscopy combined with advanced machine-learning algorithms have potential to analyze and characterize patients with PCOS.

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

BACKGROUND

Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method.

OBJECTIVE

This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians' decision making.

METHODS

Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians' decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2.

RESULTS

Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07).

CONCLUSIONS

Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines.

TRIAL REGISTRATION

PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106.

The utility of nuclear magnetic resonance spectroscopy in assisted reproduction

Infertility affects approximately 15-20% of individuals of reproductive age worldwide. Over the last 40 years, assisted reproductive technology (ART) has helped millions of childless couples. However, ART is limited by a low success rate and risk of multiple gestations. Devising methods for selecting the best gamete or embryo that increases the ART success rate and prevention of multiple gestation has become one of the key goals in ART today. Special emphasis has been placed on the development of non-invasive approaches, which do not require perturbing the embryonic cells, as the current morphology-based embryo selection approach has shortcomings in predicting the implantation potential of embryos. An observed association between embryo metabolism and viability has prompted researchers to develop metabolomics-based biomarkers. Nuclear magnetic resonance (NMR) spectroscopy provides a non-invasive approach for the metabolic profiling of tissues, gametes and embryos, with the key advantage of having a minimal sample preparation procedure. Using NMR spectroscopy, biologically important molecules can be identified and quantified in intact cells, extracts or secretomes. This, in turn, helps to map out the active metabolic pathways in a system. The present review covers the contribution of NMR spectroscopy in assisted reproduction at various stages of the process.

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

PURPOSE

Embryo genotyping in IVF clinics aims to identify aneuploid embryos, and current methodologies rely on costly, invasive and time-consuming approaches such as PGT-A screening. MALDI-ToF-based mass spectral analysis of embryo culture has been demonstrated to be a non-invasive, affordable and accurate technique that is able to capture secretome profiles from embryo culture media extremely quick. Thus, aneuploid embryo genotypes can be distinguished from euploids from these profiles towards the development of novel embryo selection tools.

METHODS

A retrospective cohort study, including 292 spent media samples from embryo cultures collected from a single IVF clinic in USA. There were 149 euploid and 165 aneuploid embryos previously analysed by PGT-A next-generation sequencing techniques. Secretome mass spectra of embryos were generated using MALDI-ToF mass spectrometry in the UK. Data was systematically analysed using a fully automated and ultra-fast bioinformatic pipeline developed for the identification of mass spectral signatures.

RESULTS

Distinct spectral patterns were found for euploid and aneuploid genotypes in embryo culture media. We identified 12 characteristic peak signatures for euploid and 17 for aneuploid embryos. Data analysis also revealed a high degree of complementarity among regions showing that 22 regions are required to differentiate between genotypes with a sensitivity of 84% and a false positive rate of 18%.

CONCLUSION

Ultra-fast and fully automated screening of an embryo genotype is possible based on multiple combinations of specific mass spectral peak signatures. This constitutes a breakthrough towards the implementation of non-invasive and ultra-fast tools for embryo selection immediately prior to transfer.

Unbalanced human embryos secrete more hyperglycosylated human chorionic gonadotrophin (hCG-H) than balanced ones

PURPOSE

The aim of this study was to compare the levels of hyperglycosylated human chorionic gonadotropin (hCG-H) secreted from balanced and unbalanced human embryos.

METHODS

Single-step culture media samples from 155 good quality embryos, derived from 90 good prognosis patients undergoing intracytoplasmic sperm injection (ICSI), were collected on the fifth day of embryo cultivation. All embryos were tested by next-generation sequencing (NGS) technique. The hCG-H levels in the culture media were evaluated by ELISA kit (Cusabio Biotech, CBS-E15803h) according to the manufacturer's instructions. Statistical analysis was performed using SPSS v.21 (IBM Corp., Armonk, NY, USA).

RESULTS

The NGS analysis revealed that 36% of the embryos (n = 56) were balanced, and 64% of the embryos were unbalanced (n = 99). The presence of hCG-H was confirmed in all embryo culture media samples but was absent in the negative control. In addition, hCG-H concentration was significantly higher in the culture media from unbalanced embryos compared with the balanced ones (0.72 ± 0.30 mIU/ml vs. 0.62 ± 0.12 mIU/ml, p = 0.02, respectively). Furthermore, the mean levels of hCG-H were significantly increased in the samples from embryos with multiple abnormalities. Finally, the highest levels of hCG-H were expressed from embryos with monosomy of chromosome 11 (1.28 ± 0.04 mIU/ml) and those with trisomies of chromosomes 21 (2.23 mIU/ml) and 4 (1.02 ± 0.35 mIU/ml).

CONCLUSION

Our results suggest that chromosomal aberrations in human embryos are associated with an increased secretion of hCG-H. However, hCG-H concentration in embryo culture media as a single biomarker is not sufficient for an accurate selection of balanced embryos.

The impact of culture conditions on blastocyst formation and aneuploidy rates: a comparison between single-step and sequential media in a large academic practice

PURPOSE

To compare a single-step medium with a sequential medium on human blastocyst development rates, aneuploidy rates, and clinical outcomes.

METHODS

Retrospective cohort study of IVF cycles that used Sage advantage sequential medium (n = 347) and uninterrupted Sage 1-step medium (n = 519) from July 1, 2016, to December 31, 2017, in an academic fertility center. Main outcome measures are blastocyst formation rates per two-pronuclear (2PN) oocyte and aneuploidy rates per biopsy.

RESULTS

Of all IVF cycles, single-step medium yielded higher blastocyst formation rate (51.7% vs 43.4%) but higher aneuploidy rate (54.0% vs 45.8%) compared with sequential medium. When stratified by maternal age, women under age 38 had no difference in blastocyst formation (52.2% vs 50.2%) but a higher aneuploidy rate (44.5% vs 36.4%) resulting in a lower number of euploid blastocysts per cycle (2.6 vs 3.3) when using single-step medium compared to sequential medium. In cycles used single-step medium, patients ≥ age 38 had higher blastocyst rate (48.0% vs 33.6%), but no difference in aneuploidy rate (68.8% vs 66.0%) or number of euploid embryos (0.8 vs 1.1). For patients reaching euploid embryo transfer, there was no difference in clinical pregnancy rates, miscarriage rates, or live birth rates between two culture media systems.

CONCLUSIONS

Our study demonstrates an increase in aneuploidy in young women whose embryos were cultured in a single-step medium compared to sequential medium. This study highlights the importance of culture conditions on embryo ploidy and the need to stratify by patient age when examining the impact of culture conditions on overall cycle potential.

Time-lapse imaging of cytoplasmic strings at the blastocyst stage suggests their association with spontaneous blastocoel collapse

RESEARCH QUESTION

To study the origin and temporal behaviour of cytoplasmic strings spanning the blastocoel (main objective) and their influence on treatment outcome (secondary objective).

DESIGN

This retrospective analysis of prospectively collected data was set up in a university medical centre. Patients who either underwent fresh (n = 95) or vitrified-warmed (n = 55) single blastocyst transfer were included. Time-lapse sequences of in-vitro developed blastocysts were screened for the presence of cytoplasmic strings. Pregnancies in string-positive and string-negative transfers were followed up to live birth.

RESULTS

A total of 387 blastocysts were obtained in the fresh cycles of 100 patients, corresponding to a blastocyst formation rate of 62.4%. Cytoplasmic strings were first detected around full stage (108.5 ± 6.4 h) in 170 blastocysts (43.9%). The number of strings varied (range: 1-7) and the duration of visibility was 5.2 ± 3.5 h. The occurrence of cytoplasmic strings was significantly associated with the presence of blastocoelic collapses (P < 0.001) but not with any of the annotated morphokinetic parameters. Live birth and neonatal outcome were the same for both string-positive and string-negative pregnancies. Moreover, collapses did not affect treatment outcome.

CONCLUSION

Time-lapse analysis of cytoplasmic strings at the blastocyst stage revealed that this morphological feature was not a negative predictor as previously reported. Although physiologically normal, at least some of the cytoplasmic strings are an artefact, possibly associated with blastocoelic collapses.