Integrating imaging-based classification and transcriptomics for quality assessment of human oocytes according to their reproductive efficiency

Purpose Utilising non-invasive imaging parameters to assess human oocyte fertilisation, development and implantation; and their influence on transcriptomic profiles. Methods A ranking tool was designed using imaging data from 957 metaphase II stage oocytes retrieved from 102 patients undergoing ART....

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Bibliographic Details
Published in:Journal of assisted reproduction and genetics 2023-11, Vol.40 (11), p.2545-2556
Main Authors: Viñals Gonzalez, Xavier, Thrasivoulou, Christopher, Naja, Roy Pascal, Seshadri, Srividya, Serhal, Paul, Gupta, Sioban Sen
Format: Article
Language:English
Subjects:
Algorithms
Cell cycle
DNA damage
Embryo Implantation
Fertilization
Gamete Biology
Gene Expression Profiling
Gynecology
Human Genetics
Humans
Medicine
Medicine & Public Health
Metaphase
Oocytes
Oogenesis - genetics
Optical density
Quality control
Reproductive Medicine
Transcriptome - genetics
Transcriptomics
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Summary:Purpose Utilising non-invasive imaging parameters to assess human oocyte fertilisation, development and implantation; and their influence on transcriptomic profiles. Methods A ranking tool was designed using imaging data from 957 metaphase II stage oocytes retrieved from 102 patients undergoing ART. Hoffman modulation contrast microscopy was conducted with an Olympus IX53 microscope. Images were acquired prior to ICSI and processed using ImageJ for optical density and grey-level co-occurrence matrices texture analysis. Single-cell RNA sequencing of twenty-three mature oocytes classified according to their competence was performed. Result(s) Overall fertilisation, blastulation and implantation rates were 73.0%, 62.6% and 50.8%, respectively. Three different algorithms were produced using binary logistic regression methods based on “optimal” quartiles, resulting in an accuracy of prediction of 76.6%, 67% and 80.7% for fertilisation, blastulation and implantation. Optical density, gradient, inverse difference moment (homogeneity) and entropy (structural complexity) were the parameters with highest predictive properties. The ranking tool showed high sensitivity (68.9–90.8%) but with limited specificity (26.5–62.5%) for outcome prediction. Furthermore, five differentially expressed genes were identified when comparing “good” versus “poor” competent oocytes. Conclusion(s) Imaging properties can be used as a tool to assess differences in the ooplasm and predict laboratory and clinical outcomes. Transcriptomic analysis suggested that oocytes with lower competence may have compromised cell cycle either by non-reparable DNA damage or insufficient ooplasmic maturation. Further development of algorithms based on image parameters is encouraged, with an increased balanced cohort and validated prospectively in multicentric studies.
ISSN:1058-0468
1573-7330
DOI:10.1007/s10815-023-02911-y