Label-free assessment of pre-implantation embryo quality by the Fluorescence Lifetime Imaging Microscopy (FLIM)-phasor approach

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Abstract

Development of quantitative, safe and rapid techniques for assessing embryo quality provides significant advances in Assisted Reproductive Technologies (ART). Instead of assessing the embryo quality by the standard morphologic evaluation, we apply the phasor-FLIM (Fluorescence Lifetime Imaging Microscopy) method to capture endogenous fluorescent biomarkers of pre-implantation embryos as a non-morphological caliber for embryo quality. Here, we identify, under hypoxic and non-hypoxic conditions, the unique spectroscopic trajectories at different stages of mouse pre-implantation development, which is referred to as the developmental, or “D-trajectory”, that consists of fluorescence lifetime from different stages of mouse pre-implantation embryos. The D-trajectory correlates with intrinsic fluorescent species from a distinctive energy metabolism and oxidized lipids, as seen with Third Harmonic Generation (THG) that changes over time. In addition, we have defined a non-morphological Embryo Viability Index (EVI) to distinguish pre-implantation embryo quality using the Distance Analysis (DA), a machine learning algorithm to process the fluorescence lifetime distribution patterns. We show, under our experimental conditions, that the phasor-FLIM approach provides a much-needed non-invasive quantitative technology for identifying healthy embryos at the early compaction stage with 86% accuracy. The DA and phasor-FLIM method may provide the opportunity to improve implantation success rates for in vitro fertilization clinics.

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  1. .

    This is an interesting study and having an assay like this could be really useful for improving IVF methods. I have two main questions:

    1. Could your method be used to actually try and optimize media and growth conditions at later stages that could help with viability and development?
    2. What kind of environmental stresses do embryos experience upon implantation? I wonder how your work connects to that key transition step.
  2. require highly-trained personnel to analyze the complex data

    I wonder if these could be more efficient now given the data analysis tools available today? the studies you cite are all in the early 2000s or even earlier. Would be cool to see how any of these orthogonal methods might further strengthen the accuracy of your approach