Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure.

The ability to evaluate sperm at the microscopic level, at high-throughput, would be useful for assisted reproductive technologies (ARTs), as it can allow specific selection of sperm cells for in vitro fertilization (IVF). The tradeoff between intrinsic imaging and external contrast agents is particularly acute in reproductive medicine. The use of fluorescence labels has enabled new cell-sorting strategies and given new insights into developmental biology. Nevertheless, using extrinsic contrast agents is often too invasive for routine clinical operation. Raising questions about cell viability, especially for single-cell selection, clinicians prefer intrinsic contrast in the form of phase-contrast, differential-interference contrast, or Hoffman modulation contrast. While such instruments are nondestructive, the resulting image suffers from a lack of specificity. In this work, we provide a template to circumvent the tradeoff between cell viability and specificity by combining high-sensitivity phase imaging with deep learning. In order to introduce specificity to label-free images, we trained a deep-convolutional neural network to perform semantic segmentation on quantitative phase maps. This approach, a form of phase imaging with computational specificity (PICS), allowed us to efficiently analyze thousands of sperm cells and identify correlations between dry-mass content and artificial-reproduction outcomes. Specifically, we found that the dry-mass content ratios between the head, midpiece, and tail of the cells can predict the percentages of success for zygote cleavage and embryo blastocyst formation.

Morphometric analysis of sperm used for IVP by three different separation methods with spatial light interference microscopy.

The goal of this study was to characterize sperm populations resulting from three different methods of sperm selection used for bovine in vitro fertilization. We compared sperm selection with discontinuous Percoll gradients, Swim-Up, and electro-channel. Spatial light interference microscopy (SLIM) was used to evaluate the morphology of the spermatozoa and computer-assisted semen analysis (CASA) was used to evaluate the motility behavior of the sperm. Using these two technologies, we analyzed morphometric parameters and the kinetic (motility) patterns of frozen-thawed Holstein bull spermatozoa after sperm selection. For the first time, we have shown that these methods used to select viable spermatozoa for in vitro fertilization (IVF) result in very different sperm subpopulations. Almost every parameter evaluated resulted in statistical differences between treatment groups. One novel observation was that the dry mass of the sperm head is heavier in spermatozoa selected with the electro-channel than in sperm selected by the other methods. These results show the potential of SLIM microscopy in reproductive biology.Abbreviations: SLIM: spatial light interference microscopy; CASA: computer aided sperm analysis; IVF: in vitro fertilization; BSA: bovine serum albumin; QPI: quantitative phase imaging; IVEP: in vitro embryo production; IACUC: institutional animal care and use committee; CSS: Certified Semen Services; AI: artificial insemination; TALP: Tyrode's Albumin Lactate Pyruvate; MEC: medium for electro-channel; PDMS: polydimethylsiloxane; EC: electro-channel; TM, %: total motility; PM, %: progressive motility; RM, %: percentage of rapid sperm motility; VAP, µm/s: average path velocity; VSL, µm/s: straight-line velocity; VCL, µm/s: curvilinear velocity; ALH, µm: amplitude of lateral head displacement; BCF, Hz: beat cross frequency; STR, %: straightness; LIN, %: and linearity; GLS: generalized least squares; ANOVA: analysis of variance; LSD: Least Significant Difference; SPSS: Statistical Package for the Social Sciences; PCA: principal components analysis.