LAUSR

ABSTRACT 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