Walker Benjamin J, Phuyal Shiva, Ishimoto Kenta, Tung Chih-Kuan, Gaffney Eamonn A
Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK.
Department of Physics, North Carolina A&T State University, Greensboro, NC 27411, USA.
R Soc Open Sci. 2020 Jun 17;7(6):200769. doi: 10.1098/rsos.200769. eCollection 2020 Jun.
Obstructed by hurdles in information extraction, handling and processing, computer-assisted sperm analysis systems have typically not considered in detail the complex flagellar waveforms of spermatozoa, despite their defining role in cell motility. Recent developments in imaging techniques and data processing have produced significantly improved methods of waveform digitization. Here, we use these improvements to demonstrate that near-complete flagellar capture is realizable on the scale of hundreds of cells, and, further, that meaningful statistical comparisons of flagellar waveforms may be readily performed with widely available tools. Representing the advent of high-fidelity computer-assisted beat-pattern analysis, we show how such a statistical approach can distinguish between samples using complex flagellar beating patterns rather than crude summary statistics. Dimensionality-reduction techniques applied to entire samples also reveal qualitatively distinct components of the beat, and a novel data-driven methodology for the generation of representative synthetic waveform data is proposed.
由于在信息提取、处理和加工方面存在障碍，计算机辅助精子分析系统通常没有详细考虑精子复杂的鞭毛波形，尽管其在细胞运动中起着决定性作用。成像技术和数据处理方面的最新进展产生了显著改进的波形数字化方法。在此，我们利用这些改进来证明，在数百个细胞的规模上实现近乎完整的鞭毛捕获是可行的，而且，使用广泛可用的工具可以很容易地对鞭毛波形进行有意义的统计比较。作为高保真计算机辅助搏动模式分析出现的代表，我们展示了这种统计方法如何利用复杂的鞭毛搏动模式而非粗略的汇总统计来区分样本。应用于整个样本的降维技术还揭示了搏动在性质上截然不同的成分，并提出了一种用于生成代表性合成波形数据的新型数据驱动方法。
