Rapid sperm capture: high-throughput flagellar waveform analysis

Abstract

STUDY QUESTION Can flagellar analyses be scaled up to provide automated tracking of motile sperm, and does knowledge of the flagellar waveform provide new insight not provided by routine head tracking? SUMMARY ANSWER High-throughput flagellar waveform tracking and analysis enable measurement of experimentally intractable quantities such as energy dissipation, disturbance of the surrounding medium and viscous stresses, which are not possible by tracking the sperm head alone. WHAT IS KNOWN ALREADY The clinical gold standard for sperm motility analysis comprises a manual analysis by a trained professional, with existing automated sperm diagnostics [computer-aided sperm analysis (CASA)] relying on tracking the sperm head and extrapolating measures. It is not currently possible with either of these approaches to track the sperm flagellar waveform for large numbers of cells in order to unlock the potential wealth of information enclosed within. STUDY DESIGN, SIZE, DURATION The software tool in this manuscript has been developed to enable high-throughput, repeatable, accurate and verifiable analysis of the sperm flagellar beat. PARTICIPANTS/MATERIALS, SETTING, METHODS Using the software tool [Flagellar Analysis and Sperm Tracking (FAST)] described in this manuscript, we have analysed 176 experimental microscopy videos and have tracked the head and flagellum of 205 progressive cells in diluted semen (DSM), 119 progressive cells in a high-viscosity medium (HVM) and 42 stuck cells in a low-viscosity medium. Unscreened donors were recruited at Birmingham Women’s and Children’s NHS Foundation Trust after giving informed consent. MAIN RESULTS AND THE ROLE OF CHANCE We describe fully automated tracking and analysis of flagellar movement for large cell numbers. The analysis is demonstrated on freely motile cells in low- and high-viscosity fluids and validated on published data of tethered cells undergoing pharmacological hyperactivation. Direct analysis of the flagellar beat reveals that the CASA measure ‘beat cross frequency’ does not measure beat frequency; attempting to fit a straight line between the two measures gives \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\mathrm{R}}^2$\end{document} values of 0.042 and 0.00054 for cells in DSM and HVM, respectively. A new measurement, track centroid speed, is validated as an accurate differentiator of progressive motility. Coupled with fluid mechanics codes, waveform data enable extraction of experimentally intractable quantities such as energy dissipation, disturbance of the surrounding medium and viscous stresses. We provide a powerful and accessible research tool, enabling connection ...

Rotational motion and rheotaxis of human sperm do not require functional CatSper channels and transmembrane Ca 2+ signaling

“…Bicarbonate (25 mM) used for capacitation stimulates cAMP synthesis (Carlson et al , ; Tresguerres et al , ; Brenker et al , ) and, thereby, accelerates the flagellar beat (Esposito et al , ; Xie et al , ) and rotation frequency (Miki & Clapham, ). The rotation frequency decreased with increasing viscosity (Fig F), in line with previous results (e.g., Nosrati et al , ; Gallagher et al , ). To study rolling of single sperm cells with high time resolution and for long recording times, we combined bright‐field microscopy with an optical tweezer (Ashkin et al , ) (Fig G).…”

The application of computer-assisted semen analysis (CASA) technology to optimise semen evaluation. A review

“…Another significant factor is related to the design of the CASA system. Although most of them are based on similar principles, they differ in optics, hardware and software characteristics, particularly in terms of the algorithms used for sperm segmentation, identification, trajectory reconstruction and silhouette identification (Gallagher et al, 2019). The CASA systems differ in their grayscale bit-depth, frame rate (Wilson-Leedy and Ingermann, 2007), number of consecutive frames analysed, grayscale thresholding method, image segmentation method to determine the pixel coordinates of the sperm, head versus midpiece tracking and strategies for handling collisions between spermatozoa (Shi et al, 2006) as well as the different morphological components of the cell (Yániz et al, 2016).…”

Computer-assisted beat-pattern analysis and the flagellar waveforms of bovine spermatozoa

“…1. Our dataset represents the digitisation of flagellar waveforms not previously captured on this scale via the common imaging modality of phase contrast, as previous distal capture is limited at approximately 70% [14]. We present this dataset of automatically captured and smoothed flagellar beats in both the Cartesian and tangent angle forms (see Methods and Materials), accompanied by the computed beating period and truncated flagellum length.…”

“…Whilst existing CASA implementations are able to approximate the beating period of spermatozoa from the oscillations of the body over time, as first suggested by Schoëvaërt-Brossault [41] and Serres et al [42] and more recently conducted via Fourier analysis, we opt to determine the period of the flagellar beat using the captured flagellar kinematics. The recent work of Gallagher et al [14] also based their estimate of the period on captured flagellar data, though utilised the Fourier spectra of points along the flagellum to identify a dominant frequency, implicitly requiring that the beating period is comprised of a single sinusoidal mode. Here we avoid this requirement by computing the period using the autocorrelation of a material point, which may be thought of as tracking the location of this point over time and identifying the period after which its trajectory is most self-similar.…”

“…Furthermore, digital videomicroscopy that captures flagellar movement potentially contains a wealth of additional information, motivating the need for more user-friendly, less user-intensive techniques to capture the moving flagellum. A recent tool has been developed for this purpose with phase contrast microscopy by Gallagher et al [14], though it is limited due to its current inability to capture the distal region of the flagellum, despite the reported importance of the distal flagellum in motility mechanics [15]. Other recent works have similarly developed techniques to capture the beating flagellum, including the distal region, most notably the three-dimensional capture of the flagellar waveform achieved by Daloglu et al [16] with holographic imaging.…”