Sperm trajectories form chiral ribbons.

We report the discovery of an entirely new three-dimensional (3D) swimming pattern observed in human and horse sperms. This motion is in the form of 'chiral ribbons', where the planar swing of the sperm head occurs on an osculating plane creating in some cases a helical ribbon and in some others a twisted ribbon. The latter, i.e., the twisted ribbon trajectory, also defines a minimal surface, exhibiting zero mean curvature for all the points on its surface. These chiral ribbon swimming patterns cannot be represented or understood by already known patterns of sperms or other micro-swimmers. The discovery of these unique patterns is enabled by holographic on-chip imaging of >33,700 sperm trajectories at >90-140 frames/sec, which revealed that only ~1.7% of human sperms exhibit chiral ribbons, whereas it increases to ~27.3% for horse sperms. These results might shed more light onto the statistics and biophysics of various micro-swimmers' 3D motion

Shape mode analysis exposes movement patterns in biology: flagella and flatworms as case studies

We illustrate shape mode analysis as a simple, yet powerful technique to concisely describe complex biological shapes and their dynamics. We characterize undulatory bending waves of beating flagella and reconstruct a limit cycle of flagellar oscillations, paying particular attention to the periodicity of angular data. As a second example, we analyze non-convex boundary outlines of gliding flatworms, which allows us to expose stereotypic body postures that can be related to two different locomotion mechanisms. Further, shape mode analysis based on principal component analysis allows to discriminate different flatworm species, despite large motion-associated shape variability. Thus, complex shape dynamics is characterized by a small number of shape scores that change in time. We present this method using descriptive examples, explaining abstract mathematics in a graphic way.Comment: 20 pages, 6 figures, accepted for publication in PLoS On

Unlabeled Semen Analysis by Means of the Holographic Imaging

The morphology, the motility, and the biochemical structure of the spermatozoon have often been correlated with the outcome of in vitro fertilization and have been shown to be the sole parameters of the semen analysis in predicting the success of intracytoplasmic sperm injection and intracytoplasmic morphologically selected sperm injection. In this context, digital holography has demonstrated to be an attractive technique to perform a label-free, noninvasive, and high-resolution technique for characterization of live spermatozoa. The aim of this chapter is to summarize the recent achievements of digital holography in order to show its high potentiality as an efficient method for healthy and fertile sperm cell selection, without injuring the specimen and to explore new possible applications of digital holography in this field

Thrifty swimming with shear-thinning

Microscale propulsion is integral to numerous biomedical systems, for example biofilm formation and human reproduction, where the surrounding fluids comprise suspensions of polymers. These polymers endow the fluid with non-Newtonian rheological properties, such as shear-thinning and viscoelasticity. Thus, the complex dynamics of non-Newtonian fluids presents numerous modelling challenges, strongly motivating experimental study. Here, we demonstrate that failing to account for "out-of-plane" effects when analysing experimental data of undulatory swimming through a shear-thinning fluid results in a significant overestimate of fluid viscosity around the model swimmer C. elegans. This miscalculation of viscosity corresponds with an overestimate of the power the swimmer expends, a key biophysical quantity important for understanding the internal mechanics of the swimmer. As experimental flow tracking techniques improve, accurate experimental estimates of power consumption using this technique will arise in similar undulatory systems, such as the planar beating of human sperm through cervical mucus, will be required to probe the interaction between internal power generation, fluid rheology, and the resulting waveform

Counting the Number of Active Spermatozoa Movements Using Improvement Adaptive Background Learning Algorithm

The most important early stage in sperm infertility research is the detection of sperm objects. The success rate in separating sperm objects from semen fluid has an important role for further analysis. This research performed the detection and calculation of human spermatozoa. The detected sperm was the moving sperm in the video data. An improvement of Adaptive Background Learning was applied to detect the moving sperm. The purpose of this method is to improve the performance of Adaptive Background Learning algorithm in background subtraction process to detect and calculate moving sperm on the microscopic video of sperm fluid. This paper also compared several other background subtraction algorithms to conclude the appropriate background subtraction algorithm for sperm detection and sperm counting. The process done in this research was preprocessing using the Gaussian filter. The next was background subtraction process, followed by morphology operation. To test or validate the detection results of any background subtraction algorithm used, the foreground mask results from the morphological operation were compared to the ground truth of moving sperm image. For visualization purposes, every BLOB area (white object in binary image) on the foreground were given a bounding box to the original frame and the number of BLOB objects present in the foreground mask were counted. This shows that the system had been able to detect and calculate moving sperm. Based on the test results, Adaptive Background Learning method had a value of F-measure of 0.9205 and succeeded in extracting sperm shape close to the original form compared to other methods

DPY-30 Domain and its Flanking Sequence Mediate the Assembly Modulation of Flagellar Radial Spoke Complexes

RIIa is known as the dimerization and docking (D/D) domain of the cyclic AMP (cAMP)-dependent protein kinase. However, numerous molecules, including radial spoke protein 2 (RSP2) in Chlamydomonas flagella, also contain an RIIa or a similar DPY-30 domain. To elucidate new roles of D/D domain-containing proteins, we investigated a panel of RSP2 mutants. An RSP2 mutant had paralyzed flagella defective in RSP2 and multiple subunits near the spokehead. New transgenic strains lacking only the DPY-30 domain in RSP2 were also paralyzed. In contrast, motility was restored in strains that lacked only RSP2’s calmodulin- binding C-terminal region. These cells swam normally in dim light but could not maintain typical swimming trajectories under bright illumination. In both deletion transgenic strains, the subunits near the spokehead were restored, but their firm attachment to the spokestalk required the DPY-30 domain. We postulate that the DPY-30–helix dimer is a conserved two-prong linker, required for normal motility, organizing duplicated subunits in the radial spoke stalk and formation of a symmetrical spokehead. Further, the dispensable calmodulin-binding region appears to fine-tune the spokehead for regulation of “steering” motility in the green algae. Thus, in general, D/D domains may function to localize molecular modules for both the assembly and modulation of macromolecular complexes

VISEM-Tracking, a human spermatozoa tracking dataset

A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-assisted sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet sperm preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa