Related Experiment Video
Updated: Jun 21, 2025
Measuring Sperm Guidance and Motility within the Caenorhabditis elegans Hermaphrodite Reproductive Tract
Published on: June 6, 2019
Characterizing the consistency of motion of spermatozoa through nanoscale motion tracing
Sunil Bhatt1, Ankit Butola2, Sebastian Acuña2
1Bio-photonics and Green-photonics Laboratory, Department of Physics, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi, India.
Objective:
To demonstrate nanoscale motion tracing of spermatozoa and present analysis of the motion traces to characterize the consistency of motion of spermatozoa as a complement to progressive motility analysis.
Design:
Anonymized sperm samples were videographed under a quantitative phase microscope, followed by generating and analyzing superresolution motion traces of individual spermatozoa.
Setting:
Not applicable.
Patient(S):
Centrifuged human sperm samples.
Intervention(S):
Not applicable.
Main Outcome Measure(S):
Precision of motion trace of individual sperms, presence of a helical pattern in the motion trace, mean and standard deviations of helical periods and radii of sperm motion traces, speed of progression.
Result(S):
Spatially sensitive quantitative phase imaging with a superresolution computational technique MUltiple SIgnal Classification ALgorithm allowed achieving motion precision of 340 nm using ×10, 0.25 numerical aperture lens whereas the diffraction-limited resolution at this setting was 1,320 nm. The motion traces thus derived facilitated new kinematic features of sperm, namely the statistics of helix period and radii per sperm. Through the analysis, 47 sperms with a speed >25 μm/s were randomly selected from the same healthy donor semen sample, it is seen that the kinematic features did not correlate with the speed of the sperms. In addition, it is noted that spermatozoa may experience changes in the periodicity and radius of the helical path over time. Further, some very fast sperms (e.g., >70 μm/s) may demonstrate irregular motion and need further investigation. Presented computational analysis can be used directly for sperm samples from both fertility patients with normal and abnormal sperm cell conditions. We note that MUltiple SIgnal Classification ALgorithm is an image analysis technique that may vaguely fall under the machine learning category, but the conventional metrics for reporting found in Enhancing the QUAlity and Transparency Of health Research network do not apply. Alternative suitable metrics are reported, and bias is avoided through random selection of regions for analysis. Detailed methods are included for reproducibility.
Conclusion(S):
Kinematic features derived from nanoscale motion traces of spermatozoa contain information complementary to the speed of the sperms, allowing further distinction among the progressively motile sperms. Some highly progressive spermatozoa may have irregular motion patterns, and whether irregularity of motion indicates poor quality regarding artificial insemination needs further investigation. The presented technique can be generalized for sperm analysis for a variety of fertility conditions.
