Discipline: Technology and Engineering
Subcategory: Electrical Engineering
Mustafa U. Daloglu - University of California, Los Angeles
Co-Author(s): Wei Luo, University of California, Los Angeles, CA; Faizan Shabbir, University of California, Los Angeles, CA; Francis Lin, University of California, Los Angeles, CA; Kevin Kim, University of California, Los Angeles, CA; Inje Lee, University of California, Los Angeles, CA; Jiaqi Jiang, University of California, Los Angeles, CA; Wenjun Cai, University of California, Los Angeles, CA; Vishwajith Ramesh, University of California, Los Angeles, CA; Mengyuan Yu, University of California, Los Angeles, CA; Aydogan Ozcan, University of California, Los Angeles, CA.
The swimming dynamics and behavior of sperm cells have been of great interest to researchers, even before the advent of automated cell tracking platforms. However, the three-dimensional (3D) swimming patterns, especially the flagellar beating and head spin, of these remarkable microswimmers have been relatively underexplored, despite many advances in computer-aided sperm analysis technologies, mainly due to the limitations of conventional lens-based optical microscopes. Poor depth resolution and the trade-off between field-of-view and lateral resolution necessitate the use of shallow chambers when imaging sperm cells with standard microscopy tools, restricting their natural 3D motion. We have designed and built a dual-angle on-chip holographic imaging platform capable of recording the complete 3D motion of free-swimming bovine sperm cells, including the head position and spin, along with their flagellar beating patterns, across a large imaging volume of 1.8 μL with a depth-of-field of 0.6 mm [1]. This computational imaging platform only consists of a CMOS image sensor and two partially coherent light sources (fiber-coupled LEDs at ~525 nm wavelength) placed in mirror symmetry. The sample chamber is placed very close to the image sensor, with a periodic light blocking structure to spatially separate the pair of interference patterns (or holograms) created by light that is scattered and transmitted by each sperm cell on the sensor, significantly improving the sensitivity. Each hologram pair is then reconstructed with the corresponding angle of incidence and depth information, resulting in a pair of 2D projections of the sperm cell and its flagellum from two symmetric perspectives which are then used to accurately determine the 3D position and orientation of the sperm head along with the 3D structure of the flagellum. Operating this platform at a high frame rate (~300 frames/second) sufficient to capture the motion of the rapidly beating flagellum without temporal under-sampling, we recorded the complete 3D motion of 2,133 bovine sperm cells. Tracking the head spin and orientation helped us to visualize the flagellar beating from a local perspective of an observer seated on the sperm head, for the first time, decoupling the flagellar motion from the translational and rotational motion of the sperm [1]. In summary, lensfree on-chip microscopy is a powerful platform for 3D tracking of sperm cells, also revealing the flagellar beating patterns and the head spin across large sample volumes, providing unique opportunities to investigate the 3D spatio-temporal kinematics of the sperm cells. References: 1. Daloglu, M.U., Luo, W., Shabbir, F., Lin, F., Kim, K., Lee, I., Jiang, J., Cai, W., Ramesh, V., Yu, M. & Ozcan, A. 2017. Label-free 3D Computational Imaging of Spermatozoon Locomotion, Head Spin and Flagellum Beating Over a Large Volume. Light: Science & Applications. Accepted article preview 16 August 2017. doi: 10.1038/LSA.2017.121
Not SubmittedFunder Acknowledgement(s): NSF EFRI Program is acknowledged.
Faculty Advisor: Aydogan Ozcan, UCLA, ozcan@ucla.edu
Role: I am the graduate student responsible from this research project, involved in all aspects of the work. I have recruited and led our multidisciplinary team of seven undergraduate students in this research, training and leading them on sample preparation and biological experiments. I have designed and built the imaging platform, also implemented and maintained our reconstruction and tracking algorithms with a colleague. I am the first-author of a journal article that resulted from this research project, published very recently and referenced accordingly in my abstract.