Localized measurements of physical parameters within human sperm cells obtained with wide‐field interferometry

Abstract

We developed a new method to identify the separate cellular compartments in the optical path delay (OPD) maps of un-labeled spermatozoa. This was conducted by comparing OPD maps of fixed, un-labeled spermatozoa to bright field images of the same cells following labeling. The labeling enabled us to identify the acrosomal and nuclear compartments in the corresponding OPD maps of the cells. We then extracted the refractive index maps of fixed cells by dividing the OPD maps of spermatozoa by the corresponding thickness maps of the same cells, obtained with AFM. Finally, the dry mass of the head, nucleus and acrosome of un-labeled immobile spermatozoa, was measured. This method provides the ability to quantitatively measure the dry mass of cellular compartments within human spermatozoa. We expect that these measurements will assist label-free selection of sperm cells for fertilization.

Stain‐free interferometric phase microscopy correlation with DNA fragmentation stain in human spermatozoa

“…In Ref. , we determined that the average dry mass of a sperm head was DM = 7.51 ±1.03×10 −12 g, and the average refractive index of a sperm head was n c = 1.515 ± 0.046. This was achieved by comparing the OPD maps with the exact height measurements of the same cells using an atomic force microscope (AFM).…”

“…From the AFM measurements obtained in Ref. , we can also obtain the average volume of the cells and calculate the average concentration of dry mass density in sperm cells; ρ = M / V = 1.013·10 −21 g/nm 3 = 1.013·10 3 g/L. This value was then used to calculate the dry mass of each cell and its compartment.…”

Automated analysis of individual sperm cells using stain‐free interferometric phase microscopy and machine learning

“…The phase map images obtained by IPM have been shown to provide meaningful, stain-free contrast that correlates to that of stained sperm cells, and have previously been shown to enable effective identification of sperm cell morphological features (6)(7)(8). Furthermore, new parameters such as the dry mass of the cells and cellular compartments can now be derived (9). In addition, the quantitative phase maps produced by IPM have been shown in the past to be an excellent source of quantitative data for the application of machine learning techniques (10)(11)(12)(13).…”

Review on methods of solving the refractive index–thickness coupling problem in digital holographic microscopy of biological cells

“…Cardenas et al [29] have integrated the DHM and AFM into a single system allowing for simultaneous measurement, where the intrusion of the AFM probe is minimal during simultaneous AFM and DHM recording due to the transparent nature and bent configuration of the optical fiber-based AFM cantilever. Balberg et al [30] have used a specialized grid to identify fixated cells imaged using DHM and correlate them with the ones measured using the AFM.…”

“…The simplest and fastest method is approximating the local thickness based on the fact that cells in suspension assume a spherical shape [21][22][23][24][25][26][27][28], yielding the integral RI 2-D profile of the cell from its quantitative phase profile with no prior knowledge other than the RI of the suspension medium. Another option is using a different imaging method to directly measure the geometrical thickness, enabling the isolation of the integral RI [29][30][31][32]. A third option is not measuring the native thickness of the sample but rather constraining the cells into a known dimensional microstructure that confines the cell in the vertical direction such that its thickness is known [21].…”