Exploring altered bovine sperm trajectories by sperm tracking in unconfined conditions.

Mammalian sperm motility is getting more relevant due to rising infertility rates worldwide, generating the need to improve conventional analysis and diagnostic approaches. Nowadays, computer assisted sperm analysis (CASA) technologies represent a popular alternative to manual examination which is generally performed by observing sperm motility in very confined geometries. However, under physiological conditions, sperm describe three-dimensional motility patterns which are not well reconstructed by the limited depth of standard acquisition chambers. Therefore, affordable and more versatile alternatives are needed. Here, a motility analysis in unconfined conditions is proposed. In details, the analysis is characterized by a significant longer duration -with respect to conventional systems- with the aim to observe eventually altered motility patterns. Brightfield acquisition in rectangular glass capillaries captured frozen-thawed bovine spermatozoa which were analyzed by means of a self-written tracking routine and classified in sub-populations, based on their curvilinear velocity. To test the versatility of our approach, cypermethrin -a commonly used pesticides- known to be responsible for changes in sperm motility was employed, assessing its effect at three different time-steps. Experimental results showed that such drug induces an increase in sperm velocity and progressiveness as well as circular pattern formation, likely independent of wall interactions. Moreover, this resulted in a redistribution of sperm with the rapid class declining in number with time, but still showing an overall velocity increase. The flexibility of the approach permits parameter modifications with the experimental needs, allowing us to conduct a comprehensive examination of sperm motility. This adaptability facilitated data acquisition which can be computed at different frame rates, extended time periods, and within deeper observation chambers. The suggested approach for sperm analysis exhibits potential as a valuable augmentation to current diagnostic instruments.

Wide-range viscoelastic compression forces in microfluidics to probe cell-dependent nuclear structural and mechanobiological responses.

The cell nucleus plays a critical role in mechanosensing and mechanotransduction processes, by adaptive changes of its envelope composition to external biophysical stimuli such as substrate rigidity and tensile forces. Current measurement approaches lack precise control in stress application on nuclei, thus significantly impairing a complete mechanobiological study of cells. Here, we present a contactless microfluidic approach capable to exert a wide range of viscoelastic compression forces (10-103 µN)-as an alternative to adhesion-related techniques-to induce cell-specific mechano-structural and biomolecular changes. We succeed in monitoring substantial nuclear modifications in Lamin A/C expression and coverage, diffusion processes of probing molecules, YAP shuttling, chromatin re-organization and cGAS pathway activation. As a result, high compression forces lead to a nuclear reinforcement (e.g. up to +20% in Lamin A/C coverage) or deconstruction (e.g. down to -45% in Lamin A/C coverage with a 30% reduction of chromatin condensation state parameter) up to cell death. We demonstrate how wide-range compression on suspended cells can be used as a tool to investigate nuclear mechanobiology and to define specific nuclear signatures for cell mechanical phenotyping.

Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters.

Cell deformability is a well-established marker of cell states for diagnostic purposes. However, the measurement of a wide range of different deformability levels is still challenging, especially in cancer, where a large heterogeneity of rheological/mechanical properties is present. Therefore, a simple, versatile and cost-effective recognition method for variable rheological/mechanical properties of cells is needed. Here, we introduce a new set of in-flow motion parameters capable of identifying heterogeneity among cell deformability, properly modified by the administration of drugs for cytoskeleton destabilization. Firstly, we measured cell deformability by identification of in-flow motions, rolling (R), tumbling (T), swinging (S) and tank-treading (TT), distinctively associated with cell rheological/mechanical properties. Secondly, from a pool of motion and structural cell parameters, an unsupervised machine learning approach based on principal component analysis (PCA) revealed dominant features: the local cell velocity (VCell/VAvg), the equilibrium position (YEq) and the orientation angle variation (Δφ). These motion parameters clearly defined cell clusters in terms of motion regimes corresponding to specific deformability. Such correlation is verified in a wide range of rheological/mechanical properties from the elastic cells moving like R until the almost viscous cells moving as TT. Thus, our approach shows how simple motion parameters allow cell deformability heterogeneity recognition, directly measuring rheological/mechanical properties.

Mechanical phenotyping of breast cell lines by in-flow deformation-dependent dynamics under tuneable compressive forces.

Cell mechanical properties are powerful biomarkers for label-free phenotyping. To date, microfluidic approaches assay mechanical properties by measuring changes in cellular shape, applying extensional or shear flows or forcing cells to pass through constrictions. In general, such approaches use high-speed imaging or transit time measurements to evaluate cell deformation, while cell dynamics in-flow after stress imposition have not yet been considered. Here, we present a microfluidic approach to apply, over a wide range, tuneable compressive forces on suspended cells, which result in well distinct signatures of deformation-dependent dynamic motions. By properly conceiving microfluidic chip geometry and rheological fluid properties, we modulate applied single-cell forces, which result in different motion regimes (rolling, tumbling or tank-treating) depending on the investigated cell line. We decided to prove our approach by testing breast cell lines, with well-known mechanical properties. We measured a set of in-flow parameters (orientation angle, aspect ratio, cell deformation and cell diameter) as a backward analysis of cell mechanical response. By such an approach, we report that the highly invasive tumour cells (MDA-MB-231) are much more deformable (6-times higher) than healthy (MCF-10A) and low invasive ones (MCF-7). Thus, we demonstrate that a microfluidic design with tuneable rheological fluid properties and direct analysis of bright-field images can be suitable for the label-free mechanical phenotyping of various cell lines.