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.

Unknown cell class distinction via neural network based scattering snapshot recognition.

Neural network-based image classification is widely used in life science applications. However, it is essential to extrapolate a correct classification method for unknown images, where no prior knowledge can be utilised. Under a closed set assumption, unknown images will be inevitably misclassified, but this can be genuinely overcome choosing an open-set classification approach, which first generates an in-distribution of identified images to successively discriminate out-of-distribution images. The testing of such image classification for single cell applications in life science scenarios has yet to be done but could broaden our expertise in quantifying the influence of prediction uncertainty in deep learning. In this framework, we implemented the open-set concept on scattering snapshots of living cells to distinguish between unknown and known cell classes, targeting four different known monoblast cell classes and a single tumoral unknown monoblast cell line. We also investigated the influence on experimental sample errors and optimised neural network hyperparameters to obtain a high unknown cell class detection accuracy. We discovered that our open-set approach exhibits robustness against sample noise, a crucial aspect for its application in life science. Moreover, the presented open-set based neural network reveals measurement uncertainty out of the cell prediction, which can be applied to a wide range of single cell classifications.

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.

Single cell classification of macrophage subtypes by label-free cell signatures and machine learning.

Pro-inflammatory (M1) and anti-inflammatory (M2) macrophage phenotypes play a fundamental role in the immune response. The interplay and consequently the classification between these two functional subtypes is significant for many therapeutic applications. Albeit, a fast classification of macrophage phenotypes is challenging. For instance, image-based classification systems need cell staining and coloration, which is usually time- and cost-consuming, such as multiple cell surface markers, transcription factors and cytokine profiles are needed. A simple alternative would be to identify such cell types by using single-cell, label-free and high throughput light scattering pattern analyses combined with a straightforward machine learning-based classification. Here, we compared different machine learning algorithms to classify distinct macrophage phenotypes based on their optical signature obtained from an ad hoc developed wide-angle static light scattering apparatus. As the main result, we were able to identify unpolarized macrophages from M1- and M2-polarized phenotypes and distinguished them from naive monocytes with an average accuracy above 85%. Therefore, we suggest that optical single-cell signatures within a lab-on-a-chip approach along with machine learning could be used as a fast, affordable, non-invasive macrophage phenotyping tool to supersede resource-intensive cell labelling.

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.

New Trends in Precision Medicine: A Pilot Study of Pure Light Scattering Analysis as a Useful Tool for Non-Small Cell Lung Cancer (NSCLC) Diagnosis.

Background: To date, in personalized medicine approaches, single-cell analyses such as circulating tumour cells (CTC) are able to reveal small structural cell modifications, and therefore can retrieve several biophysical cell properties, such as the cell dimension, the dimensional relationship between the nucleus and the cytoplasm and the optical density of cellular sub-compartments. On this basis, we present in this study a new morphological measurement approach for the detection of vital CTC from pleural washing in individual non-small cell lung cancer (NSCLC) patients. Materials and methods: After a diagnosis of pulmonary malignancy, pleural washing was collected from nine NSCLC patients. The collected samples were processed with a density gradient separation process. Light scattering analysis was performed on a single cell. The results of this analysis were used to obtain the cell's biophysical pattern and, later on, as basis for Machine Learning (ML) on unknown samples. Results: Morphological single-cell analysis followed by ML show a predictive picture for an NSCLC patient, screening that it is possible to distinguish CTC from other cells. Moreover, we find that the proposed measurement approach was fast, reliable, label-free, identifying and count CTC in a biological fluid. Conclusions: Our findings demonstrate that CTC Biophysical Profile by Pure Light Scattering in NSCLC could be used as a promising diagnostic candidate in NSCLC patients.

Non-invasive and label-free identification of human natural killer cell subclasses by biophysical single-cell features in microfluidic flow.

Natural killer (NK) cells are indicated as favorite candidates for innovative therapeutic treatment and are divided into two subclasses: immature regulatory NK CD56bright and mature cytotoxic NK CD56dim. Therefore, the ability to discriminate CD56dim from CD56bright could be very useful because of their higher cytotoxicity. Nowadays, NK cell classification is routinely performed by cytometric analysis based on surface receptor expression. Here, we present an in-flow, label-free and non-invasive biophysical analysis of NK cells through a combination of light scattering and machine learning (ML) for NK cell subclass classification. In this respect, to identify relevant biophysical cell features, we stimulated NK cells with interleukine-15 inducing a subclass transition from CD56bright to CD56dim. We trained our ML algorithm with sorted NK cell subclasses (≥86% accuracy). Next, we applied our NK cell classification algorithm to cells stimulated over time, to investigate the transition of CD56bright to CD56dim and their biophysical feature changes. Finally, we tested our approach on several proband samples, highlighting the potential of our measurement approach. We show a label-free way for the robust identification of NK cell subclasses based on biophysical features, which can be applied in both cell biology and cell therapy.

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.

CD4+ versus CD8+ T-lymphocyte identification in an integrated microfluidic chip using light scattering and machine learning.

T lymphocytes are a group of cells representing the main effectors of human adaptive immunity. Characterization of the most representative T-lymphocyte subclasses, CD4+ and CD8+, is challenging, but has a significant impact on clinical decisions. Up to now, T lymphocytes have been identified by quite complex cytometric assays, which are based on antibody labeling. However, a label-free approach based on pure biophysical evaluation at a single-cell level could enable the ability to distinguish between these subclasses. Here, we report a light-scattering approach, supported by accurate data mining, to evaluate cell biophysical properties on an integrated microfluidic chip. In order to perform single-cell optical analysis in viscoelastic fluids, such a chip is composed of mixing, alignment, readout and collection sections. In particular, we measured the cell dimensions, the refractive index of the cell nucleus, the refractive index of the cytosol, and the nucleus-to-cytosol ratio. Combining measurement of biophysical properties and machine learning allows us to both distinguish and count human CD4+ and CD8+ cells with an accuracy of 79%. An enhanced identification accuracy of 88% can be achieved by stimulating the cells with a selective anti-apoptotic protein, which results in increased biophysical differences between CD4+ and CD8+ cells. This approach has been successfully validated by analysis of samples that recapitulate physiological and pathological scenarios (CD4+/CD8+ ratios). The results are encouraging for the possible application of our approach in hematological clinical routines, as well as in diagnosis and follow-up of specific pathologies, such as human immunodeficiency virus (HIV) progression.

Comparative spallation performance of silicone versus Tygon extracorporeal circulation tubing.

OBJECTIVES: Reports ranged from mixed to marginal tubing wear and spallation effects as a complication of roller pumps in cardiopulmonary bypass (CPB). Because the rollers constantly compress part of the tubing, we sought to determine whether circuit materials behave differently under a 3-h simulation of CPB. METHODS: Two different tubing materials (silicone and Tygon) were tested with a customized experimental circuit, designed to allow in vitro simulation of CPB with priming volumes, pressures, revolutions per minute and temperatures equivalent to the clinical scenario. Samples were analysed with optical and field-emission scanning electron microscopy. We collected 200-ml fluid samples at 4 different times: before starting the CPB (T0), when the predicted revolutions per minute corresponded to about 2 min of CPB (T1), at 90 min (T2) and at 180 min (T3). At the end of CPB, we harvested 2 samples of tubing. Lastly, optical investigations and field-emission scanning electron microscopy observations were used for qualitative and quantitative analysis of circulating fragments. RESULTS: T2 and T3 fluid samples showed more particles than T1 samples. Significant differences in terms of particle numbers were detected: silicone tubing released more fragments per millilitre than Tygon tubing, with both materials releasing particles from 5 to 500 µm. Silicone tubing was associated with a time-dependent increase in small particles released (P = 0.04), whereas this did not apply to large particles or to Tygon tubing. Yet, bootstrap estimates suggested that silicone tubing was associated with the release of more small particles whereas Tygon tubing released more large particles (both P < 0.01). Unlike silicone, Tygon samples taken from the portion of the circuit not subjected to the action of the roller pump did not show any erosion on their surfaces. Samples of both materials taken from the portion subjected to the compression of the roller pump showed signs of significant deterioration. CONCLUSIONS: Silicone showed a worse spallation performance than Tygon, thus appearing less safe for more complex surgery of prolonged duration or for patients with a prior cerebral ischaemic event. Additional risk and cost-effectiveness comparisons to determine the potential benefits of one type of tubing material over the other are warranted to further expand our findings.

Electron Microscopy for 3D Scaffolds-Cell Biointerface Characterization.

Cell fate is largely determined by interactions that occur at the interface between cells and their surrounding microenvironment. For this reason, especially in the field of tissue-engineering, there is a growing interest in developing techniques that allow evaluating cell-material interaction at the nanoscale, particularly focusing on cell adhesion processes. While for 2D culturing systems a consolidated series of tools already satisfy this need, in 3D environments, more closely recapitulating complex in vivo structures, there is still a lack of procedures furthering the comprehension of cell-material interactions. Here, the use of scanning electron microscopy coupled with a focused ion beam (SEM/FIB) for the characterization of cell interactions with 3D scaffolds obtained by different fabrication techniques is reported for the first time. The results clearly show the capability of the developed approach to preserve and finely resolve scaffold-cell interfaces highlighting details such as plasma membrane arrangement, extracellular matrix architecture and composition, and cellular structures playing a role in cell adhesion to the surface. It is anticipated that the developed approach will be relevant for the design of efficient cell-instructive platforms in the study of cellular guidance strategies for tissue-engineering applications as well as for in vitro 3D models.

Biophysical investigation of living monocytes in flow by collaborative coherent imaging techniques.

We implemented a completely label-free biophysical (morphometric and optical) property characterization of living monocytes in flow, using measurements obtained from two coherent imaging techniques: a pure light scattering approach to obtain an optical signature (OS) of cells, and a digital holography (DH) approach to achieve optical cell reconstructions in flow. A precise 3D cell alignment platform, taking advantage of viscoelastic fluid properties and microfluidic channel geometry, was used to investigate the OS of cells to achieve their refractive index, ratio of the nucleus over cytoplasm, and overall cell dimension. Further quantitative phase-contrast reconstructions by DH were employed to calculate surface area, dry mass, and biovolume of monocytes by using the OS outcomes as input parameters. The results show significantly different biophysical cell properties, confirming the possibility to differentiate monocytes from other cell classes in flow, thus avoiding chemical cell staining or labeling, which are nowadays used.

Single-cell screening of multiple biophysical properties in leukemia diagnosis from peripheral blood by pure light scattering.

Histology and histopathology are based on the morphometric observations of quiescent cells. Their diagnostic potential could largely benefit from a simultaneous screening of intrinsic biophysical properties at single-cell level. For such a purpose, we analyzed light scattering signatures of individual mononuclear blood cells in microfluidic flow. In particular, we extracted a set of biophysical properties including morphometric (dimension, shape and nucleus-to-cytosol ratio) and optical (optical density) ones to clearly discriminate different cell types and stages. By considering distinctive ranges of biophysical properties along with the obtained relative cell frequencies, we can identify unique cell classes corresponding to specific clinical conditions (p < 0.01). Based on such a straightforward approach, we are able to discriminate T-, B-lymphocytes, monocytes and beyond that first results on different stages of lymphoid and myeloid leukemia cells are presented. This work shows that the simultaneous screening of only three biophysical properties enables a clear distinction between pathological and physiological mononuclear blood stream cells. We believe our approach could represent a useful tool for a label-free analysis of biophysical single-cell signatures.

Label-free analysis of mononuclear human blood cells in microfluidic flow by coherent imaging tools.

The investigation of the physical properties of peripheral blood mononuclear cells (PBMC) is of great relevance, as they play a key role in regulating human body health. Here we report the possibility to characterize human PBMC in their physiological conditions in a microfluidic-based measurement system. A viscoelastic polymer solution is adopted for 3D alignment of individual cells inflow. An optical signature (OS) acquisition of each flowing cell is performed using a wide angle light scattering apparatus. Besides, a quantitative phase imaging (QPI) holographic system is employed with the aim (i) to check the position in flow of individual cells using a holographic 3D cell tracking method; and (ii) to estimate their 3D morphometric features, such as their refractive index (RI). Results obtained by combining OS and QPI have been compared with literature values, showing good agreement. The results confirm the possibility to obtain sub-micrometric details of physical cell properties in microfluidic flow, avoiding chemical staining or fluorescent labelling.

In-flow real-time detection of spectrally encoded microgels for miRNA absolute quantification.

We present an in-flow ultrasensitive fluorescence detection of microRNAs (miRNAs) using spectrally encoded microgels. We researched and employed a viscoelastic fluid to achieve an optimal alignment of microgels in a straight measurement channel and applied a simple and inexpensive microfluidic layout, allowing continuous fluorescence signal acquisitions with several emission wavelengths. In particular, we chose microgels endowed with fluorescent emitting molecules designed for multiplex spectral analysis of specific miRNA types. We analysed in a quasi-real-time manner circa 80 microgel particles a minute at sample volumes down to a few microliters, achieving a miRNA detection limit of 202 fM in microfluidic flow conditions. Such performance opens up new routes for biosensing applications of particles within microfluidic devices.