An explicit model to extract viscoelastic properties of cells from AFM force-indentation curves

Atomic force microscopy (AFM) is widely used for quantifying the mechanical properties of soft materials such as cells. AFM force-indentation curves are conventionally fitted with a Hertzian model to extract elastic properties. These properties solely are, however, insufficient to describe the mechanical properties of cells. Here, we expand the analysis capabilities to describe the viscoelastic behavior while using the same force-indentation curves. Our model gives an explicit relation of force and indentation and extracts physically meaningful mechanical parameters. We first validated the model on simulated force-indentation curves. Then, we applied the fitting model to the force-indentation curves of two hydrogels with different crosslinking mechanisms. Finally, we characterized HeLa cells in two cell cycle phases, interphase and mitosis, and showed that mitotic cells have a higher apparent elasticity and a lower apparent viscosity. Our study provides a simple method, which can be directly integrated into the standard AFM framework for extracting the viscoelastic properties of materials

nanite: using machine learning to assess the quality of atomic force microscopy-enabled nano-indentation data

Atomic force microscopy (AFM) allows the mechanical characterization of single cells and live tissue by quantifying force-distance (FD) data in nano-indentation experiments. One of the main problems when dealing with biological tissue is the fact that the measured FD curves can be disturbed. These disturbances are caused, for instance, by passive cell movement, adhesive forces between the AFM probe and the cell, or insufficient attachment of the tissue to the supporting cover slide. In practice, the resulting artifacts are easily spotted by an experimenter who then manually sorts out curves before proceeding with data evaluation. However, this manual sorting step becomes increasingly cumbersome for studies that involve numerous measurements or for quantitative imaging based on FD maps

Combined fluorescence, optical diffraction tomography and Brillouin microscopy

Quantitative measurements of physical parameters become increasingly important for understanding biological processes. Brillouin microscopy (BM) has recently emerged as one technique providing the 3D distribution of viscoelastic properties inside biological samples — so far relying on the implicit assumption that refractive index (RI) and density can be neglected. Here, we present a novel method (FOB microscopy) combining BM with optical diffraction tomography and epi-fluorescence imaging for explicitly measuring the Brillouin shift, RI and absolute density with molecular specificity. We show that neglecting the RI and density might lead to erroneous conclusions. Investigating the cell nucleus, we find that it has lower density but higher longitudinal modulus. Thus, the longitudinal modulus is not merely sensitive to the water content of the sample — a postulate vividly discussed in the field. We demonstrate the further utility of FOB on various biological systems including adipocytes and intracellular membraneless compartments. FOB microscopy can provide unexpected scientific discoveries and shed quantitative light on processes such as phase separation and transition inside living cells

Augmenting regional and targeted delivery in the pulmonary acinus using magnetic particles

Yan Ostrovski, Philipp Hofemeier, Josué Sznitman Department of Biomedical Engineering, Technion – Israel Institute of Technology, Haifa, Israel Background: It has been hypothesized that by coupling magnetic particles to inhaled therapeutics, the ability to target specific lung regions (eg, only acinar deposition), or even more so specific points in the lung (eg, tumor targeting), can be substantially improved. Although this method has been proven feasible in seminal in vivo studies, there is still a wide gap in our basic understanding of the transport phenomena of magnetic particles in the pulmonary acinar regions of the lungs, including particle dynamics and deposition characteristics.Methods: Here, we present computational fluid dynamics-discrete element method simulations of magnetically loaded microdroplet carriers in an anatomically inspired, space-filling, multi-generation acinar airway tree. Breathing motion is modeled by kinematic sinusoidal displacements of the acinar walls, during which droplets are inhaled and exhaled. Particle dynamics are governed by viscous drag, gravity, and Brownian motion as well as the external magnetic force. In particular, we examined the roles of droplet diameter and volume fraction of magnetic material within the droplets under two different breathing maneuvers.Results and discussion: Our results indicate that by using magnetic-loaded droplets, 100% of the particles that enter are deposited in the acinar region. This is consistent across all particle sizes investigated (ie, 0.5–3.0 µm). This is best achieved through a deep inhalation maneuver combined with a breath-hold. Particles are found to penetrate deep into the acinus and disperse well, while the required amount of magnetic material is maintained low (<2.5%). Although particles in the size range of ~90–500 nm typically show the lowest deposition fractions, our results suggest that this feature could be leveraged to augment targeted delivery. Keywords: inhalation medicine, targeted delivery, SPIONs, aerosol, CF

Intelligent image-based deformation-assisted cell sorting with molecular specificity

Although label-free cell sorting is desirable for providing pristine cells for further analysis or use, current approaches lack molecular specificity and speed. Here, we combine real-time fluorescence and deformability cytometry with sorting based on standing surface acoustic waves and transfer molecular specificity to image-based sorting using an efficient deep neural network. In addition to general performance, we demonstrate the utility of this method by sorting neutrophils from whole blood without labels. Sorting RT-FDC combines real-time fluorescence and deformability cytometry with sorting based on standing surface acoustic waves to transfer molecular specificity to label-free, image-based cell sorting using an efficient deep neural network