Real-time parallel 3D multiple particle tracking with single molecule centrifugal force microscopy

Unravelling the Mystery inside Cells by Using Single-Molecule Fluorescence Imaging

Live-cell imaging is a powerful technique to study the dynamics and mechanics of various biological molecules like proteins, organelles, DNA, and RNA. With the rapid evolution of optical microscopy, our understanding of how these molecules are implicated in the cells' most critical physiological roles deepens. In this review, we focus on how spatiotemporal nanoscale live-cell imaging at the single molecule level allows for profound contributions towards new discoveries in life science. This review will start by summarizing how single-molecule tracking has been used to analyze membrane dynamics, receptor-ligand interactions, protein-protein interactions, inner- and extra-cellular transport, gene expression/transcription, and whole organelle tracking. We then move on to how current authors are trying to improve single-molecule tracking and overcome current limitations by offering new ways of labeling proteins of interest, multi-channel/color detection, improvements in time-lapse imaging, and new methods and programs to analyze the colocalization and movement of targets. We later discuss how single-molecule tracking can be a beneficial tool used for medical diagnosis. Finally, we wrap up with the limitations and future perspectives of single-molecule tracking and total internal reflection microscopy.

Deep learning-based 3D cellular force reconstruction directly from volumetric images

The forces exerted by single cells in the three-dimensional (3D) environments play a crucial role in modulating cellular functions and behaviors closely related to physiological and pathological processes. Cellular force microscopy (CFM) provides a feasible solution for quantifying the mechanical interactions, which usually regains cellular forces from deformation information of extracellular matrices embedded with fluorescent beads. Owing to computational complexity, the traditional 3D-CFM is usually extremely time-consuming, which makes it challenging for efficient force recovery and large-scale sample analysis. With the aid of deep neural networks, this study puts forward a novel data-driven 3D-CFM to reconstruct 3D cellular force fields directly from volumetric images with random fluorescence patterns. The deep learning (DL)-based network is established through stacking deep convolutional neural network (DCNN) and specific function layers. Some necessary physical information associated with constitutive relation of extracellular matrix material is coupled to the data-driven network. The mini-batch stochastic gradient descent and back-propagation algorithms are introduced to ensure its convergence and training efficiency. The network not only have good generalization ability and robustness, but also can recover 3D cellular forces directly from the input fluorescence image pairs. Particularly, the computational efficiency of the DL-based network is at least one to two orders of magnitude higher than that of the traditional 3D-CFM. This study provides a novel scheme for developing high-performance 3D cellular force microscopy to quantitatively characterize mechanical interactions between single cells and surrounding extracellular matrices, which is of vital importance for quantitative investigations in biomechanics and mechanobiology.

Modification to axial tracking for mobile magnetic microspheres

Three-dimensional particle tracking is a routine experimental procedure for various biophysical applications including magnetic tweezers. A common method for tracking the axial position of particles involves the analysis of diffraction rings whose pattern depends sensitively on the axial position of the bead relative to the focal plane. To infer the axial position, the observed rings are compared with reference images of a bead at known axial positions. Often the precision or accuracy of these algorithms is measured on immobilized beads over a limited axial range, while many experiments are performed using freely mobile beads. This inconsistency raises the possibility of incorrect estimates of experimental uncertainty. By manipulating magnetic beads in a bidirectional magnetic tweezer setup, we evaluated the error associated with tracking mobile magnetic beads and found that the error of tracking a moving magnetic bead increases by almost an order of magnitude compared to the error of tracking a stationary bead. We found that this additional error can be ameliorated by excluding the center-most region of the diffraction ring pattern from tracking analysis. Evaluation of the limitations of a tracking algorithm is essential for understanding the error associated with a measurement. These findings promise to bring increased resolution to three-dimensional bead tracking of magnetic microspheres.

Wi-Fi Live-Streaming Centrifuge Force Microscope for Benchtop Single-Molecule Experiments

The ability to apply controlled forces to individual molecules has been revolutionary in shaping our understanding of biophysics in areas as diverse as dynamic bond strength, biological motor operation, and DNA replication. However, the methodology to perform single-molecule experiments remains relatively inaccessible because of cost and complexity. In 2010, we introduced the centrifuge force microscope (CFM) as a platform for accessible and high-throughput single-molecule experimentation. The CFM consists of a rotating microscope with which prescribed centrifugal forces can be applied to microsphere-tethered biomolecules. In this work, we develop and demonstrate a next-generation Wi-Fi CFM that offers unprecedented ease of use and flexibility in design. The modular CFM unit fits within a standard benchtop centrifuge and connects by Wi-Fi to an external computer for live control and streaming at near gigabit speeds. The use of commercial wireless hardware allows for flexibility in programming and provides a streamlined upgrade path as Wi-Fi technology advances. To facilitate ease of use, detailed build and setup instructions, as well as LabVIEW-based control software and MATLAB-based analysis software, are provided. We demonstrate the instrument’s performance by analysis of force-dependent dissociation of short DNA duplexes of 7, 8, and 9 bp. We showcase the sensitivity of the approach by resolving distinct dissociation kinetic rates for a 7 bp duplex in which one G-C basepair is mutated to an A-T basepair.