Investigation of cell mechanics using single-beam acoustic tweezers as a versatile tool for the diagnosis and treatment of highly invasive breast cancer cell lines: an in vitro study

Critical review of single-cell mechanotyping approaches for biomedical applications

Accurate mechanical measurements of cells has the potential to improve diagnostics, therapeutics and advance understanding of disease mechanisms, where high-resolution mechanical information can be measured by deforming individual cells. Here we evaluate recently developed techniques for measuring cell-scale stiffness properties; while many such techniques have been developed, much of the work examining single-cell stiffness is impacted by difficulties in standardization and comparability, giving rise to large variations in reported mechanical moduli. We highlight the role of underlying mechanical theories driving this variability, and note opportunities to develop novel mechanotyping devices and theoretical models that facilitate convenient and accurate mechanical characterisation. Moreover, many high-throughput approaches are confounded by factors including cell size, surface friction, natural population heterogeneity and convolution of elastic and viscous contributions to cell deformability. We nevertheless identify key approaches based on deformability cytometry as a promising direction for further development, where both high-throughput and accurate single-cell resolutions can be realized.

Diagnosis of diabetes mellitus using high frequency ultrasound and convolutional neural network

The incidence of diabetes mellitus has been increasing, prompting the search for non-invasive diagnostic methods. Although current methods exist, these have certain limitations, such as low reliability and accuracy, difficulty in individual patient adjustment, and discomfort during use. This paper presents a novel approach for diagnosing diabetes using high-frequency ultrasound (HFU) and a convolutional neural network (CNN). This method is based on the observation that glucose in red blood cells (RBCs) forms glycated hemoglobin (HbA1c) and accumulates on its surface. The study incubated RBCs with different glucose concentrations, collected acoustic reflection signals from them using a custom-designed 90-MHz transducer, and analyzed the signals using a CNN. The CNN was applied to the frequency spectra and spectrograms of the signal to identify correlations between changes in RBC properties owing to glucose concentration and signal features. The results confirmed the efficacy of the CNN-based approach with a classification accuracy of 0.98. This non-invasive diagnostic technology using HFU and CNN holds promise for in vivo diagnosis without the need for blood collection.

Red blood cell trapping using single-beam acoustic tweezers in the Rayleigh regime

Acoustic tweezers (ATs) are a promising technology that can trap and manipulate microparticles or cells with the focused ultrasound beam without physical contact. Unlike optical tweezers, ATs may be used for in vivo studies because they can manipulate cells through tissues. However, in previous non-invasive microparticle trapping studies, ATs could only trap spherical particles, such as beads. Here, we present a theoretical analysis of how the acoustic beam traps red blood cells (RBCs) with experimental demonstration. The proposed modeling shows that the trapping of a non-spherical, biconcave-shaped RBC could be successfully done by single-beam acoustic tweezers (SBATs). We demonstrate this by trapping RBCs using SBATs in the Rayleigh regime, where the cell size is smaller than the wavelength of the beam. Suggested SBAT is a promising tool for cell transportation and sorting.

A deformability-based biochip for precise label-free stratification of metastatic subtypes using deep learning

Cellular deformability is a promising biomarker for evaluating the physiological state of cells in medical applications. Microfluidics has emerged as a powerful technique for measuring cellular deformability. However, existing microfluidic-based assays for measuring cellular deformability rely heavily on image analysis, which can limit their scalability for high-throughput applications. Here, we develop a parallel constriction-based microfluidic flow cytometry device and an integrated computational framework (ATMQcD). The ATMQcD framework includes automatic training set generation, multiple object tracking, segmentation, and cellular deformability quantification. The system was validated using cancer cell lines of varying metastatic potential, achieving a classification accuracy of 92.4% for invasiveness assessment and stratifying cancer cells before and after hypoxia treatment. The ATMQcD system also demonstrated excellent performance in distinguishing cancer cells from leukocytes (accuracy = 89.5%). We developed a mechanical model based on power-law rheology to quantify stiffness, which was fitted with measured data directly. The model evaluated metastatic potentials for multiple cancer types and mixed cell populations, even under real-world clinical conditions. Our study presents a highly robust and transferable computational framework for multiobject tracking and deformation measurement tasks in microfluidics. We believe that this platform has the potential to pave the way for high-throughput analysis in clinical applications, providing a powerful tool for evaluating cellular deformability and assessing the physiological state of cells.

Photoacoustic based evaluation of viscoelastic properties of Gram-negative and Gram-positive bacterial colonies

Mechanical properties of bacterial colonies are crucial considering both addressing their pathogenic effects and exploring their potential applications. Viscoelasticity is a key mechanical property with major impacts on the cell shapes and functions, which reflects the information about the cell envelope constituents. Hereby, we have proposed the application of photoacoustic viscoelasticity (PAVE) for studying the rheological properties of bacterial colonies. In this regard, we employed an intensity-modulated laser beam as the excitation source followed by the phase delay measurement between the generated PA signal and the reference for the characterization of colonies of two different types of Gram-positive and Gram-negative bacteria. The results of our study show that the colony of Staphylococcus aureus as Gram-positive bacteria has a significantly higher viscoelasticity ratio compared to that value for Acinetobacter baumannii as Gram-negative bacteria (77% difference). This may be due to the differing cell envelope structure between the two species, but we cannot rule out effects of biofilm formation in the colonies. Furthermore, a lumped model has been provided for the mechanical properties of bacterial colonies.

Acoustic tweezers using bisymmetric coherent surface acoustic waves for dynamic and reconfigurable manipulation of particle multimers

HYPOTHESIS

The accurate and dynamic manipulation of multiple micro-sized objects has always been a technical challenge in areas of colloid assembly, tissue engineering, and organ regeneration. The hypothesis of this paper is the precise modulation and parallel manipulation of morphology of individual and multiple colloidal multimers can be achieved by customizing acoustic field.

EXPERIMENTS

Herein, we present a colloidal multimer manipulation method by using acoustic tweezers with bisymmetric coherent surface acoustic waves (SAWs), which enables contactless morphology modulation of individual colloidal multimers and patterning arrays by regulating the shape of acoustic field to specific desired distributions with high accuracy. Rapid switching of multimer patterning arrays, morphology modulation of individual multimers, and controllable rotation can be achieved by regulating coherent wave vector configurations and phase relations in real time.

FINDINGS

To demonstrate the capabilities of this technology, we have firstly achieved eleven patterns of deterministic morphology switching for single hexamer and precise switching between three array modes. In addition, the assembly of multimers with three kinds of specific widths and controllable rotation of single multimers and arrays were demonstrated from 0 to 22.4 rpm (tetramers). Therefore, this technique enables reversible assembly and dynamic manipulation of particles and/or cells in colloid synthesis applications.

An Inverse Class-E Power Amplifier for Ultrasound Transducer

An inverse Class-E power amplifier was designed for an ultrasound transducer. The proposed inverse Class-E power amplifier can be useful because of the low series inductance values used in the output matching network that helps to reduce signal distortions. Therefore, a newly designed Class-E power amplifier can obtain a proper echo signal quality. The measured output voltage, voltage gain, voltage gain difference, and power efficiency were 50.1 V, 22.871 dB, 0.932 dB, and 55.342%, respectively. This low voltage difference and relatively high efficiency could verify the capability of the ultrasound transducer. The pulse-echo response experiment using an ultrasound transducer was performed to verify the capability of the proposed inverse Class-E power amplifier. The obtained echo signal amplitude and pulse width were 6.01 mV_p-p and 0.81 μs, respectively. The -6 dB bandwidth and center frequencies of the echo signal were 27.25 and 9.82 MHz, respectively. Consequently, the designed Class-E power amplifier did not significantly alter the performance of the center frequency of the ultrasound transducer; therefore, it could be employed particularly in certain ultrasound applications that require high linearity and reasonable power efficiency.

Ultrasonic High-Resolution Imaging and Acoustic Tweezers Using Ultrahigh Frequency Transducer: Integrative Single-Cell Analysis

Ultrasound imaging is a highly valuable tool in imaging human tissues due to its non-invasive and easily accessible nature. Despite advances in the field of ultrasound research, conventional transducers with frequencies lower than 20 MHz face limitations in resolution for cellular applications. To address this challenge, we employed ultrahigh frequency (UHF) transducers and demonstrated their potential applications in the field of biomedical engineering, specifically for cell imaging and acoustic tweezers. The lateral resolution achieved with a 110 MHz UHF transducer was 20 μm, and 6.5 μm with a 410 MHz transducer, which is capable of imaging single cells. The results of our experiments demonstrated the successful imaging of a single PC-3 cell and a 15 μm bead using an acoustic scanning microscope equipped with UHF transducers. Additionally, the dual-mode multifunctional UHF transducer was used to trap and manipulate single cells and beads, highlighting its potential for single-cell studies in areas such as cell deformability and mechanotransduction.

Flexible acoustic lens-based surface acoustic wave device for manipulation and directional transport of micro-particles

Microfluidics is an emerging technology that is playing increasingly important roles in biomedical and pharmaceutical research and development. Surface acoustic waves (SAWs) have been combined with microfluidics technology to establish a SAW-based microfluidics technology that uses the unique interaction between the two techniques to manipulate substances effectively in fluids on the surface of a substrate. This paper reports a method to generate SAWs using conventional planar ultrasonic transducers and acoustic lenses. Additionally, this method is introduced to manipulate particles effectively on a substrate surface. It is demonstrated that the particle positions can be manipulated precisely in any direction on the substrate surface, thus enabling high-precision particle manipulation. We also proposed the generation of nonplanar SAWs via appropriate design of the acoustic lens and realized directional particle transport. In addition, structures to enhance forward-propagating acoustic beams are proposed. The proposed method has potential for use in microfluidics and biomedical applications, allowing tasks such as flexible cell manipulation on a chip to be performed without complex design or micromachining.

Automated cell-type classification combining dilated convolutional neural networks with label-free acoustic sensing

This study aimed to automatically classify live cells based on their cell type by analyzing the patterns of backscattered signals of cells with minimal effect on normal cell physiology and activity. Our previous studies have demonstrated that label-free acoustic sensing using high-frequency ultrasound at a high pulse repetition frequency (PRF) can capture and analyze a single object from a heterogeneous sample. However, eliminating possible errors in the manual setting and time-consuming processes when postprocessing integrated backscattering (IB) coefficients of backscattered signals is crucial. In this study, an automated cell-type classification system that combines a label-free acoustic sensing technique with deep learning-empowered artificial intelligence models is proposed. We applied an one-dimensional (1D) convolutional autoencoder to denoise the signals and conducted data augmentation based on Gaussian noise injection to enhance the robustness of the proposed classification system to noise. Subsequently, denoised backscattered signals were classified into specific cell types using convolutional neural network (CNN) models for three types of signal data representations, including 1D CNN models for waveform and frequency spectrum analysis and two-dimensional (2D) CNN models for spectrogram analysis. We evaluated the proposed system by classifying two types of cells (e.g., RBC and PNT1A) and two types of polystyrene microspheres by analyzing their backscattered signal patterns. We attempted to discover cell physical properties reflected on backscattered signals by controlling experimental variables, such as diameter and structure material. We further evaluated the effectiveness of the neural network models and efficacy of data representations by comparing their accuracy with that of baseline methods. Therefore, the proposed system can be used to classify reliably and precisely several cell types with different intrinsic physical properties for personalized cancer medicine development.

High-Resolution Micro-object Separation by Rotating Magnetic Chromatography

The development of new technologies for the separation, selection, and isolation of microparticles such as rare target cells, circulating tumor cells, cancer stem cells, and immune cells has become increasingly important in the last few years. Microparticle separation technologies are usually applied to the analysis of disease-associated cells, but these procedures often face a cell separation problem that is often insufficient for single specific cell analyses. To overcome these limitations, a highly accurate size-based microparticle separation technique, herein called "rotating magnetic chromatography", is proposed in this work. Magnetic nanoparticles, placed in a microfluidic separation channel, are forced to move in well-defined trajectories by an external magnetic field, colliding with microparticles that are in this way separated on the basis of their dimensions with high accuracy and reproducibility. The method was optimized by using fluorescein isothiocyanate-modified polystyrene particles (chosen as a reference standard) and then applied to the analysis of cancer cells like Hep-3B and SK-Hep-1, allowing their fast and high-resolution chromatographic separation as a function of their dimensions. Due to its unmatched sub-micrometer cell separation capabilities, RMC can be considered a break-through technique that can unlock new perspectives in different scientific fields, that is, in medical oncology.

Manipulation and Mechanical Deformation of Leukemia Cells by High-Frequency Ultrasound Single Beam

Ultrasound single-beam acoustic tweezer system has attracted increasing attention in the field of biomechanics. Cell biomechanics play a pivotal role in leukemia cell functions. To better understand and compare the cell mechanics of the leukemia cells, herein, we fabricated an acoustic tweezer system in-house connected with a 50-MHz high-frequency cylinder ultrasound transducer. Selected leukemia cells (Jurkat, K562, and MV-411 cells) were cultured, trapped, and manipulated by high-frequency ultrasound single beam, which was transmitted from the ultrasound transducer without contacting any cells. The relative deformability of each leukemia cell was measured, characterized, and compared, and the leukemia cell (Jurkat cell) gaining the highest deformability was highlighted. Our results demonstrate that the high-frequency ultrasound single beam can be utilized to manipulate and characterize leukemia cells, which can be applied to study potential mechanisms in the immune system and cell biomechanics in other cell types.

Automated estimation of cancer cell deformability with machine learning and acoustic trapping

Cell deformability is a useful feature for diagnosing various diseases (e.g., the invasiveness of cancer cells). Existing methods commonly inflict pressure on cells and observe changes in cell areas, diameters, or thickness according to the degree of pressure. Then, the Young’s moduli (i.e., a measure of deformability) of cells are estimated based on the assumption that the degrees of the changes are inversely proportional to Young’s moduli. However, manual measurements of the physical changes in cells are labor-intensive, and the subjectivity of the operators can intervene during this step, thereby causing considerable uncertainty. Further, because the shapes of cells are nonuniform, we cannot ensure the assumption for linear correlations of physical changes in cells with their deformability. Therefore, this study aims at measuring non-linear elastic moduli of live cells (degrees of cell deformability) automatically by employing conventional neural networks (CNN) and multilayer perceptrons (MLP) while preserving (or enhancing) the accuracy of the manual methods. First, we obtain photomicrographs of cells on multiple pressure levels using single-beam acoustic tweezers, and then, we suggest an image preprocessing method for emphasizing changes in cell areas on the photomicrographs. The CNN model is trained to measure the ratios of the cell area change at each pressure level. Then, we apply the multilayer perceptron (MLP) to learn the correlations of the cell area change ratios according to the pressure levels with cell deformability. The accuracy of the CNN was evaluated using two types of breast cancer cells: MDA-MB-231 (invasive) and MCF-7 (noninvasive). The MLP was assessed using five different beads (Young’s moduli from 0.214 to 9.235 kPa), which provides standardized reference data of the non-linear elastic moduli of live cells. Finally, we validated the practicality of the proposed system by examining whether the non-linear elastic moduli estimated by the proposed system can distinguish invasive breast cancer cells from noninvasive ones.

Collapse pressure measurement of single hollow glass microsphere using single-beam acoustic tweezer

Microbubbles are widely used in medical ultrasound imaging and drug delivery. Many studies have attempted to quantify the collapse pressure of microbubbles using methods that vary depending on the type and population of bubbles and the frequency band of the ultrasound. However, accurate measurement of collapse pressure is difficult as a result of non-acoustic pressure factors generated by physical and chemical reactions such as dissolution, cavitation, and interaction between bubbles. In this study, we developed a method for accurately measuring collapse pressure using only ultrasound pulse acoustic pressure. Under the proposed method, the collapse pressure of a single hollow glass microsphere (HGM) is measured using a high-frequency (20-40 MHz) single-beam acoustic tweezer (SBAT), thereby eliminating the influence of additional factors. Based on these measurements, the collapse pressure is derived as a function of the HGM size using the microspheres' true density. We also developed a method for estimating high-frequency acoustic pressure, whose measurement using current hydrophone equipment is complicated by limitations in the size of the active aperture. By recording the transmit voltage at the moment of collapse and referencing it against the corresponding pressure, it is possible to estimate the acoustic pressure at the given transmit condition. These results of this study suggest a method for quantifying high-frequency acoustic pressure, provide a potential reference for the characterization of bubble collapse pressure, and demonstrate the potential use of acoustic tweezers as a tool for measuring the elastic properties of particles/cells.

Thermal Ablation and High-Resolution Imaging Using a Back-to-Back (BTB) Dual-Mode Ultrasonic Transducer: In Vivo Results

We present a back-to-back (BTB) structured, dual-mode ultrasonic device that incorporates a single-element 5.3 MHz transducer for high-intensity focused ultrasound (HIFU) treatment and a single-element 20.0 MHz transducer for high-resolution ultrasound imaging. Ultrasound image-guided surgical systems have been developed for lesion monitoring to ensure that ultrasonic treatment is correctly administered at the right locations. In this study, we developed a dual-element transducer composed of two elements that share the same housing but work independently with a BTB structure, enabling a mode change between therapy and imaging via 180-degree mechanical rotation. The optic fibers were embedded in the HIFU focal region of ex vivo chicken breasts and the temperature change was measured. Images were obtained in vivo mice before and after treatment and compared to identify the treated region. We successfully acquired B-mode and C-scan images that display the hyperechoic region indicating coagulation necrosis in the HIFU-treated volume up to a depth of 10 mm. The compact BTB dual-mode ultrasonic transducer may be used for subcutaneous thermal ablation and monitoring, minimally invasive surgery, and other clinical applications, all with ultrasound only.