Automated quantitative analysis of multiple cardiomyocytes at the single-cell level with three-dimensional holographic imaging informatics

Automated fast label-free quantification of cardiomyocyte dynamics with raw holograms for cardiotoxicity screening

Traditional cell analysis approaches based on quantitative phase imaging (QPI) necessitate a reconstruction stage, which utilizes digital holography. However, phase retrieval processing can be complicated and time-consuming since it needs numerical reconstruction and then phase unwrapping. For analysis of cardiomyocyte (CM) dynamics, it was reported that by estimating the spatial variance of the optical path difference from QPI, the spatial displacement of CMs can be quantified, thereby enabling monitoring of the excitation-contraction activity of CMs. Also, it was reported that the Farnebäck optical flow method could be combined with the holographic imaging information from QPI to characterize the contractile motion of single CMs, enabling monitoring of the mechanical beating activity of CMs for cardiotoxicity screening. However, no studies have analyzed the contractile dynamics of CMs based on raw holograms. In this paper, we present a fast, label-free, and high throughput method for contractile dynamic analysis of human-induced pluripotent stem cell-derived CMs using raw holograms or the filtered holograms, which are obtained by filtering only The proposed approach obviates the need for time-consuming numerical reconstruction and phase unwrapping for CM's dynamic analysis while still having performance comparable to that of the previous methods. Accordingly, we developed a computational algorithm to characterize the CM's functional behaviors from contractile motion waveform obtained from raw or filtered holograms, which allows the calculation of various temporal metrics related to beating activity from contraction-relaxation motion-speed profile. To the best of our knowledge, this approach is the first to analyze drug-treated CM's dynamics from raw or filtered holograms without the need for numerical phase image reconstruction. For one hologram, the reconstruction process itself in the existing methods takes at least three times longer than the process of tracking the contraction-relaxation motion-speed profile using optical flow in the proposed method. Furthermore, our proposed methodology was validated in the toxicity screening of two drugs (E-4031 and isoprenaline) with various concentrations. The findings provide information on CM contractile motion and kinetics for cardiotoxicity screening.

Label-Free Visualization and Morphological Profiling of Neuronal Differentiation and Axonal Degeneration through Quantitative Phase Imaging

Understanding the intricate processes of neuronal growth, degeneration, and neurotoxicity is paramount for unraveling nervous system function and holds significant promise in improving patient outcomes, especially in the context of chemotherapy-induced peripheral neuropathy (CIPN). These processes are influenced by a broad range of entwined events facilitated by chemical, electrical, and mechanical signals. The progress of each process is inherently linked to phenotypic changes in cells. Currently, the primary means of demonstrating morphological changes rely on measurements of neurite outgrowth and axon length. However, conventional techniques for monitoring these processes often require extensive preparation to enable manual or semi-automated measurements. Here, a label-free and non-invasive approach is employed for monitoring neuronal differentiation and degeneration using quantitative phase imaging (QPI). Operating on unlabeled specimens and offering little to no phototoxicity and photobleaching, QPI delivers quantitative maps of optical path length delays that provide an objective measure of cellular morphology and dynamics. This approach enables the visualization and quantification of axon length and other physical properties of dorsal root ganglion (DRG) neuronal cells, allowing greater understanding of neuronal responses to stimuli simulating CIPN conditions. This research paves new avenues for the development of more effective strategies in the clinical management of neurotoxicity.

Application of Digital Holographic Imaging to Monitor Real-Time Cardiomyocyte Hypertrophy Dynamics in Response to Norepinephrine Stimulation

Cardiomyocyte hypertrophy, characterized by an increase in cell size, is associated with various cardiovascular diseases driven by factors including hypertension, myocardial infarction, and valve dysfunction. In vitro primary cardiomyocyte culture models have yielded numerous insights into the intrinsic and extrinsic mechanisms driving hypertrophic growth. However, due to limitations in current approaches, the dynamics of cardiomyocyte hypertrophic responses remain poorly characterized. In this study, we evaluate the application of the Holomonitor M4 digital holographic imaging microscope to track dynamic changes in cardiomyocyte surface area and volume in response to norepinephrine treatment, a model hypertrophic stimulus. The Holomonitor M4 permits non-invasive, label-free imaging of three-dimensional changes in cell morphology with minimal phototoxicity, thus enabling long-term imaging studies. Untreated and norepinephrine-stimulated primary neonatal rat cardiomyocytes were live-imaged on the Holomonitor M4, which was followed by image segmentation and single-cell tracking using the HOLOMONITOR App Suite software version 4.0.1.546. The 24 h treatment of cultured cardiomyocytes with norepinephrine increased cardiomyocyte spreading and optical volume as expected, validating the reliability of the approach. Single-cell tracking of both cardiomyocyte surface area and three-dimensional optical volume revealed dynamic increases in these parameters throughout the 24 h imaging period, demonstrating the potential of this technology to explore cardiomyocyte hypertrophic responses with greater temporal resolution; however, technological limitations were also observed and should be considered in the experimental design and interpretation of results. Overall, leveraging the unique advantages of the Holomonitor M4 digital holographic imaging system has the potential to empower future work towards understanding the molecular and cellular mechanisms underlying cardiomyocyte hypertrophy with enhanced temporal clarity.

Automated analysis of human cardiomyocytes dynamics with holographic image-based tracking for cardiotoxicity screening

This paper proposes a new non-invasive, low-cost, and fully automated platform to quantitatively analyze dynamics of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) at the single-cell level by holographic image-based tracking for cardiotoxicity screening. A dense Farneback optical flow method and holographic imaging informatics were combined to characterize the contractile motion of a single CM, which obviates the need for costly equipment to monitor a CM's mechanical beat activity. The reliability of the proposed platform was tested by single-cell motion characterization, synchronization analysis, motion speed measurement of fixed CMs versus live CMs, and noise sensitivity. The applicability of the motion characterization method was tested to determine the pharmacological effects of two cardiovascular drugs, isoprenaline (166 nM) and E-4031 (500 μM). The experiments were done using single CMs and multiple cells, and the results were compared to control conditions. Cardiomyocytes responded to isoprenaline by increasing the action potential (AP) speed and shortening the resting period, thus increasing the beat frequency. In the presence of E-4031, the AP speed was decreased, and the resting period was prolonged, thus decreasing the beat frequency. The findings offer insights into single hiPS-CMs' contractile motion and a deep understanding of their kinetics at the single-cell level for cardiotoxicity screening.

High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks

SIGNIFICANCE

Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell level in biomedical images for biomarker discovery and disease diagnostics. However, the biological cell analysis based on phase information of images is inefficient due to the complexity of numerical phase reconstruction algorithm applied to raw hologram images. New cell study methods based on diffraction pattern directly are desirable.

AIM

Deep fully convolutional networks (FCNs) were developed on raw hologram images directly for high-throughput label-free cell detection and counting to assist the biological cell analysis in the future.

APPROACH

The raw diffraction patterns of RBCs were recorded by use of DHM. Ground-truth mask images were labeled based on phase images reconstructed from RBC holograms using numerical reconstruction algorithm. A deep FCN, which is UNet, was trained on the diffraction pattern images to achieve the label-free cell detection and counting.

RESULTS

The implemented deep FCNs provide a promising way to high-throughput and label-free counting of RBCs with a counting accuracy of 99% at a throughput rate of greater than 288 cells per second and 200  μm  ×  200  μm field of view at the single cell level. Compared to convolutional neural networks, the FCNs can get much better results in terms of accuracy and throughput rate.

CONCLUSIONS

High-throughput label-free cell detection and counting were successfully achieved from diffraction patterns with deep FCNs. It is a promising approach for biological specimen analysis based on raw hologram directly.

Diffraction-based technology for the monitoring of contraction dynamics in 3D and 2D tissue models

We present a novel optical device developed for the monitoring of dynamic behavior in extended 3D-tissue models in various culture environments based on variations in their speckle patterns. The results presented point out the benefit of the technology in terms of detection, accuracy, sensitivity and a reasonable read-out speed as well as reproducibility for the measurements and monitoring of cardiac contractions. We show that the optical read-out technology is suitable for long time monitoring and for drug screening. The method is discussed and compared to other techniques, in particular calcium imaging. The device is flexible and easily adaptable to 2D and 3D-tissue model screenings using different culture environments. The technology can be parallelized for automated read-out of different multi-well-plate formats.

Automated quantification study of human cardiomyocyte synchronization using holographic imaging

This paper investigates the rhythm strip and parameters of synchronization of human induced pluripotent stem cell (iPS) derived cardiomyocytes. The synchronization is evaluated from quantitative phase images of beating cardiomyocytes which are obtained using the time-lapse digital holographic imaging method. By quantitatively monitoring the dry mass redistribution, digital holography provides the physical contraction-relaxation signal caused by autonomous cardiac action potential. In order to analyze the synchronicity at the cell-to-cell level, we extracted single cardiac muscle cells, which contain the nuclei, from the phase images of cardiomyocytes containing multiple cells resulting from the fusion of k-means clustering and watershed segmentation algorithms. We demonstrate that mature cardiomyocyte cell synchronization can be automatically evaluated by time-lapse microscopic holographic imaging. Our proposed method can be applied for studies on cardiomyocyte disorders and drug safety testing systems.