Digital Holographic Microscopy Research Articles

DL-CSPF: Deep learning-based cell segmentation with a physical framework for digital holographic microscopy

DL-CSPF: Deep learning-based cell segmentation with a physical framework for digital holographic microscopy

Morphological and Optical Profiling of Melanocytes and SK-MEL-28 Melanoma Cells Via Digital Holographic Microscopy and Quantitative Phase Imaging.

Melanoma, which originates from pigment-producing melanocytes, is an aggressive and deadly skin cancer. Despite extensive research, its mechanisms of progression and metastasis remain unclear. This study uses quantitative phase imaging via digital holographic microscopy, Principal Component Analysis (PCA), and t-distributed Stochastic Neighbor Embedding (t-SNE) to identify the morphological, optical, and behavioral differences between normal melanocytes and SK-MEL-28 melanoma cells. Our findings reveal significant differences in cell shape, size, and internal organization, with SK-MEL-28 cells displaying greater size variability, more polygonal shapes, and higher optical thickness. Phase shift parameters and surface roughness analyses underscore melanoma cells' uniform and predictable textures. Violin plots highlight the dynamic and varied migration of SK-MEL-28 cells, contrasting with the localized movement of melanocytes. Hierarchical clustering of correlation matrices provides further insights into complex morphological and optical relationships. Integrating label-free imaging with robust analytical methods enhances understanding of melanoma's aggressive behavior, supporting targeted therapies and highlighting potential biomarkers for precise melanoma diagnostics and treatment.

Biophysical and Morphological Cell Features Retrieved by Digital Holographic Microscopy Correlate with Drug-Induced Changes in Cell Migration Behavior.

Quantitative analysis of cancer cell migration is critical for developing effective therapies to curb cancer metastasis. However, traditional methods are time-consuming and labor-intensive and lack quantitative capabilities. Cell volume change, a key physiological indicator of cell migration, is directly linked to phase change. In this work, we have developed a model that connects phase features from digital holographic microscopy (DHM) with cell healrate values from the wound healing assay. This approach aims to provide a rapid and quantitative evaluation of the breast cancer cell migration capability. Using DHM, six phase features of 231 cells treated with varying drug concentrations were extracted. It was observed that the rate of change of these phase features, termed characteristic parameters, showed a high linear correlation with cell healrate values from wound healing assays. Based on these linear correlations, a composite coefficient was derived by linearly combining the characteristic parameters of the six phase features. This composite coefficient was then linearly correlated with the cell healrate values to create a correlation model. This model establishes a strong connection between DHM-extracted morphological/biophysical features and cell migration metrics from a complementary assay. It provides a new, rapid, and quantitative method for assessing cancer cell migration in vitro and delivering valuable insights for cancer research.

Optical property discrepancies found between healthy and unhealthy skin cells using digital holographic microscopy with three wavelengths

Cancer and other health disorders can be differentiated by changes in cell optical properties such as their refractive index, thickness, and topology (height and width). Here, we employ three wavelengths simultaneously in digital holographic microscopy (3λ-DHM) to visualize the whole cell topology as 3D images through a numerical reconstruction algorithm applied to a hologram. By identifying the cell state and the changes in its optical properties, it is possible to discern between healthy and unhealthy cells. The simultaneous use of three wavelengths provides a rapid and straightforward quantitative reconstruction of the whole cell without the need for an unwrapping algorithm. This is a benefit over traditional methods, which often require complicated procedures. The performance of the approach was first validated in a known sample, a silicon dioxide thin film, where we were able to corroborate its refractive index with the values reported in the literature. Then the method was applied to fixed skin cells finding a refractive index of 1.3443 for healthy cells and 1.3246 for cells found in tumor tissue. We discuss and highlight differences based on the refractive index to demonstrate that the employed process can provide reliable information to distinguish characteristics between healthy and unhealthy cells.

Cumulative learning based segmentation aided cell mixtures classification in digital holographic microscopy

The tumor microenvironment consists of several components like tumor cells, normal healthy cells, extracellular matrix, secreted factors, and other non-cellular components. The interaction among these components is crucial for tumor progression and metastasis. Identifying the presence of tumor cells among the normal healthy cells during the initial stages of cancer will aid in its early detection and treatment. One of the main features that differentiates a cancer cell from a normal healthy cell distinctively is its morphology. In this study, the effectiveness of machine learning algorithms is evaluated in classifying cells co-cultured in a Petri dish. To achieve this, digital holographic microscopy is used to capture the quantitative phase images of human dermal fibroblast and A375 human melanoma cancer cells co-cultured at 1:1, 1:2, and 2:1 ratios. Cell segmentation was performed using a cumulative learning approach. Subsequently, morphological and phase-based features were extracted to train the machine learning algorithms for binary classification of cells. Phase-based features in addition to the morphological features were found to provide improved performance of the classifier. The classifier demonstrated an average F1-score of 0.9 during testing.

Digital Holographic Microscopy in Veterinary Medicine-A Feasibility Study to Analyze Label-Free Leukocytes in Blood and Milk of Dairy Cows.

For several years, the determination of a differential cell count of a raw milk sample has been proposed as a more accurate tool for monitoring the udder health of dairy cows compared with using the absolute somatic cell count. However, the required sample preparation and staining process can be labor- and cost-intensive. Therefore, the aim of our study was to demonstrate the feasibility of analyzing unlabeled blood and milk leukocytes from dairy cows by means of digital holographic microscopy (DHM). For this, we trained three different machine learning methods, i.e., k-Nearest Neighbor, Random Forests, and Support Vector Machine, on sorted leukocyte populations (granulocytes, lymphocytes, and monocytes/macrophages) isolated from blood and milk samples of three dairy cows by using fluorescence-activated cell sorting. Afterward, those classifiers were applied to differentiate unlabeled blood and milk samples analyzed by DHM. A total of 70 blood and 70 milk samples were used. Those samples were collected from five clinically healthy cows at 14-time points within a study period of 26 days. The outcome was compared with the results of the same samples analyzed by flow cytometry and (in the case of blood samples) also to routine analysis in an external laboratory. Moreover, a standard vaccination was used as an immune stimulus during the study to check for changes in cell morphology or cell counts. When applied to isolated leukocytes, Random Forests performed best, with a specificity of 0.93 for blood and 0.84 for milk cells and a sensitivity of 0.90 and 0.81, respectively. Although the results of the three analytical methods differed, it could be demonstrated that a DHM analysis is applicable for blood and milk leukocyte samples with high reliability. Compared with the flow cytometric results, Random Forests showed an MAE of 0.11 (SD = 0.04), an RMSE of 0.13 (SD = 0.14), and an MRE of 1.00 (SD = 1.11) for all blood leukocyte counts and an MAE of 0.20 (SD = 0.11), an RMSE of 0.21 (SD = 0.11) and an MRE of 1.95 (SD = 2.17) for all milk cell populations. Further studies with larger sample sizes and varying immune cell compositions are required to establish method-specific reference ranges.

An integrated portable system for laser speckle contrast imaging and digital holographic microscopy

A multimodal quantitative phase imaging platform combining digital holographic microscopy (DHM) with laser speckle contrast imaging (LSCI) is demonstrated for imaging weakly scattering and transparent samples in a label-free manner. It uses the principle of coherence of the light source for interferometric detection of phase of transparent and semi-transparent samples. The speckle formation resulting from the coherence property of the laser source is used to track dynamic activities in the regions of interest in the sample. Integration of these two techniques onto a microfluidic chip leads to an optofluidic real-time microscope for live cell imaging applications. In this work, we have developed an integrated multimodal system combining digital holographic microscopy and laser speckle contrast imaging system for Optofluidics and in vitro studies. The sample flowing through the microfluidic channel is imaged to record holograms and video of intensity images at the same time. This enables to map the flow within a microfluidic channel and quantify the channel as well as the particle flow through the channel. The channel morphology along with the particles flowing through the channel are quantified using DHM. The two-dimensional speckle contrast images map the flow of the dynamic microbeads and cells with very high contrast in almost real time. A low-cost portable multimodal quantitative phase microscope combining DHM and LSCI has been demonstrated for real time imaging with applications in optofluidics.

Digital holographic microscopy is suitable for lipid accumulation analysis in single cells of Yarrowia lipolytica

Digital holographic microscopy is suitable for lipid accumulation analysis in single cells of Yarrowia lipolytica

Robotic manipulation of cardiomyocytes to identify gap junction modifiers for arrhythmogenic cardiomyopathy.

Arrhythmogenic cardiomyopathy (ACM) is a leading cause of sudden cardiac death among young adults. Aberrant gap junction remodeling has been linked to disease-causative mutations in plakophilin-2 (PKP2). Although gap junctions are a key therapeutic target, measurement of gap junction function in preclinical disease models is technically challenging. To quantify gap junction function with high precision and high consistency, we developed a robotic cell manipulation system with visual feedback from digital holographic microscopy for three-dimensional and label-free imaging of human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). The robotic system can accurately determine the dynamic height changes in the cells' contraction and resting phases, microinject drug-treated healthy and diseased iPSC-CMs in their resting phase with constant injection depth across all cells, and deposit a membrane-impermeable dye that solely diffuses between cells through gap junctions for measuring the gap junction diffusion function. The robotic system was applied toward a targeted drug screen to identify gap junction modulators and potential therapeutics for ACM. Five compounds were found to dose-dependently enhance gap junction permeability in cardiomyocytes with PKP2 knockdown. In addition, PCO 400 (pinacidil) reduced beating irregularity in a mouse model of ACM expressing mutant PKP2 (R735X). These results highlight the utility of the robotic cell manipulation system to efficiently assess gap junction function in a relevant preclinical disease model, thus providing a technique to advance drug discovery for ACM and other gap junction-mediated diseases.

Imaging of Live Cells by Digital Holographic Microscopy

Imaging of microscopic objects is of fundamental importance, especially in life sciences. Recent fast progress in electronic detection and control, numerical computation, and digital image processing, has been crucial in advancing modern microscopy. Digital holography is a new field in three-dimensional imaging. Digital reconstruction of a hologram offers the remarkable capability to refocus at different depths inside a transparent or semi-transparent object. Thus, this technique is very suitable for biological cell studies in vivo and could have many biomedical and biological applications. A comprehensive review of the research carried out in the area of digital holographic microscopy (DHM) for live-cell imaging is presented. The novel microscopic technique is non-destructive and label-free and offers unmatched imaging capabilities for biological and bio-medical applications. It is also suitable for imaging and modelling of key metabolic processes in living cells, microbial communities or multicellular plant tissues. Live-cell imaging by DHM allows investigation of the dynamic processes underlying the function and morphology of cells. Future applications of DHM can include real-time cell monitoring in response to clinically relevant compounds. The effect of drugs on migration, proliferation, and apoptosis of abnormal cells is an emerging field of this novel microscopic technique.

Editorial: Advances in digital holographic microscopy and applications

Editorial: Advances in digital holographic microscopy and applications

Advancing early detection of biological events by digital holographic microscopy and simulation of microorganisms

There is a global need to advance bio-aerosol sensing for CBRN (Chemical, Biological, Radiological, and Nuclear) applications by compact and cost-effective devices. Employing digital holographic microscopy (DHM) and deep learning, we developed a system called HoloZcan to automate the analysis of airborne microbial pathogens and particles. DHM provides valuable information, but obtaining data from biological specimens for robust investigations is challenging. This paper introduces a custom simulation approach using the open-source software Meep and the finite-difference time-domain (FDTD) method to overcome limitations of existing Mie-based simulators, especially when dealing with complex microbial shapes. The simulation tool enables the modelling of specific microorganisms, offering a safer and more flexible alternative for CBRN research by bypassing ethical and logistical constraints associated with live pathogens. The study details the simulation workflow, built upon the construction of a database of optical properties of biological materials, for realistic simulations of light-microbe interactions. Evaluations on homogeneous and non-homogeneous objects demonstrate the tool’s limited intrinsic errors and superior sensitivity to refractive index changes compared to traditional Mie-based simulations. This work significantly advances our capability to accurately simulate and analyse CBRN-related scenarios, enhancing comprehensive research in bio-aerosol sensing.Graphical abstract

Quantitative phase imaging verification in large field-of-view lensless holographic microscopy via two-photon 3D printing

Large field-of-view (FOV) microscopic imaging (over 100 mm2) with high lateral resolution (1–2 μm) plays a pivotal role in biomedicine and biophotonics, especially within the label-free regime. Lensless digital holographic microscopy (LDHM) is promising in this context but ensuring accurate quantitative phase imaging (QPI) in large FOV LDHM is challenging. While phantoms, 3D printed by two-photon polymerization (TPP), have facilitated testing small FOV lens-based QPI systems, an equivalent evaluation for lensless techniques remains elusive, compounded by issues such as twin-image and beam distortions, particularly towards the detector’s edges. Here, we propose an application of TPP over large area to examine phase consistency in LDHM. Our research involves fabricating widefield phase test targets with galvo and piezo scanning, scrutinizing them under single-shot twin-image corrupted conditions and multi-frame iterative twin-image minimization scenarios. By measuring the structures near the detector’s edges, we verified LDHM phase imaging errors across the entire FOV, with less than 12% phase value difference between areas. Our findings indicate that TPP, followed by LDHM and Linnik interferometry cross-verification, requires new design considerations for precise large-area photonic manufacturing. This research paves the way for quantitative benchmarking of large FOV lensless phase imaging, enhancing understanding and further development of LDHM technique.

High-resolution lensless holographic microscopy using a physics-aware deep network.

Lensless digital inline holographic microscopy (LDIHM) is an emerging quantitative phase imaging modality that uses advanced computational methods for phase retrieval from the interference pattern. The existing end-to-end deep networks require a large training dataset with sufficient diversity to achieve high-fidelity hologram reconstruction. To mitigate this data requirement problem, physics-aware deep networks integrate the physics of holography in the loss function to reconstruct complex objects without needing prior training. However, the data fidelity term measures the data consistency with a single low-resolution hologram without any external regularization, which results in a low performance on complex biological data. We aim to mitigate the challenges with trained and physics-aware untrained deep networks separately and combine the benefits of both methods for high-resolution phase recovery from a single low-resolution hologram in LDIHM. We propose a hybrid deep framework (HDPhysNet) using a plug-and-play method that blends the benefits of trained and untrained deep models for phase recovery in LDIHM. The high-resolution phase is generated by a pre-trained high-definition generative adversarial network (HDGAN) from a single low-resolution hologram. The generated phase is then plugged into the loss function of a physics-aware untrained deep network to regulate the complex object reconstruction process. Simulation results show that the SSIM of the proposed method is increased by 0.07 over the trained and 0.04 over the untrained deep networks. The average phase-SNR is elevated by 8.2dB over trained deep models and 9.8dB over untrained deep networks on the experimental biological cells (cervical cells and red blood cells). We showed improved performance of the HDPhysNet against the unknown perturbation in the imaging parameters such as the propagation distance, the wavelength of the illuminating source, and the imaging sample compared with the trained network (HDGAN). LDIHM, combined with HDPhysNet, is a portable and technology-driven microscopy best suited for point-of-care cytology applications.

Visualizing the fine structure and dynamics of living cells with time-lapse polychromatic digital holographic microscopy

Visualizing the fine structure and dynamics of living cells with time-lapse polychromatic digital holographic microscopy

3D morphometry of endothelial cells angiogenesis in an extracellular matrix composite hydrogel

Human umbilical vein endothelial cells (HUVECs) play a fundamental role in angiogenesis. Herein, we introduce digital holographic microscopy (DHM) for the 3D quantitative morphological analysis of HUVECs in extracellular matrix (ECM)-based biomaterials as an angiogenesis model. The combination of volumetric information from DHM and the physicochemical and cytobiocompatibility data provided by fluorescence microscopy and cytology offers a comprehensive understanding of the angiogenesis-related parameters of HUVECs within the ECM. DHM enables label-free, non-contact, and non-invasive 3D monitoring of living samples in real time, in a quantitative manner. In this study, the human amniotic membrane (HAM) is decellularized, pulverized, and combined with sodium alginate hydrogel to provide an in vitro substrate for modeling HUVEC angiogenesis. Our results demonstrate that modifying alginate hydrogel with HAM enhances its biofunctionality due to the presence of ECM components. Moreover, the DHM results reveal an increase in its porous properties, which, in turn, aids in interpreting the tubulation results.

Real-time morphometric analysis of targeted therapy for neuroblastoma cells in monolayer and 3D hydrogels using digital holographic microscopy

Real-time morphometric analysis of targeted therapy for neuroblastoma cells in monolayer and 3D hydrogels using digital holographic microscopy

Self‐Assembled Biconvex Microlens Array Using Chiral Ferroelectric Nematic Liquid Crystals

AbstractRecently, it is shown (Popov et al, Sci. Rep, 2017, 7, 1603) that chiral nematic liquid crystal films adopt biconvex lens shapes underwater, which may explain the formation of insect eyes, but restrict their practical application. Here it is demonstrated that chiral ferroelectric nematic liquid crystals, where the ferroelectric polarization aligns parallel to the air interface, can spontaneously form biconvex lens arrays in air when suspended in submillimeter‐size grids. Using Digital Holographic Microscopy, it is shown that the lens has a paraboloid shape and the curvature radius at the center decreases with increasing chiral dopant concentration, i.e., with decreasing helical pitch. Simultaneous measurements of the imaging properties of the lenses show the focal length depends on the pitch, thus offering tunability. The physical mechanism of formation of the self‐assembled ferroelectric nematic microlenses is also discussed.

STU-Net: Swin Transformer U-Net for high-throughput live cell analysis with a lens-free on-chip digital holographic microscope

STU-Net: Swin Transformer U-Net for high-throughput live cell analysis with a lens-free on-chip digital holographic microscope

Computational vibration mitigation using phase interpolation in digital holographic microscopy for overlay metrology

Digital holographic microscopy retrieves amplitude and phase information of an image which allows us to computationally correct for imperfections in the imaging optics. However, digital holographic microscopy is an interferometric technique that is inherently sensitive to undesired phase variations between object and reference beam. These phase variations lower the fringe contrast if they are integrated over a finite exposure time which leads to a reduced amplitude of the retrieved image. This results in significant errors in applications that rely on a stable and accurate amplitude measurement, such as optical overlay metrology in the semiconductor industry. We present experimental results on a computational vibration mitigation method for the application of overlay metrology using phase interpolation between a sequence of measured holograms and demonstrate its capability to improve metrology precision in an overlay metrology application that uses digital holographic microscopy.