Digital holographic microscopy in label-free analysis of cultured cells' response to photodynamic treatment

Screening for urothelial carcinoma cells in urine based on digital holographic flow cytometry through machine learning and deep learning methods

The incidence of urothelial carcinoma continues to rise annually, particularly among the elderly. Prompt diagnosis and treatment can significantly enhance patient survival and quality of life. Urine cytology remains a widely-used early screening method for urothelial carcinoma, but it still has limitations including sensitivity, labor-intensive procedures, and elevated cost. In recent developments, microfluidic chip technology offers an effective and efficient approach for clinical urine specimen analysis. Digital holographic microscopy, a form of quantitative phase imaging technology, captures extensive data on the refractive index and thickness of cells. The combination of microfluidic chips and digital holographic microscopy facilitates high-throughput imaging of live cells without staining. In this study, digital holographic flow cytometry was employed to rapidly capture images of diverse cell types present in urine and to reconstruct high-precision quantitative phase images for each cell type. Then, various machine learning algorithms and deep learning models were applied to categorize these cell images, and remarkable accuracy in cancer cell identification was achieved. This research suggests that the integration of digital holographic flow cytometry with artificial intelligence algorithms offers a promising, precise, and convenient approach for early screening of urothelial carcinoma.

Computational multi-wavelength phase synthesis using convolutional neural networks [Invited]

Multi-wavelength digital holographic microscopy (MWDHM) provides indirect measurements of the refractive index for non-dispersive samples. Successive-shot MWDHM is not appropriate for dynamic samples and single-shot MWDHM significantly increases the complexity of the optical setup due to the need for multiple lasers or a wavelength tunable source. Here we consider deep learning convolutional neural networks for computational phase synthesis to obtain high-speed simultaneous phase estimates on different wavelengths and thus single-shot estimates of the integral refractive index without increased experimental complexity. This novel, to the best of our knowledge, computational concept is validated using cell phantoms consisting of internal refractive index variations representing cytoplasm and membrane-bound organelles, respectively, and a simulation of a realistic holographic recording process. Specifically, in this work we employed data-driven computational techniques to perform accurate dual-wavelength hologram synthesis (hologram-to-hologram prediction), dual-wavelength phase synthesis (unwrapped phase-to-phase prediction), direct phase-to-index prediction using a single wavelength, hologram-to-phase prediction, and 2D phase unwrapping with sharp discontinuities (wrapped-to-unwrapped phase prediction).

Significant difference in response of malignant tumor cells of individual patients to photodynamic treatment as revealed by digital holographic microscopy

The investigation of in-vitro response of cell cultures derived from tumor material of individual patients with similar tumor localizations to photodynamic treatment is presented. Tumor types included in the research were renal cell carcinoma, melanoma and alveolar, synovial, lypo- and osteo- sarcomas. Long-term observations of treatment-induced morphological changes in cells were performed by means of digital holographic microscopy. A substantial variance in response of cells of individual patients with similar tumor types and localizations to photodynamic treatment with the same dose has been observed. These peculiarities are indicative of the demand to personalized protocols of photodynamic treatment. The elevated resistance of cells of some patients to treatment at high doses highlights potential limitations of photodynamic therapy for some patients. Digital holographic microscopy is shown to be an informative label-free noninvasive tool allowing for long-term monitoring of cell samples in vitro and providing quantitative information on necrosis rate and loss of cellular dry mass. The developed methodology can be generalized for analysis of cellular response to various therapies.

Sensing morphogenesis of bone cells under microfluidic shear stress by holographic microscopy and automatic aberration compensation with deep learning

We present sensing time-lapse morphogenesis of living bone cells under micro-fluidic shear stress (FSS) by digital holographic (DH) microscopy. To remove the effect of aberrations on quantitative measurements, we propose a numerical and automatic method to compensate for aberrations based on a convolutional neural network (CNN). For the first time, the aberration compensation issue is considered as a regression task where optimal coefficients for constructing the phase aberration map act as responses corresponding to the input aberrated phase image. We adopted tens of thousands of living cells' phase images reconstructed from digital holograms for training the CNN. The experiments demonstrate that, based on the trained network, phase aberrations can be totally removed in real-time without any hypothesis of object and aberration phase, knowledge of the setup's physical parameters, and the operation of selecting background regions; hence, the morphogenesis of the bone cells under FSS is accurately detected and quantitatively analyzed. The results show that the proposed method could provide a highly efficient and versatile way to investigate the effects of micro-FSS on living biological cells in microfluidic lab-on-chip platforms thanks to the combination of phase-contrast label-free microcopy with artificial intelligence.

Displacement and strain mapping for osteocytes under fluid shear stress using digital holographic microscopy and digital image correlation

Osteocytes, as the mechano-sensors in bone, are always subjected to fluid shear stress (FSS) from the surrounding matrix. Quantification of FSS-induced cellular deformation is significant for clarifying the "perceive and transmit" process of cellular mechanotransduction. In this research, a label-free displacement and strain mapping method based on digital holographic microscopy (DHM) and digital image correlation (DIC) is introduced. The method, which is termed DHM-DIC, innovatively utilizes surface features extracted from holographic phase images instead of speckles as the metric for DIC searching. Simulation results on a hemisphere validate the feasibility of DHM-DIC. Displacement and strain maps of living osteocytes under 1.5 Pa FSS are evaluated from DHM-DIC and present good agreement with our previous finite element modeling results.

In vitro monitoring of photoinduced necrosis in HeLa cells using digital holographic microscopy and machine learning

Digital holographic microscopy supplemented with the developed cell segmentation and machine learning and classification algorithms is implemented for quantitative description of the dynamics of cellular necrosis induced by photodynamic treatment in vitro. It is demonstrated that the developed algorithms operating with a set of optical, morphological, and physiological parameters of cells, obtained from their phase images, can be used for automatic distinction between live and necrotic cells. The developed classifier provides high accuracy of about 95.5% and allows for calculation of survival rates in the course of cell death.

Quantitative assessment of changes in cellular morphology at photodynamic treatment in vitro by means of digital holographic microscopy

Temporal dependence of changes in the morphological characteristics of cells of two cultured lines of cancer origin, HeLa and A549, induced by photodynamic treatment with Radachlorin photosensitizer, have been monitored using digital holographic microscopy during first two hours after short-term irradiation. The observed post-treatment early dynamics of the phase shift in the transmitted wavefront indicated several distinct scenarios of cell behavior depending upon the irradiation dose. In particular the phase shift increased at low doses, which can be associated with apoptosis, while at high doses it decreased, which can be associated with necrosis. As shown, the two cell types responded differently to similar irradiation doses. Although the sequence of death scenarios with the increase of the irradiation dose was the same, each scenario was realized at substantially different doses. These findings suggest that the average phase shift of the transmitted wavefront can be used for quantitative non-invasive cell death characterization. The conclusions made were cofirmed by commonly used test assays using confocal fluorescent microscopy.

Multimodal Optical Diagnostics of Glycated Biological Tissues

Diabetes mellitus is a metabolic disorder characterized by chronic hyperglycemia accompanied by the disruption of carbohydrate, lipid, and proteins metabolism and development of long-term microvascular, macrovascular, and neuropathic changes. This review presents the results of spectroscopic studies on the glycation of tissues and cell proteins in organisms with naturally developing and model diabetes and in vitro glycated samples in a wide range of electromagnetic waves, from visible light to terahertz radiation. Experiments on the refractometric measurements of glycated and oxygenated hemoglobin in broad wavelength and temperature ranges using digital holographic microscopy and diffraction tomography are discussed, as well as possible application of these methods in the diabetes diagnostics. It is shown that the development and implementation of multimodal approaches based on a combination of phase diagnostics with other methods is another promising direction in the diabetes diagnostics. The possibilities of using optical clearing agents for monitoring the diffusion of substances in the glycated tissues and blood flow dynamics in the pancreas of animals with induced diabetes have also been analyzed.

Morphological changes in the ovarian carcinoma cells of Wistar rats induced by chemotherapy with cisplatin and dioxadet

The development of new express methods for the analysis of the efficacy of anti-cancer therapy on the cellular level is highly desirable for the analysis of chemotherapeutic agent performance. In this paper we suggest the use of parameters of cell morphology determined by holographic microscopy and tomography for the effective label free quantitative analysis of cell viability under antitumor chemotherapy and thus of cytostatic agent efficacy. As shown, measured phase shifts and cell morphology change dramatically as a result of chemotherapy and depend strongly on the cell type and agent applied. Experimentally, a comparative analysis of the antitumor efficacy of the two cytostatics, cisplatin and dioxadet, that are commonly used for chemotherapy of disseminated ovarian carcinoma has been performed. The experiments were carried out on the Wistar rat model. An essential difference in the morphology of cells, both normal (erythrocytes) and cancerous, present in ascitic fluid taken from the non-treated group of rats and the groups treated with either dioxadet or cisplatin, has been observed. The results obtained can be interpreted as an indication of the antitumor performance of both cytostatics at the cellular level and as a demonstration of the higher efficacy of therapy with dioxadet as compared to that with cisplatin. Differences in cell morphology are suggested to be applied as quantitative markers of cell viability and cytostatic agent efficacy. The conclusions made are supported by a comparison with the results of recent experiments based on survival rates of laboratory animals treated with these agents..

Super-resolution microscopy for biological specimens: lensless phase retrieval in noisy conditions

The paper is devoted to a computational super-resolution microscopy. A complex-valued wavefront of a transparent biological cellular specimen is restored from multiple intensity diffraction patterns registered with noise. For this problem, the recently developed lensless super-resolution phase retrieval algorithm [Optica, 4(7), 786 (2017)] is modified and tuned. This algorithm is based on a random phase coding of the wavefront and on a sparse complex-domain approximation of the specimen. It is demonstrated in experiments, that the reliable phase and amplitude imaging of the specimen is achieved for the low signal-to-noise ratio provided a low dynamic range of observations. The filterings in the observation domain and specimen variables are specific features of the applied algorithm. If these filterings are omitted the algorithm becomes a super-resolution version of the standard iterative phase retrieval algorithms. In comparison with this simplified algorithm with no filterings, our algorithm shows a valuable improvement in imaging with much smaller number of observations and shorter exposure time. In this way, presented algorithm demonstrates ability to work in a low radiation photon-limited mode.

Phase sensitivity of off-axis digital holography

In off-axis digital holography, the Fourier transform-based algorithm is commonly used for signal processing. Here, we derive the theoretical phase sensitivity of this algorithm, which can be calculated from a single 2D hologram. This algorithm sensitivity represents the best achievable sensitivity of a system using this algorithm. Our derivation treats the signal in its most general form, considering non-uniform illumination and the effect of sideband filtering. As a result, phase sensitivity varies spatially, determined by local signal-to-noise ratio. Sensitivity expressions for both shot noise and uniform noise models are given. These results are validated with simulations and experiments. Significantly, this theoretical sensitivity can serve as a baseline metric for assessing performance of a phase-imaging system, such as experimental sensitivity and hardware stability, which are critical for high-sensitivity quantitative phase imaging. In addition, the results are equally applicable to other interferometric techniques with similar interferogram patterns and signal processing algorithms.

Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning

The noninvasive, fast acquisition of quantitative phase maps using digital holographic microscopy (DHM) allows tracking of rapid cellular motility on transparent substrates. On two-dimensional surfaces in vitro, MDA-MB-231 cancer cells assume several morphologies related to the mode of migration and substrate stiffness, relevant to mechanisms of cancer invasiveness in vivo. The quantitative phase information from DHM may accurately classify adhesive cancer cell subpopulations with clinical relevance. To test this, cells from the invasive breast cancer MDA-MB-231 cell line were cultured on glass, tissue-culture treated polystyrene, and collagen hydrogels, and imaged with DHM followed by epifluorescence microscopy after staining F-actin and nuclei. Trends in cell phase parameters were tracked on the different substrates, during cell division, and during matrix adhesion, relating them to F-actin features. Support vector machine learning algorithms were trained and tested using parameters from holographic phase reconstructions and cell geometric features from conventional phase images, and used to distinguish between elongated and rounded cell morphologies. DHM was able to distinguish between elongated and rounded morphologies of MDA-MB-231 cells with 94% accuracy, compared to 83% accuracy using cell geometric features from conventional brightfield microscopy. This finding indicates the potential of DHM to detect and monitor cancer cell morphologies relevant to cell cycle phase status, substrate adhesion, and motility. © 2017 International Society for Advancement of Cytometry.

Quantitative observations on cytoskeleton changes of osteocytes at different cell parts using digital holographic microscopy

Cytoskeletons such as F-actin have different distributions in different cell parts and they are the cause of different degrees of cell collapse when the F-actin is disrupted. It is challenging to use conventional methods such as fluorescence microscopy and atomic force microscopy to conduct real-time and three-dimensional observations on the dynamic processes at different cell parts due to the slow measuring speed and the need for live-cell staining. In this study, the morphological variations of different bone cell parts caused by F-actin disruption are dynamically measured by using digital holographic microscopy (DHM). We separately analyze local parameters (cell height and cell width) and global parameters (cell projected area and cell volume) of cells to address variations of specific cell areas and quantify the changing process of the whole cell. We found significant differences in temporal variations of both local and global cell parameters between the cell body and cell process, which is consistent with the qualitative observation by fluorescence staining. Our study not only validates the unique ability of DHM to simultaneously investigate the dynamic process at different cell parts, but also provides sufficient experimental bases for exploring the mechanism for F-actin disruption.

Tunable time-resolved tick-tock pulsed digital holographic microscopy for ultrafast events

This study presents a time-resolved imaging technique for detecting ultrafast events in the sample with tunable tick-tock pulses in common-path digital holographic microscopy. The tick-tock pulses are generated from the same single femtosecond pulse source and manipulated through a spatial-multiplexing encoding/decoding scheme with two complementary binary codes for digital hologram recording and reconstruction. The spatial-multiplexing encoding/decoding scheme with compressive sensing on the Fresnel digital hologram is used to recover missing data and achieve high-fidelity wavefront reconstruction. The elapsed time of tick-tock pulses can be adjusted by changing the optical path difference between the pulse pair arms for dynamic observation at various timescales. The proposed method is applied to explore the ultrafast physical phenomenon by quantitative phase imaging with a stopwatch-like tunable timescale ranging from nanoseconds to femtoseconds.

Time-resolved spectral analysis of Radachlorin luminescence in water

We report results of spectral- and time-resolved study of Radachlorin photosensitizer luminescence in water in the spectral range of 950-1350nm and for determination of the photosensitizer triplet state and the singlet oxygen lifetimes responsible for singlet oxygen generation and degradation. At any wavelength within the explored spectral range the luminescence decay contained two major contributions: a fast decay at the ns time scale and a slow evolution at the μs time scale. The fast decay was attributed to electric dipole fluorescence transitions in photosensitizer molecules and the slow evolution to intercombination phosphorescence transitions in singlet oxygen and photosensitizer molecules. Relatively high-amplitude ns peak observed at all wavelengths suggests that singlet oxygen monitoring with spectral isolation methods alone, without additional temporal resolution can be controversial. In the applied experimental conditions the total phosphorescence signal at any wavelength contained a contribution from the photosensitizer triplet state decay, while at 1274nm the singlet oxygen phosphorescence dominated. The results obtained can be used for optimization of the methods of singlet oxygen monitoring and imaging.