Label-free Quantitative Phase Imaging Research Articles

Label-free functional analysis of root-associated microbes with dynamic quantitative oblique back-illumination microscopy

The increasing global demand for food, coupled with concerns about the environmental impact of synthetic fertilizers, underscores the urgency of developing sustainable agricultural practices. Nitrogen-fixing bacteria, known as diazotrophs, offer a potential solution by converting atmospheric nitrogen into bioavailable forms, reducing the reliance on synthetic fertilizers. However, a deeper understanding of their interactions with plants and other microbes is needed. In this study, we introduce a recently developed label-free 3D quantitative phase imaging technology called dynamic quantitative oblique back-illumination microscopy (DqOBM) to assess the functional dynamic activity of diazotrophs in vitro and in situ. Our experiments involved three different diazotrophs (Sinorhizobium meliloti, Azotobacter vinelandii, and Rahnella aquatilis) cultured on media with amendments of carbon and nitrogen sources. Over 5 days, we observed increased dynamics in nutrient-amended media. These results suggest that the observed bacterial dynamics correlate with their metabolic activity. Furthermore, we applied qOBM to visualize microbial dynamics within the root cap and elongation zone of Arabidopsis thaliana primary roots. This allowed us to identify distinct areas of microbial infiltration in plant roots without the need for fluorescent markers. Our findings demonstrate that DqOBM can effectively characterize microbial dynamics and provide insights into plant-microbe interactions in situ, offering a valuable tool for advancing our understanding of sustainable agriculture.

Label-free cell classification in holographic flow cytometry through an unbiased learning strategy.

Nowadays, label-free imaging flow cytometry at the single-cell level is considered the stepforward lab-on-a-chip technology to address challenges in clinical diagnostics, biology, life sciences and healthcare. In this framework, digital holography in microscopy promises to be a powerful imaging modality thanks to its multi-refocusing and label-free quantitative phase imaging capabilities, along with the encoding of the highest information content within the imaged samples. Moreover, the recent achievements of new data analysis tools for cell classification based on deep/machine learning, combined with holographic imaging, are urging these systems toward the effective implementation of point of care devices. However, the generalization capabilities of learning-based models may be limited from biases caused by data obtained from other holographic imaging settings and/or different processing approaches. In this paper, we propose a combination of a Mask R-CNN to detect the cells, a convolutional auto-encoder, used to the image feature extraction and operating on unlabelled data, thus overcoming the bias due to data coming from different experimental settings, and a feedforward neural network for single cell classification, that operates on the above extracted features. We demonstrate the proposed approach in the challenging classification task related to the identification of drug-resistant endometrial cancer cells.

Platelet aggregates detected using quantitative phase imaging associate with COVID-19 severity

BackgroundThe clinical spectrum of acute SARS-CoV-2 infection ranges from an asymptomatic to life-threatening disease. Considering the broad spectrum of severity, reliable biomarkers are required for early risk stratification and prediction of clinical outcomes. Despite numerous efforts, no COVID-19-specific biomarker has been established to guide further diagnostic or even therapeutic approaches, most likely due to insufficient validation, methodical complexity, or economic factors. COVID-19-associated coagulopathy is a hallmark of the disease and is mainly attributed to dysregulated immunothrombosis. This process describes an intricate interplay of platelets, innate immune cells, the coagulation cascade, and the vascular endothelium leading to both micro- and macrothrombotic complications. In this context, increased levels of immunothrombotic components, including platelet and platelet-leukocyte aggregates, have been described and linked to COVID-19 severity.MethodsHere, we describe a label-free quantitative phase imaging approach, allowing the identification of cell-aggregates and their components at single-cell resolution within 30 min, which prospectively qualifies the method as point-of-care (POC) testing.ResultsWe find a significant association between the severity of COVID-19 and the amount of platelet and platelet-leukocyte aggregates. Additionally, we observe a linkage between severity, aggregate composition, and size distribution of platelets in aggregates.ConclusionsThis study presents a POC-compatible method for rapid quantitative analysis of blood cell aggregates in patients with COVID-19.

Analysis of structural effects of sickle cell disease on brain vasculature of mice using three-dimensional quantitative phase imaging.

Although the molecular origins of sickle cell disease (SCD) have been extensively studied, the effects of SCD on the vasculature-which can influence blood clotting mechanisms, pain crises, and strokes-are not well understood. Improving this understanding can yield insight into the mechanisms and wide-ranging effects of this devastating disease. We aim to demonstrate the ability of a label-free 3D quantitative phase imaging technology, called quantitative oblique back-illumination microscopy (qOBM), to provide insight into the effects of SCD on brain vasculature. Using qOBM, we quantitatively analyze the vasculature of freshly excised, but otherwise unaltered, whole mouse brains. We use Townes sickle transgenic mice, which closely recapitulate the pathophysiology of human SCD, and sickle cell trait mice as controls. Two developmental time points are studied: 6-week-old mice and 20-week-old mice. Quantitative structural and biophysical parameters of the vessels (including the refractive index (RI), which is linearly proportional to dry mass) are extracted from the high-resolution images and analyzed. qOBM reveals structural differences in the brain blood vessel thickness (thinner for SCD in particular brain regions) and the RI of the vessel wall (higher and containing a larger variation throughout the brain for SCD). These changes were only significant in 20-week-old mice. Further, vessel breakages are observed in SCD mice at both time points. The vessel wall RI distribution near these breaks, up to away from the breaking point, shows an erratic behavior characterized by wide RI variations. Vessel diameter, tortuosity, texture within the vessel, and structural fractal patterns are found to not be statistically different. As with vessel breaks, we also observe blood vessel blockages only in mice brains with SCD. qOBM provides insight into the biophysical and structural composition of brain blood vessels in mice with SCD. Data suggest that the RI may be an indirect indicator of vessel rigidity, vessel strength, and/or tensions, which change with SCD. Future ex vivo and in vivo studies with qOBM could improve our understanding of SCD.

Live-cell analysis framework for quantitative phase imaging with slightly off-axis digital holographic microscopy

Label-free quantitative phase imaging is an essential tool for studying in vitro living cells in various research fields of life sciences. Digital holographic microscopy (DHM) is a non-destructive full-field microscopy technique that provides phase images by directly measuring the optical path differences, which facilitates cell segmentation and allows the determination of several important cellular physical features, such as dry mass. In this work, we present a systematic analysis framework for live-cell imaging and morphological characterization, terms as LAF (live-cell analysis framework). All image processing algorithms involved in this framework are implemented on the high-resolution artifact-free quantitative phase images obtained by our previously proposed slightly off-axis holographic system (FPDH) and associated reconstruction methods. A highly robust automated cell segmentation method is applied to extract the valid cellular region, followed by live-cell analysis framework algorithms to determine the physical and morphological properties, including the area, perimeter, irregularity, volume and dry mass, of each individual cell. Experiments on live HeLa cells demonstrate the validity and effectiveness of the presented framework, revealing its potential for diverse biomedical applications.

Sperm-cell DNA fragmentation prediction using label-free quantitative phase imaging and deep learning.

In intracytoplasmic sperm injection (ICSI), a single sperm cell is selected and injected into an egg. The quality of the chosen sperm and specifically its DNA fragmentation have a significant effect on the fertilization success rate. However, there is no method today to measure the DNA fragmentation of live and unstained cells during ICSI. We present a new method to predict the DNA fragmentation of sperm cells using multi-layer stain-free imaging data, including quantitative phase imaging, and lightweight deep learning architectures. The DNA fragmentation ground truth is achieved by staining the cells with acridine orange and imaging them via fluorescence microscopy. Our prediction model is based on the MobileNet convolutional neural network architecture combined with confidence measurement determined by distances between vectors in the latent space. Our results show that the mean absolute error for cells with high prediction confidence is 0.05 and the 90th percentile mean absolute error is 0.1, where the range of DNA fragmentation score is [0,1]. In the future, this model may be applied to improve cell selection by embryologists during ICSI.

Classification of tissue biopsies by Raman spectroscopy guided by quantitative phase imaging and its application to bladder cancer.

We present a multimodal label-free optical measurement approach for analyzing sliced tissue biopsies by a unique combination of quantitative phase imaging and localized Raman spectroscopy. First, label-free quantitative phase imaging of the entire unstained tissue slice is performed using automated scanning. Then, pixel-wise segmentation of the tissue layers is performed by a kernelled structural support vector machine based on Haralick texture features, which are extracted from the quantitative phase profile, and used to find the best locations for performing the label-free localized Raman measurements. We use this multimodal label-free measurement approach for segmenting the urothelium in benign and malignant bladder cancer tissues by quantitative phase imaging, followed by location-guided Raman spectroscopy measurements. We then use sparse multinomial logistic regression (SMLR) on the Raman spectroscopy measurements to classify the tissue types, demonstrating that the prior segmentation of the urothelium done by label-free quantitative phase imaging improves the Raman spectra classification accuracy from 85.7% to 94.7%.

Digital Holographic Microscopy for Label-Free Detection of Leukocyte Alternations Associated with Perioperative Inflammation after Cardiac Surgery.

In a prospective observational pilot study on patients undergoing elective cardiac surgery with cardiopulmonary bypass, we evaluated label-free quantitative phase imaging (QPI) with digital holographic microscopy (DHM) to describe perioperative inflammation by changes in biophysical cell properties of lymphocytes and monocytes. Blood samples from 25 patients were investigated prior to cardiac surgery and postoperatively at day 1, 3 and 6. Biophysical and morphological cell parameters accessible with DHM, such as cell volume, refractive index, dry mass, and cell shape related form factor, were acquired and compared to common flow cytometric blood cell markers of inflammation and selected routine laboratory parameters. In all examined patients, cardiac surgery induced an acute inflammatory response as indicated by changes in routine laboratory parameters and flow cytometric cell markers. DHM results were associated with routine laboratory and flow cytometric data and correlated with complications in the postoperative course. In a subgroup analysis, patients were classified according to the inflammation related C-reactive protein (CRP) level, treatment with epinephrine and the occurrence of postoperative complications. Patients with regular courses, without epinephrine treatment and with low CRP values showed a postoperative lymphocyte volume increase. In contrast, the group of patients with increased CRP levels indicated an even further enlarged lymphocyte volume, while for the groups of epinephrine treated patients and patients with complicative courses, no postoperative lymphocyte volume changes were detected. In summary, the study demonstrates the capability of DHM to describe biophysical cell parameters of perioperative lymphocytes and monocytes changes in cardiac surgery patients. The pattern of correlations between biophysical DHM data and laboratory parameters, flow cytometric cell markers, and the postoperative course exemplify DHM as a promising diagnostic tool for a characterization of inflammatory processes and course of disease.

Live-dead assay on unlabeled cells using phase imaging with computational specificity

Existing approaches to evaluate cell viability involve cell staining with chemical reagents. However, the step of exogenous staining makes these methods undesirable for rapid, nondestructive, and long-term investigation. Here, we present an instantaneous viability assessment of unlabeled cells using phase imaging with computation specificity. This concept utilizes deep learning techniques to compute viability markers associated with the specimen measured by label-free quantitative phase imaging. Demonstrated on different live cell cultures, the proposed method reports approximately 95% accuracy in identifying live and dead cells. The evolution of the cell dry mass and nucleus area for the labeled and unlabeled populations reveal that the chemical reagents decrease viability. The nondestructive approach presented here may find a broad range of applications, from monitoring the production of biopharmaceuticals to assessing the effectiveness of cancer treatments.

Off-Axis Gradient Light Interference Microscopy Enabled by Polarization Gratings Pair for Integrational and Sectional Label-Free Quantitative Phase Imaging

Off-Axis Gradient Light Interference Microscopy Enabled by Polarization Gratings Pair for Integrational and Sectional Label-Free Quantitative Phase Imaging

Label-Free Quantitative Phase Imaging Reveals Spatial Heterogeneity of Extracellular Vesicles in Select Colon Disorders

Three-dimensional (3D) imaging and quantitative analysis of extracellular vesicles (EVs) remain largely unexplored, mainly because of limitations in detection techniques. In this study, EVs from patients diagnosed with colorectal cancer (CRC) and ulcerative colitis were examined. To investigate the spatial heterogeneity and 3D refractive index (RI) distribution of single EVs, a label-free digital holographic tomography technique was used at a submicrometer spatial resolution. The presented image-processing algorithms were used in quantitative analysis with digital staining and 3D visualization, the determination of the EV size distribution and extraction of fractions with different RIs. Reconstructed 3D RI distributions revealed variations in the spatial heterogeneity of EVs related to tissue specificity, such as CRC, normal colonic mucosa, and ulcerative colitis, as well as the isolation procedures used. The RI values of EVs isolated from solid tissues of frozen CRC samples were also dependent on the tumor grade and cancer cell proliferation. The simultaneous examination of cell culture models confirmed the association of the RI of EVs with the tumor grade. 3D-RI data analysis generates new perspectives with the optical, contact-free, label-free examination of the individual EVs. Depending on the specific tissue and isolation method, EVs exhibit significant spatial heterogeneity. The optical parameters of single EVs enabled their classification into two unique subgroups with different RI values.

Multiscale label-free volumetric holographic histopathology of thick-tissue slides with subcellular resolution

Histopathology relies upon the staining and sectioning of biological tissues, which can be laborious and may cause artifacts and distort tissues. We develop label-free volumetric imaging of thick-tissue slides, exploiting refractive index distributions as intrinsic imaging contrast. The present method systematically exploits label-free quantitative phase imaging techniques, volumetric reconstruction of intrinsic refractive index distributions in tissues, and numerical algorithms for the seamless stitching of multiple three-dimensional tomograms and for reducing scattering-induced image distortion. We demonstrated label-free volumetric imaging of thick tissues with the field of view of 2 mm × 1.75 mm × 0.2 mm with a spatial resolution of 170 nm × 170 nm × 1400 nm. The number of optical modes, calculated as the reconstructed volume divided by the size of the point spread function, was ∼20 giga voxels. We have also demonstrated that different tumor types and a variety of precursor lesions and pathologies can be visualized with the present method.

Accelerating ptychographic reconstructions using spectral initializations.

Ptychography is a promising phase retrieval technique for label-free quantitative phase imaging. Recent advances in phase retrieval algorithms witnessed the development of spectral methods to accelerate gradient descent algorithms. Using spectral initializations on experimental data, for the first time, we report three times faster ptychographic reconstructions than with a standard gradient descent algorithm and improved resilience to noise. Coming at no additional computational cost compared to gradient-descent-based algorithms, spectral methods have the potential to be implemented in large-scale iterative ptychographic algorithms.

Deep-learning-based prediction of living cells mitosis via quantitative phase microscopy

We present a deep learning approach for living cells mitosis classification based on label-free quantitative phase imaging with transport of intensity equation methods. In the approach, we applied a pretrained deep convolutional neural network using transfer learning for binary classification of mitosis and non-mitosis. As a validation, we demonstrated the performances of the network trained by phase images and intensity images, respectively. The convolutional neural network trained by phase images achieved an average accuracy of 98.9% on the validation data, which outperforms the average accuracy 89.6% obtained by the network trained by intensity images. We believe that the quantitative phase microscopy in combination with deep learning enables researchers to predict the mitotic status of living cells noninvasively and efficiently.

Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network.

.Significance: Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase images efficiently, their applications in diagnostic testing are limited by the lack of transparency. More interpretable results such as morphological and biochemical characteristics of individual RBCs are highly desirable.Aim: An end-to-end deep-learning model was developed to efficiently discriminate thalassemic RBCs (tRBCs) from healthy RBCs (hRBCs) in quantitative phase images and segment RBCs for single-cell characterization.Approach: Two-dimensional quantitative phase images of hRBCs and tRBCs were acquired using digital holographic microscopy. A mask region-based convolutional neural network (Mask R-CNN) model was trained to discriminate tRBCs and segment individual RBCs. Characterization of tRBCs was achieved utilizing SHapley Additive exPlanation analysis and canonical correlation analysis on automatically segmented RBC phase images.Results: The implemented model achieved 97.8% accuracy in detecting tRBCs. Phase-shift statistics showed the highest influence on the correct classification of tRBCs. Associations between the phase-shift features and three-dimensional morphological features were revealed.Conclusions: The implemented Mask R-CNN model accurately identified tRBCs and segmented RBCs to provide single-RBC characterization, which has the potential to aid clinical decision-making.

Miscalibration-Tolerant Fourier Ptychography

Fourier Ptychography probes the sample from different directions to achieve label-free quantitative phase imaging with a large space-bandwidth product. However, special attention has to be paid in the calibration of the optical setup to assure the accurate knowledge of the geometrical parameters involved in the image reconstruction. Any slight misalignment can provoke incorrect synthesis of the observables and, in turn, severe phase errors in the resulting high-resolution image. Here, we present a new processing pipeline that automatically removes such a priori unknown artifacts, thus making Fourier Ptychography miscalibration-tolerant. This result is achieved through a numerical Multi-Look approach that generates and combines multiple reconstructions of the same set of observables where phase artifacts are largely uncorrelated and, thus, automatically suppress each other. The proposed method is non-iterative, fully parallelizable, and completely blind, unlocking the use of Fourier Ptychography as an easy to handle tool or add-on to existing microscopes to be employed by unskilled users, thus paving the way to biomedical and clinical practices.

Abstract 4769: Mapping cellular metabolic activity within U87MR glioblastoma cells in response to chemical and environmental insult using label-free quantitative phase imaging

Abstract Introduction: One of our drug development target diseases is Glioblastoma multiforme (GBM), a serious neurological disease with a very poor prognosis for the patients due, in part, to the large number of metabolic pathways it can follow. Materials and Methods: We are using U87-MR cells as a model. We employ time-lapse quantitative phase imaging technology that quantifies the refractive index (RI) of cells via light interference. There is no need for labeling of the cells. The RI is an intrinsic physical property of the cells which lends itself to highly precise and accurate quantitative analysis. Our basic protocol involves seeding cells, providing atmosphere and temperature control. Time-lapse images are automatically obtained at intervals ranging from 10 seconds up to 5 minutes and over time periods from minutes to days. Cells are unlabeled, and can be untreated, or treated with drugs or components of multi-drug formulations. We used drugs that have known effects on the cell cycle, mitochondria and induce apoptosis. We also tested drugs such as temozolomide (TMZ) the first line regimen in GBM therapy protocols. Results: Our first notable finding relates to U87-MR motility. The cells self-assemble into a pattern of well separated cell clusters with tubular connections. These connections are initiated by mitochondrial tunneling and are stable over time. The tunnels are reinforced with actin, and mitochondrial fusion occurs at substrate contact foci. These connections between cells also are conduits for intercellular communication and the transport of objects such as mitochondria (MT). Many drugs that have nuclear targets, including demecolcine, topoisomerase inhibitors, present effects in the cytoplasm of the cells, including vacuoles and possible the remnants of DNA repair. MT contain their DNA. The necessary genes are needed to control their own replication to replace damaged MT and to initiate alternative energy sources, such as mitophagy where cellular protein digestion is utilized as a source of energy. There are numerous factors that cause a wide range of textures within cells, including MT fission, lipid droplets, and autophagy. In many treatments, we see a definite region forming with very active textural changes. Due to its proximity to the nuclear membrane as well as the endoplasmic reticulum, we hypothesize that this may be where mTOR1 and mTOR2C activities originate. In the TMZ studies, we found a strong correlation between the dosage, and the morphology of the cells with a transformation to mesenchymal morphologies at the higher dosages. Discussion: GBM cells adapt to and control a number of different metabolic pathways which are primarily characterized by biochemical analysis. We have vastly improved-time lapse imaging capabilities. We produced first evidence of successful characterization of the metabolic changes based on RI quantifications and visual effects. Citation Format: Ed Luther, Livia P. Mendes, Vladimir P. Torchilin. Mapping cellular metabolic activity within U87MR glioblastoma cells in response to chemical and environmental insult using label-free quantitative phase imaging [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4769.

Abstract 6019: Identification of the neo-genesis of glioblastoma stem cells using label-free quantitative phase imaging

Abstract Introduction: Glioblastoma (GBM) is the most aggressive neurological cancer with a very poor prognosis due to its propensity to regenerate after medical interventions. Our experimental model is U87-MG cells, which contain subsets of cancer stem cells (CSC) and have the unusual property of spontaneously forming neurospheres in culture. We are investigating the origins of these stem cells. Materials and Methods: We employ recently developed time label-free, time-lapse quantitative phase imaging (QPI) technology that allows precise quantification of the refractive index (RI) distributions on a sub-cellular level. These measurements are stored in 2D or 3D arrays which can be presented as images or videos at various resolutions. Cells were seeded into sealable culture Petrie dishes and imaged while maintaining appropriate environmental controls. Results: We first noted CSCs in late stage cultures in exploratory experiments, where they presented as small cells (10-15 um) compared to the 50-100 um range of mature U8-MR cells. Their refractive index (RI) is 1.42, compared to about 1.40 for the cytoplasm of mature cells. QPI technology sensitivity enables almost complete separation between the two cell types in RI histograms. We exemplify a grouping of cells in an 80 um2 field of view, tracked in multiple time segments ranging from 20 minutes to 12 hours over a period of about 18 hours, containing a mature U87 which serves as a reference marker. Around this cell, there are 3 distinct pro-stem (PS) cells 12 to 16 um in size: PS-1 with numerous 0.5 um particles in the size range of mitochondria, PS-2 with a nucleus with nucleoli but no nuclear membrane, and PS-3 with a distinct nucleus, nucleoli, and cytosol. The cells are all connected in a network of nanotubes and tunnels. PS-1 and PS-2 are not very active in the early analysis, but they eventually move together and appear to fuse while still maintaining their individual shapes. PS-3 is very actively interacting with another cell on the border of the analysis window. It is extending and retracting a tube and is apparently moving materials both to and away from the other cell in a very regular and linear fashion, suggesting a primitive form of transcription. Discussion: The processes we observed are consistent with tumor stem cell generation by autophagy, mitophagy, and catabolization followed by reprograming of the remnants and formation of neoplastic stem cells. The novelty of our approach is determined by technical capabilities of our QPI platforms, which is non-invasive label-free approach coupled with ultra-high-resolution analysis, previously unobtainable in traditional time-lapse imaging systems. Citation Format: Ed Luther, Livia P. Mendes, Vladimir P. Torchilin. Identification of the neo-genesis of glioblastoma stem cells using label-free quantitative phase imaging [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6019.

Classification of cell morphology with quantitative phase microscopy and machine learning.

We describe and compare two machine learning approaches for cell classification based on label-free quantitative phase imaging with transport of intensity equation methods. In one approach, we design a multilevel integrated machine learning classifier including various individual models such as artificial neural network, extreme learning machine and generalized logistic regression. In another approach, we apply a pretrained convolutional neural network using transfer learning for the classification. As a validation, we show the performances of both approaches on classification between macrophages cultured in normal gravity and microgravity with quantitative phase imaging. The multilevel integrated classifier achieves average accuracy 93.1%, which is comparable to the average accuracy 93.5% obtained by convolutional neural network. The presented quantitative phase imaging system with two classification approaches could be helpful to biomedical scientists for easy and accurate cell analysis.

Label-free biochemical quantitative phase imaging with mid-infrared photothermal effect

Label-free optical imaging is valuable in biology and medicine because of its non-destructive nature. Quantitative phase imaging (QPI) and molecular vibrational imaging (MVI) are the two most successful label-free methods, providing morphological and biochemical information, respectively. These techniques have enabled numerous applications as they have matured over the past few decades; however, their label-free contrasts are inherently complementary and difficult to integrate due to their reliance on different light–matter interactions. Here we present a unified imaging scheme with simultaneous and in situ acquisition of quantitative phase and molecular vibrational contrasts of single cells in the QPI framework using the mid-infrared photothermal effect. The robust integration of subcellular morphological and biochemical label-free measurements may enable new analyses, especially for studying complex and fragile biological phenomena such as drug delivery, cellular disease, and stem cell development, where long-time observation of unperturbed cells is needed under low phototoxicity.