Quantitative phase imaging of stromal prognostic markers in pancreatic ductal adenocarcinoma

Quantitative phase imaging techniques for measuring scattering properties of cells and tissues: a review-part II

Significance

Quantitative phase imaging (QPI) is a non-invasive, label-free technique that provides intrinsic information about the sample under study. Such information includes the structure, function, and dynamics of the sample. QPI overcomes the limitations of conventional fluorescence microscopy in terms of phototoxicity to the sample and photobleaching of the fluorophore. As such, the application of QPI in estimating the three-dimensional (3D) structure and dynamics is well-suited for a range of samples from intracellular organelles to highly scattering multicellular samples while allowing for longer observation windows.

Aim

We aim to provide a comprehensive review of 3D QPI and related phase-based measurement techniques along with a discussion of methods for the estimation of sample dynamics.

Approach

We present information collected from 106 publications that cover the theoretical description of 3D light scattering and the implementation of related measurement techniques for the study of the structure and dynamics of the sample. We conclude with a discussion of the applications of the reviewed techniques in the biomedical field.

Results

QPI has been successfully applied to 3D sample imaging. The scattering-based contrast provides measurements of intrinsic quantities of the sample that are indicative of disease state, stage of growth, or overall dynamics.

Conclusions

We reviewed state-of-the-art QPI techniques for 3D imaging and dynamics estimation of biological samples. Both theoretical and experimental aspects of various techniques were discussed. We also presented the applications of the discussed techniques as applied to biomedicine and biology research.

Quantitative phase imaging techniques for measuring scattering properties of cells and tissues: a review-part I

Significance

Quantitative phase imaging (QPI) techniques offer intrinsic information about the sample of interest in a label-free, noninvasive manner and have an enormous potential for wide biomedical applications with negligible perturbations to the natural state of the sample in vitro.

Aim

We aim to present an in-depth review of the scattering formulation of light-matter interactions as applied to biological samples such as cells and tissues, discuss the relevant quantitative phase measurement techniques, and present a summary of various reported applications.

Approach

We start with scattering theory and scattering properties of biological samples followed by an exploration of various microscopy configurations for 2D QPI for measurement of structure and dynamics.

Results

We reviewed 157 publications and presented a range of QPI techniques and discussed suitable applications for each. We also presented the theoretical frameworks for phase reconstruction associated with the discussed techniques and highlighted their domains of validity.

Conclusions

We provide detailed theoretical as well as system-level information for a wide range of QPI techniques. Our study can serve as a guideline for new researchers looking for an exhaustive literature review of QPI methods and relevant applications.

Engineering Biomaterials to Model Immune-Tumor Interactions In Vitro

Engineered biomaterial scaffolds are becoming more prominent in research laboratories to study drug efficacy for oncological applications in vitro, but do they have a place in pharmaceutical drug screening pipelines? The low efficacy of cancer drugs in phase II/III clinical trials suggests that there are critical mechanisms not properly accounted for in the pre-clinical evaluation of drug candidates. Immune cells associated with the tumor may account for some of these failures given recent successes with cancer immunotherapies; however, there are few representative platforms to study immune cells in the context of cancer as traditional 2D culture is typically monocultures and humanized animal models have a weakened immune composition. Biomaterials that replicate tumor microenvironmental cues may provide a more relevant model with greater in vitro complexity. In this review, the authors explore the pertinent microenvironmental cues that drive tumor progression in the context of the immune system, discuss how these cues can be incorporated into hydrogel design to culture immune cells, and describe progress toward precision oncological drug screening with engineered tissues.

Panoramic quantitative phase imaging of adherent live cells in a microfluidic environment

Understanding how cells respond to external stimuli is crucial. However, there are a lack of inspection systems capable of simultaneously stimulating and imaging cells, especially in their natural states. This study presents a novel microfluidic stimulation and observation system equipped with flat-fielding quantitative phase contrast microscopy (FF-QPCM). This system allowed us to track the behavior of organelles in live cells experiencing controlled microfluidic stimulation. Using this innovative imaging platform, we successfully quantified the cellular response to shear stress including directional cellular shrinkage and mitochondrial distribution change in a label-free manner. Additionally, we detected and characterized the cellular response, particularly mitochondrial behavior, under varying fluidic conditions such as temperature and drug induction time. The proposed imaging platform is highly suitable for various microfluidic applications at the organelle level. We advocate that this platform will significantly facilitate life science research in microfluidic environments.

Quantitative Image Analysis of Fibrillar Collagens Reveals Novel Diagnostic and Prognostic Biomarkers and Histotype-dependent Aberrant Mechanobiology in Lung Cancer

Fibrillar collagens are the most abundant extracellular matrix components in non-small cell lung cancer (NSCLC). Yet, the potential of collagen fiber descriptors as a source of clinically-relevant biomarkers in NSCLC is mainly unknown. Likewise, our understanding of the aberrant collagen organization and associated tumor-promoting effects needs to be better defined. To address these limitations, we identified a digital pathology approach that can be easily implemented in pathology units based on the Curvelet Transform filtering and single Fiber Reconstruction (CT-FIRE) software analysis of picrosirius (PSR) stains of fibrillar collagens imaged with polarized light (PL). CT-FIRE settings were pre-optimized to assess a panel of collagen fiber descriptors in PSR-PL images of tissue microarrays from surgical NSCLC patients (106 adenocarcinomas (ADC), 89 squamous cell carcinomas (SCC)). Using this approach, we identified straightness as the single high-accuracy diagnostic collagen fiber descriptor (average area under the curve AUC = 0.92) and fiber density as the single descriptor consistently associated with poor prognosis in both ADC and SCC independently of the gold standard based on tumor size, lymph node involvement and metastasis (TNM) staging (Hazard ratio HR = 2.69 (1.55-4.66), p < 0.001). Moreover, we found that collagen fibers were markedly straighter, longer, and more aligned in tumors compared to paired samples from uninvolved pulmonary tissue, particularly in ADC, which is indicative of increased tumor stiffening. Consistently, we observed an increase in a panel of stiffness-associated processes in the high collagen fiber density patient group selectively in ADC, including venous/lymphatic invasion, fibroblast activation (alpha-smooth muscle actin (α-SMA)), and immune evasion (programmed death-ligand 1 (PD-L1)). Likewise, transcriptional correlation analysis supported the potential involvement of the major Yes-associated protein 1 (YAP)/TAZ mechanobiology pathway in ADC. Our results provide a proof-of-principle to use CT-FIRE analysis of PSR-PL images to assess new collagen fiber-based diagnostic and prognostic biomarkers in pathology units, which may improve the clinical management of surgical NSCLC patients. Our findings also unveil an aberrant stiff microenvironment in lung ADC that may foster immune evasion and dissemination, encouraging future work to identify therapeutic opportunities.

Multiscale Label-Free Imaging of Fibrillar Collagen in the Tumor Microenvironment

With recent advances in cancer therapeutics, there is a great need for improved imaging methods for characterizing cancer onset and progression in a quantitative and actionable way. Collagen, the most abundant extracellular matrix protein in the tumor microenvironment (and the body in general), plays a multifaceted role, both hindering and promoting cancer invasion and progression. Collagen deposition can defend the tumor with immunosuppressive effects, while aligned collagen fiber structures can enable tumor cell migration, aiding invasion and metastasis. Given the complex role of collagen fiber organization and topology, imaging has been a tool of choice to characterize these changes on multiple spatial scales, from the organ and tumor scale to cellular and subcellular level. Macroscale density already aids in the detection and diagnosis of solid cancers, but progress is being made to integrate finer microscale features into the process. Here we review imaging modalities ranging from optical methods of second harmonic generation (SHG), polarized light microscopy (PLM), and optical coherence tomography (OCT) to the medical imaging approaches of ultrasound and magnetic resonance imaging (MRI). These methods have enabled scientists and clinicians to better understand the impact collagen structure has on the tumor environment, at both the bulk scale (density) and microscale (fibrillar structure) levels. We focus on imaging methods with the potential to both examine the collagen structure in as natural a state as possible and still be clinically amenable, with an emphasis on label-free strategies, exploiting intrinsic optical properties of collagen fibers.

Label-free imaging of intracellular organelle dynamics using flat-fielding quantitative phase contrast microscopy (FF-QPCM)

Panoramic and long-term observation of nanosized organelle dynamics and interactions with high spatiotemporal resolution still hold great challenge for current imaging platforms. In this study, we propose a live-organelle imaging platform, where a flat-fielding quantitative phase contrast microscope (FF-QPCM) visualizes all the membrane-bound subcellular organelles, and an intermittent fluorescence channel assists in specific organelle identification. FF-QPCM features a high spatiotemporal resolution of 245 nm and 250 Hz and strong immunity against external disturbance. Thus, we could investigate several important dynamic processes of intracellular organelles from direct perspectives, including chromosome duplication in mitosis, mitochondrial fusion and fission, filaments, and vesicles' morphologies in apoptosis. Of note, we have captured, for the first time, a new type of mitochondrial fission (entitled mitochondrial disintegration), the generation and fusion process of vesicle-like organelles, as well as the mitochondrial vacuolization during necrosis. All these results bring us new insights into spatiotemporal dynamics and interactions among organelles, and hence aid us in understanding the real behaviors and functional implications of the organelles in cellular activities.

Synthetic aperture interference light (SAIL) microscopy for high-throughput label-free imaging

Quantitative phase imaging (QPI) is a valuable label-free modality that has gained significant interest due to its wide potentials, from basic biology to clinical applications. Most existing QPI systems measure microscopic objects via interferometry or nonlinear iterative phase reconstructions from intensity measurements. However, all imaging systems compromise spatial resolution for the field of view and vice versa, i.e., suffer from a limited space bandwidth product. Current solutions to this problem involve computational phase retrieval algorithms, which are time-consuming and often suffer from convergence problems. In this article, we presented synthetic aperture interference light (SAIL) microscopy as a solution for high-resolution, wide field of view QPI. The proposed approach employs low-coherence interferometry to directly measure the optical phase delay under different illumination angles and produces large space-bandwidth product label-free imaging. We validate the performance of SAIL on standard samples and illustrate the biomedical applications on various specimens: pathology slides, entire insects, and dynamic live cells in large cultures. The reconstructed images have a synthetic numeric aperture of 0.45 and a field of view of 2.6 × 2.6 mm2. Due to its direct measurement of the phase information, SAIL microscopy does not require long computational time, eliminates data redundancy, and always converges.

Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity

Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A virus), HAdV (adenovirus), and ZIKV (Zika virus). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic-processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically.

Monitoring reactivation of latent HIV by label-free gradient light interference microscopy

Human immunodeficiency virus (HIV) can infect cells and take a quiescent and nonexpressive state called latency. In this study, we report insights provided by label-free, gradient light interference microscopy (GLIM) about the changes in dry mass, diameter, and dry mass density associated with infected cells that occur upon reactivation. We discovered that the mean cell dry mass and mean diameter of latently infected cells treated with reactivating drug, TNF-α, are higher for latent cells that reactivate than those of the cells that did not reactivate. Cells with mean dry mass and diameter less than approximately 10 pg and 8 μm, respectively, remain exclusively in the latent state. Also, cells with mean dry mass greater than approximately 28-30 pg and mean diameter greater than 11–12 μm have a higher probability of reactivating. This study is significant as it presents a new label-free approach to quantify latent reactivation of a virus in single cells.

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GLIM imaging reveals differences between latent and reactivated HIV in JLat cells

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Cells with reactivated HIV have higher dry mass and diameter

Spatial light interference microscopy: principle and applications to biomedicine

In this paper, we review spatial light interference microscopy (SLIM), a common-path, phase-shifting interferometer, built onto a phase-contrast microscope, with white-light illumination. As one of the most sensitive quantitative phase imaging (QPI) methods, SLIM allows for speckle-free phase reconstruction with sub-nanometer path-length stability. We first review image formation in QPI, scattering, and full-field methods. Then, we outline SLIM imaging from theory and instrumentation to diffraction tomography. Zernike's phase-contrast microscopy, phase retrieval in SLIM, and halo removal algorithms are discussed. Next, we discuss the requirements for operation, with a focus on software developed in-house for SLIM that enables high-throughput acquisition, whole slide scanning, mosaic tile registration, and imaging with a color camera. We introduce two methods for solving the inverse problem using SLIM, white-light tomography, and Wolf phase tomography. Lastly, we review the applications of SLIM in basic science and clinical studies. SLIM can study cell dynamics, cell growth and proliferation, cell migration, mass transport, etc. In clinical settings, SLIM can assist with cancer studies, reproductive technology, blood testing, etc. Finally, we review an emerging trend, where SLIM imaging in conjunction with artificial intelligence brings computational specificity and, in turn, offers new solutions to outstanding challenges in cell biology and pathology.

Computational interference microscopy enabled by deep learning

Quantitative phase imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light interference microscopy (SLIM) is a highly sensitive QPI method due to its partially coherent illumination and common path interferometry geometry. However, SLIM's acquisition rate is limited because of the four-frame phase-shifting scheme. On the other hand, off-axis methods such as diffraction phase microscopy (DPM) allow for single-shot QPI. However, the laser-based DPM system is plagued by spatial noise due to speckles and multiple reflections. In a parallel development, deep learning was proven valuable in the field of bioimaging, especially due to its ability to translate one form of contrast into another. Here, we propose using deep learning to produce synthetic, SLIM-quality, and high-sensitivity phase maps from DPM using single-shot images as the input. We used an inverted microscope with its two ports connected to the DPM and SLIM modules such that we have access to the two types of images on the same field of view. We constructed a deep learning model based on U-net and trained on over 1000 pairs of DPM and SLIM images. The model learned to remove the speckles in laser DPM and overcame the background phase noise in both the test set and new data. The average peak signal-to-noise ratio, Pearson correlation coefficient, and structural similarity index measure were 29.97, 0.79, and 0.82 for the test dataset. Furthermore, we implemented the neural network inference into the live acquisition software, which now allows a DPM user to observe in real-time an extremely low-noise phase image. We demonstrated this principle of computational interference microscopy imaging using blood smears, as they contain both erythrocytes and leukocytes, under static and dynamic conditions.

Fabrication and Bonding of Refractive Index Matched Microfluidics for Precise Measurements of Cell Mass

The optical properties of polymer materials used for microfluidic device fabrication can impact device performance when used for optical measurements. In particular, conventional polymer materials used for microfluidic devices have a large difference in refractive index relative to aqueous media generally used for biomedical applications. This can create artifacts when used for microscopy-based assays. Fluorination can reduce polymer refractive index, but at the cost of reduced adhesion, creating issues with device bonding. Here, we present a novel fabrication technique for bonding microfluidic devices made of NOA1348, which is a fluorinated, UV-curable polymer with a refractive index similar to that of water, to a glass substrate. This technique is compatible with soft lithography techniques, making this approach readily integrated into existing microfabrication workflows. We also demonstrate that this material is compatible with quantitative phase imaging, which we used to validate the refractive index of the material post-fabrication. Finally, we demonstrate the use of this material with a novel image processing approach to precisely quantify the mass of cells in the microchannel without the use of cell segmentation or tracking. The novel image processing approach combined with this low refractive index material eliminates an important source of error, allowing for high-precision measurements of cell mass with a coefficient of variance of 1%.

Navigating the Collagen Jungle: The Biomedical Potential of Fiber Organization in Cancer

Recent research has highlighted the importance of key tumor microenvironment features, notably the collagen-rich extracellular matrix (ECM) in characterizing tumor invasion and progression. This led to great interest from both basic researchers and clinicians, including pathologists, to include collagen fiber evaluation as part of the investigation of cancer development and progression. Fibrillar collagen is the most abundant in the normal extracellular matrix, and was revealed to be upregulated in many cancers. Recent studies suggested an emerging theme across multiple cancer types in which specific collagen fiber organization patterns differ between benign and malignant tissue and also appear to be associated with disease stage, prognosis, treatment response, and other clinical features. There is great potential for developing image-based collagen fiber biomarkers for clinical applications, but its adoption in standard clinical practice is dependent on further translational and clinical evaluations. Here, we offer a comprehensive review of the current literature of fibrillar collagen structure and organization as a candidate cancer biomarker, and new perspectives on the challenges and next steps for researchers and clinicians seeking to exploit this information in biomedical research and clinical workflows.

High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition

Sperm cell motility and morphology observed under the bright field microscopy are the only criteria for selecting a particular sperm cell during Intracytoplasmic Sperm Injection (ICSI) procedure of Assisted Reproductive Technology (ART). Several factors such as oxidative stress, cryopreservation, heat, smoking and alcohol consumption, are negatively associated with the quality of sperm cell and fertilization potential due to the changing of subcellular structures and functions which are overlooked. However, bright field imaging contrast is insufficient to distinguish tiniest morphological cell features that might influence the fertilizing ability of sperm cell. We developed a partially spatially coherent digital holographic microscope (PSC-DHM) for quantitative phase imaging (QPI) in order to distinguish normal sperm cells from sperm cells under different stress conditions such as cryopreservation, exposure to hydrogen peroxide and ethanol. Phase maps of total 10,163 sperm cells (2,400 control cells, 2,750 spermatozoa after cryopreservation, 2,515 and 2,498 cells under hydrogen peroxide and ethanol respectively) are reconstructed using the data acquired from the PSC-DHM system. Total of seven feedforward deep neural networks (DNN) are employed for the classification of the phase maps for normal and stress affected sperm cells. When validated against the test dataset, the DNN provided an average sensitivity, specificity and accuracy of 85.5%, 94.7% and 85.6%, respectively. The current QPI + DNN framework is applicable for further improving ICSI procedure and the diagnostic efficiency for the classification of semen quality in regard to their fertilization potential and other biomedical applications in general.