Multimodal imaging of a liver-on-a-chip model using labelled and label-free optical microscopy techniques.

A liver-on-a-chip model is an advanced complex in vitro model (CIVM) that incorporates different cell types and extracellular matrix to mimic the microenvironment of the human liver in a laboratory setting. Given the heterogenous and complex nature of liver-on-a-chip models, brightfield and fluorescence-based imaging techniques are widely utilized for assessing the changes occurring in these models with different treatment and environmental conditions. However, the utilization of optical microscopy techniques for structural and functional evaluation of the liver CIVMs have been limited by the reduced light penetration depth and lack of 3D information obtained using these imaging techniques. In this study, the potential of both labelled as well as label-free multimodal optical imaging techniques for visualization and characterization of the cellular and sub-cellular features of a liver-on-a-chip model was investigated. (1) Cellular uptake and distribution of Alexa 488 (A488)-labelled non-targeted and targeted antisense oligonucleotides (ASO and ASO-GalNAc) in the liver-on-a-chip model was determined using multiphoton microscopy. (2) Hyperspectral stimulated Raman scattering (SRS) microscopy of the C-H region was used to determine the heterogeneity of chemical composition of circular and cuboidal hepatocytes in the liver-on-a-chip model in a label-free manner. Additionally, the spatial overlap between the intracellular localization of ASO and lipid droplets was explored using simultaneous hyperspectral SRS and fluorescence microscopy. (3) The capability of light sheet fluorescence microscopy (LSFM) for full-depth 3D visualization of sub-cellular distribution of A488-ASO and cellular phenotypes in the liver-on-a-chip model was demonstrated. In summary, multimodal optical microscopy is a promising platform that can be utilized for visualization and quantification of 3D cellular organization, drug distribution and functional changes occurring in liver-on-a-chip models, and can provide valuable insights into liver biology and drug uptake mechanisms by enabling better characterization of these liver models.

Predicting DNA damage response in non-small cell lung cancer organoids via simultaneous label-free autofluorescence multiharmonic microscopy.

The DNA damage response (DDR) is a fundamental readout for evaluating efficacy of cancer therapeutics, many of which target DNA associated processes. Current techniques to evaluate DDR rely on immunostaining for phosphorylated histone H2AX (γH2AX), which is an indicator of DNA double-strand breaks. While γH2AX immunostaining can provide a snapshot of DDR in fixed cell and tissue samples, this method is technically cumbersome due to temporal monitoring of DDR requiring timepoint replicates, extensive assay development efforts for 3D cell culture samples such as organoids, and time-consuming protocols for γH2AX immunostaining and its evaluation. The goal of this current study is to reduce overall burden on assay duration and development in non-small cell lung cancer (NSCLC) organoids by leveraging label-free multiphoton imaging. In this study, simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy was used to provide rich intracellular information based on endogenous contrasts. SLAM microscopy enables imaging of live samples eliminating the need to generate sacrificial sample replicates and has improved image acquisition in 3D space over conventional confocal microscopy. Predictive modeling between label-free SLAM microscopy and γH2AX immunostained images confirmed strong correlation between SLAM image features and γH2AX signal. Across multiple DNA targeting chemotherapeutics and multiple patient-derived NSCLC organoid lines, the optical redox ratio and third harmonic generation channels were used to robustly predict DDR. Imaging via SLAM microscopy can be used to more rapidly predict DDR in live 3D NSCLC organoids with minimal sample handling and without labeling.

Label-free nonlinear optical signatures of extracellular vesicles in liquid and tissue biopsies of human breast cancer.

Extracellular vesicles (EVs) have been implicated in metastasis and proposed as cancer biomarkers. However, heterogeneity and small size makes assessments of EVs challenging. Often, EVs are isolated from biofluids, losing spatial and temporal context and thus lacking the ability to access EVs in situ in their native microenvironment. This work examines the capabilities of label-free nonlinear optical microscopy to extract biochemical optical metrics of EVs in ex vivo tissue and EVs isolated from biofluids in cases of human breast cancer, comparing these metrics within and between EV sources. Before surgery, fresh urine and blood serum samples were obtained from human participants scheduled for breast tumor surgery (24 malignant, 6 benign) or healthy participants scheduled for breast reduction surgery (4 control). EVs were directly imaged both in intact ex vivo tissue that was removed during surgery and in samples isolated from biofluids by differential ultracentrifugation. Isolated EVs and freshly excised ex vivo breast tissue samples were imaged with custom nonlinear optical microscopes to extract single-EV optical metabolic signatures of NAD(P)H and FAD autofluorescence. Optical metrics were significantly altered in cases of malignant breast cancer in biofluid-derived EVs and intact tissue EVs compared to control samples. Specifically, urinary isolated EVs showed elevated NAD(P)H fluorescence lifetime in cases of malignant cancer, serum-derived isolated EVs showed decreased optical redox ratio in stage II cancer, but not earlier stages, and ex vivo breast tissue showed an elevated number of EVs in cases of malignant cancer. Results further indicated significant differences in the measured optical metabolic signature based on EV source (urine, serum and tissue) within individuals.

Probing delivery of a lipid nanoparticle encapsulated self-amplifying mRNA vaccine using coherent Raman microscopy and multiphoton imaging.

The COVID-19 pandemic triggered the resurgence of synthetic RNA vaccine platforms allowing rapid, scalable, low-cost manufacturing, and safe administration of therapeutic vaccines. Self-amplifying mRNA (SAM), which self-replicates upon delivery into the cellular cytoplasm, leads to a strong and sustained immune response. Such mRNAs are encapsulated within lipid nanoparticles (LNPs) that act as a vehicle for delivery to the cell cytoplasm. A better understanding of LNP-mediated SAM uptake and release mechanisms in different types of cells is critical for designing effective vaccines. Here, we investigated the cellular uptake of a SAM-LNP formulation and subsequent intracellular expression of SAM in baby hamster kidney (BHK-21) cells using hyperspectral coherent anti-Stokes Raman scattering (HS-CARS) microscopy and multiphoton-excited fluorescence lifetime imaging microscopy (FLIM). Cell classification pipelines based on HS-CARS and FLIM features were developed to obtain insights on spectral and metabolic changes associated with SAM-LNPs uptake. We observed elevated lipid intensities with the HS-CARS modality in cells treated with LNPs versus PBS-treated cells, and simultaneous fluorescence images revealed SAM expression inside BHK-21 cell nuclei and cytoplasm within 5 h of treatment. In a separate experiment, we observed a strong correlation between the SAM expression and mean fluorescence lifetime of the bound NAD(P)H population. This work demonstrates the ability and significance of multimodal optical imaging techniques to assess the cellular uptake of SAM-LNPs and the subsequent changes occurring in the cellular microenvironment following the vaccine expression.

In vivo label-free optical signatures of chemotherapy response in human pancreatic ductal adenocarcinoma patient-derived xenografts.

Pancreatic cancer is a devastating disease often detected at later stages, necessitating swift and effective chemotherapy treatment. However, chemoresistance is common and its mechanisms are poorly understood. Here, label-free multi-modal nonlinear optical microscopy was applied to study microstructural and functional features of pancreatic tumors in vivo to monitor inter- and intra-tumor heterogeneity and treatment response. Patient-derived xenografts with human pancreatic ductal adenocarcinoma were implanted into mice and characterized over five weeks of intraperitoneal chemotherapy (FIRINOX or Gem/NabP) with known responsiveness/resistance. Resistant and responsive tumors exhibited a similar initial metabolic response, but by week 5 the resistant tumor deviated significantly from the responsive tumor, indicating that a representative response may take up to five weeks to appear. This biphasic metabolic response in a chemoresistant tumor reveals the possibility of intra-tumor spatiotemporal heterogeneity of drug responsiveness. These results, though limited by small sample size, suggest the possibility for further work characterizing chemoresistance mechanisms using nonlinear optical microscopy.

Localization of unlabeled bepirovirsen antisense oligonucleotide in murine tissues using in situ hybridization and CARS imaging.

Current methods for detecting unlabeled antisense oligonucleotide (ASO) drugs rely on immunohistochemistry (IHC) and/or conjugated molecules, which lack sufficient sensitivity, specificity, and resolution to fully investigate their biodistribution. Our aim was to demonstrate the qualitative and quantitative distribution of unlabeled bepirovirsen, a clinical stage ASO, in livers and kidneys of dosed mice using novel staining and imaging technologies at subcellular resolution. ASOs were detected in formalin-fixed paraffin-embedded (FFPE) and frozen tissues using an automated chromogenic in situ hybridization (ISH) assay: miRNAscope. This was then combined with immunohistochemical detection of cell lineage markers. ASO distribution in hepatocytes versus nonparenchymal cell lineages was quantified using HALO AI image analysis. To complement this, hyperspectral coherent anti-Stokes Raman scattering (HS-CARS) imaging microscopy was used to specifically detect the unique cellular Raman spectral signatures following ASO treatment. Bepirovirsen was localized primarily in nonparenchymal liver cells and proximal renal tubules. Codetection of ASO with distinct cell lineage markers of liver and kidney populations aided target cell identity facilitating quantification. Positive liver signal was quantified using HALO AI, with 12.9% of the ASO localized to the hepatocytes and 87.1% in nonparenchymal cells. HS-CARS imaging specifically detected ASO fingerprints based on the unique vibrational signatures following unlabeled ASO treatment in a totally nonperturbative manner at subcellular resolution. Together, these novel detection and imaging modalities represent a significant increase in our ability to detect unlabeled ASOs in tissues, demonstrating improved levels of specificity and resolution. These methods help us understand their underlying mechanisms of action and ultimately improve the therapeutic potential of these important drugs for treating globally significant human diseases.

Weakly Supervised Identification and Localization of Drug Fingerprints Based on Label-Free Hyperspectral CARS Microscopy.

Understanding drug fingerprints in complex biological samples is essential for the development of a drug. Hyperspectral coherent anti-Stokes Raman scattering (HS-CARS) microscopy, a label-free nondestructive chemical imaging technique, can profile biological samples based on their endogenous vibrational contrast. Here, we propose a deep learning-assisted HS-CARS imaging approach for the investigation of drug fingerprints and their localization at single-cell resolution. To identify and localize drug fingerprints in complex biological systems, an attention-based deep neural network, hyperspectral attention net (HAN), was developed. By formulating the task to a multiple instance learning problem, HAN highlights informative regions through the attention mechanism when being trained on whole-image labels. Using the proposed technique, we investigated the drug fingerprints of a hepatitis B virus therapy in murine liver tissues. With the increase in drug dosage, higher classification accuracy was observed, with an average area under the curve (AUC) of 0.942 for the high-dose group. Besides, highly informative tissue structures predicted by HAN demonstrated a high degree of similarity with the drug localization shown by the in situ hybridization staining results. These results demonstrate the potential of the proposed deep learning-assisted optical imaging technique for the label-free profiling, identification, and localization of drug fingerprints in biological samples, which can be extended to nonperturbative investigations of complex biological systems under various biological conditions.

Weakly supervised identification of microscopic human breast cancer-related optical signatures from normal-appearing breast tissue.

With the latest advancements in optical bioimaging, rich structural and functional information has been generated from biological samples, which calls for capable computational tools to identify patterns and uncover relationships between optical characteristics and various biomedical conditions. Constrained by the existing knowledge of the novel signals obtained by those bioimaging techniques, precise and accurate ground truth annotations can be difficult to obtain. Here we present a weakly supervised deep learning framework for optical signature discovery based on inexact and incomplete supervision. The framework consists of a multiple instance learning-based classifier for the identification of regions of interest in coarsely labeled images and model interpretation techniques for optical signature discovery. We applied this framework to investigate human breast cancer-related optical signatures based on virtual histopathology enabled by simultaneous label-free autofluorescence multiharmonic microscopy (SLAM), with the goal of exploring unconventional cancer-related optical signatures from normal-appearing breast tissues. The framework has achieved an average area under the curve (AUC) of 0.975 on the cancer diagnosis task. In addition to well-known cancer biomarkers, non-obvious cancer-related patterns were revealed by the framework, including NAD(P)H-rich extracellular vesicles observed in normal-appearing breast cancer tissue, which facilitate new insights into the tumor microenvironment and field cancerization. This framework can be further extended to diverse imaging modalities and optical signature discovery tasks.

Deep convolutional neural networks for detection of abnormalities in chest X-rays trained on the very large dataset.

One of the main challenges in the current pandemic is the detection of coronavirus. Conventional techniques (PT-PCR) have their limitations such as long response time and limited accessibility. On the other hand, X-ray machines are widely available and they are already digitized in the health systems. Thus, their usage is faster and more available. Therefore, in this research, we evaluate how well deep CNNs do when it comes to classifying normal versus pathological chest X-rays. Compared to the previous research, we trained our network on the largest number of images, 103,468 in total, including 5 classes such as COPD signs, COVID, normal, others and Pneumonia. We achieved COVID accuracy of 97% and overall accuracy of 81%. Additionally, we achieved classification accuracy of 84% for categorization into normal (78%) and abnormal (88%).

Computational Photon Counting Using Multithreshold Peak Detection for Fast Fluorescence Lifetime Imaging Microscopy.

Time-resolved photon counting methods have a finite bandwidth that restricts the acquisition speed of techniques like fluorescence lifetime imaging microscopy (FLIM). To enable faster imaging, computational methods can be employed to count photons when the output of a detector is directly digitized at a high sampling rate. Here, we present computational photon counting using a hybrid photodetector in conjunction with multithreshold peak detection to count instances where one or more photons arrive at the detector within the detector response time. This method can be used to distinguish up to five photon counts per digitized point, whereas previous demonstrations of computational photon counting on data acquired with photomultiplier tubes have only counted one photon at a time. We demonstrate in both freely moving C. elegans and a human breast cancer cell line undergoing apoptosis that this novel multithreshold peak detection method can accurately characterize the intensity and fluorescence lifetime of samples producing photon rates up to 223%, higher than previously demonstrated photon counting FLIM systems.

Label-free optical redox ratio from urinary extracellular vesicles as a screening biomarker for bladder cancer.

Extracellular vesicles (EVs) have been studied for their potential applications in cancer screening, diagnosis, and treatment monitoring. Most studies have focused on the bulk content of EVs; however, it is also informative to investigate their metabolic status, and changes under different physiological and environmental conditions. In this study, noninvasive, multimodal, label-free nonlinear optical microscopy was used to evaluate the optical redox ratio of large EVs (microvesicles) isolated from the urine of 11 dogs in three cohorts (4 healthy, 4 transitional cell carcinoma (TCC) of the bladder, and 3 prostate cancer). The optical redox ratio is a common metric comparing the autofluorescence intensities of metabolic cofactors FAD and NAD(P)H to characterize the metabolic profile of cells and tissues, and has recently been applied to EVs. The optical redox ratio revealed that dogs with TCC of the bladder had a more than 2-fold increase in NAD(P)H-rich urinary EVs (uEVs) when compared to healthy dogs, whereas dogs with prostate cancer had no significant difference. The optical redox ratio values of uEVs kept at -20°C for 48 hours were significantly different from those of freshly isolated uEVs, indicating that this parameter is more reliable when assessing freshly isolated uEVs. These results suggest that the label-free optical redox ratio of uEVs, indicating relative rates of glycolysis and oxidative phosphorylation of parent cells and tissues, may act as a potential screening biomarker for bladder cancer.

Tracking the binding of multi-functional fluorescent tags for Alzheimer's disease using quantitative multiphoton microscopy.

A recent theranostic approach to address Alzheimer's disease (AD) utilizes multifunctional targets that both tag and negate the toxicity of AD biomarkers. These compounds, which emit fluorescence with both an activation and a spectral shift in the presence of Aß, were previously characterized with traditional fluorescence imaging for binary characterization. However, these multifunctional compounds have broad and dynamic emission spectra that are dependent on factors such as the local environment, presence of Aß deposits, etc. Since quantitative multiphoton microscopy is sensitive to the binding dynamics of molecules, we characterized the performance of two such compounds, LS-4 and ZY-12-OMe, using Simultaneous Label-free Autofluorescence Multi-harmonic (SLAM) microscopy and Fast Optical Coherence, Autofluorescence Lifetime imaging and Second harmonic generation (FOCALS) microscopy. This study shows that the combination of quantitative multiphoton imaging with multifunctional tags for AD offers new insights into the interaction of these tags with AD biomarkers and the theranostic mechanisms.

Label-free multimodal nonlinear optical imaging of needle biopsy cores for intraoperative cancer diagnosis.

SIGNIFICANCE: Needle biopsy (NB) procedures are important for the initial diagnosis of many types of cancer. However, the possibility of NB specimens being unable to provide diagnostic information, (i.e., non-diagnostic sampling) and the time-consuming histological evaluation process can cause delays in diagnoses that affect patient care. AIM: We aim to demonstrate the advantages of this label-free multimodal nonlinear optical imaging (NLOI) technique as a non-destructive point-of-procedure evaluation method for NB tissue cores, for the visualization and characterization of the tissue microenvironment. APPROACH: A portable, label-free, multimodal NLOI system combined second-harmonic generation (SHG) and third-harmonic generation and two- and three-photon autofluorescence (2PF, 3PF) microscopy. It was used for intraoperative imaging of fresh NB tissue cores acquired during canine cancer surgeries, which involved liver, lung, and mammary tumors as well as soft-tissue sarcoma; in total, eight canine patients were recruited. An added tissue culture chamber enabled the use of this NLOI system for longitudinal imaging of fresh NB tissue cores taken from an induced rat mammary tumor and healthy mouse livers. RESULTS: The intraoperative NLOI system was used to assess fresh canine NB specimens during veterinary cancer surgeries. Histology-like morphological features were visualized by the combination of four NLOI modalities at the point-of-procedure. The NLOI results provided quantitative information on the tissue microenvironment such as the collagen fiber orientation using Fourier-domain SHG analysis and metabolic profiling by optical redox ratio (ORR) defined by 2PF/(2PF + 3PF). The analyses showed that the canine mammary tumor had more randomly oriented collagen fibers compared to the tumor margin, and hepatocarcinoma had a wider distribution of ORR with a lower mean value compared to the liver fibrosis and the normal-appearing liver. Moreover, the loss of metabolic information during tissue degradation of fresh murine NB specimens was shown by overall intensity decreases in all channels and an increase of mean ORR from 0.94 (standard deviation 0.099) to 0.97 (standard deviation 0.077) during 1-h longitudinal imaging of a rat mammary tumor NB specimen. The tissue response to staurosporine (STS), an apoptotic inducer, from fresh murine liver NB specimens was also observed. The mean ORR decreased from 0.86 to 0.74 in the first 40 min and then increased to 0.8 during the rest of the hour of imaging, compared to the imaging results without the addition of STS, which showed a continuous increase of ORR from 0.72 to 0.75. CONCLUSIONS: A label-free, multimodal NLOI platform reveals microstructural and metabolic information of the fresh NB cores during intraoperative cancer imaging. This system has been demonstrated on animal models to show its potential to provide a more comprehensive histological assessment and a better understanding of the unperturbed tumor microenvironment. Considering tissue degradation, or loss of viability upon fixation, this intraoperative NLOI system has the advantage of immediate assessment of freshly excised tissue specimens at the point of procedure.

Label-free metabolic and structural profiling of dynamic biological samples using multimodal optical microscopy with sensorless adaptive optics.

Label-free optical microscopy has matured as a noninvasive tool for biological imaging; yet, it is criticized for its lack of specificity, slow acquisition and processing times, and weak and noisy optical signals that lead to inaccuracies in quantification. We introduce FOCALS (Fast Optical Coherence, Autofluorescence Lifetime imaging, and Second harmonic generation) microscopy capable of generating NAD(P)H fluorescence lifetime, second harmonic generation (SHG), and polarization-sensitive optical coherence microscopy (OCM) images simultaneously. Multimodal imaging generates quantitative metabolic and morphological profiles of biological samples in vitro, ex vivo, and in vivo. Fast analog detection of fluorescence lifetime and real-time processing on a graphical processing unit enables longitudinal imaging of biological dynamics. We detail the effect of optical aberrations on the accuracy of FLIM beyond the context of undistorting image features. To compensate for the sample-induced aberrations, we implemented a closed-loop single-shot sensorless adaptive optics solution, which uses computational adaptive optics of OCM for wavefront estimation within 2 s and improves the quality of quantitative fluorescence imaging in thick tissues. Multimodal imaging with complementary contrasts improves the specificity and enables multidimensional quantification of the optical signatures in vitro, ex vivo, and in vivo, fast acquisition and real-time processing improve imaging speed by 4-40 × while maintaining enough signal for quantitative nonlinear microscopy, and adaptive optics improves the overall versatility, which enable FOCALS microscopy to overcome the limits of traditional label-free imaging techniques.

Differential Uptake of Antisense Oligonucleotides in Mouse Hepatocytes and Macrophages Revealed by Simultaneous Two-Photon Excited Fluorescence and Coherent Raman Imaging.

Antisense oligonucleotides (ASOs), a novel paradigm in modern therapeutics, modulate cellular gene expression by binding to complementary messenger RNA (mRNA) sequences. While advances in ASO medicinal chemistry have greatly improved the efficiency of cellular uptake, selective uptake by specific cell types has been difficult to achieve. For more efficient and selective uptake, ASOs are often conjugated with molecules with high binding affinity for transmembrane receptors. Triantennary N-acetyl-galactosamine conjugated phosphorothioate ASOs (GalNAc-PS-ASOs) were developed to enhance targeted ASO delivery into liver through the hepatocyte-specific asialoglycoprotein receptor (ASGR). We assessed the kinetics of uptake and subsequent intracellular distribution of AlexaFluor 488 (AF488)-labeled PS-ASOs and GalNAc-PS-ASOs in J774A.1 mouse macrophages and primary mouse or rat hepatocytes using simultaneous coherent anti-Stokes Raman scattering (CARS) and two-photon fluorescence (2PF) imaging. The CARS modality captured the dynamic lipid distributions and overall morphology of the cells; two-photon fluorescence (2PF) measured the time- and dose-dependent localization of ASOs delivered by a modified treatment of suspension cells. Our results show that in macrophages, the uptake rate of PS-ASOs did not significantly differ from that of GalNAc-PS-ASOs. However, in hepatocytes, GalNAc-PS-ASOs exhibited a peripheral uptake distribution compared to a polar uptake distribution observed in macrophages. The peripheral distribution correlated with a significantly larger amount of internalized GalNAc-PS-ASOs compared to the PS-ASOs. This work demonstrates the relevance of multimodal imaging for elucidating the uptake mechanism, accumulation, and fate of different ASOs in liver cells that can be used further in complex in vitro models and liver tissues to evaluate ASO distribution and activity.

Single-photon peak event detection (SPEED): a computational method for fast photon counting in fluorescence lifetime imaging microscopy.

Fluorescence lifetime imaging microscopy (FLIM) characterizes samples by examining the temporal properties of fluorescence emission, providing useful contrast within samples based on the local physical and biochemical environment of fluorophores. Despite this, FLIM applications have been limited in scope by either poor accuracy or long acquisition times. Here, we present a method for computational single-photon counting of directly sampled time-domain FLIM data that is capable of accurate fluorescence lifetime and intensity measurements while acquiring over 160 Mega-counts-per-second with sub-nanosecond time resolution between consecutive photon counts. We demonstrate that our novel method of Single-photon PEak Event Detection (SPEED) is more accurate than direct pulse sampling and faster than established photon counting FLIM methods. We further show that SPEED can be implemented for imaging and quantifying samples that benefit from higher -throughput and -dynamic range imaging with real-time GPU-accelerated processing and use this capability to examine the NAD(P)H-related metabolic dynamics of apoptosis in human breast cancer cells. Computational methods for photon counting such as SPEED open up more opportunities for fast and accurate FLIM imaging and additionally provide a basis for future innovation into alternative FLIM techniques.

Differentiation of breast tissue types for surgical margin assessment using machine learning and polarization-sensitive optical coherence tomography.

We report an automated differentiation model for classifying malignant tumor, fibro-adipose, and stroma in human breast tissues based on polarization-sensitive optical coherence tomography (PS-OCT). A total of 720 PS-OCT images from 72 sites of 41 patients with H&E histology-confirmed diagnoses as the gold standard were employed in this study. The differentiation model is trained by the features extracted from both one standard OCT-based metric (i.e., intensity) and four PS-OCT-based metrics (i.e., phase difference between two channels (PD), phase retardation (PR), local phase retardation (LPR), and degree of polarization uniformity (DOPU)). Further optimized by forward searching and validated by leave-one-site-out-cross-validation (LOSOCV) method, the best feature subset was acquired with the highest overall accuracy of 93.5% for the model. Furthermore, to show the superiority of our differentiation model based on PS-OCT images over standard OCT images, the best model trained by intensity-only features (usually obtained by standard OCT systems) was also obtained with an overall accuracy of 82.9%, demonstrating the significance of the polarization information in breast tissue differentiation. The high performance of our differentiation model suggests the potential of using PS-OCT for intraoperative human breast tissue differentiation during the surgical resection of breast cancer.

Label-Free Multimodal Multiphoton Intravital Imaging.

Label-free intravital optical imaging is an emergent visualization tool that is not only useful for basic biological research, but also for preclinical research with potential translational clinical applications. The complete absence of exogenous labeling or genetic alterations avoids plausible harmful perturbation to biological processes and the pristine physiological environment, as the endogenous biomolecules enable intrinsic imaging contrasts to interrogate various live multicellular organisms of interest. This tool has evolved from single-modality, single-photon imaging into multimodal multiphoton imaging, in order to gain different contrasts simultaneously during imaging sessions, and permit long-term time-lapse studies that have begun to spawn more diverse applications.

Longitudinal monitoring of cell metabolism in biopharmaceutical production using label-free fluorescence lifetime imaging microscopy.

Chinese hamster ovary (CHO) cells are routinely used in the biopharmaceutical industry for production of therapeutic monoclonal antibodies (mAbs). Although multiple offline and time-consuming measurements of spent media composition and cell viability assays are used to monitor the status of culture in biopharmaceutical manufacturing, the day-to-day changes in the cellular microenvironment need further in-depth characterization. In this study, two-photon fluorescence lifetime imaging microscopy (2P-FLIM) was used as a tool to directly probe into the health of CHO cells from a bioreactor, exploiting the autofluorescence of intracellular nicotinamide adenine dinucleotide phosphate (NAD(P)H), an enzymatic cofactor that determines the redox state of the cells. A custom-built multimodal microscope with two-photon FLIM capability was utilized to monitor changes in NAD(P)H fluorescence for longitudinal characterization of a changing environment during cell culture processes. Three different cell lines were cultured in 0.5 L shake flasks and 3 L bioreactors. The resulting FLIM data revealed differences in the fluorescence lifetime parameters, which were an indicator of alterations in metabolic activity. In addition, a simple principal component analysis (PCA) of these optical parameters was able to identify differences in metabolic progression of two cell lines cultured in bioreactors. Improved understanding of cell health during antibody production processes can result in better streamlining of process development, thereby improving product titer and verification of scale-up. To our knowledge, this is the first study to use FLIM as a label-free measure of cellular metabolism in a biopharmaceutically relevant and clinically important CHO cell line.

Computational adaptive optics for polarization-sensitive optical coherence tomography.

Defocus aberration in optical systems, including optical coherence tomography (OCT) systems employing Gaussian illumination, gives rise to the well-known compromise between transverse resolution and depth-of-field. This results in blurry images when out-of-focus, whilst other low-order aberrations (e.g., astigmatism, coma, etc.) present in both the OCT system and biological samples further reduce image resolution and contrast. Computational adaptive optics (CAO) is a computed optical interferometric imaging technique that modifies the phase of the OCT data in the spatial frequency domain to correct optical aberrations and provide improvement of the image quality throughout the three-dimensional (3D) volume. In this Letter, we report the first implementation of CAO for polarization-sensitive OCT to correct defocus and other low-order aberrations, providing enhanced polarization-sensitive imaging contrast (i.e., intensity and phase retardation) on a 3D OCT phantom, molded plastics, ex vivo chicken breast tissue, and ex vivo human breast cancer tissue.