In Vivo Autofluorescence Imaging of Tumor Heterogeneity in Response to Treatment

Evaluation of Cellpose segmentation with sequential thresholding for instance segmentation of cytoplasms within autofluorescence images

BACKGROUND

Autofluorescence imaging of the coenzyme, reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H), provides a label-free technique to assess cellular metabolism. Because NAD(P)H is localized in the cytosol and mitochondria, instance segmentation of cell cytoplasms from NAD(P)H images allows quantification of metabolism with cellular resolution. However, accurate cytoplasmic segmentation of autofluorescence images is difficult due to irregular cell shapes and cell clusters.

METHOD

Here, a cytoplasm segmentation method is presented and tested. First, autofluorescence images are segmented into cells via either hand-segmentation or Cellpose, a deep learning-based segmentation method. Then, a cytoplasmic post-processing algorithm (CPPA) is applied for cytoplasmic segmentation. CPPA uses a binarized segmentation image to remove non-segmented pixels from the NAD(P)H image and then applies an intensity-based threshold to identify nuclei regions. Errors at cell edges are removed using a distance transform algorithm. The nucleus mask is then subtracted from the cell segmented image to yield the cytoplasm mask image. CPPA was tested on five NAD(P)H images of three different cell samples, quiescent T cells, activated T cells, and MCF7 cells.

RESULTS

Using POSEA, an evaluation method tailored for instance segmentation, the CPPA yielded F-measure values of 0.89, 0.87, and 0.94 for quiescent T cells, activated T cells, and MCF7 cells, respectively, for cytoplasm identification of hand-segmented cells. CPPA achieved F-measure values of 0.84, 0.74, and 0.72 for Cellpose segmented cells.

CONCLUSION

These results exceed the F-measure value of a comparative cell segmentation method (CellProfiler, ∼0.50-0.60) and support the use of artificial intelligence and post-processing techniques for accurate segmentation of autofluorescence images for single-cell metabolic analyses.

Insights into metabolic heterogeneity of colorectal cancer gained from fluorescence lifetime imaging

Heterogeneity of tumor metabolism is an important, but still poorly understood aspect of tumor biology. Present work is focused on the visualization and quantification of cellular metabolic heterogeneity of colorectal cancer using fluorescence lifetime imaging (FLIM) of redox cofactor NAD(P)H. FLIM-microscopy of NAD(P)H was performed in vitro in four cancer cell lines (HT29, HCT116, CaCo2 and CT26), in vivo in the four types of colorectal tumors in mice and ex vivo in patients' tumor samples. The dispersion and bimodality of the decay parameters were evaluated to quantify the intercellular metabolic heterogeneity. Our results demonstrate that patients' colorectal tumors have significantly higher heterogeneity of energy metabolism compared with cultured cells and tumor xenografts, which was displayed as a wider and frequently bimodal distribution of a contribution of a free (glycolytic) fraction of NAD(P)H within a sample. Among patients' tumors, the dispersion was larger in the high-grade and early stage ones, without, however, any association with bimodality. These results indicate that cell-level metabolic heterogeneity assessed from NAD(P)H FLIM has a potential to become a clinical prognostic factor.

Cellpose as a reliable method for single-cell segmentation of autofluorescence microscopy images

Autofluorescence microscopy uses intrinsic sources of molecular contrast to provide cellular-level information without extrinsic labels. However, traditional cell segmentation tools are often optimized for high signal-to-noise ratio (SNR) images, such as fluorescently labeled cells, and unsurprisingly perform poorly on low SNR autofluorescence images. Therefore, new cell segmentation tools are needed for autofluorescence microscopy. Cellpose is a deep learning network that is generalizable across diverse cell microscopy images and automatically segments single cells to improve throughput and reduce inter-human biases. This study aims to validate Cellpose for autofluorescence imaging, specifically from multiphoton intensity images of NAD(P)H. Manually segmented nuclear masks of NAD(P)H images were used to train new Cellpose models. These models were applied to PANC-1 cells treated with metabolic inhibitors and patient-derived cancer organoids (across 9 patients) treated with chemotherapies. These datasets include co-registered fluorescence lifetime imaging microscopy (FLIM) of NAD(P)H and FAD, so fluorescence decay parameters and the optical redox ratio (ORR) were compared between masks generated by the new Cellpose model and manual segmentation. The Dice score between repeated manually segmented masks was significantly lower than that of repeated Cellpose masks (p<0.0001) indicating greater reproducibility between Cellpose masks. There was also a high correlation (R2>0.9) between Cellpose and manually segmented masks for the ORR, mean NAD(P)H lifetime, and mean FAD lifetime across 2D and 3D cell culture treatment conditions. Masks generated from Cellpose and manual segmentation also maintain similar means, variances, and effect sizes between treatments for the ORR and FLIM parameters. Overall, Cellpose provides a fast, reliable, reproducible, and accurate method to segment single cells in autofluorescence microscopy images such that functional changes in cells are accurately captured in both 2D and 3D culture.

Monitoring the Intracellular pH and Metabolic State of Cancer Cells in Response to Chemotherapy Using a Combination of Phosphorescence Lifetime Imaging Microscopy and Fluorescence Lifetime Imaging Microscopy

The extracellular matrix (ECM), in which collagen is the most abundant protein, impacts many aspects of tumor physiology, including cellular metabolism and intracellular pH (pHi), as well as the efficacy of chemotherapy. Meanwhile, the role of collagen in differential cell responses to treatment within heterogeneous tumor environments remains poorly investigated. In the present study, we simultaneously monitored the changes in pHi and metabolism in living colorectal cancer cells in vitro upon treatment with a chemotherapeutic combination, FOLFOX (5-fluorouracil, oxaliplatin and leucovorin). The pHi was followed using the new pH-sensitive probe BC-Ga-Ir, working in the mode of phosphorescence lifetime imaging (PLIM), and metabolism was assessed from the autofluorescence of the metabolic cofactor NAD(P)H using fluorescence lifetime imaging (FLIM) with a two-photon laser scanning microscope. To model the ECM, 3D collagen-based hydrogels were used, and comparisons with conventional monolayer cells were made. It was found that FOLFOX treatment caused an early temporal intracellular acidification (reduction in pHi), followed by a shift to more alkaline values, and changed cellular metabolism to a more oxidative state. The presence of unstructured collagen markedly reduced the cytotoxic effects of FOLFOX, and delayed and diminished the pHi and metabolic responses. These results support the observation that collagen is a factor in the heterogeneous response of cancer cells to chemotherapy and a powerful regulator of their metabolic behavior.

Metabolic Imaging as a Tool to Characterize Chemoresistance and Guide Therapy in Triple-Negative Breast Cancer (TNBC)

After an initial response to chemotherapy, tumor relapse is frequent. This event is reflective of both the spatiotemporal heterogeneities of the tumor microenvironment as well as the evolutionary propensity of cancer cell populations to adapt to variable conditions. Because the cause of this adaptation could be genetic or epigenetic, studying phenotypic properties such as tumor metabolism is useful as it reflects molecular, cellular, and tissue-level dynamics. In triple-negative breast cancer (TNBC), the characteristic metabolic phenotype is a highly fermentative state. However, during treatment, the spatial and temporal dynamics of the metabolic landscape are highly unstable, with surviving populations taking on a variety of metabolic states. Thus, longitudinally imaging tumor metabolism provides a promising approach to inform therapeutic strategies, and to monitor treatment responses to understand and mitigate recurrence. Here we summarize some examples of the metabolic plasticity reported in TNBC following chemotherapy and review the current metabolic imaging techniques available in monitoring chemotherapy responses clinically and preclinically. The ensemble of imaging technologies we describe has distinct attributes that make them uniquely suited for a particular length scale, biological model, and/or features that can be captured. We focus on TNBC to highlight the potential of each of these technological advances in understanding evolution-based therapeutic resistance.

Emerging clinical applications in oncology for non-invasive multi- and hyperspectral imaging of cell and tissue autofluorescence

Hyperspectral and multispectral imaging of cell and tissue autofluorescence is an emerging technology in which fluorescence imaging is applied to biological materials across multiple spectral channels. This produces a stack of images where each matched pixel contains information about the sample's spectral properties at that location. This allows precise collection of molecularly specific data from a broad range of native fluorophores. Importantly, complex information, directly reflective of biological status, is collected without staining and tissues can be characterised in situ, without biopsy. For oncology, this can spare the collection of biopsies from sensitive regions and enable accurate tumour mapping. For in vivo tumour analysis, the greatest focus has been on oral cancer, whereas for ex vivo assessment head-and-neck cancers along with colon cancer have been the most studied, followed by oral and eye cancer. This review details the scope and progress of research undertaken towards clinical translation in oncology.

Light-sheet autofluorescence lifetime imaging with a single-photon avalanche diode array

Significance

Fluorescence lifetime imaging microscopy (FLIM) of the metabolic co-enzyme nicotinamide adenine dinucleotide (phosphate) [NAD(P)H] is a popular method to monitor single-cell metabolism within unperturbed, living 3D systems. However, FLIM of NAD(P)H has not been performed in a light-sheet geometry, which is advantageous for rapid imaging of cells within live 3D samples.

Aim

We aim to design, validate, and demonstrate a proof-of-concept light-sheet system for NAD(P)H FLIM.

Approach

A single-photon avalanche diode camera was integrated into a light-sheet microscope to achieve optical sectioning and limit out-of-focus contributions for NAD(P)H FLIM of single cells.

Results

An NAD(P)H light-sheet FLIM system was built and validated with fluorescence lifetime standards and with time-course imaging of metabolic perturbations in pancreas cancer cells with 10 s integration times. NAD(P)H light-sheet FLIM in vivo was demonstrated with live neutrophil imaging in a larval zebrafish tail wound also with 10 s integration times. Finally, the theoretical and practical imaging speeds for NAD(P)H FLIM were compared across laser scanning and light-sheet geometries, indicating a 30 × to 6 × acquisition speed advantage for the light sheet compared to the laser scanning geometry.

Conclusions

FLIM of NAD(P)H is feasible in a light-sheet geometry and is attractive for 3D live cell imaging applications, such as monitoring immune cell metabolism and migration within an organism.

Label-free, multi-parametric assessments of cell metabolism and matrix remodeling within human and early-stage murine osteoarthritic articular cartilage

Osteoarthritis (OA) is characterized by the progressive deterioration of articular cartilage, involving complicated cell-matrix interactions. Systematic investigations of dynamic cellular and matrix changes during OA progression are lacking. In this study, we use label-free two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) imaging to assess cellular and extracellular matrix features of murine articular cartilage during several time points at early stages of OA development following destabilization of medial meniscus surgery. We detect significant changes in the organization of collagen fibers and crosslink-associated fluorescence of the superficial zone as early as one week following surgery. Such changes become significant within the deeper transitional and radial zones at later time-points, highlighting the importance of high spatial resolution. Cellular metabolic changes exhibit a highly dynamic behavior, and indicate metabolic reprogramming from enhanced oxidative phosphorylation to enhanced glycolysis or fatty acid oxidation over the ten-week observation period. The optical metabolic and matrix changes detected within this mouse model are consistent with differences identified in excised human cartilage specimens from OA and healthy cartilage specimens. Thus, our studies reveal important cell-matrix interactions at the onset of OA that may enable improved understanding of OA development and identification of new potential treatment targets.

Light sheet autofluorescence lifetime imaging with a single photon avalanche diode array

Single photon avalanche diode (SPAD) array sensors can increase the imaging speed for fluorescence lifetime imaging microscopy (FLIM) by transitioning from laser scanning to widefield geometries. While a SPAD camera in epi-fluorescence geometry enables widefield FLIM of fluorescently labeled samples, label-free imaging of single-cell autofluorescence is not feasible in an epi-fluorescence geometry because background fluorescence from out-of-focus features masks weak cell autofluorescence and biases lifetime measurements. Here, we address this problem by integrating the SPAD camera in a light sheet illumination geometry to achieve optical sectioning and limit out-of-focus contributions, enabling fast label-free FLIM of single-cell NAD(P)H autofluorescence. The feasibility of this NAD(P)H light sheet FLIM system was confirmed with time-course imaging of metabolic perturbations in pancreas cancer cells with 10 s integration times, and in vivo NAD(P)H light sheet FLIM was demonstrated with live neutrophil imaging in a zebrafish tail wound, also with 10 s integration times. Finally, the theoretical and practical imaging speeds for NAD(P)H FLIM were compared across laser scanning and light sheet geometries, indicating a 30X to 6X frame rate advantage for the light sheet compared to the laser scanning geometry. This light sheet system provides faster frame rates for 3D NAD(P)H FLIM for live cell imaging applications such as monitoring single cell metabolism and immune cell migration throughout an entire living organism.

Mapping the 3D remodeling of the extracellular matrix in human hypertrophic scar by multi-parametric multiphoton imaging using endogenous contrast

The hypertrophic scar is an aberrant form of wound healing process, whose clinical efficacy is limited by a lack of understanding of its pathophysiology. Remodeling of collagen and elastin fibers in the extracellular matrix (ECM) is closely associated with scar progression. Herein, we perform label-free multiphoton microscopy (MPM) of both fiber components from human skin specimens and propose a multi-fiber metrics (MFM) analysis model for mapping the structural remodeling of the ECM in hypertrophic scars in a highly-sensitive, three-dimensional (3D) manner. We find that both fiber components become wavier and more disorganized in scar tissues, while content accumulation is observed from elastin fibers only. The 3D MFM analysis can effectively distinguish normal and scar tissues with better than 95% in accuracy and 0.999 in the area under the curve value of the receiver operating characteristic curve. Further, unique organizational features with orderly alignment of both fibers are observed in scar-normal adjacent regions, and an optimized combination of features from 3D MFM analysis enables successful identification of all the boundaries. This imaging and analysis system uncovers the 3D architecture of the ECM in hypertrophic scars and exhibits great translational potential for evaluating scars in vivo and identifying individualized treatment targets.

Spatial Alignment of Organoids Tracking Subclonal Chemotherapy Resistance in Pancreatic and Ampullary Cancer

Pancreatic and ampullary cancers remain highly morbid diseases for which accurate clinical predictions are needed for precise therapeutic predictions. Patient-derived cancer organoids have been widely adopted; however, prior work has focused on well-level therapeutic sensitivity. To characterize individual oligoclonal units of therapeutic response, we introduce a low-volume screening assay, including an automated alignment algorithm. The oligoclonal growth response was compared against validated markers of response, including well-level viability and markers of single-cell viability. Line-specific sensitivities were compared with clinical outcomes. Automated alignment algorithms were generated to match organoids across time using coordinates across a single projection of Z-stacked images. After screening for baseline size (50 μm) and circularity (>0.4), the match efficiency was found to be optimized by accepting the diffusion thresholded with the root mean standard deviation of 75 μm. Validated well-level viability showed a limited correlation with the mean organoid size (R = 0.408), and a normalized growth assayed by normalized changes in area (R = 0.474) and area (R = 0.486). Subclonal populations were defined by both residual growth and the failure to induce apoptosis and necrosis. For a culture with clinical resistance to gemcitabine and nab-paclitaxel, while a therapeutic challenge induced a robust effect in inhibiting cell growth (GΔ = 1.53), residual oligoclonal populations were able to limit the effect on the ability to induce apoptosis (GΔ = 0.52) and cell necrosis (GΔ = 1.07). Bioengineered approaches are feasible to capture oligoclonal heterogeneity in organotypic cultures, integrating ongoing efforts for utilizing organoids across cancer types as integral biomarkers and in novel therapeutic development.

POSEA: A novel algorithm to evaluate the performance of multi-object instance image segmentation

Many techniques and software packages have been developed to segment individual cells within microscopy images, necessitating a robust method to evaluate images segmented into a large number of unique objects. Currently, segmented images are often compared with ground-truth images at a pixel level; however, this standard pixel-level approach fails to compute errors due to pixels incorrectly assigned to adjacent objects. Here, we define a per-object segmentation evaluation algorithm (POSEA) that calculates segmentation accuracy metrics for each segmented object relative to a ground truth segmented image. To demonstrate the performance of POSEA, precision, recall, and f-measure metrics are computed and compared with the standard pixel-level evaluation for simulated images and segmented fluorescence microscopy images of three different cell samples. POSEA yields lower accuracy metrics than the standard pixel-level evaluation due to correct accounting of misclassified pixels of adjacent objects. Therefore, POSEA provides accurate evaluation metrics for objects with pixels incorrectly assigned to adjacent objects and is robust for use across a variety of applications that require evaluation of the segmentation of unique adjacent objects.

Bendable long graded index lens microendoscopy

Graded index (GRIN) lens endoscopy has broadly benefited biomedical microscopic imaging by enabling accessibility to sites not reachable by traditional benchtop microscopes. It is a long-held notion that GRIN lenses can only be used as rigid probes, which may limit their potential for certain applications. Here, we describe bendable and long-range GRIN microimaging probes for a variety of potential micro-endoscopic biomedical applications. Using a two-photon fluorescence imaging system, we have experimentally demonstrated the feasibility of three-dimensional imaging through a 500-µm-diameter and ∼11 cm long GRIN lens subject to a cantilever beam-like deflection with a minimum bend radius of ∼25 cm. Bend-induced perturbation to the field of view and resolution has also been investigated quantitatively. Our development alters the conventional notion of GRIN lenses and enables a range of innovative applications. For example, the demonstrated flexibility is highly desirable for implementation into current and emerging minimally invasive clinical procedures, including a pioneering microdevice for high-throughput cancer drug selection.

Technologies to Assess Drug Response and Heterogeneity in Patient-Derived Cancer Organoids

Patient-derived cancer organoids (PDCOs) are organotypic 3D cultures grown from patient tumor samples. PDCOs provide an exciting opportunity to study drug response and heterogeneity within and between patients. This research can guide new drug development and inform clinical treatment planning. We review technologies to assess PDCO drug response and heterogeneity, discuss best practices for clinically relevant drug screens, and assert the importance of quantifying single-cell and organoid heterogeneity to characterize response. Autofluorescence imaging of PDCO growth and metabolic activity is highlighted as a compelling method to monitor single-cell and single-organoid response robustly and reproducibly. We also speculate on the future of PDCOs in clinical practice and drug discovery.Future development will require standardization of assessment methods for both morphology and function in PDCOs, increased throughput for new drug development, prospective validation with patient outcomes, and robust classification algorithms.

Label-free sensing of cells with fluorescence lifetime imaging: The quest for metabolic heterogeneity

Molecular, morphological, and physiological heterogeneity is the inherent property of cells which governs differences in their response to external influence. Tumor cell metabolic heterogeneity is of a special interest due to its clinical relevance to tumor progression and therapeutic outcomes. Rapid, sensitive, and noninvasive assessment of metabolic heterogeneity of cells is a great demand for biomedical sciences. Fluorescence lifetime imaging (FLIM), which is an all-optical technique, is an emerging tool for sensing and quantifying cellular metabolism by measuring fluorescence decay parameters of endogenous fluorophores, such as NAD(P)H. To achieve accurate discrimination between metabolically diverse cellular subpopulations, appropriate approaches to FLIM data collection and analysis are needed. In this paper, the unique capability of FLIM to attain the overarching goal of discriminating metabolic heterogeneity is demonstrated. This has been achieved using an approach to data analysis based on the nonparametric analysis, which revealed a much better sensitivity to the presence of metabolically distinct subpopulations compared to more traditional approaches of FLIM measurements and analysis. The approach was further validated for imaging cultured cancer cells treated with chemotherapy. These results pave the way for accurate detection and quantification of cellular metabolic heterogeneity using FLIM, which will be valuable for assessing therapeutic vulnerabilities and predicting clinical outcomes.

Identification of Rare Cell Populations in Autofluorescence Lifetime Image Data

Drug‐resistant cells and anti‐inflammatory immune cells within tumor masses contribute to tumor aggression, invasion, and worse patient outcomes. These cells can be a small proportion (<10%) of the total cell population of the tumor. Due to their small quantity, the identification of rare cells is challenging with traditional assays. Single cell analysis of autofluorescence images provides a live‐cell assay to quantify cellular heterogeneity. Fluorescence intensities and lifetimes of the metabolic coenzymes reduced nicotinamide adenine dinucleotide and oxidized flavin adenine dinucleotide allow quantification of cellular metabolism and provide features for classification of cells with different metabolic phenotypes. In this study, Gaussian distribution modeling and machine learning classification algorithms are used for the identification of rare cells within simulated autofluorescence lifetime image data of a large tumor comprised of tumor cells and T cells. A Random Forest machine learning algorithm achieved an overall accuracy of 95% for the identification of cell type from the simulated optical metabolic imaging data of a heterogeneous tumor of 20,000 cells consisting of 70% drug responsive breast cancer cells, 5% drug resistant breast cancer cells, 20% quiescent T cells and 5% activated T cells. High resolution imaging methods combined with single‐cell quantitative analyses allows identification and quantification of rare populations of cells within heterogeneous cultures

Metabolic therapy and bioenergetic analysis: The missing piece of the puzzle

BACKGROUND

Aberrant metabolism is recognized as a hallmark of cancer, a pillar necessary for cellular proliferation. Regarding bioenergetics (ATP generation), most cancers display a preference not only toward aerobic glycolysis ("Warburg effect") and glutaminolysis (mitochondrial substrate level-phosphorylation) but also toward other metabolites such as lactate, pyruvate, and fat-derived sources. These secondary metabolites can assist in proliferation but cannot fully cover ATP demands.

SCOPE OF REVIEW

The concept of a static metabolic profile is challenged by instances of heterogeneity and flexibility to meet fuel/anaplerotic demands. Although metabolic therapies are a promising tool to improve therapeutic outcomes, either via pharmacological targets or press-pulse interventions, metabolic plasticity is rarely considered. Lack of bioenergetic analysis in vitro and patient-derived models is hindering translational potential. Here, we review the bioenergetics of cancer and propose a simple analysis of major metabolic pathways, encompassing both affordable and advanced techniques. A comprehensive compendium of Seahorse XF bioenergetic measurements is presented for the first time.

MAJOR CONCLUSIONS

Standardization of principal readouts might help researchers to collect a complete metabolic picture of cancer using the most appropriate methods depending on the sample of interest.

Social motility of biofilm-like microcolonies in a gliding bacterium

Bacterial biofilms are aggregates of surface-associated cells embedded in an extracellular polysaccharide (EPS) matrix, and are typically stationary. Studies of bacterial collective movement have largely focused on swarming motility mediated by flagella or pili, in the absence of a biofilm. Here, we describe a unique mode of collective movement by a self-propelled, surface-associated biofilm-like multicellular structure. Flavobacterium johnsoniae cells, which move by gliding motility, self-assemble into spherical microcolonies with EPS cores when observed by an under-oil open microfluidic system. Small microcolonies merge, creating larger ones. Microscopic analysis and computer simulation indicate that microcolonies move by cells at the base of the structure, attached to the surface by one pole of the cell. Biochemical and mutant analyses show that an active process drives microcolony self-assembly and motility, which depend on the bacterial gliding apparatus. We hypothesize that this mode of collective bacterial movement on solid surfaces may play potential roles in biofilm dynamics, bacterial cargo transport, or microbial adaptation. However, whether this collective motility occurs on plant roots or soil particles, the native environment for F. johnsoniae, is unknown.

Bacterial biofilms are aggregates of surface-associated cells embedded in an extracellular polysaccharide (EPS) matrix. Here, the authors describe a unique mode of collective movement by self-propelled, surface-associated spherical microcolonies with EPS cores in the gliding bacterium Flavobacterium johnsoniae.

Label-free imaging for quality control of cardiomyocyte differentiation

Human pluripotent stem cell (hPSC)-derived cardiomyocytes provide a promising regenerative cell therapy for cardiovascular patients and an important model system to accelerate drug discovery. However, cost-effective and time-efficient platforms must be developed to evaluate the quality of hPSC-derived cardiomyocytes during biomanufacturing. Here, we develop a non-invasive label-free live cell imaging platform to predict the efficiency of hPSC differentiation into cardiomyocytes. Autofluorescence imaging of metabolic co-enzymes is performed under varying differentiation conditions (cell density, concentration of Wnt signaling activator) across five hPSC lines. Live cell autofluorescence imaging and multivariate classification models provide high accuracy to separate low (< 50%) and high (≥ 50%) differentiation efficiency groups (quantified by cTnT expression on day 12) within 1 day after initiating differentiation (area under the receiver operating characteristic curve, 0.91). This non-invasive and label-free method could be used to avoid batch-to-batch and line-to-line variability in cell manufacturing from hPSCs.

Differentiation of hPSCs to cardiomyocytes suffers from high variability. Here the authors report a label-free live cell imaging platform based on autofluorescence imaging to enable the prediction of cardiomyocyte differentiation efficiency from hPSCs.

Autofluorescence Imaging of Treatment Response in Neuroendocrine Tumor Organoids

Gastroenteropancreatic neuroendocrine tumors (GEP-NET) account for roughly 60% of all neuroendocrine tumors. Low/intermediate grade human GEP-NETs have relatively low proliferation rates that animal models and cell lines fail to recapitulate. Short-term patient-derived cancer organoids (PDCOs) are a 3D model system that holds great promise for recapitulating well-differentiated human GEP-NETs. However, traditional measurements of drug response (i.e., growth, proliferation) are not effective in GEP-NET PDCOs due to the small volume of tissue and low proliferation rates that are characteristic of the disease. Here, we test a label-free, non-destructive optical metabolic imaging (OMI) method to measure drug response in live GEP-NET PDCOs. OMI captures the fluorescence lifetime and intensity of endogenous metabolic cofactors NAD(P)H and FAD. OMI has previously provided accurate predictions of drug response on a single cell level in other cancer types, but this is the first study to apply OMI to GEP-NETs. OMI tested the response to novel drug combination on GEP-NET PDCOs, specifically ABT263 (navitoclax), a Bcl-2 family inhibitor, and everolimus, a standard GEP-NET treatment that inhibits mTOR. Treatment response to ABT263, everolimus, and the combination were tested in GEP-NET PDCO lines derived from seven patients, using two-photon OMI. OMI measured a response to the combination treatment in 5 PDCO lines, at 72 h post-treatment. In one of the non-responsive PDCO lines, heterogeneous response was identified with two distinct subpopulations of cell metabolism. Overall, this work shows that OMI provides single-cell metabolic measurements of drug response in PDCOs to guide drug development for GEP-NET patients.

Patient-derived cancer organoid tracking with wide-field one-photon redox imaging to assess treatment response

SIGNIFICANCE

Accessible tools are needed for rapid, non-destructive imaging of patient-derived cancer organoid (PCO) treatment response to accelerate drug discovery and streamline treatment planning for individual patients.

AIM

To segment and track individual PCOs with wide-field one-photon redox imaging to extract morphological and metabolic variables of treatment response.

APPROACH

Redox imaging of the endogenous fluorophores, nicotinamide dinucleotide (NADH), nicotinamide dinucleotide phosphate (NADPH), and flavin adenine dinucleotide (FAD), was used to monitor the metabolic state and morphology of PCOs. Redox imaging was performed on a wide-field one-photon epifluorescence microscope to evaluate drug response in two colorectal PCO lines. An automated image analysis framework was developed to track PCOs across multiple time points over 48 h. Variables quantified for each PCO captured metabolic and morphological response to drug treatment, including the optical redox ratio (ORR) and organoid area.

RESULTS

The ORR (NAD(P)H/(FAD + NAD(P)H)) was independent of PCO morphology pretreatment. Drugs that induced cell death decreased the ORR and growth rate compared to control. Multivariate analysis of redox and morphology variables identified distinct PCO subpopulations. Single-organoid tracking improved sensitivity to drug treatment compared to pooled organoid analysis.

CONCLUSIONS

Wide-field one-photon redox imaging can monitor metabolic and morphological changes on a single organoid-level, providing an accessible, non-destructive tool to screen drugs in patient-matched samples.

Optical and magnetic resonance imaging approaches for investigating the tumour microenvironment: state-of-the-art review and future trends

The tumour microenvironment (TME) strongly influences tumorigenesis and metastasis. Two of the most characterized properties of the TME are acidosis and hypoxia, both of which are considered hallmarks of tumours as well as critical factors in response to anticancer treatments. Currently, various imaging approaches exist to measure acidosis and hypoxia in the TME, including magnetic resonance imaging (MRI), positron emission tomography and optical imaging. In this review, we will focus on the latest fluorescent-based methods for optical sensing of cell metabolism and MRI as diagnostic imaging tools applied both in vitro and in vivo. The primary emphasis will be on describing the current and future uses of systems that can measure intra- and extra-cellular pH and oxygen changes at high spatial and temporal resolution. In addition, the suitability of these approaches for mapping tumour heterogeneity, and assessing response or failure to therapeutics will also be covered.

Novel Quantitative Analysis Using Optical Imaging (VELscope) and Spectroscopy (Raman) Techniques for Oral Cancer Detection

In this study, we developed a novel quantitative analysis method to enhance the detection capability for oral cancer screening. We combined two different optical techniques, a light-based detection technique (visually enhanced lesion scope) and a vibrational spectroscopic technique (Raman spectroscopy). Materials and methods: Thirty-five oral cancer patients who went through surgery were enrolled. Thirty-five cancer lesions and thirty-five control samples with normal oral mucosa (adjacent to the cancer lesion) were analyzed. Thirty-five autofluorescence images and 70 Raman spectra were taken from 35 cancer and 35 control group cryopreserved samples. The normalized intensity and heterogeneity of the 70 regions of interest (ROIs) were calculated along with 70 averaged Raman spectra. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to differentiate the cancer and control groups (normal). The classifications rates were validated using two different validation methods, leave-one-out cross-validation (LOOCV) and k-fold cross-validation. Results: The cryopreserved normal and tumor tissues were differentiated using the PCA-LDA and PCA-QDA models. The PCA-LDA of Raman spectroscopy (RS) had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while ROIs on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. The combination of two optical techniques differentiated cancer and normal group with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. Conclusion: In this study, we combined the data of two different optical techniques. Furthermore, PCA-LDA and PCA-QDA quantitative analysis models were used to differentiate tumor and normal groups, creating a complementary pathway for efficient tumor diagnosis. The error rates of RS and VELcope analysis were 17.10% and 10%, respectively, which was reduced to 3% when the two optical techniques were combined.

Simultaneous two-photon activation and imaging of neural activity based on spectral-temporal modulation of supercontinuum light

SIGNIFICANCE

Recent advances in nonlinear optics in neuroscience have focused on using two ultrafast lasers for activity imaging and optogenetic stimulation. Broadband femtosecond light sources can obviate the need for multiple lasers by spectral separation for chromatically targeted excitation.

AIM

We present a photonic crystal fiber (PCF)-based supercontinuum source for spectrally resolved two-photon (2P) imaging and excitation of GCaMP6s and C1V1-mCherry, respectively.

APPROACH

A PCF is pumped using a 20-MHz repetition rate femtosecond laser to generate a supercontinuum of light, which is spectrally separated, compressed, and recombined to image GCaMP6s (930 nm excitation) and stimulate the optogenetic protein, C1V1-mCherry (1060 nm excitation). Galvanometric spiral scanning is employed on a single-cell level for multiphoton excitation and high-speed resonant scanning is employed for imaging of calcium activity.

RESULTS

Continuous wave lasers were used to verify functionality of optogenetic activation followed by directed 2P excitation. Results from these experiments demonstrate the utility of a supercontinuum light source for simultaneous, single-cell excitation and calcium imaging.

CONCLUSIONS

A PCF-based supercontinuum light source was employed for simultaneous imaging and excitation of calcium dynamics in brain tissue. Pumped PCFs can serve as powerful light sources for imaging and activation of neural activity, and overcome the limited spectra and space associated with multilaser approaches.

Metabolic Heterogeneity in Patient Tumor-Derived Organoids by Primary Site and Drug Treatment

New tools are needed to match cancer patients with effective treatments. Patient-derived organoids offer a high-throughput platform to personalize treatments and discover novel therapies. Currently, methods to evaluate drug response in organoids are limited because they overlook cellular heterogeneity. In this study, non-invasive optical metabolic imaging (OMI) of cellular heterogeneity was characterized in breast cancer (BC) and pancreatic cancer (PC) patient-derived organoids. Baseline heterogeneity was analyzed for each patient, demonstrating that single-cell techniques, such as OMI, are required to capture the complete picture of heterogeneity present in a sample. Treatment-induced changes in heterogeneity were also analyzed, further demonstrating that these measurements greatly complement current techniques that only gauge average cellular response. Finally, OMI of cellular heterogeneity in organoids was evaluated as a predictor of clinical treatment response for the first time. Organoids were treated with the same drugs as the patient's prescribed regimen, and OMI measurements of heterogeneity were compared to patient outcome. OMI distinguished subpopulations of cells with divergent and dynamic responses to treatment in living organoids without the use of labels or dyes. OMI of organoids agreed with long-term therapeutic response in patients. With these capabilities, OMI could serve as a sensitive high-throughput tool to identify optimal therapies for individual patients, and to develop new effective therapies that address cellular heterogeneity in cancer.

Label-free, High-Resolution Optical Metabolic Imaging of Human Cervical Precancers Reveals Potential for Intraepithelial Neoplasia Diagnosis

While metabolic changes are considered a cancer hallmark, their assessment has not been incorporated in the detection of early or precancers, when treatment is most effective. Here, we demonstrate that metabolic changes are detected in freshly excised human cervical precancerous tissues using label-free, non-destructive imaging of the entire epithelium. The images rely on two-photon excited fluorescence from two metabolic co-enzymes, NAD(P)H and FAD, and have micron-level resolution, enabling sensitive assessments of the redox ratio and mitochondrial fragmentation, which yield metrics of metabolic function and heterogeneity. Simultaneous characterization of morphological features, such as the depth-dependent variation of the nuclear:cytoplasmic ratio, is demonstrated. Multi-parametric analysis combining several metabolic metrics with morphological ones enhances significantly the diagnostic accuracy of identifying high-grade squamous intraepithelial lesions. Our results motivate the translation of such functional metabolic imaging to in vivo studies, which may enable improved identification of cervical lesions, and other precancers, at the bedside.

Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications

SIGNIFICANCE

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to distinguish the unique molecular environment of fluorophores. FLIM measures the time a fluorophore remains in an excited state before emitting a photon, and detects molecular variations of fluorophores that are not apparent with spectral techniques alone. FLIM is sensitive to multiple biomedical processes including disease progression and drug efficacy.

AIM

We provide an overview of FLIM principles, instrumentation, and analysis while highlighting the latest developments and biological applications.

APPROACH

This review covers FLIM principles and theory, including advantages over intensity-based fluorescence measurements. Fundamentals of FLIM instrumentation in time- and frequency-domains are summarized, along with recent developments. Image segmentation and analysis strategies that quantify spatial and molecular features of cellular heterogeneity are reviewed. Finally, representative applications are provided including high-resolution FLIM of cell- and organelle-level molecular changes, use of exogenous and endogenous fluorophores, and imaging protein-protein interactions with Förster resonance energy transfer (FRET). Advantages and limitations of FLIM are also discussed.

CONCLUSIONS

FLIM is advantageous for probing molecular environments of fluorophores to inform on fluorophore behavior that cannot be elucidated with intensity measurements alone. Development of FLIM technologies, analysis, and applications will further advance biological research and clinical assessments.

Metabolic mapping of glioblastoma stem cells reveals NADH fluxes associated with glioblastoma phenotype and survival

SIGNIFICANCE

Glioblastoma multiforme (GBM) is the most frequently diagnosed adult primary brain malignancy with poor patient prognosis. GBM can recur despite aggressive treatment due to therapeutically resistant glioblastoma stem cells (GSCs) that may exhibit metabolic plasticity.

AIM

Intrinsic nicotinamide adenine dinucleotide (NADH) fluorescence can be acquired with fluorescence lifetime imaging microscopy (FLIM) to examine its bound and free metabolic states in GSC and GBM tissues.

APPROACH

We compared the mean NADH fluorescence lifetime in live human GSCs and normal neural stem cells and validated those results by measuring oxygen consumption rates (OCRs). We also examined the role that invasive versus less-invasive GSCs had on tumor metabolism by measuring the mean NADH lifetimes and the relative amount of the longer-lived component of NADH and correlated these results with survival in an orthotopic mouse xenograft model.

RESULTS

Mean NADH lifetime, amount of bound NADH, and OCR were increased in GSCs. Compared with normal mouse brain, mean NADH lifetimes were longer for all GBM tissues. Invasive xenografts had higher relative amounts of the longer-lived NADH component, and this correlated with decreased survival.

CONCLUSIONS

FLIM offers cellular resolution quantification of metabolic flux in GBM phenotypes, potentially informing biomedical researchers on improved therapeutic approaches.

Optical changes in THP-1 macrophage metabolism in response to pro- and anti-inflammatory stimuli reported by label-free two-photon imaging

Temporal changes in macrophage metabolism are likely crucial to their role in inflammatory diseases. Label-free two-photon excited fluorescence (TPEF) and fluorescence lifetime imaging microscopy are well suited to track dynamic changes in macrophage metabolism. We performed TPEF imaging of human macrophages following either pro- or an anti-inflammatory stimulation. Two endogenous fluorophores, NAD(P)H and FAD, coenzymes involved in key metabolic pathways, provided contrast. We used the corresponding intensity images to determine the optical redox ratio of FAD to FAD + NAD(P)H. We also analyzed the intensity fluctuation patterns within NAD(P)H TPEF images to determine mitochondrial clustering patterns. Finally, we acquired NAD(P)H TPEF lifetime images to assess the relative levels of bound NAD(P)H. Our studies indicate that the redox ratio increases, whereas mitochondrial clustering decreases in response to both pro- and anti-inflammatory stimuli; however, these changes are enhanced in pro-inflammatory macrophages. Interestingly, we did not detect any significant changes in the corresponding NAD(P)H bound fraction. A combination of optical metabolic metrics could be used to classify pro- and anti-inflammatory macrophages with high accuracy. Contributions from alterations in different metabolic pathways may explain our findings, which highlight the potential of label-free two-photon imaging to assess nondestructively macrophage functional state.

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning

Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic.

Quantitative Spatial Analysis of Metabolic Heterogeneity Across in vivo and in vitro Tumor Models

Metabolic preferences of tumor cells vary within a single tumor, contributing to tumor heterogeneity, drug resistance, and patient relapse. However, the relationship between tumor treatment response and metabolically distinct tumor cell populations is not well-understood. Here, a quantitative approach was developed to characterize spatial patterns of metabolic heterogeneity in tumor cell populations within in vivo xenografts and 3D in vitro cultures (i.e., organoids) of head and neck cancer. Label-free images of cell metabolism were acquired using two-photon fluorescence lifetime microscopy of the metabolic co-enzymes NAD(P)H and FAD. Previous studies have shown that NAD(P)H mean fluorescence lifetimes can identify metabolically distinct cells with varying drug response. Thus, density-based clustering of the NAD(P)H mean fluorescence lifetime was used to identify metabolic sub-populations of cells, then assessed in control, cetuximab-, cisplatin-, and combination-treated xenografts 13 days post-treatment and organoids 24 h post-treatment. Proximity analysis of these metabolically distinct cells was designed to quantify differences in spatial patterns between treatment groups and between xenografts and organoids. Multivariate spatial autocorrelation and principal components analyses of all autofluorescence intensity and lifetime variables were developed to further improve separation between cell sub-populations. Spatial principal components analysis and Z-score calculations of autofluorescence and spatial distribution variables also visualized differences between models. This analysis captures spatial distributions of tumor cell sub-populations influenced by treatment conditions and model-specific environments. Overall, this novel spatial analysis could provide new insights into tumor growth, treatment resistance, and more effective drug treatments across a range of microscopic imaging modalities (e.g., immunofluorescence, imaging mass spectrometry).

Simultaneous label-free autofluorescence and multi-harmonic imaging reveals in vivo structural and metabolic changes in murine skin

Simultaneous quantification of multifarious cellular metabolites and the extracellular matrix in vivo has been long sought. Simultaneous label-free autofluorescence and multi-harmonic (SLAM) microscopy has achieved simultaneous four-channel nonlinear imaging to study tissue structure and metabolism. In this study, we implemented two laser systems and directly compared SLAM microscopy with conventional two-photon microscopy for in vivo imaging. We found that three-photon imaging of adenine dinucleotide (phosphate) (NAD(P)H) in SLAM microscopy using our tailored laser source provided better resolution, contrast, and background suppression than conventional two-photon imaging of NAD(P)H. We also integrated fluorescence lifetime imaging with SLAM microscopy, and enabled differentiation of free and bound NAD(P)H. We imaged murine skin in vivo and showed that changes in tissue structure, cell dynamics, and metabolism can be monitored simultaneously in real-time. We also discovered an increase in metabolism and protein-bound NAD(P)H in skin cells during the early stages of wound healing.

Patient-Derived Cancer Organoid Cultures to Predict Sensitivity to Chemotherapy and Radiation

PURPOSE

Cancer treatment is limited by inaccurate predictors of patient-specific therapeutic response. Therefore, some patients are exposed to unnecessary side effects and delays in starting effective therapy. A clinical tool that predicts treatment sensitivity for individual patients is needed.

EXPERIMENTAL DESIGN

Patient-derived cancer organoids were derived across multiple histologies. The histologic characteristics, mutation profile, clonal structure, and response to chemotherapy and radiation were assessed using bright-field and optical metabolic imaging on spheroid and single-cell levels, respectively.

RESULTS

We demonstrate that patient-derived cancer organoids represent the cancers from which they were derived, including key histologic and molecular features. These cultures were generated from numerous cancers, various biopsy sample types, and in different clinical settings. Next-generation sequencing reveals the presence of subclonal populations within the organoid cultures. These cultures allow for the detection of clonal heterogeneity with a greater sensitivity than bulk tumor sequencing. Optical metabolic imaging of these organoids provides cell-level quantification of treatment response and tumor heterogeneity allowing for resolution of therapeutic differences between patient samples. Using this technology, we prospectively predict treatment response for a patient with metastatic colorectal cancer.

CONCLUSIONS

These studies add to the literature demonstrating feasibility to grow clinical patient-derived organotypic cultures for treatment effectiveness testing. Together, these culture methods and response assessment techniques hold great promise to predict treatment sensitivity for patients with cancer undergoing chemotherapy and/or radiation.

Cellular Metabolic Heterogeneity In Vivo Is Recapitulated in Tumor Organoids

Heterogeneous populations within a tumor have varying metabolic profiles, which can muddle the interpretation of bulk tumor imaging studies of treatment response. Although methods to study tumor metabolism at the cellular level are emerging, these methods provide a single time point “snapshot” of tumor metabolism and require a significant time and animal burden while failing to capture the longitudinal metabolic response of a single tumor to treatment. Here, we investigated a novel method for longitudinal, single-cell tracking of metabolism across heterogeneous tumor cell populations using optical metabolic imaging (OMI), which measures autofluorescence of metabolic coenzymes as a report of metabolic activity. We also investigated whether in vivo cellular metabolic heterogeneity can be accurately captured using tumor-derived three-dimensional organoids in a genetically engineered mouse model of breast cancer. OMI measurements of response to paclitaxel and the phosphatidylinositol-3-kinase inhibitor XL147 in tumors and organoids taken at single cell resolution revealed parallel shifts in metaboltruic heterogeneity. Interestingly, these previously unappreciated heterogeneous metabolic responses in tumors and organoids could not be attributed to tumor cell fate or varying leukocyte content within the microenvironment, suggesting that heightened metabolic heterogeneity upon treatment is largely due to heterogeneous metabolic shifts within tumor cells. Together, these studies show that OMI revealed remarkable heterogeneity in response to treatment, which could provide a novel approach to predict the presence of potentially unresponsive tumor cell subpopulations lurking within a largely responsive bulk tumor population, which might otherwise be overlooked by traditional measurements.

Targeting flavin-containing enzymes eliminates cancer stem cells (CSCs), by inhibiting mitochondrial respiration: Vitamin B2 (Riboflavin) in cancer therapy

Here, we performed high-throughput drug-screening to identify new non-toxic mitochondrial inhibitors. This screening platform was specifically designed to detect compounds that selectively deplete cellular ATP levels, but have little or no toxic side effects on cell viability. Using this approach, we identified DPI (Diphenyleneiodonium chloride) as a new potential therapeutic agent. Mechanistically, DPI potently blocks mitochondrial respiration by inhibiting flavin-containing enzymes (FMN and FAD-dependent), which form part of Complex I and II. Interestingly, DPI induced a chemo-quiescence phenotype that potently inhibited the propagation of CSCs, with an IC-50 of 3.2 nano-molar. Virtually identical results were obtained using CSC markers, such as CD44 and CD24. We further validated the effects of DPI on cellular metabolism. At 10 nM, DPI inhibited oxidative mitochondrial metabolism (OXPHOS), reducing mitochondrial driven ATP production by >90%. This resulted in a purely glycolytic phenotype, with elevated L-lactate production. We show that this metabolic inflexibility could be rapidly-induced, after only 1 hour of DPI treatment. Remarkably, the mitochondrial inhibitory effects of DPI were reversible, and DPI did not induce ROS production. Cells maintained in DPI for 1 month showed little or no mitochondrial activity, but remained viable. Thus, it appears that DPI behaves as a new type of mitochondrial inhibitor, which maintains cells in a state of metabolic-quiescence or “suspended animation”. In conclusion, DPI treatment can be used to acutely confer a mitochondrial-deficient phenotype, which we show effectively depletes CSCs from the heterogeneous cancer cell population. These findings have significant therapeutic implications for potently targeting CSCs, while minimizing toxic side effects. We also discuss the possible implications of DPI for the aging process. Interestingly, previous studies in C. elegans have shown that DPI prevents the accumulation of lipofuscin (an aging-associated hallmark), during the response to oxidative stress. Our current results are consistent with data showing that flavins (FAD, FMN and/or Riboflavin) are auto-fluorescent markers of i) increased mitochondrial “power” (OXPHOS) and ii) elevated CSC activity. Finally, we believe that DPI is one of the most potent and highly selective CSC inhibitors discovered to date. Therefore, our current findings suggest a new impetus to create novel analogues of i) DPI (Diphenyleneiodonium chloride) and ii) DPI-related compounds (Diphenyliodonium chloride), using medicinal chemistry, to optimize this very promising and potent anti-CSC activity. We propose to call these new molecules “Mitoflavoscins”. For example, DPI is ∼30 times more potent than Palbociclib (IC-50 = 100 nM), which is an FDA-approved CDK4/6 inhibitor, that broadly targets proliferation in any cell type, including CSCs.

"Energetic" Cancer Stem Cells (e-CSCs): A New Hyper-Metabolic and Proliferative Tumor Cell Phenotype, Driven by Mitochondrial Energy

Here, we provide the necessary evidence that mitochondrial metabolism drives the anchorage-independent proliferation of CSCs. Two human breast cancer cell lines, MCF7 [ER(+)] and MDA-MB-468 (triple-negative), were used as model systems. To directly address the issue of metabolic heterogeneity in cancer, we purified a new distinct sub-population of CSCs, based solely on their energetic profile. We propose the term “energetic” cancer stem cells (e-CSCs), to better describe this novel cellular phenotype. In a single step, we first isolated an auto-fluorescent cell sub-population, based on their high flavin-content, using flow-cytometry. Then, these cells were further subjected to a detailed phenotypic characterization. More specifically, e-CSCs were more glycolytic, with higher mitochondrial mass and showed significantly elevated oxidative metabolism. e-CSCs also demonstrated an increased capacity to undergo cell cycle progression, as well as enhanced anchorage-independent growth and ALDH-positivity. Most importantly, these e-CSCs could be effectively targeted by treatments with either (i) OXPHOS inhibitors (DPI) or (ii) a CDK4/6 inhibitor (Ribociclib). Finally, we were able to distinguish two distinct phenotypic sub-types of e-CSCs, depending on whether they were grown as 2D-monolayers or as 3D-spheroids. Remarkably, under 3D anchorage-independent growth conditions, e-CSCs were strictly dependent on oxidative mitochondrial metabolism. Unbiased proteomics analysis demonstrated the up-regulation of gene products specifically related to the anti-oxidant response, mitochondrial energy production, and mitochondrial biogenesis. Therefore, mitochondrial inhibitors should be further developed as promising anti-cancer agents, to directly target and eliminate the “fittest” e-CSCs. Our results have important implications for using e-CSCs, especially those derived from 3D-spheroids, (i) in tumor tissue bio-banking and (ii) as a new cellular platform for drug development.

MTORC1/2 Inhibition as a Therapeutic Strategy for PIK3CA Mutant Cancers

PIK3CA mutations are common in clinical molecular profiling, yet an effective means to target these cancers has yet to be developed. MTORC1 inhibitors are often used off-label for patients with PIK3CA mutant cancers with only limited data to support this approach. Here we describe a cohort of patients treated with cancers possessing mutations activating the PI3K signaling cascade with minimal benefit to treatment with the MTORC1 inhibitor everolimus. Previously, we demonstrated that dual PI3K/mTOR inhibition could decrease proliferation, induce differentiation, and result in a treatment response in APC and PIK3CA mutant colorectal cancer. However, reactivation of AKT was identified, indicating that the majority of the benefit may be secondary to MTORC1/2 inhibition. TAK-228, an MTORC1/2 inhibitor, was compared with dual PI3K/mTOR inhibition using BEZ235 in murine colorectal cancer spheroids. A reduction in spheroid size was observed with TAK-228 and BEZ235 (-13% and -14%, respectively) compared with an increase of >200% in control (P < 0.001). These spheroids were resistant to MTORC1 inhibition. In transgenic mice possessing Pik3ca and Apc mutations, BEZ235 and TAK-228 resulted in a median reduction in colon tumor size of 19% and 20%, respectively, with control tumors having a median increase of 18% (P = 0.02 and 0.004, respectively). This response correlated with a decrease in the phosphorylation of 4EBP1 and RPS6. MTORC1/2 inhibition is sufficient to overcome resistance to everolimus and induce a treatment response in PIK3CA mutant colorectal cancers and deserves investigation in clinical trials and in future combination regimens.

In-vivo correlations between skin metabolic oscillations and vasomotion in wild-type mice and in a model of oxidative stress

Arterioles in the cutaneous microcirculation frequently display an oscillatory phenomenon defined vasomotion, consistent with periodic diameter variations in the micro-vessels associated with particular physiological or abnormal conditions. The cellular mechanisms underlying vasomotion and its physiological role have not been completely elucidated. Various mechanisms were demonstrated, based on cell Ca²⁺ oscillations determined by the activity of channels in the plasma membrane or sarcoplasmic reticulum of vascular cells. However, the possible engagement in vasomotion of cell metabolic oscillations of mitochondrial or glycolytic origin has been poorly explored. Metabolic oscillations associated with the production of ATP energy were previously described in cells, while limited studies have investigated these fluctuations in-vivo. Here, we characterised a low-frequency metabolic oscillator (MO-1) in skin from live wild-type and Nrf2^−/− mice, by combination of fluorescence spectroscopy and wavelet transform processing technique. Furthermore, the relationships between metabolic and microvascular oscillators were examined during phenylephrine-induced vasoconstriction. We found a significant interaction between MO-1 and the endothelial EDHF vasomotor mechanism that was reduced in the presence of oxidative stress (Nrf2^−/− mice). Our findings suggest indirectly that metabolic oscillations may be involved in the mechanisms underlying endothelium-mediated skin vasomotion, which might be altered in the presence of metabolic disturbance.

Quantitative Label-Free Imaging of 3D Vascular Networks Self-Assembled in Synthetic Hydrogels

Vascularization is an important strategy to overcome diffusion limits and enable the formation of complex, physiologically relevant engineered tissues and organoids. Self-assembly is a technique to generate in vitro vascular networks, but engineering the necessary network morphology and function remains challenging. Here, autofluorescence multiphoton microscopy (aMPM), a label-free imaging technique, is used to quantitatively evaluate in vitro vascular network morphology. Vascular networks are generated using human embryonic stem cell-derived endothelial cells and primary human pericytes encapsulated in synthetic poly(ethylene glycol)-based hydrogels. Two custom-built bioreactors are used to generate distinct fluid flow patterns during vascular network formation: recirculating flow or continuous flow. aMPM is used to image these 3D vascular networks without the need for fixation, labels, or dyes. Image processing and analysis algorithms are developed to extract quantitative morphological parameters from these label-free images. It is observed with aMPM that both bioreactors promote formation of vascular networks with lower network anisotropy compared to static conditions, and the continuous flow bioreactor induces more branch points compared to static conditions. Importantly, these results agree with trends observed with immunocytochemistry. These studies demonstrate that aMPM allows label-free monitoring of vascular network morphology to streamline optimization of growth conditions and provide quality control of engineered tissues.

Two-channel autofluorescence analysis for oral cancer

We created a two-channel autofluorescence test to detect oral cancer. The wavelengths 375 and 460 nm, with filters of 479 and 525 nm, were designed to excite and detect reduced-form nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) autofluorescence. Patients with oral cancer or with precancerous lesions, and a control group with healthy oral mucosae, were enrolled. The lesion in the autofluorescent image was the region of interest. The average intensity and heterogeneity of the NADH and FAD were calculated. The redox ratio [(NADH)/(NADH + FAD)] was also computed. A quadratic discriminant analysis (QDA) was used to compute boundaries based on sensitivity and specificity. We analyzed 49 oral cancer lesions, 34 precancerous lesions, and 77 healthy oral mucosae. A boundary (sensitivity: 0.974 and specificity: 0.898) between the oral cancer lesions and healthy oral mucosae was validated. Oral cancer and precancerous lesions were also differentiated from healthy oral mucosae (sensitivity: 0.919 and specificity: 0.755). The two-channel autofluorescence detection device and analyses of the intensity and heterogeneity of NADH, and of FAD, and the redox ratio combined with a QDA classifier can differentiate oral cancer and precancerous lesions from healthy oral mucosae.

Organotypic microfluidic breast cancer model reveals starvation-induced spatial-temporal metabolic adaptations

BACKGROUND

Ductal carcinoma in situ (DCIS) is the earliest stage of breast cancer. During DCIS, tumor cells remain inside the mammary duct, growing under a microenvironment characterized by hypoxia, nutrient starvation, and waste product accumulation; this harsh microenvironment promotes genomic instability and eventually cell invasion. However, there is a lack of biomarkers to predict what patients will transition to a more invasive tumor or how DCIS cells manage to survive in this harsh microenvironment.

METHODS

In this work, we have developed a microfluidic model that recapitulates the DCIS microenvironment. In the microdevice, a DCIS model cell line was grown inside a luminal mammary duct model, embedded in a 3D hydrogel with mammary fibroblasts. Cell behavior was monitored by confocal microscopy and optical metabolic imaging. Additionally, metabolite profile was studied by NMR whereas gene expression was analyzed by RT-qPCR.

FINDINGS

DCIS cell metabolism led to hypoxia and nutrient starvation; revealing an altered metabolism focused on glycolysis and other hypoxia-associated pathways. In response to this starvation and hypoxia, DCIS cells modified the expression of multiple genes, and a gradient of different metabolic phenotypes was observed across the mammary duct model. These genetic changes observed in the model were in good agreement with patient genomic profiles; identifying multiple compounds targeting the affected pathways. In this context, the hypoxia-activated prodrug tirapazamine selectively destroyed hypoxic DCIS cells.

INTERPRETATION

The results showed the capacity of the microfluidic model to mimic the DCIS structure, identifying multiple cellular adaptations to endure the hypoxia and nutrient starvation generated within the mammary duct. These findings may suggest new potential therapeutic directions to treat DCIS. In summary, given the lack of in vitro models to study DCIS, this microfluidic device holds great potential to find new DCIS predictors and therapies and translate them to the clinic.

In vivo metabolic and SHG imaging for monitoring of tumor response to chemotherapy

Although chemotherapy remains one of the main types of treatment for cancer, treatment failure is a frequent occurrence, emphasizing the need for new approaches to the early assessment of tumor response. The aim of this study was to search for indicators based on optical imaging of cellular metabolism and of collagen in tumors in vivo that enable evaluation of their response to chemotherapy. The study was performed on a mouse colorectal cancer model with the use of cisplatin, paclitaxel, and irinotecan. The metabolic activity of the tumor cells was assessed using fluorescence lifetime imaging of the metabolic cofactor reduced nicotinamide adenine dinucleotide (phosphate), NAD(P)H. Second harmonic generation (SHG) imaging was used to analyze the extent and properties of collagen within the tumors. We detected an early decrease in the free/bound NAD(P)H ratio in all treated tumors, indicating a shift toward a more oxidative metabolism. Monitoring of collagen showed an early increase in the amount of collagen followed by an increase in the extent of its orientation in tumors treated with cisplatin and paclitaxel, and decrease in collagen content in the case of irinotecan. Our study suggests that changes in cellular metabolism and fibrotic stroma organization precede morphological alterations and tumor size reduction, and that this indicates that NAD(P)H and collagen can be considered as intrinsic indicators of the response to treatment. This is the first time that these parameters have been investigated in tumors in vivo in the course of chemotherapy with drugs having different mechanisms of action. © 2018 International Society for Advancement of Cytometry.

NADH Autofluorescence-A Marker on its Way to Boost Bioenergetic Research

More than 60 years ago, the idea was introduced that NADH autofluorescence could be used as a marker of cellular redox state and indirectly also of cellular energy metabolism. Fluorescence lifetime imaging microscopy of NADH autofluorescence offers a marker-free readout of the mitochondrial function of cells in their natural microenvironment and allows different pools of NADH to be distinguished within a cell. Despite its many advantages in terms of spatial resolution and in vivo applicability, this technique still requires improvement in order to be fully useful in bioenergetics research. In the present review, we give a summary of technical and biological challenges that have so far limited the spread of this powerful technology. To help overcome these challenges, we provide a comprehensible overview of biological applications of NADH imaging, along with a detailed summary of valid imaging approaches that may be used to tackle many biological questions. This review is meant to provide all scientists interested in bioenergetics with support on how to embed successfully NADH imaging in their research. © 2018 International Society for Advancement of Cytometry.

A Radiosensitizing Inhibitor of HIF-1 alters the Optical Redox State of Human Lung Cancer Cells In Vitro

Treatment failure caused by a radiation-resistant cell phenotype remains an impediment to the success of radiation therapy in cancer. We recently showed that a radiation-resistant isogenic line of human A549 lung cancer cells had significantly elevated expression of hypoxia-inducible factor (HIF-1α), and increased glucose catabolism compared with the parental, radiation-sensitive cell line. The objective of this study was to investigate the longitudinal metabolic changes in radiation-resistant and sensitive A549 lung cancer cells after treatment with a combination of radiation therapy and YC-1, a potent HIF-1 inhibitor. Using label-free two-photon excited fluorescence microscopy, we determined changes in the optical redox ratio of FAD/(NADH and FAD) over a period of 24 hours following treatment with YC-1, radiation, and both radiation and YC-1. To complement the optical redox ratio, we also evaluated changes in mitochondrial organization, glucose uptake, reactive oxygen species (ROS), and reduced glutathione. We observed significant differences in the optical redox ratio of radiation-resistant and sensitive A549 cells in response to radiation or YC-1 treatment alone; however, combined treatment eliminated these differences. Our results demonstrate that the optical redox ratio can elucidate radiosensitization of previously radiation-resistant A549 cancer cells, and provide a method for evaluating treatment response in patient-derived tumor biopsies.

Metabolic cofactors NAD(P)H and FAD as potential indicators of cancer cell response to chemotherapy with paclitaxel

Paclitaxel, a widely used antimicrotubular agent, predominantly eliminates rapidly proliferating cancer cells, while slowly proliferating and quiescent cells can survive the treatment, which is one of the main reasons for tumor recurrence and non-responsiveness to the drug. To improve the efficacy of chemotherapy, biomarkers need to be developed to enable monitoring of tumor responses. In this study we considered the auto-fluorescent metabolic cofactors NAD(P)H and FAD as possible indicators of cancer cell response to therapy with paclitaxel. It was found that, among the tested parameters (the fluorescence intensity-based redox ratio FAD/NAD(P)H, and the fluorescence lifetimes of NAD(P)H and FAD), the fluorescence lifetime of NAD(P)H is the most sensitive in tracking the drug response, and is capable of indicating heterogeneous cellular responses both in cell monolayers and in multicellular tumor spheroids. We observed that metabolic reorganization to a more oxidative state preceded the morphological manifestation of cell death and developed faster in cells that were more responsive to the drug. Our results suggest that noninvasive, label-free monitoring of the drug-induced metabolic changes by noting the NAD(P)H fluorescence lifetime is a valuable approach to characterize the responses of cancer cells to anti-cancer treatments and, therefore, to predict the effectiveness of chemotherapy.

Mapping metabolic changes by noninvasive, multiparametric, high-resolution imaging using endogenous contrast

Monitoring subcellular functional and structural changes associated with metabolism is essential for understanding healthy tissue development and the progression of numerous diseases, including cancer, diabetes, and cardiovascular and neurodegenerative disorders. Unfortunately, established methods for this purpose either are destructive or require the use of exogenous agents. Recent work has highlighted the potential of endogenous two-photon excited fluorescence (TPEF) as a method to monitor subtle metabolic changes; however, mechanistic understanding of the connections between the detected optical signal and the underlying metabolic pathways has been lacking. We present a quantitative approach to detecting both functional and structural metabolic biomarkers noninvasively, relying on endogenous TPEF from two coenzymes, NADH (reduced form of nicotinamide adenine dinucleotide) and FAD (flavin adenine dinucleotide). We perform multiparametric analysis of three optical biomarkers within intact, living cells and three-dimensional tissues: cellular redox state, NADH fluorescence lifetime, and mitochondrial clustering. We monitor the biomarkers in cells and tissues subjected to metabolic perturbations that trigger changes in distinct metabolic processes, including glycolysis and glutaminolysis, extrinsic and intrinsic mitochondrial uncoupling, and fatty acid oxidation and synthesis. We demonstrate that these optical biomarkers provide complementary insights into the underlying biological mechanisms. Thus, when used in combination, these biomarkers can serve as a valuable tool for sensitive, label-free identification of changes in specific metabolic pathways and characterization of the heterogeneity of the elicited responses with single-cell resolution.

Autofluorescence imaging identifies tumor cell-cycle status on a single-cell level

The goal of this study is to validate fluorescence intensity and lifetime imaging of metabolic co-enzymes NAD(P)H and FAD (optical metabolic imaging, or OMI) as a method to quantify cell-cycle status of tumor cells. Heterogeneity in tumor cell-cycle status (e. g. proliferation, quiescence, apoptosis) increases drug resistance and tumor recurrence. Cell-cycle status is closely linked to cellular metabolism. Thus, this study applies cell-level metabolic imaging to distinguish proliferating, quiescent, and apoptotic populations. Two-photon microscopy and time-correlated single photon counting are used to measure optical redox ratio (NAD(P)H fluorescence intensity divided by FAD intensity), NAD(P)H and FAD fluorescence lifetime parameters. Redox ratio, NAD(P)H and FAD lifetime parameters alone exhibit significant differences (p<0.05) between population means. To improve separation between populations, linear combination models derived from partial least squares - discriminant analysis (PLS-DA) are used to exploit all measurements together. Leave-one-out cross validation of the model yielded high classification accuracies (92.4 and 90.1 % for two and three populations, respectively). OMI and PLS-DA also identifies each sub-population within heterogeneous samples. These results establish single-cell analysis with OMI and PLS-DA as a label-free method to distinguish cell-cycle status within intact samples. This approach could be used to incorporate cell-level tumor heterogeneity in cancer drug development.

Vital ex vivo tissue labeling and pathology-guided micropunching to characterize cellular heterogeneity in the tissue microenvironment

Cellular heterogeneity within the tissue microenvironment may underlie chemotherapeutic resistance and response, enabling tumor evolution; however, this heterogeneity it is difficult to characterize. Here, we present a new approach-pathology-guided micropunching (PGM)-that enables identification and characterization of heterogeneous foci identified in viable human and animal model tissue slices. This technique consists of live-cell tissue labeling using fluorescent antibodies/small molecules to identify heterogeneous foci (e.g., immune infiltrates or cells with high levels of reactive oxygen species) in viable tissues, coupled with a micropunch step to isolate cells from these heterogeneous foci for downstream molecular or vital functional analysis. Micropunches obtained from epithelial or stromal fibroblast foci in human prostate tissue show 6- to 12-fold enrichment in transcripts specific for EpCam/cytokeratin 8 and vimentin/a-smooth muscle actin/integrin 1-α, respectively. Transcriptional enrichment efficiency agrees with epithelial and stromal laser capture microdissection samples isolated from human prostate. Micropunched foci show a loss of cellular viability in the periphery, but centrally localized cells retained viability before and after dissociation and grew out in culture.