Multiphoton microscopy and fluorescence lifetime imaging microscopy (FLIM) to monitor metastasis and the tumor microenvironment

Exploiting Matrix Stiffness to Overcome Drug Resistance

Drug resistance is arguably one of the biggest challenges facing cancer research today. Understanding the underlying mechanisms of drug resistance in tumor progression and metastasis are essential in developing better treatment modalities. Given the matrix stiffness affecting the mechanotransduction capabilities of cancer cells, characterization of the related signal transduction pathways can provide a better understanding for developing novel therapeutic strategies. In this review, we aimed to summarize the recent advancements in tumor matrix biology in parallel to therapeutic approaches targeting matrix stiffness and its consequences in cellular processes in tumor progression and metastasis. The cellular processes governed by signal transduction pathways and their aberrant activation may result in activating the epithelial-to-mesenchymal transition, cancer stemness, and autophagy, which can be attributed to drug resistance. Developing therapeutic strategies to target these cellular processes in cancer biology will offer novel therapeutic approaches to tailor better personalized treatment modalities for clinical studies.

Label-free fluorescence lifetime imaging for the assessment of cell viability in living tumor fragments

Significance

To enable non-destructive longitudinal assessment of drug agents in intact tumor tissue without the use of disruptive probes, we have designed a label-free method to quantify the health of individual tumor cells in excised tumor tissue using multiphoton fluorescence lifetime imaging microscopy (MP-FLIM).

Aim

Using murine tumor fragments which preserve the native tumor microenvironment, we seek to demonstrate signals generated by the intrinsically fluorescent metabolic co-factors nicotinamide adenine dinucleotide phosphate [NAD(P)H] and flavin adenine dinucleotide (FAD) correlate with irreversible cascades leading to cell death.

Approach

We use MP-FLIM of NAD(P)H and FAD on tissues and confirm viability using standard apoptosis and live/dead (Caspase 3/7 and propidium iodide, respectively) assays.

Results

Through a statistical approach, reproducible shifts in FLIM data, determined through phasor analysis, are shown to correlate with loss of cell viability. With this, we demonstrate that cell death achieved through either apoptosis/necrosis or necroptosis can be discriminated. In addition, specific responses to common chemotherapeutic treatment inducing cell death were detected.

Conclusions

These data demonstrate that MP-FLIM can detect and quantify cell viability without the use of potentially toxic dyes, thus enabling longitudinal multi-day studies assessing the effects of therapeutic agents on tumor fragments.

Implementation of FRET Spectrometry Using Temporally Resolved Fluorescence: A Feasibility Study

Förster resonance energy transfer (FRET) spectrometry is a method for determining the quaternary structure of protein oligomers from distributions of FRET efficiencies that are drawn from pixels of fluorescence images of cells expressing the proteins of interest. FRET spectrometry protocols currently rely on obtaining spectrally resolved fluorescence data from intensity-based experiments. Another imaging method, fluorescence lifetime imaging microscopy (FLIM), is a widely used alternative to compute FRET efficiencies for each pixel in an image from the reduction of the fluorescence lifetime of the donors caused by FRET. In FLIM studies of oligomers with different proportions of donors and acceptors, the donor lifetimes may be obtained by fitting the temporally resolved fluorescence decay data with a predetermined number of exponential decay curves. However, this requires knowledge of the number and the relative arrangement of the fluorescent proteins in the sample, which is precisely the goal of FRET spectrometry, thus creating a conundrum that has prevented users of FLIM instruments from performing FRET spectrometry. Here, we describe an attempt to implement FRET spectrometry on temporally resolved fluorescence microscopes by using an integration-based method of computing the FRET efficiency from fluorescence decay curves. This method, which we dubbed time-integrated FRET (or tiFRET), was tested on oligomeric fluorescent protein constructs expressed in the cytoplasm of living cells. The present results show that tiFRET is a promising way of implementing FRET spectrometry and suggest potential instrument adjustments for increasing accuracy and resolution in this kind of study.

Time-resolved fluorescence and diffuse reflectance for lung squamous carcinoma margin detection

OBJECTIVES

A major challenge in non-small cell lung cancer surgery is the occurrence of positive tumor margins. This may lead to the need for additional surgeries and has been linked to poor patient prognosis. This study aims to develop an in vivo surgical tool that can differentiate cancerous from noncancerous lung tissue at the margin.

METHODS

A time-resolved fluorescence and diffuse reflectance bimodal device was used to measure the lifetime, spectra, and intensities of endogenous fluorophores as well as optical properties of lung tissue. The tumor and fibrotic tissue data, each containing 36 samples, was obtained from patients who underwent surgical removal of lung tissue after being diagnosed with squamous carcinoma but before any other treatment was administered. The normal lung tissue data were obtained from nine normal tissue samples.

RESULTS

The results show a statistically significant difference between cancerous and noncancerous tissue. The results also show a difference in metabolic related optical properties between fibrotic and normal lung tissue samples.

CONCLUSIONS

This work demonstrates the feasibility of a device that can differentiate cancerous and noncancerous lung tissue for patients diagnosed with squamous cell carcinoma.

Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning

According to the World Health Organization, the proportion of the world's population over 60 years will approximately double by 2050. This progressive increase in the elderly population will lead to a dramatic growth of age-related diseases, resulting in tremendous pressure on the sustainability of healthcare systems globally. In this context, finding more efficient ways to address cancers, a set of diseases whose incidence is correlated with age, is of utmost importance. Prevention of cancers to decrease morbidity relies on the identification of precursor lesions before the onset of the disease, or at least diagnosis at an early stage. In this article, after briefly discussing some of the most prominent endoscopic approaches for gastric cancer diagnostics, we review relevant progress in three emerging technologies that have significant potential to play pivotal roles in next-generation endoscopy systems: biomimetic vision (with special focus on compound eye cameras), non-linear optical microscopies, and Deep Learning. Such systems are urgently needed to enhance the three major steps required for the successful diagnostics of gastrointestinal cancers: detection, characterization, and confirmation of suspicious lesions. In the final part, we discuss challenges that lie en route to translating these technologies to next-generation endoscopes that could enhance gastrointestinal imaging, and depict a possible configuration of a system capable of (i) biomimetic endoscopic vision enabling easier detection of lesions, (ii) label-free in vivo tissue characterization, and (iii) intelligently automated gastrointestinal cancer diagnostic.

Label-free photothermal optical coherence microscopy to locate desired regions of interest in multiphoton imaging of volumetric specimens

Biochip-based research is currently evolving into a three-dimensional and large-scale basis similar to the in vivo microenvironment. For the long-term live and high-resolution imaging in these specimens, nonlinear microscopy capable of label-free and multiscale imaging is becoming increasingly important. Combination with non-destructive contrast imaging will be useful for effectively locating regions of interest (ROI) in large specimens and consequently minimizing photodamage. In this study, a label-free photothermal optical coherence microscopy (OCM) serves as a new approach to locate the desired ROI within biological samples which are under investigation by multiphoton microscopy (MPM). The weak photothermal perturbation in sample by the MPM laser with reduced power was detected at the endogenous photothermal particles within the ROI using the highly sensitive phase-differentiated photothermal (PD-PT) OCM. By monitoring the temporal change of the photothermal response signal of the PD-PT OCM, the hotspot generated within the sample focused by the MPM laser was located on the ROI. Combined with automated sample movement in the x-y axis, the focal plane of MPM could be effectively navigated to the desired portion of a volumetric sample for high-resolution targeted MPM imaging. We demonstrated the feasibility of the proposed method in second harmonic generation microscopy using two phantom samples and a biological sample, a fixed insect on microscope slide, with dimensions of 4 mm wide, 4 mm long, and 1 mm thick.

Intravital imaging to study cancer progression and metastasis

Navigation through the bulk tumour, entry into the blood vasculature, survival in the circulation, exit at distant sites and resumption of proliferation are all steps necessary for tumour cells to successfully metastasize. The ability of tumour cells to complete these steps is highly dependent on the timing and sequence of the interactions that these cells have with the tumour microenvironment (TME), including stromal cells, the extracellular matrix and soluble factors. The TME thus plays a major role in determining the overall metastatic phenotype of tumours. The complexity and cause-and-effect dynamics of the TME cannot currently be recapitulated in vitro or inferred from studies of fixed tissue, and are best studied in vivo, in real time and at single-cell resolution. Intravital imaging (IVI) offers these capabilities, and recent years have been a time of immense growth and innovation in the field. Here we review some of the recent advances in IVI of mammalian models of cancer and describe how IVI is being used to understand cancer progression and metastasis, and to develop novel treatments and therapies. We describe new techniques that allow access to a range of tissue and cancer types, novel fluorescent reporters and biosensors that allow fate mapping and the probing of functional and phenotypic states, and the clinical applications that have arisen from applying these techniques, reporters and biosensors to study cancer. We finish by presenting some of the challenges that remain in the field, how to address them and future perspectives.

A preclinical model of cutaneous melanoma based on reconstructed human epidermis

Malignant melanoma is among the tumor entities with the highest increase of incidence worldwide. To elucidate melanoma progression and develop new effective therapies, rodent models are commonly used. While these do not adequately reflect human physiology, two-dimensional cell cultures lack crucial elements of the tumor microenvironment. To address this shortcoming, we have developed a melanoma skin equivalent based on an open-source epidermal model. Melanoma cell lines with different driver mutations were incorporated into these models forming distinguishable tumor aggregates within a stratified epidermis. Although barrier properties of the skin equivalents were not affected by incorporation of melanoma cells, their presence resulted in a higher metabolic activity indicated by an increased glucose consumption. Furthermore, we re-isolated single cells from the models to characterize the proliferation state within the respective model. The applicability of our model for tumor therapeutics was demonstrated by treatment with a commonly used v-raf murine sarcoma viral oncogene homolog B (BRAF) inhibitor vemurafenib. This selective BRAF inhibitor successfully reduced tumor growth in the models harboring BRAF-mutated melanoma cells. Hence, our model is a promising tool to investigate melanoma development and as a preclinical model for drug discovery.

Assessment of MRI to estimate metastatic dissemination risk and prometastatic effects of chemotherapy

Metastatic dissemination in breast cancer is regulated by specialized intravasation sites called “tumor microenvironment of metastasis” (TMEM) doorways, composed of a tumor cell expressing the actin-regulatory protein Mena, a perivascular macrophage, and an endothelial cell, all in stable physical contact. High TMEM doorway number is associated with an increased risk of distant metastasis in human breast cancer and mouse models of breast carcinoma. Here, we developed a novel magnetic resonance imaging (MRI) methodology, called TMEM Activity-MRI, to detect TMEM-associated vascular openings that serve as the portal of entry for cancer cell intravasation and metastatic dissemination. We demonstrate that TMEM Activity-MRI correlates with primary tumor TMEM doorway counts in both breast cancer patients and mouse models, including MMTV-PyMT and patient-derived xenograft models. In addition, TMEM Activity-MRI is reduced in mouse models upon treatment with rebastinib, a specific and potent TMEM doorway inhibitor. TMEM Activity-MRI is an assay that specifically measures TMEM-associated vascular opening (TAVO) events in the tumor microenvironment, and as such, can be utilized in mechanistic studies investigating molecular pathways of cancer cell dissemination and metastasis. Finally, we demonstrate that TMEM Activity-MRI increases upon treatment with paclitaxel in mouse models, consistent with prior observations that chemotherapy enhances TMEM doorway assembly and activity in human breast cancer. Our findings suggest that TMEM Activity-MRI is a promising precision medicine tool for localized breast cancer that could be used as a non-invasive test to determine metastatic risk and serve as an intermediate pharmacodynamic biomarker to monitor therapeutic response to agents that block TMEM doorway-mediated dissemination.

Label-free metabolic imaging for sensitive and robust monitoring of anti-CD47 immunotherapy response in triple-negative breast cancer

Background

Immunotherapy is revolutionizing cancer treatment from conventional radiotherapies and chemotherapies to immune checkpoint inhibitors which use patients’ immune system to recognize and attack cancer cells. Despite the huge clinical success and vigorous development of immunotherapies, there is a significant unmet need for a robust tool to identify responders to specific immunotherapy. Early and accurate monitoring of immunotherapy response is indispensable for personalized treatment and effective drug development.

Methods

We established a label-free metabolic intravital imaging (LMII) technique to detect two-photon excited autofluorescence signals from two coenzymes, NAD(P)H (reduced nicotinamide adenine dinucleotide (phosphate) hydrogen) and FAD (flavin adenine dinucleotide) as robust imaging markers to monitor metabolic responses to immunotherapy. Murine models of triple-negative breast cancer (TNBC) were established and tested with different therapeutic regimens including anti-cluster of differentiation 47 (CD47) immunotherapy to monitor time-course treatment responses using the developed metabolic imaging technique.

Results

We first imaged the mechanisms of the CD47-signal regulatory protein alpha pathway in vivo, which unravels macrophage-mediated antibody-dependent cellular phagocytosis and illustrates the metabolism of TNBC cells and macrophages. We further visualized the autofluorescence of NAD(P)H and FAD and found a significant increase during tumor growth. Following anti-CD47 immunotherapy, the imaging signal was dramatically decreased demonstrating the sensitive monitoring capability of NAD(P)H and FAD imaging for therapeutic response. NAD(P)H and FAD intravital imaging also showed a marked decrease after chemotherapy and radiotherapy. A comparative study with conventional whole-body bioluminescence and fluorescent glucose imaging demonstrated superior sensitivity of metabolic imaging. Flow cytometry validated metabolic imaging results. In vivo immunofluorescent staining revealed the targeting ability of NAD(P)H imaging mainly for tumor cells and a small portion of immune-active cells and that of FAD imaging mainly for immunosuppressive cells such as M2-like tumor-associated macrophages.

Conclusions

Collectively, this study showcases the potential of the LMII technique as a powerful tool to visualize dynamic changes of heterogeneous cell metabolism of cancer cells and immune infiltrates in response to immunotherapy thus providing sensitive and complete monitoring. Leveraged on ability to differentiate cancer cells and immunosuppressive macrophages, the presented imaging approach provides particularly useful imaging biomarkers for emerged innate immune checkpoint inhibitors such as anti-CD47 therapy.

Deep learning identification of stiffness markers in breast cancer

While essential to our understanding of solid tumor progression, the study of cell and tissue mechanics has yet to find traction in the clinic. Determining tissue stiffness, a mechanical property known to promote a malignant phenotype in vitro and in vivo, is not part of the standard algorithm for the diagnosis and treatment of breast cancer. Instead, clinicians routinely use mammograms to identify malignant lesions and radiographically dense breast tissue is associated with an increased risk of developing cancer. Whether breast density is related to tumor tissue stiffness, and what cellular and non-cellular components of the tumor contribute the most to its stiffness are not well understood. Through training of a deep learning network and mechanical measurements of fresh patient tissue, we create a bridge in understanding between clinical and mechanical markers. The automatic identification of cellular and extracellular features from hematoxylin and eosin (H&E)-stained slides reveals that global and local breast tissue stiffness best correlate with the percentage of straight collagen. Importantly, the percentage of dense breast tissue does not directly correlate with tissue stiffness or straight collagen content.

Web-Based Application for Biomedical Image Registry, Analysis, and Translation (BiRAT)

Imaging has become an invaluable tool in preclinical research for its capability to non-invasively detect and monitor disease and assess treatment response. With the increased use of preclinical imaging, large volumes of image data are being generated requiring critical data management tools. Due to proprietary issues and continuous technology development, preclinical images, unlike DICOM-based images, are often stored in an unstructured data file in company-specific proprietary formats. This limits the available DICOM-based image management database to be effectively used for preclinical applications. A centralized image registry and management tool is essential for advances in preclinical imaging research. Specifically, such tools may have a high impact in generating large image datasets for the evolving artificial intelligence applications and performing retrospective analyses of previously acquired images. In this study, a web-based server application is developed to address some of these issues. The application is designed to reflect the actual experimentation workflow maintaining detailed records of both individual images and experimental data relevant to specific studies and/or projects. The application also includes a web-based 3D/4D image viewer to easily and quickly view and evaluate images. This paper briefly describes the initial implementation of the web-based application.

Visualization of Polymer Dynamics in Cellulose Nanocrystal Matrices Using Fluorescence Lifetime Measurements

Polymer nanocomposites containing self-assembled cellulose nanocrystals (CNCs) are ideal for advanced applications requiring both strength and toughness as the helicoidal structure of the CNCs deflects crack propagation and the polymer matrix dissipates impact energy. However, any adsorbed water layer surrounding the CNCs may compromise the interfacial adhesion between the polymer matrix and the CNCs, thus impacting stress transfer at that interface. Therefore, it is critical to study the role of water at the interface in connecting the polymer dynamics and the resulting mechanical performance of the nanocomposite. Here, we explore the effect of polymer confinement and water content on polymer dynamics in CNC nanocomposites by covalently attaching a fluorogenic water-sensitive dye to poly(diethylene glycol methyl ether methacrylate) (PMEO₂MA), to provide insights into the observed mechanical performance. Utilizing fluorescence lifetime imaging microscopy (FLIM), the lifetime of dye fluorescence decay was measured to probe the polymer chain dynamics of PMEO₂MA in CNC nanocomposite films. The PMEO₂MA chains experienced distinct regions of differing dynamics within Bouligand structures. A correlation was observed between the average fluorescence lifetime and the mechanical performance of CNC films, indicating that polymer chains with high mobility improved the strain and toughness. These studies demonstrated FLIM as a method to investigate polymer dynamics at the nanosecond timescale that can readily be applied to other composite systems.

Application of Multiphoton Microscopic Imaging in Study of Gastric Cancer

Multiphoton microscopy (MPM) imaging relies on the nonlinear interaction between ultrashort optical pulses and the samples to achieve image contrast. Featuring larger penetration depth, less phototoxicity, 3-dimensional sectioning capability, no need for labeling, MPM become a powerful medical imaging technique that can identify structural characteristics of tissues at the cellular and subcellular levels. In this review paper, we introduce the working principle of MPM imaging, present the current results of MPM imaging applied to the study of gastric tumors, and discuss the future prospects of this interdisciplinary research field.

Quantifying molecular- to cellular-level forces in living cells

Mechanical cues have been suggested to play an important role in cell functions and cell fate determination, however, such physical quantities are challenging to directly measure in living cells with single molecule sensitivity and resolution. In this review, we focus on two main technologies that are promising in probing forces at the single molecule level. We review their theoretical fundamentals, recent technical advancements, and future directions, tailored specifically for interrogating mechanosensitive molecules in live cells.

Nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to predict the histologic grade in breast cancer

The purpose of this study is to develop and validate a new nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to differentiate tumor grade in patients with invasive breast cancer. A total of 543 patients were included in this study. We used computer-generated random numbers to assign 328 of these patients to the training cohort and 215 patients to the validation cohort. Macroscopic tumor-associated collagen signatures (TACS1-8) were obtained by multiphoton microscopy at the invasion front and inside of the breast primary tumor. TACS corresponding microscopic features (TCMF) including morphology and texture features were extracted from the segmented regions of interest using Matlab 2016b. Using ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8, and the least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust TCMF features to build a TCMF-score. Univariate logistic regression analysis demonstrates that the TACS-score and TCMF-score are significantly associated with histologic grade (odds ratio, 2.994; 95% CI, 2.013-4.452; P < 0.001; 4.245, 2.876-6.264, P < 0.001 in the training cohort). The nomogram (collagen) model combining the TACS-score and TCMF-score could stratify patients into Grade1 and Grade2/3 groups with the AUC of 0.859 and 0.863 in the training and validation cohorts. The predictive performance can be further improved by combining the clinical factors, achieving the AUC of 0.874 in both data cohorts. The nomogram model combining the TACS-score and TCMF-score can be useful in differentiating breast tumor patients with Grade1 and Grade2/3.

Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics

MOTIVATION

Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell membrane mostly relies on the mean-squared displacement plots, much information is lost when producing these plots from the trajectories. Here we employ deep learning to classify breast cancer cell types based on the trajectories of epidermal growth factor receptor (EGFR). Our model is an artificial neural network trained on the EGFR motions acquired from six breast cancer cell lines of varying invasiveness and receptor status: MCF7 (hormone receptor positive), BT474 (HER2-positive), SKBR3 (HER2-positive), MDA-MB-468 (triple negative, TN), MDA-MB-231 (TN) and BT549 (TN).

RESULTS

The model successfully classified the trajectories within individual cell lines with 83% accuracy and predicted receptor status with 85% accuracy. To further validate the method, epithelial-mesenchymal transition (EMT) was induced in benign MCF10A cells, noninvasive MCF7 cancer cells and highly invasive MDA-MB-231 cancer cells, and EGFR trajectories from these cells were tested. As expected, after EMT induction, both MCF10A and MCF7 cells showed higher rates of classification as TN cells, but not the MDA-MB-231 cells. Whereas deep learning-based cancer cell classifications are primarily based on the optical transmission images of cell morphology and the fluorescence images of cell organelles or cytoskeletal structures, here we demonstrated an alternative way to classify cancer cells using a dynamic, biophysical feature that is readily accessible.

AVAILABILITY AND IMPLEMENTATION

A python implementation of deep learning-based classification can be found at https://github.com/soonwoohong/Deep-learning-for-EGFR-trajectory-classification.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Live-cell imaging of glucose-induced metabolic coupling of β and α cell metabolism in health and type 2 diabetes

Type 2 diabetes is characterized by β and α cell dysfunction. We used phasor-FLIM (Fluorescence Lifetime Imaging Microscopy) to monitor oxidative phosphorylation and glycolysis in living islet cells before and after glucose stimulation. In healthy cells, glucose enhanced oxidative phosphorylation in β cells and suppressed oxidative phosphorylation in α cells. In Type 2 diabetes, glucose increased glycolysis in β cells, and only partially suppressed oxidative phosphorylation in α cells. FLIM uncovers key perturbations in glucose induced metabolism in living islet cells and provides a sensitive tool for drug discovery in diabetes.

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

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

Advances in understanding and in multi-disciplinary methodology used to assess lipid regulation of signalling cascades from the cancer cell plasma membrane

The lipid bilayer is a functional component of cells, forming a stable platform for the initiation of key biological processes, including cell signalling. There are distinct changes in the lipid composition of cell membranes during oncogenic transformation resulting in aberrant activation and inactivation of signalling transduction pathways. Studying the role of the cell membrane in cell signalling is challenging, since techniques are often limited to by timescale, resolution, sensitivity, and averaging. To overcome these limitations, combining 'computational', 'wet-lab' and 'semi-dry' approaches offers the best opportunity to resolving complex biological processes involved in membrane organisation. In this review, we highlight analytical tools that have been applied for the study of cell signalling initiation from the cancer cell membranes through computational microscopy, biological assays, and membrane biophysics. The cancer therapeutic potential of extracellular membrane-modulating agents, such as cholesterol-reducing agents is also discussed, as is the need for future collaborative inter-disciplinary research for studying the role of the cell membrane and its components in cancer therapy.

HIFα independent mechanisms in renal carcinoma cells modulate divergent outcomes in fibronectin assembly mediated by hypoxia and CoCl2

Fibronectin (FN) is a core matrix protein that assembles to form a dynamic cellular scaffold, frequently perturbed during oncogenic transformation. Tumor hypoxia, characterized by low oxygen concentrations in the microenvironment of most solid tumors has been shown to accelerate FN assembly in fibroblasts and cancer-associated fibroblasts, cell types that produce abundant amounts of FN protein. Nevertheless, FN matrix regulation in epithelial cancer cells during hypoxia remains less well defined. In this study we investigate the assembly of the FN matrix during hypoxia in renal cancer epithelial cells, the cells of origin of renal cell carcinoma (RCC). We show that hypoxia (1% O₂) specifically increases matrix disassembly and increases migratory propensity in renal cancer cells. However, HIFα stabilization using hypoxia mimetics, does not recapitulate the effect of hypoxia on FN matrix reorganization or cell migration. Using a combination of knockdown and inhibitor-based approaches, our work characterizes the signaling events that mediate these two disparate changes on the matrix and explores its functional significance on chemotactic cell migration. Our study systematically reexamines the role of hypoxia mimetics as experimental substitutes for hypoxia and provides new findings on HIFα stabilization and the FN matrix in the context of renal cancer.

Machine Learning Methods for Fluorescence Lifetime Imaging (FLIM) Based Label-Free Detection of Microglia

Automated computational analysis techniques utilizing machine learning have been demonstrated to be able to extract more data from different imaging modalities compared to traditional analysis techniques. One new approach is to use machine learning techniques to existing multiphoton imaging modalities to better interpret intrinsically fluorescent cellular signals to characterize different cell types. Fluorescence Lifetime Imaging Microscopy (FLIM) is a high-resolution quantitative imaging tool that can detect metabolic cellular signatures based on the lifetime variations of intrinsically fluorescent metabolic co-factors such as nicotinamide adenine dinucleotide [NAD(P)H]. NAD(P)H lifetime-based discrimination techniques have previously been used to develop metabolic cell signatures for diverse cell types including immune cells such as macrophages. However, FLIM could be even more effective in characterizing cell types if machine learning was used to classify cells by utilizing FLIM parameters for classification. Here, we demonstrate the potential for FLIM-based, label-free NAD(P)H imaging to distinguish different cell types using Artificial Neural Network (ANN)-based machine learning. For our biological use case, we used the challenge of differentiating microglia from other glia cell types in the brain. Microglia are the resident macrophages of the brain and spinal cord and play a critical role in maintaining the neural environment and responding to injury. Microglia are challenging to identify as most fluorescent labeling approaches cross-react with other immune cell types, are often insensitive to activation state, and require the use of multiple specialized antibody labels. Furthermore, the use of these extrinsic antibody labels prevents application in in vivo animal models and possible future clinical adaptations such as neurodegenerative pathologies. With the ANN-based NAD(P)H FLIM analysis approach, we found that microglia in cell culture mixed with other glial cells can be identified with more than 0.9 True Positive Rate (TPR). We also extended our approach to identify microglia in fixed brain tissue with a TPR of 0.79. In both cases the False Discovery Rate was around 30%. This method can be further extended to potentially study and better understand microglia’s role in neurodegenerative disease with improved detection accuracy.

FLIm-Guided Raman Imaging to Study Cross-Linking and Calcification of Bovine Pericardium

Bovine pericardium (BP) is a vascular biomaterial used in cardiovascular surgery that is typically cross-linked for masking antigenicity and enhance stability. There is a need for biochemical evaluation of the tissue properties prior to implantation to ensure that quality and reliability standards are met. Here, engineered antigen removed BP (ARBP) that was cross-linked with 0.2% and 0.6% glutaraldehyde (GA), and further calcified in vitro to simulate graft calcifications upon implantation was characterized nondestructively using fluorescence lifetime imaging (FLIm) to identify regions of interest which were then assessed by Raman spectroscopy. We observed that the tissue fluorescence lifetime shortened, and that Raman bands at 856, 935, 1282, and 1682 cm-1 decreased, and at 1032 and 1627 cm-1 increased with increasing GA cross-linking. Independent classification analysis based on fluorescence lifetime and on Raman spectra discriminated between GA-ARBP and untreated ARBP with an accuracy of 91% and 66%, respectively. Pearson's correlation analysis showed a strong correlation between pyridinium cross-links measured with high-performance liquid chromatography and fluorescence lifetime measured at 380-400 nm (R = -0.76, p = 0.00094), as well as Raman bands at 856 cm-1 for hydroxy-proline (R = -0.68, p = 0.0056) and at 1032 cm-1 for hydroxy-pyridinium (R = 0.74, p = 0.0016). Calcified areas of GA cross-linked tissue showed characteristic hydroxyapatite (959 and 1038 cm-1) bands in the Raman spectrum and fluorescence lifetime shortened by 0.4 ns compared to uncalcified regions. FLIm-guided Raman imaging could rapidly identify degrees of cross-linking and detected calcified regions with high chemical specificity, an ability that can be used to monitor tissue engineering processes for applications in regenerative medicine.

Heparanase Promotes Syndecan-1 Expression to Mediate Fibrillar Collagen and Mammographic Density in Human Breast Tissue Cultured ex vivo

Mammographic density (MD) is a strong and independent factor for breast cancer (BC) risk and is increasingly associated with BC progression. We have previously shown in mice that high MD, which is characterized by the preponderance of a fibrous stroma, facilitates BC xenograft growth and metastasis. This stroma is rich in extracellular matrix (ECM) factors, including heparan sulfate proteoglycans (HSPGs), such as the BC-associated syndecan-1 (SDC1). These proteoglycans tether growth factors, which are released by heparanase (HPSE). MD is positively associated with estrogen exposure and, in cell models, estrogen has been implicated in the upregulation of HPSE, the activity of which promotes SDC expression. Herein we describe a novel measurement approach (single-sided NMR) using a patient-derived explant (PDE) model of normal human (female) mammary tissue cultured ex vivo to investigate the role(s) of HPSE and SDC1 on MD. Relative HSPG gene and protein analyses determined in patient-paired high vs. low MD tissues identified SDC1 and SDC4 as potential mediators of MD. Using the PDE model we demonstrate that HPSE promotes SDC1 rather than SDC4 expression and cleavage, leading to increased MD. In this model system, synstatin (SSTN), an SDC1 inhibitory peptide designed to decouple SDC1-ITGαvβ3 parallel collagen alignment, reduced the abundance of fibrillar collagen as assessed by picrosirius red viewed under polarized light, and reduced MD. Our results reveal a potential role for HPSE in maintaining MD via its direct regulation of SDC1, which in turn physically tethers collagen into aligned fibers characteristic of MD. We propose that inhibitors of HPSE and/or SDC1 may afford an opportunity to reduce MD in high BC risk individuals and reduce MD-associated BC progression in conjunction with established BC therapies.

Drosophila TRIM32 cooperates with glycolytic enzymes to promote cell growth

Cell growth and/or proliferation may require the reprogramming of metabolic pathways, whereby a switch from oxidative to glycolytic metabolism diverts glycolytic intermediates towards anabolic pathways. Herein, we identify a novel role for TRIM32 in the maintenance of glycolytic flux mediated by biochemical interactions with the glycolytic enzymes Aldolase and Phosphoglycerate mutase. Loss of Drosophila TRIM32, encoded by thin (tn), shows reduced levels of glycolytic intermediates and amino acids. This altered metabolic profile correlates with a reduction in the size of glycolytic larval muscle and brain tissue. Consistent with a role for metabolic intermediates in glycolysis-driven biomass production, dietary amino acid supplementation in tn mutants improves muscle mass. Remarkably, TRIM32 is also required for ectopic growth - loss of TRIM32 in a wing disc-associated tumor model reduces glycolytic metabolism and restricts growth. Overall, our results reveal a novel role for TRIM32 for controlling glycolysis in the context of both normal development and tumor growth.

Report on fluorescence lifetime imaging using multiphoton laser scanning microscopy targeting sentinel lymph node diagnostics

SIGNIFICANCE

Sentinel lymph node (SLN) biopsy is an important method for metastasis staging in, e.g., patients with malignant melanoma. Tools enabling prompt histopathological analysis are expected to facilitate diagnostics; optical technologies are explored for this purpose.

AIM

The objective of this exploratory study was to investigate the potential of adopting multiphoton laser scanning microscopy (MPM) together with fluorescence lifetime analysis (FLIM) for the examination of lymph node (LN) tissue ex vivo.

APPROACH

Five LN tissue samples (three metastasis positive and two negative) were acquired from a biobank comprising tissues from melanoma patients. Tissues were deparaffinized and subjected to MPM-FLIM using an experimental MPM set-up equipped with a time correlated single photon counting module enabling FLIM.

RESULTS

The data confirm that morphological features similar to conventional histology were observed. In addition, FLIM analysis revealed elevated morphological contrast, particularly for discriminating between metastatic cells, lymphocytes, and erythrocytes.

CONCLUSIONS

Taken together, the results from this investigation show promise for adopting MPM-FLIM in the context of SLN diagnostics and encourage further translational studies on fresh tissue samples.

In vivo study of metabolic dynamics and heterogeneity in brown and beige fat by label-free multiphoton redox and fluorescence lifetime microscopy

In this work, the metabolic characteristics of adipose tissues in live mouse model were investigated using a multiphoton redox ratio and fluorescence lifetime imaging technology. By analyzing the intrinsic fluorescence of metabolic coenzymes, we measured the optical redox ratios of adipocytes in vivo and studied their responses to thermogenesis. The fluorescence lifetime imaging further revealed changes in protein bindings of metabolic coenzymes in the adipocytes during thermogenesis. Our study uncovered significant heterogeneity in the cellular structures and metabolic characteristics of thermogenic adipocytes in brown and beige fat. Subgroups of brown and beige adipocytes were identified based on the distinct lipid size distributions, redox ratios, fluorescence lifetimes and thermogenic capacities. The results of our study show that this label-free imaging technique can shed new light on in vivo study of metabolic dynamics and heterogeneity of adipose tissues in live organisms.

Single-Photon, Time-Gated, Phasor-Based Fluorescence Lifetime Imaging through Highly Scattering Medium

Fluorescence lifetime imaging (FLI) is increasingly recognized as a powerful tool for biochemical and cellular investigations, including in vivo applications. Fluorescence lifetime is an intrinsic characteristic of any fluorescent dye which, to a large extent, does not depend on excitation intensity and signal level. In particular, it allows distinguishing dyes with similar emission spectra, offering additional multiplexing capabilities. However, in vivo FLI in the visible range is complicated by the contamination by (i) tissue autofluorescence, which decreases contrast, and by (ii) light scattering and absorption in tissues, which significantly reduce fluorescence intensity and modify the temporal profile of the signal. Here, we demonstrate how these issues can be accounted for and overcome, using a new time-gated single-photon avalanche diode array camera, SwissSPAD2, combined with phasor analysis to provide a simple and fast visual method for lifetime imaging. In particular, we show how phasor dispersion increases with increasing scattering and/or decreasing fluorescence intensity. Next, we show that as long as the fluorescence signal of interest is larger than the phantom autofluorescence, the presence of a distinct lifetime can be clearly identified with appropriate background correction. We use these results to demonstrate the detection of A459 cells expressing the fluorescent protein mCyRFP1 through highly scattering and autofluorescent phantom layers. These results showcase the possibility to perform FLI in challenging conditions, using standard, bright, visible fluorophore or fluorescence proteins.

Molecular Imaging in Oncology: Advanced Microscopy Techniques

Preclinical studies usually require high levels of morphological, functional, and biochemical information at subcellular resolution. This type of information cannot be obtained from clinical imaging techniques, such as MRI, PET/CT, or US. Luckily, many microscopy techniques exist that can offer this information, also for malignant tissues and therapeutic approaches. In this overview, we discuss the various advanced optical microscopy techniques and their applications in oncological research. After a short introduction in Sect. 16.1, we continue in Sect. 16.2 with a discussion on fluorescent labelling strategies, followed in Sect. 16.3 by an in-depth description of confocal, light-sheet, two-photon, and super-resolution microscopy. We end in Sect. 16.4 with a focus on the applications, specifically in oncology.

Laser induced autofluorescence lifetime to identify larynx squamous cell carcinoma: Short series ex vivo study

Laser induced autofluorescence (LIAF) lifetime is useful to distinguish between normal laryngeal tissues and squamous cell carcinoma (SCC) based on variations of their biochemical composition and structure alterations. LIAF was collected from samples constituted by pairs of normal and malignant tissue, which were excised from three patients. Exclusion criteria for samples harvest were: (i) macroscopic changes of normal vocal cord observed during surgery; (ii) previous surgical intervention on vocal cord, (iii) patients treated only with chemotherapy or radiotherapy for carcinoma. Inclusion conditions: men, aged 57-68, non-smokers. A pulsed laser diode excited LIAF at 375 nm and 31 MHz repetition rate; beam full-time width at half-maximum was 87 ps at an average power of 0.49 mW. Mean LIAF lifetime for normal tissues was (3.75 ± 0.49) ns and for malignant (4.37 ± 0.85) ns: it is longer in malignant than in normal tissue. Variance analysis made with Fisher's test has shown no significant difference between patients for normal tissues; the same was true for malignant. Though, when malignant tissue was compared to normal for the same patients as well as between patients, a significant difference (significance level of 5%) was evidenced. Time-resolved LIAF may allow better differentiation between normal and malignant tissues in patients diagnosed with larynx SCC.

Systematic Enzyme Mapping of Cellular Metabolism by Phasor-Analyzed Label-Free NAD(P)H Fluorescence Lifetime Imaging

In the past years, cellular metabolism of the immune system experienced a revival, as it has become clear that it is not merely responsible for the cellular energy supply, but also impacts on many signaling pathways and, thus, on diverse cellular functions. Label-free fluorescence lifetime imaging of the ubiquitous coenzymes NADH and NADPH (NAD(P)H-FLIM) makes it possible to monitor cellular metabolism in living cells and tissues and has already been applied to study metabolic changes both under physiologic and pathologic conditions. However, due to the complex distribution of NAD(P)H-dependent enzymes in cells, whose distribution continuously changes over time, a thorough interpretation of NAD(P)H-FLIM results, in particular, resolving the contribution of various enzymes to the overall metabolic activity, remains challenging. We developed a systematic framework based on angle similarities of the phase vectors and their length to analyze NAD(P)H-FLIM data of cells and tissues based on a generally valid reference system of highly abundant NAD(P)H-dependent enzymes in cells. By using our analysis framework, we retrieve information not only about the overall metabolic activity, i.e., the fraction of free to enzyme-bound NAD(P)H, but also identified the enzymes predominantly active within the sample at a certain time point with subcellular resolution. We verified the performance of the approach by applying NAD(P)H-FLIM on a stromal-like cell line and identified a different group of enzymes that were active in the cell nuclei as compared to the cytoplasm. As the systematic phasor-based analysis framework of label-free NAD(P)H-FLIM can be applied both in vitro and in vivo, it retains the unique power to enable dynamic enzyme-based metabolic investigations, at subcellular resolution, in genuine environments.

NAD(P)H fluorescence lifetime measurements in fixed biological tissues

Autofluorescence based fluorescence lifetime imaging microscopy (AF-FLIM) techniques have come a long way from early studies on cancer characterization and have now been widely employed in several cellular and animal studies covering a wide range of diseases. The majority of research in autofluorescence imaging (AFI) study metabolic fluxes in live biological samples. However, tissues from clinical or scientific studies are often chemically fixed for preservation and stabilization of tissue morphology. Fixation is particularly crucial for enzymatic, functional, or histopathology studies. Interpretations of metabolic imaging such as optical redox intensity imaging and AF-FLIM, have often been viewed as potentially unreliable in a fixed sample due to lack of studies in this field. In this study, we carefully evaluate the possibility of extracting microenvironment information in fixed tissues using reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) endogenous fluorescence. The ability to distinguish changes such as metabolism and pH using intrinsic fluorescence in fixed tissues has great pathological value. In this work, we show that the lifetime based metabolic contrast in a sample is preserved after chemical fixation. The fluorescence lifetime of a sample increases with an additive fixative like formaldehyde; however, the fixed tissues retain metabolic signatures even after fixation. This study presents an opportunity to successfully image archived unstained histopathology tissues, and generate useful AF-FLIM signatures. We demonstrate the capability to draw metabolic interpretations in fixed tissues even after long periods of storage.

Recent trends in two-photon auto-fluorescence lifetime imaging (2P-FLIM) and its biomedical applications

Two photon fluorescence microscopy and the numerous technical advances to it have served as valuable tools in biomedical research. The fluorophores (exogenous or endogenous) absorb light and emit lower energy photons than the absorption energy and the emission (fluorescence) signal is measured using a fluorescence decay graph. Additionally, high spatial resolution images can be acquired in two photon fluorescence lifetime imaging (2P-FLIM) with improved penetration depth which helps in detection of fluorescence signal in vivo. 2P-FLIM is a non-invasive imaging technique in order to visualize cellular metabolic, by tracking intrinsic fluorophores present in it, such as nicotinamide adenine dinucleotide, flavin adenine dinucleotide and tryptophan etc. 2P-FLIM of these molecules enable the visualization of metabolic alterations, non-invasively. This comprehensive review discusses the numerous applications of 2P-FLIM towards cancer, neuro-degenerative, infectious diseases, and wound healing.

Whole-Section Tumor Micro-Architecture Analysis by a Two-Dimensional Phasor-Based Approach Applied to Polarization-Dependent Second Harmonic Imaging

Second Harmonic Generation (SHG) microscopy has gained much interest in the histopathology field since it allows label-free imaging of tissues simultaneously providing information on their morphology and on the collagen microarchitecture, thereby highlighting the onset of pathologies and diseases. A wide request of image analysis tools is growing, with the aim to increase the reliability of the analysis of the huge amount of acquired data and to assist pathologists in a user-independent way during their diagnosis. In this light, we exploit here a set of phasor-parameters that, coupled to a 2-dimensional phasor-based approach (μMAPPS, Microscopic Multiparametric Analysis by Phasor projection of Polarization-dependent SHG signal) and a clustering algorithm, allow to automatically recover different collagen microarchitectures in the tissues extracellular matrix. The collagen fibrils microscopic parameters (orientation and anisotropy) are analyzed at a mesoscopic level by quantifying their local spatial heterogeneity in histopathology sections (few mm in size) from two cancer xenografts in mice, in order to maximally discriminate different collagen organizations, allowing in this case to identify the tumor area with respect to the surrounding skin tissue. We show that the "fibril entropy" parameter, which describes the tissue order on a selected spatial scale, is the most effective in enlightening the tumor edges, opening the possibility of their automatic segmentation. Our method, therefore, combined with tissue morphology information, has the potential to become a support to standard histopathology in diseases diagnosis.

Diffuse and nonlinear imaging of multiscale vascular parameters for in vivo monitoring of preclinical mammary tumors

Diffuse optical imaging (DOI) techniques provide a wide-field or macro assessment of the functional tumor state and have shown substantial promise for monitoring treatment efficacy in cancer. Conversely, intravital microscopy provides a high-resolution view of the tumor state and has played a key role in characterizing treatment response in the preclinical setting. There has been little prior work in investigating how the macro and micro spatial scales can be combined to develop a more comprehensive and translational view of treatment response. To address this, a new multiscale preclinical imaging technique called diffuse and nonlinear imaging (DNI) was developed. DNI combines multiphoton microscopy with spatial frequency domain imaging (SFDI) to provide multiscale data sets of tumor microvascular architecture coregistered within wide-field hemodynamic maps. A novel method was developed to match the imaging depths of both modalities by utilizing informed SFDI spatial frequency selection. An in vivo DNI study of murine mammary tumors revealed multiscale relationships between tumor oxygen saturation and microvessel diameter, and tumor oxygen saturation and microvessel length (|Pearson's ρ| ≥ 0.5, P < 0.05). Going forward, DNI will be uniquely enabling for the investigation of multiscale relationships in tumors during treatment.

Multimodal multiphoton imaging for label-free monitoring of early gastric cancer

Background

Early gastric cancer is associated with a much better prognosis than advanced disease, and strategies to improve prognosis is strictly dependent on earlier detection and accurate diagnosis. Therefore, a label-free, non-invasive imaging technique that allows the precise identification of morphologic changes in early gastric cancer would be of considerable clinical interest.

Methods

In this study, multiphoton microscopy (MPM) using two-photon excited fluorescence combined with second-harmonic generation was used for the identification of early gastric cancer.

Results

This microscope was able to directly reveal improved cellular detail and stromal changes during the development of early gastric cancer. Furthermore, two features were quantified from MPM images to assess the cell change in size and stromal collagen change as gastric lesion developed from normal to early cancer.

Conclusions

These results clearly show that multiphoton microscopy can be used to examine early gastric cancer at the cellular level without the need for exogenous contrast agents. This study would be helpful for early diagnosis and treatment of gastric cancer, and may provide the groundwork for further exploration into the application of multiphoton microscopy in clinical practice.

Multimodal imaging to explore endogenous fluorescence of fresh and fixed human healthy and tumor brain tissues

To complement a project toward label-free optical biopsy and enhanced resection which the overall goal is to develop a multimodal nonlinear endomicroscope, this multimodal approach aims to enhance the accuracy in classifying brain tissue into solid tumor, infiltration and normal tissue intraoperatively. Multiple optical measurements based on one- and two-photon spectral and lifetime autofluorescence, including second harmonic generation imaging, were acquired. As a prerequisite, studying the effect of the time of measurement postexcision on tissue's spectral/lifetime fluorescence properties was warranted, so spectral and lifetime fluorescences of fresh brain tissues were measured using a point-based linear endoscope. Additionally, a comparative study on tissue's optical properties obtained by multimodal nonlinear optical imaging microscope from fresh and fixed tissue was necessary to test whether clinical validation of the nonlinear endomicroscope is feasible by extracting optical signatures from fixed tissue rather than from freshly excised samples. The former is generally chosen for convenience. Results of this study suggest that an hour is necessary postexcision to have consistent fluorescence intensities\lifetimes. The fresh (a,b,c) vs fixed (d,e,f) tissue study indicates that while all optical signals differ after fixation. The characteristic features extracted from one- and two-photon excitation still discriminate normal brain (a,d) cortical tissue, glioblastoma (GBM) (b,e) and metastases (c,f).

Bright Dots and Smart Optical Microscopy to Probe Intracellular Events in Single Cells

Probing intracellular events is a key step in developing new biomedical methodologies. Optical microscopy has been one of the best options to observe biological samples at single cell and sub-cellular resolutions. Morphological changes are readily detectable in brightfield images. When stained with fluorescent molecules, distributions of intracellular organelles, and biological molecules are made visible using fluorescence microscopes. In addition to these morphological views of cells, optical microscopy can reveal the chemical and physical status of defined intracellular spaces. This review begins with a brief overview of genetically encoded fluorescent probes and small fluorescent chemical dyes. Although these are the most common approaches, probing is also made possible by using tiny materials that are incorporated into cells. When these tiny materials emit enough photons, it is possible to draw conclusions about the environment in which the tiny material resides. Recent advances in these tiny but sufficiently bright fluorescent materials are nextly reviewed to show their applications in tracking target molecules and in temperature imaging of intracellular spots. The last section of this review addresses purely optical methods for reading intracellular status without staining with probes. These non-labeling methods are especially essential when biospecimens are thereafter required for in vivo uses, such as in regenerative medicine.

Autofluorescence lifetime imaging of cellular metabolism: Sensitivity toward cell density, pH, intracellular, and intercellular heterogeneity

Autofluorescence imaging (AFI) has greatly accelerated in the last decade, way past its origins in detecting endogenous signals in biological tissues to identify differences between samples. There are many endogenous fluorescence sources of contrast but the most robust and widely utilized have been those associated with metabolism. The intrinsically fluorescent metabolic cofactors nicotinamide adenine dinucleotide (NAD+ /NADH) and flavin adenine dinucleotide (FAD/FADH2 ) have been utilized in a number of AFI applications including basic research, clinical, and pharmaceutical studies. Fluorescence lifetime imaging microscopy (FLIM) has emerged as one of the more powerful AFI tools for NADH and FAD characterization due to its unique ability to noninvasively detect metabolite bound and free states and quantitate cellular redox ratio. However, despite this widespread biological use, many standardization methods are still needed to extend FLIM-based AFI into a fully robust research and clinical diagnostic tools. FLIM is sensitive to a wide range of factors in the fluorophore microenvironment, and there are a number of analysis variables as well. To this end, there has been an emphasis on developing imaging standards and ways to make the image acquisition and analysis more consistent. However, biological conditions during FLIM-based AFI imaging are rarely considered as key sources of FLIM variability. Here, we present several experimental factors with supporting data of the cellular microenvironment such as confluency, pH, inter-/intracellular heterogeneity, and choice of cell line that need to be considered for accurate quantitative FLIM-based AFI measurement of cellular metabolism. © 2018 International Society for Advancement of Cytometry.

Collagen density modulates triple-negative breast cancer cell metabolism through adhesion-mediated contractility

Extracellular matrix (ECM) mechanical properties upregulate cancer invasion, cell contractility, and focal adhesion formation. Alteration in energy metabolism is a known characteristic of cancer cells (i.e., Warburg effect) and modulates cell invasion. There is little evidence to show if collagen density can alter cancer cell metabolism. We investigated changes in energy metabolism due to collagen density in five breast cell lines by measuring the fluorescence lifetime of NADH. We found that only triple-negative breast cancer cells, MDA-MB231 and MDA-MB468 cells, had an increased population of bound NADH, indicating an oxidative phosphorylation (OXPHOS) signature, as collagen density decreased. When inhibiting ROCK and cell contractility, MDA-MB231 cells on glass shifted from glycolysis (GLY) to OXPHOS, confirming the intricate relationship between mechanosensing and metabolism. MCF10A cells showed less significant changes in metabolism, shifting towards GLY as collagen density decreased. The MCF-7 and T-47D, less invasive breast cancer cells, compared to the MDA-MB231 and MDA-MB468 cells, showed no changes regardless of substrate. In addition, OXPHOS or GLY inhibitors in MDA-MB231 cells showed dramatic shifts from OXPHOS to GLY or vice versa. These results provide an important link between cellular metabolism, contractility, and collagen density in human breast cancer.

Collagen density modulates triple-negative breast cancer cell metabolism through adhesion-mediated contractility

Extracellular matrix (ECM) mechanical properties upregulate cancer invasion, cell contractility, and focal adhesion formation. Alteration in energy metabolism is a known characteristic of cancer cells (i.e., Warburg effect) and modulates cell invasion. There is little evidence to show if collagen density can alter cancer cell metabolism. We investigated changes in energy metabolism due to collagen density in five breast cell lines by measuring the fluorescence lifetime of NADH. We found that only triple-negative breast cancer cells, MDA-MB231 and MDA-MB468 cells, had an increased population of bound NADH, indicating an oxidative phosphorylation (OXPHOS) signature, as collagen density decreased. When inhibiting ROCK and cell contractility, MDA-MB231 cells on glass shifted from glycolysis (GLY) to OXPHOS, confirming the intricate relationship between mechanosensing and metabolism. MCF10A cells showed less significant changes in metabolism, shifting towards GLY as collagen density decreased. The MCF-7 and T-47D, less invasive breast cancer cells, compared to the MDA-MB231 and MDA-MB468 cells, showed no changes regardless of substrate. In addition, OXPHOS or GLY inhibitors in MDA-MB231 cells showed dramatic shifts from OXPHOS to GLY or vice versa. These results provide an important link between cellular metabolism, contractility, and collagen density in human breast cancer.

Types of advanced optical microscopy techniques for breast cancer research: a review

A cancerous cell is characterized by morphological and metabolic changes which are the key features of carcinogenesis. Adenosine triphosphate (ATP) in cancer cells is primarily produced by aerobic glycolysis rather than oxidative phosphorylation. In normal cellular metabolism, nicotinamide adenine dinucleotide (NADH) is considered as a principle electron donor and flavin adenine dinucleotide (FAD) as an electron acceptor. During metabolism in a cancerous cell, a net increase in NADH is found as the pathway switched from oxidative phosphorylation to aerobic glycolysis. Often during initiation and progression of cancer, the developmental regulation of extracellular matrix (ECM) is restricted and becomes disorganized. Tumor cell behavior is regulated by the ECM in the tumor micro environment. Collagen, which forms the scaffold of tumor micro-environment also influences its behavior. Advanced optical microscopy techniques are useful for determining the metabolic characteristics of cancerous, normal cells and tissues. They can be used to identify the collagen microstructure and the function of NADH, FAD, and lipids in living system. In this review article, various optical microscopy techniques applied for breast cancer research are discussed including fluorescence, confocal, second harmonic generation (SHG), coherent anti-Stokes Raman scattering (CARS), and fluorescence lifetime imaging (FLIM).

Real-time Brain Tumor imaging with endogenous fluorophores: a diagnosis proof-of-concept study on fresh human samples

The primary line of therapy for high-grade brain tumor is surgical resection, however, identifying tumor margins in vivo remains a major challenge. Despite the progress in computer-assisted imaging techniques, biopsy analysis remains the standard diagnostic tool when it comes to delineating tumor margins. Our group aims to answer this challenge by exploiting optical imaging of endogenous fluorescence in order to provide a reliable and reproducible diagnosis close to neuropathology. In this study, we first establish the ability of two-photon microscopy (TPM) to discriminate normal brain tissue from glioblastomas and brain metastasis using the endogenous fluorescence response of fresh human brain sample. Two-photon fluorescence images were compared to gold standard neuropathology. "Blind" diagnosis realized by a neuropathologist on a group of TPM images show a good sensitivity, 100%, and specificity, 50% to discriminate non tumoral brain tissue versus glioblastoma or brain metastasis. Quantitative analysis on spectral and fluorescence lifetime measurements resulted in building a scoring system to discriminate brain tissue samples.

Deciphering the relative contribution of vascular inflammation and blood rheology in metastatic spreading

Vascular adhesion of circulating tumor cells (CTCs) is a key step in cancer spreading. If inflammation is recognized to favor the formation of vascular "metastatic niches," little is known about the contribution of blood rheology to CTC deposition. Herein, a microfluidic chip, covered by a confluent monolayer of endothelial cells, is used for analyzing the adhesion and rolling of colorectal (HCT-15) and breast (MDA-MB-231) cancer cells under different biophysical conditions. These include the analysis of cell transport in a physiological solution and whole blood over a healthy and a TNF-α inflamed endothelium with a flow rate of 50 and 100 nl/min. Upon stimulation of the endothelial monolayer with TNF-α (25 ng/ml), CTC adhesion increases from 2 to 4 times whilst cell rolling velocity only slightly reduces. Notably, whole blood also enhances cancer cell deposition from 2 to 3 times, but only on the unstimulated vasculature. For all tested conditions, no statistically significant difference is observed between the two cancer cell types. Finally, a computational model for CTC transport demonstrates that a rigid cell approximation reasonably predicts rolling velocities while cell deformability is needed to model adhesion. These results would suggest that, within microvascular networks, blood rheology and inflammation contribute similarly to CTC deposition, thereby facilitating the formation of metastatic niches along the entire network, including the healthy endothelium. In microfluidic-based assays, neglecting blood rheology would significantly underestimate the metastatic potential of cancer cells.

Discovering Macrophage Functions Using In Vivo Optical Imaging Techniques

Macrophages are an important component of host defense and inflammation and play a pivotal role in immune regulation, tissue remodeling, and metabolic regulation. Since macrophages are ubiquitous in human bodies and have versatile physiological functions, they are involved in virtually every disease, including cancer, diabetes, multiple sclerosis, and atherosclerosis. Molecular biological and histological methods have provided critical information on macrophage biology. However, many in vivo dynamic behaviors of macrophages are poorly understood and yet to be discovered. A better understanding of macrophage functions and dynamics in pathogenesis will open new opportunities for better diagnosis, prognostic assessment, and therapeutic intervention. In this article, we will review the advances in macrophage tracking and analysis with in vivo optical imaging in the context of different diseases. Moreover, this review will cover the challenges and solutions for optical imaging techniques during macrophage intravital imaging.

The Need for Imaging Methods in Bioengineering of Three-Dimensional Cell Cultures

The analysis of live cells and tissue is gaining increasing importance in research and development. A number of demands are thus being made on the cultivation and analysis of cell response as well. For certain research approaches, in vitro three-dimensional (3D) cultivation best mimics natural conditions. Nevertheless, the imaging of such 3D environments is still a problem. To understand cell function in 3D, a number of in vivo imaging methods have been developed. This article introduces this field and proposes some interesting and promising imaging techniques for this approach.

Quantitative evaluation of redox ratio and collagen characteristics during breast cancer chemotherapy using two-photon intrinsic imaging

Preoperative neoadjuvant treatment in locally advanced breast cancer is recognized as an effective adjuvant therapy, as it improves treatment outcomes. However, the potential complications remain a threat, so there is an urgent clinical need to assess both the tumor response and changes in its microenvironment using non-invasive and precise identification techniques. Here, two-photon microscopy was employed to detect morphological alterations in breast cancer progression and recession throughout chemotherapy. The changes in structure were analyzed based on the autofluorescence and collagen of differing statuses. Parameters, including optical redox ratio, the ratio of second harmonic generation and auto-fluorescence signal, collagen density, and collagen shape orientation, were studied. Results indicate that these parameters are potential indicators for evaluating breast tumors and their microenvironment changes during progression and chemotherapy. Combined analyses of these parameters could provide a quantitative, novel method for monitoring tumor therapy.