Rapid quantification of mitochondrial fractal dimension in individual cells

AutoMitoNetwork: Software for analyzing mitochondrial networks in autofluorescence images to enable label-free cell classification

High-resolution mitochondria imaging in combination with image analysis tools have significantly advanced our understanding of cellular function in health and disease. However, most image analysis tools for mitochondrial studies have been designed to work with fluorescently labeled images only. Additionally, efforts to integrate features describing mitochondrial networks with machine learning techniques for the differentiation of cell types have been limited. Herein, we present AutoMitoNetwork software for image-based assessment of mitochondrial networks in label-free autofluorescence images using a range of interpretable morphological, intensity, and textural features. To demonstrate its utility, we characterized unstained mitochondrial networks in healthy retinal cells and in retinal cells exposed to two types of treatments: rotenone, which directly inhibited mitochondrial respiration and ATP production, and iodoacetic acid, which had a milder impact on mitochondrial networks via the inhibition of anaerobic glycolysis. For both cases, our multi-dimensional feature analysis combined with a support vector machine classifier distinguished between healthy cells and those treated with rotenone or iodoacetic acid. Subtle changes in morphological features were measured including increased fragmentation in the treated retinal cells, pointing to an association with metabolic mechanisms. AutoMitoNetwork opens new options for image-based machine learning in label-free imaging, diagnostics, and mitochondrial disease drug development.

Label-Free Optical Metabolic Imaging in Cells and Tissues

Over the last half century, the autofluorescence of the metabolic cofactors NADH (reduced nicotinamide adenine dinucleotide) and FAD (flavin adenine dinucleotide) has been quantified in a variety of cell types and disease states. With the spread of nonlinear optical microscopy techniques in biomedical research, NADH and FAD imaging has offered an attractive solution to noninvasively monitor cell and tissue status and elucidate dynamic changes in cell or tissue metabolism. Various tools and methods to measure the temporal, spectral, and spatial properties of NADH and FAD autofluorescence have been developed. Specifically, an optical redox ratio of cofactor fluorescence intensities and NADH fluorescence lifetime parameters have been used in numerous applications, but significant work remains to mature this technology for understanding dynamic changes in metabolism. This article describes the current understanding of our optical sensitivity to different metabolic pathways and highlights current challenges in the field. Recent progress in addressing these challenges and acquiring more quantitative information in faster and more metabolically relevant formats is also discussed.

A highly generalized classifier for osteoporosis radiography based on multiscale fractal, lacunarity, and entropy distributions

Background: Osteoporosis is a common degenerative disease with high incidence among aging populations. However, in regular radiographic diagnostics, asymptomatic osteoporosis is often overlooked and does not include tests for bone mineral density or bone trabecular condition. Therefore, we proposed a highly generalized classifier for osteoporosis radiography based on the multiscale fractal, lacunarity, and entropy distributions. Methods: We collected a total of 104 radiographs (92 for training and 12 for testing) of lumbar spine L4 and divided them into three groups (normal, osteopenia, and osteoporosis). In parallel, 174 radiographs (116 for training and 58 for testing) of calcaneus from health and osteoporotic fracture groups were collected. The texture feature data of all the radiographs were pulled out and analyzed. The Davies-Bouldin index was applied to optimize hyperparameters of feature counting. Neighborhood component analysis was performed to reduce feature dimension and increase generalization. A support vector machine classifier was trained with only the most effective six features for each binary classification scenario. The accuracy and sensitivity performance were estimated by calculating the area under the curve. Results: Interpretable feature trends of osteoporotic pathological changes were depicted. On the spine test dataset, the accuracy and sensitivity of binary classifiers were 0.851 (95% CI: 0.730-0.922), 0.813 (95% CI: 0.718-0.878), and 0.936 (95% CI: 0.826-1) for osteoporosis diagnosis; 0.721 (95% CI: 0.578-0.824), 0.675 (95% CI: 0.563-0.772), and 0.774 (95% CI: 0.635-0.878) for osteopenia diagnosis; and 0.935 (95% CI: 0.830-0.968), 0.928 (95% CI: 0.863-0.963), and 0.910 (95% CI: 0.746-1) for osteoporosis diagnosis from osteopenia. On the calcaneus test dataset, they were 0.767 (95% CI: 0.629-0.879), 0.672 (95% CI: 0.545-0.793), and 0.790 (95% CI: 0.621-0.923) for osteoporosis diagnosis. Conclusion: This method showed the capacity of resisting disturbance on lateral spine radiographs and high generalization on the calcaneus dataset. Pixel-wise texture features not only helped to understand osteoporosis on radiographs better but also shed new light on computer-aided osteopenia and osteoporosis diagnosis.

Label-Free Multiphoton Microscopy for the Detection and Monitoring of Calcific Aortic Valve Disease

Calcific aortic valve disease (CAVD) is the most common valvular heart disease. CAVD results in a considerable socio-economic burden, especially considering the aging population in Europe and North America. The only treatment standard is surgical valve replacement as early diagnostic, mitigation, and drug strategies remain underdeveloped. Novel diagnostic techniques and biomarkers for early detection and monitoring of CAVD progression are thus a pressing need. Additionally, non-destructive tools are required for longitudinal in vitro and in vivo assessment of CAVD initiation and progression that can be translated into clinical practice in the future. Multiphoton microscopy (MPM) facilitates label-free and non-destructive imaging to obtain quantitative, optical biomarkers that have been shown to correlate with key events during CAVD progression. MPM can also be used to obtain spatiotemporal readouts of metabolic changes that occur in the cells. While cellular metabolism has been extensively explored for various cardiovascular disorders like atherosclerosis, hypertension, and heart failure, and has shown potential in elucidating key pathophysiological processes in heart valve diseases, it has yet to gain traction in the study of CAVD. Furthermore, MPM also provides structural, functional, and metabolic readouts that have the potential to correlate with key pathophysiological events in CAVD progression. This review outlines the applicability of MPM and its derived quantitative metrics for the detection and monitoring of early CAVD progression. The review will further focus on the MPM-detectable metabolic biomarkers that correlate with key biological events during valve pathogenesis and their potential role in assessing CAVD pathophysiology.

Influences of microwave irradiation on performances of membrane filtration and catalytic degradation of perfluorooctanoic acid (PFOA)

Perfluorooctanoic Acid (PFOA), one of the common per- and poly fluorinated alkylated substances (PFASs), is increasingly detected in the environment due to the diverse industrial applications and high resistance to degradation processes. This study evaluated degradation of PFOA in microwave-assistant catalytic membrane filtration, a process that integrates microwave catalytic reactions into a ceramic membrane filtration. First, water permeation of the pristine and catalyst-coated membranes were examined under the influence of microwave irradiation to analyse the impacts of the coating layer and water temperature increase on permeate flux, which were well interpreted by the Carman-Kozeny and Hagen-Posieulle (non-slipping and slit-like) models. Then, the PFOA removal was first assessed in a continuous filtration mode with and without microwave irradiation. Our results show that PFOA first adsorbed on membrane and catalyst materials, and then fully penetrated the membrane filter after reaching adsorption equilibrium. Under microwave irradiation (7.2 W·cm^-2), approximate 65.9% of PFOA (25 μg·L^-1) in the feed solution was degraded within a hydraulic time of 2 min (at the permeate flow rate of 43 LMH) due to the microwave-Fenton like reactions. In addition, low flow rates and moderate catalyst coating densities are critical for optimizing PFOA removal. Finally, potential degradation mechanisms of PFOA were proposed through the analysis of degradation by-products (e.g., PFPeA). The findings may provide new insight into the development of reactive membrane-enabled systems for destruction of refractory PFAS.

Label-free metabolic biomarkers for assessing valve interstitial cell calcific progression

Calcific aortic valve disease (CAVD) is the most common form of valve disease where the only available treatment strategy is surgical valve replacement. Technologies for the early detection of CAVD would benefit the development of prevention, mitigation and alternate therapeutic strategies. Two-photon excited fluorescence (TPEF) microscopy is a label-free, non-destructive imaging technique that has been shown to correlate with multiple markers for cellular differentiation and phenotypic changes in cancer and wound healing. Here we show how specific TPEF markers, namely, the optical redox ratio and mitochondrial fractal dimension, correlate with structural, functional and phenotypic changes occurring in the aortic valve interstitial cells (VICs) during osteogenic differentiation. The optical redox ratio, and fractal dimension of mitochondria were assessed and correlated with gene expression and nuclear morphology of VICs. The optical redox ratio decreased for VICs during early osteogenic differentiation and correlated with biological markers for CAVD progression. Fractal dimension correlated with structural and osteogenic markers as well as measures of nuclear morphology. Our study suggests that TPEF imaging markers, specifically the optical redox ratio and mitochondrial fractal dimension, can be potentially used as a tool for assessing early CAVD progression in vitro.

Optical Imaging Approaches to Investigating Radiation Resistance

Radiation therapy is frequently the first line of treatment for over 50% of cancer patients. While great advances have been made in improving treatment response rates and reducing damage to normal tissue, radiation resistance remains a persistent clinical problem. While hypoxia or a lack of tumor oxygenation has long been considered a key factor in causing treatment failure, recent evidence points to metabolic reprogramming under well-oxygenated conditions as a potential route to promoting radiation resistance. In this review, we present recent studies from our lab and others that use high-resolution optical imaging as well as clinical translational optical spectroscopy to shine light on the biological basis of radiation resistance. Two-photon microscopy of endogenous cellular metabolism has identified key changes in both mitochondrial structure and function that are specific to radiation-resistant cells and help promote cell survival in response to radiation. Optical spectroscopic approaches, such as diffuse reflectance and Raman spectroscopy have demonstrated functional and molecular differences between radiation-resistant and sensitive tumors in response to radiation. These studies have uncovered key changes in metabolic pathways and present a viable route to clinical translation of optical technologies to determine radiation resistance at a very early stage in the clinic.

The Mitochondrion as an Emerging Therapeutic Target in Cancer

Mitochondria have emerged as important pharmacological targets because of their key role in cellular proliferation and death. In tumor tissues, mitochondria can switch metabolic phenotypes to meet the challenges of high energy demand and macromolecular synthesis. Furthermore, mitochondria can engage in crosstalk with the tumor microenvironment, and signals from cancer-associated fibroblasts can impinge on mitochondria. Cancer cells can also acquire a hybrid phenotype in which both glycolysis and oxidative phosphorylation (OXPHOS) can be utilized. This hybrid phenotype can facilitate metabolic plasticity of cancer cells more specifically in metastasis and therapy-resistance. In light of the metabolic heterogeneity and plasticity of cancer cells that had until recently remained unappreciated, strategies targeting cancer metabolic dependency appear to be promising in the development of novel and effective cancer therapeutics.