Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer

Asymmetric cell division of ALDH1-positive cancer stem cells generates glycolytic metabolically diverse cell populations

Metabolic heterogeneity in various cancer cells within a tumor causes resistance to medical therapies and promotes tumor recurrence and metastasis. However, the mechanisms by which tumors acquire metabolic heterogeneity are poorly understood. Here, we revealed that PKCλ-dependent asymmetric division of ALDH1-positive cancer stem cells (CSCs) led to an uneven distribution of glycolytic capacity, which is crucial for understanding metabolic heterogeneity within a tumor. The rate-limiting enzyme PFKP and the metabolic probe CDG in glycolysis codistributed with the ALDH1A3 protein during the post-cell division phase, highlighting a mechanism for acquiring metabolic diversity. PKCλ deficiency reduced the asymmetric distribution of these proteins in ALDH1high cells with high ALDH1 activity, suggesting a fundamental role for PKCλ in metabolic heterogeneity. We identified 28 distinct distribution patterns combining PFKP and CDG distributions, demonstrating the complexity of glycolytic heterogeneity. Furthermore, validation and prediction of cell distribution patterns via a probabilistic model confirmed that PKCλ deficiency diminished glycolytic diversity in individual cells within a cancer cell colony generated from an ALDH1-positive CSC. These findings suggest that PKCλ-dependent asymmetric cell division of ALDH1-positive CSCs is crucial for glycolytic heterogeneity in cancer cells within a tumor, potentially offering new therapeutic targets against tumor resistance and metastasis.

Subclonal response heterogeneity to define cancer organoid therapeutic sensitivity

Tumor heterogeneity is predicted to confer inferior clinical outcomes with precision-based strategies, however, modeling heterogeneity in a manner that still represents the tumor of origin remains a formidable challenge. Sequencing technologies are limited in their ability to identify rare subclonal populations and predict response to treatments for patients. Patient-derived organotypic cultures have significantly improved the modeling of cancer biology by faithfully representing the molecular features of primary malignant tissues. Patient-derived cancer organoid (PCO) cultures contain subclonal populations with the potential to recapitulate heterogeneity, although treatment response assessments commonly ignore diversity in the molecular profile or treatment response. Here, we demonstrate the advantage of evaluating individual PCO heterogeneity to enhance the sensitivity of these assays for predicting clinical response. Additionally, organoid subcultures identify subclonal populations with altered treatment response. Finally, dose escalation studies of PCOs to targeted anti-EGFR therapy are utilized which reveal divergent pathway expression when compared to pretreatment cultures. Overall, these studies demonstrate the importance of population-based organoid response assessments, the use of PCOs to identify molecular heterogeneity not observed with bulk tumor sequencing, and PCO heterogeneity for understanding therapeutic resistance mechanisms.

Autofluorescence lifetime imaging classifies human B and NK cell activation state

New non-destructive tools with single-cell resolution are needed to reliably assess B cell and NK cell function for applications including adoptive cell therapy and immune profiling. Optical metabolic imaging (OMI) is a label-free method that measures the autofluorescence intensity and lifetime of the metabolic cofactors NAD(P)H and FAD to quantify metabolism at a single-cell level. Here, we demonstrate that OMI can resolve metabolic changes between primary human quiescent and IL-4/anti-CD40 activated B cells and between quiescent and IL-12/IL-15/IL-18 activated NK cells. We found that stimulated B and NK cells had an increased proportion of free compared to protein-bound NAD(P)H, a reduced redox state, and produced more lactate compared to control cells. The NAD(P)H mean fluorescence lifetime decreased in the stimulated B and NK cells compared to control cells. Random forest models classified B cells and NK cells according to activation state (CD69+) based on OMI variables with an accuracy of 93%. Our results show that autofluorescence lifetime imaging can accurately assess B and NK cell activation in a label-free, non-destructive manner.

MRS and Optical Imaging Studies of Therapeutic Response to Combination Therapy Targeting BRAF/MEK in Murine Melanomas

RATIONALE AND OBJECTIVES

Melanoma, an aggressive skin cancer, often harbors BRAFV600E mutations driving tumor progression via the mitogen-activated protein kinase (MAPK) pathway. While targeted therapies like BRAF (dabrafenib) and MEK (trametinib) inhibitors have improved outcomes, resistance linked to metabolic reprogramming remains a challenge. This study investigates metabolic changes induced by dual BRAF/MEK inhibition in a BRAFV600E-mutant murine melanoma model using magnetic resonance spectroscopy (MRS), optical redox imaging (ORI), and biochemical assays. We aim to identify metabolic biomarkers for predicting therapeutic response or resistance.

MATERIALS AND METHODS

YUMM1.7 murine melanoma cells and tumored mice were treated with dabrafenib and trametinib. ORI assessed mitochondrial redox status by measuring reduced nicotinamide adenine dinucleotide (NADH), oxidized flavoproteins (Fp), and the redox ratio (Fp/(NADH+Fp)) in vitro. Glucose consumption and lactate production were analyzed using a YSI Biochemical Analyzer. In vivo metabolic changes were monitored via ¹H and ³¹P MRS, evaluating lactate, alanine, pH, βNTP/Pi, and total NAD(P)(H), which represents combined oxidized nicotinamide adenine dinucleotide (NAD+), NADH, and reduced nicotinamide adenine dinucleotide phosphate (NADPH).

RESULTS

Under the combined therapeutic regimen of dabrafenib and trametinib, YUMM1.7 murine melanoma cells exhibited significant inhibition of lactate generation, non-significant reduction of glucose utilization, decreased intracellular levels of NADH and total NAD(P)(H), and more oxidized redox status in vitro, which can be interpreted as inhibition of the Warburg effect and improved OXPHOS efficiency by targeting BRAF/MEK signaling activities. Furthermore, YUMM1.7 mouse tumors demonstrated less tissue acidification and improved bioenergetics (βNTP/Pi), in agreement with the in vitro data.

CONCLUSION

MRS, ORI, and biochemical assays identified critical metabolic changes, highlighting potential biomarkers and supporting the integration of metabolic inhibitors with MAPK-targeted therapies to improve clinical outcomes.

Fluorescence Lifetime Imaging Techniques-A Review on Principles, Applications and Clinical Relevance

This article gives an overview of the most frequently used fluorescence-lifetime imaging (FLIM) techniques, their capabilities, and typical applications. Starting from a general introduction to fluorescence and phosphorescence lifetime, we will show that the fluorescence lifetime or, more accurately, the fluorescence decay function of a fluorophore is a direct indicator of the interaction with its molecular environment. FLIM is therefore more than a simple contrast technique in microscopy-it is a technique of molecular imaging. FLIM techniques can be classified into time-domain and frequency-domain techniques, analogue and photon counting techniques, and scanning and wide-field techniques. Starting from an overview of these general technical principles we will describe the features and peculiarities of the different FLIM techniques in use. An extended section is dedicated to TCSPC FLIM, addressing unique capabilities that make the technique especially interesting to FLIM of biological systems.

Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning

The selection of high-performing cell lines is crucial for biopharmaceutical production but is often time-consuming and labor-intensive. We investigated label-free multimodal nonlinear optical microscopy for non-perturbative profiling of biopharmaceutical cell lines based on their intrinsic molecular contrast. Employing simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy with fluorescence lifetime imaging microscopy (FLIM), we characterized Chinese hamster ovary (CHO) cell lines at early passages (0-2). A machine learning (ML)-assisted analysis pipeline leveraged high-dimensional information to classify single cells into their respective lines. Remarkably, the monoclonal cell line classifiers achieved balanced accuracies exceeding 96.8% as early as passage 2. Correlation features and FLIM modality played pivotal roles in early classification. This integrated optical bioimaging and machine learning approach presents a promising solution to expedite cell line selection process while ensuring identification of high-performing biopharmaceutical cell lines. The techniques have potential for broader single-cell characterization applications in stem cell research, immunology, cancer biology and beyond.

Differentiation of Tumors of the Upper Respiratory Tract Using Optical Metabolic Imaging

OBJECTIVES

With over 184,000 new cases and more than 99,000 deaths per year, malignancies of the larynx are a global health problem. Currently, a dedicated screening method enabling a direct onsite diagnosis is missing. This can lead to delayed diagnosis and worse outcomes of the patients. An endoscopic optical method enabling a direct distinction between healthy tissue, dysplastic tissue and cancerous tissue would be an ideal tool for the detection of tumors of the upper aerodigestive tract (UADT). Healthy and tumor cells differ significantly in their metabolic state due to the different metabolic pathways they use (more oxidative phosphorylation in healthy cells, more glycolysis in tumor cells). Optical metabolic imaging (OMI) measuring relative intracellular concentration of NAD(P)H and FAD redox pairs could be a promising approach for early tumor detection and differentiation of suspicious mucosal lesions.

METHODS

In this study, a specially designed endoscopic two-beam two-photon fluorescence lifetime imaging (FLIM) system was used to perform two-photon two-beam FLIM of NAD(P)H and FAD to image the metabolic state in different tissue samples of the UADT. FLIM data sets of 27 tissue samples from 16 patients were recorded directly after surgery ex vivo in a special tissue culture medium at 37°C on a dedicated microscope using multiphoton excitation.

RESULTS

Based on the FLIM measurements of NAD(P)H and FAD, six of the most common indices for the characterization of the cells' metabolism were calculated. Three of them, the ratio of the exponential coefficients (amplitudes) of the short and long lifetime components both for NAD(P)H and FAD (NAD(P)H a1/a2 ratio and FAD a1/a2 ratio) and the fluorescence lifetime redox ratio (FLIRR) enabled differentiation between healthy tissue, benign lesions, dysplastic tissue, and cancer tissue with statistical significance.

CONCLUSIONS

We showed by measurements on freshly collected tissue samples that mucosal lesions of the UADT can be differentiated using our newly designed endoscopic FLIM device. In vivo measurements in healthy volunteers were also possible. By means of this technology, differentiation of cancerous, pre-cancerous, and healthy tissue in the UADT by OMI could be possible. Of six indices used to characterize cell metabolism we calculated, the FLIRR showed the most significant differences between tissue types.

Metabolic rewiring in skin epidermis drives tolerance to oncogenic mutations

Skin epithelial stem cells correct aberrancies induced by oncogenic mutations. Oncogenes invoke different strategies of epithelial tolerance; while wild-type cells outcompete β-catenin-gain-of-function (βcatGOF) cells, HrasG12V cells outcompete wild-type cells. Here we ask how metabolic states change as wild-type stem cells interface with mutant cells and drive different cell-competition outcomes. By tracking the endogenous redox ratio (NAD(P)H/FAD) with single-cell resolution in the same mouse over time, we discover that βcatGOF and HrasG12V mutations, when interfaced with wild-type epidermal stem cells, lead to a rapid drop in redox ratios, indicating more oxidized cellular redox. However, the resultant redox differential persists through time in βcatGOF, whereas it is flattened rapidly in the HrasG12Vmodel. Using 13C liquid chromatography-tandem mass spectrometry, we find that the βcatGOF and HrasG12V mutant epidermis increase the fractional contribution of glucose through the oxidative tricarboxylic acid cycle. Treatment with metformin, a modifier of cytosolic redox, inhibits downstream mutant phenotypes and reverses cell-competition outcomes of both mutant models.

Optical metabolic imaging identifies metabolic shifts and mitochondria heterogeneity in POLG mutator macrophages

The polymerase gamma (POLG) gene mutation is associated with mitochondria and metabolism disorders, resulting in heterogeneous responses to immunological activation and posing challenges for mitochondrial disease therapy. Optical metabolic imaging captures the autofluorescent signal of two coenzymes, NADH and FAD, and offers a label-free approach to detect cellular metabolic phenotypes, track mitochondria morphology, and quantify metabolic heterogeneity. In this study, fluorescence lifetime imaging (FLIM) of NAD(P)H and FAD revealed that POLG mutator macrophages exhibit a decreased NAD(P)H lifetime, and optical redox ratio compared to the wild-type macrophages, indicating an increased dependence on glycolysis. FLIM revealed that both wild-type and POLG mutator macrophages switch to a decreased NAD(P)H τ 1 , and τ m after immune stimulation by Lipopolysaccharides (LPS). Furthermore, a bimodality index of subpopulation analysis identified heterogenous populations of POLG mutator macrophage responses under immune challenge by LPS. Moreover, to quantify the mitochondria variations in POLG mutator macrophages, a customized thresholding image processing pipeline was developed to segment mitochondria regions within each cell from the NADH image, allowing for the feature analysis of mitochondria clusters. Consequently, the wild-type macrophages exhibited a higher percentage of mitochondria-containing pixels and longer lengths of connected mitochondria, as compared with POLG mutated macrophages. Altogether, these results illustrate the potential of optical metabolic imaging for non-invasive detection and quantification of cellular metabolism, metabolic heterogeneity within cell populations, and intra-cellular mitochondria morphology differences in POLG mutator macrophages. Optical metabolic imaging will be valuable for studying POLG-mutation diseases and evaluating efficacy of potential therapies.

Optical imaging provides flow-cytometry-like single-cell level analysis of HIF-1α-mediated metabolic changes in radioresistant head and neck squamous carcinoma cells

Significance

Radioresistance remains a significant problem for head and neck squamous cell carcinoma (HNSCC) patients. To mitigate this, the cellular and molecular pathways used by radioresistant HNSCC that drive recurrence must be studied.

Aim

We aim to demonstrate optical imaging strategies to provide flow cytometry-like single-cell level analysis of hypoxia-inducible factor 1-alpha (HIF-1α)-mediated metabolic changes in the radioresistant and radiosensitive HNSCC cells but in a more efficient, cost-effective, and non-destructive manner. Through both optical imaging and flow cytometry studies, we will reveal the role of radiation-induced HIF-1α overexpression and the following metabolic changes in the radioresistance development for HNSCC.

Approach

We optimized the use of two metabolic probes: 2-[N-(7-nitrobenz-2-oxa-1, 3-diazol-4-yl) amino]-2-deoxy-D-glucose (2-NBDG) (to report glucose uptake) and Tetramethylrhodamine ethyl ester (TMRE) (to report mitochondrial membrane potential) with both a standard fluorescence microscope and a flow cytometry device, to report the changes in metabolism between radioresistant (rSCC-61) and radiosensitive (SCC-61) HNSCC cell lines under radiation stresses with or without HIF-1α inhibition.

Results

We found that the matched HNSCC cell lines had different baseline metabolic phenotypes, and their metabolism responded differently to radiation stress along with significantly enhanced HIF-1α expressions in the rSCC-61 cells. HIF-1α inhibition during the radiation treatment modulates the metabolic changes and radio-sensitizes the rSCC-61 cells. Through these studies, we demonstrated that a standard fluorescence microscope along with proper image processing methods can provide flow cytometry-like single-cell level analysis of HIF-1α-mediated metabolic changes in the radioresistant and radiosensitive HNSCC cells.

Conclusions

Our reported optical imaging strategies may enable one to study the role of metabolism reprogramming in cancer therapeutic resistance development at the single-cell level in a more efficient, cost-effective, and non-destructive manner. Our understanding of radiation resistance mechanisms using our imaging methods will offer opportunities to design targeted radiotherapy for improved treatment outcomes for HNSCC patients.

Measuring Metabolic Changes in Cancer Cells Using Two-Photon Fluorescence Lifetime Imaging Microscopy and Machine-Learning Analysis

Two-photon (2P) fluorescence lifetime imaging microscopy (FLIM) was used to track cellular metabolism with drug treatment of auto-fluorescent coenzymes NAD(P)H and FAD in living cancer cells. Simultaneous excitation at 800 nm of both coenzymes was compared with traditional sequential 740/890 nm plus another alternative of 740/800 nm, before and after adding doxorubicin in an imaging time course. Changes of redox states at single cell resolution were compared by three analysis methods: our recently introduced fluorescence lifetime redox ratio (FLIRR: NAD(P)H-a 2%/FAD-a 1%), machine-learning (ML) algorithms using principal component (PCA) and non-linear multi-Feature autoencoder (AE) analysis. While all three led to similar biological conclusions (early drug response), the ML models provided statistically the most robust significant results. The advantage of the single 800 nm excitation of both coenzymes for metabolic imaging in above mentioned analysis is demonstrated.

Differential Mitochondrial Redox Responses to the Inhibition of NAD+ Salvage Pathway of Triple Negative Breast Cancer Cells

Background/Objectives: Cancer cells rely on metabolic reprogramming that is supported by altered mitochondrial redox status and an increased demand for NAD+. Over expression of Nampt, the rate-limiting enzyme of the NAD+ biosynthesis salvage pathway, is common in breast cancer cells, and more so in triple negative breast cancer (TNBC) cells. Targeting the salvage pathway has been pursued for cancer therapy. However, TNBC cells have heterogeneous responses to Nampt inhibition, which contributes to the diverse outcomes. There is a lack of imaging biomarkers to differentiate among TNBC cells under metabolic stress and identify which are responsive. We aimed to characterize and differentiate among a panel of TNBC cell lines under NAD-deficient stress and identify which subtypes are more dependent on the NAD salvage pathway. Methods: Optical redox imaging (ORI), a label-free live cell imaging microscopy technique was utilized to acquire intrinsic fluorescence intensities of NADH and FAD-containing flavoproteins (Fp) thus the mitochondrial redox ratio Fp/(NADH + Fp) in a panel of TNBC cell lines. Various fluorescence probes were then added to the cultures to image the mitochondrial ROS, mitochondrial membrane potential, mitochondrial mass, and cell number. Results: Various TNBC subtypes are sensitive to Nampt inhibition in a dose- and time-dependent manner, they have differential mitochondrial redox responses; furthermore, the mitochondrial redox indices linearly correlated with mitochondrial ROS induced by various doses of a Nampt inhibitor. Moreover, the changes in the redox indices correlated with growth inhibition. Additionally, the redox state was found fully recovered after removing the Nampt inhibitor. Conclusions: This study supports the utility of ORI in rapid metabolic phenotyping of TNBC cells under NAD-deficient stress to identify responsive cells and biomarkers of treatment response, facilitating combination therapy strategies.

Deep and dynamic metabolic and structural imaging in living tissues

Label-free imaging through two-photon autofluorescence of NAD(P)H allows for nondestructive, high-resolution visualization of cellular activities in living systems. However, its application to thick tissues has been restricted by its limited penetration depth within 300 μm, largely due to light scattering. Here, we demonstrate that the imaging depth for NAD(P)H can be extended to more than 700 μm in living engineered human multicellular microtissues by adopting multimode fiber-based, low repetition rate, high peak power, three-photon excitation of NAD(P)H at 1100 nm. This is achieved by having more than 0.5 megawatts peak power at the band of 1100 ± 25 nm through adaptively modulating multimodal nonlinear pulse propagation with a compact fiber shaper. Moreover, the eightfold increase in pulse energy enables faster imaging of monocyte behaviors in the living multicellular models. These results represent a substantial advance for deep and dynamic imaging of intact living biosystems. The modular design is anticipated to allow wide adoption for demanding imaging applications, including cancer research, immune responses, and tissue engineering.

Organoid models of breast cancer in precision medicine and translational research

One of the most famous and heterogeneous cancers worldwide is breast cancer (BC). Owing to differences in the gene expression profiles and clinical features of distinct BC subtypes, different treatments are prescribed for patients. However, even with more thorough pathological evaluations of tumors than in the past, available treatments do not perform equally well for all individuals. Precision medicine is a new approach that considers the effects of patients' genes, lifestyle, and environment to choose the right treatment for an individual patient. As a powerful tool, the organoid culture system can maintain the morphological and genetic characteristics of patients' tumors. Evidence also shows that organoids have high predictive value for patient treatment. In this review, a variety of BC studies performed on organoid culture systems are evaluated. Additionally, the potential of using organoid models in BC translational research, especially in immunotherapy, drug screening, and precision medicine, has been reported.

Passage dependence of NADH redox status and reactive oxygen species level in vitro in triple-negative breast cancer cell lines with different invasiveness

Background

The redox status of nicotinamide adenine dinucleotide (NAD; including oxidized form NAD+ and reduced form NADH) plays key roles in both health and disease and has been actively studied to develop cancer biomarkers and therapeutic strategies. With the optical redox imaging (ORI) technique, we have been investigating the relationship of NADH redox status, reactive oxygen species (ROS), and invasiveness in breast cancer cell cultures, and have associated higher invasiveness with more oxidized NADH redox state. However, the cell cultures may have phenotypic drift and metabolic change with increased passage numbers.

Methods

We investigated the passage-dependence of NADH redox status and ROS levels in two triple-negative breast cancer (TNBC) cell cultures: the more invasive/metastatic MDA-MB-231 and the less invasive/metastatic HCC1806 cell lines. We measured the NADH redox status, redox plasticity, and cytoplasmic and mitochondrial ROS levels under the basal condition and metabolic perturbations of the mitochondrial electron transport chain. We evaluated the dependence of redox and ROS profiles on the cell passage number by comparing the early (<20 passages) with the late (>60 passages) passage cells.

Results

(I) NADH redox and ROS baselines are stable and independent of cell passage number, but can vary with passage number under metabolic perturbations depending on specific perturbation and cell line; (II) NADH redox status and intracellular ROS levels can change discordantly in cancer cells; (III) under both basal and metabolically perturbed conditions, the more invasive cell line has a more oxidized NADH redox status with a higher basal cytoplasmic ROS level than the less invasive line, regardless of passage number.

Conclusions

The general correlation between redox, ROS, and invasiveness in studied TNBC cells is not very sensitive to passage number. These results indicate that NADH redox and basal ROS status in TNBC likely reflect the intrinsic progressive nature of TNBC cells.

Overcoming photon and spatiotemporal sparsity in fluorescence lifetime imaging with SparseFLIM

Fluorescence lifetime imaging microscopy (FLIM) provides quantitative readouts of biochemical microenvironments, holding great promise for biomedical imaging. However, conventional FLIM relies on slow photon counting routines to accumulate sufficient photon statistics, restricting acquisition speeds. Here we demonstrate SparseFLIM, an intelligent paradigm for achieving high-fidelity FLIM reconstruction from sparse photon measurements. We develop a coupled bidirectional propagation network that enriches photon counts and recovers hidden spatial-temporal information. Quantitative analysis shows over tenfold photon enrichment, dramatically improving signal-to-noise ratio, lifetime accuracy, and correlation compared to the original sparse data. SparseFLIM enables reconstructing spatially and temporally undersampled FLIM at full resolution and channel count. The model exhibits strong generalization across experimental modalities including multispectral FLIM and in vivo endoscopic FLIM. This work establishes deep learning as a promising approach to enhance fluorescence lifetime imaging and transcend limitations imposed by the inherent codependence between measurement duration and information content.

Detection and characterization of colorectal cancer by autofluorescence lifetime imaging on surgical specimens

Colorectal cancer (CRC) ranks among the most prevalent malignancies worldwide, driving a quest for comprehensive characterization methods. We report a characterization of the ex vivo autofluorescence lifetime fingerprint of colorectal tissues obtained from 73 patients that underwent surgical resection. We specifically target the autofluorescence characteristics of collagens, reduced nicotine adenine (phosphate) dinucleotide (NAD(P)H), and flavins employing a fiber-based dual excitation (375 nm and 445 nm) optical imaging system. Autofluorescence-derived parameters obtained from normal tissues, adenomatous lesions, and adenocarcinomas were analyzed considering the underlying clinicopathological features. Our results indicate that differences between tissues are primarily driven by collagen and flavins autofluorescence parameters. We also report changes in the autofluorescence parameters associated with NAD(P)H that we tentatively attribute to intratumoral heterogeneity, potentially associated to the presence of distinct metabolic subpopulations. Changes in autofluorescence signatures of malignant tumors were also observed with lymphatic and venous invasion, differentiation grade, and microsatellite instability. Finally, we characterized the impact of radiative treatment in the autofluorescence fingerprints of rectal tissues and observed a generalized increase in the mean lifetime of radiated adenocarcinomas, which is suggestive of altered metabolism and structural remodeling. Overall, our preliminary findings indicate that multiparametric autofluorescence lifetime measurements have the potential to significantly enhance clinical decision-making in CRC, spanning from initial diagnosis to ongoing management. We believe that our results will provide a foundational framework for future investigations to further understand and combat CRC exploiting autofluorescence measurements.

Metabolic light absorption, scattering, and emission (MetaLASE) microscopy

Optical imaging of metabolism can provide key information about health and disease progression in cells and tissues; however, current methods have lacked gold-standard information about histological structure. Conversely, histology and virtual histology methods have lacked metabolic contrast. Here, we present metabolic light absorption, scattering, and emission (MetaLASE) microscopy, which rapidly provides a virtual histology and optical metabolic readout simultaneously. Hematoxylin-like nucleic contrast and eosin-like cytoplasmic contrast are obtained using photoacoustic remote sensing and ultraviolet reflectance microscopy, respectively. The same ultraviolet source excites endogenous Nicotinamide adenine dinucleotide (phosphate), flavin adenine dinucleotide, and collagen autofluorescence, providing a map of optical redox ratios to visualize metabolic variations including in areas of invasive carcinoma. Benign chronic inflammation and glands also are seen to exhibit hypermetabolism. MetaLASE microscopy offers promise for future applications in intraoperative margin analysis and in research applications where greater insights into metabolic activity could be correlated with cell and tissue types.

Light-field tomographic fluorescence lifetime imaging microscopy

Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging technique that enables the visualization of biological samples at the molecular level by measuring the fluorescence decay rate of fluorescent probes. This provides critical information about molecular interactions, environmental changes, and localization within biological systems. However, creating high-resolution lifetime maps using conventional FLIM systems can be challenging, as it often requires extensive scanning that can significantly lengthen acquisition times. This issue is further compounded in three-dimensional (3D) imaging because it demands additional scanning along the depth axis. To tackle this challenge, we developed a computational imaging technique called light-field tomographic FLIM (LIFT-FLIM). Our approach allows for the acquisition of volumetric fluorescence lifetime images in a highly data-efficient manner, significantly reducing the number of scanning steps required compared to conventional point-scanning or line-scanning FLIM imagers. Moreover, LIFT-FLIM enables the measurement of high-dimensional data using low-dimensional detectors, which are typically low cost and feature a higher temporal bandwidth. We demonstrated LIFT-FLIM using a linear single-photon avalanche diode array on various biological systems, showcasing unparalleled single-photon detection sensitivity. Additionally, we expanded the functionality of our method to spectral FLIM and demonstrated its application in high-content multiplexed imaging of lung organoids. LIFT-FLIM has the potential to open up broad avenues in both basic and translational biomedical research.

The Use of Patient-Derived Organoids in the Study of Molecular Metabolic Adaptation in Breast Cancer

Around 13% of women will likely develop breast cancer during their lifetime. Advances in cancer metabolism research have identified a range of metabolic reprogramming events, such as altered glucose and amino acid uptake, increased reliance on glycolysis, and interactions with the tumor microenvironment (TME), all of which present new opportunities for targeted therapies. However, studying these metabolic networks is challenging in traditional 2D cell cultures, which often fail to replicate the three-dimensional architecture and dynamic interactions of real tumors. To address this, organoid models have emerged as powerful tools. Tumor organoids are 3D cultures, often derived from patient tissue, that more accurately mimic the structural and functional properties of actual tumor tissues in vivo, offering a more realistic model for investigating cancer metabolism. This review explores the unique metabolic adaptations of breast cancer and discusses how organoid models can provide deeper insights into these processes. We evaluate the most advanced tools for studying cancer metabolism in three-dimensional culture models, including optical metabolic imaging (OMI), matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), and recent advances in conventional techniques applied to 3D cultures. Finally, we explore the progress made in identifying and targeting potential therapeutic targets in breast cancer metabolism.

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.

Metabolic changes in Toxoplasma gondii-infected host cells measured by autofluorescence imaging

Toxoplasma gondii, the causative agent of toxoplasmosis, is an obligate intracellular parasite that infects warm-blooded vertebrates across the world. In humans, seropositivity rates of T. gondii range from 10% to 90% across communities. Despite its prevalence, few studies address how T. gondii infection changes the metabolism of host cells. In this study, we investigate how T. gondii manipulates the host cell metabolic environment by monitoring the metabolic response over time using noninvasive autofluorescence lifetime imaging of single cells, metabolite analysis, extracellular flux analysis, and reactive oxygen species (ROS) production. Autofluorescence lifetime imaging indicates that infected host cells become more oxidized and have an increased proportion of bound NAD(P)H compared to uninfected controls. Over time, infected cells also show decreases in levels of intracellular glucose and lactate, increases in oxygen consumption, and variability in ROS production. We further examined changes associated with the pre-invasion "kiss and spit" process using autofluorescence lifetime imaging, which also showed a more oxidized host cell with an increased proportion of bound NAD(P)H over 48 hours compared to uninfected controls, suggesting that metabolic changes in host cells are induced by T. gondii kiss and spit even without invasion.IMPORTANCEThis study sheds light on previously unexplored changes in host cell metabolism induced by T. gondii infection using noninvasive, label-free autofluorescence imaging. In this study, we use optical metabolic imaging (OMI) to measure the optical redox ratio (ORR) in conjunction with fluorescence lifetime imaging microscopy (FLIM) to noninvasively monitor single host cell response to T. gondii infection over 48 hours. Collectively, our results affirm the value of using autofluorescence lifetime imaging to noninvasively monitor metabolic changes in host cells over the time course of a microbial infection. Understanding this metabolic relationship between the host cell and the parasite could uncover new treatment and prevention options for T. gondii infections worldwide.

Pixel-level classification of pigmented skin cancer lesions using multispectral autofluorescence lifetime dermoscopy imaging

There is no clinical tool available to primary care physicians or dermatologists that could provide objective identification of suspicious skin cancer lesions. Multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy enables label-free biochemical and metabolic imaging of skin lesions. This study investigated the use of pixel-level maFLIM dermoscopy features for objective discrimination of malignant from visually similar benign pigmented skin lesions. Clinical maFLIM dermoscopy images were acquired from 60 pigmented skin lesions before undergoing a biopsy examination. Random forest and deep neural networks classification models were explored, as they do not require explicit feature selection. Feature pools with either spectral intensity or bi-exponential maFLIM features, and a combined feature pool, were independently evaluated with each classification model. A rigorous cross-validation strategy tailored for small-size datasets was adopted to estimate classification performance. Time-resolved bi-exponential autofluorescence features were found to be critical for accurate detection of malignant pigmented skin lesions. The deep neural network model produced the best lesion-level classification, with sensitivity and specificity of 76.84%±12.49% and 78.29%±5.50%, respectively, while the random forest classifier produced sensitivity and specificity of 74.73%±14.66% and 76.83%±9.58%, respectively. Results from this study indicate that machine-learning driven maFLIM dermoscopy has the potential to assist doctors with identifying patients in real need of biopsy examination, thus facilitating early detection while reducing the rate of unnecessary biopsies.

Label-Free Optical Technologies to Enhance Noninvasive Endoscopic Imaging of Early-Stage Cancers

White light endoscopic imaging allows for the examination of internal human organs and is essential in the detection and treatment of early-stage cancers. To facilitate diagnosis of precancerous changes and early-stage cancers, label-free optical technologies that provide enhanced malignancy-specific contrast and depth information have been extensively researched. The rapid development of technology in the past two decades has enabled integration of these optical technologies into clinical endoscopy. In recent years, the significant advantages of using these adjunct optical devices have been shown, suggesting readiness for clinical translation. In this review, we provide an overview of the working principles and miniaturization considerations and summarize the clinical and preclinical demonstrations of several such techniques for early-stage cancer detection. We also offer an outlook for the integration of multiple technologies and the use of computer-aided diagnosis in clinical endoscopy.

Metabolic priming of GD2 TRAC-CAR T cells during manufacturing promotes memory phenotypes while enhancing persistence

Manufacturing chimeric antigen receptor (CAR) T cell therapies is complex, with limited understanding of how medium composition impacts T cell phenotypes. CRISPR-Cas9 ribonucleoproteins can precisely insert a CAR sequence while disrupting the endogenous T cell receptor alpha constant (TRAC) gene resulting in TRAC-CAR T cells with an enriched stem cell memory T cell population, a process that could be further optimized through modifications to the medium composition. In this study we generated anti-GD2 TRAC-CAR T cells using "metabolic priming" (MP), where the cells were activated in glucose/glutamine-low medium and then expanded in glucose/glutamine-high medium. T cell products were evaluated using spectral flow cytometry, metabolic assays, cytokine production, cytotoxicity assays in vitro, and potency against human GD2+ xenograft neuroblastoma models in vivo. Compared with standard TRAC-CAR T cells, MP TRAC-CAR T cells showed less glycolysis, higher CCR7/CD62L expression, more bound NAD(P)H activity, and reduced IFN-γ, IL-2, IP-10, IL-1β, IL-17, and TGF-β production at the end of manufacturing ex vivo, with increased central memory CAR T cells and better persistence observed in vivo. MP with medium during CAR T cell biomanufacturing can minimize glycolysis and enrich memory phenotypes ex vivo, which could lead to better responses against solid tumors in vivo.

Multimodal Phasor Approach to study breast cancer cells invasion in 3D spheroid model

We implemented a multimodal set of functional imaging techniques optimized for deep-tissue imaging to investigate how cancer cells invade surrounding tissues and how their physiological properties change in the process. As a model for cancer invasion of the extracellular matrix, we created 3D spheroids from triple-negative breast cancer cells (MDA-MB-231) and non-tumorigenic breast epithelial cells (MCF-10A). We analyzed multiple hallmarks of cancer within the same spheroid by combining a number of imaging techniques, such as metabolic imaging of NADH by Fluorescence Lifetime Imaging Microscopy (NADH-FLIM), hyperspectral imaging of a solvatochromic lipophilic dye (Nile Red) and extracellular matrix imaging by Second Harmonic Generation (SHG). We included phasor-based bioimage analysis of spheroids at three different time points, tracking both morphological and biological properties, including cellular metabolism, fatty acids storage, and collagen organization. Employing this multimodal deep-imaging framework, we observed and quantified cancer cell plasticity in response to changes in the environment composition.

Free Salivary Amino Acid Profile in Breast Cancer: Clinicopathological and Molecular Biological Features

The study of salivary amino acid profiles has attracted the attention of researchers, since amino acids are actively involved in most metabolic processes, including breast cancer. In this study, we analyzed the amino acid profile of saliva in a sample including all molecular biological subtypes of breast cancer to obtain a more complete picture and evaluate the potential utility of individual amino acids or their combinations for diagnostic purposes. This study included 116 patients with breast cancer, 24 patients with benign breast disease, and 25 healthy controls. From all patients, strictly before the start of treatment, saliva samples were collected, and the quantitative content of 26 amino acids was determined. Statistically significant differences between the three groups are shown in the content of Asp, Gly, Leu + Ile, Orn, Phe, Pro, Thr, and Tyr. To differentiate the three groups from each other, a decision tree was built. To construct it, we selected those amino acids for which the change in concentrations in the subgroups was multidirectional (GABA, Hyl, Arg, His, Pro, and Car). For the first time, it is shown that the amino acid profile of saliva depends on the molecular biological subtype of breast cancer. The most significant differences are shown for the luminal B HER2-positive and TNBC subgroups. In our opinion, it is critically important to consider the molecular biological subtype of breast cancer when searching for potential diagnostic markers.

Dual excitation spectral autofluorescence lifetime and reflectance imaging for fast macroscopic characterization of tissues

Advancements in optical imaging techniques have revolutionized the field of biomedical research, allowing for the comprehensive characterization of tissues and their underlying biological processes. Yet, there is still a lack of tools to provide quantitative and objective characterization of tissues that can aid clinical assessment in vivo to enhance diagnostic and therapeutic interventions. Here, we present a clinically viable fiber-based imaging system combining time-resolved spectrofluorimetry and reflectance spectroscopy to achieve fast multiparametric macroscopic characterization of tissues. An essential feature of the setup is its ability to perform dual wavelength excitation in combination with recording time-resolved fluorescence data in several spectral intervals. Initial validation of this bimodal system was carried out in freshly resected human colorectal cancer specimens, where we demonstrated the ability of the system to differentiate normal from malignant tissues based on their autofluorescence and reflectance properties. To further highlight the complementarity of autofluorescence and reflectance measurements and demonstrate viability in a clinically relevant scenario, we also collected in vivo data from the skin of a volunteer. Altogether, integration of these modalities in a single platform can offer multidimensional characterization of tissues, thus facilitating a deeper understanding of biological processes and potentially advancing diagnostic and therapeutic approaches in various medical applications.

The determinants of metabolic discrepancies in aerobic glycolysis: Providing potential targets for breast cancer treatment

Altered aerobic glycolysis is the robust mechanism to support cancer cell survival and proliferation beyond the maintenance of cellular energy metabolism. Several investigators portrayed the important role of deregulated glycolysis in different cancers, including breast cancer. Breast cancer is the most ubiquitous form of cancer and the primary cause of cancer death in women worldwide. Breast cancer with increased glycolytic flux is hampered to eradicate with current therapies and can result in tumor recurrence. In spite of the low order efficiency of ATP production, cancer cells are highly addicted to glycolysis. The glycolytic dependency of cancer cells provides potential therapeutic strategies to preferentially kill cancer cells by inhibiting glycolysis using antiglycolytic agents. The present review emphasizes the most recent research on the implication of glycolytic enzymes, including glucose transporters (GLUTs), hexokinase (HK), phosphofructokinase (PFK), pyruvate kinase (PK), lactate dehydrogenase-A (LDHA), associated signalling pathways and transcription factors, as well as the antiglycolytic agents that target key glycolytic enzymes in breast cancer. The potential activity of glycolytic inhibitors impinges cancer prevalence and cellular resistance to conventional drugs even under worse physiological conditions such as hypoxia. As a single agent or in combination with other chemotherapeutic drugs, it provides the feasibility of new therapeutic modalities against a wide spectrum of human cancers.

Quantitative Optical Redox Imaging of Melanoma Xenografts with Different Metastatic Potentials

To develop imaging biomarkers for tumors aggressiveness, our previous optical redox imaging (ORI) studies of the reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavoproteins (Fp, containing flavin adenine dinucleotide, i.e., FAD) in tumor xenografts of human melanoma associated the high optical redox ratio (ORR = Fp/(Fp + NADH)) and its heterogeneity to the high invasive/metastatic potential, without having reported quantitative results for NADH and Fp. Here, we implemented a calibration procedure to facilitate imaging the nominal concentrations of tissue NADH and Fp in the mouse xenografts of two human melanoma lines, an indolent less metastatic A375P and a more metastatic C8161. Images of the redox indices (NADH, Fp, ORR) revealed the existence of more oxidized areas (OAs) and more reduced areas (RAs) within individual tumors. ORR was found to be higher and NADH lower in C8161 compared to that of A375P xenografts, both globally for the whole tumors and locally in OAs. The ORR in the OA can differentiate xenografts with a higher statistical significance than the global averaged ORR. H&E staining of the tumors indicated that the redox differences we identified were more likely due to intrinsically different cell metabolism, rather than variations in cell density.

Functional Plasmonic Microscope: Characterizing the Metabolic Activity of Single Cells via Sub-nm Membrane Fluctuations

Metabolic abnormalities are at the center of many diseases, and the capability to film and quantify the metabolic activities of a single cell is important for understanding the heterogeneities in these abnormalities. In this paper, a functional plasmonic microscope (FPM) is used to image and measure metabolic activities without fluorescent labels at a single-cell level. The FPM can accurately image and quantify the subnanometer membrane fluctuations with a spatial resolution of 0.5 μm in real time. These active cell membrane fluctuations are caused by metabolic activities across the cell membrane. A three-dimensional (3D) morphology of the bottom cell membrane was imaged and reconstructed with FPM to illustrate the capability of the microscope for cell membrane characterization. Then, the subnanometer cell membrane fluctuations of single cells were imaged and quantified with the FPM using HeLa cells. Cell metabolic heterogeneity is analyzed based on membrane fluctuations of each individual cell that is exposed to similar environmental conditions. In addition, we demonstrated that the FPM could be used to evaluate the therapeutic responses of metabolic inhibitors (glycolysis pathway inhibitor STF 31) on a single-cell level. The result showed that the metabolic activities significantly decrease over time, but the nature of this response varies, depicting cell heterogeneity. A low-concentration dose showed a reduced fluctuation frequency with consistent fluctuation amplitudes, while the high-concentration dose showcased a decreasing trend in both cases. These results have demonstrated the capabilities of the functional plasmonic microscope to measure and quantify metabolic activities for drug discovery.

Metabolic heterogeneity affects trastuzumab response and survival in HER2-positive advanced gastric cancer

BACKGROUND

Trastuzumab is the only first-line treatment targeted against the human epidermal growth factor receptor 2 (HER2) approved for patients with HER2-positive advanced gastric cancer. The impact of metabolic heterogeneity on trastuzumab treatment efficacy remains unclear.

METHODS

Spatial metabolomics via high mass resolution imaging mass spectrometry was performed in pretherapeutic biopsies of patients with HER2-positive advanced gastric cancer in a prospective multicentre observational study. The mass spectra, representing the metabolic heterogeneity within tumour areas, were grouped by K-means clustering algorithm. Simpson's diversity index was applied to compare the metabolic heterogeneity level of individual patients.

RESULTS

Clustering analysis revealed metabolic heterogeneity in HER2-positive gastric cancer patients and uncovered nine tumour subpopulations. High metabolic heterogeneity was shown as a factor indicating sensitivity to trastuzumab (p = 0.008) and favourable prognosis at trend level. Two of the nine tumour subpopulations associated with favourable prognosis and trastuzumab sensitivity, and one subpopulation associated with poor prognosis and trastuzumab resistance.

CONCLUSIONS

This work revealed that tumour metabolic heterogeneity associated with prognosis and trastuzumab response based on tissue metabolomics of HER2-positive gastric cancer. Tumour metabolic subpopulations may provide an association with trastuzumab therapy efficacy.

CLINICAL TRIAL REGISTRATION

The patient cohort was conducted from a multicentre observational study (VARIANZ;NCT02305043).

Metabolic priming of GD2 TRAC -CAR T cells during manufacturing promotes memory phenotypes while enhancing persistence

Manufacturing Chimeric Antigen Receptor (CAR) T cell therapies is complex, with limited understanding of how media composition impact T-cell phenotypes. CRISPR/Cas9 ribonucleoproteins can precisely insert a CAR sequence while disrupting the endogenous T cell receptor alpha constant ( TRAC ) gene resulting in TRAC -CAR T cells with an enriched stem cell memory T-cell population, a process that could be further optimized through modifications to the media composition. In this study we generated anti-GD2 TRAC -CAR T cells using "metabolic priming" (MP), where the cells were activated in glucose/glutamine low media and then expanded in glucose/glutamine high media. T cell products were evaluated using spectral flow cytometry, metabolic assays, cytokine production, cytotoxicity assays in vitro and potency against human GD2+ xenograft neuroblastoma models in vivo . Compared to standard TRAC -CAR T cells, MP TRAC -CAR T cells showed less glycolysis, higher CCR7/CD62L expression, more bound NAD(P)H activity and reduced IFN-γ, IL-2, IP-10, IL-1β, IL-17, and TGFβ production at the end of manufacturing ex vivo , with increased central memory CAR T cells and better persistence observed in vivo . Metabolic priming with media during CAR T cell biomanufacturing can minimize glycolysis and enrich memory phenotypes ex vivo , which could lead to better responses against solid tumors in vivo .

3D convolutional neural networks predict cellular metabolic pathway use from fluorescence lifetime decay data

Fluorescence lifetime imaging of the co-enzyme reduced nicotinamide adenine dinucleotide (NADH) offers a label-free approach for detecting cellular metabolic perturbations. However, the relationships between variations in NADH lifetime and metabolic pathway changes are complex, preventing robust interpretation of NADH lifetime data relative to metabolic phenotypes. Here, a three-dimensional convolutional neural network (3D CNN) trained at the cell level with 3D NAD(P)H lifetime decay images (two spatial dimensions and one time dimension) was developed to identify metabolic pathway usage by cancer cells. NADH fluorescence lifetime images of MCF7 breast cancer cells with three isolated metabolic pathways, glycolysis, oxidative phosphorylation, and glutaminolysis were obtained by a multiphoton fluorescence lifetime microscope and then segmented into individual cells as the input data for the classification models. The 3D CNN models achieved over 90% accuracy in identifying cancer cells reliant on glycolysis, oxidative phosphorylation, or glutaminolysis. Furthermore, the model trained with human breast cancer cell data successfully predicted the differences in metabolic phenotypes of macrophages from control and POLG-mutated mice. These results suggest that the integration of autofluorescence lifetime imaging with 3D CNNs enables intracellular spatial patterns of NADH intensity and temporal dynamics of the lifetime decay to discriminate multiple metabolic phenotypes. Furthermore, the use of 3D CNNs to identify metabolic phenotypes from NADH fluorescence lifetime decay images eliminates the need for time- and expertise-demanding exponential decay fitting procedures. In summary, metabolic-prediction CNNs will enable live-cell and in vivo metabolic measurements with single-cell resolution, filling a current gap in metabolic measurement technologies.

Applications of machine learning in time-domain fluorescence lifetime imaging: a review

Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources. In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics. However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm. In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.

Preclinical Models of Anal Cancer Combined-Modality Therapy

INTRODUCTION

There have been no significant changes in anal cancer treatment options in 4 decades. In this study, we highlight two preclinical models designed to assess anal cancer treatments.

MATERIALS AND METHODS

Transgenic K14E6/E7 mice were treated with 7, 12-dimethylbenz(a)anthracene until anal tumors developed. Mice were treated with localized radiation in addition to chemotherapy (combined-modality therapy [CMT]) and compared to no treatment control (NTC). K14E6/E7 mouse anal spheroids with and without Pik3ca mutations were isolated and treated with vehicle, LY3023414 (LY3) (a drug previously shown to be effective in cancer prevention), CMT, or CMT + LY3.

RESULTS

In the in vivo model, there was a significant increase in survival in the CMT group compared to the NTC group (P = 0.0392). In the ex vivo model, there was a significant decrease in the mean diameter of CMT and CMT + LY3-treated spheroids compared to vehicle (P ≤ 0.0001). For LY3 alone compared to vehicle, there was a statistically significant decrease in spheroid size in the K14E6/E7 group without mutation (P = 0.0004).

CONCLUSIONS

We have provided proof of concept for two preclinical anal cancer treatment models that allow for the future testing of novel therapies for anal cancer.

Label-free single-cell live imaging reveals fast metabolic switch in T lymphocytes

T-cell activation induces a metabolic switch generating energy for proliferation, survival, and functions. We used noninvasive label-free two-photon fluorescence lifetime microscopy (2P-FLIM) to map the spatial and temporal dynamics of the metabolic NAD(P)H co-enzyme during T lymphocyte activation. This provides a readout of the OXPHOS and glycolysis rates at a single-cell level. Analyzes were performed in the CD4+ leukemic T cell line Jurkat, and in human CD4+ primary T cells. Cells were activated on glass surfaces coated with activating antibodies mimicking immune synapse formation. Comparing the fraction of bound NAD(P)H between resting and activated T cells, we show that T-cell activation induces a rapid switch toward glycolysis. This occurs after 10 min and remains stable for one hour. Three-dimensional analyzes revealed that the intracellular distribution of fraction of bound NAD(P)H increases at the immune synapse in activated cells. Finally, we show that fraction of bound NAD(P)H tends to negatively correlate with spreading of activated T cells, suggesting a link between actin remodeling and metabolic changes. This study highlights that 2P-FLIM measurement of fraction of bound NAD(P)H is well suited to follow a fast metabolic switch in three dimensions, in single T lymphocytes with subcellular resolution.

UVA Hyperspectral Light-Sheet Microscopy for Volumetric Metabolic Imaging: Application to Preimplantation Embryo Development

Cellular metabolism is a key regulator of energetics, cell growth, regeneration, and homeostasis. Spatially mapping the heterogeneity of cellular metabolic activity is of great importance for unraveling the overall cell and tissue health. In this regard, imaging the endogenous metabolic cofactors, nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD), with subcellular resolution and in a noninvasive manner would be useful to determine tissue and cell viability in a clinical environment, but practical use is limited by current imaging techniques. In this paper, we demonstrate the use of phasor-based hyperspectral light-sheet (HS-LS) microscopy using a single UVA excitation wavelength as a route to mapping metabolism in three dimensions. We show that excitation solely at a UVA wavelength of 375 nm can simultaneously excite NAD(P)H and FAD autofluorescence, while their relative contributions can be readily quantified using a hardware-based spectral phasor analysis. We demonstrate the potential of our HS-LS system by capturing dynamic changes in metabolic activity during preimplantation embryo development. To validate our approach, we delineate metabolic changes during preimplantation embryo development from volumetric maps of metabolic activity. Importantly, our approach overcomes the need for multiple excitation wavelengths, two-photon imaging, or significant postprocessing of data, paving the way toward clinical translation, such as in situ, noninvasive assessment of embryo viability.

Multimodal Imaging of Pancreatic Cancer Microenvironment in Response to an Antiglycolytic Drug

Lipid metabolism and glycolysis play crucial roles in the progression and metastasis of cancer, and the use of 3-bromopyruvate (3-BP) as an antiglycolytic agent has shown promise in killing pancreatic cancer cells. However, developing an effective strategy to avoid chemoresistance requires the ability to probe the interaction of cancer drugs with complex tumor-associated microenvironments (TAMs). Unfortunately, no robust and multiplexed molecular imaging technology is currently available to analyze TAMs. In this study, the simultaneous profiling of three protein biomarkers using SERS nanotags and antibody-functionalized nanoparticles in a syngeneic mouse model of pancreatic cancer (PC) is demonstrated. This allows for comprehensive information about biomarkers and TAM alterations before and after treatment. These multimodal imaging techniques include surface-enhanced Raman spectroscopy (SERS), immunohistochemistry (IHC), polarized light microscopy, second harmonic generation (SHG) microscopy, fluorescence lifetime imaging microscopy (FLIM), and untargeted liquid chromatography and mass spectrometry (LC-MS) analysis. The study reveals the efficacy of 3-BP in treating pancreatic cancer and identifies drug treatment-induced lipid species remodeling and associated pathways through bioinformatics analysis.

Label-free spatially maintained measurements of metabolic phenotypes in cells

Metabolic reprogramming at a cellular level contributes to many diseases including cancer, yet few assays are capable of measuring metabolic pathway usage by individual cells within living samples. Here, autofluorescence lifetime imaging is combined with single-cell segmentation and machine-learning models to predict the metabolic pathway usage of cancer cells. The metabolic activities of MCF7 breast cancer cells and HepG2 liver cancer cells were controlled by growing the cells in culture media with specific substrates and metabolic inhibitors. Fluorescence lifetime images of two endogenous metabolic coenzymes, reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavin adenine dinucleotide (FAD), were acquired by a multi-photon fluorescence lifetime microscope and analyzed at the cellular level. Quantitative changes of NADH and FAD lifetime components were observed for cells using glycolysis, oxidative phosphorylation, and glutaminolysis. Conventional machine learning models trained with the autofluorescence features classified cells as dependent on glycolytic or oxidative metabolism with 90%-92% accuracy. Furthermore, adapting convolutional neural networks to predict cancer cell metabolic perturbations from the autofluorescence lifetime images provided improved performance, 95% accuracy, over traditional models trained via extracted features. Additionally, the model trained with the lifetime features of cancer cells could be transferred to autofluorescence lifetime images of T cells, with a prediction that 80% of activated T cells were glycolytic, and 97% of quiescent T cells were oxidative. In summary, autofluorescence lifetime imaging combined with machine learning models can detect metabolic perturbations between glycolysis and oxidative metabolism of living samples at a cellular level, providing a label-free technology to study cellular metabolism and metabolic heterogeneity.

Relationship Between Metabolic Activity, Cellularity, Histopathological Features of Primary Tumors and Distant Metastatic Potential in Breast Cancer

Objectives

The aim of this study was to evaluate the relationship between the types of distant metastatic spread, histopathological features, and imaging features of primary tumor on positron emission tomography/magnetic resonance imaging (PET/MRI) for primary staging in newly diagnosed breast invasive ductal carcinoma (IDC) patients.

Methods

Data from 289 female patients were retrospectively evaluated. Maximum standardized uptake value, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and minimum apparent diffusion coefficient (ADCmin) values of primary tumors were obtained from PET/MRI. The patients were grouped as non-metastatic, oligometastatic (1-5 metastatic lesions) and multimetastatic (>5 metastatic lesions) disease according to the number of distant metastases, and divided into two groups as isolated bone metastasis (IBM) and mixed/soft tissue metastasis (M-SM) groups according to the sites of metastatic spread.

Results

Metabolic parameters had higher values and ADCmin had lower values in the multimetastatic and oligometastatic groups than in the non-metastatic group. MTV was the only parameter that showed significant difference between the multimetastatic and oligometastatic groups. MTV and TLG were significantly higher in the M-SM group than in the IBM group. 18F-fluorodeoxyglucose PET parameters had significantly higher values in grade 3, hormone receptor negative, human epidermal growth factor receptor 2 positive, triple negative, and highly proliferative (Ki-67 ≥14%) tumors. The prediction models that included imaging parameters to predict the presence of distant metastasis had higher discriminatory powers than the prediction models that included only histopathological parameters.

Conclusion

Primary tumors with higher metabolic-glycolytic activity and higher cellularity were more aggressive and had higher metastatic potential in breast IDC. Compared with histopathological parameters alone, the combination of imaging parameters and histopathological features of primary tumors may help to better understand tumor biology and behavior.

Probing organoid metabolism using fluorescence lifetime imaging microscopy (FLIM): The next frontier of drug discovery and disease understanding

Organoid models have been used to address important questions in developmental and cancer biology, tissue repair, advanced modelling of disease and therapies, among other bioengineering applications. Such 3D microenvironmental models can investigate the regulation of cell metabolism, and provide key insights into the mechanisms at the basis of cell growth, differentiation, communication, interactions with the environment and cell death. Their accessibility and complexity, based on 3D spatial and temporal heterogeneity, make organoids suitable for the application of novel, dynamic imaging microscopy methods, such as fluorescence lifetime imaging microscopy (FLIM) and related decay time-assessing readouts. Several biomarkers and assays have been proposed to study cell metabolism by FLIM in various organoid models. Herein, we present an expert-opinion discussion on the principles of FLIM and PLIM, instrumentation and data collection and analysis protocols, and general and emerging biosensor-based approaches, to highlight the pioneering work being performed in this field.

Metabolic changes to host cells with Toxoplasma gondii infection

Toxoplasma gondii, the causative agent of toxoplasmosis, is an obligate intracellular parasite that infects warm-blooded vertebrates across the world. In humans, seropositivity rates of T. gondii range from 10% to 90%. Despite its prevalence, few studies address how T. gondii infection changes the metabolism of host cells. Here, we investigate how T. gondii manipulates the host cell metabolic environment by monitoring metabolic response over time using non-invasive autofluorescence lifetime imaging of single cells, seahorse metabolic flux analysis, reactive oxygen species (ROS) production, and metabolomics. Autofluorescence lifetime imaging indicates that infected host cells become more oxidized and have an increased proportion of bound NAD(P)H with infection. These findings are consistent with changes in mitochondrial and glycolytic function, decrease of intracellular glucose, fluctuations in lactate and ROS production in infected cells over time. We also examined changes associated with the pre-invasion "kiss and spit" process using autofluorescence lifetime imaging, which similarly showed a more oxidized host cell with an increased proportion of bound NAD(P)H over 48 hours. Glucose metabolic flux analysis indicated that these changes are driven by NADH and NADP+ in T. gondii infection. In sum, metabolic changes in host cells with T. gondii infection were similar during full infection, and kiss and spit. Autofluorescence lifetime imaging can non-invasively monitor metabolic changes in host cells over a microbial infection time-course.

Portable optical spectroscopic assay for non-destructive measurement of key metabolic parameters on in vitro cancer cells and organotypic fresh tumor slices

To enable non-destructive metabolic characterizations on in vitro cancer cells and organotypic tumor models for therapeutic studies in an easy-to-access way, we report a highly portable optical spectroscopic assay for simultaneous measurement of glucose uptake and mitochondrial function on various cancer models with high sensitivity. Well-established breast cancer cell lines (MCF-7 and MDA-MB-231) were used to validate the optical spectroscopic assay for metabolic characterizations, while fresh tumor samples harvested from both animals and human cancer patients were used to test the feasibility of our optical metabolic assay for non-destructive measurement of key metabolic parameters on organotypic tumor slices. Our optical metabolic assay captured that MCF-7 cells had higher mitochondrial metabolism, but lower glucose uptake compared to the MDA-MB-231 cells, which is consistent with our microscopy imaging and flow cytometry data, as well as the published Seahorse Assay data. Moreover, we demonstrated that our optical assay could non-destructively measure both glucose uptake and mitochondrial metabolism on the same cancer cell samples at one time, which remains challenging by existing metabolic tools. Our pilot tests on thin fresh tumor slices showed that our optical assay captured increased metabolic activities in tumors compared to normal tissues. Our non-destructive optical metabolic assay provides a cost-effective way for future longitudinal therapeutic studies using patient-derived organotypic fresh tumor slices through the lens of tumor energetics, which will significantly advance translational cancer research.

Balance between the cell viability and death in 3D

Cell death is a phenomenon, frequently perceived as an absolute event for cell, tissue and the organ. However, the rising popularity and complexity of such 3D multicellular 'tissue building blocks' as heterocellular spheroids, organoids, and 'assembloids' prompts to revise the definition and quantification of cell viability and death. It raises several questions on the overall viability of all the cells within 3D volume and on choosing the appropriate, continuous, and non-destructive viability assay enabling for a single-cell analysis. In this review, we look at cell viability and cell death modalities with attention to the intrinsic features of such 3D models as spheroids, organoids, and bioprints. Furthermore, we look at emerging and promising methodologies, which can help define and understand the balance between cell viability and death in dynamic and complex 3D environments. We conclude that the recent innovations in biofabrication, biosensor probe development, and fluorescence microscopy can help answer these questions.

Microphysiological head and neck cancer model identifies novel role of lymphatically secreted monocyte migration inhibitory factor in cancer cell migration and metabolism

Regional metastasis of head and neck cancer (HNC) is prevalent (approximately 50% of patients at diagnosis), yet the underlying drivers and mechanisms of lymphatic spread remain unclear. The complex tumor microenvironment (TME) of HNC plays a crucial role in disease maintenance and progression; however, the contribution of the lymphatics remains underexplored. We created a primary patient cell derived microphysiological system that incorporates cancer-associated-fibroblasts from patients with HNC alongside a HNC tumor spheroid and a lymphatic microvessel to create an in vitro TME platform to investigate metastasis. Screening of soluble factor signaling identified novel secretion of macrophage migration inhibitory factor (MIF) by lymphatic endothelial cells conditioned in the TME. Importantly, we also observed patient-to-patient heterogeneity in cancer cell migration similar to the heterogeneity observed in clinical disease. Optical metabolic imaging at the single cell level identified a distinct metabolic profile of migratory versus non-migratory HNC cells in a microenvironment dependent manner. Additionally, we report a unique role of MIF in increasing HNC reliance on glycolysis over oxidative phosphorylation. This multicellular, microfluidic platform expands the tools available to explore HNC biology in vitro through multiple orthogonal outputs and establishes a system with enough resolution to visualize and quantify patient-to-patient heterogeneity.

Comparison of phasor analysis and biexponential decay curve fitting of autofluorescence lifetime imaging data for machine learning prediction of cellular phenotypes

Introduction: Autofluorescence imaging of the coenzymes reduced nicotinamide (phosphate) dinucleotide (NAD(P)H) and oxidized flavin adenine dinucleotide (FAD) provides a label-free method to detect cellular metabolism and phenotypes. Time-domain fluorescence lifetime data can be analyzed by exponential decay fitting to extract fluorescence lifetimes or by a fit-free phasor transformation to compute phasor coordinates. Methods: Here, fluorescence lifetime data analysis by biexponential decay curve fitting is compared with phasor coordinate analysis as input data to machine learning models to predict cell phenotypes. Glycolysis and oxidative phosphorylation of MCF7 breast cancer cells were chemically inhibited with 2-deoxy-d-glucose and sodium cyanide, respectively; and fluorescence lifetime images of NAD(P)H and FAD were obtained using a multiphoton microscope. Results: Machine learning algorithms built from either the extracted lifetime values or phasor coordinates predict MCF7 metabolism with a high accuracy (∼88%). Similarly, fluorescence lifetime images of M0, M1, and M2 macrophages were acquired and analyzed by decay fitting and phasor analysis. Machine learning models trained with features from curve fitting discriminate different macrophage phenotypes with improved performance over models trained using only phasor coordinates. Discussion: Altogether, the results demonstrate that both curve fitting and phasor analysis of autofluorescence lifetime images can be used in machine learning models for classification of cell phenotype from the lifetime data.

Evolution of cisplatin resistance through coordinated metabolic reprogramming of the cellular reductive state

BACKGROUND

Cisplatin (CDDP) is a mainstay treatment for advanced head and neck squamous cell carcinomas (HNSCC) despite a high frequency of innate and acquired resistance. We hypothesised that tumours acquire CDDP resistance through an enhanced reductive state dependent on metabolic rewiring.

METHODS

To validate this model and understand how an adaptive metabolic programme might be imprinted, we performed an integrated analysis of CDDP-resistant HNSCC clones from multiple genomic backgrounds by whole-exome sequencing, RNA-seq, mass spectrometry, steady state and flux metabolomics.

RESULTS

Inactivating KEAP1 mutations or reductions in KEAP1 RNA correlated with Nrf2 activation in CDDP-resistant cells, which functionally contributed to resistance. Proteomics identified elevation of downstream Nrf2 targets and the enrichment of enzymes involved in generation of biomass and reducing equivalents, metabolism of glucose, glutathione, NAD(P), and oxoacids. This was accompanied by biochemical and metabolic evidence of an enhanced reductive state dependent on coordinated glucose and glutamine catabolism, associated with reduced energy production and proliferation, despite normal mitochondrial structure and function.

CONCLUSIONS

Our analysis identified coordinated metabolic changes associated with CDDP resistance that may provide new therapeutic avenues through targeting of these convergent pathways.

Light-field tomographic fluorescence lifetime imaging microscopy

Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging technique that enables the visualization of biological samples at the molecular level by measuring the fluorescence decay rate of fluorescent probes. This provides critical information about molecular interactions, environmental changes, and localization within biological systems. However, creating high-resolution lifetime maps using conventional FLIM systems can be challenging, as it often requires extensive scanning that can significantly lengthen acquisition times. This issue is further compounded in three-dimensional (3D) imaging because it demands additional scanning along the depth axis. To tackle this challenge, we developed a novel computational imaging technique called light field tomographic FLIM (LIFT-FLIM). Our approach allows for the acquisition of volumetric fluorescence lifetime images in a highly data-efficient manner, significantly reducing the number of scanning steps required compared to conventional point-scanning or line-scanning FLIM imagers. Moreover, LIFT-FLIM enables the measurement of high-dimensional data using low-dimensional detectors, which are typically low-cost and feature a higher temporal bandwidth. We demonstrated LIFT-FLIM using a linear single-photon avalanche diode array on various biological systems, showcasing unparalleled single-photon detection sensitivity. Additionally, we expanded the functionality of our method to spectral FLIM and demonstrated its application in high-content multiplexed imaging of lung organoids. LIFT-FLIM has the potential to open up new avenues in both basic and translational biomedical research.

Fluorescence Lifetime Imaging Microscopy (FLIM) reveals spatial-metabolic changes in 3D breast cancer spheroids

Cancer cells are mechanically sensitive to physical properties of the microenvironment, which can affect downstream signaling to promote malignancy, in part through the modulation of metabolic pathways. Fluorescence Lifetime Imaging Microscopy (FLIM) can be used to measure the fluorescence lifetime of endogenous fluorophores, such as the metabolic co-factors NAD(P)H and FAD, in live samples. We used multiphoton FLIM to investigate the changes in cellular metabolism of 3D breast spheroids derived from MCF-10A and MD-MB-231 cell lines embedded in collagen with varying densities (1 vs. 4 mg/ml) over time (Day 0 vs. Day 3). MCF-10A spheroids demonstrated spatial gradients, with the cells closest to the spheroid edge exhibiting FLIM changes consistent with a shift towards oxidative phosphorylation (OXPHOS) while the spheroid core had changes consistent with a shift towards glycolysis. The MDA-MB-231 spheroids had a large shift consistent with increased OXPHOS with a more pronounced change at the higher collagen concentration. The MDA-MB-231 spheroids invaded into the collagen gel over time and cells that traveled the farthest had the largest changes consistent with a shift towards OXPHOS. Overall, these results suggest that the cells in contact with the extracellular matrix (ECM) and those that migrated the farthest had changes consistent with a metabolic shift towards OXPHOS. More generally, these results demonstrate the ability of multiphoton FLIM to characterize how spheroids metabolism and spatial metabolic gradients are modified by physical properties of the 3D ECM.