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.

Carbomer-based adjuvant elicits CD8 T-cell immunity by inducing a distinct metabolic state in cross-presenting dendritic cells.

There is a critical need for adjuvants that can safely elicit potent and durable T cell-based immunity to intracellular pathogens. Here, we report that parenteral vaccination with a carbomer-based adjuvant, Adjuplex (ADJ), stimulated robust CD8 T-cell responses to subunit antigens and afforded effective immunity against respiratory challenge with a virus and a systemic intracellular bacterial infection. Studies to understand the metabolic and molecular basis for ADJ's effect on antigen cross-presentation by dendritic cells (DCs) revealed several unique and distinctive mechanisms. ADJ-stimulated DCs produced IL-1ß and IL-18, suggestive of inflammasome activation, but in vivo activation of CD8 T cells was unaffected in caspase 1-deficient mice. Cross-presentation induced by TLR agonists requires a critical switch to anabolic metabolism, but ADJ enhanced cross presentation without this metabolic switch in DCs. Instead, ADJ induced in DCs, an unique metabolic state, typified by dampened oxidative phosphorylation and basal levels of glycolysis. In the absence of increased glycolytic flux, ADJ modulated multiple steps in the cytosolic pathway of cross-presentation by enabling accumulation of degraded antigen, reducing endosomal acidity and promoting antigen localization to early endosomes. Further, by increasing ROS production and lipid peroxidation, ADJ promoted antigen escape from endosomes to the cytosol for degradation by proteasomes into peptides for MHC I loading by TAP-dependent pathways. Furthermore, we found that induction of lipid bodies (LBs) and alterations in LB composition mediated by ADJ were also critical for DC cross-presentation. Collectively, our model challenges the prevailing metabolic paradigm by suggesting that DCs can perform effective DC cross-presentation, independent of glycolysis to induce robust T cell-dependent protective immunity to intracellular pathogens. These findings have strong implications in the rational development of safe and effective immune adjuvants to potentiate robust T-cell based immunity.

Identification of rare cell populations in autofluorescence lifetime image data.

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

Decellularized organ biomatrices facilitate quantifiable in vitro 3D cancer metastasis models.

Metastatic cancers are chemoresistant, involving complex interplay between disseminated cancer cell aggregates and the distant organ microenvironment (extracellular matrix and stromal cells). Conventional metastasis surrogates (scratch/wound healing, Transwell migration assays) lack 3D architecture and ECM presence. Metastasis studies can therefore significantly benefit from biomimetic 3D in vitro models recapitulating the complex cascade of distant organ invasion and colonization by collective clusters of cells. We aimed to engineer reproducible and quantifiable 3D models of highly therapy-resistant cancer processes: (i) colorectal cancer liver metastasis; and (ii) breast cancer lung metastasis. Metastatic seeds are engineered using 3D tumor spheroids to recapitulate the 3D aggregation of cancer cells both in the tumor and in circulation throughout the metastatic cascade of many cancers. Metastatic soil was engineered by decellularizing porcine livers and lungs to generate biomatrix scaffolds, followed by extensive materials characterization. HCT116 colorectal and MDA-MB-231 breast cancer spheroids were generated on hanging drop arrays to initiate clustered metastatic seeding into liver and lung biomatrix scaffolds, respectively. Between days 3-7, biomatrix cellular colonization was apparent with increased metabolic activity and the presence of cellular nests evaluated via multiphoton microscopy. HCT116 and MDA-MB-231 cells colonized liver and lung biomatrices, and at least 15% of the cells invaded more than 20 µm from the surface. Engineered metastases also expressed increased signatures of genes associated with the metastatic epithelial to mesenchymal transition (EMT). Importantly, inhibition of matrix metalloproteinase-9 inhibited metastatic invasion into the biomatrix. Furthermore, metastatic nests were significantly more chemoresistant (>3 times) to the anti-cancer drug oxaliplatin, compared to 3D spheroids. Together, our data indicated that HCT116 and MDA-MB-231 spheroids invade, colonize, and proliferate in livers and lungs establishing metastatic nests in 3D settings in vitro. The metastatic nature of these cells was confirmed with functional readouts regarding EMT and chemoresistance. Modeling the dynamic metastatic cascade in vitro has potential to identify therapeutic targets to treat or prevent metastatic progression in chemoresistant metastatic cancers.

Signal Transducer and Activator of Transcription 3, Mediated Remodeling of the Tumor Microenvironment Results in Enhanced Tumor Drug Delivery in a Mouse Model of Pancreatic Cancer.

BACKGROUND & AIMS: A hallmark of pancreatic ductal adenocarcinoma (PDAC) is the presence of a dense desmoplastic reaction (stroma) that impedes drug delivery to the tumor. Attempts to deplete the tumor stroma have resulted in formation of more aggressive tumors. We have identified signal transducer and activator of transcription (STAT) 3 as a biomarker of resistance to cytotoxic and molecularly targeted therapy in PDAC. The purpose of this study is to investigate the effects of targeting STAT3 on the PDAC stroma and on therapeutic resistance. METHODS: Activated STAT3 protein expression was determined in human pancreatic tissues and tumor cell lines. In vivo effects of AZD1480, a JAK/STAT3 inhibitor, gemcitabine or the combination were determined in Ptf1a(cre/+);LSL-Kras(G12D/+);Tgfbr2(flox/flox) (PKT) mice and in orthotopic tumor xenografts. Drug delivery was analyzed by matrix-assisted laser desorption/ionization imaging mass spectrometry. Collagen second harmonic generation imaging quantified tumor collagen alignment and density. RESULTS: STAT3 activation correlates with decreased survival and advanced tumor stage in patients with PDAC. STAT3 inhibition combined with gemcitabine significantly inhibits tumor growth in both an orthotopic and the PKT mouse model of PDAC. This combined therapy attenuates in vivo expression of SPARC, increases microvessel density, and enhances drug delivery to the tumor without depletion of stromal collagen or hyaluronan. Instead, the PDAC tumors demonstrate vascular normalization, remodeling of the tumor stroma, and down-regulation of cytidine deaminase. CONCLUSIONS: Targeted inhibition of STAT3 combined with gemcitabine enhances in vivo drug delivery and therapeutic response in PDAC. These effects occur through tumor stromal remodeling and down-regulation of cytidine deaminase without depletion of tumor stromal content.

Deconvolution of fluorescence lifetime imaging microscopy by a library of exponentials.

Fluorescence lifetime microscopy imaging (FLIM) is an optic technique that allows a quantitative characterization of the fluorescent components of a sample. However, for an accurate interpretation of FLIM, an initial processing step is required to deconvolve the instrument response of the system from the measured fluorescence decays. In this paper, we present a novel strategy for the deconvolution of FLIM data based on a library of exponentials. Our approach searches for the scaling coefficients of the library by non-negative least squares approximations plus Thikonov/l(2) or l(1) regularization terms. The parameters of the library are given by the lower and upper bounds in the characteristic lifetimes of the exponential functions and the size of the library, where we observe that this last variable is not a limiting factor in the resulting fitting accuracy. We compare our proposal to nonlinear least squares and global non-linear least squares estimations with a multi-exponential model, and also to constrained Laguerre-base expansions, where we visualize an advantage of our proposal based on Thikonov/l(2) regularization in terms of estimation accuracy, computational time, and tuning strategy. Our validation strategy considers synthetic datasets subject to both shot and Gaussian noise and samples with different lifetime maps, and experimental FLIM data of ex-vivo atherosclerotic plaques and human breast cancer cells.

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.

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.

Comparison of polystyrene and hydrogel microcarriers for optical imaging of adherent cells.

Significance: The ability to observe and monitor cell density and morphology has been imperative for assessing the health of a cell culture and for producing high quality, high yield cell cultures for decades. Microcarrier-based cultures, used for large-scale cellular expansion processes, are not compatible with traditional visualization-based methods, such as widefield microscopy, due to their thickness and material composition. Aim: Here, we assess the optical imaging compatibilities of commercial polystyrene microcarriers versus custom-fabricated gelatin methacryloyl (gelMA) microcarriers for non-destructive and non-invasive visualization of the entire microcarrier surface, direct cell enumeration, and sub-cellular visualization of mesenchymal stem/stromal cells. Approach: Mie scattering and wavefront error simulations of the polystyrene and gelMA microcarriers were performed to assess the potential for elastic scattering-based imaging of adherent cells. A Zeiss Z.1 light-sheet microscope was adapted to perform light-sheet tomography using label-free elastic scattering contrast from planar side illumination to achieve optical sectioning and permit non-invasive and non-destructive, in toto, three-dimensional, high-resolution visualization of cells cultured on microcarriers. Results: The polystyrene microcarrier prevents visualization of cells on the distal half of the microcarrier using either fluorescence or elastic scattering contrast, whereas the gelMA microcarrier allows for high fidelity visualization of cell morphology and quantification of cell density using light-sheet fluorescence microscopy and tomography. Conclusions: The combination of optical-quality gelMA microcarriers and label-free light-sheet tomography will facilitate enhanced control of bioreactor-microcarrier cell culture processes.

Autofluorescence is a biomarker of neural stem cell activation state.

Neural stem cells (NSCs) must exit quiescence to produce neurons; however, our understanding of this process remains constrained by the technical limitations of current technologies. Fluorescence lifetime imaging (FLIM) of autofluorescent metabolic cofactors has been used in other cell types to study shifts in cell states driven by metabolic remodeling that change the optical properties of these endogenous fluorophores. Using this non-destructive, live-cell, and label-free strategy, we found that quiescent NSCs (qNSCs) and activated NSCs (aNSCs) have unique autofluorescence profiles. Specifically, qNSCs display an enrichment of autofluorescence localizing to a subset of lysosomes, which can be used as a graded marker of NSC quiescence to predict cell behavior at single-cell resolution. Coupling autofluorescence imaging with single-cell RNA sequencing, we provide resources revealing transcriptional features linked to deep quiescence and rapid NSC activation. Together, we describe an approach for tracking mouse NSC activation state and expand our understanding of adult neurogenesis.

Quantitative optical imaging of vascular response in vivo in a model of peripheral arterial disease.

The mouse hind limb ischemia (HLI) model is well established for studying collateral vessel formation and testing therapies for peripheral arterial disease, but there is a lack of quantitative techniques for intravitally analyzing blood vessel structure and function. To address this need, non-invasive, quantitative optical imaging techniques were developed to assess the time-course of recovery in the mouse HLI model. Hyperspectral imaging and optical coherence tomography (OCT) were used to non-invasively image hemoglobin oxygen saturation and microvessel morphology plus blood flow, respectively, in the anesthetized mouse after induction of HLI. Hyperspectral imaging detected significant increases in hemoglobin saturation in the ischemic paw as early as 3 days after femoral artery ligation (P < 0.01), and significant increases in distal blood flow were first detected with OCT 14 days postsurgery (P < 0.01). Intravital OCT images of the adductor muscle vasculature revealed corkscrew collateral vessels characteristic of the arteriogenic response to HLI. The hyperspectral imaging and OCT data significantly correlated with each other and with laser Doppler perfusion imaging (LDPI) and tissue oxygenation sensor data (P < 0.01). However, OCT measurements acquired depth-resolved information and revealed more sustained flow deficits following surgery that may be masked by more superficial measurements (LDPI, hyperspectral imaging). Therefore, intravital OCT may provide a robust biomarker for the late stages of ischemic limb recovery. This work validates non-invasive acquisition of both functional and morphological data with hyperspectral imaging and OCT. Together, these techniques provide cardiovascular researchers an unprecedented and comprehensive view of the temporal dynamics of HLI recovery in living mice.

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

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

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.

Label-free optical imaging of cell function and collagen structure for cell-based therapies.

Cell-based therapies harness functional cells or tissues to mediate healing and treat disease. Assessment of cellular therapeutics requires methods that are non-destructive to ensure therapies remain viable and uncontaminated for use in patients. Optical imaging of endogenous collagen, by second-harmonic generation, and the metabolic coenzymes NADH and FAD, by autofluorescence microscopy, provides tissue structure and cellular information. Here, we review applications of label-free nonlinear optical imaging of cellular metabolism and collagen second-harmonic generation for assessing cell-based therapies. Additionally, we discuss the potential of label-free imaging for quality control of cell-based therapies, as well as the current limitations and potential future directions of label-free imaging technologies.

Perfusability and immunogenicity of implantable pre-vascularized tissues recapitulating features of native capillary network.

Vascularization is a key pre-requisite to engineered anatomical scale three dimensional (3-D) constructs to ensure their nutrient and oxygen supply upon implantation. Presently, engineered pre-vascularized 3-D tissues are limited to only micro-scale hydrogels, which meet neither the anatomical scale needs nor the complexity of natural extracellular matrix (ECM) environments. Anatomical scale perfusable constructs are critically needed for translational applications. To overcome this challenge, we previously developed pre-vascularized ECM sheets with long and oriented dense microvascular networks. The present study further evaluated the patency, perfusability and innate immune response toward these pre-vascularized constructs. Macrophage-co-cultured pre-vascularized constructs were evaluated in vitro to confirm micro-vessel patency and perturbations in macrophage metabolism. Subcutaneously implanted pre-vascularized constructs remained viable and formed a functional anastomosis with host vasculature within 3 days of implantation. This completely biological pre-vascularized construct holds great potential as a building block to engineer perfusable anatomical scale tissues.

Volumetric imaging of human mesenchymal stem cells (hMSCs) for non-destructive quantification of 3D cell culture growth.

The adoption of cell-based therapies into the clinic will require tremendous large-scale expansion to satisfy future demand, and bioreactor-microcarrier cultures are best suited to meet this challenge. The use of spherical microcarriers, however, precludes in-process visualization and monitoring of cell number, morphology, and culture health. The development of novel expansion methods also motivates the advancement of analytical methods used to characterize these microcarrier cultures. A robust optical imaging and image-analysis assay to non-destructively quantify cell number and cell volume was developed. This method preserves 3D cell morphology and does not require membrane lysing, cellular detachment, or exogenous labeling. Complex cellular networks formed in microcarrier aggregates were imaged and analyzed in toto. Direct cell enumeration of large aggregates was performed in toto for the first time. This assay was successfully applied to monitor cellular growth of mesenchymal stem cells attached to spherical hydrogel microcarriers over time. Elastic scattering and fluorescence lightsheet microscopy were used to quantify cell volume and cell number at varying spatial scales. The presented study motivates the development of on-line optical imaging and image analysis systems for robust, automated, and non-destructive monitoring of bioreactor-microcarrier cell cultures.

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.

Autofluorescence lifetime imaging classifies human lymphocyte activation and subtype.

New non-destructive tools are needed to reliably assess lymphocyte function for immune profiling and adoptive cell therapy. Optical metabolic imaging (OMI) is a label-free method that measures the autofluorescence intensity and lifetime of metabolic cofactors NAD(P)H and FAD to quantify metabolism at a single-cell level. Here, we investigate whether OMI can resolve metabolic changes between human quiescent versus IL4/CD40 activated B cells and IL12/IL15/IL18 activated memory-like NK cells. We found that quiescent B and NK cells were more oxidized compared to activated cells. Additionally, the NAD(P)H mean fluorescence lifetime decreased and the fraction of unbound NAD(P)H increased in the activated B and NK cells compared to quiescent cells. Machine learning classified B cells and NK cells according to activation state (CD69+) based on OMI parameters with up to 93.4% and 92.6% accuracy, respectively. Leveraging our previously published OMI data from activated and quiescent T cells, we found that the NAD(P)H mean fluorescence lifetime increased in NK cells compared to T cells, and further increased in B cells compared to NK cells. Random forest models based on OMI classified lymphocytes according to subtype (B, NK, T cell) with 97.8% accuracy, and according to activation state (quiescent or activated) and subtype (B, NK, T cell) with 90.0% accuracy. Our results show that autofluorescence lifetime imaging can accurately assess lymphocyte activation and subtype in a label-free, non-destructive manner.

The effect of temperature on the autofluorescence of scattering and non-scattering tissue.

BACKGROUND AND OBJECTIVES: With the increasing use of fluorescence in medical applications, a comprehensive understanding of the effect of temperature on tissue autofluorescence is essential. The purpose of this study is to explore the effect of temperature on the fluorescence of porcine cornea and rat skin and determine the relative contributions of irreversible changes in optical properties and in fluorescence yield. STUDY DESIGN/MATERIALS AND METHODS: Fluorescence, diffuse reflectance, and temperature measurements were acquired from excised porcine cornea and rat skin over a temperature range of 0-80 °C. A dual excitation system was used with a 337 nm pulsed nitrogen laser for the fluorescence and a white light source for the diffuse reflectance measurements. A thermal camera measured tissue temperature. Optical property changes were inferred from diffuse reflectance measurements. The reversibility of the change in fluorescence was examined by acquiring measurements while the tissue sample cooled from the highest induced temperature to room temperature. RESULTS: The fluorescence intensity decreased with increasing tissue temperature. This fluorescence change was reversible when the tissue was heated to a temperature of 45 °C, but irreversible when heated to a temperature of 80 °C. CONCLUSION: Auto-fluorescence intensity dependence on temperature appears to be a combination of temperature-induced optical property changes and reduced fluorescence quantum yield due to changes in collagen structure. Temperature-induced changes in measured fluorescence must be taken into consideration in applications where fluorescence is used to diagnose disease or guide therapy.

In vivo fluorescence lifetime imaging of macrophage intracellular metabolism during wound responses in zebrafish.

The function of macrophages in vitro is linked to their metabolic rewiring. However, macrophage metabolism remains poorly characterized in situ. Here, we used two-photon intensity and lifetime imaging of autofluorescent metabolic coenzymes, nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD), to assess the metabolism of macrophages in the wound microenvironment. Inhibiting glycolysis reduced NAD(P)H mean lifetime and made the intracellular redox state of macrophages more oxidized, as indicated by reduced optical redox ratio. We found that TNFα+ macrophages had lower NAD(P)H mean lifetime and were more oxidized compared to TNFα- macrophages. Both infection and thermal injury induced a macrophage population with a more oxidized redox state in wounded tissues. Kinetic analysis detected temporal changes in the optical redox ratio during tissue repair, revealing a shift toward a more reduced redox state over time. Metformin reduced TNFα+ wound macrophages, made intracellular redox state more reduced and improved tissue repair. By contrast, depletion of STAT6 increased TNFα+ wound macrophages, made redox state more oxidized and impaired regeneration. Our findings suggest that autofluorescence of NAD(P)H and FAD is sensitive to dynamic changes in intracellular metabolism in tissues and can be used to probe the temporal and spatial regulation of macrophage metabolism during tissue damage and repair.