Classification of T-cell activation via autofluorescence lifetime imaging

Perspectives on label-free microscopy of heterogeneous and dynamic biological systems

Significance

Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging).

Aim

This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs.

Approach

Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended.

Conclusion

Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Computational Hyperspectral Microflow Cytometry

Miniaturized flow cytometry has significant potential for portable applications, such as cell-based diagnostics and the monitoring of therapeutic cell manufacturing, however, the performance of current techniques is often limited by the inability to resolve spectrally-overlapping fluorescence labels. Here, the study presents a computational hyperspectral microflow cytometer (CHC) that enables accurate discrimination of spectrally-overlapping fluorophores labeling single cells. CHC employs a dispersive optical element and an optimization algorithm to detect the full fluorescence emission spectrum from flowing cells, with a high spectral resolution of ≈3 nm in the range from 450 to 650 nm. CHC also includes a dedicated microfluidic device that ensures in-focus imaging through viscoelastic sheathless focusing, thereby enhancing the accuracy and reliability of microflow cytometry analysis. The potential of CHC for analyzing T lymphocyte subpopulations and monitoring changes in cell composition during T cell expansion is demonstrated. Overall, CHC represents a major breakthrough in microflow cytometry and can facilitate its use for immune cell monitoring.

Label-free metabolic optical biomarkers track stem cell fate transition in real time

Tracking stem cell fate transition is crucial for understanding their development and optimizing biomanufacturing. Destructive single-cell methods provide a pseudotemporal landscape of stem cell differentiation but cannot monitor stem cell fate in real time. We established a metabolic optical metric using label-free fluorescence lifetime imaging microscopy (FLIM), feature extraction and machine learning-assisted analysis, for real-time cell fate tracking. From a library of 205 metabolic optical biomarker (MOB) features, we identified 56 associated with hematopoietic stem cell (HSC) differentiation. These features collectively describe HSC fate transition and detect its bifurcate lineage choice. We further derived a MOB score measuring the "metabolic stemness" of single cells and distinguishing their division patterns. This score reveals a distinct role of asymmetric division in rescuing stem cells with compromised metabolic stemness and a unique mechanism of PI3K inhibition in promoting ex vivo HSC maintenance. MOB profiling is a powerful tool for tracking stem cell fate transition and improving their biomanufacturing from a single-cell perspective.

MEN1 deficiency stabilizes PD-L1 and promotes tumor immune evasion of lung cancer

Multiple Endocrine Neoplasia 1 gene (MEN1), which is known to be a tumor suppressor gene in lung tissues, encodes a 610 amino acid protein menin. Previous research has proven that MEN1 deficiency promotes the malignant progression of lung cancer. However, the biological role of this gene in the immune microenvironment of lung cancer remains unclear. In this study, we found that programmed cell death-ligand 1 (PD-L1) is upregulated in lung-specific KrasG12D mutation-induced lung adenocarcinoma in mice, after Men1 deficiency. Simultaneously, CD8+ and CD3+ T cells are depleted, and their cytotoxic effects are suppressed. In vitro, PD-L1 is inhibited by the overexpression of menin. Mechanistically, we found that MEN1 inactivation promotes the deubiquitinating activity of COP9 signalosome subunit 5 (CSN5) and subsequently increases the level of PD-L1.

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.

Targeting DNMT3A-mediated oxidative phosphorylation to overcome ibrutinib resistance in mantle cell lymphoma

The use of Bruton tyrosine kinase (BTK) inhibitors such as ibrutinib achieves a remarkable clinical response in mantle cell lymphoma (MCL). Acquired drug resistance, however, is significant and affects long-term survival of MCL patients. Here, we demonstrate that DNA methyltransferase 3A (DNMT3A) is involved in ibrutinib resistance. We find that DNMT3A expression is upregulated upon ibrutinib treatment in ibrutinib-resistant MCL cells. Genetic and pharmacological analyses reveal that DNMT3A mediates ibrutinib resistance independent of its DNA-methylation function. Mechanistically, DNMT3A induces the expression of MYC target genes through interaction with the transcription factors MEF2B and MYC, thus mediating metabolic reprogramming to oxidative phosphorylation (OXPHOS). Targeting DNMT3A with low-dose decitabine inhibits the growth of ibrutinib-resistant lymphoma cells both in vitro and in a patient-derived xenograft mouse model. These findings suggest that targeting DNMT3A-mediated metabolic reprogramming to OXPHOS with decitabine provides a potential therapeutic strategy to overcome ibrutinib resistance in relapsed/refractory MCL.

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.

Machine learning predictions of T cell antigen specificity from intracellular calcium dynamics

Adoptive T cell therapies rely on the production of T cells with an antigen receptor that directs their specificity toward tumor-specific antigens. Methods for identifying relevant T cell receptor (TCR) sequences, predominantly achieved through the enrichment of antigen-specific T cells, represent a major bottleneck in the production of TCR-engineered cell therapies. Fluctuation of intracellular calcium is a proximal readout of TCR signaling and candidate marker for antigen-specific T cell identification that does not require T cell expansion; however, calcium fluctuations downstream of TCR engagement are highly variable. We propose that machine learning algorithms may allow for T cell classification from complex datasets such as polyclonal T cell signaling events. Using deep learning tools, we demonstrate accurate prediction of TCR-transgenic CD8+ T cell activation based on calcium fluctuations and test the algorithm against T cells bearing a distinct TCR as well as polyclonal T cells. This provides the foundation for an antigen-specific TCR sequence identification pipeline for adoptive T cell therapies.

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.

Assessing cell viability with dynamic optical coherence microscopy

Assessing cell viability is important in many fields of research. Current optical methods to assess cell viability typically involve fluorescent dyes, which are often less reliable and have poor permeability in primary tissues. Dynamic optical coherence microscopy (dOCM) is an emerging tool that provides label-free contrast reflecting changes in cellular metabolism. In this work, we compare the live contrast obtained from dOCM to viability dyes, and for the first time to our knowledge, demonstrate that dOCM can distinguish live cells from dead cells in murine syngeneic tumors. We further demonstrate a strong correlation between dOCM live contrast and optical redox ratio by metabolic imaging in primary mouse liver tissue. The dOCM technique opens a new avenue to apply label-free imaging to assess the effects of immuno-oncology agents, targeted therapies, chemotherapy, and cell therapies using live tumor tissues.

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.

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.

2P-FLIM unveils time-dependent metabolic shifts during osteogenic differentiation with a key role of lactate to fuel osteogenesis via glutaminolysis identified

BACKGROUND

Human mesenchymal stem cells (hMSCs) utilize discrete biosynthetic pathways to self-renew and differentiate into specific cell lineages, with undifferentiated hMSCs harbouring reliance on glycolysis and hMSCs differentiating towards an osteogenic phenotype relying on oxidative phosphorylation as an energy source.

METHODS

In this study, the osteogenic differentiation of hMSCs was assessed and classified over 14 days using a non-invasive live-cell imaging modality-two-photon fluorescence lifetime imaging microscopy (2P-FLIM). This technique images and measures NADH fluorescence from which cellular metabolism is inferred.

RESULTS

During osteogenesis, we observe a higher dependence on oxidative phosphorylation (OxPhos) for cellular energy, concomitant with an increased reliance on anabolic pathways. Guided by these non-invasive observations, we validated this metabolic profile using qPCR and extracellular metabolite analysis and observed a higher reliance on glutaminolysis in the earlier time points of osteogenic differentiation. Based on the results obtained, we sought to promote glutaminolysis further by using lactate, to improve the osteogenic potential of hMSCs. Higher levels of mineral deposition and osteogenic gene expression were achieved when treating hMSCs with lactate, in addition to an upregulation of lactate metabolism and transmembrane cellular lactate transporters. To further clarify the interplay between glutaminolysis and lactate metabolism in osteogenic differentiation, we blocked these pathways using BPTES and α-CHC respectively. A reduction in mineralization was found after treatment with BPTES and α-CHC, demonstrating the reliance of hMSC osteogenesis on glutaminolysis and lactate metabolism.

CONCLUSION

In summary, we demonstrate that the osteogenic differentiation of hMSCs has a temporal metabolic profile and shift that is observed as early as day 3 of cell culture using 2P-FLIM. Furthermore, extracellular lactate is shown as an essential metabolite and metabolic fuel to ensure efficient osteogenic differentiation and as a signalling molecule to promote glutaminolysis. These findings have significant impact in the use of 2P-FLIM to discover potent approaches towards bone tissue engineering in vitro and in vivo by engaging directly with metabolite-driven osteogenesis.

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.

Label-free biomedical optical imaging

Label-free optical imaging employs natural and nondestructive approaches for the visualisation of biomedical samples for both biological assays and clinical diagnosis. Currently, this field revolves around multiple broad technology-oriented communities, each with a specific focus on a particular modality despite the existence of shared challenges and applications. As a result, biologists or clinical researchers who require label-free imaging are often not aware of the most appropriate modality to use. This manuscript presents a comprehensive review of and comparison among different label-free imaging modalities and discusses common challenges and applications. We expect this review to facilitate collaborative interactions between imaging communities, push the field forward and foster technological advancements, biophysical discoveries, as well as clinical detection, diagnosis, and monitoring of disease.

Expanding access to CAR T cell therapies through local manufacturing

Chimeric antigen receptor (CAR) T cells are changing the therapeutic landscape for hematological malignancies. To date, all six CAR T cell products approved by the US Food and Drug Administration (FDA) are autologous and centrally manufactured. As the numbers of approved products and indications continue to grow, new strategies to increase cell-manufacturing capacity are urgently needed to ensure patient access. Distributed manufacturing at the point of care or at other local manufacturing sites would go a long way toward meeting the rising demand. To ensure successful implementation, it is imperative to harness novel technologies to achieve uniform product quality across geographically dispersed facilities. This includes the use of automated cell-production systems, in-line sensors and process simulation for enhanced quality control and efficient supply chain management. A comprehensive effort to understand the critical quality attributes of CAR T cells would enable better definition of widely attainable release criteria. To supplement oversight by national regulatory agencies, we recommend expansion of the role of accreditation bodies. Moreover, regulatory standards may need to be amended to accommodate the unique characteristics of distributed manufacturing models.

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.

Smart probes for optical imaging of T cells and screening of anti-cancer immunotherapies

T cells are an essential part of the immune system with crucial roles in adaptive response and the maintenance of tissue homeostasis. Depending on their microenvironment, T cells can be differentiated into multiple states with distinct functions. This myriad of cellular activities have prompted the development of numerous smart probes, ranging from small molecule fluorophores to nanoconstructs with variable molecular architectures and fluorescence emission mechanisms. In this Tutorial Review, we summarize recent efforts in the design, synthesis and application of smart probes for imaging T cells in tumors and inflammation sites by targeting metabolic and enzymatic biomarkers as well as specific surface receptors. Finally, we briefly review current strategies for how smart probes are employed to monitor the response of T cells to anti-cancer immunotherapies. We hope that this Review may help chemists, biologists and immunologists to design the next generation of molecular imaging probes for T cells and anti-cancer immunotherapies.

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.

Quantitative multiphoton imaging of cell metabolism, stromal fibers, and keratinization enables label-free discrimination of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) features atypical clinical manifestations and a low 5-year survival rate (< 5% in many developing countries where most of the disease occurs). Precise ESCC detection and grading toward timely and effective intervention are therefore crucial. In this study, we propose a multidimensional, slicing-free, and label-free histopathological evaluation method based on multispectral multiphoton fluorescence lifetime imaging microscopy (MM-FLIM) for precise ESCC identification. To assess the feasibility of this method, comparative imaging on fresh human biopsy specimens of different ESCC grades is performed. By constructing fluorescence spectrum- and lifetime-coded images, ESCC-induced morphological variations are unveiled. Further quantification of cell metabolism and stromal fibers reveals potential indicators for ESCC detection and grading. The specific identification of keratin pearls provides additional support for the early detection of ESCC. These findings demonstrate the viability of using MM-FLIM and the series of derived indicators for histopathological evaluation of ESCC. As there is an increasing interest in developing multiphoton endoscopes and multiphoton FLIM systems for clinical use, the proposed method would probably allow noninvasive, label-free, and multidimensional histological detection and grading of ESCC in the future.

On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning

We present a rapid label-free imaging flow cytometry and cell classification approach based directly on raw digital holograms. Off-axis holography enables real-time acquisition of cells during rapid flow. However, classification of the cells typically requires reconstruction of their quantitative phase profiles, which is time-consuming. Here, we present a new approach for label-free classification of individual cells based directly on the raw off-axis holographic images, each of which contains the complete complex wavefront (amplitude and quantitative phase profiles) of the cell. To obtain this, we built a convolutional neural network, which is invariant to the spatial frequencies and directions of the interference fringes of the off-axis holograms. We demonstrate the effectiveness of this approach using four types of cancer cells. This approach has the potential to significantly improve both speed and robustness of imaging flow cytometry, enabling real-time label-free classification of individual cells.

Spatial topology of organelle is a new breast cancer cell classifier

Genomics and proteomics have been central to identify tumor cell populations, but more accurate approaches to classify cell subtypes are still lacking. We propose a new methodology to accurately classify cancer cells based on their organelle spatial topology. Herein, we developed an organelle topology-based cell classification pipeline (OTCCP), which integrates artificial intelligence (AI) and imaging quantification to analyze organelle spatial distribution and inter-organelle topology. OTCCP was used to classify a panel of human breast cancer cells, grown as 2D monolayer or 3D tumor spheroids using early endosomes, mitochondria, and their inter-organelle contacts. Organelle topology allows for a highly precise differentiation between cell lines of different subtypes and aggressiveness. These findings lay the groundwork for using organelle topological profiling as a fast and efficient method for phenotyping breast cancer function as well as a discovery tool to advance our understanding of cancer cell biology at the subcellular level.

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

Significance

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

Aim

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

Approach

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

Results

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

Conclusions

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

Fluorescence Lifetime Imaging of NAD(P)H T Cells Autofluorescence in the Lymphatic Nodes to Assess the Effectiveness of Anti-CTLA-4 Immunotherapy

The main problem in the field of tumor immunotherapy is the lack of reliable biomarkers that allow pre-determining the susceptibility of individual patients to treatment, as well as insufficient knowledge about the resistance mechanisms. Biomarkers based on the autofluorescence of metabolic coenzymes in immune cells can become a powerful new predictor of early tumor response to treatment, whereas the optical FLIM method can be a tool to predict the effectiveness of immunotherapy, which allows preserving the spatial structure of the sample and obtaining results on the metabolic status of immune cells in real time. The aim of the study is to conduct a metabolic autofluorescence imaging study of the NAD(P)H metabolic coenzyme in immune cells of freshly isolated lymph nodes as a potential marker for assessing the effectiveness of an early response to immunotherapy.

Materials and Methods

The study was carried out on C57Bl/6 FoxP3-EGFP mice with B16F0 melanoma implanted near the inguinal lymph node. The mice were injected with antibodies to CTLA-4 (Bio X Cell, USA) (250 μg per mouse, intraperitoneally on days 7, 8, 11, and 12 of the tumor growth). FLIM images in the nicotinamide adenine dinucleotide (phosphate) coenzyme (NAD(P)H) channel (excitation - 375 nm, reception - 435-485 nm) were received using an LSM 880 fluorescent confocal laser scanning microscope (Carl Zeiss, Germany) equipped with a FLIM Simple-Tau module 152 TCSPC (Becker & Hickl GmbH, Germany). Flow cytometry was conducted using a BD FACSAria III cell sorter (BD Biosciences, USA).

Results

Immunotherapy with checkpoint inhibitors resulted in marked metabolic rearrangements in T cells of freshly isolated lymph nodes in responder mice, with inhibition of the tumor growth. Fluorescence lifetime imaging data on NAD(P)H indicated an increase in the free fraction of NADH α1, a form associated with glycolysis to meet high demands of the activated T cells and pro-inflammatory cytokine synthesis. In contrast, non-responder mice with advanced tumors showed low values of the ratio of free fraction to bound α1/α2, which may be related to mechanisms of resistance to therapy.The response to immunotherapy was verified by data on the expression of activation and proliferation markers by means of flow cytometry. The authors observed an increase in the production of the pro-inflammatory cytokine IFN-γ in effector T cells in responder mice compared to untreated controls and non-responders. In addition, an increase in the expression of the surface activation markers CD25 and CD69 was registered compared to untreated controls.

Conclusion

Use of the FLIM method allowed to demonstrate that autofluorescence of the NAD(P)H coenzyme is sensitive to the response to checkpoint immunotherapy and can be used as a reliable marker of the effectiveness of response to treatment.

Immune heterogeneity in cardiovascular diseases from a single-cell perspective

A variety of immune cell subsets occupy different niches in the cardiovascular system, causing changes in the structure and function of the heart and vascular system, and driving the progress of cardiovascular diseases (CVDs). The immune cells infiltrating the injury site are highly diverse and integrate into a broad dynamic immune network that controls the dynamic changes of CVDs. Due to technical limitations, the effects and molecular mechanisms of these dynamic immune networks on CVDs have not been fully revealed. With recent advances in single-cell technologies such as single-cell RNA sequencing, systematic interrogation of the immune cell subsets is feasible and will provide insights into the way we understand the integrative behavior of immune populations. We no longer lightly ignore the role of individual cells, especially certain highly heterogeneous or rare subpopulations. We summarize the phenotypic diversity of immune cell subsets and their significance in three CVDs of atherosclerosis, myocardial ischemia and heart failure. We believe that such a review could enhance our understanding of how immune heterogeneity drives the progression of CVDs, help to elucidate the regulatory roles of immune cell subsets in disease, and thus guide the development of new immunotherapies.

Selectable encapsulated cell quantity in droplets via label-free electrical screening and impedance-activated sorting

Single-cell encapsulation in droplets has become a powerful tool in immunotherapy, medicine discovery, and single-cell analysis, thanks to its capability for cell confinement in picoliter volumes. However, the purity and throughput of single-cell droplets are limited by random encapsulation process, which resuts in a majority of empty and multi-cells droplets. Herein we introduce the first label-free selectable cell quantity encapsulation in droplets sorting system to overcome this problem. The system utilizes a simple and reliable electrical impedance based screening (98.9% of accuracy) integrated with biocompatible acoustic sorting to select single-cell droplets, achieving 90.3% of efficiency and up to 200 ​Hz of throughput, by removing multi-cells (∼60% of rejection) and empty droplets (∼90% of rejection). We demonstrate the use of the droplet sorting to improve the throughput of single-cell encapsulation by ∼9-fold compared to the conventional random encapsulation process.

Label-free macrophage phenotype classification using machine learning methods

Macrophages are heterogeneous innate immune cells that are functionally shaped by their surrounding microenvironment. Diverse macrophage populations have multifaceted differences related to their morphology, metabolism, expressed markers, and functions, where the identification of the different phenotypes is of an utmost importance in modelling immune response. While expressed markers are the most used signature to classify phenotypes, multiple reports indicate that macrophage morphology and autofluorescence are also valuable clues that can be used in the identification process. In this work, we investigated macrophage autofluorescence as a distinct feature for classifying six different macrophage phenotypes, namely: M0, M1, M2a, M2b, M2c, and M2d. The identification was based on extracted signals from multi-channel/multi-wavelength flow cytometer. To achieve the identification, we constructed a dataset containing 152,438 cell events each having a response vector of 45 optical signals fingerprint. Based on this dataset, we applied different supervised machine learning methods to detect phenotype specific fingerprint from the response vector, where the fully connected neural network architecture provided the highest classification accuracy of 75.8% for the six phenotypes compared simultaneously. Furthermore, by restricting the number of phenotypes in the experiment, the proposed framework produces higher classification accuracies, averaging 92.0%, 91.9%, 84.2%, and 80.4% for a pool of two, three, four, five phenotypes, respectively. These results indicate the potential of the intrinsic autofluorescence for classifying macrophage phenotypes, with the proposed method being quick, simple, and cost-effective way to accelerate the discovery of macrophage phenotypical diversity.

Inflammatory CD4/CD8 double-positive human T cells arise from reactive CD8 T cells and are sufficient to mediate GVHD pathology

An important paradigm in allogeneic hematopoietic cell transplantations (allo-HCTs) is the prevention of graft-versus-host disease (GVHD) while preserving the graft-versus-leukemia (GVL) activity of donor T cells. From an observational clinical study of adult allo-HCT recipients, we identified a CD4+/CD8+ double-positive T cell (DPT) population, not present in starting grafts, whose presence was predictive of ≥ grade 2 GVHD. Using an established xenogeneic transplant model, we reveal that the DPT population develops from antigen-stimulated CD8 T cells, which become transcriptionally, metabolically, and phenotypically distinct from single-positive CD4 and CD8 T cells. Isolated DPTs were sufficient to mediate xeno-GVHD pathology when retransplanted into naïve mice but provided no survival benefit when mice were challenged with a human B-ALL cell line. Overall, this study reveals human DPTs as a T cell population directly involved with GVHD pathology.

Touch-free optical technologies to streamline the production of T cell therapies

Currently approved adoptive T cell therapy relies on autologous (obtained from the same patient) T cells, which often suffer from poor quality that diminishes treatment efficacy. Due to the heterogeneous nature of T cell quality between and within patients, significant efforts are aimed at optimizing cell manipulation and growth conditions for potent T cell products. We believe that touch-free imaging and sensing technologies are critical to monitor single-cell features during T cell manufacturing to ensure consistent and optimally timed methods for cell manipulation and growth. Here, we discuss emerging label-free optical imaging and sensing methods, along with machine learning techniques that could enable in-line feedback to optimize T cell quality at multiple stages during manufacturing. These methods have the potential to streamline current workflow, accelerate the manufacture of safe high-quality T cell therapies, and improve our understanding of the dynamic, heterogeneous processes of T cell manufacturing.

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.

Visualization of preimplantation uterine fluid absorption in mice using Alexa Fluor™ 488 Hydrazide†

Uterine fluid plays important roles in supporting early pregnancy events and its timely absorption is critical for embryo implantation. In mice, its volume is maximum on day 0.5 post-coitum (D0.5) and approaches minimum upon embryo attachment ~D4.0. Its secretion and absorption in ovariectomized rodents were shown to be promoted by estrogen and progesterone (P4), respectively. The temporal mechanisms in preimplantation uterine fluid absorption remain to be elucidated. We have established an approach using intraluminally injected Alexa Fluor™ 488 Hydrazide (AH) in preimplantation control (RhoAf/f) and P4-deficient RhoAf/fPgrCre/+ mice. In control mice, bulk entry (seen as smeared cellular staining) via uterine luminal epithelium (LE) decreases from D0.5 to D3.5. In P4-deficient RhoAf/fPgrCre/+ mice, bulk entry on D0.5 and D3.5 is impaired. Exogenous P4 treatment on D1.5 and D2.5 increases bulk entry in D3.5 P4-deficient RhoAf/fPgrCre/+ LE, while progesterone receptor (PR) antagonist RU486 treatment on D1.5 and D2.5 diminishes bulk entry in D3.5 control LE. The abundance of autofluorescent apical fine dots, presumptively endocytic vesicles to reflect endocytosis, in the LE cells is generally increased from D0.5 to D3.5 but its regulation by exogenous P4 or RU486 is not obvious under our experimental setting. In the glandular epithelium (GE), bulk entry is rarely observed and green cellular dots do not show any consistent differences among all the investigated conditions. This study demonstrates the dominant role of LE but not GE, the temporal mechanisms of bulk entry and endocytosis in the LE, and the inhibitory effects of P4-deficiency and RU486 on bulk entry in the LE in preimplantation uterine fluid absorption.

Real-time detection of T cell activation by visualizing TCR nanoclusters with a cholesterol derived aggregation-induced emission probe

Successful T-cell based immunotherapy usually depends on the activation of T cells. Most of commonly used methods for assessing T cell activity rely on the antibody-based technology, which focus on detecting protein-centered activation markers, including CD25, cytokines and so on. However, these methods always involve tedious sample-preparation process, labor-consuming and costly, which could not be utilized in real-time detection. The T cell receptor (TCR) clustering is another kind of essential T cell activation marker on the membrane, which increases during the activation state of T cells. We herein developed a cholesterol derived aggregation-induced emission (AIE) fluorescent probe (R-TPE-PEG-Chol) for detecting T cell activation in real-time. Five probes were first designed and synthesized and among them COOH-TPE-PEG-Chol displayed the best imaging effects, which had no significant impact on the key physiological functions of T cells. In addition, we have proved that COOH-TPE-PEG-Chol was introduced onto the naïve T cell membrane in its molecularly dissolved form without fluorescent emission. While during T cell activation, the formation of TCR nanoclusters would induce aggregation of membrane cholesterol, which could provoke the fluorescence signal of the COOH-TPE-PEG-Chol due to the AIE characteristic. Moreover, the enhancement of the fluorescence intensity was positively related to the activation state of T cells. Our study demonstrated the concept of cholesterol-derived AIE fluorescent probes for deciphering the spatiotemporal arrangements of TCR on the membrane during T cell activation, and consequently provided a novel and complementary strategy for detecting T cell activation in real-time.

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

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

Identifying lipid particle sub-types in live Caenorhabditis elegans with two-photon fluorescence lifetime imaging

Fat metabolism is an important modifier of aging and longevity in Caenorhabditis elegans. Given the anatomy and hermaphroditic nature of C. elegans, a major challenge is to distinguish fats that serve the energetic needs of the parent from those that are allocated to the progeny. Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy has revealed that the composition and dynamics of lipid particles are heterogeneous both within and between different tissues of this organism. Using BCARS, we have previously succeeded in distinguishing lipid-rich particles that serve as energetic reservoirs of the parent from those that are destined for the progeny. While BCARS microscopy produces high-resolution images with very high information content, it is not yet a widely available platform. Here we report a new approach combining the lipophilic vital dye Nile Red and two-photon fluorescence lifetime imaging microscopy (2p-FLIM) for the in vivo discrimination of lipid particle sub-types. While it is widely accepted that Nile Red staining yields unreliable results for detecting lipid structures in live C. elegans due to strong interference of autofluorescence and non-specific staining signals, our results show that simple FLIM phasor analysis can effectively separate those signals and is capable of differentiating the non-polar lipid-dominant (lipid-storage), polar lipid-dominant (yolk lipoprotein) particles, and the intermediates that have been observed using BCARS microscopy. An advantage of this approach is that images can be acquired using common, commercially available 2p-FLIM systems within about 10% of the time required to generate a BCARS image. Our work provides a novel, broadly accessible approach for analyzing lipid-containing structures in a complex, live whole organism context.

Engineered Nanoprobes for Immune Activation Monitoring

The activation of the immune system is critical for cancer immunotherapy and treatments of inflammatory diseases. Non-invasive visualization of immunoactivation is designed to monitor the dynamic nature of the immune response and facilitate the assessment of therapeutic outcomes, which, however, remains challenging. Conventional imaging modalities, such as positron emission tomography, computed tomography, etc., were utilized for imaging immune-related biomarkers. To explore the dynamic immune monitoring, probes with signals correlated to biomarkers of immune activation or prognosis are urgently needed. These emerging molecular probes, which turn on the signal only in the presence of the intended biomarker, can improve the detection specificity. These probes with "turn on" signals enable non-invasive, dynamic, and real-time imaging with high sensitivity and efficiency, showing significance for multifunctionality/multimodality imaging. As a result, more and more innovative engineered nanoprobes combined with diverse imaging modalities were developed to assess the activation of the immune system. In this work, we comprehensively review the recent and emerging advances in engineered nanoprobes for monitoring immune activation in cancer or other immune-mediated inflammatory diseases and discuss the potential in predicting the efficacy following treatments. Research on real-time in vivo immunoimaging is still under exploration, and this review can provide guidance and facilitate the development and application of next-generation imaging technologies.

FLIM of NAD(P)H in Lymphatic Nodes Resolves T-Cell Immune Response to the Tumor

Assessment of T-cell response to the tumor is important for diagnosis of the disease and monitoring of therapeutic efficacy. For this, new non-destructive label-free methods are required. Fluorescence lifetime imaging (FLIM) of metabolic coenzymes is a promising innovative technology for the assessment of the functional status of cells. The purpose of this work was to test whether FLIM can resolve metabolic alterations that accompany T-cell reactivation to the tumors. The study was carried out on C57Bl/6 FoxP3-EGFP mice bearing B16F0 melanoma. Autofluorescence of the immune cells in fresh lymphatic nodes (LNs) was investigated. It was found that fluorescence lifetime parameters of nicotinamide adenine dinucleotide (phosphate) NAD(P)H are sensitive to tumor development. Effector T-cells in the LNs displayed higher contribution of free NADH, the form associated with glycolysis, in all tumors and the presence of protein-bound NADPH, associated with biosynthetic processes, in the tumors of large size. Flow cytometry showed that the changes in the NADH fraction of the effector T-cells correlated with their activation, while changes in NADPH correlated with cell proliferation. In conclusion, FLIM of NAD(P)H in fresh lymphoid tissue is a powerful tool for assessing the immune response to tumor development.

Conditionally immortalised equine skeletal muscle cell lines for in vitro analysis

Background

Thoroughbred racehorse performance is largely influenced by a major quantitative trait locus at the myostatin (MSTN) gene which determines aptitude for certain race distances due to a promoter region insertion mutation influencing functional phenotypes in skeletal muscle. To develop an in vitro system for functional experiments we established three novel equine skeletal muscle cell lines reflecting the variation in phenotype associated with MSTN genotype (CC/II, CT/IN and TT/NN for SNP g.66493737C > T/SINE insertion 227 bp polymorphism). Primary equine skeletal muscle myoblasts, isolated from Thoroughbred horse gluteus medius, were conditionally immortalised and evaluated to determine whether cell phenotype and metabolic function were comparable to functional characteristics previously reported for ex vivo skeletal muscle isolated from Thoroughbred horses with each genotype.

Results

Primary myoblasts conditionally immortalised with the temperature sensitive SV40TtsA58 lentivirus vector successfully proliferated and could revert to their primary cell phenotype and differentiate into multinucleated myotubes. Skeletal muscle fibre type, MSTN gene expression, mitochondrial abundance, and mitochondrial function of the three MSTN genotype cell lines, were consistent with equivalent characterisation of ex vivo skeletal muscle samples with these genotypes. Furthermore, addition of coenzyme Q₁₀ (CoQ₁₀) to the cell lines improved mitochondrial function, an observation consistent with ex vivo skeletal muscle samples with these genotypes following supplementation with CoQ₁₀ in the diet.

Conclusions

The observation that the phenotypic characteristics and metabolic function of the cells lines are equivalent to ex vivo skeletal muscle indicates that this in vitro system will enable efficient and cost-effective analyses of equine skeletal muscle for a range of different applications including understanding metabolic function, testing of nutritional supplements, drug test development and gene doping test development. In the multi-billion-euro international Thoroughbred horse industry research advances in the biological function of skeletal muscle are likely to have considerable impact. Furthermore, this novel genotype-specific system may be adapted and applied to human biomedicine to improve understanding of the effects of myostatin in human physiology and medicine.

Prostate cancer cells demonstrate unique metabolism and substrate adaptability acutely after androgen deprivation therapy

BACKGROUND

Androgen deprivation therapy (ADT) has been the standard of care for advanced hormone-sensitive prostate cancer (PC), yet tumors invariably develop resistance resulting in castrate-resistant PC. The acute response of cancer cells to ADT includes apoptosis and cell death, but a large fraction remains arrested but viable. In this study, we focused on intensively characterizing the early metabolic changes that result after ADT to define potential metabolic targets for treatment.

METHODS

A combination of mass spectrometry, optical metabolic imaging which noninvasively measures drug responses in cells, oxygen consumption rate, and protein expression analysis was used to characterize and block metabolic pathways over several days in multiple PC cell lines with variable hormone response status including ADT sensitive lines LNCaP and VCaP, and resistant C4-2 and DU145.

RESULTS

Mass spectrometry analysis of LNCaP pre- and postexposure to ADT revealed an abundance of glycolytic intermediates after ADT. In LNCaP and VCaP, a reduction in the optical redox ratio [NAD(P)H/FAD], extracellular acidification rate, and a downregulation of key regulatory enzymes for fatty acid and glutamine utilization was acutely observed after ADT. Screening several metabolic inhibitors revealed that blocking fatty acid oxidation and synthesis reversed this stress response in the optical redox ratio seen with ADT alone in LNCaP and VCaP. In contrast, both cell lines demonstrated increased sensitivity to the glycolytic inhibitor 2-Deoxy- d-glucose(2-DG) and maintained sensitivity to electron transport chain inhibitor Malonate after ADT exposure. ADT followed by 2-DG results in synergistic cell death, a result not seen with simultaneous administration.

CONCLUSIONS

Hormone-sensitive PC cells displayed altered metabolic profiles early after ADT including an overall depression in energy metabolism, induction of a quiescent/senescent phenotype, and sensitivity to selected metabolic inhibitors. Glycolytic blocking agents (e.g., 2-DG) as a sequential treatment after ADT may be promising.

Computational macroscopic lifetime imaging and concentration unmixing of autofluorescence

Single-pixel computational imaging can leverage highly sensitive detectors that concurrently acquire data across spectral and temporal domains. For molecular imaging, such methodology enables to collect rich intensity and lifetime multiplexed fluorescence datasets. Herein we report on the application of a single-pixel structured light-based platform for macroscopic imaging of tissue autofluorescence. The super-continuum visible excitation and hyperspectral single-pixel detection allow for parallel characterization of autofluorescence intensity and lifetime. Furthermore, we exploit a deep learning based data processing pipeline, to perform autofluorescence unmixing while yielding the autofluorophores' concentrations. The full scheme (setup and processing) is validated in silico and in vitro with clinically relevant autofluorophores flavin adenine dinucleotide, riboflavin, and protoporphyrin. The presented results demonstrate the potential of the methodology for macroscopically quantifying the intensity and lifetime of autofluorophores, with higher specificity for cases of mixed emissions, which are ubiquitous in autofluorescence and multiplexed in vivo imaging.

Deep learning-assisted co-registration of full-spectral autofluorescence lifetime microscopic images with H&E-stained histology images

Autofluorescence lifetime images reveal unique characteristics of endogenous fluorescence in biological samples. Comprehensive understanding and clinical diagnosis rely on co-registration with the gold standard, histology images, which is extremely challenging due to the difference of both images. Here, we show an unsupervised image-to-image translation network that significantly improves the success of the co-registration using a conventional optimisation-based regression network, applicable to autofluorescence lifetime images at different emission wavelengths. A preliminary blind comparison by experienced researchers shows the superiority of our method on co-registration. The results also indicate that the approach is applicable to various image formats, like fluorescence in-tensity images. With the registration, stitching outcomes illustrate the distinct differences of the spectral lifetime across an unstained tissue, enabling macro-level rapid visual identification of lung cancer and cellular-level characterisation of cell variants and common types. The approach could be effortlessly extended to lifetime images beyond this range and other staining technologies.

Phasor Analysis of Fluorescence Lifetime Enables Quantitative Multiplexed Molecular Imaging of Three Probes

The excited-state lifetime is an intrinsic property of fluorescent molecules that can be leveraged for multiplexed imaging. An advantage of fluorescence lifetime-based multiplexing is that signals from multiple probes can be gathered simultaneously, whereas traditional spectral fluorescence imaging typically requires multiple images at different excitation and emission wavelengths. Additionally, lifetime and spectra could both be utilized to expand the multiplexing capacity of fluorescence. However, resolving exogenous molecular probes based exclusively on the fluorescence lifetime has been limited by technical challenges in analyzing lifetime data. The phasor approach to lifetime analysis offers a simple, graphical solution that has increasingly been used to assess endogenous cellular autofluorescence to quantify metabolic factors. In this study, we employed the phasor analysis of FLIM to quantitatively resolve three exogenous, antibody-targeted fluorescent probes with similar spectral properties based on lifetime information alone. First, we demonstrated that three biomarkers that were spatially restricted to the cell membrane, cytosol, or nucleus could be accurately distinguished using FLIM and phasor analysis. Next, we successfully resolved and quantified three probes that were all targeted to cell surface biomarkers. Finally, we demonstrated that lifetime-based quantitation accuracy can be improved through intensity matching of various probe-biomarker combinations, which will expand the utility of this technique. Importantly, we reconstructed images for each individual probe, as well as an overlay of all three probes, from a single FLIM image. Our results demonstrate that FLIM and phasor analysis can be leveraged as a powerful tool for simultaneous detection of multiple biomarkers with high sensitivity and accuracy.

Non-invasive classification of macrophage polarisation by 2P-FLIM and machine learning

In this study, we utilise fluorescence lifetime imaging of NAD(P)H-based cellular autofluorescence as a non-invasive modality to classify two contrasting states of human macrophages by proxy of their governing metabolic state. Macrophages derived from human blood-circulating monocytes were polarised using established protocols and metabolically challenged using small molecules to validate their responding metabolic actions in extracellular acidification and oxygen consumption. Large field-of-view images of individual polarised macrophages were obtained using fluorescence lifetime imaging microscopy (FLIM). These were challenged in real time with small-molecule perturbations of metabolism during imaging. We uncovered FLIM parameters that are pronounced under the action of carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP), which strongly stratifies the phenotype of polarised human macrophages; however, this performance is impacted by donor variability when analysing the data at a single-cell level. The stratification and parameters emanating from a full field-of-view and single-cell FLIM approach serve as the basis for machine learning models. Applying a random forests model, we identify three strongly governing FLIM parameters, achieving an area under the receiver operating characteristics curve (ROC-AUC) value of 0.944 and out-of-bag (OBB) error rate of 16.67% when classifying human macrophages in a full field-of-view image. To conclude, 2P-FLIM with the integration of machine learning models is showed to be a powerful technique for analysis of both human macrophage metabolism and polarisation at full FoV and single-cell level.

A preclinical model of cutaneous melanoma based on reconstructed human epidermis

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

In vivo tumor immune microenvironment phenotypes correlate with inflammation and vasculature to predict immunotherapy response

Response to immunotherapies can be variable and unpredictable. Pathology-based phenotyping of tumors into ‘hot’ and ‘cold’ is static, relying solely on T-cell infiltration in single-time single-site biopsies, resulting in suboptimal treatment response prediction. Dynamic vascular events (tumor angiogenesis, leukocyte trafficking) within tumor immune microenvironment (TiME) also influence anti-tumor immunity and treatment response. Here, we report dynamic cellular-level TiME phenotyping in vivo that combines inflammation profiles with vascular features through non-invasive reflectance confocal microscopic imaging. In skin cancer patients, we demonstrate three main TiME phenotypes that correlate with gene and protein expression, and response to toll-like receptor agonist immune-therapy. Notably, phenotypes with high inflammation associate with immunostimulatory signatures and those with high vasculature with angiogenic and endothelial anergy signatures. Moreover, phenotypes with high inflammation and low vasculature demonstrate the best treatment response. This non-invasive in vivo phenotyping approach integrating dynamic vasculature with inflammation serves as a reliable predictor of response to topical immune-therapy in patients.

Standard assessment of immune infiltration of biopsies is not sufficient to accurately predict response to immunotherapy. Here, the authors show that reflectance confocal microscopy can be used to quantify dynamic vasculature and inflammatory features to better predict treatment response in skin cancers.

Label-free hematology analysis method based on defocusing phase-contrast imaging under illumination of 415 nm light

Label-free imaging technology is a trending way to simplify and improve conventional hematology analysis by bypassing lengthy and laborious staining procedures. However, the existing methods do not well balance system complexity, data acquisition efficiency, and data analysis accuracy, which severely impedes their clinical translation. Here, we propose defocusing phase-contrast imaging under the illumination of 415 nm light to realize label-free hematology analysis. We have verified that the subcellular morphology of blood components can be visualized without complex staining due to the factor that defocusing can convert the second-order derivative distribution of samples' optical phase into intensity and the illumination of 415 nm light can significantly enhance the contrast. It is demonstrated that the defocusing phase-contrast images for the five leucocyte subtypes can be automatically discriminated by a trained deep-learning program with high accuracy (the mean F1 score: 0.986 and mean average precision: 0.980). Since this technique is based on a regular microscope, it simultaneously realizes low system complexity and high data acquisition efficiency with remarkable quantitative analysis ability. It supplies a label-free, reliable, easy-to-use, fast approach to simplifying and reforming the conventional way of hematology analysis.

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

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