Label-free imaging for quality control of cardiomyocyte differentiation

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.

Shenmai Injection Reduces Cardiomyocyte Apoptosis Induced by Doxorubicin through miR-30a/Bcl-2

OBJECTIVE

To explore the molecular mechanism of Shenmai Injection (SMI) against doxorubicin (DOX) induced cardiomyocyte apoptosis.

METHODS

A total of 40 specific pathogen-free (SPF) male Sprague Dawley (SD) male rats were divided into 5 groups based on the random number table, including the control group, the model group, miR-30a agomir group, SMI low-dose (SMI-L) group, and SMI high-dose (SMI-H) group, with 8 rats in each group. Except for the control group, the rats were injected weekly with DOX (2 mg/kg) in the tail vein for 4 weeks to induce myocardial injury, and were given different regimens of continuous intervention for 2 weeks. Cardiac function was detected by echocardiography and myocardial pathological changes were observed by Van Gieson (VG) staining. Myocardial injury serum markers, including creatine kinase (CK), lactate dehydrogenase (LDH), troponin T (cTnT), N-terminal pro-brain natriuretic peptide (NT-proBNP), soluble ST2 (sST2), and growth differentiation factor-15 (GDF-15) were detected by enzyme linked immunosorbent assay (ELISA). Cardiomyocyte apoptosis was observed by terminal deoxynucleotidyl transferase-mediated biotinylated dUTP triphosphate nick end labeling (TUNEL) and transmission electron microscopy, and the expressions of target proteins and mRNA were detected by Western blot and quantitative real time polymerase chain reaction (qRT-RCR), respectively.

RESULTS

The treatment with different doses of SMI reduced rat heart mass index and left ventricular mass index (P<0.05), significantly improved the left ventricular ejection fraction (P<0.05), decreased the levels of serum CK, LDH, cTnT, and NT-proBNP (P<0.05 or P<0.01), reduced the levels of serum sST2 and GDF-15 (P<0.05 or P<0.01), decreased the collagen volume fraction, reduced the expressions of rat myocardial type I and type III collagen (P<0.05 or P<0.01), and effectively alleviated myocardial fibrosis. And the study found that SMI promoted the expression levels of miR-30a and Bcl-2 in myocardium, and down-regulated the expression of Bax, which inhibited the activation of Caspase-3 and Caspase-9 (P<0.05 or P<0.01), and improved myocardial cell apoptosis.

CONCLUSIONS

SMI can alleviate myocardial injury and apoptosis caused by DOX, and its mechanism possibly by promoting the targeted expression of myocardial Bcl-2 protein through miR-30a.

Using magnetic resonance relaxometry to evaluate the safety and quality of induced pluripotent stem cell-derived spinal cord progenitor cells

BACKGROUND

The emergence of induced pluripotent stem cells (iPSCs) offers a promising approach for replacing damaged neurons and glial cells, particularly in spinal cord injuries (SCI). Despite its merits, iPSC differentiation into spinal cord progenitor cells (SCPCs) is variable, necessitating reliable assessment of differentiation and validation of cell quality and safety. Phenotyping is often performed via label-based methods including immunofluorescent staining or flow cytometry analysis. These approaches are often expensive, laborious, time-consuming, destructive, and severely limits their use in large scale cell therapy manufacturing settings. On the other hand, cellular biophysical properties have demonstrated a strong correlation to cell state, quality and functionality and can be measured with ingenious label-free technologies in a rapid and non-destructive manner.

METHOD

In this study, we report the use of Magnetic Resonance Relaxometry (MRR), a rapid and label-free method that indicates iron levels based on its readout (T2). Briefly, we differentiated human iPSCs into SCPCs and compared key iPSC and SCPC cellular markers to their intracellular iron content (Fe3+) at different stages of the differentiation process.

RESULTS

With MRR, we found that intracellular iron of iPSCs and SCPCs were distinctively different allowing us to accurately reflect varying levels of residual undifferentiated iPSCs (i.e., OCT4+ cells) in any given population of SCPCs. MRR was also able to predict Day 10 SCPC OCT4 levels from Day 1 undifferentiated iPSC T2 values and identified poorly differentiated SCPCs with lower T2, indicative of lower neural progenitor (SOX1) and stem cell (Nestin) marker expression levels. Lastly, MRR was able to provide predictive indications for the extent of differentiation to Day 28 spinal cord motor neurons (ISL-1/SMI-32) based on the T2 values of Day 10 SCPCs.

CONCLUSION

MRR measurements of iPSCs and SCPCs has clearly indicated its capabilities to identify and quantify key phenotypes of iPSCs and SCPCs for end-point validation of safety and quality parameters. Thus, our technology provides a rapid label-free method to determine critical quality attributes in iPSC-derived progenies and is ideally suited as a quality control tool in cell therapy manufacturing.

Compact lens-free imager using a thin-film transistor for long-term quantitative monitoring of stem cell culture and cardiomyocyte production

With advancements in human induced pluripotent stem cell (hiPSC) technology, there is an increasing demand for quality control techniques to manage the long-term process of target cell production effectively. While monitoring systems designed for use within incubators are promising for assessing culture quality, existing systems still face challenges in terms of compactness, throughput, and available metrics. To address these limitations, we have developed a compact and high-throughput lens-free imaging device named INSPCTOR. The device is as small as a standard culture plate, which allows for the installation of multiple units within an incubator. INSPCTOR utilises a large thin-film transistor image sensor, enabling simultaneous observation of six independent culture environments, each approximately 1 cm2. With this device, we successfully monitored the confluency of hiPSC cultures and identified the onset timing of epithelial-to-mesenchymal transition during mesodermal induction. Additionally, we quantified the beating frequency and conduction of hiPSC-derived cardiomyocytes by using high-speed imaging modes. This enabled us to identify the onset of spontaneous beating during differentiation and assess chronotropic responses in drug evaluations. Moreover, by tracking beating frequency over 10 days of cardiomyocyte maturation, we identified week-scale and daily-scale fluctuations, the latter of which correlated with cellular metabolic activity. The metrics derived from this device would enhance the reproducibility and quality of target cell production.

Autofluorescence lifetime flow cytometry with time-correlated single photon counting

Autofluorescence lifetime imaging microscopy (FLIM) is sensitive to metabolic changes in single cells based on changes in the protein-binding activities of the metabolic co-enzymes NAD(P)H. However, FLIM typically relies on time-correlated single-photon counting (TCSPC) detection electronics on laser-scanning microscopes, which are expensive, low-throughput, and require substantial post-processing time for cell segmentation and analysis. Here, we present a fluorescence lifetime-sensitive flow cytometer that offers the same TCSPC temporal resolution in a flow geometry, with low-cost single-photon excitation sources, a throughput of tens of cells per second, and real-time single-cell analysis. The system uses a 375 nm picosecond-pulsed diode laser operating at 50 MHz, alkali photomultiplier tubes, an FPGA-based time tagger, and can provide real-time phasor-based classification (i.e., gating) of flowing cells. A CMOS camera produces simultaneous brightfield images using far-red illumination. A second PMT provides two-color analysis. Cells are injected into the microfluidic channel using a syringe pump at 2-5 mm/s with nearly 5 ms integration time per cell, resulting in a light dose of 2.65 J/cm2 that is well below damage thresholds (25 J/cm2 at 375 nm). Our results show that cells remain viable after measurement, and the system is sensitive to autofluorescence lifetime changes in Jurkat T cells with metabolic perturbation (sodium cyanide), quiescent versus activated (CD3/CD28/CD2) primary human T cells, and quiescent versus activated primary adult mouse neural stem cells, consistent with prior studies using multiphoton FLIM. This TCSPC-based autofluorescence lifetime flow cytometer provides a valuable label-free method for real-time analysis of single-cell function and metabolism with higher throughput than laser-scanning microscopy systems.

in vivo and in situ robust quantitative optical biopsy of hepatocellular carcinoma and metastasis based on two-photon autofluorescence imaging

Two-photon autofluorescence (TPAF) imaging is able to offer precise cellular metabolic information with high spatiotemporal resolution, making it a promising biopsy tool. The technique is greatly hampered by the complexity of either the optical system or data processing. Here, the excitation wavelength was optimized to simultaneously excite both flavin adenine dinucleotide and nicotinamide adenine dinucleotide and eliminate the unexpected TPAF. The optical redox ratio (ORR) images were robustly achieved without additional calibration under the optimized single-wavelength excitation. The in vitro, ex vivo, and in vivo biopsy by the TPAF method were systematically studied and compared using hepato-cellular carcinoma and metastasis as examples. It was demonstrated that the proposed TPAF method simplified the optical system, improved the robustness of ORR, and enabled early-stage cancer diagnosis, showing distinguished advantages as compared with previous methods.

Autofluorescence lifetime flow cytometry with time-correlated single photon counting

Autofluorescence lifetime imaging microscopy (FLIM) is sensitive to metabolic changes in single cells based on changes in the protein-binding activities of the metabolic co-enzymes NAD(P)H. However, FLIM typically relies on time-correlated single-photon counting (TCSPC) detection electronics on laser-scanning microscopes, which are expensive, low-throughput, and require substantial post-processing time for cell segmentation and analysis. Here, we present a fluorescence lifetime-sensitive flow cytometer that offers the same TCSPC temporal resolution in a flow geometry, with low-cost single-photon excitation sources, a throughput of tens of cells per second, and real-time single-cell analysis. The system uses a 375nm picosecond-pulsed diode laser operating at 50MHz, alkali photomultiplier tubes, an FPGA-based time tagger, and can provide real-time phasor-based classification ( i.e ., gating) of flowing cells. A CMOS camera produces simultaneous brightfield images using far-red illumination. A second PMT provides two-color analysis. Cells are injected into the microfluidic channel using a syringe pump at 2-5 mm/s with nearly 5ms integration time per cell, resulting in a light dose of 2.65 J/cm 2 that is well below damage thresholds (25 J/cm 2 at 375 nm). Our results show that cells remain viable after measurement, and the system is sensitive to autofluorescence lifetime changes in Jurkat T cells with metabolic perturbation (sodium cyanide), quiescent vs. activated (CD3/CD28/CD2) primary human T cells, and quiescent vs. activated primary adult mouse neural stem cells, consistent with prior studies using multiphoton FLIM. This TCSPC-based autofluorescence lifetime flow cytometer provides a valuable label-free method for real-time analysis of single-cell function and metabolism with higher throughput than laser-scanning microscopy systems.

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.

Optical redox imaging to screen synthetic hydrogels for stem cell-derived cardiomyocyte differentiation and maturation

Significance

Heart disease is the leading cause of death in the United States, yet research is limited by the inability to culture primary cardiac cells. Cardiomyocytes (CMs) derived from human induced pluripotent stem cells (iPSCs) are a promising solution for drug screening and disease modeling.

Aim

Induced pluripotent stem cell-derived CM (iPSC-CM) differentiation and maturation studies typically use heterogeneous substrates for growth and destructive verification methods. Reproducible, tunable substrates and touch-free monitoring are needed to identify ideal conditions to produce homogenous, functional CMs.

Approach

We generated synthetic polyethylene glycol-based hydrogels for iPSC-CM differentiation and maturation. Peptide concentrations, combinations, and gel stiffness were tuned independently. Label-free optical redox imaging (ORI) was performed on a widefield microscope in a 96-well screen of gel formulations. We performed live-cell imaging throughout differentiation and early to late maturation to identify key metabolic shifts.

Results

Label-free ORI confirmed the expected metabolic shifts toward oxidative phosphorylation throughout the differentiation and maturation processes of iPSC-CMs on synthetic hydrogels. Furthermore, ORI distinguished high and low differentiation efficiency cell batches in the cardiac progenitor stage.

Conclusions

We established a workflow for medium throughput screening of synthetic hydrogel conditions with the ability to perform repeated live-cell measurements and confirm expected metabolic shifts. These methods have implications for reproducible iPSC-CM generation in biomanufacturing.

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

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

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

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

Cardiac multiscale bioimaging: from nano- through micro- to mesoscales

Cardiac multiscale bioimaging is an emerging field that aims to provide a comprehensive understanding of the heart and its functions at various levels, from the molecular to the entire organ. It combines both physiologically and clinically relevant dimensions: from nano- and micrometer resolution imaging based on vibrational spectroscopy and high-resolution microscopy to assess molecular processes in cardiac cells and myocardial tissue, to mesoscale structural investigations to improve the understanding of cardiac (patho)physiology. Tailored super-resolution deep microscopy with advanced proteomic methods and hands-on experience are thus strategically combined to improve the quality of cardiovascular research and support future medical decision-making by gaining additional biomolecular information for translational and diagnostic applications.

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.

Microenvironmental stiffness induces metabolic reprogramming in glioblastoma

The mechanical properties of solid tumors influence tumor cell phenotype and the ability to invade surrounding tissues. Using bioengineered scaffolds to provide a matrix microenvironment for patient-derived glioblastoma (GBM) spheroids, this study demonstrates that a soft, brain-like matrix induces GBM cells to shift to a glycolysis-weighted metabolic state, which supports invasive behavior. We first show that orthotopic murine GBM tumors are stiffer than peritumoral brain tissues, but tumor stiffness is heterogeneous where tumor edges are softer than the tumor core. We then developed 3D scaffolds with μ-compressive moduli resembling either stiffer tumor core or softer peritumoral brain tissue. We demonstrate that the softer matrix microenvironment induces a shift in GBM cell metabolism toward glycolysis, which manifests in lower proliferation rate and increased migration activities. Finally, we show that these mechanical cues are transduced from the matrix via CD44 and integrin receptors to induce metabolic and phenotypic changes in cancer cells.

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

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

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.

A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems

The differentiation of pluripotent stem cells (PSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variabilities, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, PSC-to-cardiomyocyte (CM) differentiation is vulnerable to inappropriate doses of CHIR99021 (CHIR) that are applied in the initial stage of mesoderm differentiation. Here, by harnessing live-cell bright-field imaging and machine learning (ML), we realize real-time cell recognition in the entire differentiation process, e.g., CMs, cardiac progenitor cells (CPCs), PSC clones, and even misdifferentiated cells. This enables non-invasive prediction of differentiation efficiency, purification of ML-recognized CMs and CPCs for reducing cell contamination, early assessment of the CHIR dose for correcting the misdifferentiation trajectory, and evaluation of initial PSC colonies for controlling the start point of differentiation, all of which provide a more invulnerable differentiation method with resistance to variability. Moreover, with the established ML models as a readout for the chemical screen, we identify a CDK8 inhibitor that can further improve the cell resistance to the overdose of CHIR. Together, this study indicates that artificial intelligence is able to guide and iteratively optimize PSC differentiation to achieve consistently high efficiency across cell lines and batches, providing a better understanding and rational modulation of the differentiation process for functional cell manufacturing in biomedical applications.

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.

Progress in Nano-Biosensors for Non-Invasive Monitoring of Stem Cell Differentiation

Non-invasive, non-destructive, and label-free sensing techniques are required to monitor real-time stem cell differentiation. However, conventional analysis methods, such as immunocytochemistry, polymerase chain reaction, and Western blot, involve invasive processes and are complicated and time-consuming. Unlike traditional cellular sensing methods, electrochemical and optical sensing techniques allow non-invasive qualitative identification of cellular phenotypes and quantitative analysis of stem cell differentiation. In addition, various nano- and micromaterials with cell-friendly properties can greatly improve the performance of existing sensors. This review focuses on nano- and micromaterials that have been reported to improve sensing capabilities, including sensitivity and selectivity, of biosensors towards target analytes associated with specific stem cell differentiation. The information presented aims to motivate further research into nano-and micromaterials with advantageous properties for developing or improving existing nano-biosensors to achieve the practical evaluation of stem cell differentiation and efficient stem cell-based therapies.

Label-free optical imaging and sensing for quality control of stem cell manufacturing

Human stem cells provide emerging methods for drug screening, disease modeling, and personalized patient therapies. To meet this growing demand for scale-up, stem cell manufacturing methods must be streamlined with continuous monitoring technologies and automated feedback to optimize growth conditions for high production and consistency. Label-free optical imaging and sensing, including multiphoton microscopy, Raman spectroscopy, and low-cost methods such as phase and transmitted light microscopy, can provide rapid, repeatable, and non-invasive monitoring of stem cells throughout cell differentiation and maturation. Machine learning algorithms trained on label-free optical imaging and sensing features could identify viable cells and predict optimal manufacturing conditions. These techniques have the potential to streamline stem cell manufacturing and accelerate their use in regenerative medicine.

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.

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.

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.

Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning

Cervical cancer has high morbidity and mortality rates, affecting hundreds of thousands of women worldwide and requiring more accurate screening for early intervention and follow-up treatment. Cytology is the current dominant clinical screening approach, and though it has been used for decades, it has unsatisfactory sensitivity and specificity. In this work, fluorescence lifetime imaging microscopy (FLIM) was used for the imaging of exfoliated cervical cells in which an endogenous coenzyme involved in metabolism, namely, reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H], was detected to evaluate the metabolic status of cells. FLIM images from 71 participants were analyzed by the unsupervised machine learning method to build a prediction model for cervical cancer risk. The FLIM method combined with unsupervised machine learning (FLIM-ML) had a sensitivity and specificity of 90.9% and 100%, respectively, significantly higher than those of the cytology approach. One cancer recurrence case was predicted as high-risk several months earlier using this method as compared to using current clinical methods, implying that FLIM-ML may be very helpful for follow-up cancer care. This study illustrates the clinical applicability of FLIM-ML as a detection method for cervical cancer screening and a convenient tool for follow-up cancer care.

Fluorescence Spectroscopy of Low-Level Endogenous β-adrenergic Receptor Expression at the Plasma Membrane of Differentiating Human iPSC-Derived Cardiomyocytes

The potential of human-induced pluripotent stem cells (hiPSCs) to be differentiated into cardiomyocytes (CMs) mimicking adult CMs functional morphology, marker genes and signaling characteristics has been investigated since over a decade. The evolution of the membrane localization of CM-specific G protein-coupled receptors throughout differentiation has received, however, only limited attention to date. We employ here advanced fluorescent spectroscopy, namely linescan Fluorescence Correlation Spectroscopy (FCS), to observe how the plasma membrane abundance of the β1- and β2-adrenergic receptors (β1/2-ARs), labelled using a bright and photostable fluorescent antagonist, evolves during the long-term monolayer culture of hiPSC-derived CMs. We compare it to the kinetics of observed mRNA levels in wildtype (WT) hiPSCs and in two CRISPR/Cas9 knock-in clones. We conduct these observations against the backdrop of our recent report of cell-to-cell expression variability, as well as of the subcellular localization heterogeneity of β-ARs in adult CMs.

NAD(P)H fluorescence lifetime imaging of live intestinal nematodes reveals metabolic crosstalk between parasite and host

Infections with intestinal nematodes have an equivocal impact: they represent a burden for human health and animal husbandry, but, at the same time, may ameliorate auto-immune diseases due to the immunomodulatory effect of the parasites. Thus, it is key to understand how intestinal nematodes arrive and persist in their luminal niche and interact with the host over long periods of time. One basic mechanism governing parasite and host cellular and tissue functions, metabolism, has largely been neglected in the study of intestinal nematode infections. Here we use NADH (nicotinamide adenine dinucleotide) and NADPH (nicotinamide adenine dinucleotide phosphate) fluorescence lifetime imaging of explanted murine duodenum infected with the natural nematode Heligmosomoides polygyrus and define the link between general metabolic activity and possible metabolic pathways in parasite and host tissue, during acute infection. In both healthy and infected host intestine, energy is effectively produced, mainly via metabolic pathways resembling oxidative phosphorylation/aerobic glycolysis features. In contrast, the nematodes shift their energy production from balanced fast anaerobic glycolysis-like and effective oxidative phosphorylation-like metabolic pathways, towards mainly anaerobic glycolysis-like pathways, back to oxidative phosphorylation/aerobic glycolysis-like pathways during their different life cycle phases in the submucosa versus the intestinal lumen. Additionally, we found an increased NADPH oxidase (NOX) enzymes-dependent oxidative burst in infected intestinal host tissue as compared to healthy tissue, which was mirrored by a similar defense reaction in the parasites. We expect that, the here presented application of NAD(P)H-FLIM in live tissues constitutes a unique tool to study possible shifts between metabolic pathways in host-parasite crosstalk, in various parasitic intestinal infections.

Label-Free Imaging to Track Reprogramming of Human Somatic Cells

The process of reprogramming patient samples to human-induced pluripotent stem cells (iPSCs) is stochastic, asynchronous, and inefficient, leading to a heterogeneous population of cells. In this study, we track the reprogramming status of patient-derived erythroid progenitor cells (EPCs) at the single-cell level during reprogramming with label-free live-cell imaging of cellular metabolism and nuclear morphometry to identify high-quality iPSCs. EPCs isolated from human peripheral blood of three donors were used for our proof-of-principle study. We found distinct patterns of autofluorescence lifetime for the reduced form of nicotinamide adenine dinucleotide (phosphate) and flavin adenine dinucleotide during reprogramming. Random forest models classified iPSCs with ∼95% accuracy, which enabled the successful isolation of iPSC lines from reprogramming cultures. Reprogramming trajectories resolved at the single-cell level indicated significant reprogramming heterogeneity along different branches of cell states. This combination of micropatterning, autofluorescence imaging, and machine learning provides a unique, real-time, and nondestructive method to assess the quality of iPSCs in a biomanufacturing process, which could have downstream impacts in regenerative medicine, cell/gene therapy, and disease modeling.

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.

Distinct Mitochondrial Remodeling During Mesoderm Differentiation in a Human-Based Stem Cell Model

Given the considerable interest in using stem cells for modeling and treating disease, it is essential to understand what regulates self-renewal and differentiation. Remodeling of mitochondria and metabolism, with the shift from glycolysis to oxidative phosphorylation (OXPHOS), plays a fundamental role in maintaining pluripotency and stem cell fate. It has been suggested that the metabolic “switch” from glycolysis to OXPHOS is germ layer-specific as glycolysis remains active during early ectoderm commitment but is downregulated during the transition to mesoderm and endoderm lineages. How mitochondria adapt during these metabolic changes and whether mitochondria remodeling is tissue specific remain unclear. Here, we address the question of mitochondrial adaptation by examining the differentiation of human pluripotent stem cells to cardiac progenitors and further to differentiated mesodermal derivatives, including functional cardiomyocytes. In contrast to recent findings in neuronal differentiation, we found that mitochondrial content decreases continuously during mesoderm differentiation, despite increased mitochondrial activity and higher levels of ATP-linked respiration. Thus, our work highlights similarities in mitochondrial remodeling during the transition from pluripotent to multipotent state in ectodermal and mesodermal lineages, while at the same time demonstrating cell-lineage-specific adaptations upon further differentiation. Our results improve the understanding of how mitochondrial remodeling and the metabolism interact during mesoderm differentiation and show that it is erroneous to assume that increased OXPHOS activity during differentiation requires a simultaneous expansion of mitochondrial content.