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.

Label-free imaging for quality control of cardiomyocyte differentiation.

Human pluripotent stem cell (hPSC)-derived cardiomyocytes provide a promising regenerative cell therapy for cardiovascular patients and an important model system to accelerate drug discovery. However, cost-effective and time-efficient platforms must be developed to evaluate the quality of hPSC-derived cardiomyocytes during biomanufacturing. Here, we develop a non-invasive label-free live cell imaging platform to predict the efficiency of hPSC differentiation into cardiomyocytes. Autofluorescence imaging of metabolic co-enzymes is performed under varying differentiation conditions (cell density, concentration of Wnt signaling activator) across five hPSC lines. Live cell autofluorescence imaging and multivariate classification models provide high accuracy to separate low (< 50%) and high (≥ 50%) differentiation efficiency groups (quantified by cTnT expression on day 12) within 1 day after initiating differentiation (area under the receiver operating characteristic curve, 0.91). This non-invasive and label-free method could be used to avoid batch-to-batch and line-to-line variability in cell manufacturing from hPSCs.

Intravital Metabolic Autofluorescence Imaging Captures Macrophage Heterogeneity Across Normal and Cancerous Tissue.

Macrophages are dynamic immune cells that govern both normal tissue function and disease progression. However, standard methods to measure heterogeneity in macrophage function within tissues require tissue excision and fixation, which limits our understanding of diverse macrophage function in vivo. Two-photon microscopy of the endogenous metabolic co-enzymes NAD(P)H and flavin adenine dinucleotide (FAD) (metabolic autofluorescence imaging) enables dynamic imaging of mouse models in vivo. Here, we demonstrate metabolic autofluorescence imaging to assess cell-level macrophage heterogeneity in response to normal and cancerous tissue microenvironments in vivo. NAD(P)H and FAD fluorescence intensities and lifetimes were measured for both tissue-resident macrophages in mouse ear dermis and tumor-associated macrophages in pancreatic flank tumors. Metabolic and spatial organization of macrophages were determined by performing metabolic autofluorescence imaging and single macrophage segmentation in mice engineered for macrophage-specific fluorescent protein expression. Tumor-associated macrophages exhibited decreased optical redox ratio [NAD(P)H divided by FAD intensity] compared to dermal macrophages, indicating that tumor-associated macrophages are more oxidized than dermal macrophages. The mean fluorescence lifetimes of NAD(P)H and FAD were longer in dermal macrophages than in tumor-associated macrophages, which reflects changes in NAD(P)H and FAD protein-binding activities. Dermal macrophages had greater heterogeneity in optical redox ratio, NAD(P)H mean lifetime, and FAD mean lifetime compared to tumor-associated macrophages. Similarly, standard markers of macrophage phenotype (CD206 and CD86) assessed by immunofluorescence revealed greater heterogeneity in dermal macrophages compared to tumor-associated macrophages. Ultimately, metabolic autofluorescence imaging provides a novel tool to assess tissue-specific macrophage behavior and cell-level heterogeneity in vivo in animal models.

Microfluidic tumor-on-a-chip model to evaluate the role of tumor environmental stress on NK cell exhaustion.

Solid tumors generate a suppressive environment that imposes an overwhelming burden on the immune system. Nutrient depletion, waste product accumulation, hypoxia, and pH acidification severely compromise the capacity of effector immune cells such as T and natural killer (NK) cells to destroy cancer cells. However, the specific molecular mechanisms driving immune suppression, as well as the capacity of immune cells to adapt to the suppressive environment, are not completely understood. Thus, here, we used an in vitro microfluidic tumor-on-a-chip platform to evaluate how NK cells respond to the tumor-induced suppressive environment. The results demonstrated that the suppressive environment created by the tumor gradually eroded NK cell cytotoxic capacity, leading to compromised NK cell surveillance and tumor tolerance. Further, NK cell exhaustion persisted for an extended period of time after removing NK cells from the microfluidic platform. Last, the addition of checkpoint inhibitors and immunomodulatory agents alleviated NK cell exhaustion.

Autofluorescence Imaging of 3D Tumor-Macrophage Microscale Cultures Resolves Spatial and Temporal Dynamics of Macrophage Metabolism.

Macrophages within the tumor microenvironment (TME) exhibit a spectrum of protumor and antitumor functions, yet it is unclear how the TME regulates this macrophage heterogeneity. Standard methods to measure macrophage heterogeneity require destructive processing, limiting spatiotemporal studies of function within the live, intact 3D TME. Here, we demonstrate two-photon autofluorescence imaging of NAD(P)H and FAD to nondestructively resolve spatiotemporal metabolic heterogeneity of individual macrophages within 3D microscale TME models. Fluorescence lifetimes and intensities of NAD(P)H and FAD were acquired at 24, 48, and 72 hours poststimulation for mouse macrophages (RAW264.7) stimulated with IFNγ or IL4 plus IL13 in 2D culture, confirming that autofluorescence measurements capture known metabolic phenotypes. To quantify metabolic dynamics of macrophages within the TME, mouse macrophages or human monocytes (RAW264.7 or THP-1) were cultured alone or with breast cancer cells (mouse polyoma-middle T virus or primary human IDC) in 3D microfluidic platforms. Human monocytes and mouse macrophages in tumor cocultures exhibited significantly different FAD mean lifetimes and greater migration than monocultures at 24, 48, and 72 hours postseeding. In cocultures with primary human cancer cells, actively migrating monocyte-derived macrophages had greater redox ratios [NAD(P)H/FAD intensity] compared with passively migrating monocytes at 24 and 48 hours postseeding, reflecting metabolic heterogeneity in this subpopulation of monocytes. Genetic analyses further confirmed this metabolic heterogeneity. These results establish label-free autofluorescence imaging to quantify dynamic metabolism, polarization, and migration of macrophages at single-cell resolution within 3D microscale models. This combined culture and imaging system provides unique insights into spatiotemporal tumor-immune cross-talk within the 3D TME. SIGNIFICANCE: Label-free metabolic imaging and microscale culture technologies enable monitoring of single-cell macrophage metabolism, migration, and function in the 3D tumor microenvironment.

Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications.

SIGNIFICANCE: Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to distinguish the unique molecular environment of fluorophores. FLIM measures the time a fluorophore remains in an excited state before emitting a photon, and detects molecular variations of fluorophores that are not apparent with spectral techniques alone. FLIM is sensitive to multiple biomedical processes including disease progression and drug efficacy. AIM: We provide an overview of FLIM principles, instrumentation, and analysis while highlighting the latest developments and biological applications. APPROACH: This review covers FLIM principles and theory, including advantages over intensity-based fluorescence measurements. Fundamentals of FLIM instrumentation in time- and frequency-domains are summarized, along with recent developments. Image segmentation and analysis strategies that quantify spatial and molecular features of cellular heterogeneity are reviewed. Finally, representative applications are provided including high-resolution FLIM of cell- and organelle-level molecular changes, use of exogenous and endogenous fluorophores, and imaging protein-protein interactions with Förster resonance energy transfer (FRET). Advantages and limitations of FLIM are also discussed. CONCLUSIONS: FLIM is advantageous for probing molecular environments of fluorophores to inform on fluorophore behavior that cannot be elucidated with intensity measurements alone. Development of FLIM technologies, analysis, and applications will further advance biological research and clinical assessments.

Quantitative Spatial Analysis of Metabolic Heterogeneity Across in vivo and in vitro Tumor Models.

Metabolic preferences of tumor cells vary within a single tumor, contributing to tumor heterogeneity, drug resistance, and patient relapse. However, the relationship between tumor treatment response and metabolically distinct tumor cell populations is not well-understood. Here, a quantitative approach was developed to characterize spatial patterns of metabolic heterogeneity in tumor cell populations within in vivo xenografts and 3D in vitro cultures (i.e., organoids) of head and neck cancer. Label-free images of cell metabolism were acquired using two-photon fluorescence lifetime microscopy of the metabolic co-enzymes NAD(P)H and FAD. Previous studies have shown that NAD(P)H mean fluorescence lifetimes can identify metabolically distinct cells with varying drug response. Thus, density-based clustering of the NAD(P)H mean fluorescence lifetime was used to identify metabolic sub-populations of cells, then assessed in control, cetuximab-, cisplatin-, and combination-treated xenografts 13 days post-treatment and organoids 24 h post-treatment. Proximity analysis of these metabolically distinct cells was designed to quantify differences in spatial patterns between treatment groups and between xenografts and organoids. Multivariate spatial autocorrelation and principal components analyses of all autofluorescence intensity and lifetime variables were developed to further improve separation between cell sub-populations. Spatial principal components analysis and Z-score calculations of autofluorescence and spatial distribution variables also visualized differences between models. This analysis captures spatial distributions of tumor cell sub-populations influenced by treatment conditions and model-specific environments. Overall, this novel spatial analysis could provide new insights into tumor growth, treatment resistance, and more effective drug treatments across a range of microscopic imaging modalities (e.g., immunofluorescence, imaging mass spectrometry).

BET inhibitors reduce cell size and induce reversible cell cycle arrest in AML.

Inhibitors of the bromodomain and extraterminal domain family (BETi) offer a new approach to treat hematological malignancies, with leukemias containing mixed lineage leukemia rearrangements being especially sensitive due to a reliance on the regulation of transcription elongation. We explored the mechanism of action of BETi in cells expressing the t(8;21), and show that these compounds reduced the size of acute myeloid leukemia cells, triggered a rapid but reversible G0 /G1 arrest, and with time, cause cell death. Meta-analysis of PRO-seq data identified ribosomal genes, which are regulated by MYC, were downregulated within 3 hours of addition of the BETi. This reduction of MYC regulated metabolic genes coincided with the loss of mitochondrial respiration and large reductions in the glycolytic rate. In addition, gene expression analysis showed that transcription of BCL2 was rapidly affected by BETi but this did not cause dramatic increases in cell death. Cell cycle arrest, lowered metabolic activity, and reduced BCL2 levels suggested that a second compound was needed to push these cells over the apoptotic threshold. Indeed, low doses of the BCL2 inhibitor, venetoclax, in combination with the BETi was a potent combination in t(8;21) containing cells. Thus, BET inhibitors that affect MYC and BCL2 expression should be considered for combination therapy with venetoclax.

Organotypic microfluidic breast cancer model reveals starvation-induced spatial-temporal metabolic adaptations.

BACKGROUND: Ductal carcinoma in situ (DCIS) is the earliest stage of breast cancer. During DCIS, tumor cells remain inside the mammary duct, growing under a microenvironment characterized by hypoxia, nutrient starvation, and waste product accumulation; this harsh microenvironment promotes genomic instability and eventually cell invasion. However, there is a lack of biomarkers to predict what patients will transition to a more invasive tumor or how DCIS cells manage to survive in this harsh microenvironment. METHODS: In this work, we have developed a microfluidic model that recapitulates the DCIS microenvironment. In the microdevice, a DCIS model cell line was grown inside a luminal mammary duct model, embedded in a 3D hydrogel with mammary fibroblasts. Cell behavior was monitored by confocal microscopy and optical metabolic imaging. Additionally, metabolite profile was studied by NMR whereas gene expression was analyzed by RT-qPCR. FINDINGS: DCIS cell metabolism led to hypoxia and nutrient starvation; revealing an altered metabolism focused on glycolysis and other hypoxia-associated pathways. In response to this starvation and hypoxia, DCIS cells modified the expression of multiple genes, and a gradient of different metabolic phenotypes was observed across the mammary duct model. These genetic changes observed in the model were in good agreement with patient genomic profiles; identifying multiple compounds targeting the affected pathways. In this context, the hypoxia-activated prodrug tirapazamine selectively destroyed hypoxic DCIS cells. INTERPRETATION: The results showed the capacity of the microfluidic model to mimic the DCIS structure, identifying multiple cellular adaptations to endure the hypoxia and nutrient starvation generated within the mammary duct. These findings may suggest new potential therapeutic directions to treat DCIS. In summary, given the lack of in vitro models to study DCIS, this microfluidic device holds great potential to find new DCIS predictors and therapies and translate them to the clinic.

Autofluorescence imaging identifies tumor cell-cycle status on a single-cell level.

The goal of this study is to validate fluorescence intensity and lifetime imaging of metabolic co-enzymes NAD(P)H and FAD (optical metabolic imaging, or OMI) as a method to quantify cell-cycle status of tumor cells. Heterogeneity in tumor cell-cycle status (e. g. proliferation, quiescence, apoptosis) increases drug resistance and tumor recurrence. Cell-cycle status is closely linked to cellular metabolism. Thus, this study applies cell-level metabolic imaging to distinguish proliferating, quiescent, and apoptotic populations. Two-photon microscopy and time-correlated single photon counting are used to measure optical redox ratio (NAD(P)H fluorescence intensity divided by FAD intensity), NAD(P)H and FAD fluorescence lifetime parameters. Redox ratio, NAD(P)H and FAD lifetime parameters alone exhibit significant differences (p<0.05) between population means. To improve separation between populations, linear combination models derived from partial least squares - discriminant analysis (PLS-DA) are used to exploit all measurements together. Leave-one-out cross validation of the model yielded high classification accuracies (92.4 and 90.1 % for two and three populations, respectively). OMI and PLS-DA also identifies each sub-population within heterogeneous samples. These results establish single-cell analysis with OMI and PLS-DA as a label-free method to distinguish cell-cycle status within intact samples. This approach could be used to incorporate cell-level tumor heterogeneity in cancer drug development.

Metabolic Imaging of Head and Neck Cancer Organoids.

Head and neck cancer patients suffer from toxicities, morbidities, and mortalities, and these ailments could be minimized through improved therapies. Drug discovery is a long, expensive, and complex process, so optimized assays can improve the success rate of drug candidates. This study applies optical imaging of cell metabolism to three-dimensional in vitro cultures of head and neck cancer grown from primary tumor tissue (organoids). This technique is advantageous because it measures cell metabolism using intrinsic fluorescence from NAD(P)H and FAD on a single cell level for a three-dimensional in vitro model. Head and neck cancer organoids are characterized alone and after treatment with standard therapies, including an antibody therapy, a chemotherapy, and combination therapy. Additionally, organoid cellular heterogeneity is analyzed quantitatively and qualitatively. Gold standard measures of treatment response, including cell proliferation, cell death, and in vivo tumor volume, validate therapeutic efficacy for each treatment group in a parallel study. Results indicate that optical metabolic imaging is sensitive to therapeutic response in organoids after 1 day of treatment (p<0.05) and resolves cell subpopulations with distinct metabolic phenotypes. Ultimately, this platform could provide a sensitive high-throughput assay to streamline the drug discovery process for head and neck cancer.

Excitation-scanning hyperspectral imaging microscope.

Hyperspectral imaging is a versatile tool that has recently been applied to a variety of biomedical applications, notably live-cell and whole-tissue signaling. Traditional hyperspectral imaging approaches filter the fluorescence emission over a broad wavelength range while exciting at a single band. However, these emission-scanning approaches have shown reduced sensitivity due to light attenuation from spectral filtering. Consequently, emission scanning has limited applicability for time-sensitive studies and photosensitive applications. In this work, we have developed an excitation-scanning hyperspectral imaging microscope that overcomes these limitations by providing high transmission with short acquisition times. This is achieved by filtering the fluorescence excitation rather than the emission. We tested the efficacy of the excitation-scanning microscope in a side-by-side comparison with emission scanning for detection of green fluorescent protein (GFP)-expressing endothelial cells in highly autofluorescent lung tissue. Excitation scanning provided higher signal-to-noise characteristics, as well as shorter acquisition times (300 ms/wavelength band with excitation scanning versus 3 s/wavelength band with emission scanning). Excitation scanning also provided higher delineation of nuclear and cell borders, and increased identification of GFP regions in highly autofluorescent tissue. These results demonstrate excitation scanning has utility in a wide range of time-dependent and photosensitive applications.