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.

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.

Cellpose as a reliable method for single-cell segmentation of autofluorescence microscopy images.

Autofluorescence microscopy uses intrinsic sources of molecular contrast to provide cellular-level information without extrinsic labels. However, traditional cell segmentation tools are often optimized for high signal-to-noise ratio (SNR) images, such as fluorescently labeled cells, and unsurprisingly perform poorly on low SNR autofluorescence images. Therefore, new cell segmentation tools are needed for autofluorescence microscopy. Cellpose is a deep learning network that is generalizable across diverse cell microscopy images and automatically segments single cells to improve throughput and reduce inter-human biases. This study aims to validate Cellpose for autofluorescence imaging, specifically from multiphoton intensity images of NAD(P)H. Manually segmented nuclear masks of NAD(P)H images were used to train new Cellpose models. These models were applied to PANC-1 cells treated with metabolic inhibitors and patient-derived cancer organoids (across 9 patients) treated with chemotherapies. These datasets include co-registered fluorescence lifetime imaging microscopy (FLIM) of NAD(P)H and FAD, so fluorescence decay parameters and the optical redox ratio (ORR) were compared between masks generated by the new Cellpose model and manual segmentation. The Dice score between repeated manually segmented masks was significantly lower than that of repeated Cellpose masks (p<0.0001) indicating greater reproducibility between Cellpose masks. There was also a high correlation (R2>0.9) between Cellpose and manually segmented masks for the ORR, mean NAD(P)H lifetime, and mean FAD lifetime across 2D and 3D cell culture treatment conditions. Masks generated from Cellpose and manual segmentation also maintain similar means, variances, and effect sizes between treatments for the ORR and FLIM parameters. Overall, Cellpose provides a fast, reliable, reproducible, and accurate method to segment single cells in autofluorescence microscopy images such that functional changes in cells are accurately captured in both 2D and 3D culture.

Same People, Different Results: Categorizing Cancer Registry Cases Across the Rural-Urban Continuum.

BACKGROUND: Many rural-urban indexes are utilized in cancer research. This variation introduces inconsistencies between studies. Recommendations on index use have prioritized geographical unit over feasibility of inclusion in analysis. We evaluated rural-urban indexes and recommend one for use to increase comparability across studies. METHODS: We assessed 9 US rural-urban indexes regarding their respective rural and urban code ranges; geographical unit, land area, and population distributions; percent agreement; suitability for analysis; and integration feasibility for national, state, and local cancer research. We referenced 1569 Wisconsin Pancreatic Cancer Registry patients to demonstrate how index choice affects patient categorization. RESULTS: Six indexes categorized rural and urban areas. Indexes agreed on binary rural-urban designation for 88.8% of the US population. As ternary variables, they agreed for 83.4%. For cancer registry patients, this decreased to 73.4% and 60.4% agreement, respectively. Rural-Urban Continuum Codes (RUCC) performed the best in differentiating metropolitan, micropolitan, and rural counties; availability for retrospective and prospective studies; and continuous coding for analysis. CONCLUSIONS: Urban/rural patient categorization changed with index selection. We conclude that RUCC is an appropriate and feasible rural-urban index to include in cancer research, as it is standardly available in national cancer registries, can be matched to patient's county of residence for local research, and it had the least amount of fluctuation of the indices analyzed. Utilizing RUCC as a continuous variable across studies with a rural-urban component will increase reproducibility and comparability of results and eliminate rural-urban index choice as a potential source of discrepancy between studies.

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.

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

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

Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence.

Neural stem cells (NSCs) divide and produce newborn neurons in the adult brain through a process called adult neurogenesis. Adult NSCs are primarily quiescent, a reversible cell state where they have exited the cell cycle (G0) yet remain responsive to the environment. In the first step of adult neurogenesis, quiescent NSCs (qNSCs) receive a signal and activate, exiting quiescence and re-entering the cell cycle. Thus, understanding the regulators of NSC quiescence and quiescence exit is critical for future strategies targeting adult neurogenesis. However, our understanding of NSC quiescence is limited by technical constraints in identifying quiescent NSCs (qNSCs) and activated NSCs (aNSCs). This protocol describes a new approach to identify and enrich qNSCs and aNSCs generated in in vitro cultures by imaging NSC autofluorescence. First, this protocol describes how to use a confocal microscope to identify autofluorescent markers of qNSCs and aNSCs to classify NSC activation state using autofluorescence intensity. Second, this protocol describes how to use a fluorescent activated cell sorter (FACS) to classify NSC activation state and enrich samples for qNSCs or aNSCs using autofluorescence intensity. Third, this protocol describes how to use a multiphoton microscope to perform fluorescence lifetime imaging (FLIM) at single-cell resolution, classify NSC activation state, and track the dynamics of quiescent exit using both autofluorescence intensities and fluorescence lifetimes. Thus, this protocol provides a live-cell, label-free, single-cell resolution toolkit for studying NSC quiescence and quiescence exit.

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

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

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.

Naive primary neutrophils play a dual role in the tumor microenvironment.

The tumor microenvironment (TME) is characterized by a network of cancer cells, recruited immune cells and extracellular matrix (ECM) in a hypoxic microenvironment. However, the specific role of neutrophils during tumor development, and their interactions with other immune cells is still not well understood. Thus, there is a need to investigate the interaction between primary neutrophils and natural killer cells and the resulting effects on tumor development. Here we use both standard well plate culture and an under oil microfluidic (UOM) assay with an integrated extracellular cell matrix (ECM) bridge to elucidate how naive primary neutrophils respond to both patient derived tumor cells and tumor cell lines. Our data demonstrated that both patient derived head and neck squamous cell carcinoma (HNSCC) tumor cells and MDA-MB-231 breast cancer cells trigger cluster formation in neutrophils, and the swarm of neutrophils restricts tumor invasion through the generation of reactive oxygen species (ROS) and neutrophil extracellular trap (NETs) release within the neutrophil cluster. However, we also observed that the presence of neutrophils downregulates granzyme B in NK-92 cells and the resulting NETs can obstruct NK cells from penetrating the tumor mass in vitro suggesting a dual role for neutrophils in the TME. Further, using label-free optical metabolic imaging (OMI) we observed changes in the metabolic activities of primary neutrophils during the different swarming phases when challenged with tumor cells. Finally, our data demonstrates that neutrophils in direct contact, or in close proximity, with tumor cells exhibit greater metabolic activities (lower nicotinamide adenine dinucleotide phosphate (NAD(P)H) mean lifetime) compared to non-contact neutrophils.

Development of a multilayer fetal membrane material model calibrated using bulge inflation mechanical tests.

The fetal membranes are an essential mechanical structure for pregnancy, protecting the developing fetus in an amniotic fluid environment and rupturing before birth. In cooperation with the cervix and the uterus, the fetal membranes support the mechanical loads of pregnancy. Structurally, the fetal membranes comprise two main layers: the amnion and the chorion. The mechanical characterization of each layer is crucial to understanding how each layer contributes to the structural performance of the whole membrane. The in-vivo mechanical loading of the fetal membranes and the amount of tissue stress generated in each layer throughout gestation remains poorly understood, as it is difficult to perform direct measurements on pregnant patients. Finite element analysis of pregnancy offers a computational method to explore how anatomical and tissue remodeling factors influence the load-sharing of the uterus, cervix, and fetal membranes. To aid in the formulation of such computational models of pregnancy, this work develops a fiber-based multilayer fetal membrane model that captures its response to previously published bulge inflation loading data. First, material models for the amnion, chorion, and maternal decidua are formulated, informed, and validated by published data. Then, the behavior of the fetal membrane as a layered structure was analyzed, focusing on the respective stress distribution and thickness variation in each layer. The layered computational model captures the overall behavior of the fetal membranes, with the amnion being the mechanically dominant layer. The inclusion of fibers in the amnion material model is an important factor in obtaining reliable fetal membrane behavior according to the experimental dataset. These results highlight the potential of this layered model to be integrated into larger biomechanical models of the gravid uterus and cervix to study the mechanical mechanisms of preterm birth.

Preclinical Models of Anal Cancer Combined-Modality Therapy.

INTRODUCTION: There have been no significant changes in anal cancer treatment options in 4 decades. In this study, we highlight two preclinical models designed to assess anal cancer treatments. MATERIALS AND METHODS: Transgenic K14E6/E7 mice were treated with 7, 12-dimethylbenz(a)anthracene until anal tumors developed. Mice were treated with localized radiation in addition to chemotherapy (combined-modality therapy [CMT]) and compared to no treatment control (NTC). K14E6/E7 mouse anal spheroids with and without Pik3ca mutations were isolated and treated with vehicle, LY3023414 (LY3) (a drug previously shown to be effective in cancer prevention), CMT, or CMT + LY3. RESULTS: In the in vivo model, there was a significant increase in survival in the CMT group compared to the NTC group (P = 0.0392). In the ex vivo model, there was a significant decrease in the mean diameter of CMT and CMT + LY3-treated spheroids compared to vehicle (P ≤ 0.0001). For LY3 alone compared to vehicle, there was a statistically significant decrease in spheroid size in the K14E6/E7 group without mutation (P = 0.0004). CONCLUSIONS: We have provided proof of concept for two preclinical anal cancer treatment models that allow for the future testing of novel therapies for anal cancer.

Quantifying in vivo collagen reorganization during immunotherapy in murine melanoma with second harmonic generation imaging.

Significance: Increased collagen linearization and deposition during tumorigenesis can impede immune cell infiltration and lead to tumor metastasis. Although melanoma is well studied in immunotherapy research, studies that quantify collagen changes during melanoma progression and treatment are lacking. Aim: Image in vivo collagen in preclinical melanoma models during immunotherapy and quantify the collagen phenotype in treated and control mice. Approach: Second harmonic generation imaging of collagen was performed in mouse melanoma tumors in vivo over a treatment time-course. Animals were treated with a curative radiation and immunotherapy combination. Collagen morphology was quantified over time at an image and single fiber level using CurveAlign and CT-FIRE software. Results: In immunotherapy-treated mice, collagen reorganized toward a healthy phenotype, including shorter, wider, curlier collagen fibers, with modestly higher collagen density. Temporally, collagen fiber straightness and length changed late in treatment (Day 9 and 12) while width and density changed early (Day 6) compared to control mice. Single fiber level collagen analysis was most sensitive to the changes between treatment groups compared to image level analysis. Conclusions: Quantitative second harmonic generation imaging can provide insight into collagen dynamics in vivo during immunotherapy, with key implications in improving immunotherapy response in melanoma and other cancers.

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.

Microphysiological model reveals the promise of memory-like natural killer cell immunotherapy for HIV± cancer.

Numerous studies are exploring the use of cell adoptive therapies to treat hematological malignancies as well as solid tumors. However, there are numerous factors that dampen the immune response, including viruses like human immunodeficiency virus. In this study, we leverage human-derived microphysiological models to reverse-engineer the HIV-immune system interaction and evaluate the potential of memory-like natural killer cells for HIV+ head and neck cancer, one of the most common tumors in patients living with human immunodeficiency virus. Here, we evaluate multiple aspects of the memory-like natural killer cell response in human-derived bioengineered environments, including immune cell extravasation, tumor penetration, tumor killing, T cell dependence, virus suppression, and compatibility with retroviral medication. Overall, these results suggest that memory-like natural killer cells are capable of operating without T cell assistance and could simultaneously destroy head and neck cancer cells as well as reduce viral latency.

Griddient: a microfluidic array to generate reconfigurable gradients on-demand for spatial biology applications.

Biological tissues are highly organized structures where spatial-temporal gradients (e.g., nutrients, hypoxia, cytokines) modulate multiple physiological and pathological processes including inflammation, tissue regeneration, embryogenesis, and cancer progression. Current in vitro technologies struggle to capture the complexity of these transient microenvironmental gradients, do not provide dynamic control over the gradient profile, are complex and poorly suited for high throughput applications. Therefore, we have designed Griddent, a user-friendly platform with the capability of generating controllable and reversible gradients in a 3D microenvironment. Our platform consists of an array of 32 microfluidic chambers connected to a 384 well-array through a diffusion port at the bottom of each reservoir well. The diffusion ports are optimized to ensure gradient stability and facilitate manual micropipette loading. This platform is compatible with molecular and functional spatial biology as well as optical and fluorescence microscopy. In this work, we have used this platform to study cancer progression.

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.

Microfluidic device with reconfigurable spatial temporal gradients reveals plastic astrocyte response to stroke and reperfusion.

As a leading cause of mortality and morbidity, stroke constitutes a significant global health burden. Ischemic stroke accounts for 80% of cases and occurs due to an arterial thrombus, which impedes cerebral blood flow and rapidly leads to cell death. As the most abundant cell type within the central nervous system, astrocytes play a critical role within the injured brain. We developed a novel microphysiological platform that permits the induction of spatiotemporally controlled nutrient gradients, allowing us to study astrocytic response during and after transient nutrient deprivation. Within 24 h of inducing starvation in the platform, nutrient deprivation led to multiple changes in astrocyte response, from metabolic perturbations to gene expression changes, and cell viability. Furthermore, we observed that nutrient restoration did not reverse the functional changes in astrocyte metabolism, which mirrors reperfusion injury observed in vivo. We also identified alterations in numerous glucose metabolism-associated genes, many of which remained upregulated or downregulated even after restoration of the nutrient supply. Together, these findings suggest that astrocyte activation during and after nutrient starvation induces plastic changes that may underpin persistent stroke-induced functional impairment. Overall, our innovative device presents interesting potential to be used in the development of new therapies to improve tissue repair and even cognitive recovery after stroke.