Neural computations occurring simultaneously in multiple cerebral cortical regions are critical for mediating behaviors. Progress has been made in understanding how neural activity in specific cortical regions contributes to behavior. However, there is a lack of tools that allow simultaneous monitoring and perturbing neural activity from multiple cortical regions. We engineered ‘See-Shells’—digitally designed, morphologically realistic, transparent polymer skulls that allow long-term (>300 days) optical access to 45 mm 2 of the dorsal cerebral cortex in the mouse. We demonstrate the ability to perform mesoscopic imaging, as well as cellular and subcellular resolution two-photon imaging of neural structures up to 600 µm deep. See-Shells allow calcium imaging from multiple, non-contiguous regions across the cortex. Perforated See-Shells enable introducing penetrating neural probes to perturb or record neural activity simultaneously with whole cortex imaging. See-Shells are constructed using common desktop fabrication tools, providing a powerful tool for investigating brain structure and function.

By causing mitochondrial DNA (mtDNA) mutations and oxidation of mitochondrial proteins, reactive oxygen species (ROS) leads to perturbations in mitochondrial proteostasis. Several studies have linked mtDNA mutations to metastasis of cancer cells but the nature of the mtDNA species involved remains unclear. Our data suggests that no common mtDNA mutation identifies metastatic cells; rather the metastatic potential of several ROS-generating mutations is largely determined by their mtDNA genomic landscapes, which can act either as an enhancer or repressor of metastasis. However, mtDNA landscapes of all metastatic cells are characterized by activation of the SIRT/FOXO/SOD2 axis of the mitochondrial unfolded protein response (UPR mt ). The UPR mt promotes a complex transcription program ultimately increasing mitochondrial integrity and fitness in response to oxidative proteotoxic stress. Using SOD2 as a surrogate marker of the UPR mt , we found that in primary breast cancers, SOD2 is significantly increased in metastatic lesions. We propose that the ability of selected mtDNA species to activate the UPR mt is a process that is exploited by cancer cells to maintain mitochondrial fitness and facilitate metastasis.

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SUMMARY GSK3β is a serine threonine kinase implicated in the progression of Alzheimer’s disease. Although the role of GSK3β in growth and pathology has been extensively studied, little is known about the metabolic consequences of GSK3β manipulation, particularly in the brain. Here, we show that GSK3β regulates mitochondrial energy metabolism in human H4 neuroglioma cells and rat PC12-derived neuronal cells and that inhibition of GSK3β in mice in vivo alters metabolism in the hippocampus in a region-specific manner. We demonstrate that GSK3β inhibition increases mitochondrial respiration and membrane potential and alters NAD(P)H metabolism. These metabolic effects are associated with increased PGC-1α protein stabilization, enhanced nuclear localization, and increased transcriptional co-activation. In mice treated with the GSK3β inhibitor lithium carbonate, changes in hippocampal energy metabolism are linked to increased PGC-1α. These data highlight a metabolic role for brain GSK3β and suggest that the GSK3β/PGC-1α axis may be important in neuronal metabolic integrity.

Cell growth and/or proliferation may require the reprogramming of metabolic pathways, whereby a switch from oxidative to glycolytic metabolism diverts glycolytic intermediates towards anabolic pathways. Herein, we identify a novel role for TRIM32 in the maintenance of glycolytic flux mediated by biochemical interactions with the glycolytic enzymes Aldolase and Phosphoglycerate mutase. Loss of Drosophila TRIM32, encoded by thin (tn), shows reduced levels of glycolytic intermediates and amino acids. This altered metabolic profile correlates with a reduction in the size of glycolytic larval muscle and brain tissue. Consistent with a role for metabolic intermediates in glycolysis-driven biomass production, dietary amino acid supplementation in tn mutants improves muscle mass. Remarkably, TRIM32 is also required for ectopic growth - loss of TRIM32 in a wing disc-associated tumor model reduces glycolytic metabolism and restricts growth. Overall, our results reveal a novel role for TRIM32 for controlling glycolysis in the context of both normal development and tumor growth.

Control of tissue and organismal size requires the continual reprogramming of metabolic pathways to integrate biosynthetic and degradative signals. During cell growth and/or proliferation, one such mechanism that promotes the accumulation of cellular material is a switch from oxidative to glycolytic metabolism, whereby glycolytic intermediates are diverted towards anabolic pathways. How this switch is regulated in different tissues is not clear. Herein we identify a novel role for the tripartite motif (TRIM) family member, TRIM32, in the maintenance of glycolytic flux. Using a proteomics approach, we uncovered the glycolytic enzymes Aldolase (Ald) and Phosphoglycerate mutase 78 (Pglym) as TRIM32 interacting proteins. Loss of Drosophila TRIM32, encoded by the thin (tn) gene, showed a reduction in glycolytic activity and amino acid abundance. This altered metabolic profile caused a striking reduction in the overall size of two inherently glycolytic larval tissues – somatic muscles and the developing brain. Consistent with a role for metabolic intermediates in glycolysis‐driven biomass production, nutritional supplementation of amino acids in tn mutants restored muscle mass. Many tumors favor glycolytic metabolism to maximize substrate production for uncontrolled cell growth and proliferation. Remarkably, wing disc‐associated tumor growth is abolished upon loss of TRIM32. Our results reveal a novel connection between TRIM32 and the maintenance of glycolytic enzyme levels and upregulated pathway activity for the sustained growth of normal and cancerous tissue growth. Support or Funding Information This work was supported by a grant through the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) to E.R.G (R01AR060788). J.M.T. is supported by a MIRA award from NIGMS (R35GM119557).

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging tool used to study the molecular environment of flurophores. In time domain FLIM, extracting lifetime from fluorophores signals entails fitting data to a decaying exponential distribution function. However, most existing techniques for this purpose need large amounts of photons at each pixel and a long computation time, thus making it difficult to obtain reliable inference in applications requiring either short acquisition or minimal computation time. In this work, we introduce a new nonparametric empirical Bayesian framework for FLIM data analysis (NEB-FLIM), leading to both improved pixel-wise lifetime estimation and a more robust and computationally efficient integral property inference. This framework is developed based on a newly proposed hierarchical statistical model for FLIM data and adopts a novel nonparametric maximum likelihood estimator to estimate the prior distribution. To demonstrate the merit of the proposed framework, we applied it on both simulated and real biological datasets and compared it with previous classical methods on these datasets.