Diabetes is a common comorbidity in stroke patients and a strong predictor of poor functional outcome. To provide a more mechanistic understanding of this clinically relevant problem, we focused on how diabetes affects blood-brain barrier (BBB) function after stroke. Because the BBB can be compromised for days after stroke and thus further exacerbate ischemic injury, manipulating its function presents a unique opportunity for enhancing stroke recovery long after the window for thrombolytics has passed. Using a mouse model of Type 1 diabetes, we discovered that ischemic stroke leads to an abnormal and persistent increase in vascular endothelial growth factor receptor 2 (VEGF-R2) expression in peri-infarct vascular networks. Correlating with this, BBB permeability was markedly increased in diabetic mice, which could not be prevented with insulin treatment after stroke. Imaging of capillary ultrastructure revealed that BBB permeability was associated with an increase in endothelial transcytosis rather than a loss of tight junctions. Pharmacological inhibition (initiated 2.5 d after stroke) or vascular-specific knockdown of VEGF-R2 after stroke attenuated BBB permeability, loss of synaptic structure in peri-infarct regions, and improved recovery of forepaw function. However, the beneficial effects of VEGF-R2 inhibition on stroke recovery were restricted to diabetic mice and appeared to worsen BBB permeability in nondiabetic mice. Collectively, these results suggest that aberrant VEGF signaling and BBB dysfunction after stroke plays a crucial role in limiting functional recovery in an experimental model of diabetes. Furthermore, our data highlight the need to develop more personalized stroke treatments for a heterogeneous clinical population.

Cellular homeostasis relies on having dedicated and coordinated responses to a variety of stresses. The accumulation of unfolded proteins in the endoplasmic reticulum (ER) is a common stress that triggers a conserved pathway called the unfolded protein response (UPR) that mitigates damage, and dysregulation of UPR underlies several debilitating diseases. Here, we discover that a previously uncharacterized 54-amino acid microprotein PIGBOS regulates UPR. PIGBOS localizes to the mitochondrial outer membrane where it interacts with the ER protein CLCC1 at ER–mitochondria contact sites. Functional studies reveal that the loss of PIGBOS leads to heightened UPR and increased cell death. The characterization of PIGBOS reveals an undiscovered role for a mitochondrial protein, in this case a microprotein, in the regulation of UPR originating in the ER. This study demonstrates microproteins to be an unappreciated class of genes that are critical for inter-organelle communication, homeostasis, and cell survival.

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Point-scanning imaging systems are among the most widely used tools for high-resolution cellular and tissue imaging, benefitting from arbitrarily defined pixel sizes. The resolution, speed, sample preservation, and signal-to-noise ratio (SNR) of point-scanning systems are difficult to optimize simultaneously. We show these limitations can be mitigated via the use of Deep Learning-based supersampling of undersampled images acquired on a point-scanning system, which we term point-scanning super-resolution (PSSR) imaging. We designed a “crappifier” that computationally degrades high SNR, high pixel resolution ground truth images to simulate low SNR, low-resolution counterparts for training PSSR models that can restore real-world undersampled images. For high spatiotemporal resolution fluorescence timelapse data, we developed a “multi-frame” PSSR approach that utilizes information in adjacent frames to improve model predictions. In conclusion, PSSR facilitates point-scanning image acquisition with otherwise unattainable resolution, speed, and sensitivity. All the training data, models, and code for PSSR are publicly available at 3DEM.org . Editor’s summary Point-scanning super-resolution imaging uses deep learning to supersample undersampled images and enable time-lapse imaging of subcellular events. An accompanying “crappifier” rapidly generates quality training data for robust performance.

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