In vivo mapping of hemodynamic responses mediated by tubuloglomerular feedback in hypertensive kidneys.

The kidney has a sophisticated vascular structure that performs the unique function of filtering blood and managing blood pressure. Tubuloglomerular feedback is an intra-nephron negative feedback mechanism stabilizing single-nephron blood flow, glomerular filtration rate, and tubular flow rate, which is exhibited as self-sustained oscillations in single-nephron blood flow. We report the application of multi-scale laser speckle imaging to monitor global blood flow changes across the kidney surface (low zoom) and local changes in individual microvessels (high zoom) in normotensive and spontaneously hypertensive rats in vivo. We reveal significant differences in the parameters of TGF-mediated hemodynamics and patterns of synchronization. Furthermore, systemic infusion of a glucagon-like-peptide-1 receptor agonist, a potential renoprotective agent, induces vasodilation in both groups but only alters the magnitude of the TGF in Sprague Dawleys, although the underlying mechanisms remain unclear.

Renoprotective effects of GLP-1 receptor agonists and SGLT-2 inhibitors-is hemodynamics the key point?

Two novel treatments for diabetic kidney disease have emerged after decades with little progression. Both agents were developed for improved glycemic control in patients with type-2 diabetes. However, large clinical trials showed renoprotective effects beyond their ability to lower plasma glucose levels, body weight, and blood pressure. How this renal protection occurs is unknown. We will discuss their physiological effects, with special focus on the renal effects. We discuss how these drugs affect the function of the diabetic and nondiabetic kidneys to elucidate mechanisms by which the renoprotection could arise. Diabetic kidney disease affects the glomerular capillaries, which are usually protected by the renal autoregulatory mechanisms, the myogenic response, and the tubuloglomerular feedback mechanism. Animal models with reduced renal autoregulatory capacity develop chronic kidney disease. Despite different cellular targets, both drugs are suspected to affect renal hemodynamics through changes in the renal autoregulatory mechanisms. The glucagon-like peptide-1 receptor agonists (GLP-1RAs) exert a direct vasodilatory effect on the afferent arteriole (AA) positioned just before the glomerulus. Paradoxically, this effect is expected to increase glomerular capillary pressure, causing glomerular injury. In contrast, the sodium-glucose transporter-2 inhibitors (SGLT2i) are believed to activate the tubuloglomerular feedback mechanism to elicit vasoconstriction of the afferent arteriole. Because of their opposing effects on the renal afferent arterioles, it appears unlikely that their renoprotective effects can be explained by common effects of renal hemodynamics, but both drugs appear to add protection to the kidney beyond what can be obtained with classical treatment targeted at lowering blood glucose levels and blood pressure.

The assessment of cortical hemodynamic responses induced by tubuloglomerular feedback using in vivo imaging.

The tubuloglomerular feedback (TGF) mechanism modulates renal hemodynamics and glomerular filtration rate in individual nephrons. Our study aimed to evaluate the TGF-induced vascular responses by inhibiting Na-K-2Cl co-transporters and sodium-glucose co-transporters in rats. We assessed cortical hemodynamics with high-resolution laser speckle contrast imaging, which enabled the evaluation of blood flow in individual microvessels and analysis of their dynamical patterns in the time-frequency domain. We demonstrated that a systemic administration of furosemide abolishes TGF-mediated hemodynamic responses. Furthermore, we showed that the local microcirculatory blood flow decreased, and the TGF-induced hemodynamic oscillations were sustained but weakened after inhibiting sodium-glucose co-transporters in Sprague-Dawley rats.

Multi-scale laser speckle contrast imaging of microcirculatory vasoreactivity.

Laser speckle contrast imaging is a robust and versatile blood flow imaging tool in basic and clinical research for its relatively simple construction and ease of customization. One of its key features is the scalability of the imaged field of view. With minimal changes to the system or analysis, laser speckle contrast imaging allows for high-resolution blood flow imaging through cranial windows or low-resolution perfusion visualization of perfusion over large areas, e.g. in human skin. We further utilize this feature and introduce a multi-scale laser speckle contrast imaging system, which we apply to study vasoreactivity in renal microcirculation. We combine high resolution (small field of view) to segment blood flow in individual vessels with low resolution (large field of view) to monitor global blood flow changes across the renal surface. Furthermore, we compare their performance when analyzing blood flow dynamics potentially associated with a single nephron and show that the previously published approaches, based on low-zoom imaging alone, provide inaccurate results in such applications.

Functional Ultrasound Speckle Decorrelation-Based Velocimetry of the Brain.

A high-speed, contrast-free, quantitative ultrasound velocimetry (vUS) for blood flow velocity imaging throughout the rodent brain is developed based on the normalized first-order temporal autocorrelation function of the ultrasound field signal. vUS is able to quantify blood flow velocity in both transverse and axial directions, and is validated with numerical simulation, phantom experiments, and in vivo measurements. The functional imaging ability of vUS is demonstrated by monitoring the blood flow velocity changes during whisker stimulation in awake mice. Compared to existing Power-Doppler- and Color-Doppler-based functional ultrasound imaging techniques, vUS shows quantitative accuracy in estimating both axial and transverse flow speeds and resistance to acoustic attenuation and high-frequency noise.

Improving the characterization of ex vivo human brain optical properties using high numerical aperture optical coherence tomography by spatially constraining the confocal parameters.

Significance: The optical properties of biological samples provide information about the structural characteristics of the tissue and any changes arising from pathological conditions. Optical coherence tomography (OCT) has proven to be capable of extracting tissue's optical properties using a model that combines the exponential decay due to tissue scattering and the axial point spread function that arises from the confocal nature of the detection system, particularly for higher numerical aperture (NA) measurements. A weakness in estimating the optical properties is the inter-parameter cross-talk between tissue scattering and the confocal parameters defined by the Rayleigh range and the focus depth. Aim: In this study, we develop a systematic method to improve the characterization of optical properties with high-NA OCT. Approach: We developed a method that spatially parameterizes the confocal parameters in a previously established model for estimating the optical properties from the depth profiles of high-NA OCT. Results: The proposed parametrization model was first evaluated on a set of intralipid phantoms and then validated using a low-NA objective in which cross-talk from the confocal parameters is negligible. We then utilize our spatially parameterized model to characterize optical property changes introduced by a tissue index matching process using a simple immersion agent, 2,2'-thiodiethonal. Conclusions: Our approach improves the confidence of parameter estimation by reducing the degrees of freedom in the non-linear fitting model.

Printable microscale interfaces for long-term peripheral nerve mapping and precision control.

The nascent field of bioelectronic medicine seeks to decode and modulate peripheral nervous system signals to obtain therapeutic control of targeted end organs and effectors. Current approaches rely heavily on electrode-based devices, but size scalability, material and microfabrication challenges, limited surgical accessibility, and the biomechanically dynamic implantation environment are significant impediments to developing and deploying peripheral interfacing technologies. Here, we present a microscale implantable device - the nanoclip - for chronic interfacing with fine peripheral nerves in small animal models that begins to meet these constraints. We demonstrate the capability to make stable, high signal-to-noise ratio recordings of behaviorally-linked nerve activity over multi-week timescales. In addition, we show that multi-channel, current-steering-based stimulation within the confines of the small device can achieve multi-dimensional control of a small nerve. These results highlight the potential of new microscale design and fabrication techniques for realizing viable devices for long-term peripheral interfacing.

Chronic Imaging of Mouse Brain: From Optical Systems to Functional Ultrasound.

Utilization of functional ultrasound (fUS) in cerebral vascular imaging is gaining popularity among neuroscientists. In this article, we describe a chronic surgical preparation method that allows longitudinal studies and therefore is applicable to a wide range of studies, especially on aging, stroke, and neurodegenerative diseases. This method can also be used with awake mice; hence, the deleterious effects of anesthesia on neurovascular responses can be avoided. In addition to fUS imaging, this surgical preparation allows researchers to take advantage of common optical imaging methods to acquire complementary datasets to help increase the technical rigor of studies. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Surgical preparation of mouse chronic cranial windows using polymethylpentene Basic Protocol 2: Imaging of mice with chronic cranial windows.

Awake chronic mouse model of targeted pial vessel occlusion via photothrombosis.

Animal models of stroke are used extensively to study the mechanisms involved in the acute and chronic phases of recovery following stroke. A translatable animal model that closely mimics the mechanisms of a human stroke is essential in understanding recovery processes as well as developing therapies that improve functional outcomes. We describe a photothrombosis stroke model that is capable of targeting a single distal pial branch of the middle cerebral artery with minimal damage to the surrounding parenchyma in awake head-fixed mice. Mice are implanted with chronic cranial windows above one hemisphere of the brain that allow optical access to study recovery mechanisms for over a month following occlusion. Additionally, we study the effect of laser spot size used for occlusion and demonstrate that a spot size with small axial and lateral resolution has the advantage of minimizing unwanted photodamage while still monitoring macroscopic changes to cerebral blood flow during photothrombosis. We show that temporally guiding illumination using real-time feedback of blood flow dynamics also minimized unwanted photodamage to the vascular network. Finally, through quantifiable behavior deficits and chronic imaging we show that this model can be used to study recovery mechanisms or the effects of therapeutics longitudinally.

Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning.

Objective and Impact Statement. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. Introduction. Vascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems and is able to segment large-scale angiograms. Methods. We employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and total variation regularization on the network's output. Its effectiveness is demonstrated on experimentally acquired in vivo angiograms from mouse brains of dimensions up to 808×808×702 µm. Results. To demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art. Conclusion. Our work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning-based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.