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Several neuronal populations in the hypothalamus and brainstem express thyrotropin-releasing hormone (TRH). While TRH neurons in the paraventricular nucleus (PVN) regulate the thyroid axis, the roles of other TRH-producing neurons remain largely unknown. Here we investigate the role of TRH neurons in the PVN, the dorsomedial hypothalamus (DMH), the medial preoptic area (MPA), and the rostral raphe pallidus (RPa) for metabolism in mice. Selective activation of these populations using chemogenetics in mice revealed that TRH neurons of the hypothalamus increase food intake and influence energy homeostasis in different ways. Specifically, TRH neurons in the PVN and DMH enhance brown adipose tissue activity via a polysynaptic circuit, while MPA-located neurons increase locomotor activity and maintain cold tolerance. These effects were independent of the thyroid axis, demonstrating that TRH neurons have distinct, subtype-specific ways to increase energy expenditure beyond regulating the thyroid axis in mice. TRH neurons are located in brain regions controlling energy balance. Here, authors show that distinct hypothalamic TRH neurons regulate feeding, energy use, body temperature, and brown fat, and are involved in counteracting hypothermia.

Coronavirus disease 2019 (COVID-19) can damage cerebral small vessels and cause neurological symptoms. Here we describe structural changes in cerebral small vessels of patients with COVID-19 and elucidate potential mechanisms underlying the vascular pathology. In brains of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected individuals and animal models, we found an increased number of empty basement membrane tubes, so-called string vessels representing remnants of lost capillaries. We obtained evidence that brain endothelial cells are infected and that the main protease of SARS-CoV-2 (M ) cleaves NEMO, the essential modulator of nuclear factor-κB. By ablating NEMO, M induces the death of human brain endothelial cells and the occurrence of string vessels in mice. Deletion of receptor-interacting protein kinase (RIPK) 3, a mediator of regulated cell death, blocks the vessel rarefaction and disruption of the blood-brain barrier due to NEMO ablation. Importantly, a pharmacological inhibitor of RIPK signaling prevented the M -induced microvascular pathology. Our data suggest RIPK as a potential therapeutic target to treat the neuropathology of COVID-19.

Two-photon laser-scanning microscopy has revolutionized our view on vital processes by revealing motility and interaction patterns of various cell subsets in hardly accessible organs (e.g. brain) in living animals. However, current technology is still insufficient to elucidate the mechanisms of organ dysfunction as a prerequisite for developing new therapeutic strategies, since it renders only sparse information about the molecular basis of cellular response within tissues in health and disease. In the context of imaging, Förster resonant energy transfer (FRET) is one of the most adequate tools to probe molecular mechanisms of cell function. As a calibration-free technique, fluorescence lifetime imaging (FLIM) is superior for quantifying FRET in vivo. Currently, its main limitation is the acquisition speed in the context of deep-tissue 3D and 4D imaging. Here we present a parallelized time-correlated single-photon counting point detector (p-TCSPC) (i) for dynamic single-beam scanning FLIM of large 3D areas on the range of hundreds of milliseconds relevant in the context of immune-induced pathologies as well as (ii) for ultrafast 2D FLIM in the range of tens of milliseconds, a scale relevant for cell physiology. We demonstrate its power in dynamic deep-tissue intravital imaging, as compared to multi-beam scanning time-gated FLIM suitable for fast data acquisition and compared to highly sensitive single-channel TCSPC adequate to detect low fluorescence signals. Using p-TCSPC, 256×256 pixel FLIM maps (300×300 µm(2)) are acquired within 468 ms while 131×131 pixel FLIM maps (75×75 µm(2)) can be acquired every 82 ms in 115 µm depth in the spinal cord of CerTN L15 mice. The CerTN L15 mice express a FRET-based Ca-biosensor in certain neuronal subsets. Our new technology allows us to perform time-lapse 3D intravital FLIM (4D FLIM) in the brain stem of CerTN L15 mice affected by experimental autoimmune encephalomyelitis and, thereby, to truly quantify neuronal dysfunction in neuroinflammation.

Compressed sensing is an image reconstruction technique to achieve high-quality results from limited amount of data. In order to achieve this, it utilizes prior knowledge about the samples that shall be reconstructed. Focusing on image reconstruction in nanotomography, this work proposes enhancements by including additional problem-specific knowledge. In more detail, we propose further classes of algebraic inequalities that are added to the compressed sensing model. The first consists in a valid upper bound on the pixel brightness. It only exploits general information about the projections and is thus applicable to a broad range of reconstruction problems. The second class is applicable whenever the sample material is of roughly homogeneous composition. The model favors a constant density and penalizes deviations from it. The resulting mathematical optimization models are algorithmically tractable and can be solved to global optimality by state-of-the-art available implementations of interior point methods. In order to evaluate the novel models, obtained results are compared to existing image reconstruction methods, tested on simulated and experimental data sets. The experimental data comprise one 360 electron tomography tilt series of a macroporous zeolite particle and one absorption contrast nano X-ray computed tomography (nano-CT) data set of a copper microlattice structure. The enriched models are optimized quickly and show improved reconstruction quality, outperforming the existing models. Promisingly, our approach yields superior reconstruction results, particularly when information about the samples is available for a small number of tilt angles only