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Recording cell-specific neuronal activity while monitoring behaviors of freely moving subjects can provide some of the most significant insights into brain function. Current means for monitoring calcium dynamics in genetically targeted populations of neurons rely on delivery of light and recording of fluorescent signals through optical fibers that can reduce subject mobility, induce motion artifacts, and limit experimental paradigms to isolated subjects in open, two-dimensional (2D) spaces. Wireless alternatives eliminate constraints associated with optical fibers, but their use of head stages with batteries adds bulk and weight that can affect behaviors, with limited operational lifetimes. The systems introduced here avoid drawbacks of both types of technologies, by combining highly miniaturized electronics and energy harvesters with injectable photometric modules in a class of fully wireless, battery-free photometer that is fully implantable subdermally to allow for the interrogation of neural dynamics in freely behaving subjects, without limitations set by fiber optic tethers or operational lifetimes constrained by traditional power supplies. The unique capabilities of these systems, their compatibility with magnetic resonant imaging and computed tomography and the ability to manufacture them with techniques in widespread use for consumer electronics, suggest a potential for broad adoption in neuroscience research.

Fiber photometry (FP) is an adaptable method for recording in vivo neural activity in freely behaving animals. It has become a popular tool in neuroscience due to its ease of use, low cost, the ability to combine FP with freely moving behavior, among other advantages. However, analysis of FP data can be challenging for new users, especially those with a limited programming background. Here, we present Guided Photometry Analysis in Python (GuPPy), a free and open-source FP analysis tool. GuPPy is designed to operate across computing platforms and can accept data from a variety of FP data acquisition systems. The program presents users with a set of graphic user interfaces (GUIs) to load data and provide input parameters. Graphs are produced that can be easily exported for integration into scientific figures. As an open-source tool, GuPPy can be modified by users with knowledge of Python to fit their specific needs.

Eleven hours after the detection of gravitational wave source GW170817 by the Laser Interferometer Gravitational-Wave Observatory and Virgo Interferometers, an associated optical transient, SSS17a, was identified in the galaxy NGC 4993. Although the gravitational wave data indicate that GW170817 is consistent with the merger of two compact objects, the electromagnetic observations provide independent constraints on the nature of that system. We synthesize the optical to near-infrared photometry and spectroscopy of SSS17a collected by the One-Meter Two-Hemisphere collaboration, finding that SSS17a is unlike other known transients. The source is best described by theoretical models of a kilonova consisting of radioactive elements produced by rapid neutron capture (the r-process). We conclude that SSS17a was the result of a binary neutron star merger, reinforcing the gravitational wave result.

The paraventricular thalamus is a relay station connecting brainstem and hypothalamic signals that represent internal states with the limbic forebrain that performs associative functions in emotional contexts. Zhu et al. found that paraventricular thalamic neurons represent multiple salient features of sensory stimuli, including reward, aversiveness, novelty, and surprise. The nucleus thus provides context-dependent salience encoding. The thalamus gates sensory information and contributes to the sleep-wake cycle through its interactions with the cerebral cortex. Ren et al. recorded from neurons in the paraventricular thalamus and observed that both population and single-neuron activity were tightly coupled with wakefulness. Science , this issue p. 423 , p. 429 Neurons in the paraventricular thalamic nucleus are both necessary and sufficient for maintaining arousal. Clinical observations indicate that the paramedian region of the thalamus is a critical node for controlling wakefulness. However, the specific nucleus and neural circuitry for this function remain unknown. Using in vivo fiber photometry or multichannel electrophysiological recordings in mice, we found that glutamatergic neurons of the paraventricular thalamus (PVT) exhibited high activities during wakefulness. Suppression of PVT neuronal activity caused a reduction in wakefulness, whereas activation of PVT neurons induced a transition from sleep to wakefulness and an acceleration of emergence from general anesthesia. Moreover, our findings indicate that the PVT–nucleus accumbens projections and hypocretin neurons in the lateral hypothalamus to PVT glutamatergic neurons’ projections are the effector pathways for wakefulness control. These results demonstrate that the PVT is a key wakefulness-controlling nucleus in the thalamus.

The increasing demand for continuous, comprehensive physiological information captured by skin-interfaced wireless sensors is hindered by their relatively high-power consumption and the associated patient discomfort that can follow from the use of high capacity batteries. This paper presents an adaptive electronics platform and a tri-modal energy harvesting approach to reduce the need for battery power. Specifically, the schemes focus on sensors that involve light in their operation, through use of (i) photometric methods, where ambient light contributes directly to the measurement process, (ii) multijunction photovoltaic cells, where ambient light powers operation and/or charges an integrated battery, and (iii) photoluminescent packaging, where ambient light activates light-emitting species to enhance the first two schemes. Additional features of interest are in (i) in-sensor computational approaches that decrease the bandwidth and thus the energy consumption in wireless data communication and (ii) radio frequency power transfer for battery charging. These ideas have utility across broad other classes of wearable devices as well as small, portable electronic gadgetry. Wearable sensors typically require innovative methods of energy harvesting for low-power electronics. Here, the authors present an adaptive power management system with a trimodal energy harvesting approach.

The homeostatic control of body temperature is essential for survival in mammals and is known to be regulated in part by temperature-sensitive neurons in the hypothalamus. However, the specific neural pathways and corresponding neural populations have not been fully elucidated. To identify these pathways, we used cFos staining to identify neurons that are activated by a thermal challenge and found induced expression in subsets of neurons within the ventral part of the lateral preoptic nucleus (vLPO) and the dorsal part of the dorsomedial hypothalamus (DMD). Activation of GABAergic neurons in the vLPO using optogenetics reduced body temperature, along with a decrease in physical activity. Optogenetic inhibition of these neurons resulted in fever-level hyperthermia. These GABAergic neurons project from the vLPO to the DMD and optogenetic stimulation of the nerve terminals in the DMD also reduced body temperature and activity. Electrophysiological recording revealed that the vLPO GABAergic neurons suppressed neural activity in DMD neurons, and fiber photometry of calcium transients revealed that DMD neurons were activated by cold. Accordingly, activation of DMD neurons using designer receptors exclusively activated by designer drugs (DREADDs) or optogenetics increased body temperature with a strong increase in energy expenditure and activity. Finally, optogenetic inhibition of DMD neurons triggered hypothermia, similar to stimulation of the GABAergic neurons in the vLPO. Thus, vLPO GABAergic neurons suppressed the thermogenic effect of DMD neurons. In aggregate, our data identify vLPO→DMD neural pathways that reduce core temperature in response to a thermal challenge, and we show that outputs from the DMD can induce activity-induced thermogenesis.

The emission of singly ionized carbon is used to identify two galaxies with redshifts of nearly 7—corresponding to the Universe’s first billion years—and with velocity structures suggestive of rotation. Rotation in two high-redshift galaxies The forbidden emission line of singly ionized carbon [C ɪɪ] at a wavelength of 157.7 micrometres is one of the main lines for cooling gas in nearby star-forming galaxies, and has been expected, although not yet proved, to be bright in the early Universe. Renske Smit and collaborators report spectroscopic confirmation of the redshifts of two infrared-selected galaxies at redshifts of 6.85 and 6.81, using the [C ɪɪ] line. The galaxies are luminous, with velocity gradients across their surfaces. If those gradients represent rotation, then the galaxies have dynamical properties like those of Hα-bright galaxies two billion years later in the history of the Universe. The earliest galaxies are thought to have emerged during the first billion years of cosmic history, initiating the ionization of the neutral hydrogen that pervaded the Universe at this time. Studying this ‘epoch of reionization’ involves looking for the spectral signatures of ancient galaxies that are, owing to the expansion of the Universe, now very distant from Earth and therefore exhibit large redshifts. However, finding these spectral fingerprints is challenging. One spectral characteristic of ancient and distant galaxies is strong hydrogen-emission lines (known as Lyman-α lines), but the neutral intergalactic medium that was present early in the epoch of reionization scatters such Lyman-α photons. Another potential spectral identifier is the line at wavelength 157.4 micrometres of the singly ionized state of carbon (the [C ii ] λ  = 157.74 μm line), which signifies cooling gas and is expected to have been bright in the early Universe. However, so far Lyman-α-emitting galaxies from the epoch of reionization have demonstrated much fainter [C ii ] luminosities than would be expected from local scaling relations 1 , 2 , 3 , 4 , 5 , and searches for the [C ii ] line in sources without Lyman-α emission but with photometric redshifts greater than 6 (corresponding to the first billion years of the Universe) have been unsuccessful. Here we identify [C ii ] λ  = 157.74 μm emission from two sources that we selected as high-redshift candidates on the basis of near-infrared photometry; we confirm that these sources are two galaxies at redshifts of z  = 6.8540 ± 0.0003 and z  = 6.8076 ± 0.0002. Notably, the luminosity of the [C ii ] line from these galaxies is higher than that found previously in star-forming galaxies with redshifts greater than 6.5. The luminous and extended [C ii ] lines reveal clear velocity gradients that, if interpreted as rotation, would indicate that these galaxies have similar dynamic properties to the turbulent yet rotation-dominated disks that have been observed in Hα-emitting galaxies two billion years later, at ‘cosmic noon’.

Optical approaches to monitor neural activity are transforming neuroscience, owing to a fast-evolving palette of genetically encoded molecular reporters. However, the field still requires robust and label-free technologies to monitor the multifaceted biomolecular changes accompanying brain development, aging or disease. Here, we have developed vibrational fiber photometry as a low-invasive method for label-free monitoring of the biomolecular content of arbitrarily deep regions of the mouse brain in vivo through spontaneous Raman spectroscopy. Using a tapered fiber probe as thin as 1 µm at its tip, we elucidate the cytoarchitecture of the mouse brain, monitor molecular alterations caused by traumatic brain injury, as well as detect markers of brain metastasis with high accuracy. We view our approach, which introduces a deep learning algorithm to suppress probe background, as a promising complement to the existing palette of tools for the optical interrogation of neural function, with application to fundamental and preclinical investigations of the brain and other organs. Vibrational fiber photometry enables Raman spectroscopy in the mouse brain with low impact, owing to the use of tapered fibers.

The quantification of membrane-associated biomolecular interactions is crucial to our understanding of various cellular processes. State-of-the-art single-molecule approaches rely largely on the addition of fluorescent labels, which complicates the quantification of the involved stoichiometries and dynamics because of low temporal resolution and the inherent limitations associated with labeling efficiency, photoblinking and photobleaching. Here, we demonstrate dynamic mass photometry, a method for label-free imaging, tracking and mass measurement of individual membrane-associated proteins diffusing on supported lipid bilayers. Application of this method to the membrane remodeling GTPase, dynamin-1, reveals heterogeneous mixtures of dimer-based oligomers, oligomer-dependent mobilities, membrane affinities and (dis)association of individual complexes. These capabilities, together with assay-based advances for studying integral membrane proteins, will enable the elucidation of biomolecular mechanisms in and on lipid bilayers.Dynamic mass photometry allows label-free tracking and mass measurement of individual membrane-associated proteins diffusing on supported lipid bilayers. The approach can be used to monitor dynamic (dis)assembly of protein complexes.