Cortical neurons exhibit temporally irregular spiking patterns and heterogeneous firing rates. These features arise in model circuits operating in a 'fluctuation-driven regime', in which fluctuations in membrane potentials emerge from the network dynamics. However, it is still debated whether the cortex operates in such a regime. We evaluated the fluctuation-driven hypothesis by analyzing spiking and sub-threshold membrane potentials of neurons in the frontal cortex of mice performing a decision-making task. We showed that while standard fluctuation-driven models successfully account for spiking statistics, they fall short in capturing the heterogeneity in sub-threshold activity. This limitation is an inevitable outcome of bombarding single-compartment neurons with a large number of pre-synaptic inputs, thereby clamping the voltage of all neurons to more or less the same average voltage. To address this, we effectively incorporated dendritic morphology into the standard models. Inclusion of dendritic morphology in the neuronal models increased neuronal selectivity and reduced error trials, suggesting a functional role for dendrites during decision-making. Our work suggests that, during decision-making, cortical neurons in high-order cortical areas operate in a fluctuation-driven regime.

A major frontier in single cell biology is decoding how transcriptional states result in cellular-level architectural changes, ultimately driving function. A remarkable example of this cellular remodelling program is the differentiation of airway stem cells into the human respiratory multiciliated epithelium, a tissue barrier protecting against bacteria, viruses and particulate matter. Here, we present the first isotropic three-dimensional map of the airway epithelium at the nanometre scale unveiling the coordinated changes in cellular organisation, organelle topology and contacts, occurring during multiciliogenesis. This analysis led us to discover a cellular mechanism of communication whereby motile cilia relay mechanical information to mitochondria through striated cytoskeletal fibres, the rootlets, to promote effective ciliary motility and ATP generation. Altogether, this study integrates nanometre-scale structural, functional and dynamic insights to elucidate fundamental mechanisms responsible for airway defence.

Incentives tend to drive improvements in performance. But when incentives get too high, we can "choke under pressure" and underperform right when it matters most. What neural processes might lead to choking under pressure? We studied rhesus monkeys performing a challenging reaching task in which they underperformed when an unusually large "jackpot" reward was at stake, and we sought a neural mechanism that might result in that underperformance. We found that increases in reward drive neural activity during movement preparation into, and then past, a zone of optimal performance. We conclude that neural signals of reward and motor preparation interact in the motor cortex (MC) in a manner that can explain why we choke under pressure.

Signaling pathways induce stereotyped transcriptional changes as stem cells progress into mature cell types during embryogenesis. Signaling perturbations are necessary to discover which genes are responsive or insensitive to pathway activity. However, gene regulation is additionally dependent on cell state-specific factors like chromatin modifications or transcription factor binding. Thus, transcriptional profiles need to be assayed in single cells to identify potentially multiple, distinct perturbation responses among heterogeneous cell states in an embryo. In perturbation studies, comparing heterogeneous transcriptional states among experimental conditions often requires samples to be collected over multiple independent experiments. Datasets produced in such complex experimental designs can be confounded by batch effects. We present Design-Aware Integration of Single Cell ExpEriments (DAISEE), a new algorithm that models perturbation responses in single-cell datasets with a complex experimental design. We demonstrate that DAISEE improves upon a previously available integrative non-negative matrix factorization framework, more efficiently separating perturbation responses from confounding variation. We use DAISEE to integrate newly collected single-cell RNA-sequencing datasets from 5-hour old zebrafish embryos expressing optimized photoswitchable MEK (psMEK), which globally activates the extracellular signal-regulated kinase (ERK), a signaling molecule involved in many cell specification events. psMEK drives some cells that are normally not exposed to ERK signals towards other wild type states and induces novel states expressing a mixture of transcriptional programs, including precociously activated endothelial genes. ERK signaling is therefore capable of introducing profoundly new gene expression states in developing embryos.Significance Statement Signaling perturbations produce heterogeneous transcriptional responses that must be measured at the single-cell level. Data integration techniques allow us to model these responses which, however, can be confounded by batch effects. We present a computational tool (DAISEE) for extracting the common and perturbation-specific features of single-cell datasets representing multiple experimental conditions while achieving efficient batch effect correction. DAISEE outperforms its predecessor and will enable accurate analysis of a broad range of single-cell datasets. DAISEE applied to new single-cell RNA sequencing data from zebrafish embryos shows that gain-of-function signaling perturbations can induce novel states. Our analysis suggests that a wild type endothelial cell-specification program can be activated in abnormal developmental contexts when the extracellular signal-regulated kinase (ERK) pathway is deregulated.

How the body interacts with the brain to perform vital life functions, such as feeding, is a fundamental issue in physiology and neuroscience. Here, we use a whole-animal scanning transmission electron microscopy volume of Drosophila to map the neuronal circuits that connect the entire enteric nervous system to the brain via the insect vagus nerve at synaptic resolution. We identify a gut-brain feedback loop in which Piezo-expressing mechanosensory neurons in the esophagus convey food passage information to a cluster of six serotonergic neurons in the brain. Together with information on food value, these central serotonergic neurons enhance the activity of serotonin receptor 7-expressing motor neurons that drive swallowing. This elemental circuit architecture includes an axo-axonic synaptic connection from the glutamatergic motor neurons innervating the esophageal muscles onto the mechanosensory neurons that signal to the serotonergic neurons. Our analysis elucidates a neuromodulatory sensory-motor system in which ongoing motor activity is strengthened through serotonin upon completion of a biologically meaningful action, and it may represent an ancient form of motor learning.

Motor systems implement diverse motor programs to pattern behavioral sequences, yet how different motor actions are controlled on a moment-by-moment basis remains unclear. Here, we investigated the neural circuit mechanisms underlying the control of distinct courtship songs in Drosophila. Courting males rapidly alternate between two types of song: pulse and sine. By recording calcium signals in the ventral nerve cord in singing flies, we found that one neural population is active during both songs, whereas an expanded neural population, which includes neurons from the first population, is active during pulse song. Brain recordings showed that this nested activation pattern is present in two descending pathways required for singing. Connectomic analysis reveals that these two descending pathways provide structured input to ventral nerve cord neurons in a manner consistent with their activation patterns. These results suggest that nested premotor circuit activity, directed by distinct descending signals, enables rapid switching between motor actions.

To survive, animals must be able quickly infer the state of their surroundings. For example, to successfully escape an approaching predator, prey must quickly estimate the direction of approach from incoming sensory stimuli. Such rapid inferences are particularly challenging because the animal has only a brief window of time to gather sensory stimuli, and yet the accuracy of inference is critical for survival. Due to evolutionary pressures, nervous systems have likely evolved effective computational strategies that enable accurate inferences under strong time limitations. Traditionally, the relationship between the speed and accuracy of inference has been described by the "speed-accuracy tradeoff" (SAT), which quantifies how the average performance of an ideal observer improves as the observer has more time to collect incoming stimuli. While this trial-averaged description can reasonably account for individual inferences made over long timescales, it does not capture individual inferences on short timescales, when trial-to-trial variability gives rise to diverse patterns of error dynamics. We show that an ideal observer can exploit this single-trial structure by adaptively tracking the dynamics of its belief about the state of the environment, which enables it make more rapid inferences and more reliably track its own error but also causes it to violate the SAT. We show that these features can be used to improve overall performance during rapid escape. The resulting behavior qualitatively reproduces features of escape behavior in the fruit fly Drosophila melanogaster, whose escapes have presumably been highly optimized by natural selection.

Expansion microscopy (ExM) is an innovative approach to achieve super-resolution images without using super-resolution microscopes, based on the physical expansion of the sample. The advent of ExM has unlocked the detail of super-resolution images for a broader scientific circle, lowering the cost and entry skill requirements for the field. One of its branches, ultrastructure expansion microscopy (U-ExM), has become popular among research groups studying apicomplexan parasites, including the acute stage of Toxoplasma gondii infection. Here, we show that the chronic cyst-forming stage of Toxoplasma, however, resists U-ExM expansion, impeding precise protein localization. We then solve the in vitro cyst's resistance to denaturation required for successful U-ExM. As the cyst's main structural protein CST1 contains a mucin domain, we added an enzymatic digestion step using the pan-mucinase StcE prior to the expansion protocol. This allowed full expansion of the cysts in fibroblasts and primary neuronal cell culture without disrupting immunofluorescence analysis of parasite proteins. Using StcE-enhanced U-ExM, we clarified the localization of the GRA2 protein, which is important for establishing a normal cyst, observing GRA2 granules spanning across the CST1 cyst wall. The StcE-U-ExM protocol allows accurate pinpointing of proteins in the bradyzoite cyst, which will greatly facilitate investigation of the underlying biology of cyst formation and its vulnerabilities.

Cognition is produced by the continuous interactions between many regions across the brain, but has typically been studied one brain region at a time. How signals in different regions coordinate to achieve a single coherent action remains unclear. Here, we address this question by characterizing the simultaneous interactions between up to 20 brain regions across the brain (10 targeted regions per hemisphere), of rats performing the “Poisson Clicks” task, a decision-making task that demands the gradual accumulation of momentary evidence. Using 8 Neuropixels probes in each animal, we recorded simultaneously in prefrontal cortex, striatum, motor cortex, hippocampus, amygdala, and thalamus. To assess decision-related interactions between regions, we quantified correlations of each region’s “decision variable”: moment-to-moment co-fluctuations along the axis in neural state space that best predicts the upcoming choice. This revealed a network of strongly correlated brain regions that include the dorsomedial frontal cortex (dmFC), anterior dorsal striatum (ADS), and primary motor cortex (M1), whose decision variables also led the rest of the brain. If coordinated activity within this subnetwork reflects an ongoing evidence accumulation process, these correlations should cease at the time of decision commitment. We therefore compared correlations before versus after “nTc”, a recently reported estimator for the time of internal decision commitment. We found that correlations in the decision variables between different brain regions decayed to near-zero after nTc. Additionally, we found that choice-predictive activity steadily increased over time before nTc, but abruptly stopped growing at nTc, consistent with an evidence accumulation process that has stopped evolving at that time. Assessing nTc from the activity of individual regions revealed that nTc could be reliably detected earlier in M1 than other regions. These results show that evidence accumulation involves coordination within a network of frontal cortical and striatal regions, and suggests that termination of this process may initiate in M1.

Zebrafish larvae are used to model the pathogenesis of multiple bacteria. This transparent model offers the unique advantage of allowing quantification of fluorescent bacterial burdens (fluorescent pixel counts: FPC) in vivo by facile microscopical methods, replacing enumeration of bacteria using time-intensive plating of lysates on bacteriological media. Accurate FPC measurements require laborious manual image processing to mark the outside borders of the animals so as to delineate the bacteria inside the animals from those in the culture medium that they are in. Here, we have developed an automated ImageJ/Fiji-based macro that accurately detect the outside borders of Mycobacterium marinum-infected larvae.