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 super-resolution imaging for a broader scientific circle, lowering the cost and entry skill requirements to the field. One of its branches, ultrastructure ExM (U-ExM), has become popular among research groups studying Apicomplexan parasites, including the acute stage of Toxoplasma gondii infection. The chronic cyst-forming stage of Toxoplasma, however, resists U-ExM expansion, impeding precise protein localisation. Here, we solve the in vitro cyst’s resistance to denaturation required for successful U-ExM of the encapsulated parasites. 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 interference with the epitopes of the cyst-wall associated proteins. Using StcE-enhanced U-ExM, we clarified the shape and location of the GRA2 protein important for establishing a normal cyst. Expanded cysts revealed GRA2 granules spanning across the cyst wall, with a notable presence observed outside on both sides of the CST1-positive layer.

Importance Toxoplasma gondii is an intracellular parasite capable of establishing long-term chronic infection in nearly all warm-blooded animals. During the chronic stage, parasites encapsulate into cysts in a wide range of tissues but particularly in neurons of the central nervous system and in skeletal muscle. Current anti-Toxoplasma drugs do not eradicate chronic parasites and leave behind a reservoir of infection. As the cyst is critical for both transmission and pathology of the disease, we need to understand more fully the biology of the cyst and its vulnerabilities.

The advent of a new super-resolution approach called ultrastructure expansion microscopy allowed in-depth studies of the acute stage of Toxoplasma infection but not the cyst-forming stage, which resists protocol-specific denaturation. Here, we show that an additional step of enzymatic digestion using mucinase StcE allows full expansion of the Toxoplasma cysts, offering a new avenue for a comprehensive examination of the chronic stage of infection using an accessible super-resolution technique.

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300 µm) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine novel spatially and molecularly defined subregions. EASI-FISH also facilitates iterative re-analysis of scRNA-Seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization.

The excitability of individual dendritic branches is a plastic property of neurons. We found that experience in an enriched environment increased propagation of dendritic Na(+) spikes in a subset of individual dendritic branches in rat hippocampal CA1 pyramidal neurons and that this effect was mainly mediated by localized downregulation of A-type K(+) channel function. Thus, dendritic plasticity might be used to store recent experience in individual branches of the dendritic arbor.

The hippocampus is critical for producing stable representations of familiar spaces. How these representations arise is poorly understood, largely because changes to hippocampal inputs have not been measured during spatial learning. Here, using intracellular recording, we monitored inputs and plasticity-inducing complex spikes (CSs) in CA1 neurons while mice explored novel and familiar virtual environments. Inputs driving place field spiking increased in amplitude - often suddenly - during novel environment exploration. However, these increases were not sustained in familiar environments. Rather, the spatial tuning of inputs became increasingly similar across repeated traversals of the environment with experience - both within fields and throughout the whole environment. In novel environments, CSs were not necessary for place field formation. Our findings support a model in which initial inhomogeneities in inputs are amplified to produce robust place field activity, then plasticity refines this representation into one with less strongly modulated, but more stable, inputs for long-term storage.

Synaptic plasticity in adult neural circuits may involve the strengthening or weakening of existing synapses as well as structural plasticity, including synapse formation and elimination. Indeed, long-term in vivo imaging studies are beginning to reveal the structural dynamics of neocortical neurons in the normal and injured adult brain. Although the overall cell-specific morphology of axons and dendrites, as well as of a subpopulation of small synaptic structures, are remarkably stable, there is increasing evidence that experience-dependent plasticity of specific circuits in the somatosensory and visual cortex involves cell type-specific structural plasticity: some boutons and dendritic spines appear and disappear, accompanied by synapse formation and elimination, respectively. This Review focuses on recent evidence for such structural forms of synaptic plasticity in the mammalian cortex and outlines open questions.

From 1980 to 1992, a series of influential papers reported on the discovery, genetics, and evolution of a periodic cycling of the interval between Drosophila male courtship song pulses. The molecular mechanisms underlying this periodicity were never described. To reinitiate investigation of this phenomenon, we previously performed automated segmentation of songs but failed to detect the proposed rhythm [Arthur BJ, et al. (2013) BMC Biol 11:11; Stern DL (2014) BMC Biol 12:38]. Kyriacou et al. [Kyriacou CP, et al. (2017) Proc Natl Acad Sci USA 114:1970-1975] report that we failed to detect song rhythms because (i) our flies did not sing enough and (ii) our segmenter did not identify many of the song pulses. Kyriacou et al. manually annotated a subset of our recordings and reported that two strains displayed rhythms with genotype-specific periodicity, in agreement with their original reports. We cannot replicate this finding and show that the manually annotated data, the original automatically segmented data, and a new dataset provide no evidence for either the existence of song rhythms or song periodicity differences between genotypes. Furthermore, we have reexamined our methods and analysis and find that our automated segmentation method was not biased to prevent detection of putative song periodicity. We conclude that there is no evidence for the existence of Drosophila courtship song rhythms.

Adult zebra finches require auditory feedback to maintain their songs. It has been proposed that the lateral magnocellular nucleus of the anterior nidopallium (LMAN) mediates song plasticity based on auditory feedback. In this model, neurons in LMAN, tuned to the spectral and temporal properties of the bird’s own song (BOS), are thought to compute the difference between the auditory feedback from the bird’s vocalizations and an internal song template. This error-correction signal is then used to initiate changes in the motor system that make future vocalizations a better match to the song template. This model was tested by recording from single LMAN neurons while manipulating the auditory feedback heard by singing birds. In contrast to the model predictions, LMAN spike patterns are insensitive to manipulations of auditory feedback. These results suggest that BOS tuning in LMAN is not used for error detection and constrain the nature of any error signal from LMAN to the motor system. Finally, LMAN neurons produce spikes locked precisely to the bird’s song, independent of the auditory feedback heard by the bird. This finding suggests that a large portion of the input to this nucleus is from the motor control signals that generate the song rather than from auditory feedback.

We took advantage of the unusual genomic organization of the ciliate Oxytricha trifallax to screen for eukaryotic non-coding RNA (ncRNA) genes. Ciliates have two types of nuclei: a germ line micronucleus that is usually transcriptionally inactive, and a somatic macronucleus that contains a reduced, fragmented and rearranged genome that expresses all genes required for growth and asexual reproduction. In some ciliates including Oxytricha, the macronuclear genome is particularly extreme, consisting of thousands of tiny ’nanochromosomes’, each of which usually contains only a single gene. Because the organism itself identifies and isolates most of its genes on single-gene nanochromosomes, nanochromosome structure could facilitate the discovery of unusual genes or gene classes, such as ncRNA genes. Using a draft Oxytricha genome assembly and a custom-written protein-coding genefinding program, we identified a subset of nanochromosomes that lack any detectable protein-coding gene, thereby strongly enriching for nanochromosomes that carry ncRNA genes. We found only a small proportion of non-coding nanochromosomes, suggesting that Oxytricha has few independent ncRNA genes besides homologs of already known RNAs. Other than new members of known ncRNA classes including C/D and H/ACA snoRNAs, our screen identified one new family of small RNA genes, named the Arisong RNAs, which share some of the features of small nuclear RNAs.

After finding food, a foraging animal must decide whether to continue feeding, or to explore the environment for potentially better options. One strategy to negotiate this tradeoff is to perform local searches around the food but repeatedly return to feed. We studied this behavior in flies and used genetic tools to uncover the underlying mechanisms. Over time, flies gradually expand their search, shifting from primarily exploiting food sources to exploring the environment, a change that is likely driven by increases in satiety. We found that flies’ search patterns preserve these dynamics even as the overall scale of the search is modulated by starvation-induced changes in metabolic state. In contrast, search induced by optogenetic activation of sugar sensing neurons does not show these dynamics. We asked what navigational strategies underlie local search. Using a generative model, we found that a change in locomotor pattern after food consumption could account for repeated returns to the food, but failed to capture relatively direct, long return trajectories. Alternative strategies, such as path integration or sensory taxis could allow flies to return from larger distances. We tested this by individually silencing the fly’s head direction system, olfaction and hygrosensation, and found that the only substantial effect was from perturbing hygrosensation, which reduced the number of long exploratory trips. Our study illustrates that local search is composed of multiple behavioral features that evolve over time based on both internal and external factors, providing a path towards uncovering the underlying neural mechanisms.

Many problems in voice recognition and audio processing involve feature extraction from raw waveforms. The goal of feature extraction is to reduce the dimensionality of the audio signal while preserving the informative signatures that, for example, distinguish different phonemes in speech or identify particular instruments in music. If the acoustic variability of a data set is described by a small number of continuous features, then we can imagine the data as lying on a low dimensional manifold in the high dimensional space of all possible waveforms. Locally linear embedding (LLE) is an unsupervised learning algorithm for feature extraction in this setting. In this paper, we present results from the exploratory analysis and visualization of speech and music by LLE.