Cellular geometry and epithelial-mesenchymal plasticity intersect with PIEZO1 in breast cancer cells.

Differences in shape can be a distinguishing feature between different cell types, but the shape of a cell can also be dynamic. Changes in cell shape are critical when cancer cells escape from the primary tumor and undergo major morphological changes that allow them to squeeze between endothelial cells, enter the vasculature, and metastasize to other areas of the body. A shift from rounded to spindly cellular geometry is a consequence of epithelial-mesenchymal plasticity, which is also associated with changes in gene expression, increased invasiveness, and therapeutic resistance. However, the consequences and functional impacts of cell shape changes and the mechanisms through which they occur are still poorly understood. Here, we demonstrate that altering the morphology of a cell produces a remodeling of calcium influx via the ion channel PIEZO1 and identify PIEZO1 as an inducer of features of epithelial-to-mesenchymal plasticity. Combining automated epifluorescence microscopy and a genetically encoded calcium indicator, we demonstrate that activation of the PIEZO1 force channel with the PIEZO1 agonist, YODA 1, induces features of epithelial-to-mesenchymal plasticity in breast cancer cells. These findings suggest that PIEZO1 is a critical point of convergence between shape-induced changes in cellular signaling and epithelial-mesenchymal plasticity in breast cancer cells.

Cortical activity emerges in region-specific patterns during early brain development.

The development of precise neural circuits in the brain requires spontaneous patterns of neural activity prior to functional maturation. In the rodent cerebral cortex, patchwork and wave patterns of activity develop in somatosensory and visual regions, respectively, and are present at birth. However, whether such activity patterns occur in noneutherian mammals, as well as when and how they arise during development, remain open questions relevant for understanding brain formation in health and disease. Since the onset of patterned cortical activity is challenging to study prenatally in eutherians, here we offer an approach in a minimally invasive manner using marsupial dunnarts, whose cortex forms postnatally. We discovered similar patchwork and travelling waves in the dunnart somatosensory and visual cortices at stage 27 (equivalent to newborn mice) and examined earlier stages of development to determine the onset of these patterns and how they first emerge. We observed that these patterns of activity emerge in a region-specific and sequential manner, becoming evident as early as stage 24 in somatosensory and stage 25 in visual cortices (equivalent to embryonic day 16 and 17, respectively, in mice), as cortical layers establish and thalamic axons innervate the cortex. In addition to sculpting synaptic connections of existing circuits, evolutionarily conserved patterns of neural activity could therefore help regulate other early events in cortical development.

Whole-brain imaging of freely-moving zebrafish.

One of the holy grails of neuroscience is to record the activity of every neuron in the brain while an animal moves freely and performs complex behavioral tasks. While important steps forward have been taken recently in large-scale neural recording in rodent models, single neuron resolution across the entire mammalian brain remains elusive. In contrast the larval zebrafish offers great promise in this regard. Zebrafish are a vertebrate model with substantial homology to the mammalian brain, but their transparency allows whole-brain recordings of genetically-encoded fluorescent indicators at single-neuron resolution using optical microscopy techniques. Furthermore zebrafish begin to show a complex repertoire of natural behavior from an early age, including hunting small, fast-moving prey using visual cues. Until recently work to address the neural bases of these behaviors mostly relied on assays where the fish was immobilized under the microscope objective, and stimuli such as prey were presented virtually. However significant progress has recently been made in developing brain imaging techniques for zebrafish which are not immobilized. Here we discuss recent advances, focusing particularly on techniques based on light-field microscopy. We also draw attention to several important outstanding issues which remain to be addressed to increase the ecological validity of the results obtained.

From perception to behavior: The neural circuits underlying prey hunting in larval zebrafish.

A key challenge for neural systems is to extract relevant information from the environment and make appropriate behavioral responses. The larval zebrafish offers an exciting opportunity for studying these sensing processes and sensory-motor transformations. Prey hunting is an instinctual behavior of zebrafish that requires the brain to extract and combine different attributes of the sensory input and form appropriate motor outputs. Due to its small size and transparency the larval zebrafish brain allows optical recording of whole-brain activity to reveal the neural mechanisms involved in prey hunting and capture. In this review we discuss how the larval zebrafish brain processes visual information to identify and locate prey, the neural circuits governing the generation of motor commands in response to prey, how hunting behavior can be modulated by internal states and experience, and some outstanding questions for the field.

Cortical activity emerges in region-specific patterns during early brain development.

The development of precise neural circuits in the brain requires spontaneous patterns of neural activity prior to functional maturation. In the rodent cerebral cortex patchwork and wave patterns of activity develop in somatosensory and visual regions, respectively, and are present at birth. However, whether such activity patterns occur in non-eutherian mammals, as well as when and how they arise during development remain open questions relevant to understand brain formation in health and disease. Since the onset of patterned cortical activity is challenging to study prenatally in eutherians, here we offer a new approach in a minimally invasive manner using marsupial dunnarts, whose cortex forms postnatally. We discovered similar patchwork and travelling waves in the dunnart somatosensory and visual cortices at stage 27 (equivalent to newborn mice), and examined progressively earlier stages of development to determine their onset and how they first emerge. We observed that these patterns of activity emerge in a region-specific and sequential manner, becoming evident as early as stage 24 in somatosensory and stage 25 in visual cortices (equivalent to embryonic day 16 and 17, respectively, in mice), as cortical layers establish and thalamic axons innervate the cortex. In addition to sculpting synaptic connections of existing circuits, evolutionarily conserved patterns of neural activity could therefore help regulate early events in cortical development. Significance Statement: Region-specific patterns of neural activity are present at birth in rodents and are thought to refine synaptic connections during critical periods of cerebral cortex development. Marsupials are born much more immature than rodents, allowing the investigation of how these patterns arise in vivo. We discovered that cortical activity patterns are remarkably similar in marsupial dunnarts and rodents, and that they emerge very early, before cortical neurogenesis is complete. Moreover, they arise from the outset in different patterns specific to somatosensory and visual areas (i.e., patchworks and waves) indicating they may also play evolutionarily conserved roles in cortical regionalization during development.

fmr1 Mutation Alters the Early Development of Sensory Coding and Hunting and Social Behaviors in Larval Zebrafish.

Autism spectrum disorders (ASDs) are developmental in origin; however, little is known about how they affect the early development of behavior and sensory coding. The most common inherited form of autism is Fragile X syndrome (FXS), caused by a mutation in FMR1 Mutation of fmr1 in zebrafish causes anxiety-like behavior, hyperactivity, and hypersensitivity in auditory and visual processing. Here, we show that zebrafish fmr1-/- mutant larvae of either sex also display changes in hunting behavior, tectal coding, and social interaction. During hunting, they were less successful at catching prey and displayed altered behavioral sequences. In the tectum, representations of prey-like stimuli were more diffuse and had higher dimensionality. In a social behavioral assay, they spent more time observing a conspecific but responded more slowly to social cues. However, when given a choice of rearing environment fmr1-/- larvae preferred one with reduced visual stimulation, and rearing them in this environment reduced genotype-specific effects on tectal excitability. Together, these results shed new light on how fmr1-/- changes the early development of neural systems and behavior in a vertebrate.SIGNIFICANCE STATEMENT Autism spectrum disorders (ASDs) are caused by changes in early neural development. Animal models of ASDs offer the opportunity to study these developmental processes in greater detail than in humans. Here, we found that a zebrafish mutant for a gene which in humans causes one type of ASD showed early alterations in hunting behavior, social behavior, and how visual stimuli are represented in the brain. However, we also found that mutant fish preferred reduced visual stimulation, and rearing them in this environment reduced alterations in neural activity patterns. These results suggest interesting new directions for using zebrafish as a model to study the development of brain and behavior in ASDs, and how the impact of ASDs could potentially be reduced.

Inference of Multiplicative Factors Underlying Neural Variability in Calcium Imaging Data.

Understanding brain function requires disentangling the high-dimensional activity of populations of neurons. Calcium imaging is an increasingly popular technique for monitoring such neural activity, but computational tools for interpreting extracted calcium signals are lacking. While there has been a substantial development of factor analysis-type methods for neural spike train analysis, similar methods targeted at calcium imaging data are only beginning to emerge. Here we develop a flexible modeling framework that identifies low-dimensional latent factors in calcium imaging data with distinct additive and multiplicative modulatory effects. Our model includes spike-and-slab sparse priors that regularize additive factor activity and gaussian process priors that constrain multiplicative effects to vary only gradually, allowing for the identification of smooth and interpretable changes in multiplicative gain. These factors are estimated from the data using a variational expectation-maximization algorithm that requires a differentiable reparameterization of both continuous and discrete latent variables. After demonstrating our method on simulated data, we apply it to experimental data from the zebrafish optic tectum, uncovering low-dimensional fluctuations in multiplicative excitability that govern trial-to-trial variation in evoked responses.

Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain.

Neural computation has evolved to optimize the behaviors that enable our survival. Although much previous work in neuroscience has focused on constrained task behaviors, recent advances in computer vision are fueling a trend toward the study of naturalistic behaviors. Automated tracking of fine-scale behaviors is generating rich datasets for animal models including rodents, fruit flies, zebrafish, and worms. However, extracting meaning from these large and complex data often requires sophisticated computational techniques. Here we review the latest methods and modeling approaches providing new insights into the brain from behavior. We focus on unsupervised methods for identifying stereotyped behaviors and for resolving details of the structure and dynamics of behavioral sequences.

Zebrafish Chromosome 14 Gene Differential Expression in the fmr1 h u2787 Model of Fragile X Syndrome.

Zebrafish represent a valuable model for investigating the molecular and cellular basis of Fragile X syndrome (FXS). Reduced expression of the zebrafish FMR1 orthologous gene, fmr1, causes developmental and behavioural phenotypes related to FXS. Zebrafish homozygous for the hu2787 non-sense mutation allele of fmr1 are widely used to model FXS, although FXS-relevant phenotypes seen from morpholino antisense oligonucleotide (morpholino) suppression of fmr1 transcript translation were not observed when hu2787 was first described. The subsequent discovery of transcriptional adaptation (a form of genetic compensation), whereby mutations causing non-sense-mediated decay of transcripts can drive compensatory upregulation of homologous transcripts independent of protein feedback loops, suggested an explanation for the differences reported. We examined the whole-embryo transcriptome effects of homozygosity for fmr1 h u2787 at 2 days post fertilisation. We observed statistically significant changes in expression of a number of gene transcripts, but none from genes showing sequence homology to fmr1. Enrichment testing of differentially expressed genes implied effects on lysosome function and glycosphingolipid biosynthesis. The majority of the differentially expressed genes are located, like fmr1, on Chromosome 14. Quantitative PCR tests did not support that this was artefactual due to changes in relative chromosome abundance. Enrichment testing of the "leading edge" differentially expressed genes from Chromosome 14 revealed that their co-location on this chromosome may be associated with roles in brain development and function. The differential expression of functionally related genes due to mutation of fmr1, and located on the same chromosome as fmr1, is consistent with R.A. Fisher's assertion that the selective advantage of co-segregation of particular combinations of alleles of genes will favour, during evolution, chromosomal rearrangements that place them in linkage disequilibrium on the same chromosome. However, we cannot exclude that the apparent differential expression of genes on Chromosome 14 genes was, (if only in part), caused by differences between the expression of alleles of genes unrelated to the effects of the fmr1 h u2787 mutation and made manifest due to the limited, but non-zero, allelic diversity between the genotypes compared.

Spontaneous and evoked activity patterns diverge over development.

The immature brain is highly spontaneously active. Over development this activity must be integrated with emerging patterns of stimulus-evoked activity, but little is known about how this occurs. Here we investigated this question by recording spontaneous and evoked neural activity in the larval zebrafish tectum from 4 to 15 days post-fertilisation. Correlations within spontaneous and evoked activity epochs were comparable over development, and their neural assemblies refined in similar ways. However, both the similarity between evoked and spontaneous assemblies, and also the geometric distance between spontaneous and evoked patterns, decreased over development. At all stages of development, evoked activity was of higher dimension than spontaneous activity. Thus, spontaneous and evoked activity do not converge over development in this system, and these results do not support the hypothesis that spontaneous activity evolves to form a Bayesian prior for evoked activity.

Limitations to Estimating Mutual Information in Large Neural Populations.

Information theory provides a powerful framework to analyse the representation of sensory stimuli in neural population activity. However, estimating the quantities involved such as entropy and mutual information from finite samples is notoriously hard and any direct estimate is known to be heavily biased. This is especially true when considering large neural populations. We study a simple model of sensory processing and show through a combinatorial argument that, with high probability, for large neural populations any finite number of samples of neural activity in response to a set of stimuli is mutually distinct. As a consequence, the mutual information when estimated directly from empirical histograms will be equal to the stimulus entropy. Importantly, this is the case irrespective of the precise relation between stimulus and neural activity and corresponds to a maximal bias. This argument is general and applies to any application of information theory, where the state space is large and one relies on empirical histograms. Overall, this work highlights the need for alternative approaches for an information theoretic analysis when dealing with large neural populations.

Modular transient nanoclustering of activated ß2-adrenergic receptors revealed by single-molecule tracking of conformation-specific nanobodies.

None of the current superresolution microscopy techniques can reliably image the changes in endogenous protein nanoclustering dynamics associated with specific conformations in live cells. Single-domain nanobodies have been invaluable tools to isolate defined conformational states of proteins, and we reasoned that expressing these nanobodies coupled to single-molecule imaging-amenable tags could allow superresolution analysis of endogenous proteins in discrete conformational states. Here, we used anti-GFP nanobodies tagged with photoconvertible mEos expressed as intrabodies, as a proof-of-concept to perform single-particle tracking on a range of GFP proteins expressed in live cells, neurons, and small organisms. We next expressed highly specialized nanobodies that target conformation-specific endogenous ß2-adrenoreceptor (ß2-AR) in neurosecretory cells, unveiling real-time mobility behaviors of activated and inactivated endogenous conformers during agonist treatment in living cells. We showed that activated ß2-AR (Nb80) is highly immobile and organized in nanoclusters. The Gαs-GPCR complex detected with Nb37 displayed higher mobility with surprisingly similar nanoclustering dynamics to that of Nb80. Activated conformers are highly sensitive to dynamin inhibition, suggesting selective targeting for endocytosis. Inactivated ß2-AR (Nb60) molecules are also largely immobile but relatively less sensitive to endocytic blockade. Expression of single-domain nanobodies therefore provides a unique opportunity to capture highly transient changes in the dynamic nanoscale organization of endogenous proteins.

Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data.

The pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. By not averaging away potentially important information encoded in spontaneous activity, this broadly applicable model brings new insight into population-level neural activity within single trials.

Altered brain-wide auditory networks in a zebrafish model of fragile X syndrome.

BACKGROUND: Loss or disrupted expression of the FMR1 gene causes fragile X syndrome (FXS), the most common monogenetic form of autism in humans. Although disruptions in sensory processing are core traits of FXS and autism, the neural underpinnings of these phenotypes are poorly understood. Using calcium imaging to record from the entire brain at cellular resolution, we investigated neuronal responses to visual and auditory stimuli in larval zebrafish, using fmr1 mutants to model FXS. The purpose of this study was to model the alterations of sensory networks, brain-wide and at cellular resolution, that underlie the sensory aspects of FXS and autism. RESULTS: Combining functional analyses with the neurons' anatomical positions, we found that fmr1-/- animals have normal responses to visual motion. However, there were several alterations in the auditory processing of fmr1-/- animals. Auditory responses were more plentiful in hindbrain structures and in the thalamus. The thalamus, torus semicircularis, and tegmentum had clusters of neurons that responded more strongly to auditory stimuli in fmr1-/- animals. Functional connectivity networks showed more inter-regional connectivity at lower sound intensities (a - 3 to - 6 dB shift) in fmr1-/- larvae compared to wild type. Finally, the decoding capacities of specific components of the ascending auditory pathway were altered: the octavolateralis nucleus within the hindbrain had significantly stronger decoding of auditory amplitude while the telencephalon had weaker decoding in fmr1-/- mutants. CONCLUSIONS: We demonstrated that fmr1-/- larvae are hypersensitive to sound, with a 3-6 dB shift in sensitivity, and identified four sub-cortical brain regions with more plentiful responses and/or greater response strengths to auditory stimuli. We also constructed an experimentally supported model of how auditory information may be processed brain-wide in fmr1-/- larvae. Our model suggests that the early ascending auditory pathway transmits more auditory information, with less filtering and modulation, in this model of FXS.

Behavioral Signatures of a Developing Neural Code.

During early life, neural codes must develop to appropriately transform sensory inputs into behavioral outputs. Here, we demonstrate a link between the maturity of neural coding in the visual brain and developmental changes in visually guided behavior. In zebrafish larvae, we show that visually driven hunting behavior improves from 4 to 15 days post-fertilization, becoming faster and more accurate. During the same period, population activity in parts of the optic tectum refines, improving decoding and information transmission for particular spatial positions. Remarkably, individual differences in decoding can predict each fish's hunting success. Together, these results help reveal how the neural codes required for a natural behavior emerge during development.

Correction to: Detecting neural assemblies in calcium imaging data.

Upon publication of the original article, [1], the authors noticed that the first authors' affiliation contained an error.

Probabilistic Encoding Models for Multivariate Neural Data.

A key problem in systems neuroscience is to characterize how populations of neurons encode information in their patterns of activity. An understanding of the encoding process is essential both for gaining insight into the origins of perception and for the development of brain-computer interfaces. However, this characterization is complicated by the highly variable nature of neural responses, and thus usually requires probabilistic methods for analysis. Drawing on techniques from statistical modeling and machine learning, we review recent methods for extracting important variables that quantitatively describe how sensory information is encoded in neural activity. In particular, we discuss methods for estimating receptive fields, modeling neural population dynamics, and inferring low dimensional latent structure from a population of neurons, in the context of both electrophysiology and calcium imaging data.

Detecting neural assemblies in calcium imaging data.

BACKGROUND: Activity in populations of neurons often takes the form of assemblies, where specific groups of neurons tend to activate at the same time. However, in calcium imaging data, reliably identifying these assemblies is a challenging problem, and the relative performance of different assembly-detection algorithms is unknown. RESULTS: To test the performance of several recently proposed assembly-detection algorithms, we first generated large surrogate datasets of calcium imaging data with predefined assembly structures and characterised the ability of the algorithms to recover known assemblies. The algorithms we tested are based on independent component analysis (ICA), principal component analysis (Promax), similarity analysis (CORE), singular value decomposition (SVD), graph theory (SGC), and frequent item set mining (FIM-X). When applied to the simulated data and tested against parameters such as array size, number of assemblies, assembly size and overlap, and signal strength, the SGC and ICA algorithms and a modified form of the Promax algorithm performed well, while PCA-Promax and FIM-X did less well, for instance, showing a strong dependence on the size of the neural array. Notably, we identified additional analyses that can improve their importance. Next, we applied the same algorithms to a dataset of activity in the zebrafish optic tectum evoked by simple visual stimuli, and found that the SGC algorithm recovered assemblies closest to the averaged responses. CONCLUSIONS: Our findings suggest that the neural assemblies recovered from calcium imaging data can vary considerably with the choice of algorithm, but that some algorithms reliably perform better than others. This suggests that previous results using these algorithms may need to be reevaluated in this light.