The central nervous system can generate various behaviors, including motor responses, which we can observe through video recordings. Recent advances in gene manipulation, automated behavioral acquisition at scale, and machine learning enable us to causally link behaviors to their underlying neural mechanisms. Moreover, in some animals, such as the Drosophila melanogaster larva, this mapping is possible at the unprecedented scale of single neurons, allowing us to identify the neural microcircuits generating particular behaviors. These high-throughput screening efforts, linking the activation or suppression of specific neurons to behavioral patterns in millions of animals, provide a rich dataset to explore the diversity of nervous system responses to the same stimuli. However, important challenges remain in identifying subtle behaviors, including immediate and delayed responses to neural activation or suppression, and understanding these behaviors on a large scale. We here introduce several statistically robust methods for analyzing behavioral data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioral spaces aimed at detecting subtle deviations in behavior. 3) A generative model for larval behavioral sequences, providing a benchmark for identifying higher-order behavioral changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioral screen focused on responses to an air puff, analyzing data from 280 716 larvae across 569 genetic lines.

Preprint: https://www.biorxiv.org/content/10.1101/2024.05.03.591825v1

The central nervous system can generate various behaviours, including motor responses, which we can observe through video recordings. Recent advancements in genetics, automated behavioural acquisition at scale, and machine learning enable us to link behaviours to their underlying neural mechanisms causally. Moreover, in some animals, such as the Drosophila larva, this mapping is possible at unprecedented scales of millions of animals and single neurons, allowing us to identify the neural circuits generating particular behaviours.These high-throughput screening efforts are invaluable, linking the activation or suppression of specific neurons to behavioural patterns in millions of animals. This provides a rich dataset to explore how diverse nervous system responses can be to the same stimuli. However, challenges remain in identifying subtle behaviours from these large datasets, including immediate and delayed responses to neural activation or suppression, and understanding these behaviours on a large scale. We introduce several statistically robust methods for analyzing behavioural data in response to these challenges: 1) A generative physical model that regularizes the inference of larval shapes across the entire dataset. 2) An unsupervised kernel-based method for statistical testing in learned behavioural spaces aimed at detecting subtle deviations in behaviour. 3) A generative model for larval behavioural sequences, providing a benchmark for identifying complex behavioural changes. 4) A comprehensive analysis technique using suffix trees to categorize genetic lines into clusters based on common action sequences. We showcase these methodologies through a behavioural screen focused on responses to an air puff, analyzing data from 280,716 larvae across 568 genetic lines.Author Summary There is a significant gap in understanding between the architecture of neural circuits and the mechanisms of action selection and behaviour generation.Drosophila larvae have emerged as an ideal platform for simultaneously probing behaviour and the underlying neuronal computation [1]. Modern genetic tools allow efficient activation or silencing of individual and small groups of neurons. Combining these techniques with standardized stimuli over thousands of individuals makes it possible to relate neurons to behaviour causally. However, extracting these relationships from massive and noisy recordings requires the development of new statistically robust approaches. We introduce a suite of statistical methods that utilize individual behavioural data and the overarching structure of the behavioural screen to deduce subtle behavioural changes from raw data. Given our study’s extensive number of larvae, addressing and preempting potential challenges in body shape recognition is critical for enhancing behaviour detection. To this end, we have adopted a physics-informed inference model. Our first group of techniques enables robust statistical analysis within a learned continuous behaviour latent space, facilitating the detection of subtle behavioural shifts relative to reference genetic lines. A second array of methods probes for subtle variations in action sequences by comparing them to a bespoke generative model. Together, these strategies have enabled us to construct representations of behavioural patterns specific to a lineage and identify a roster of ”hit” neurons with the potential to influence behaviour subtly.

Understanding how animals coordinate movements to achieve goals is a fundamental pursuit in neuroscience. Here we explore how neurons that reside in posterior lower-order regions of a locomotor system and project to anterior higher-order regions influence steering and navigation. We characterized the anatomy and functional role of a population of ascending interneurons in the ventral nerve cord of Drosophila larvae. Through electron microscopy reconstructions and light microscopy, we determined that the cholinergic 19f cells receive input primarily from premotor interneurons and synapse upon a diverse array of postsynaptic targets within the anterior segments including other 19f cells. Calcium imaging of 19f activity in isolated CNS preparations in relation to motor neurons revealed that 19f neurons are recruited into most larval motor programmes. 19f activity lags behind motor neuron activity and as a population, the cells encode spatio-temporal patterns of locomotor activity in the larval CNS. Optogenetic manipulations of 19f cell activity in isolated CNS preparations revealed that they coordinate the activity of central pattern generators underlying exploratory headsweeps and forward locomotion in a context and location specific manner. In behaving animals, activating 19f cells suppressed exploratory headsweeps and slowed forward locomotion, while inhibition of 19f activity potentiated headsweeps, slowing forward movement. Inhibiting activity in 19f cells ultimately affected the ability of larvae to remain in the vicinity of an odor source during an olfactory navigation task. Overall, our findings provide insights into how ascending interneurons monitor motor activity and shape interactions amongst rhythm generators underlying complex navigational tasks.

Building a sizable, complex brain requires both cellular expansion and diversification. One mechanism to achieve these goals is production of multiple transiently amplifying intermediate neural progenitors (INPs) from a single neural stem cell. Like mammalian neural stem cells, Drosophila type II neuroblasts utilize INPs to produce neurons and glia. Within a given lineage, the consecutively born INPs produce morphologically distinct progeny, presumably due to differential inheritance of temporal factors. To uncover the underlying temporal fating mechanisms, we profiled type II neuroblasts' transcriptome across time. Our results reveal opposing temporal gradients of Imp and Syp RNA-binding proteins (descending and ascending, respectively). Maintaining high Imp throughout serial INP production expands the number of neurons and glia with early temporal fate at the expense of cells with late fate. Conversely, precocious upregulation of Syp reduces the number of cells with early fate. Furthermore, we reveal that the transcription factor Seven-up initiates progression of the Imp/Syp gradients. Interestingly, neuroblasts that maintain initial Imp/Syp levels can still yield progeny with a small range of early fates. We therefore propose that the Seven-up-initiated Imp/Syp gradients create coarse temporal windows within type II neuroblasts to pattern INPs, which subsequently undergo fine-tuned subtemporal patterning.

Adult stem cells are responsible for life-long tissue maintenance. They reside in and interact with specialized tissue microenvironments (niches). Using murine hair follicle as a model, we show that when junctional perturbations in the niche disrupt barrier function, adjacent stem cells dramatically change their transcriptome independent of bacterial invasion and become capable of directly signaling to and recruiting immune cells. Additionally, these stem cells elevate cell cycle transcripts which reduce their quiescence threshold, enabling them to selectively proliferate within this microenvironment of immune distress cues. However, rather than mobilizing to fuel new tissue regeneration, these ectopically proliferative stem cells remain within their niche to contain the breach. Together, our findings expose a potential communication relay system that operates from the niche to the stem cells to the immune system and back. The repurposing of proliferation by these stem cells patch the breached barrier, stoke the immune response and restore niche integrity.

The estrogen receptor (ER), glucocorticoid receptor (GR), and forkhead box protein 1 (FoxA1) are significant factors in breast cancer progression. FoxA1 has been implicated in establishing ER-binding patterns though its unique ability to serve as a pioneer factor. However, the molecular interplay between ER, GR, and FoxA1 requires further investigation. Here we show that ER and GR both have the ability to alter the genomic distribution of the FoxA1 pioneer factor. Single-molecule tracking experiments in live cells reveal a highly dynamic interaction of FoxA1 with chromatin in vivo. Furthermore, the FoxA1 factor is not associated with detectable footprints at its binding sites throughout the genome. These findings support a model wherein interactions between transcription factors and pioneer factors are highly dynamic. Moreover, at a subset of genomic sites, the role of pioneer can be reversed, with the steroid receptors serving to enhance binding of FoxA1.

To date, it has been difficult to reveal physiological Ca(2+) events occurring within the fine astrocytic processes of mature animals. The objective of the study was to explore whether neuronal activity evokes astrocytic Ca(2+) signals at glutamatergic synapses of adult mice. We stimulated the Schaffer collateral/commissural fibers in acute hippocampal slices from adult mice transduced with the genetically encoded Ca(2+) indicator GCaMP5E driven by the glial fibrillary acidic protein promoter. Two-photon imaging revealed global stimulation-evoked astrocytic Ca(2+) signals with distinct latencies, rise rates, and amplitudes in fine processes and somata. Specifically, the Ca(2+) signals in the processes were faster and of higher amplitude than those in the somata. A combination of P2 purinergic and group I/II metabotropic glutamate receptor (mGluR) antagonists reduced the amplitude of the Ca(2+) transients by 30-40% in both astrocytic compartments. Blockage of the mGluRs alone only modestly reduced the magnitude of the stimulation-evoked Ca(2+) signals in processes and failed to affect the somatic Ca(2+) response. Local application of group I or I/II mGluR agonists or adenosine triphosphate (ATP) elicited global astrocytic Ca(2+) signals that mimicked the stimulation-evoked astrocytic Ca(2+) responses. We conclude that stimulation-evoked Ca(2+) signals in astrocytic processes at CA3-CA1 synapses of adult mice (1) differ from those in astrocytic somata and (2) are modulated by glutamate and ATP.

Orientation columns exist in the primary visual cortex (V1) of cat and primates but not mouse. Intriguingly, some recent studies reported the presence of orientation and direction columns in the mouse superficial superior colliculus (sSC), while others reported a lack of columnar organization therein. Using in vivo calcium imaging of sSC in the awake mouse brain, we found that the presence of columns is highly stimulus dependent. Specifically, we observed orientation and direction columns formed by sSC neurons retinotopically mapped to the edge of grating stimuli. For both excitatory and inhibitory neurons in sSC, orientation selectivity can be induced by the edge with their preferred orientation perpendicular to the edge orientation. Furthermore, we found that this edge-induced orientation selectivity is associated with saliency encoding. These findings indicate that the tuning properties of sSC neurons are not fixed by circuit architecture but rather dependent on the spatiotemporal properties of the stimulus.

Sparse coding is thought to improve discrimination of sensory stimuli by reducing overlap between their representations. Two factors, however, can offset sparse coding's advantages. Similar sensory stimuli have significant overlap, and responses vary across trials. To elucidate the effect of these two factors, we analyzed odor responses in the fly and mouse olfactory regions implicated in learning and discrimination --- the Mushroom Body (MB) and the Piriform Cortex (PCx). In both species, we show that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic. Computationally, we show that the range of observed variability arises from probabilistic synapses in inhibitory feedback connections within central circuits rather than sensory noise, as is traditionally assumed. We propose this coding scheme to be advantageous for coarse- and fine-odor discrimination. More reliable cells enable quick discrimination between dissimilar odors. For similar odors, however, these cells overlap, and do not provide distinguishing information. By contrast, more unreliable cells are decorrelated for similar odors, providing distinguishing information, though this requires extended training with more trials. Overall, we have uncovered a stochastic coding scheme that is conserved in vertebrates and invertebrates, and we identify a candidate mechanism, based on variability in a winner-take-all inhibitory circuit, that improves discrimination with training.

Nondestructive chemical processing of porous samples such as fixed biological tissues typically relies on molecular diffusion. Diffusion into a porous structure is a slow process that significantly delays completion of chemical processing. Here, we present a novel electrokinetic method termed stochastic electrotransport for rapid nondestructive processing of porous samples. This method uses a rotational electric field to selectively disperse highly electromobile molecules throughout a porous sample without displacing the low-electromobility molecules that constitute the sample. Using computational models, we show that stochastic electrotransport can rapidly disperse electromobile molecules in a porous medium. We apply this method to completely clear mouse organs within 1–3 days and to stain them with nuclear dyes, proteins, and antibodies within 1 day. Our results demonstrate the potential of stochastic electrotransport to process large and dense tissue samples that were previously infeasible in time when relying on diffusion.