Mammalian development is characterized with transitions from homogeneous populations of precursor to heterogeneous population of specified cells. We review here the main dynamical mechanisms through which such transitions are conceptualized, and discuss that the differentiation timing, robust cell-type proportions and recovery upon perturbation are emergent property of proliferating and communicating cell populations. We argue that studying developmental systems using transitions in collective system states is necessary to describe observed experimental features, and propose additionally the basis of a novel analytical method to deduce the relationship between single-cell dynamics and the collective, symmetry-broken states in cellular populations.

We consider the problem of estimating discrete selfexciting point process models from limited binary observations, where the history of the process serves as the covariate. We analyze the performance of two classes of estimators, namely the `1-regularized maximum likelihood and greedy estimators, for a canonical self-exciting point process and characterize the sampling tradeoffs required for stable recovery in the non-asymptotic regime. Our results extend those of compressed sensing for linear and generalized linear models with i.i.d. covariates to point processes with highly inter-dependent covariates. We further provide simulation studies as well as application to real spiking data from mouse’s lateral geniculate nucleus and ferret’s retinal ganglion cells which agree with our theoretical predictions.

Single-particle tracking (SPT) has become an important method to bridge biochemistry and cell biology since it allows direct observation of protein binding and diffusion dynamics in live cells. However, accurately inferring information from SPT studies is challenging due to biases in both data analysis and experimental design. To address analysis bias, we introduce 'Spot-On', an intuitive web-interface. Spot-On implements a kinetic modeling framework that accounts for known biases, including molecules moving out-of-focus, and robustly infers diffusion constants and subpopulations from pooled single-molecule trajectories. To minimize inherent experimental biases, we implement and validate stroboscopic photo-activation SPT (spaSPT), which minimizes motion-blur bias and tracking errors. We validate Spot-On using experimentally realistic simulations and show that Spot-On outperforms other methods. We then apply Spot-On to spaSPT data from live mammalian cells spanning a wide range of nuclear dynamics and demonstrate that Spot-On consistently and robustly infers subpopulation fractions and diffusion constants.

Neural activity maintains representations that bridge past and future events, often over many seconds. Network models can produce persistent and ramping activity, but the positive feedback that is critical for these slow dynamics can cause sensitivity to perturbations. Here we use electrophysiology and optogenetic perturbations in the mouse premotor cortex to probe the robustness of persistent neural representations during motor planning. We show that preparatory activity is remarkably robust to large-scale unilateral silencing: detailed neural dynamics that drive specific future movements were quickly and selectively restored by the network. Selectivity did not recover after bilateral silencing of the premotor cortex. Perturbations to one hemisphere are thus corrected by information from the other hemisphere. Corpus callosum bisections demonstrated that premotor cortex hemispheres can maintain preparatory activity independently. Redundancy across selectively coupled modules, as we observed in the premotor cortex, is a hallmark of robust control systems. Network models incorporating these principles show robustness that is consistent with data.

Multi-modal image registration is a challenging task that is vital to fuse complementary signals for subsequent analyses. Despite much research into cost functions addressing this challenge, there exist cases in which these are ineffective. In this work, we show that (1) this is true for the registration of in-vivo Drosophila brain volumes visualizing genetically encoded calcium indicators to an nc82 atlas and (2) that machine learning based contrast synthesis can yield improvements. More specifically, the number of subjects for which the registration outright failed was greatly reduced (from 40% to 15%) by using a synthesized image.

Calcium imaging is a powerful method to record the activity of neural populations in many species, but inferring spike times from calcium signals is a challenging problem. We compared multiple approaches using multiple datasets with ground truth electrophysiology, and found that simple non-negative deconvolution (NND) outperformed all other algorithms on out-of-sample test data. We introduce a novel benchmark applicable to recordings without electrophysiological ground truth, based on the correlation of responses to two stimulus repeats, and used this to show that unconstrained NND also outperformed the other algorithms when run on "zoomed out" datasets of ∼10,000 cell recordings from the visual cortex of mice of either sex. Finally, we show that NND-based methods match the performance of a supervised method based on convolutional neural networks, while avoiding some of the biases of such methods, and at much faster running times. We therefore recommend that spikes be inferred from calcium traces using simple NND, due to its simplicity, efficiency and accuracy.The experimental method that currently allows for recordings of the largest numbers of cells simultaneously is two-photon calcium imaging. However, use of this powerful method requires that neuronal firing times be inferred correctly from the large resulting datasets. Previous studies have claimed that complex supervised learning algorithms outperform simple deconvolution methods at this task. Unfortunately, these studies suffered from several problems and biases. When we repeated the analysis, using the same data and correcting these problems, we found that simpler spike inference methods perform better. Even more importantly, we found that supervised learning methods can introduce artifactual structure into spike trains, that can in turn lead to erroneous scientific conclusions. Of the algorithms we evaluated, we found that an extremely simple method performed best in all circumstances tested, was much faster to run, and was insensitive to parameter choices, making incorrect scientific conclusions much less likely.

Staphylococcus aureus responds to changing extracellular environments in part by adjusting its proteome through alterations of transcriptional priorities and selective degradation of the preexisting pool of proteins. In Bacillus subtilis, the proteolytic adaptor protein MecA has been shown to play a role in assisting with the proteolytic degradation of proteins involved in competence and the oxidative stress response. However, the targets of TrfA, the MecA homolog in S. aureus, have not been well characterized. In this work, we investigated how TrfA assists chaperones and proteases to regulate the proteolysis of several classes of proteins in S. aureus. By fusing the last 3 amino acids of the SsrA degradation tag to Venus, a rapidly folding yellow fluorescent protein, we obtained both fluorescence-based and Western blot assay-based evidence that TrfA and ClpCP are the adaptor and protease, respectively, responsible for the degradation of the SsrA-tagged protein in S. aureus. Notably, the impact of TrfA on degradation was most prominent during late log phase and early stationary phase, due in part to a combination of transcriptional regulation and proteolytic degradation of TrfA by ClpCP. We also characterized the temporal transcriptional regulation governing TrfA activity, wherein Spx, a redox-sensitive transcriptional regulator degraded by ClpXP, activates trfA transcription while repressing its own promoter. Finally, the scope of TrfA-mediated proteolysis was expanded by identifying TrfA as the adaptor that works with ClpCP to degrade antitoxins in S. aureus. Together, these results indicate that the adaptor TrfA adds temporal nuance to protein degradation by ClpCP in S. aureus.

The secondary neurons generated in the thoracic central nervous system of Drosophila arise from a hemisegmental set of 25 neuronal stem cells, the neuroblasts (NBs). Each NB undergoes repeated asymmetric divisions to produce a series of smaller ganglion mother cells (GMCs), which typically divide once to form two daughter neurons. We find that the two daughters of the GMC consistently have distinct fates. Using both loss-of-function and gain-of-function approaches, we examined the role of Notch signaling in establishing neuronal fates within all of the thoracic secondary lineages. In all cases, the ’A’ (Notch(ON)) sibling assumes one fate and the ’B’ (Notch(OFF)) sibling assumes another, and this relationship holds throughout the neurogenic period, resulting in two major neuronal classes: the A and B hemilineages. Apparent monotypic lineages typically result from the death of one sibling throughout the lineage, resulting in a single, surviving hemilineage. Projection neurons are predominantly from the B hemilineages, whereas local interneurons are typically from A hemilineages. Although sibling fate is dependent on Notch signaling, it is not necessarily dependent on numb, a gene classically involved in biasing Notch activation. When Numb was removed at the start of larval neurogenesis, both A and B hemilineages were still generated, but by the start of the third larval instar, the removal of Numb resulted in all neurons assuming the A fate. The need for Numb to direct Notch signaling correlated with a decrease in NB cell cycle time and may be a means for coping with multiple sibling pairs simultaneously undergoing fate decisions.

Chemotaxis is a powerful paradigm to investigate how nervous systems represent and integrate changes in sensory signals to direct navigational decisions. In the Drosophila melanogaster larva, chemotaxis mainly consists of an alternation of distinct behavioral modes: runs and directed turns. During locomotion, turns are triggered by the integration of temporal changes in the intensity of the stimulus. Upon completion of a turning maneuver, the direction of motion is typically realigned toward the odor gradient. While the anatomy of the peripheral olfactory circuits and the locomotor system of the larva are reasonably well documented, the neural circuits connecting the sensory neurons to the motor neurons remain unknown. We combined a loss-of-function behavioral screen with optogenetics-based clonal gain-of-function manipulations to identify neurons that are necessary and sufficient for the initiation of reorientation maneuvers in odor gradients. Our results indicate that a small subset of neurons residing in the subesophageal zone controls the rate of transition from runs to turns-a premotor function compatible with previous observations made in other invertebrates. After having shown that this function pertains to the processing of inputs from different sensory modalities (olfaction, vision, thermosensation), we conclude that the subesophageal zone operates as a general premotor center that regulates the selection of different behavioral programs based on the integration of sensory stimuli. The present analysis paves the way for a systematic investigation of the neural computations underlying action selection in a miniature brain amenable to genetic manipulations.

Fluorescent in-situ hybridization (FISH)-based methods are powerful tools to study molecular processes with subcellular resolution, relying on accurate identification and localization of diffraction-limited spots in microscopy images. We developed the Radial Symmetry-FISH (RS-FISH) software that accurately, robustly, and quickly detects single-molecule spots in two and three dimensions, making it applicable to several key assays, including single-molecule FISH (smFISH), spatial transcriptomics, and spatial genomics. RS-FISH allows interactive parameter tuning and scales to large sets of images as well as tera-byte sized image volumes such as entire brain scans using straight-forward distributed processing on workstations, clusters, and in the cloud.