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, 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. 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. 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.

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.

Morphogenesis in the Drosophila retina initiates at the posterior margin of the eye imaginal disc by an unknown mechanism. Upon initiation, a wave of differentiation, its forward edge marked by the morphogenetic furrow (MF), proceeds anteriorly across the disc. Progression of the MF is driven by hedgehog (hh), expressed by differentiating photoreceptor cells. The TGF-beta homolog encoded by decapentaplegic (dpp) is expressed at the disc's posterior margin prior to initiation and in the furrow, under the control of hh, during MF progression. While dpp has been implicated in eye disc growth and morphogenesis, its precise role in retinal differentiation has not been determined. To address the role of dpp in initiation and progression of retinal differentiation we analyzed the consequences of reduced and increased dpp function during eye development. We find that dpp is not only required for normal MF initiation, but is sufficient to induce ectopic initiation of differentiation. Inappropriate initiation is normally inhibited by wingless (wg). Loss of dpp function is accompanied by expansion of wg expression, while increased dpp function leads to loss of wg transcription. In addition, dpp is required to maintain, and sufficient to induce, its own expression along the disc's margins. We postulate that dpp autoregulation and dpp-mediated inhibition of wg expression are required for the coordinated regulation of furrow initiation and progression. Finally, we show that in the later stages of retinal differentiation, reduction of dpp function leads to an arrest in MF progression.

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.

The Drosophila retina is a crystalline array of 800 ommatidia whose organization and assembly suggest polarization of the retinal epithelium along anteroposterior and dorsoventral axes. The retina develops by a stepwise process following the posterior-to-anterior progression of the morphogenetic furrow across the eye disc. Ectopic expression of hedgehog or local removal of patched function generates ectopic furrows that can progress in any direction across the disc leaving in their wake differentiating fields of ectopic ommatidia. We have studied the effect of these ectopic furrows on the polarity of ommatidial assembly and rotation. We find that the anteroposterior asymmetry of ommatidial assembly parallels the progression of ectopic furrows, regardless of their direction. In addition, ommatidia developing behind ectopic furrows rotate coordinately, forming equators in various regions of the disc. Interestingly, the expression of a marker normally restricted to the equator is induced in ectopic ommatidial fields. Ectopic equators are stable as they persist to adulthood, where they can coexist with the normal equator. Our results suggest that ectopic furrows can impart polarity to the disc epithelium, regarding the direction of both assembly and rotation of ommatidia. We propose that these processes are polarized as a consequence of furrow propagation, while more global determinants of dorsoventral and anteroposterior polarity may act less directly by determining the site of furrow initiation.