Recordings of the physiological history of cells provide insights into biological processes, yet obtaining such recordings is a challenge. To address this, we introduce a method to record transient cellular events for later analysis. We designed proteins that become labeled in the presence of both a specific cellular activity and a fluorescent substrate. The recording period is set by the presence of the substrate, whereas the cellular activity controls the degree of the labeling. The use of distinguishable substrates enabled the recording of successive periods of activity. We recorded protein-protein interactions, G protein-coupled receptor activation, and increases in intracellular calcium. Recordings of elevated calcium levels allowed selections of cells from heterogeneous populations for transcriptomic analysis and tracking of neuronal activities in flies and zebrafish.

Insects have evolved sophisticated reflexes to right themselves in mid-air. Their recovery mechanisms involve complex interactions among the physical senses, muscles, body, and wings, and they must obey the laws of flight. We sought to understand the key mechanisms involved in dragonfly righting reflexes and to develop physics-based models for understanding the control strategies of flight maneuvers. Using kinematic analyses, physical modeling, and three-dimensional flight simulations, we found that a dragonfly uses left-right wing pitch asymmetry to roll its body 180 degrees to recover from falling upside down in ~200 milliseconds. Experiments of dragonflies with blocked vision further revealed that this rolling maneuver is initiated by their ocelli and compound eyes. These results suggest a pathway from the dragonfly's visual system to the muscles regulating wing pitch that underly the recovery. The methods developed here offer quantitative tools for inferring insects' internal actions from their acrobatics, and are applicable to a broad class of natural and robotic flying systems.

Maintaining proper tension is critical for the organization and function of the plasma membrane. To study the mechanisms by which yeast restores normal plasma membrane tension, we used a microfluidics device to expose yeast to hyperosmotic conditions, which reduced cell volume and caused a ∼20% drop in cell surface area. The resulting low tension plasma membrane exhibited large clusters of negatively-charged glycerophospholipids together with nutrient transporters, suggesting phase segregation of the membrane. We found that endocytosis was blocked by the phase segregation and thus was not involved in removing excess membrane. In contrast, rapid recovery of plasma membrane tension was dependent on 1) eisosome morphology changes that were able to absorb most of the excess surface area and 2) lipid transport from the plasma membrane to the endoplasmic reticulum, where lipids were shunted into newly formed lipid droplets.

Neural computation involves diverse types of GABAergic inhibitory interneurons that are integrated with excitatory (E) neurons into precisely structured circuits. To understand how each neuron type shapes sensory representations, we measured firing patterns of defined types of neurons in the barrel cortex while mice performed an active, whisker-dependent object localization task. Touch excited fast-spiking (FS) interneurons at short latency, followed by activation of E neurons and somatostatin-expressing (SST) interneurons. Touch only weakly modulated vasoactive intestinal polypeptide-expressing (VIP) interneurons. Voluntary whisker movement activated FS neurons in the ventral posteromedial nucleus (VPM) target layers, a subset of SST neurons and a majority of VIP neurons. Together, FS neurons track thalamic input, mediating feedforward inhibition. SST neurons monitor local excitation, providing feedback inhibition. VIP neurons are activated by non-sensory inputs, disinhibiting E and FS neurons. Our data reveal rules of recruitment for interneuron types during behavior, providing foundations for understanding computation in cortical microcircuits.

Dopaminergic neurons (DANs) drive learning across the animal kingdom, but the upstream circuits that regulate their activity and thereby learning remain poorly understood. We provide a synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mushroom body of Drosophila larva. We discover afferent sensory pathways and a large population of neurons that provide feedback from mushroom body output neurons and link distinct memory systems (aversive and appetitive). We combine this with functional studies of DANs and their presynaptic partners and with comprehensive circuit modeling. We find that DANs compare convergent feedback from aversive and appetitive systems, which enables the computation of integrated predictions that may improve future learning. Computational modeling reveals that the discovered feedback motifs increase model flexibility and performance on learning tasks. Our study provides the most detailed view to date of biological circuit motifs that support associative learning.

Most cortical synapses are local and excitatory. Local recurrent circuits could implement amplification, allowing pattern completion and other computations. Cortical circuits contain subnetworks that consist of neurons with similar receptive fields and increased connectivity relative to the network average. Cortical neurons that encode different types of information are spatially intermingled and distributed over large brain volumes, and this complexity has hindered attempts to probe the function of these subnetworks by perturbing them individually. Here we use computational modelling, optical recordings and manipulations to probe the function of recurrent coupling in layer 2/3 of the mouse vibrissal somatosensory cortex during active tactile discrimination. A neural circuit model of layer 2/3 revealed that recurrent excitation enhances sensory signals by amplification, but only for subnetworks with increased connectivity. Model networks with high amplification were sensitive to damage: loss of a few members of the subnetwork degraded stimulus encoding. We tested this prediction by mapping neuronal selectivity and photoablating neurons with specific selectivity. Ablation of a small proportion of layer 2/3 neurons (10-20, less than 5% of the total) representing touch markedly reduced responses in the spared touch representation, but not in other representations. Ablations most strongly affected neurons with stimulus responses that were similar to those of the ablated population, which is also consistent with network models. Recurrence among cortical neurons with similar selectivity therefore drives input-specific amplification during behaviour.

Population neural recordings with long-range temporal structure are often best understood in terms of a shared underlying low-dimensional dynamical process. Advances in recording technology provide access to an ever larger fraction of the population, but the standard computational approaches available to identify the collective dynamics scale poorly with the size of the dataset. Here we describe a new, scalable approach to discovering the low-dimensional dynamics that underlie simultaneously recorded spike trains from a neural population. Our method is based on recurrent linear models (RLMs), and relates closely to timeseries models based on recurrent neural networks. We formulate RLMs for neural data by generalising the Kalman-filter-based likelihood calculation for latent linear dynamical systems (LDS) models to incorporate a generalised-linear observation process. We show that RLMs describe motor-cortical population data better than either directly-coupled generalised-linear models or latent linear dynamical system models with generalised-linear observations. We also introduce the cascaded linear model (CLM) to capture low-dimensional instantaneous correlations in neural populations. The CLM describes the cortical recordings better than either Ising or Gaussian models and, like the RLM, can be fit exactly and quickly. The CLM can also be seen as a generalization of a low-rank Gaussian model, in this case factor analysis. The computational tractability of the RLM and CLM allow both to scale to very high-dimensional neural data.

The first step to probing any potential interaction between two biomolecules is to determine their spatial association. In other words, if two biomolecules localize similarly within a cell, then it is plausible they could interact. Traditionally, this is quantified through various colocalization metrics. These measures infer this association by estimating the degree to which fluorescent signals from each biomolecule overlap or correlate. However, these metrics are, at best, proxies, and they depend strongly on various experimental choices. Alternatively, here we define a new strategy which leverages multispectral imaging and phasor analysis, termed the Phasor Mixing Coefficient (PMC). PMC measures the precise mixing of fluorescent signals in each pixel. We demonstrate how PMC captures complex biological subtlety by offering two distinct values, a global measure of overall color mixing and the homogeneity thereof. We additionally show that PMC exhibits less sensitivity to signal-to-noise ratio, intensity threshold, and background signal compared to canonical methods. Moreover, this method provides a means to visualize color mixing at each pixel. We show that PMC offers users a nuanced and robust metric to quantify biological association.

Traditional approaches for increasing the affinity of protein-protein complexes focus on constructing highly complementary binding surfaces. Recent theoretical simulations and experimental results suggest that electrostatic steering forces can also be manipulated to increase association rates while leaving dissociation rates unchanged, thus increasing affinity. Here we demonstrate that electrostatic attraction can be enhanced between an antibody fragment and its cognate antigen through application of a few simple rules to identify potential on-rate amplification sites that lie at the periphery of the antigen-antibody interface.

We developed a series of statistical potentials to recognize the native protein from decoys, particularly when using only a reduced representation in which each side chain is treated as a single C(beta) atom. Beginning with a highly successful all-atom statistical potential, the Discrete Optimized Protein Energy function (DOPE), we considered the implications of including additional information in the all-atom statistical potential and subsequently reducing to the C(beta) representation. One of the potentials includes interaction energies conditional on backbone geometries. A second potential separates sequence local from sequence nonlocal interactions and introduces a novel reference state for the sequence local interactions. The resultant potentials perform better than the original DOPE statistical potential in decoy identification. Moreover, even upon passing to a reduced C(beta) representation, these statistical potentials outscore the original (all-atom) DOPE potential in identifying native states for sets of decoys. Interestingly, the backbone-dependent statistical potential is shown to retain nearly all of the information content of the all-atom representation in the C(beta) representation. In addition, these new statistical potentials are combined with existing potentials to model hydrogen bonding, torsion energies, and solvation energies to produce even better performing potentials. The ability of the C(beta) statistical potentials to accurately represent protein interactions bodes well for computational efficiency in protein folding calculations using reduced backbone representations, while the extensions to DOPE illustrate general principles for improving knowledge-based potentials.