Cells work together to accomplish complex tasks. For example, both neutrophils and Dictyostelid collectives use self-generated multicellular signaling gradients to coordinate aggregation over large areas through local interactions. However, these aggregation programs occur for different reasons that necessitate different implementations. Dictyostelids are soil-dwelling amoeba that aggregate when starving to facilitate dispersal to new locations. These aggregates do not require specific locations or group sizes. In contrast, neutrophils are innate immune cells that collectively migrate to sites of injury and infection. These swarms need to occur in specific locations and must be constrained in size to avoid collateral damage to the host. Here, we review how these evolutionarily divergent systems sculpt long-range gradients at the molecular and cellular levels, discussing their similarities and differences in light of their distinctive goals. Convergence on self-generated gradients for aggregation despite different goals suggests that it is an optimal strategy to bring individuals together in complex environments.

Hundreds of millions of structured proteins sustain life through chemical interactions and catalytic reactions1. Though dynamic, these proteins are assumed to be built upon fixed scaffolds of secondary structure, α-helices and β-sheets. Experimentally determined structures of over >58,000 non-redundant proteins support this assumption, though it has recently been challenged by ∼100 fold-switching proteins2. These “metamorphic3” proteins, though ostensibly rare, raise the question of how many uncharacterized proteins have shapeshifting–rather than fixed–secondary structures. To address this question, we developed a comparative sequence-based approach that predicts fold-switching proteins from differences in secondary structure propensity. We applied this approach to the universally conserved NusG transcription factor family of ∼15,000 proteins, one of which has a 50-residue regulatory subunit experimentally shown to switch between α-helical and β-sheet folds4. Our approach predicted that 25% of the sequences in this family undergo similar α-helix ⇌ β-sheet transitions, a frequency two orders of magnitude larger than previously observed. Our predictions evade state-of-the-art computational methods but were confirmed experimentally by circular dichroism and nuclear magnetic resonance spectroscopy for all 10 assiduously chosen dissimilar variants. These results suggest that fold switching is a pervasive mechanism of transcriptional regulation in all kingdoms of life and imply that numerous uncharacterized proteins may also switch folds.

The protein folding paradigm asserts that the three-dimensional structure of a protein is determined by its amino acid sequence. Here we show that a substantial population of proteins from the NusG superfamily of transcription factors do not adhere to this paradigm. Previous work demonstrated that one member of this superfamily has a regulatory domain that completely switches between α-helical and β-sheet folds, but the pervasiveness of this fold-switching mechanism is uncertain. To address this question, we developed a sequence-based predictor, which revealed that thousands of proteins from this superfamily switch folds. Circular dichroism and nuclear magnetic resonance spectroscopies of 10 sequence-diverse variants confirmed our predictions. By contrast, state-of-the-art methods based on the protein folding paradigm assume that related sequences adopt the same fold and thus predicted that the regulatory domains of all variants adopt only the β-sheet fold. Removal of this bias revealed that residue-residue contacts from both α-helical and β-sheet folds are conserved in a large subpopulation of fold-switching domains, poising them to assume disparate conformations. Our results suggest that fold switching is a pervasive mechanism of transcriptional regulation in all kingdoms of life and indicate that expanding the protein folding paradigm may reveal the involvement of fold-switching proteins in diverse biological processes.

We have developed and characterized a method, based on reflection interference contrast microscopy, to simultaneously determine the three-dimensional positions of multiple particles in a colloidal monolayer. To evaluate this method, the interaction of 6.8 microm (+/-5%) diameter lipid-derivatized silica microspheres with an underlying planar borosilicate substrate is studied. Measured colloidal height distributions are consistent with expectations for an electrostatically levitated colloidal monolayer. The precision of the method is analyzed using experimental techniques in addition to computational bootstrapping algorithms. In its present implementation, this technique achieves 16 nm lateral and 1 nm vertical precision.

The hindbrain of larval zebrafish contains a relatively simple ground plan in which the neurons throughout it are arranged into stripes that represent broad neuronal classes that differ in transmitter identity, morphology, and transcription factor expression. Within the stripes, neurons are stacked continuously according to age as well as structural and functional properties, such as axonal extent, input resistance, and the speed at which they are recruited during movements. Here we address the question of how particular networks among the many different sensory-motor networks in hindbrain arise from such an orderly plan. We use a combination of transgenic lines and pairwise patch recording to identify excitatory and inhibitory interneurons in the hindbrain network for escape behaviors initiated by the Mauthner cell. We map this network onto the ground plan to show that an individual hindbrain network is built by drawing components in predictable ways from the underlying broad patterning of cell types stacked within stripes according to their age and structural and functional properties. Many different specialized hindbrain networks may arise similarly from a simple early patterning.

Drosophila is a marvelous system to study the underlying principles that govern how neural circuits govern behaviors. The scale of the fly brain (approximately 100,000 neurons) and the complexity of the behaviors the fly can perform make it a tractable experimental model organism. In addition, 100 years and hundreds of labs have contributed to an extensive array of tools and techniques that can be used to dissect the function and organization of the fly nervous system. This review discusses both the conceptual challenges and the specific tools for a neurogenetic approach to circuit mapping in Drosophila.

Understanding brain function requires monitoring and interpreting the activity of large networks of neurons during behavior. Advances in recording technology are greatly increasing the size and complexity of neural data. Analyzing such data will pose a fundamental bottleneck for neuroscience. We present a library of analytical tools called Thunder built on the open-source Apache Spark platform for large-scale distributed computing. The library implements a variety of univariate and multivariate analyses with a modular, extendable structure well-suited to interactive exploration and analysis development. We demonstrate how these analyses find structure in large-scale neural data, including whole-brain light-sheet imaging data from fictively behaving larval zebrafish, and two-photon imaging data from behaving mouse. The analyses relate neuronal responses to sensory input and behavior, run in minutes or less and can be used on a private cluster or in the cloud. Our open-source framework thus holds promise for turning brain activity mapping efforts into biological insights.

Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of cortical computation. Despite the ubiquity of this observation, the constraints arising from such mapping are poorly understood. Here we consider a specific example of mapping low-dimensional dynamics to high-dimensional neural activity-the neural engineering framework. We analytically solve the framework for the classic ring model-a neural network encoding a static or dynamic angular variable. Our results provide a complete characterization of the success and failure modes for this model. Based on similarities between this and other frameworks, we speculate that these results could apply to more general scenarios.

Mapping mammalian synaptic connectivity has long been an important goal of neuroscientists since it is considered crucial for explaining human perception and behavior. Yet, despite enormous efforts, the overwhelming complexity of the neural circuitry and the lack of appropriate techniques to unravel it have limited the success of efforts to map connectivity. However, recent technological advances designed to overcome the limitations of conventional methods for connectivity mapping may bring about a turning point. Here, we address the promises and pitfalls of these new mapping technologies.