Persistent and ramping neural activity in the frontal cortex anticipates specific movements. Preparatory activity is distributed across several brain regions, but it is unclear which brain areas are involved and how this activity is mediated by multi-regional interactions. The cerebellum is thought to be primarily involved in the short-timescale control of movement; however, roles for this structure in cognitive processes have also been proposed. In humans, cerebellar damage can cause defects in planning and working memory. Here we show that persistent representation of information in the frontal cortex during motor planning is dependent on the cerebellum. Mice performed a sensory discrimination task in which they used short-term memory to plan a future directional movement. A transient perturbation in the medial deep cerebellar nucleus (fastigial nucleus) disrupted subsequent correct responses without hampering movement execution. Preparatory activity was observed in both the frontal cortex and the cerebellar nuclei, seconds before the onset of movement. The silencing of frontal cortex activity abolished preparatory activity in the cerebellar nuclei, and fastigial activity was necessary to maintain cortical preparatory activity. Fastigial output selectively targeted the behaviourally relevant part of the frontal cortex through the thalamus, thus closing a cortico-cerebellar loop. Our results support the view that persistent neural dynamics during motor planning is maintained by neural circuits that span multiple brain regions, and that cerebellar computations extend beyond online motor control.

We describe the implementation and use of an adaptive imaging framework for optimizing spatial resolution and signal strength in a light-sheet microscope. The framework, termed AutoPilot, comprises hardware and software modules for automatically measuring and compensating for mismatches between light-sheet and detection focal planes in living specimens. Our protocol enables researchers to introduce adaptive imaging capabilities in an existing light-sheet microscope or use our SiMView microscope blueprint to set up a new adaptive multiview light-sheet microscope. The protocol describes (i) the mechano-optical implementation of the adaptive imaging hardware, including technical drawings for all custom microscope components; (ii) the algorithms and software library for automated adaptive imaging, including the pseudocode and annotated source code for all software modules; and (iii) the execution of adaptive imaging experiments, as well as the configuration and practical use of the AutoPilot framework. Setup of the adaptive imaging hardware and software takes 1-2 weeks each. Previous experience with light-sheet microscopy and some familiarity with software engineering and building of optical instruments are recommended. Successful implementation of the protocol recovers near diffraction-limited performance in many parts of typical multicellular organisms studied with light-sheet microscopy, such as fruit fly and zebrafish embryos, for which resolution and signal strength are improved two- to fivefold.

Maintenance of the bacterial homeostasis initially emanates from interactions between proteins and the bacterial nucleoid. Investigating their spatial correlation requires high spatial resolution, especially in tiny, highly confined and crowded bacterial cells. Here, we present super-resolution microscopy using a palette of fluorescent labels that bind transiently to either the membrane or the nucleoid of fixed E. coli cells. The presented labels are easily applicable, versatile and allow long-term single-molecule super-resolution imaging independent of photobleaching. The different spectral properties allow for multiplexed imaging in combination with other localisation-based super-resolution imaging techniques. As examples for applications, we demonstrate correlated super-resolution imaging of the bacterial nucleoid with the position of genetic loci, of nascent DNA in correlation to the entire nucleoid, and of the nucleoid of metabolically arrested cells. We furthermore show that DNA- and membrane-targeting labels can be combined with photoactivatable fluorescent proteins and visualise the nano-scale distribution of RNA polymerase relative to the nucleoid in drug-treated E. coli cells.

Animals strategically scan the environment to form an accurate perception of their surroundings. Here we investigated the neuronal representations that mediate this behavior. Ca imaging and selective optogenetic manipulation during an active sensing task reveals that layer 5 pyramidal neurons in the vibrissae cortex produce a diverse and distributed representation that is required for mice to adapt their whisking motor strategy to changing sensory cues. The optogenetic perturbation degraded single-neuron selectivity and network population encoding through a selective inhibition of active dendritic integration. Together the data indicate that active dendritic integration in pyramidal neurons produces a nonlinearly mixed network representation of joint sensorimotor parameters that is used to transform sensory information into motor commands during adaptive behavior. The prevalence of the layer 5 cortical circuit motif suggests that this is a general circuit computation.

The ability of fluorescence microscopy to simultaneously image multiple specific molecules of interest has allowed biologists to infer macromolecular organization and colocalization in fixed and live samples. However, a number of factors could affect these analyses, and colocalization is a misnomer. We propose that image similarity coefficient as a better and more descriptive term. In this chapter we will discuss many of the factors involved with determining image similarity including our perception of color in images. In addition, the correct use of several commonly accepted methods such as Pearson’s correlation coefficient, Manders’ overlap coefficient, and Spearman’s ranked correlation coefficient is discussed.

New reconstruction techniques are generating connectomes of unprecedented size. These must be analyzed to generate human comprehensible results. The analyses being used fall into three general categories. The first is interactive tools used during reconstruction, to help guide the effort, look for possible errors, identify potential cell classes, and answer other preliminary questions. The second type of analysis is support for formal documents such as papers and theses. Scientific norms here require that the data be archived and accessible, and the analysis reproducible. In contrast to some other "omic" fields such as genomics, where a few specific analyses dominate usage, connectomics is rapidly evolving and the analyses used are often specific to the connectome being analyzed. These analyses are typically performed in a variety of conventional programming language, such as Matlab, R, Python, or C++, and read the connectomic data either from a file or through database queries, neither of which are standardized. In the short term we see no alternative to the use of specific analyses, so the best that can be done is to publish the analysis code, and the interface by which it reads connectomic data. A similar situation exists for archiving connectome data. Each group independently makes their data available, but there is no standardized format and long-term accessibility is neither enforced nor funded. In the long term, as connectomics becomes more common, a natural evolution would be a central facility for storing and querying connectomic data, playing a role similar to the National Center for Biotechnology Information for genomes. The final form of analysis is the import of connectome data into downstream tools such as neural simulation or machine learning. In this process, there are two main problems that need to be addressed. First, the reconstructed circuits contain huge amounts of detail, which must be intelligently reduced to a form the downstream tools can use. Second, much of the data needed for these downstream operations must be obtained by other methods (such as genetic or optical) and must be merged with the extracted connectome.

Many brain functions depend on the ability of neural networks to temporally integrate transient inputs to produce sustained discharges. This can occur through cell-autonomous mechanisms in individual neurons and through reverberating activity in recurrently connected neural networks. We report a third mechanism involving temporal integration of neural activity by a network of astrocytes. Previously, we showed that some types of interneurons can generate long-lasting trains of action potentials (barrage firing) following repeated depolarizing stimuli. Here we show that calcium signaling in an astrocytic network correlates with barrage firing; that active depolarization of astrocyte networks by chemical or optogenetic stimulation enhances; and that chelating internal calcium, inhibiting release from internal stores, or inhibiting GABA transporters or metabotropic glutamate receptors inhibits barrage firing. Thus, networks of astrocytes influence the spatiotemporal dynamics of neural networks by directly integrating neural activity and driving barrages of action potentials in some populations of inhibitory interneurons.

Diverse traits often covary between species [1-3]. The possibility that a single mutation could contribute to the evolution of several characters between species [3] is rarely investigated as relatively few cases are dissected at the nucleotide level. Drosophila santomea has evolved additional sex comb sensory teeth on its legs and has lost two sensory bristles on its genitalia. We present evidence that a single nucleotide substitution in an enhancer of the scute gene contributes to both changes. The mutation alters a binding site for the Hox protein Abdominal-B in the developing genitalia, leading to bristle loss, and for another factor in the developing leg, leading to bristle gain. Our study suggests that morphological evolution between species can occur through a single nucleotide change affecting several sexually dimorphic traits. VIDEO ABSTRACT.

Abstract ingle molecule localisation microscopy (SMLM), experimentally achieved over a decade ago, has become a routinely used analytical tool across the life sciences. Synergistic advances in probe chemistry, optical physics and data analysis has propelled SMLM into the quantitative realm, enabling unprecedented access to the cellular machinery at the nanoscale. In its early years, SMLM primarily served as a platform for impressive rendered images of sub diffraction scale structures, however more recently a shift towards interrogating SMLM point pattern data in a robust mathematical framework has occurred. A prevalent theme in the SMLM field is the need for quantitative analytical methods, to better understand the underlying processes on which SMLM reports and to extract statistically valid biological insights. Whilst some forms of post processing analytics, for example cluster analysis, have been widely studied, others such as fibre analysis remain in their infancy. Here, we review the current state of the art of cluster analysis and fibre analysis and present new methods for their implementation in both 3D SMLM data sets and multi-colour data.

Activity in the motor cortex predicts movements, seconds before they are initiated. This preparatory activity has been observed across cortical layers, including in descending pyramidal tract neurons in layer 5. A key question is how preparatory activity is maintained without causing movement, and is ultimately converted to a motor command to trigger appropriate movements. Here, using single-cell transcriptional profiling and axonal reconstructions, we identify two types of pyramidal tract neuron. Both types project to several targets in the basal ganglia and brainstem. One type projects to thalamic regions that connect back to motor cortex; populations of these neurons produced early preparatory activity that persisted until the movement was initiated. The second type projects to motor centres in the medulla and mainly produced late preparatory activity and motor commands. These results indicate that two types of motor cortex output neurons have specialized roles in motor control.