Biological tissue is often composed of cells with similar morphologies replicated throughout large volumes and many biological applications rely on the accurate identification of these cells and their locations from image data. Here we develop a generative model that captures the regularities present in images composed of repeating elements of a few different types. Formally, the model can be described as convolutional sparse block coding. For inference we use a variant of convolutional matching pursuit adapted to block-based representations. We extend the K-SVD learning algorithm to subspaces by retaining several principal vectors from the SVD decomposition instead of just one. Good models with little cross-talk between subspaces can be obtained by learning the blocks incrementally. We perform extensive experiments on simulated images and the inference algorithm consistently recovers a large proportion of the cells with a small number of false positives. We fit the convolutional model to noisy GCaMP6 two-photon images of spiking neurons and to Nissl-stained slices of cortical tissue and show that it recovers cell body locations without supervision. The flexibility of the block-based representation is reflected in the variability of the recovered cell shapes.

An often-overlooked aspect of neural plasticity is the plasticity of neuronal composition, in which the numbers of neurons of particular classes are altered in response to environment and experience. The Drosophila brain features several well-characterized lineages in which a single neuroblast gives rise to multiple neuronal classes in a stereotyped sequence during development [1]. We find that in the intrinsic mushroom body neuron lineage, the numbers for each class are highly plastic, depending on the timing of temporal fate transitions and the rate of neuroblast proliferation. For example, mushroom body neuroblast cycling can continue under starvation conditions, uncoupled from temporal fate transitions that depend on extrinsic cues reflecting organismal growth and development. In contrast, the proliferation rates of antennal lobe lineages are closely associated with organismal development, and their temporal fate changes appear to be cell cycle-dependent, such that the same numbers and types of uniglomerular projection neurons innervate the antennal lobe following various perturbations. We propose that this surprising difference in plasticity for these brain lineages is adaptive, given their respective roles as parallel processors versus discrete carriers of olfactory information.

Many animals rely on vision to navigate through their environment. The pattern of changes in the visual scene induced by self-motion is the optic flow1, which is first estimated in local patches by directionally selective (DS) neurons2–4. But how should the arrays of DS neurons, each responsive to motion in a preferred direction at a specific retinal position, be organized to support robust decoding of optic flow by downstream circuits? Understanding this global organization is challenging because it requires mapping fine, local features of neurons across the animal’s field of view3. In Drosophila, the asymmetric dendrites of the T4 and T5 DS neurons establish their preferred direction, making it possible to predict DS responses from anatomy4,5. Here we report that the preferred directions of fly DS neurons vary at different retinal positions and show that this spatial variation is established by the anatomy of the compound eye. To estimate the preferred directions across the visual field, we reconstructed hundreds of T4 neurons in a full brain EM volume6 and discovered unexpectedly stereotypical dendritic arborizations that are independent of location. We then used whole-head μCT scans to map the viewing directions of all compound eye facets and found a non-uniform sampling of visual space that explains the spatial variation in preferred directions. Our findings show that the organization of preferred directions in the fly is largely determined by the compound eye, exposing an intimate and unexpected connection between the peripheral structure of the eye, functional properties of neurons deep in the brain, and the control of body movements.

In Drosophila, pattern formation at multiple stages of embryonic and imaginal development depends on the same intercellular signaling pathways. We have identified a novel gene, eyelid (eld), which is required for embryonic segmentation, development of the notum and wing margin, and photoreceptor differentiation. In these tissues, eld mutations have effects opposite to those caused by wingless (wg) mutations. eld encodes a widely expressed nuclear protein with a region homologous to a novel family of DNA-binding domains. Based on this homology and on the phenotypic analysis, we suggest that Eld could act as a transcription factor antagonistic to the Wg pathway.

Visualizing the formation of multinucleated giant cells (MGCs) from living specimens has been challenging due to the fact that most live imaging techniques require propagation of light through glass, but on glass macrophage fusion is a rare event. This protocol presents the fabrication of several optical-quality glass surfaces where adsorption of compounds containing long-chain hydrocarbons transforms glass into a fusogenic surface. First, preparation of clean glass surfaces as starting material for surface modification is described. Second, a method is provided for the adsorption of compounds containing long-chain hydrocarbons to convert non-fusogenic glass into a fusogenic substrate. Third, this protocol describes fabrication of surface micropatterns that promote a high degree of spatiotemporal control over MGC formation. Finally, fabricating glass bottom dishes is described. Examples of use of this in vitro cell system as a model to study macrophage fusion and MGC formation are shown.

Recent studies in mice have shown that orofacial behaviors drive a large fraction of neural activity across the brain. To understand the nature and function of these signals, we need better computational models to characterize the behaviors and relate them to neural activity. Here we developed Facemap, a framework consisting of a keypoint tracking algorithm and a deep neural network encoder for predicting neural activity. We used the Facemap keypoints as input for the deep neural network to predict the activity of ∼50,000 simultaneously-recorded neurons and in visual cortex we doubled the amount of explained variance compared to previous methods. Our keypoint tracking algorithm was more accurate than existing pose estimation tools, while the inference speed was several times faster, making it a powerful tool for closed-loop behavioral experiments. The Facemap tracker was easy to adapt to data from new labs, requiring as few as 10 annotated frames for near-optimal performance. We used Facemap to find that the neuronal activity clusters which were highly driven by behaviors were more spatially spread-out across cortex. We also found that the deep keypoint features inferred by the model had time-asymmetrical state dynamics that were not apparent in the raw keypoint data. In summary, Facemap provides a stepping stone towards understanding the function of the brainwide neural signals and their relation to behavior.

Despite the apparent simplicity of the xanthene fluorophores, the preparation of caged derivatives with free carboxy groups remains a synthetic challenge. A straightforward and flexible strategy for preparing rhodamine and fluorescein derivatives was developed using reduced, “leuco” intermediates.

Efforts to map neural circuits have been galvanized by the development of genetic technologies that permit the manipulation of targeted sets of neurons in the brains of freely behaving animals. The success of these efforts relies on the experimenter's ability to target arbitrarily small subsets of neurons for manipulation, but such specificity of targeting cannot routinely be achieved using existing methods. In Drosophila melanogaster, a widely used technique for refined cell-type specific manipulation is the Split GAL4 system, which augments the targeting specificity of the binary GAL4-UAS system by making GAL4 transcriptional activity contingent upon two enhancers, rather than one. To permit more refined targeting, we introduce here the "Killer Zipper" (KZip(+)), a suppressor that makes Split GAL4 targeting contingent upon a third enhancer. KZip(+) acts by disrupting both the formation and activity of Split GAL4 heterodimers, and we show how this added layer of control can be used to selectively remove unwanted cells from a Split GAL4 expression pattern or to subtract neurons of interest from a pattern to determine their requirement in generating a given phenotype. To facilitate application of the KZip(+) technology, we have developed a versatile set of LexAop-KZip(+) fly lines that can be used directly with the large number of LexA driver lines with known expression patterns. The Killer Zipper significantly sharpens the precision of neuronal genetic control available in Drosophila and may be extended to other organisms where Split GAL4-like systems are used.