Despite considerable progress in recent decades in dissecting the genetic causes of natural morphological variation, there is limited understanding of how variation within species ultimately contributes to species differences. We have studied patterning of the non-sensory hairs, commonly known as "trichomes," on the dorsal cuticle of first-instar larvae of Drosophila. Most Drosophila species produce a dense lawn of dorsal trichomes, but a subset of these trichomes were lost in D. sechellia and D. ezoana due entirely to regulatory evolution of the shavenbaby (svb) gene. Here, we describe intraspecific variation in dorsal trichome patterns of first-instar larvae of D. virilis that is similar to the trichome pattern variation identified previously between species. We found that a single large effect QTL, which includes svb, explains most of the trichome number difference between two D. virilis strains and that svb expression correlates with the trichome difference between strains. This QTL does not explain the entire difference between strains, implying that additional loci contribute to variation in trichome numbers. Thus, the genetic architecture of intraspecific variation exhibits similarities and differences with interspecific variation that may reflect differences in long-term and short-term evolutionary processes.

We have developed new open-source software calledTEM (computational imaging system for transmission electron microscopy) for the processing of data for high-resolution electron cryo-microscopy and single-particle averaging.TEM features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full processing pipeline including movie processing, image defocus determination, automatic particle picking, 2D classification, ab-initio 3D map generation from random parameters, 3D classification, and high-resolution refinement and reconstruction. Some of these steps implement newly-developed algorithms; others were adapted from previously published algorithms. The software is optimized to enable processing of typical datasets (2000 micrographs, 200k - 300k particles) on a high-end, CPU-based workstation in half a day or less, comparable to GPU-accelerated processing. Jobs can also be scheduled on large computer clusters using flexible run profiles that can be adapted for most computing environments.TEM is available for download from cistem.org.

We present CLADES (Cell Lineage Access Driven by an Edition Sequence), a technology for cell lineage studies based on CRISPR/Cas9. CLADES relies on a system of genetic switches to activate and inactivate reporter genes in a pre-determined order. Targeting CLADES to progenitor cells allows the progeny to inherit a sequential cascade of reporters, coupling birth order with reporter expression. This gives us temporal resolution of lineage development that can be used to deconstruct an extended cell lineage by tracking the reporters expressed in the progeny. When targeted to the germ line, the same cascade progresses across animal generations, marking each generation with the corresponding combination of reporters. CLADES thus offers an innovative strategy for making programmable cascades of genes that can be used for genetic manipulation or to record serial biological events.

Neural circuits carry out complex computations that allow animals to evaluate food, select mates, move toward attractive stimuli, and move away from threats. In insects, the subesophageal zone (SEZ) is a brain region that receives gustatory, pheromonal, and mechanosensory inputs and contributes to the control of diverse behaviors, including feeding, grooming, and locomotion. Despite its importance in sensorimotor transformations, the study of SEZ circuits has been hindered by limited knowledge of the underlying diversity of SEZ neurons. Here, we generate a collection of split-GAL4 lines that provides precise genetic targeting of 138 different SEZ cell types in adult , comprising approximately one third of all SEZ neurons. We characterize the single cell anatomy of these neurons and find that they cluster by morphology into six supergroups that organize the SEZ into discrete anatomical domains. We find that the majority of local SEZ interneurons are not classically polarized, suggesting rich local processing, whereas SEZ projection neurons tend to be classically polarized, conveying information to a limited number of higher brain regions. This study provides insight into the anatomical organization of the SEZ and generates resources that will facilitate further study of SEZ neurons and their contributions to sensory processing and behavior.

Detecting meaningful structure in neural activity and connectivity data is challenging in the presence of hidden nonlinearities, where traditional eigenvalue-based methods may be misleading. We introduce a novel approach to matrix analysis, called clique topology, that extracts features of the data invariant under nonlinear monotone transformations. These features can be used to detect both random and geometric structure, and depend only on the relative ordering of matrix entries. We then analyzed the activity of pyramidal neurons in rat hippocampus, recorded while the animal was exploring a 2D environment, and confirmed that our method is able to detect geometric organization using only the intrinsic pattern of neural correlations. Remarkably, we found similar results during nonspatial behaviors such as wheel running and rapid eye movement (REM) sleep. This suggests that the geometric structure of correlations is shaped by the underlying hippocampal circuits and is not merely a consequence of position coding. We propose that clique topology is a powerful new tool for matrix analysis in biological settings, where the relationship of observed quantities to more meaningful variables is often nonlinear and unknown.

BACKGROUND: The insect brain can be divided into neuropils that are formed by neurites of both local and remote origin. The complexity of the interconnections obscures how these neuropils are established and interconnected through development. The Drosophila central brain develops from a fixed number of neuroblasts (NBs) that deposit neurons in regional clusters. RESULTS: By determining individual NB clones and pursuing their projections into specific neuropils, we unravel the regional development of the brain neural network. Exhaustive clonal analysis revealed 95 stereotyped neuronal lineages with characteristic cell-body locations and neurite trajectories. Most clones show complex projection patterns, but despite the complexity, neighboring clones often coinnervate the same local neuropil or neuropils and further target a restricted set of distant neuropils. CONCLUSIONS: These observations argue for regional clonal development of both neuropils and neuropil connectivity throughout the Drosophila central brain.

Advances in single-cell RNA-sequencing technology have resulted in a wealth of studies aiming to identify transcriptomic cell types in various biological systems. There are multiple experimental approaches to isolate and profile single cells, which provide different levels of cellular and tissue coverage. In addition, multiple computational strategies have been proposed to identify putative cell types from single-cell data. From a data generation perspective, recent single-cell studies can be classified into two groups: those that distribute reads shallowly over large numbers of cells and those that distribute reads more deeply over a smaller cell population. Although there are advantages to both approaches in terms of cellular and tissue coverage, it is unclear whether different computational cell type identification methods are better suited to one or the other experimental paradigm. This study reviews three cell type clustering algorithms, each representing one of three broad approaches, and finds that PCA-based algorithms appear most suited to low read depth data sets, whereas gene clustering-based and biclustering algorithms perform better on high read depth data sets. In addition, highly related cell classes are better distinguished by higher-depth data, given the same total number of reads; however, simultaneous discovery of distinct and similar types is better served by lower-depth, higher cell number data. Overall, this study suggests that the depth of profiling should be determined by initial assumptions about the diversity of cells in the population, and that the selection of clustering algorithm(s) is subsequently based on the depth of profiling will allow for better identification of putative transcriptomic cell types.

The evolution of sexual traits often involves correlated changes in morphology and behavior. For example, in Drosophila, divergent mating displays are often accompanied by divergent pigment patterns. To better understand how such traits co-evolve, we investigated the genetic basis of correlated divergence in wing pigmentation and mating display between the sibling species Drosophila elegans and D. gunungcola. Drosophila elegans males have an area of black pigment on their wings known as a wing spot and appear to display this spot to females by extending their wings laterally during courtship. By contrast, D. gunungcola lost both of these traits. Using Multiplexed Shotgun Genotyping (MSG), we identified a ∼440 kb region on the X chromosome that behaves like a genetic switch controlling the presence or absence of male-specific wing spots. This region includes the candidate gene optomotor-blind (omb), which plays a critical role in patterning the Drosophila wing. The genetic basis of divergent wing display is more complex, with at least two loci on the X chromosome and two loci on autosomes contributing to its evolution. Introgressing the X-linked region affecting wing spot development from D. gunungcola into D. elegans reduced pigmentation in the wing spots but did not affect the wing display, indicating that these are genetically separable traits. Consistent with this observation, broader sampling of wild D. gunungcola populations confirmed the wing spot and wing display are evolving independently: some D. gunungcola males performed wing displays similar to D. elegans despite lacking wing spots. These data suggest that correlated selection pressures rather than physical linkage or pleiotropy are responsible for the coevolution of these morphological and behavioral traits. They also suggest that the change in morphology evolved prior to the change in behavior. This article is protected by copyright. All rights reserved.

Many genomes contain rapidly evolving and highly divergent genes whose homology to genes of known function often cannot be determined from sequence similarity alone. However, coding sequence-independent features of genes, such as intron-exon boundaries, often evolve more slowly than coding sequences and can provide complementary evidence for homology. We found that a linear logistic regression classifier using only structural features of rapidly evolving bicycle aphid effector genes identified many putative bicycle homologs in aphids, phylloxerids, and scale insects, whereas sequence similarity search methods yielded few homologs in most aphids and no homologs in phylloxerids and scale insects. Subsequent examination of sequence features and intron locations supported homology assignments. Differential expression studies of newly-identified bicycle homologs, together with prior proteomic studies, support the hypothesis that BICYCLE proteins act as plant effector proteins in many aphid species and perhaps also in phylloxerids and scale insects.