The development of two-component expression systems in Drosophila melanogaster, one of the most powerful genetic models, has allowed the precise manipulation of gene function in specific cell populations. These expression systems, in combination with site-specific recombination approaches, have also led to the development of new methods for clonal lineage analysis. We present a hands-on user guide to the techniques and approaches that have greatly increased resolution of genetic analysis in the fly, with a special focus on their application for lineage analysis. Our intention is to provide guidance and suggestions regarding which genetic tools are most suitable for addressing different developmental questions.

Summary In the Drosophila optic lobes, 800 retinotopically organized columns in the medulla act as functional units for processing visual information. The medulla contains over 80 types of neurons, which belong to two classes: uni-columnar neurons have a stoichiometry of one per column, while multi-columnar neurons contact multiple columns. We show that combinatorial inputs from temporal and spatial axes generate this neuronal diversity: All neuroblasts switch fates over time to produce different neurons. The neuroepithelium that generates neuroblasts is also sub-divided into six compartments by the expression of specific factors. Uni-columnar neurons are produced in all spatial compartments independently of spatial input; they innervate the neuropil where they are generated. Multi-columnar neurons are generated in smaller numbers in restricted compartments and later move to their final position. The integration of spatial inputs by a fixed temporal neuroblast cascade thus acts as a powerful mechanism for generating neural diversity, regulating stoichiometry and the formation of retinotopy.

In Drosophila, widely-used mitotic recombination-based strategies generate mosaic flies with positive readout for only one daughter cell after division. To differentially label both daughter cells, we developed the Twin Spot Generator technique (TSG) and demonstrate that through mitotic recombination, TSG generates green and red twin spots in internal fly tissues, visible even as single cells. We discuss the wide applications of TSG to lineage and genetic mosaic studies.

SUMMARY How neuronal and glial fates are specified from neural precursor cells is an important question for developmental neurobiologists. We address this question in the Drosophila optic lobe, composed of the lamina, medulla, and lobula complex. We show that two gliogenic regions posterior to the prospective lamina also produce lamina wide-field (Lawf) neurons, which share common progenitors with lamina glia. These progenitors express neither canonical neuroblast nor lamina precursor cell markers. They bifurcate into two sub-lineages in response to Notch signaling, generating lamina glia or Lawf neurons, respectively. The newly born glia and Lawfs then migrate tangentially over substantial distances to reach their target tissue. Thus, Lawf neurogenesis, which includes a common origin with glia, as well as neuronal migration, resembles several aspects of vertebrate neurogenesis.

The optic lobes of the fruit fly Drosophila melanogaster form a highly wired neural network composed of roughly 130.000 neurons of more than 80 different types. How neuronal diversity arises from very few cell progenitors is a central question in developmental neurobiology. We use the optic lobe of the fruit fly as a paradigm to understand how neuroblasts, the neural stem cells, generate multiple neuron types. Although the development of the fly brain has been the subject of extensive research, very little is known about the lineage relationships of the cell types forming the adult optic lobes. Here we perform a large-scale lineage bioinformatics analysis using the graph theory. We generated a large collection of cell clones that genetically label the progeny of neuroblasts and built a database to draw graphs showing the lineage relationships between cell types. By establishing biological criteria that measures the strength of the neuronal relationships and applying community detection tools we have identified eight clusters of neurons. Each cluster contains different cell types that we pose are the product of eight distinct classes of neuroblasts. Three of these clusters match the available lineage data, supporting the predictive value of the analysis. Finally, we show that the neuronal progeny of a neuroblast do not have preferential innervation patterns, but instead become part of different layers and neuropils. Here we establish a new methodology that helps understanding the logic of Drosophila brain development and can be applied to the more complex vertebrate brains.