The Drosophila brain is a work horse in neuroscience. Single-cell transcriptome analysis 1-5 , 3D morphological classification 6 , and detailed EM mapping of the connectome 7-10 have revealed an immense diversity of neuronal and glial cell types that underlie the wide array of functional and behavioral traits in the fruit fly. The identities of these cell types are controlled by -still unknowngene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. To characterize the GRN for each cell type in the Drosophila brain, we profiled chromatin accessibility of 240,919 single cells spanning nine developmental timepoints, and integrated this data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which around 70,000 are linked to specific developmental trajectories, involving neurogenesis, reprogramming and maturation. For 40 cell types, their uniquely accessible regions could be associated with their expressed transcription factors and downstream target genes, through a combination of motif discovery, network inference techniques, and deep learning. We illustrate how these "enhancer-GRNs" can be used to reveal enhancer architectures leading to a better understanding of neuronal regulatory diversity. Finally, our atlas of regulatory elements can be used to design genetic driver lines for specific cell types at specific timepoints, facilitating the characterization of brain cell types and the manipulation of brain function. MainThe brain consists of a myriad of different neuronal and glial types, each unique in their morphology and function. The Drosophila brain, which contains around 100,000 cells, is uniquely positioned as a model in which the diversity of brain cell types can be investigated. Recent advances in electron microscopy have allowed the creation of connectome maps of the different regions in the Drosophila brain 7-10 , while the availability of genetic driver lines 11 provides genetic access to many cell types for understanding neuronal function 12 . Furthermore, this diversity of cell types has been bolstered by single-cell transcriptomics on the adult brain 1-5 , the larval brain [13][14][15] , and the ventral nerve cord 16 . The recent development of single-cell assay for transposase accessible chromatin by sequencing (scATAC-seq), makes it possible to measure chromatin accessibility of single cells in high throughput 17,18 , providing an additional crucial layer of information underlying neuronal identity: which genomic regions encode the regulatory information to create and maintain each cell type. The integrated analysis of transcriptomics and chromatin accessibility makes it then possible to jointly study enhancers and gene expression to discover precise regulatory programs across cell types [19][20][21] .Cell type identity is defined by the activity of GRNs in which combinations of transcription factors activate or repress target genes....

Two methods of inoculum preparation for filamentous fungi were compared: counting with a hematocytometer and spectrophotometric adjustment. One hundred eighty-two filamentous fungi pathogenic for humans were used. Colony counts were done for all inoculum preparations. The agreement between the hematocytometer counts and the colony counts (CFU per milliliter) was 97.2%. The reproducibility between the hematocytometer counts and the colony counts by means of an intraclass correlation coefficient was 0.70. Pearson's correlation index for hematocytometer counts versus colony counts was 0.56, whereas that for optical density versus colony counts was 0.008. Both methods can be used for inoculum size adjustment. However, the use of the spectrophotometric method requires that each species be standardized separately.An increasing prevalence of infections caused by filamentous fungi has been detected in humans in recent years (1). This increasing prevalence has stimulated interest in a reliable and reproducible antifungal susceptibility testing method for molds. Recently, the National Committee for Clinical Laboratory Standards (NCCLS) proposed a reference method (the NCCLS M38-P method) for the susceptibility testing of conidium-forming filamentous fungi (7). The NCCLS method recommends inoculum preparation by a spectrophotometric procedure (2-4, 7). However, it is well known that larger objects such as spores or filaments do not obey the rule that established a proportional relationship between dry weight concentration and optical density (6). In addition, the inoculum size has a great influence on the MICs for filamentous fungi (3-5, 7). Other variables such as the color of the spores can also influence the optical density value.In the work described here, two procedures for inoculum size adjustment were evaluated, and the results were compared with those obtained by colony counting. MATERIALS AND METHODSIsolates. A set of clinical isolates was tested. Each isolate was obtained from a different patient and was sent to the laboratory for identification or antifungal susceptibility testing.The following species were included: 31 Aspergillus fumigatus, 28 Aspergillus flavus, 25 Aspergillus terreus, 16 Aspergillus niger, 18 Scedosporium apiospermum, 27 Scedosporium prolificans, 24 Fusarium solani, and 13 Fusarium oxysporum isolates. The isolates were maintained as a suspension in sterile distilled water at 4°C until testing was performed.Inoculum preparation. The isolates were subcultured from the stock water suspensions on Sabouraud agar and on potato dextrose agar. Isolates of all species except members of the genus Fusarium were incubated at 35°C; members of the genus Fusarium were incubated at 30°C. Inoculum suspensions were prepared from fresh, mature (3-to 5-day-old) cultures grown on Sabouraud agar or potato dextrose agar slants. The colonies were covered with 5 ml of distilled sterile water. Tween 20 (5%) was added to facilitate the preparation of Aspergillus inocula. For Aspergillus spp., the inocula were achieved...

During development eukaryotic gene expression is coordinated by dynamic changes in chromatin structure. Measurements of accessible chromatin are used extensively to identify genomic regulatory elements. Whilst chromatin landscapes of pluripotent stem cells are well characterised, chromatin accessibility changes in the development of somatic lineages are not well defined. Here we show that cell-specific chromatin accessibility data can be produced via ectopic expression of E. coli Dam methylase in vivo, without the requirement for cell-sorting (CATaDa). We have profiled chromatin accessibility in individual cell-types of Drosophila neural and midgut lineages. Functional cell-type-specific enhancers were identified, as well as novel motifs enriched at different stages of development. Finally, we show global changes in the accessibility of chromatin between stem-cells and their differentiated progeny. Our results demonstrate the dynamic nature of chromatin accessibility in somatic tissues during stem cell differentiation and provide a novel approach to understanding gene regulatory mechanisms underlying development.

SUMMARYIdentification of the genetic mechanisms underlying the specification of large numbers of different neuronal cell fates from limited numbers of progenitor cells is at the forefront of developmental neurobiology. In Drosophila, the identities of the different neuronal progenitor cells, the neuroblasts, are specified by a combination of spatial cues. These cues are integrated with temporal competence transitions within each neuroblast to give rise to a specific repertoire of cell types within each lineage. However, the nature of this integration is poorly understood. To begin addressing this issue, we analyze the specification of a small set of peptidergic cells: the abdominal leucokinergic neurons. We identify the progenitors of these neurons, the temporal window in which they are specified and the influence of the Notch signaling pathway on their specification. We also show that the products of the genes klumpfuss, nab and castor play important roles in their specification via a genetic cascade.