Knock-in mutations of scarecrow, a Drosophila homolog of mammalian Nkx2.1, reveal a novel function required for development of the optic lobe in Drosophila melanogaster

Yoo, Siuk; Nair, Sudershana; Kim, Hyun‐Jin; Kim, Yujin; Lee, Chansong; Lee, Gyunghee; Park, Jae H.

doi:10.1016/j.ydbio.2020.02.008

Cited by 19 publications

(37 citation statements)

References 53 publications

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“…The differentiation into medulla neuroblasts co-occurs with a phase with pointed (pnt) and scute (sc) 60 , followed by E(bx) and finally the emergence of a neuro-GGG motif 58,59 and known neuroblast factors Ham and Kr 62,63 . To study the differences between neural progenitors in more detail, we performed motif enrichment on differential peaks between LPC, OL NBs, and CB NBs, which highlighted additional regulators like so for lamina precursor cells (LPC) 64 and scro for the optic lobe progenitors 65,66 , while the GGG motif is detected in both CB and OL neuroblasts (Extended Data Fig. 13e).…”

Section: Dynamic Changes In Chromatin Accessibility During Brain Deve...mentioning

confidence: 99%

Decoding gene regulation in the fly brain

Janssens

Aibar

Taskiran

et al. 2022

Nature

126

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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....

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Section: Dynamic Changes In Chromatin Accessibility During Brain Deve...mentioning

confidence: 99%

Decoding gene regulation in the fly brain

Janssens

Aibar

Taskiran

et al. 2022

Nature

126

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“…Based on our scRNA-seq data, scro mRNA is expressed in NBs starting at a similar time as Slp 1 and 2. The scro gene encodes a NK-2 homeobox transcription factor, the expression of which in the medulla has been indicated by its knock-in mutant alleles in a recent study 71,72 .…”

Section: Scro Is Required To Promote Slp Expression To the Threshold Level For Temporal Transitionmentioning

confidence: 99%

“…Females of yw, hs-Flp 1.22 ;; FRT80B, eyBAC, Ubi-GFP/TM6B,Tb; ey J5. 71 were crossed to males with genotype hs-Flp 1.22 ;; FRT80B; ey J5. 71 / In(4)ci D (Ref 18 ).…”

Section: Fly Lines and Crossesmentioning

confidence: 99%

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A comprehensive temporal patterning gene network in Drosophila medulla neuroblasts revealed by single-cell RNA sequencing

Zhu

et al. 2021

Preprint

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During development, neural stem cells are temporally patterned to sequentially generate a variety of neural types before exiting the cell cycle. Temporal patterning is well-studied in Drosophila, where neural stem cells called neuroblasts sequentially express cascades of Temporal Transcription Factors (TTFs) to control the birth-order dependent neural specification. However, currently known TTFs were mostly identified through candidate approaches and may not be complete. In addition, many fundamental questions remain concerning the TTF cascade initiation, progression, and termination. It is also not known why temporal progression only happens in neuroblasts but not in their differentiated progeny. In this work, we performed single-cell RNA sequencing of Drosophila medulla neuroblasts of all ages to study the temporal patterning process with single-cell resolution. Our scRNA-seq data revealed that sets of genes involved in different biological processes show high to low or low to high gradients as neuroblasts age. We also identified a list of novel TTFs, and experimentally characterized their roles in the temporal progression and neural fate specification. Our study revealed a comprehensive temporal gene network that patterns medulla neuroblasts from start to end. Furthermore, we found that the progression and termination of this temporal cascade also require transcription factors differentially expressed along the differentiation axis (neuroblasts -> -> neurons). Lola proteins function as a speed modulator of temporal progression in neuroblasts; while Nerfin-1, a factor required to suppress de-differentiation in post-mitotic neurons, acts at the final temporal stage together with the last TTF of the cascade, to promote the switch to gliogenesis and the cell cycle exit. Our comprehensive study of the medulla neuroblast temporal cascade illustrated mechanisms that might be conserved in the temporal patterning of neural stem cells.

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“…The cell clusters coming from the OL, the primary visual neuropil of the honey bee brain and also one of the largest and most molecularly diverse regions (63), clustered together by the dissimilarity score across TFs (right-hand side, Supplementary Figure S8C). These clusters were identified by expression of OL-specific markers like scr, gad1, drgx, SoxN (64)(65)(66), and others (Supplementary Excel File 2), and represent neuronal subtypes exclusive to the insect visual system that would have been completely removed for the mushroom body sample. Furthermore, OL neurons 3, the OL cluster specifically annotated as lobular T4/T5 neurons based on expression of drgx, SoxN, and slo (65-68), yielded one of the highest overall dissimilarity scores.…”

Section: Simic Infers Regulatory Dynamics On Non-model Organismsmentioning

confidence: 99%

A single-cell gene regulatory network inference method for identifying complex regulatory dynamics across cell phenotypes

Peng

Chembazhi

Bangru

et al. 2020

Preprint

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Motivation: With the use of single-cell RNA sequencing (scRNA-Seq) technologies, it is now possible to acquire gene expression data for each individual cell in samples containing up to millions of cells. These cells can be further grouped into different states along an inferred cell differentiation path, which are potentially characterized by similar, but distinct enough, gene regulatory networks (GRNs). Hence, it would be desirable for scRNA-Seq GRN inference methods to capture the GRN dynamics across cell states. However, current GRN inference methods produce a unique GRN per input dataset (or independent GRNs per cell state), failing to capture these regulatory dynamics. Results: We propose a novel single-cell GRN inference method, named SimiC, that jointly infers the GRNs corresponding to each state. SimiC models the GRN inference problem as a LASSO optimization problem with an added similarity constraint, on the GRNs associated to contiguous cell states, that captures the intercell-state homogeneity. We show on a mouse hepatocyte single-cell data generated after partial hepatectomy that, contrary to previous GRN methods for scRNA-Seq data, SimiC is able to capture the transcription factor (TF) dynamics across liver regeneration, as well as the cell-level behavior for the regulatory program of each TF across cell states. In addition, on a honey bee scRNA-Seq experiment, SimiC is able to capture the increased heterogeneity of cells on whole-brain tissue with respect to a regional analysis tissue, and the TFs associated specifically to each sequenced tissue. Availability: SimiC is written in Python and includes an R API. It can be downloaded from https:// These networks are usually represented as graphs, where the nodes are genes, and the edges represent a regulatory (or co-expression) relationship between the genes that they connect. These graphs can be classified, among others, as: directed, if the regulatory direction is known; weighted, where the weight of each edge represents the regulatory strength of the connection; or bipartite, where genes are split into disjoint sets and edges only connect genes of distinct sets. In addition, some GRN inference methods follow a module-based approach, where genes are first clustered in modules and then a GRN is inferred per module, in contrast to other methods that build a unique single GRN for the data [16,30].Until recently, most available gene expression data were derived from bulk RNA sequencing (RNA-Seq). These sequencing techniques are inherently agnostic to differences among diversity of cell type within a given sample, and can therefore only give an average measure of the gene expressions across all cells. Hence, GRNs inferred from bulk RNA-Seq data represent the transcriptional regulatory landscape of the sequenced tissue rather than of the individual cells.With the advancement of single-cell RNA Sequencing technologies (scRNA-Seq), it is now possible to acquire gene expression data for individual cells in samples containing up to millions of cells. scRNA-Seq ...

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Knock-in mutations of scarecrow, a Drosophila homolog of mammalian Nkx2.1, reveal a novel function required for development of the optic lobe in Drosophila melanogaster

Cited by 19 publications

References 53 publications

Decoding gene regulation in the fly brain

Decoding gene regulation in the fly brain

A comprehensive temporal patterning gene network in Drosophila medulla neuroblasts revealed by single-cell RNA sequencing

A single-cell gene regulatory network inference method for identifying complex regulatory dynamics across cell phenotypes

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