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Single cell transcriptome atlas of the Drosophila larval brain

  1. Clarisse Brunet Avalos
  2. G Larisa Maier
  3. Rémy Bruggmann
  4. Simon G Sprecher  Is a corresponding author
  1. University of Fribourg, Switzerland
  2. University of Bern, Switzerland
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Cite this article as: eLife 2019;8:e50354 doi: 10.7554/eLife.50354

Abstract

Cell diversity of the brain and how it is affected by starvation, remains largely unknown. Here, we introduce a single cell transcriptome atlas of the entire Drosophila first instar larval brain. We first assigned cell-type identity based on known marker genes, distinguishing five major groups: neural progenitors, differentiated neurons, glia, undifferentiated neurons and non-neural cells. All major classes were further subdivided into multiple subtypes, revealing biological features of various cell-types. We further assessed transcriptional changes in response to starvation at the single-cell level. While after starvation the composition of the brain remains unaffected, transcriptional profile of several cell clusters changed. Intriguingly, different cell-types show very distinct responses to starvation, suggesting the presence of cell-specific programs for nutrition availability. Establishing a single-cell transcriptome atlas of the larval brain provides a powerful tool to explore cell diversity and assess genetic profiles from developmental, functional and behavioral perspectives.

Introduction

The brain, as the central organ of the nervous system, shows high complexity and diversity of cell-types. Numerous tasks need to be synchronously orchestrated, singular areas are committed to specific functions and ultimately cause or modulate an array of complex behaviors. The first instar Drosophila melanogaster larval central nervous system (CNS) is composed of an estimated 10,000 cells (Scott et al., 2001). Only 2000 of these cells populate the two larval cerebral lobes, the remaining cells are distributed among segmental ganglia of the ventral nerve cord (VNC). The cells populating the larval brain develop from neuroblasts delaminated from the procephalic neurectoderm, during early embryonic stages. At the end of embryogenesis neurons are fully differentiated and form the functional neural circuits of the larval brain, while neuroblasts enter a mitotic quiescence phase and are only reactivated at the end of the first larval instar. Neuroblasts will re-enter proliferation and generate different cell-types that form the adult brain. During these steps, nutrient accessibility plays a key role. It has been previously described that some glial cells, in close proximity to the neuroblast populations, release insulin-like peptides upon nutrient-sensing. This signal is later incorporated by neuroblasts through the InR/PI3K/TORC1 pathway, to ultimately induce reactivation and exit from quiescence (Chell and Brand, 2010; Sousa-Nunes et al., 2011). Surprisingly, at late-larval stages, NPCs seem to be able to proliferate even in aversive feeding conditions, independently of the InR/PI3K/TORC1 signaling pathway (Cheng et al., 2011). Thus, the lack of nutriments may affect the molecular profile of the specified cell-types, consequently modifying the cellular state and composition of the larval brain. Therefore, identifying genetic responses during brain development in normal feeding condition versus starvation may allow a better and more complete understanding of the processes regulated by the intake of nutrients at early life stages.

The simplicity in cell number, in comparison to other animals, makes Drosophila larva an ideal candidate to establish a comprehensive catalogue of brain cell-types based on morphologies, developmental trajectories and synaptic connections between each other. Recently, the advent of single-cell RNA sequencing (scRNA-seq) analysis further provides a high-resolution transcriptomic approach to decipher the molecular footprint at cellular resolution, as done to reveal the cell atlas of the adult brain (Croset et al., 2018; Davie et al., 2018; Konstantinides et al., 2018).

Here, we used a single-cell transcriptomic approach to establish a molecular cell atlas of the first instar larval brain. In this way, we identified five major cell-types: neural progenitor cells, neurons, glial cells, undifferentiated neurons and non-neural cell-types. Among differentiated cells we characterized expression and co-expression of distinct types of neurotransmitters, neuromodulators and neuropeptides, as well as distinct types of glial cells. We differentiated three major classes of neural progenitor cells (NPCs): neuroblasts, optic lobe precursors as well as mushroom body neuroblasts. We further analyzed non-neural cells from tissues that are anatomically closely associated with the brain, such as the prothoracic gland, the ring gland, corpora allata, fat body and muscles. Moreover, we observed the presence of presumptive undifferentiated neurons. For the various classes of brain cell-types and subtypes, our work further extended the list of previously described marker genes, which in turn may be used for developmental or functional studies. Finally, we described single-cell changes at the transcriptional level in the larval brain driven by starvation. We identified different cell clusters that show strong responses in gene expression upon starvation. Expectedly, the most striking differences were observed in NPCs, glial cells and undifferentiated cells. Interestingly, the response to starvation differed between distinct clusters, suggesting the presence of cell specific programs to nutritional changes.

Results

Single-Cell RNA-Seq of the Drosophila larval brain reveals different categories of characteristic cell-types

To investigate cellular and molecular diversity in the Drosophila larval brain, we performed single-cell transcriptomics analysis by applying 10X Genomics technology. In short, we dissected the CNS of late first instar larvae and separated the brain lobes from the VNC to enrich for cells populating the larval brain (Figure 1A). We obtained a cell atlas of the first instar larval brain with a total of 9353 cells and a median of 1658 genes per cell. The atlas is composed by cells from two different feeding conditions, normal feeding and 4 hr starvation prior brain dissection. Each experimental condition was treated separately and the libraries obtained were sequenced, aggregated and processed according to the standard pipeline for 10X single cell gene expression. In summary, the normal dataset comprised 4708 cells with a median of 1434 genes per cell and 836,393 mean reads per cell. While the starvation dataset comprised 4645 cells with a median of 1962 genes per cell and 545,512 mean reads per cell (Supplementary file 1).

Figure 1 with 2 supplements see all
Cellular composition of the Larval brain by Single-Cell RNA-Seq.

(A) Experimental procedure. Drosophila late first instar larval brains/ventral nerve cords (VNC) were dissected and the VNCs were removed. Brains were collected and dissociated into a suspension of single cells. (B) Cell atlas of the larval brain reveals five main cell-types: neurons, NPCs, glial cells, UNs and other cell-types, represented in a Seurat UMAP plot. Groups are color coded. (C) Cell-types are recognized based on the expression of previously characterized marker-genes. A simplified heatmap illustrates this process: a subgroup of cholinergic neurons expresses the pan-neuronal marker elav and ChAT, a protein that catalyzes the biosynthesis of the neurotransmitter acetylcholine; NPCs are recognized by the expression of Notch and dpn and glial cells by repo expression. (D–D’’) Validation of the markers used to identify the different cell-types within the larval brain by immunostainings. The images display one lobe of the larval brain. Nuclei were labeled with DAPI and cellular borders with Dlg (disc large). Scale bar: 10 µm.

To characterize the cellular population of the larval brain under normal feeding conditions, we further analyze the 4708 cells belonging to the normal experimental condition. These cells were filtered, scaled and normalized using Seurat R package (Butler et al., 2018; Stuart et al., 2018). The resulting 4349 cells, with 12,942 genes detected, were later clustered generating 29 initial cell-clusters that were subsequently visually represented using a novel learning technique for dimensional reduction, Uniform Manifold Approximation and Projection (UMAP) (McInnes et al., 2018) (Figure 1—figure supplement 1A). Clusters were manually annotated based on previously identified marker genes for specific cell-types, typically assessing the top 10 differentially expressed genes across clusters (Figure 1—source data 1). Among the 29 clusters, we distinguished five main cell type categories: differentiated neurons, NPCs, glial cells, undifferentiated neurons and a group of non-neural cells (Figure 1B). Differentiated neurons could be defined by the expression of the pan-neuronal marker embryonic lethal abnormal vision (elav), presynaptic genes or widely expressed neurotransmitters, NPCs by Notch (N) or deadpan (dpn) expression, and glial cells by the expression of reversed polarity (repo) (Figure 1C,D, Figure 1—figure supplement 1B). Some of these cells displayed particular overlaps between their expression profiles, enabling a more simplified version of the UMAP plot with only 15 clusters (Figure 1—figure supplement 1A). Within these clusters we distinguished mushroom body Kenyon Cells (KC), neurons expressing distinct peptides and neurotransmitter-releasing neurons, among the differentiated cells. While in the group of non-neural cells, we could differentiate cells coming from the ring gland, corpora allata, imaginal discs, hemolymph, lymph gland and fat tissue (Figure 1—figure supplement 2A). The identification of these last category demonstrates that during the dissection protocol, while the VNC was removed, different organs remained at least partly attached to the brain. Interestingly these cells clustered separately, further highlighting the power of the methodology.

Mapping neural progenitor cell diversity reveals presence of characteristic neurogenic cell-types

Several clusters showed prominent expression of key genes involved in different steps of neurogenesis. We could identify a population of Notch (N) positive cells, divided into several clusters closely located in our dimensionality reduction representation. To further confirm the identity of these cells, we analyzed the expression of known NPCs markers. In addition to N, these cells were expressing dpn, asense (ase), klumpfuss (klu), pointed (pnt) and prospero (pros) (Figure 2—figure supplement 1A). During embryonic development, neuroblasts are known to follow a temporally sequential expression of transcription factors, also known as temporal cascade: Hunchback (Hb), Kruppel (Kr), Pdm1/Pdm2 (Pdm), Castor (Cas) and Grainy head (grh) (Allan and Thor, 2015). The early cascade gene Kr was observed to be expressed in differentiated cells, while the late gene grh was almost exclusively expressed in cells from the neurogenic clusters (Figure 2A).

Figure 2 with 2 supplements see all
Identification of neural progenitor cell populations.

(A) Expression-pattern of genes from the temporal cascade distinguish population of cells. Two dimensions UMAP plot labeling genes from the temporal cascade. Grh, grainy head in green and Kr, Kruppel in red. Grh is broadly expressed in the recently born neuroprogenitors, while Kr is expressed in mature cells. (B) Re-analysis of the neurogenic populations by sub-clustering the original NPCs population, plotted in a Seurat UMAP plot, identifies 11 sub-populations of cells. NPCs are further divided into neuroblasts and optic lobe epithelium (OLE). (C) lncRNAs are present in NPCs expression profile. Color coded UMAP plot showing the abundance of long non-coding RNAs (lncRNAs). The scale represents gene expression levels. (D) OLE can be distinguished from the remaining neuroblasts based on the expression of characterized marker genes. Dot size corresponds to the percentage of cells expressing a particular gene, while color intensity represents gene expression levels. (E) Cell-cycle scores can be estimated transcriptionally. UMAP plot showing the different NPCs color coded based on cell-cycle phases. The pie graph illustrates the percentage of NPCs in each phase, G1, G2M and S. (F) EdU incorporation and NPCs proliferation. Immunostaining of the larval brain illustrating Dpn+ proliferating NPCs at 16 hr ALH. Proliferation index (PI) was calculated as the ratio between Dpn+EdU+ NPCs and Dpn+ NPCs. Error bars represent standard error. N = 5. Nuclei were labeled with DAPI. Scale bar: 20 µm.

To further resolve NPCs, we sub-clustered all the neurogenic populations, obtaining a new representation for these cells now grouped in 11 clusters (Figure 2B). Next, we examined the top differentially expressed genes among each group and we observed a high degree of similarity in their transcriptional programs. Interestingly, we detected a high number of cells expressing long non-coding RNAs (lncRNAs); some of them with reported functions, as it was the case of cherub (lncRNA:CR43283), known to be involved in tumorigenesis and in normal brain development (Landskron et al., 2018; Malin and Desplan, 2018), but also lncRNAs with unknown functions, like lncRNA:CR30009 (Figure 2C). In parallel, we observed expression of genes reported to be actively transcribed in the optic lobe neuroepithelium, such as Ocho (Ocho), Twin of m4 (Tom) and Bearded (Brd) (Egger et al., 2010) in a subpopulation of cells; therefore, this group of cells was annotated as the optic lobe epithelium (OLE) (Figure 2D). In addition to these genes, we identified basic helix-loop-helix (bHLH) transcription factors, such as E(spl)m8-HLH and E(spl)mgamma-HLH (Figure 2D). The remaining clusters showed different combinations of the following genes, all reported to be involved in neurogenesis: deadpan (dpn), klumpfuss (klu), asense (ase), earmuff (erm), pointed (pnt) and prospero (pros), among others (Figure 2—figure supplement 1B).

Knowing that NPCs upon embryogenesis, beside mushroom body and lateral neuroblasts, enter a quiescence state and that later, upon nutritional-signals reactivate (Egger et al., 2008); we evaluated cell-cycle genes expression in the NPCs population. We observed that the different NPCs displayed distinct cell-cycle marker genes, resulting in a heterogenic population of cells. We found a larger number of cells in G1 and G2/M phases, in comparison to S phase (Figure 2E). These results further support recent observations, where G2 phase was shown to be an entry point to quiescence, alongside G0 phase (Otsuki and Brand, 2018). Surprisingly, a relevant number of NPCs expressing marker genes for S phase were observed, indicating that these cells may be replicating their genomic content in order to proliferate. Therefore, we performed EdU incorporation experiments, together with dpn immunostaining, and identified approximately 30% of dpn positive EdU positive cells, from the total of dpn positive cells (Figure 2F); suggesting that at 16 hr ALH (after larval hatching) some NPCs were already exiting their dormant state and reassuming proliferation.

In close proximity to the neurogenic population in the UMAP plot, we identified two clusters of cells that lack expression of neurogenic marker genes, but express neuronal markers such as elav, Syt1 (Synaptotagmin 1) and nervana 3 (nrv3). However, these clusters lacked of marked expression of genes required for neurotransmitter biosynthesis. In addition, we found high expression of headcase (hdc) and unkempt (unk), two genes implicated in controlling proper timing of neural differentiation (Avet-Rochex et al., 2014). Other genes enriched in these cells were Thor, scylla (scyl), hikaru genki (hig) and Broad-Z3 (br) (Figure 2—figure supplement 2A). Since these cells clustered in vicinity of NPCs, apart from the remaining differentiated neurons, their lack of neurotransmitter markers and their expression of differentiation genes, we labeled these cells as presumptive undifferentiated neurons (UNs): UNs 1 and UNs 2. However, further analyses and validations are required to establish their bona fide identity.

Neuronal cell-types co-express different neuroactive molecules

We next analyzed the population of mature cells, more precisely those cells positive for synaptic markers: synaptobrevin (nSyb) and synaptotagmin (Syt) (DiAntonio et al., 1993), and evaluated the expression of genes involved in the release or synthesis of neurotransmitters: glutamate (Glu), acetylcholine (ACh), gamma-aminobutyric acid (GABA) and monoamines. We classified cells as glutamatergic, cholinergic, GABAergic and monoaminergic neurons; as they expressed: vesicular glutamate transporter (VGlut), vesicular acetylcholine transporter (VAChT), Glutamic acid decarboxylase 1 (Gad1) or vesicular monoamine transporter (Vmat), respectively. Glu and Ach were found to be the most abundant neurotransmitters, as they were each expressed in 24% of the total number of neurons. GABAergic and monoaminergic neurons occupied the third and fourth place, with 17% and 10% of the total number of neurons, respectively. (Figure 3A–B).

Figure 3 with 2 supplements see all
Neurotransmitter expression and co-expression in the larval neuronal population.

(A) Neurons are classified based on the expression of neurotransmitters. Seurat UMAP plot showing the distribution of the five main neuronal cell-types. (B) Simplified heatmap, representing neuronal subpopulations. Genes displayed are the main markers analyzed to identify neuronal cell-identities. The x axis represents individual cells, each line corresponds to one neuron. Gene expression levels are color coded. (C) Neurotransmitters are co-expressed in a subset of neurons. Dot size corresponds to the percentage of cells expressing a particular gene, while color intensity represents gene expression levels. (D) UpSet plot (Conway et al., 2017) illustrating the co-expression of neurotransmitters. Light and bold numbers represent number of cells and percentages of cells, respectively. The percentages were calculated based on positive cells for a particular neurotransmitter or a combination of them, in comparison to the total of neurons from the dataset. (E) Co-expression analysis based on the simultaneous expression of ple and VGlut. Blend UMAP plot showing only ple positive cells. Cells co-expressing ple and VGlut are shown in yellow. Threshold: 0,4.

In past years, the co-existence of more than one neurotransmitter in the same neuron has been extensively studied in mammals. A neuron could release multiple neurotransmitter types from the same synaptic vesicle, from different vesicles at the same synapse or even at different synaptic boutons (Vaaga et al., 2014). The recent publications on the Drosophila adult brain cell atlas re-opened this subject, by providing a list of co-expressed neuroactive molecules in the brain (Croset et al., 2018; Davie et al., 2018; Nässel, 2018). Therefore, we investigated the phenomenon of dual or multiple transmitter neurons in the larval brain. We analyzed all the possible combinations of the four very well-known marker genes for neurotransmitters: VGlut; VAChT; Gad1 and Vmat. Remarkably, we found an overlap in all combinations, although the occurrence of these events represents a small fraction in the whole dataset. Dual-transmitter neurons were more frequent than triple-transmitter neurons, with the three highest overlap occurring between VGlut and Vmat (3%), followed by VGlut and VAChT (2%) and VGlut and Gad1 (2%). Triple-transmitter expressing neurons only represent approximately 1% of the total of neurons (Figure 3C–E). Next, we validated these observations with immunostainings. We observed an overlapping between VGlut and pale (ple), a tyrosine hydroxylase involved in the synthesis of dopamine (Neckameyer and Quinn, 1989); as well as between VGlut and Gad1 and VAChT and Gad1 (Figure 3—figure supplement 1). Thereby, a subset of neurons actively co-expressed different neurotransmitters, showing that this phenomenon also occurs in the Drosophila larval brain.

Next, we further characterized the group of monoaminergic neurons, defined by the expression of Vmat. We analyzed the expression of specific markers: dopa decarboxylase (Ddc), Tyramine β hydroxylase (Tbh)/Tyrosine decarboxylase 2 (Tdc2) (Monastirioti et al., 1996; Roeder, 2005) and serotonin transporter (SerT); in order to recognize dopaminergic (DA), octopaminergic(OA)/tyraminergic(TA) and serotonergic neurons, respectively. Interestingly, only Ddc and ple were expressed at high levels in monoaminergic neurons, therefore this cluster was labeled as dopaminergic neurons (Figure 3—figure supplement 2A). OA/TA and serotonergic neurons were displayed in different clusters, always in the presence of another neurotransmitter, further supporting our findings of dual expression of transmitter-molecules in the larval brain neurons (Figure 3—figure supplement 2B).

Characterization of neuropeptide expression in the Drosophila larval brain

Among the population of mature cells, we found a cluster of cells that differentially expressed genes involved in protein synthesis, as well as the pan-neuronal marker elav. Since neuropeptide biogenesis requires the protein synthesis machinery, we wondered if these neurons were peptidergic. After analyzing the different genes being expressed, we observed that these cells contained different known neuropeptides, therefore we classified them as peptidergic neurons. The different neuropeptides followed different expression patterns; some were broadly expressed, while others were only expressed in a small number of cells.

To further characterize the peptidergic neurons (Figure 4A), we subdivided those cells that were positive for a specific neuropeptide and investigated their expression profiles. Knowing that the larval brain possesses five lateral neurons (LNs) per hemisphere, of which only four express the neuropeptide Pigment-dispersing factor (Pdf) (Collins et al., 2012; Helfrich-Förster, 1997), we decided to further characterize this cell-type. We distinguished the Pdf-LNs of the larval brain, and in addition to Pdf, these cells expressed core clock component genes: cryptochrome (cry), period (per), clock (clk) and timeless (tim). In addition, PDF neurons also expressed the Pigment-dispersing factor receptor (Pdfr), suggesting a self-regulatory function for Pdf secretion. Moreover, genes implicated in circadian processes showed to be co-expressed with Pdf, as it was the case for the nuclear hormone receptors, Hr51 and Hr38, and the transcription factor vrille (vri) (Abruzzi et al., 2017). Furthermore, we identified novel marker genes for this particular cell-type, some of them with described functions, such as the Cyclic-AMP response element binding protein A (CrebA), Matrix metalloproteinase 2 (Mmp2), Allatostatin C receptor 2 (AstC-R2) and the transcription factor stripe (sr); as well as other uncategorized genes, such as CG11221, CG44153, CG43902 and CG12541 (Figure 4B). In close vicinity with these cells, another population of neuropeptide-producing cells is involved in the secretion of the Prothoracicotropic hormone (Ptth). We mapped these cells to our dataset and we found that this gene was not being transcribed at high levels, compared to other neuropeptides, but it was indeed present in the larval brain (Figure 4C).

Neurosecretory cells distribution in the larval brain.

(A) Schematic representation of some neurosecretory cells present in the larval brain and the ring gland. VNC, ventral nerve cord. (B, D, E) Heatmaps showing gene expression profiles of PDF neurons, IPCs and CRZ neurons. In each case, only Pdf, Ilp2 and Crz highly positive cells were considered, respectively. White vertical lines separate individual cells, each column represents one cell. Gene expression levels are color coded. (C) Dot plot showing the presence of PTTH and Akh neurons, in peptidergic neurons and prothoracic gland, respectively. Dot size represents percentage of cells expressing a particular gene, while color intensity represents gene expression levels. (F) Neurosecretory cells express one or multiple neurotransmitters. Violin plot illustrating co-expression of neurotransmitters and neuropeptides. Violin plots represent the probability density of the data integrated with kernel density estimation. Wider sections of the violin plots represent a higher probability of cells with the indicated gene expression level, while skinnier sections represent lower probabilities.

Following the previous analyses, we then focused on another neurosecretory cell-type, the insulin producing cells (IPCs). These cells are responsible for the synthesis of 3 main insulin like peptides (Ilps): Ilp2, Ilp3 and Ilp5 (Nässel and Vanden Broeck, 2016). We found a small set of Ilp positive cells, therefore we classified them as IPCs (Figure 4D). The secretion of these neuropeptides is thought to be regulated through nutrient sensing in cells located in the ring gland, more precisely in the cells releasing Adipokinetic hormone (Akh). Consequently, we decided to study the expression of Akh in cells considered to be non-neural cell-types, and we observed that Akh was present in the thought-to-be the prothoracic gland and not in the larval brain (Figure 4C). Additionally, some neuropeptides are known to require the peptidase amontillado (amon) in order to become bioactive, we checked if this was also the case for the Ilps and noticed that amon mRNA levels were significantly high in the IPCs found in the larval brain (Figure 4D).

Next, we identified a set of neurons involved in the release of Corazonin (Crz), the CRZ neurons. However, in contrast to Pdf-expressing neurons, we did not observe an overlap between Crz and Corazonin receptor (CrzR) in these cells. Interestingly, we found different transcription factors being co-expressed in the CRZ neurons, such as Distal-less (Dll) and Sp1 (Figure 4E). In addition, we observed the short neuropeptide F (sNPF) being co-synthesized in Crz positive cells, as it has been previously described (Johard et al., 2008; Lee et al., 2004). Nevertheless, sNPF has been reported to exhibit a wide expression in the larval brain (Nässel et al., 2008), and in addition to its expression in Crz neurons, it was also expressed in the mushroom body Kenyon cells (Figure 5B).

Characterization of the mushroom body cellular composition.

(A) Identification of three main cell types among the mushroom body cluster of cells. Left panel: Re-clustering of the MB cluster led to three different cell populations: MBNBs, MB neuroblasts; MBUNs, MB undifferentiated neurons and MBKCs; MB Kenyon cells. Right panel: cell cycle analysis of the MB population further confirms previously mentioned cell-types. S, S phase; G1, G1 phase and G2M, G2/mitosis phases of the cell cycle. (B) The different cell types express distinct genes and validates their classification. Dot plot representation of the main markers found for each MB subpopulation. Dot size corresponds to percentage of cells expressing a particular gene, while color intensity represents gene expression levels. (C) Validation of marker genes found for the MBKCs. Immunostaining showing the distribution of prt in the larval brain MB. The MB was labeled with Fas2 and nuclei with DAPI. In the merge condition, prt antibody colocalizes with Fas2, indicating their co-expression in the MB. Scale bar: 100 µm.

Finally, we analyzed the expression of genes required for neurotransmitter synthesis and transport in peptidergic neurons. We observed that PDF neurons were mostly glutamatergic, while IPCs, PTTH and CRZ neurons seemed to be more promiscuous, co-releasing different neurotransmitters. These findings are in agreement with the notion that a single neuron could in principle make use of different types of molecules for intercellular communication (Figure 4F).

Larval mushroom body characterization identifies three major cell-types

A well-studied structure in the Drosophila brain is the mushroom body (MB), a region where olfactory learning and memory take place (Davis, 2011). In the adult fly each MB consists of approximately 2000 neurons, called Kenyon cells (KCs), these cells are produced by four mushroom body neuroblasts (MBNBs) per hemisphere, that arise during embryogenesis and continue dividing until pupal stages (Kunz et al., 2012). The neurons populating the larval MB, are embryonic-born KCs (Pauls et al., 2010). We next addressed if we could identify KCs based on known marker genes for the adult MB.

We first sub-clustered the population identified as mushroom body, identifying three types of cells based on cell-cycle phases and marker genes: mushroom body Kenyon cells (MBKCs), mushroom body neuroblasts (MBNBs) and cells undergoing differentiation (MBUNs) (Figure 5A). MBKCs expressed characteristic MB receptors: Dopamine 1-like receptor 1 (Dop1R1); Dopamine 1-like receptor 2 (Dop1R2) and Dopamine 2-like receptor (Dop2R). In addition, these cells were enriched for Fasciclin 2 (Fas2), short neuropeptide F (sNPF) and Neprilysin 1 (Nep1), among other genes (Figure 5B). Remarkably, portabella (prt), an orphan vesicular transporter previously reported to be expressed in the mushroom body (Brooks et al., 2011), was enriched in the larval KCs (Figure 5B). We confirmed this finding by immunostaining, where we observed an accumulation of prt in the MBKCs, more precisely in the calyx, penduncle, and vertical and medial lobes (Figure 5C). As expected, MBKCs appeared to be cholinergic (VAChT).

On the other hand, MBNBs were found to be constantly proliferating, as their cell-cycle scores indicated (Figure 5A). Moreover, these cells expressed characteristic neuroprogenitor marker genes, such as N, asense (ase), Delta (Dl) and tailless (tll). In addition, target of Poxn (tap), a gene playing a key role during mushroom body development was also upregulated in this cell population (Figure 5B). Finally, MBUNs expressed the neuronal markers, nSyb and VAChT, but at lower levels in comparison to MBKCs, indicating that they are not yet fully differentiated. Therefore, the particular composition and expression profiles of the MB cells could explain its location in the UMAP plot, as they are found closer to the NPCs than to neuronal clusters.

Characterization of glial cell-types populating the larval brain

A major cell-type of the brain are the glial cells. Different types have been described in the adult brain: cortex, surface, neuropil and astrocyte like glia (Croset et al., 2018; Konstantinides et al., 2018). To identify the glial cell populations in the larval brain, we selected the marker genes for each cell-type and evaluated their expression. We observed three different clusters: astrocytes/neuropil glia, cortex/chiasm glia and surface glia, enriched for specific marker genes (Figure 6A).

Classification of glial cell-types.

(A) Analysis on the glial cell population revealed different glial cell types. After sub-clustering of the initially identified glial cells, three main clusters were identified: surface glia, astrocyte/neuropil glia and chiasm/cortex glia. (B) Each particular glial cell type possesses a particular expression profile. The differentially expressed genes are represented in a dot plot, where dot size corresponds to the percentage of cells expressing a particular gene and color to gene expression intensity levels. Red: high expression, blue: low expression.

Astrocytes like glia were identified based on expression of known marker genes: astrocyte leucine-rich repeat molecule (alrm), Excitatory amino acid transporter 1 (Eaat1), GABA transporter (Gat), Glutamine synthetase 2 (Gs2) and wunen-2 (wun2) (Huang et al., 2015). Since we found another cluster of cells expressing Eaat1 and Gs2 in addition to ebony (e), and knowing that the last one is only expressed in neuropil glia, we annotated this cluster as astrocytes/neuropil glia. Then, cortex/chiasm glia was identified by the expression of wrapper (wrapper) (Konstantinides et al., 2018; Noordermeer et al., 1998) and hoepel1 (hoe1). Lastly, surface glia was characterized by the following marker genes: I'm not dead yet (Indy) and CG6126 (Figure 6B).

Starvation effects on larval brain cellular composition

Changes in the nutritional state of a brain may alter ongoing transcriptional programs and require additional or parallel ones. Here we focused on understanding how distinct cell-types respond to a specific brain state. Therefore, we set out to alter the availability of nutrients by starving first instar larvae for four hours. We observed that at this time point the initial larval population was reduced approximately two times, indicating the strong effect that starvation has on the survival rate (Figure 7—figure supplement 1). We then compared both larval brain cell atlases, normal versus starvation. We found that both datasets overlap for most clusters containing cells from each condition, with two clusters being present in only one condition. This suggests that after starvation, some cell-types were more strongly affected than others and that the changes in their transcriptional programs made them cluster separately (Figure 7A). Therefore, we proceeded to identify each cluster in order to understand where the effect of starvation had an impact on the larval brain cellular composition (Figure 7B).

Figure 7 with 3 supplements see all
Effects of starvation on the cellular composition of the larval brain.

(A) Starvation reveals sensitive cell types. Overlapping UMAP plots for the normal and starved condition, evidence the overall similarity among cell atlases, with few exceptions. (B) Integration across conditions recapitulates the principal cell types largely described in the previous sections. (C) A closer look to the Neurons X reveals its composition. Different neurotransmitters seemed to be equally expressed in the cells of Neurons X cluster. (D) UNs 1 and UNs 2 shared common features. The dot plot representation illustrates some of the marker genes for this particular cell type and the effect of starvation on their expression levels. Dot size corresponds to percentage of cells expressing a particular gene, while color intensity represents gene expression levels.

First, we manually annotated the different cell clusters upon combining the two conditions, resulting in approximately the same cell-types largely described above. Among all the clusters, only two were missing after starvation: UNs 2 and Neurons X. To characterize these cell-types, we analyzed their expression profiles and we found that Neurons X exhibited a combination of different neurotransmitters that could be further subdivided into smaller clusters based on the expression of particular neurotransmitters (Figure 7C). Thus, a subset of cells of different neurotransmitter identities respond probably in a similar way so that they now form a joint cluster. Nevertheless, it is also possible that cells populating these clusters were stochastically lost during sample preparation and its posterior manipulation.

The second cluster missing upon starvation was UNs 2, which showed an overall similarity with the non-altered UNs 1 cluster. Both expressed genes involved in TORC1 signaling pathway: Repressed by TOR (REPTOR), REPTOR-binding partner (REPTOR-BP) and Thor (Figure 7D). In addition to these genes, we observed expression of hdc and unk, whose products form a complex and regulate cell cycle progression trough binding TORC1 components, in response to nutrient intake (Li et al., 2019). Furthermore, transcripts of genes involved in cell growth regulation were also detected, such as charybde (chrb) and scylla (scyl). Therefore, UNs 2 appear to be more sensitive to starvation conditions than the remaining cell types, explaining their absence after starvation (Figure 7A). Since hdc was found to be expressed in both NPCs and UNs, but only enriched in UNs, we quantified hdc expression in normal and starved brains. We observed a decreased number of hdc positive UNs upon starvation, as suggested in silico. In addition, these cells displayed lower levels of the pan-neuronal marker elav, in comparison to the surrounding mature neurons. These results suggest that UNs can be characterized by a combination of high levels of hdc expression an low levels of elav expression; and that in fact these group of cells were severely affected by the lack of nutrients (Figure 7—figure supplement 2).

In early larval stages neuroblasts are arrested in G0 or G2 phases of the cell-cycle (Otsuki and Brand, 2018) and a complex signaling mechanism between fat-body, glial cells and neuroblasts triggers their reactivation upon nutrient-sensing (Chell and Brand, 2010; Sousa-Nunes et al., 2011). Thus, we evaluated how cell-cycle marker genes expression was affected by the lack of nutrients. We observed that the above described cell-cycle heterogeneity was maintained upon starvation, but the proportion among cell cycle phases was altered. In addition, we noticed a decreased number of cells in G2M phases, and an increase in G1 and S phases; suggesting that upon starvation neuroblasts do not exit quiescence and therefore less cells undergo mitosis (Figure 7—figure supplement 3).

Transcriptional responses to starvation

Once cell identities were assigned, we investigated changes at the transcriptional level. First we analyzed the expression of genes involved in fatty acid metabolism (Zinke et al., 2002). We found withered (whd), a carnitine O-palmitoyltransferase, being primarily upregulated in glial cells upon starvation. Moreover, other genes with key roles in fat catabolism were also upregulated, as was the case of the long-chain-fatty-acid-CoA ligase, pudgy (pdgy) and the Lipase 4 (Lip4). Among genes that were downregulated after starvation we found the fatty acid synthase 1 (FASN1) (Figure 8A). These results indicate that under nutrient-stressful conditions, glial cells potentiate catabolic pathways to survive, and pause anabolic pathways to avoid energy waste.

Figure 8 with 1 supplement see all
Variation in gene expression upon starvation.

(A) Genes involved in fat acid metabolism are up and downregulated upon starvation in glial cells. Composed violin plot showing changes in gene expression across experimental conditions. Violin plots represent the probability density of the data integrated with kernel density estimation. Wider sections represent higher probability of cells with the indicated gene expression level, while skinnier sections represent lower probability. Each violin plot is split by condition, half corresponds to the normal condition and half to starved condition. Conditions are color coded. (B) Neuropeptides respond differently to nutrient restriction, some of them were upregulated and some others downregulated. Blue indicates high expression, while gray indicates low expression. (C) lncRNAs are also affected by starvation. lncRNA:CR40469 and lncRNA:CR42862 positive cells vary after nutrient restriction. In red, cells expressing a particular lncRNA; in gray, remaining cells. Pie graphs represent the percentage of lncRNA positive cells in the total of NPCs and glial cells. (D) Genes downstream of REPTOR/REPTOR-BP complex are affected upon starvation. Barplot showing changes in the number of cells expressing a particular gene, upon TORC1 downregulation due to starvation. Experimental conditions are color coded.

Next, to determine genes up or downregulated upon starvation, we scatter plotted the average expression of cells under normal feeding condition and starvation (Supplementary file 2, Source data 1). To identify outliers, we calculated the average fold change across conditions. We observed that the expression profile of differentiated cells, like neurons releasing neurotransmitters, remained invariable; indicating that their expression programs were not affected by the lack of nutrients. We found that upon food deprivation many neuropeptides were upregulated, which are known to regulate feeding behavior. Leucokinin (Lk) and Adipokinetic hormone (Akh) expression appeared upregulated after starvation, a predictable result since Lk negatively regulates food intake and Akh triggers accumulation and availability of storage lipids and glycogen (Gáliková et al., 2015; Yurgel et al., 2019). Moreover, the neuropeptides Hug and SIFa, controlling growth and metabolism and promoting feeding behavior upon hunger signals (Martelli et al., 2017), respectively; were both upregulated after starvation. Additionally, the upregulated neuropeptides identified in our dataset were also regulating water homeostasis and locomotor activity, as it was the case for the Ion transport peptide (ITP). In contrast, other neuropeptides were downregulated as Allatostatin A (AstA), which has been previously reported to reduce feeding and promote sleep, regulated by Pdf expression (Chen et al., 2016) (Figure 8B). These results suggest that cells involved in feeding behavior and homeostasis are more sensitive to starvation than the remaining cell types, and that genes associated to these mechanisms show the strongest and fastest response to food deprivation in order to alert larvae about their stressful environment.

Additionally, we observed different lncRNAs being affected by the lack of nutrients. Specially, we noticed that lncRNA:CR40469 and lncRNA:CR42862 were upregulated in starved glial cells and NPCs (Figure 8C). Similarly, we detected changes in the expression levels of lncRNA:CR42491 and lncRNA:CR44832 in NPCs and glial cells upon starvation (Figure 8—figure supplement 1B).

Then, we tested whether components of the TORC1 signaling pathway were affected in the larval brain upon starvation. The TORC1 complex, composed by Target of rapamycin (Tor) and Raptor (raptor), was lowly expressed in both conditions in different cell-types of the larval brain. Since TORC1, under nutrient stress condition, is inactive (Tiebe et al., 2015); we wondered if the change was detectable already at mRNA levels. We did not observe substantial changes at the transcriptional level upon starvation. However, as previously mentioned, two genes known to be acting downstream of TORC1: REPTOR and REPTOR-BP, showed to respond to starvation. Upon TORC1 inactivation, REPTOR is dephosphorylated and translocated to the nucleus, where it binds REPTOR-BP to form a complex and mediate transcriptional responses (Tiebe et al., 2015). We found that REPTOR expression levels were not significantly different upon starvation, but we noticed an increase in the number of cells expressing this gene, particularly in glial cells. To confirm these findings, we investigated the expression levels of the genes downstream of the REPTOR/REPTOR-BP complex. Unk, Thor, nop5, CG16721, CG6770 and CG11658 among other genes were affected upon starvation, as the number of positive cells for these genes showed to be different after starvation (Figure 8D). Finally, knowing that REPTOR and forkhead box, sub-group O (foxo) have overlapping target genes (Tiebe et al., 2015), we expected to observe an upregulation of foxo upon starvation. Indeed, we detected higher levels of foxo transcripts in this condition (Figure 8D). Thus, we could not detect changes in the expression levels of TORC1 components, but we did observe responses due to starvation in genes acting downstream of it, possibly indicating the presence of additional mechanisms mediating this activation.

Discussion

Here we have introduced an extensive cell atlas of the first instar larval brain at single cell resolution applying 10X Genomics technology, across two different conditions: normal feeding and starvation. Single-cell RNA sequencing has innovated the way in which tissues, organs and entire organisms are studied. Accessibility to the transcriptome of individual cells results in cell-type recognition and a better understanding of cellular processes and biological mechanisms. But large tissues display a greater number of cells, while smaller organisms are capable of performing a variety of tasks with a simplified cellular composition. Thus, Drosophila melanogaster has been chosen to better understand the composition of the brain. While the adult fly brain has been extensively described, we decided to fill the existent gap regarding the composition of the brain at early developmental stages. With only about 2000 cells in the brain, a more complete coverage may be achieved more rapidly. Our data set comprises about 9400 cells, suggesting that we have likely close to a 5x cell-coverage of the larval brain.

Unsupervised cluster analysis led us to identify major cell-type categories as well as more resolved cell-types. Canonical markers (e.g. Notch, elav) grouped all cells into major categories, which based on subtype makers (e.g. ChAT, dpn, alrm) further divide them into different cell-types. This analysis further allowed the depiction of putatively novel and intriguing biological features. An example of this is the multiple-neurotransmitter-releasing neurons, which express more than one neurotransmitter, as previously found in the adult brain (Croset et al., 2018). Even though these cells represent a small fraction of the total of cells populating the larval brain, it is interesting to understand the need of a neuron to produce different neurotransmitter types. Even more, deciphering the biological processes, in which these neurons are involved and the regulation behind the co-releasing phenomenon, opens a new field that requires further investigation. Similarly, co-expression of neurotransmitters and neuropeptides appears to be a common feature among peptidergic cells, suggesting different modalities of intercellular communication.

Interestingly cells of the mushroom body are closer to the neurogenic population in the UMAP plot than to the remaining differentiated neurons. Looking in detail to this group of cells, one can observe cells at different stages of differentiation: neuroprogenitors, cells undergoing differentiation and mature neurons. Knowing that the mushroom body neuroblasts continuously divide from the embryonic to the late pupal stage, and that they produce a specific neuron type (Kunz et al., 2012), could partially explain the localization of this cluster of cells closer to undifferentiated cells. In addition to the MBNBs, which are constantly proliferating, a subset of NPCs showed to be actively replicating its genomic content at 16 hr ALH, indicating that neuroblasts in the brain may exit their quiescent state earlier than what has been previously reported.

An intriguing, yet not fully understood, cell-type are the UNs, which in the UMAP plot locate in close proximity with NPCs. While these cells show expression of neuronal differentiation markers such as elav or Syt-1, they lack expression of genes required for neurotransmitter synthesis. Similarly, a recent study revealed a new quite abundant, neuronal cell-type. These cells identified as small undifferentiated neurons (SUs), are characterized by reduced dimensions, a dense heterochromatin and the absence of synapses (Andrade et al., 2019). Therefore, we speculate that our UNs may correspond to the previously described SUs. It is intriguing to speculate that hdc and unk, two genes previously described to restrict cell cycle progression in response to nutrition restriction (Li et al., 2019), present in neurogenic cells but enriched in UNs, have a role in maintaining these cells undifferentiated.

Moreover, we explored the changes in the larval brain caused by starvation at single-cell resolution. Food intake has a critical effect on survival and development. Nevertheless, how the brain composition and cellular programs are affected upon food deprivation remains largely unknown. The cellular content showed to be almost invariable, only few clusters disappeared after starvation. The remaining cell-types clustered together, independently of the experimental condition, indicating that cell-type identity is maintained once it has been established. Moreover, and as it was observed with aging (Davie et al., 2018), the effects of starvation are cell specific; transcriptional programs in neurons remained unaffected, while glial cells, NPCs and undifferentiated neurons experienced changes at the transcriptional level or even disappear due to stressful conditions. Additionally, the most susceptible genes are those involved in fat metabolism as well as some lncRNAs, which we hypothesize may have a regulatory function. Neuropeptides are also sensitive to nutritional levels. As expected, when cells face nutrient deficiency, protein synthesis is affected. In this case, cells favor the production of those neuropeptides involved in food intake and water homeostasis, downregulating the remaining ones in an attempt to survive. Nevertheless, is important to also consider and not exclude the possibility that these cell types could have been loss during sample preparation and subsequent manipulation. To confidently address this point, further repetitions are required.

Starvation is directly linked to TORC1, which integrates information about nutrient conditions of individual cells to trigger physiological responses (Dibble and Manning, 2013). We did not observe substantial changes at the transcriptional level for the genes in the TORC1 upon food deprivation, suggesting that cells regulate TORC1 activity at the protein level rather than at the transcriptional level. It is interesting to analyze those genes downstream of TORC1. We focused on the TORC1 signaling pathway, rather than in the insulin pathway, in an attempt to better understand nutrient sensing responses in a cell-autonomous fashion and not at a systemic level. Recently, REPTOR and REPTOR-BP were shown to be transcription factors responsible for mediating some of the responses of reduced TORC1 activity (Tiebe et al., 2015). As expected, REPTOR and its binding protein are both upregulated upon starvation, as well as their target genes. Under limited nutrient conditions, larvae have to choose between anabolic or catabolic processes. Here, we observe that under our extreme starvation condition, larvae have no other option than to catabolize their storages to obtain some energy and survive as long as possible. Even though intracellular nutrient-sensing activates TORC1, extracellular sensing must also be required to mount a complete physiological response. In this way, integration between different signaling pathways, Insulin-Foxo and TORC1-REPTOR, must be required.

Furthermore, the availability of the wildtype brain cell atlas as well as the starvation induced changes provide a resource for further functional analysis of neural circuits of the larval brain as well as developmental studies at the fundamental unit of biological organization: the cell (Regev et al., 2017).

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional
information
AntibodyAnti-GFP
(Chicken polyclonal)
AbcamCat# ab13970, RRID:AB_300798IF(1:1000)
AntibodyAnti-GFP
(Rabbit polyclonal)
Thermo FischerCat# A-6455, RRID:AB_221570IF(1:1000)
AntibodyAnti-Dpn (Guinea pig polyclonal)Spéder and Brand, 2014IF(1:5000)
AntibodyAnti-Dlg (Mouse monoclonal)Iowa H.B.Cat# 4F3, RRID:AB_528203IF(1:100)
AntibodyAnti-Fas2 (Mouse monoclonal)Iowa H.B.Cat# 1D4, RRID:AB_528235IF(1:20)
AntibodyAnti-hdc (Mouse monoclonal)Iowa H.B.Cat# U33, RRID:AB_10659722IF(1:5)
AntibodyAnti-repo (Mouse monoclonal)Iowa H.B.Cat# 8D12, RRID:AB_528448IF(1:20)
AntibodyAnti-prt (Rabbit polyclonal)David E. KrantzIF(1:300)
AntibodyAnti-Tyrosine Hydroxylase (Rabbit polyclonal)MilliporeCat# AB152, RRID:AB_390204IF(1:100)
AntibodyAnti-Elav (Rat monoclonal)Iowa H.B.Cat# 7E8A10IF(3:100)
AntibodyAnti-DsRed (Rabbit polyclonal)Takara BioCat# 632496,
RRID:AB_10013483
IF(1:1000)
AntibodyAnti-Rabbit Alexa 647 (Goat polyclonal)Molecular ProbesCat# A-21244,
RRID: AB_2535812
IF(1:200)
AntibodyAnti-Rabbit Alexa 488 (Goat polyclonal)Molecular ProbesCat# A-11008,
RRID: AB_143165
IF(1:200)
AntibodyAnti-Mouse Alexa 647 (Goat polyclonal)Molecular ProbesCat# A-21235,
RRID: AB_2535804
IF(1:200)
AntibodyAnti-Guinea pig Alexa 488 (Goat polyclonal)Molecular ProbesCat# A-11073,
RRID: AB_2534117
IF(1:200)
AntibodyAnti-Chicken Alexa 488 (Goat polyclonal)Molecular ProbesCat# A-11039,
RRID: AB_2534096
IF(1:200)
AntibodyAnti-Rat Alexa 647 (Goat polyclonal)Molecular ProbesCat# A-21247,
RRID: AB_141778
IF(1:200)
AntibodyAnti-Guinea pig Alexa 647 (Goat polyclonal)Molecular ProbesCat# A-21450,
RRID: AB_2535867
IF(1:200)
Genetic reagent (D. melanogaster)VGlut-Gal4BDSCRRID: BDSC_24635
Genetic reagent (D. melanogaster)UAS-myrGFPBDSCRRID: BDSC_32198
Genetic reagent (D. melanogaster)Gad1-GFPBDSCRRID: BDSC_59304
Genetic reagent (D. melanogaster)VAChT-Gal4BDSCRRID: BDSC_39078
Genetic reagent (D. melanogaster)UAS-mCD8::RFPBDSCRRID: BDSC_32219

Fly strains

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Drosophila melanogaster Canton-S was used as the wild type strain. Other fly strains used are described in the key resources table.. All flies were kept at 25°C in a 12 hr/12 hr light-dark cycle in vials containing conventional cornmeal agar medium.

Larval culture

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Larvae were grown and kept at 25°C in the same conditions as the adult flies and late first instar larvae were collected at 16 hr after larval hatching (ALH). For the starved condition, larvae were collected at 12 ALH, quickly washed to remove food leftovers and transferred to a Petri dish with 2% of agar, humidified with PBS, for 4 hr at 25°C.

Survival rate

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Larvae were grown as described before and at 12 ALH transferred to a Petri dish with 2% of agar, humidified with PBS at 25°C. After 4 hr of starvation, dead and alive larvae were counted and the percentages were calculated based on the initial number of larvae in the Petri dishes.

Immunofluorescence

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First instar larval brains were dissected in ice-cold PBS, placed on a 22 × 22 cover slip and fixed in 4% paraformaldehyde for 18 min at room temperature with slow agitation. Fixed brains were washed three times in PBST (PBS 0,3% Triton X-100), for 20 min each, at room temperature with slow agitation. All these steps were performed in a Columbia staining jar. Primary antibodies (see Key resources table) were prepared in PBST and the cover slips with the brains were incubated overnight at 4°C in a humid chamber. Primary antibody solutions were removed and brains were washed again three times in PBST for 20 min each at room temperature with slow agitation. Next, the secondary antibody (see Key resources table) solutions were added and incubated as described. After overnight incubation, secondary antibody solutions were removed and washes with PBST were performed. Brains were mounted in Vectashield (Vector Laboratories) antifade mounting medium. Images were acquired using Leica TCS SP5 confocal microscope and images were assembled using Fiji and Adobe Illustrator CC 2018. DAPI was added together with the secondary antibodies.

EdU incorporation

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Larval brains were dissected in ice-cold PBS and immediately incubated in PBS containing 200 µg/ml of EdU (Thermo Fischer Scientific - C10637) for 30 min at room temperature. Brains were quickly washed with PBS and fixed in 4% paraformaldehyde for 18 min at room temperature. Upon fixation, brains were washed with PBST and EdU detection was performed following the manufacturer’s recommendations. Dpn antibody staining was executed as described above. The proliferation index (PI) was calculated with following formula.

PI=Dpn+EdU+Dpn+

Brain dissection, dissociation and single cell suspension

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First instar larvae (40-45) were collected and quickly washed in water to remove food leftovers and yeast. Larvae were placed in drops of ice-cold PBS on the inside of a plastic petri dish lid. Fine forceps were used to dissect the larval brain. Once the larval brain was exposed, the ventral nerve cord was cut out using a pair of pin holders (Figure 1A), and the intact brain-lobes were collected in a low DNA binding tube containing 250 µl of ice-cold RNA free PBS for a maximum of one hour. The tubes containing the brains were centrifuged at 2000 rpm for 5 min at 4°C. After centrifugation, the supernatant was carefully removed and replaced with 200 µl of collagenase (1 mg/ml Sigma-Aldrich C9722) and incubated for 1 hr at 25°C with continuous agitation. To guarantee full brain digestion, the suspension was pipetted up and down each 10 min. The enzymatic reaction was arrested by diluting the suspension with 1 ml of PBS 0.04% BSA (Thermo Fischer Scientific AM2616). After washing the cells, the cell suspension was filtered through a 40 µm Flowmi Cell strainer (Bel-Art H13680-0040). The filtered suspension was centrifuged at 2000 rpm for 5 min at 4°C. The supernatant was discarded, cells were resuspended in 50 µl of PBS 0.04% BSA and further dissociation was ensured by gently pipetting the entire volume, approximately 200 times. Cell concentration was determined using a hemocytometer (Neubauer improved – Optik Labor) under a Leica DM 100 led microscope.

10x genomics and sequencing

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scRNA-seq libraries were prepared using the Chromium Single Cell 3’ Library and Gel Bead Kit v3 (10X Genomics), according to the manufacturer’s protocol (User Guide). Chips were loaded after calculating the accurate volumes using the ‘Cell Suspension Volume Calculator Table’. With an initial single-cell suspension concentration equal to 1000 cells/µl, we targeted to recover approximately 10,000 cells. Once GEMs were obtained, reverse transcription and cDNA amplification steps were performed. Sequencing was done on Illumina NovaSeq 6000 S2 flow cell generating paired-end reads. Different sequencing cycles were performed for the different reads, R1 and R2. R1, contained 10X barcodes and UMIs, in addition to an Illumina i7 index. While R2 contained the transcript-specific sequences.

10x data processing

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The sequenced libraries were processed according to Cell Ranger (version 2.2.0) count and aggr (aggregation) pipelines, provided by 10X Genomics. The reference genome was built based on the 3rd 2018 FlyBase release (D. melanogaster r6.22). The number of cells detected at the end of each experiment was determined by Cell Ranger with the number of barcodes associated with cell-containing partitions, estimated from the barcode UMI count distribution. Two final datasets were obtaining upon aggregation of individual experiments, non-starved and starved datasets, producing single feature-barcode matrices. The first one was built aggregating the libraries from three biological replicates, corresponding to larvae kept in conventional cornmeal agar medium prior to dissection (considered as the normal condition). This dataset resulted in a total of 4708 cells with a median of 1434 genes per cell. The second dataset was built in a similar way, but aggregating libraries from two biological replicates, corresponding to larvae starved for 4 hr prior to dissection (considered as the starvation condition), resulting in a dataset of 4645 cells with a median of 1962 genes per cell. The different aggregations were carried without specifying any normalization mode.

Seurat data processing

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Seurat version 3.0 (Butler et al., 2018; Satija et al., 2015; Stuart et al., 2018) pipeline was adapted and executed on the normal (non-starved) dataset and on a combination of both conditions: ‘normal’ and ‘starvation’ datasets. The matrices produced by cell ranger were processed and duplets, or eventually multiplets, were discarded based on the overall gene expression per cell. Cell quality was assessed by the percentage of mitochondrial gene expression per cell. Thus, cells with unique feature counts between 200 and 4500, and with less than 20% of mitochondrial genes were kept for downstream processing. Additionally, genes expressed in at least one cell were considered for the analysis. The final processed datasets resulted in 4349 and 4347 cells with a total of 12,942 and 13,589 identified genes, for the non-starved and starved datasets, respectively. Once Seurat objects were built, pre-processing steps were performed before downstream analysis. First, a log-normalization with a scale factor of 10,000 was applied to normalize gene expression of individual cells by the total gene expression of each dataset. Second, a linear transformation was executed to remove unwanted source of variation. Lastly, highly variable genes were determined applying FindVariableFeatures function with default parameters, producing a total of 2000 variables genes following the R package developer’s recommendations.

Upon preprocessing, the highly variable genes were considered for the dimensional reduction analysis to highly biological significance. To define the true dimensionality of the dataset, several approaches were considered: Elbow-Plot and JackStraw-Plot tests together with an evaluation of PC-heatmaps. Finally, principal components (PCs) were selected visually by carefully inspecting Elbow-Plots (Supplementary file 3) in order to assess the percentage of variance explained by each PC. In this way, 31 PCs were considered to identify cell clusters with a graph-based approach. To better resolve clusters, we modified the resolution, as increasing this parameter helps to subdivide existing clusters and gain granularity. Therefore, we used a resolution equal to two based on the number of cells and a carefully inspection of the dataset. Then, a non-linear dimensional reduction was performed to visualize the results in UMAP plots. Lastly, cluster identities were assigned after determining top 10 differentially expressed genes across cell clusters using a Wilcoxon Rank Sum test. Some of these clusters were later subset and re-analyzed for further characterization.

Integration between conditions was performed by identifying common anchors across both datasets, normal and starvation, to later combine them into a single Seurat object by applying the ‘IntegrateData’ function, which produced a batch-corrected expression matrix. The pipeline was applied with default parameters. Downstream analysis was performed as described above. Finally, Identification of differentially expressed genes across conditions was achieved by comparing the expression profile of each cluster across experimental conditions. These results were later represented in scatter plots to visualize outliers. For simplicity, only genes with a fold change higher than one or smaller than −1 were labeled in these representations. In the case where many genes resulted as outliers, only the top 10 genes with the highest scored were included in the graphs (Supplementary file 2, Source data 1).

Cell-cycle scoring

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Cell cycle phase scores were assigned by applying ‘CellCycleScoring’ function from Seurat R package, by providing a list of marker genes for S and G2 and M (G2/M) phases. Cells that did not express marker genes for neither phases S nor G2/M, were considered to be in G1 phase.

Re-clustering and further seurat analyisis

Neural progenitor cells

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Cells characterized as neurogenic were reanalyzed and subclustered, keeping only those cells with more than 200 expressed genes per cell. The data was normalized and a new PCA was computed. Then, 13 PCs were chosen, as previously described, and clusters were visualized in a UMAP plot, with a resolution equal to 1. Identities were assigned after analyzing differentially expressed genes, following the same principle described above.

Glial cells

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For glial cells, a similar analysis was performed. In this case, 11 PCs with a resolution equal to one were selected to generate a UMAP plot and represent new clusters. Cell identities were assigned after visually evaluating enriched marker genes expression.

Mushroom body

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MB cells were reanalyzed, subclustered and identified, as it was described for NPCs. This time 10 PCs were chosen and clusters were visualized in a UMAP plot, with a resolution of 0.8.

Peptidergic neurons

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Peptidergic neurons were reanalyzed individually. This particular analysis only considered cells with high expression levels of the peptide of interest. In this way, IPCs and PDF, PTTH and CRZ neurons were selected as follows: Ilp2 >4, Pdf >4, PTTH >3 and Crz > 6, respectively. Subsequent analysis was performed based on the expression of reported marker genes for each cell-type.

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Decision letter

  1. K VijayRaghavan
    Senior and Reviewing Editor; National Centre for Biological Sciences, Tata Institute of Fundamental Research, India

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

In this study, the authors lay out single-cell transcriptome maps in the first-instar larval brain of the fruitfly, Drosophila. This extensive and substantial map covers two central nervous system lobes. Single-cell RNAseq in this study illuminates a more representative transcriptional landscape of cellular composition than other similar studies. By analyzing sequence data, the authors also confirm that the maps are consistent with information available in the literature, highlighting that the resource offered by the study will be a useful and dependable reference for future studies. This work will be a considerable contribution to the field of neurogenesis and 'metabolic-neuroscience'.

Decision letter after peer review:

Thank you for submitting your article "Single-cell transcriptome atlas of the Drosophila larval brain" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and K VijayRaghavan as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

The authors lay out single-cell transcriptome maps in the first instar larval brain under two conditions – nutritionally normal and starved. This attempt is very extensive and substantial, as the map covers ~1.75x of two CNS lobes. Single-cell RNAseq in this study illuminates more representative transcriptional landscape of cellular composition than other similar studies (for example, Davie et al., 2018 in Cell- more sequenced cell numbers but 1x coverage) by exploiting the model organism with reasonable neuronal complexity and the size benefit. By analyzing the seq data, Avalos et al. confirmed that the maps are consistent with information available in the literature, highlighting that the resource offered by the study will be a useful and dependable reference for future studies.

In the cells from the nutritionally normal condition the authors identify 14 clusters of cells that correspond to five cell types – 1) Neural stem cells, 2) Differentiated neurons, 3) Immature neurons 4) Glia, and 5) Non-neural cells.

Amongst the neural stem cells, the author distinguished optic lobe precursors and all other stem cells. They focus on neurotransmitter identity of the neurons and show that glutamatergic neurons are the most abundant, followed by cholinergic, GABAergic and monoaminergic. They find low levels of co-expression of neurotransmitters, which has also been reported in other scRNA data from adult brains. They also identify the peptidergic PDF neurons, insulin-producing cells, and corazonin positive cells and identity some markers for some of them. They show that most of the peptidergic neurons are also glutamatergic. In their glial cluster, they identify surface glia, cortex glia, astrocytes and chiasm glia based on known molecular markers.

Finally, they analyse the cells from the starved condition. Interestingly, they identify two clusters that were absent upon starvation. One of these clusters consists of differentiated neurons of various neurotransmitter identities, the other of immature neurons – they don't discuss these further. Instead, they look at the transcriptome of all remaining cell types and show that genes involved in fatty acid metabolism are upregulated and those involved in fatty acid synthesis are downregulated in glial cells. They also show upregulation, downregulation and 'mis'-regulation of many neuropeptides and long non-coding RNAs in many cells, including glia upon starvation.

While the paper can be a considerable contribution to the field of neurogenesis and metabolic neuroscience, several concerns need to be addressed. In particular, the description of the experiments are inadequate and this makes it hard to properly assess the quality of the data, and therefore the results.

Essential revisions:

1) Because the entire manuscript relies on single-cell sequencing, we would like to have seen a more rigorous description of the quality of data they obtained. It's possible that the authors have done these quality checks but have not mentioned it in the manuscript. But, given that this is a rapidly improving technology, we would request the authors to please present this aspect of their data. For example:

– Have they done replicates of their experiments? How were batch effects between the two runs (which could arise due to various factors such as changes in sequencing depth, RNA capture efficiency, RNA->cDNA conversion efficiency, etc.) accounted for, particularly when comparing gene expression between normal and starved conditions? Was this automatically done by Seurat, and if so, was this done with Seurat's default settings?

– How many reads do they have per cell? Based on the text, the numbers don't add up. If the authors have 764,000 reads in total, they have anywhere between 70-200 reads per cell (depending on the answer to the next question). This cannot correspond to 1500 genes, unless they are pooling reads within clusters – are they?

– Have they sequenced the normal and starved brains in the same run or different runs? If the latter, then, could the authors provide more information about each individual sequencing run, namely, the median number of genes obtained in the starved and fed cases? Currently, paragraph one of subsection “Single-Cell RNA-Seq of the Drosophila larval brain reveals different categories of characteristic cell-types” does not convey information about the individual libraries.

– 3000 odd cells per condition seem rather low given that other similar experiments get 20,000-60,000 (Corset et al., 2018; Davie et al., 2018; Konstntinides et al., 2018).

2) The authors identify 28 clusters of cells, which they manually annotate. Their source data shows the top 10 genes they've used for this purpose. Could the authors please edit this table to include (a) the names of each cluster, (b) whether the markers used are based on prior literature or reported in this work based on in-silico considerations, and (c) the number of cells in each cluster for both the conditions?

3) The Supplementary file 2 shows average gene expression as scatter plots for each cluster of cells of the normal and starved conditions. These plots are very useful in visually identifying outliers. However, the criterion for identifying outliers seems to change between panels. Could the authors please explain what made them pick some genes on the outer boundaries of the scatter-plot as outliers, and others not? To clarify this, could the authors please compute the fold-change in mean expression between fed- and starved- (or the other way around) conditions and add this information to the statements made in paragraph two of subsection “Transcriptional responses to starvation” –. Alternately, they could define a criterion to define outliers as genes where the fold-change in mean expression between fed- and starved- conditions was greater than X, where X is 2 or 3?

4) The loss of the UN-2 and neurons-1 clusters is quite striking! An expected response to starvation at that stage is the failure of neural stem cells to exit quiescence and re-enter cell division. This could show up as clusters of cells missing. However, more mundane possibilities that would be useful to rule out would be: (1) that UN-2 and neurons-1 are cell types that are very rare in the larval brain, and hence, could stochastically be lost during sample processing from the starved larvae, and (2) that cells from the UN-2 and neurons-1 clusters are indeed present in the data, but due to a low read depth from those cells, have been assigned to other clusters that are present in the starvation condition?

5) In the VNC, the neural stem cells that exit quiescence at 15hours ALH are the G2 arrested stem cells. Have the authors considered looking at their neural stem cell cluster in the fed vs starved conditions to see if they can distinguish G0 and G2 arrested stem cells, and the effect of starvation on them?

6) Are there any molecular markers in the UN-2 and neurons-1 clusters, that the authors could use in vivo to demonstrate the loss of those cell types upon starvation?

7) A few aspects in the description of the methodology is lacking in detail in order to be able to reproduce the analyses in the paper for future readers. Specifically:

– Results section “Altogether, our datasets displayed a median of 764,000 reads and

1500 genes per cell, and in total of about 12,000 of genes were detected”; – Is this the total number of reads, or the number of mapped reads?

– Paragraph three of subsection “Single-Cell RNA-Seq of the Drosophila larval brain reveals different categories of characteristic cell-types” –: Can the authors state how many cells (what proportion) finally fell into non-neuronal clusters?

– Paragraph two of subsection “Mapping neural progenitor cell diversity reveals presence of characteristic neurogenic cell-types” –: How was the similarity of expression profiles quantified? Was it in terms of which genes had non-zero expression? "…examined the top differentially expressed genes…"? Is this the top 10 differentially expressed genes, or a different number than 10?

– Subsection “Seurat data processing” –: Can the authors describe how many genes and cells were finally considered for analyses in the paper after the filtering criteria mentioned were imposed? Or are the numbers described in the first paragraph – of the Results section obtained after the filtering criteria are imposed?

– Subsection “Seurat data processing” –: The sequence of operations carried out is unclear. Was the PCA calculated by taking all genes in each cell into account, or only based on the 2000 most highly variable genes? If it was the latter, could the authors explain why the PCA wasn't done with all genes wasn't performed, and add this to the Materials and methods section? On the other hand, if the PCA was calculated with the expression of all genes in each cell, could they explain why the set of the 2000 most variable genes was constructed? Was this set of variable genes used for identifying marker genes or for clustering?

– Subsection “Seurat data processing”: – What is the linear transformation that was applied before PCA was carried out? Why was this linear transformation needed?

– In the same section – it appears that three different methodologies were considered for choosing the number of principal components. Could the authors mention which methodology was finally followed for choosing the number of principal components? It will be useful to add these plots as a supplemental figure. Alongside this, could the authors also explain the amount of variance captured each time a given number of principal components is chosen?

– UMAP resolution – could the authors add a couple of sentences explaining the impact of increasing or decreasing the UMAP resolution used for clustering? Is this supposed to aid in sub-clustering?

– Finally, in the interest of reproducibility, it would be nice to make available (through a URL) the scripts that were used to carry out the analyses in the manuscript.

8) Cell numbers: If we go with their estimate of 2000 cells per brain, and they analyse 3500 cells (after filtering), then they are sampling the brain with less than a two-fold coverage. Given the tremendous diversity in the Drosophila CNS it would seem that this minimal coverage risks missing a whole range of rare cell types. And this of course has major bearing on the comparison to the starvation conditions as well, which was also less than two-fold. Granted, they do identify some rare cells, such as PDF neurons, but nevertheless, a less than two-fold coverage of an extremely diverse system risks resulting in misinterpretations, especially when comparing between conditions.

9) 4-hour starvation seems very short for getting any major transcriptomic changes, and indeed the changes appear to be minor. Is there any evidence for any physiological effect of a 4-hour starvation? Why was a 4-hour starvation chosen? Previously published larval nutrient restriction schemes have been e.g., 24-36 hours (e.g., PMID 21346761). As it now stands, it is difficult to relate the transcriptional changes to any physiological effect(s), and this brings down the impact of the study. Any progress in this area would have greatly strengthened the study.

10) Related to this issue, studies by the Alex Gould and Andrea Brand labs have revolved around the role of starvation in gating how/when neural progenitors emergence from quiescence during larval stages. This has revealed input from InR, PI3K, Akt, Alk/Jeb and TOR signalling, and some unknown signals, in part provided from the periphery, possibly relayed via CNS surface glia and onto progenitors. The authors of the current study, intriguingly, do find TOR signalling components altered, but do not relate this to the previous studies from Brand and Gould. In fact, somewhat disconcertingly, none of these seminal publications, in Nature, Cell, CellRep, are even mentioned in the current study (PMIDs: 21346761, 21816278, 23478023, 21183078). By analysing responses to starvation separately in glia versus progenitors, the authors have a golden opportunity to shed further light on this intriguing issue, but do not even appear to be aware of the literature in this field.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Single cell transcriptome atlas of the Drosophila larval brain" for further consideration by eLife. The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

1) In the revised version of this manuscript, the authors have addressed and satisfied most of the reviewers' concerns. They have clarified and explained most of the text that made interpretation of their data difficult. They have also added the second replicate of their starved condition. In light of these changes, their work is both interesting and valuable to the field and is recommended for publication.

A couple of important points comment, though. There might be an error in their supplementary xls sheet for marker genes. The log-fold changes and p-values associated with some genes in the xls sheet do not seem plausible: there are astronomically high log-fold changes and p-values that are greater than one. For example, consider genes #6,#8, #9 from the "cholinergic neurons 1" cluster. These genes have log-fold-changes of 129,541,248,584,619, 0.616 and 107,496,381,242,778, with associated adjusted p-values of 8.63e-35, 3.49e-17 and 6.24e07.

It's possible that these large values occur partly due to the transformations applied to the read counts of the genes in each cell.

P-values – adjusted or otherwise – cannot exceed 1, but the p-value of gene #9 is 62.4 million.

One might expect p-values to decrease as log-fold-changes increase. So, gene #8, with a fold-change of 0.616, would be expected to have a larger (less significant) p-value than gene #9, which has a fold-change of 107,496,381,242,778. But instead, we see that gene #6 has a p-value of 3.49e-17 while gene #9 has an implausible p-value of 62.4 million.

2) Introduction section: "nutriments" should be "nutrients"

3) Results section paragraph one: I find the mean reads/cell to be very high. From our own experience, and from numerous papers, you typically detect 25,000-50,000 gene reads/cell, resulting in the detection of 1000-2500 genes/cell. They list "836,393 mean reads per cell".

https://doi.org/10.7554/eLife.50354.sa1

Author response

Essential revisions:

1) Because the entire manuscript relies on single-cell sequencing, we would like to have seen a more rigorous description of the quality of data they obtained. It's possible that the authors have done these quality checks but have not mentioned it in the manuscript. But, given that this is a rapidly improving technology, we would request the authors to please present this aspect of their data. For example:

This is indeed an important point and we apologize for previously not including more information. We have of course performed quality checks and now included this information in the manuscript (see points below).

– Have they done replicates of their experiments? How were batch effects between the two runs (which could arise due to various factors such as changes in sequencing depth, RNA capture efficiency, RNA->cDNA conversion efficiency, etc.) accounted for, particularly when comparing gene expression between normal and starved conditions? Was this automatically done by Seurat, and if so, was this done with Seurat's default settings?

We have performed the experiments in three biological replicates for normal conditions and two biological replicates for starvation conditions. Replicates were processed with 10X Genomics’ software for single-cell RNA-seq experiments. First, each replicate was processed with “cell ranger count”. Then, all the experiments from the same experimental condition were aggregated with cell “ranger aggr”, to produce a single feature-barcode matrix containing all the data. Since we only used Single Cell Gene Expression v2, there was no need to correct for possible batch effects due to the chemistry version. Finally, the datasets were quality checked, scaled and normalized with Seurat R package.

For the comparison between conditions, we followed the recommendations given by Seurat v3 developers. We performed first an anchors identification step, to later combine the two datasets applying “IntegrateData” function, which produced a batch-corrected expression matrix. We now describe these steps in-depth in the Materials and methods section.

– How many reads do they have per cell? Based on the text, the numbers don't add up. If the authors have 764,000 reads in total, they have anywhere between 70-200 reads per cell (depending on the answer to the next question). This cannot correspond to 1500 genes, unless they are pooling reads within clusters – are they?

We apologize for not describing this clearly. In line with the previous point, we have now included this information in the Materials and methods section. We have also now included a supplement table describing the sequencing metrics for each experiment (Supplementary file 1). For the normal condition, the total number of reads is equal to 3,937,742,005 and the estimated number of cells is 4708 with 836,393 mean reads per cell.

– Have they sequenced the normal and starved brains in the same run or different runs? If the latter, then, could the authors provide more information about each individual sequencing run, namely, the median number of genes obtained in the starved and fed cases? Currently, paragraph one of subsection “Single-Cell RNA-Seq of the Drosophila larval brain reveals different categories of characteristic cell-types” does not convey information about the individual libraries.

This is indeed a valid point and we apologize for not including this information before. We have now included a supplementary table with all the sequencing metrics for each experiment (Supplementary file 1). In this file, we describe: total number of reads, mean reads per cells, expected number of cells, total number of genes detected, sequencing saturation among other parameters.

– 3000 odd cells per condition seem rather low given that other similar experiments get 20,000-60,000 (Corset et al., 2018; Davie et al., 2018; Konstntinides et al., 2018).

We have now performed additional single-cell experiments for each condition. Summarizing, now the normal and starved conditions consist of three and two biological replicas, respectively. In total, our dataset now comprises of 9353 cells. We hereby have an approximate 5x coverage of the first instar larval brain.

2) The authors identify 28 clusters of cells, which they manually annotate. Their source data shows the top 10 genes they've used for this purpose. Could the authors please edit this table to include (a) the names of each cluster, (b) whether the markers used are based on prior literature or reported in this work based on in-silico considerations, and (c) the number of cells in each cluster for both the conditions?

This is an excellent suggestion. We feel that such data may provide a valuable resource for various topics and have now included further details to Figure 1—source data 1, in order to clarify the annotation process. In this table, one can find the top 10 marker genes used to identify cell types. In a second sheet, we have specified the number of cells per cluster. And, in a third sheet, we added a table illustrating the literature-based approach used during the annotation step. In addition, we now include a new source file for Figure 7 (Figure 7—source data 1), containing the number of cells per cluster and per condition.

Moreover, to further gain experimental support for the cluster analysis and annotation we have extended the analysis of neurotransmitter co-expression. We now show immunostainings supporting co-expression of Glutamate and GABA as well as GABA and Acetylcholine (Figure 3—figure supplement 1).

3) The Supplementary file 2 shows average gene expression as scatter plots for each cluster of cells of the normal and starved conditions. These plots are very useful in visually identifying outliers. However, the criterion for identifying outliers seems to change between panels. Could the authors please explain what made them pick some genes on the outer boundaries of the scatter-plot as outliers, and others not? To clarify this, could the authors please compute the fold-change in mean expression between fed- and starved- (or the other way around) conditions and add this information to the statements made in paragraph two of subsection “Transcriptional responses to starvation”. Alternately, they could define a criterion to define outliers as genes where the fold-change in mean expression between fed- and starved- conditions was greater than X, where X is 2 or 3?

This is indeed a valid point and we apologized for the missing information. To address this point, we have now adapted the result section and included a more detailed description in the Materials and methods section. Previously, we had labeled those genes that appeared to be visual outliers. Now, we calculated the logFC and represented those with a higher value than 1 or lower than -1. In the case of a large number of outliers, we represented the top 10 genes with the highest variability. These genes are labeled in the scatter plots. Moreover, we have included a complete table of the genes (Supplementary file 2—source data 1) that are differentially regulated for all clusters.

4) The loss of the UN-2 and neurons-1 clusters is quite striking! An expected response to starvation at that stage is the failure of neural stem cells to exit quiescence and re-enter cell division. This could show up as clusters of cells missing. However, more mundane possibilities that would be useful to rule out would be: (1) that UN-2 and neurons-1 are cell types that are very rare in the larval brain, and hence, could stochastically be lost during sample processing from the starved larvae, and (2) that cells from the UN-2 and neurons-1 clusters are indeed present in the data, but due to a low read depth from those cells, have been assigned to other clusters that are present in the starvation condition?

We appreciate that the reviewers brought this point to our attention. Indeed, we cannot exclude the possibility that these cells could stochastically have been lost during sample processing. We have therefore included this possibility in the respective section (“Starvation effects on larval brain cellular composition”) as well as in the Discussion. Nevertheless, among replicates the loss of these clusters continues to be consistent. However, we reason that the sequencing depth is high enough with a saturation higher than 90%.

5) In the VNC, the neural stem cells that exit quiescence at 15hours ALH are the G2 arrested stem cells. Have the authors considered looking at their neural stem cell cluster in the fed vs starved conditions to see if they can distinguish G0 and G2 arrested stem cells, and the effect of starvation on them?

This is a very interesting suggestion and we addressed this in-silico and in-vivo. We have now incorporated an analysis for the NPCs cell cycle. In additions, we have performed EdU incorporation experiments to demonstrate the reactivation of a subset of neuroblasts prior to 16 hours ALH. Moreover, we have also analyzed the effects of starvation on the NPCs cell-cycle scores upon starvation. This data has been included in the result section and can be now visualized in Figure 2 and Figure 7—figure supplement 3.

6) Are there any molecular markers in the UN-2 and neurons-1 clusters, that the authors could use in vivo to demonstrate the loss of those cell types upon starvation?

This is a very important and challenging point. Neurons-1 (now labeled as Neurons X) represents a cluster of neurons expressing a combination of different neurotransmitters, however we could not identify any unique or cluster-specific marker. Similarly, it remains challenging to distinguish UN2s from “classical” neurons.

However, we performed an experiment supporting the loss of UN2s:

We found that hdc, was preferentially expressed in UNs in comparison to the remaining cell-clusters, which we confirmed using anti-Hdc immunostaining. Thus, we quantified its expression in normal and starved brains. We found a decreased number of hdc positive UNs, a result that supports our in-silico observations. Nevertheless, we could only assed this for a combination of UNs1 and UNs2, as for either cluster cell-type specific markers remain unknown. These results were added to the corresponding section (“Starvation effects on larval brain cellular composition”) and are shown in Figure 7—figure supplement 2.

7) A few aspects in the description of the methodology is lacking in detail in order to be able to reproduce the analyses in the paper for future readers. Specifically:

We thank the reviewers for these suggestions and have incorporated the required changes to the manuscript. As this point consists of several subpoints, we have addressed each of them individually.

– Results section “Altogether, our datasets displayed a median of 764,000 reads and

1500 genes per cell, and in total of about 12,000 of genes were detected”; Is this the total number of reads, or the number of mapped reads?

– Paragraph three of subsection “Single-Cell RNA-Seq of the Drosophila larval brain reveals different categories of characteristic cell-types”: Can the authors state how many cells (what proportion) finally fell into non-neuronal clusters?

We apologize for the missing information and have now added a supplementary table with all the sequencing metrics (Supplementary file 1). Regarding the proportion of cells, approximately 60% of the cells were classified in neuronal clusters, the remaining 40% were assigned to glial cell, NPCs, UNs and nonneural cells. In addition, we have now incorporated a table with the number of cells per cluster (Figure 1—source data 1) and per condition (Figure 1—source data 1).

– Paragraph two of subsection “Mapping neural progenitor cell diversity reveals presence of characteristic neurogenic cell-types”: How was the similarity of expression profiles quantified? Was it in terms of which genes had non-zero expression? "…examined the top differentially expressed genes…"? Is this the top 10 differentially expressed genes, or a different number than 10?

The annotation of each cluster was possible by examining the list of differentially expressed genes, output from “FindAllMarkers” Seurat function. Later, the top 10 genes for each cluster were analyzed to finally confer cluster-identities. We have now modified Figure 1—source data 1 to contain information about the top 10 differentially expressed genes for each cluster, number of cells per cluster and example references that were used to confer cell identity. This information can also be found in the Materials and methods section.

– Subsection “Seurat data processing”: Can the authors describe how many genes and cells were finally considered for analyses in the paper after the filtering criteria mentioned were imposed? Or are the numbers described in the first paragraph of the Results section obtained after the filtering criteria are imposed?

We apologize for not being clear. We have now modified this section and we include both, the raw and filtered number of cells, as well as the total number of genes detected. We first introduced the raw numbers and then described the final number of cells kept for downstream analysis.

“These cells were filtered, scaled and normalized… The resulting 4349 cells, with 12,942 genes detected, were later clustered generating…”

– Subsection “Seurat data processing”: The sequence of operations carried out is unclear. Was the PCA calculated by taking all genes in each cell into account, or only based on the 2000 most highly variable genes? If it was the latter, could the authors explain why the PCA wasn't done with all genes wasn't performed, and add this to the Materials and methods section? On the other hand, if the PCA was calculated with the expression of all genes in each cell, could they explain why the set of the 2000 most variable genes was constructed? Was this set of variable genes used for identifying marker genes or for clustering?

We clarified the processing pipeline by introducing a more detailed description in the Materials and methods section. In brief, 2000 genes were selected based on the recommendations of the R package provider, in order to assess real biological features. These 2000 genes were later considered for downstream processing and analysis.

– Subsection “Seurat data processing”: What is the linear transformation that was applied before PCA was carried out? Why was this linear transformation needed?

We apologize for the brief description and we have now extended the pipeline description this in the Materials and methods section as follows:

“First, a log-normalization with a scale factor of 10,000 was applied to normalize gene expression of individual cells by the total gene expression of each dataset. Second, a linear transformation was executed to remove unwanted source of variation”

– In the same section it appears that three different methodologies were considered for choosing the number of principal components. Could the authors mention which methodology was finally followed for choosing the number of principal components? It will be useful to add these plots as a supplemental figure. Alongside this, could the authors also explain the amount of variance captured each time a given number of principal components is chosen?

This is valid point and it is known the complexity of the process of determining the real dimensionality of a single-cell RNA-seq dataset. Therefore, we have described how we finally determined the number of relevant PCs:

“To define the true dimensionality of the dataset, several approaches were considered: Elbow-Plot and JackStraw-Plot tests together with an evaluation of PC-heatmaps. Finally, principal components (PCs) were selected visually by carefully inspecting Elbow-Plots in order to assess the percentage of variance explained by each PC. In this way, 31 PCs were considered to identify clusters of cells on graph-based approach. To better resolve clusters, a resolution equal to 2 was considered, this parameter helps to subdivide existing clusters and increase the granularity.”

In addition, we have also incorporated a new supplement file containing different Elbow-plots in order to facilitate this process to other researchers.

– UMAP resolution – could the authors add a couple of sentences explaining the impact of increasing or decreasing the UMAP resolution used for clustering? Is this supposed to aid in sub-clustering?

This is correct and we apologize for not describing it previously. This information can be now found in the Materials and methods section:

“In this way, 31 PCs were considered to identify cell clusters with a graph-based approach. To better resolve clusters, we modified the resolution, as increasing this parameter helps to subdivide existing clusters and gain granularity. Therefore, we used a resolution equal to 2 based on the total number of cells and after carefully inspecting different values.”

– Finally, in the interest of reproducibility, it would be nice to make available (through a URL) the scripts that were used to carry out the analyses in the manuscript.

This is indeed a great suggestion and we now provided the scripts used for the analysis as supplementary information.

8) Cell numbers: If we go with their estimate of 2,000 cells per brain, and they analyse 3500 cells (after filtering), then they are sampling the brain with less than a two-fold coverage. Given the tremendous diversity in the Drosophila CNS it would seem that this minimal coverage risks missing a whole range of rare cell types. And this of course has major bearing on the comparison to the starvation conditions as well, which was also less than two-fold. Granted, they do identify some rare cells, such as PDF neurons, but nevertheless, a less than two-fold coverage of an extremely diverse system risks resulting in misinterpretations, especially when comparing between conditions.

This is indeed a valid point and we have performed new single-cell RNA-seq experiments for each condition, adding new biological replicates to each dataset. Now, we have a cell atlas of the first instar larval brain consisting of 9353 cells, representing an approximate 5x coverage of the larval brain.

9) 4-hour starvation seems very short for getting any major transcriptomic changes, and indeed the changes appear to be minor. Is there any evidence for any physiological effect of a 4-hour starvation? Why was a 4-hour starvation chosen? Previously published larval nutrient restriction schemes have been e.g., 24-36 hours (e.g., PMID 21346761). As it now stands, it is difficult to relate the transcriptional changes to any physiological effect(s), and this brings down the impact of the study. Any progress in this area would have greatly strengthened the study.

This is a very relevant point and we thank the reviewers for pointing this out. We have introduced a more detailed description explaining why we decided on the 4 hours starvation.

Since first instar larvae are particularly susceptible to starvation, we could not extend the starvation time. To illustrate this, we have included an experiment showing that after 4 hours of starvation 45.2% of the larvae died. This can be visualized in Figure 7—figure supplement 1. Nevertheless, we strongly agree with the reviewers and we are considering analysis different time points for future experiments, most likely moving to third instar larvae.

10) Related to this issue, studies by the Alex Gould and Andrea Brand labs have revolved around the role of starvation in gating how/when neural progenitors emergence from quiescence during larval stages. This has revealed input from InR, PI3K, Akt, Alk/Jeb and TOR signalling, and some unknown signals, in part provided from the periphery, possibly relayed via CNS surface glia and onto progenitors. The authors of the current study, intriguingly, do find TOR signalling components altered, but do not relate this to the previous studies from Brand and Gould. In fact, somewhat disconcertingly, none of these seminal publications, in Nature, Cell, CellRep, are even mentioned in the current study (PMIDs: 21346761, 21816278, 23478023, 21183078). By analysing responses to starvation separately in glia versus progenitors, the authors have a golden opportunity to shed further light on this intriguing issue, but do not even appear to be aware of the literature in this field.

We thank the reviewers for this indeed valid point. We have now included the mentioned references in our analysis. On the other hand, by analyzing the population of glial cells we observed changes in fat acid metabolism, which can be observed in Figure 8A.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

1) In the revised version of this manuscript, the authors have addressed and satisfied most of the reviewers' concerns. They have clarified and explained most of the text that made interpretation of their data difficult. They have also added the second replicate of their starved condition. In light of these changes, their work is both interesting and valuable to the field and is recommended for publication.

A couple of important points comment, though. There might be an error in their supplementary xls sheet for marker genes. The log-fold changes and p-values associated with some genes in the xls sheet do not seem plausible: there are astronomically high log-fold changes and p-values that are greater than one. For example, consider genes #6,#8, #9 from the "cholinergic neurons 1" cluster. These genes have log-fold-changes of 129,541,248,584,619, 0.616 and 107,496,381,242,778, with associated adjusted p-values of 8.63e-35, 3.49e-17 and 6.24e07.

It's possible that these large values occur partly due to the transformations applied to the read counts of the genes in each cell.

P-values – adjusted or otherwise – cannot exceed 1, but the p-value of gene #9 is 62.4 million.

One might expect p-values to decrease as log-fold-changes increase. So, gene #8, with a fold-change of 0.616, would be expected to have a larger (less significant) p-value than gene #9, which has a fold-change of 107,496,381,242,778. But instead, we see that gene #6 has a p-value of 3.49e-17 while gene #9 has an implausible p-value of 62.4 million.

Thank you for pointing this out. Indeed, we missed that some values were unexpectedly altered after transforming an R output file into a csv file in the previous version of “Figure 1—source data 1”. We have now corrected the error and carefully inspected the numbers in the updated “Figure 1—source data 1”. We apologize for the mistake.

2) Introduction section: "nutriments" should be "nutrients"

This is a valid point and we have now corrected this typo in the manuscript.

3) Results section paragraph one: I find the mean reads/cell to be very high. From our own experience, and from numerous papers, you typically detect 25,000-50,000 gene reads/cell, resulting in the detection of 1000-2500 genes/cell. They list "836,393 mean reads per cell".

Indeed, since the larval brain has fewer cells than the adult counterpart we decided to sequence deeper than it was previously done for the adult brain, we therefore expected a higher number of reads per cell. In the supplementary file we detailed this information for each experiment.

https://doi.org/10.7554/eLife.50354.sa2

Article and author information

Author details

  1. Clarisse Brunet Avalos

    Department of Biology, University of Fribourg, Fribourg, Switzerland
    Contribution
    Formal analysis, Investigation, Visualization, Methodology, Writing—original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7781-004X
  2. G Larisa Maier

    Department of Biology, University of Fribourg, Fribourg, Switzerland
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  3. Rémy Bruggmann

    Interfaculty Bioinformatics Unit, University of Bern, Bern, Switzerland
    Contribution
    Conceptualization, Data curation, Software, Funding acquisition, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4733-7922
  4. Simon G Sprecher

    Department of Biology, University of Fribourg, Fribourg, Switzerland
    Contribution
    Conceptualization, Supervision, Funding acquisition, Project administration, Writing—review and editing
    For correspondence
    simon.sprecher@unifr.ch
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9060-3750

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (31003A_149499)

  • Simon G Sprecher

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (SystemsX- SynaptiX RTD)

  • Rémy Bruggmann
  • Simon G Sprecher

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (310030_188471)

  • Simon G Sprecher

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We are grateful to Boris Egger, Cornelia Fritsch, Lucia de Andres Bragado, Jules Duruz and other members of the Sprecher lab for discussion and sharing advices. We thank the Next Generation Sequencing (NGS) Platform at the University of Bern and the Light Microscopy and Image Analysis Facility at the University of Fribourg for providing the necessary equipment and technical support for our experiments, as well as the Developmental Studies Hybridoma Bank at the University of Iowa and the Bloomington Stock Center for sharing reagents and flies. We would also like to thank David E Krantz for providing anti-Prt antibody. This work was supported by the Swiss National Science Foundation.

Senior and Reviewing Editor

  1. K VijayRaghavan, National Centre for Biological Sciences, Tata Institute of Fundamental Research, India

Publication history

  1. Received: July 19, 2019
  2. Accepted: November 19, 2019
  3. Accepted Manuscript published: November 20, 2019 (version 1)
  4. Version of Record published: December 5, 2019 (version 2)

Copyright

© 2019, Brunet Avalos et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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