Single‐cell RNA sequencing of motoneurons identifies regulators of synaptic wiring in Drosophila embryos
2022; Springer Nature; Volume: 18; Issue: 3 Linguagem: Inglês
10.15252/msb.202110255
ISSN1744-4292
AutoresJessica Velten, Xuefan Gao, Patrick van Nierop y Sanchez, Katrin Domsch, Rashi Agarwal, Lena Bognar, Malte Paulsen, Lars Velten, Ingrid Lohmann,
Tópico(s)Invertebrate Immune Response Mechanisms
ResumoArticle28 February 2022Open Access Transparent process Single-cell RNA sequencing of motoneurons identifies regulators of synaptic wiring in Drosophila embryos Jessica Velten Jessica Velten Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany The Barcelona Institute of Science and Technology, Centre for Genomic Regulation (CRG), Barcelona, Spain Flow Cytometry Core Facility, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Contribution: Conceptualization, Validation, Investigation, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Xuefan Gao Xuefan Gao orcid.org/0000-0001-8114-6404 Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany Contribution: Data curation, Formal analysis, Validation Search for more papers by this author Patrick Van Nierop y Sanchez Patrick Van Nierop y Sanchez orcid.org/0000-0002-8182-7063 Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany Contribution: Investigation, Methodology, Writing - review & editing Search for more papers by this author Katrin Domsch Katrin Domsch Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany Developmental Biology, Erlangen-Nürnberg University, Erlangen, Germany Contribution: Investigation, Methodology, Writing - review & editing Search for more papers by this author Rashi Agarwal Rashi Agarwal orcid.org/0000-0002-0504-899X Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany Contribution: Investigation Search for more papers by this author Lena Bognar Lena Bognar Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany Contribution: Investigation Search for more papers by this author Malte Paulsen Malte Paulsen Flow Cytometry Core Facility, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Contribution: Investigation, Methodology Search for more papers by this author Lars Velten Corresponding Author Lars Velten [email protected] orcid.org/0000-0002-1233-5874 The Barcelona Institute of Science and Technology, Centre for Genomic Regulation (CRG), Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain Contribution: Data curation, Software, Investigation, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Ingrid Lohmann Corresponding Author Ingrid Lohmann [email protected] orcid.org/0000-0002-0918-2758 Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany Contribution: Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Jessica Velten Jessica Velten Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany The Barcelona Institute of Science and Technology, Centre for Genomic Regulation (CRG), Barcelona, Spain Flow Cytometry Core Facility, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Contribution: Conceptualization, Validation, Investigation, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Xuefan Gao Xuefan Gao orcid.org/0000-0001-8114-6404 Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany Contribution: Data curation, Formal analysis, Validation Search for more papers by this author Patrick Van Nierop y Sanchez Patrick Van Nierop y Sanchez orcid.org/0000-0002-8182-7063 Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany Contribution: Investigation, Methodology, Writing - review & editing Search for more papers by this author Katrin Domsch Katrin Domsch Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany Developmental Biology, Erlangen-Nürnberg University, Erlangen, Germany Contribution: Investigation, Methodology, Writing - review & editing Search for more papers by this author Rashi Agarwal Rashi Agarwal orcid.org/0000-0002-0504-899X Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany Contribution: Investigation Search for more papers by this author Lena Bognar Lena Bognar Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany Contribution: Investigation Search for more papers by this author Malte Paulsen Malte Paulsen Flow Cytometry Core Facility, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany Contribution: Investigation, Methodology Search for more papers by this author Lars Velten Corresponding Author Lars Velten [email protected] orcid.org/0000-0002-1233-5874 The Barcelona Institute of Science and Technology, Centre for Genomic Regulation (CRG), Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain Contribution: Data curation, Software, Investigation, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Ingrid Lohmann Corresponding Author Ingrid Lohmann [email protected] orcid.org/0000-0002-0918-2758 Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany Contribution: Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Author Information Jessica Velten1,2,3, Xuefan Gao1, Patrick Van Nierop y Sanchez1, Katrin Domsch1,4, Rashi Agarwal1, Lena Bognar1, Malte Paulsen3, Lars Velten *,2,5 and Ingrid Lohmann *,1 1Department of Developmental Biology, Centre for Organismal Studies (COS) Heidelberg, Heidelberg, Germany 2The Barcelona Institute of Science and Technology, Centre for Genomic Regulation (CRG), Barcelona, Spain 3Flow Cytometry Core Facility, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany 4Developmental Biology, Erlangen-Nürnberg University, Erlangen, Germany 5Universitat Pompeu Fabra (UPF), Barcelona, Spain *Corresponding author. Tel: +34 93 316 0185; E-mail: [email protected] *Corresponding author. Tel: +49 6221 545523; Fax: +49 6221 546424; E-mail: [email protected] Molecular Systems Biology (2022)18:e10255https://doi.org/10.15252/msb.202110255 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The correct wiring of neuronal circuits is one of the most complex processes in development, since axons form highly specific connections out of a vast number of possibilities. Circuit structure is genetically determined in vertebrates and invertebrates, but the mechanisms guiding each axon to precisely innervate a unique pre-specified target cell are poorly understood. We investigated Drosophila embryonic motoneurons using single-cell genomics, imaging, and genetics. We show that a cell-specific combination of homeodomain transcription factors and downstream immunoglobulin domain proteins is expressed in individual cells and plays an important role in determining cell-specific connections between differentiated motoneurons and target muscles. We provide genetic evidence for a functional role of five homeodomain transcription factors and four immunoglobulins in the neuromuscular wiring. Knockdown and ectopic expression of these homeodomain transcription factors induces cell-specific synaptic wiring defects that are partly phenocopied by genetic modulations of their immunoglobulin targets. Taken together, our data suggest that homeodomain transcription factor and immunoglobulin molecule expression could be directly linked and function as a crucial determinant of neuronal circuit structure. Synopsis Single-cell RNA-seq of Drosophila embryonic motoneurons combined with imaging and genetic perturbation suggests a linked homeodomain transcription factor – immunoglobulin program critical for synaptic wiring in the neuromuscular system. Single-cell transcriptomes of Drosophila embryonic motoneurons were mapped along the AP axis using Hox gene expression as spatial markers. Homeodomain transcription factor (TF) and Immunoglobulin (Ig) genes were found to be highly variably expressed within twitlow motoneurons. Functional analysis suggested a regulatory and functional link between homeodomain TFs and Ig domain proteins in synaptic wiring. Common signatures of homeodomain TF expression were found in matching synaptic partners in the neuromuscular system. Introduction Neuronal circuits in mammals as well as Drosophila are stereotypically wired for the precise execution of functional tasks critical for organismal survival. The formation of such circuits is a step-wise process, which starts with the specification of neuronal cell types and their accurate arrangements in space, followed by the correct wiring of individual cells and their final integration into a functional network. To ensure such precision, the structure and connectivity of neural circuits is genetically specified. However, how these complex interconnected processes are encoded in the genome and executed by the cellular protein machinery is still not fully understood. According to the “labelled pathway hypothesis” (Sperry, 1963), neurons stochastically and transiently form contacts with many possible targets after their specification, while the expression of specific cell surface proteins (CSPs) is thought to stabilize the correct connections, a process called synaptic specificity (Sanes & Zipursky, 2020). Many lines of evidence support this hypothesis. Recent work has shown that combinations of immunoglobulin superfamily (IgSF) cell surface proteins (Dprs) are differentially expressed in distinct neuronal clusters and bind to specific Dpr binding proteins (DIPs) expressed in synaptic partners (Nakamura et al, 2002; Özkan et al, 2013; Carrillo et al, 2015). In the visual system, combinations of CSPs are differentially expressed between layers (Tan et al, 2015), while in olfactory neurons, a combinatorial expression of transcription factors (TFs) and CSPs maps neurons with the same olfactory receptor to the same glomerulus (Couto et al, 2005; Li et al, 2017, 2020; McLaughlin et al, 2021). All these studies explain how groups of similar neuronal cells are molecularly defined and provide a hypothesis on how stereotypic connections to another neuronal cell type are formed. By contrast, how the specificity of circuits is specified and controlled at the level of single cells is still not completely resolved. In the Drosophila neuromuscular system, every single motoneuron (MN) forms unique and stereotypic connections with target muscles already during embryogenesis, but the molecular mechanisms underlying this specificity is unclear (Allan & Thor, 2015). MNs are progressively specified from anterior to posterior by segment-specific TFs (Bossing et al, 1996; Schmidt et al, 1997; Angelini & Kaufman, 2005) and further along the dorsal to ventral axis (Broihier & Skeath, 2002; Broihier et al, 2004; Landgraf & Thor, 2006) before extending their nerve projections to predefined locations specified in all three spatial dimensions (Landgraf et al, 1997; Thor et al, 1999; Broihier & Skeath, 2002; Broihier et al, 2004; Zarin et al, 2014; Hessinger et al, 2016). These observations have suggested region-specific mechanisms in the determination of connectivity patterns. However, such a regional model alone is unlikely to explain the precise connectivity patterns of single cells (Nassif et al, 1998; Landgraf et al, 2003). In addition, elegant studies in the vertebrate central nervous system and classical transplantation experiments demonstrated that positional identity and connectivity patterns of single neurons are stably maintained even after experimental relocation of cells (Demireva et al, 2011). Thus, there seems to be a molecular mechanism that stably imprints cellular identity and instructs the formation of cellular connectivity. Homeodomain TFs have long been known to play important roles in the specification, differentiation and maintenance of neurons, also in MNs, in different organisms (Thor et al, 1999; Thor & Thomas, 2002; Urbach et al, 2006, 2016; Sanguinetto et al, 2008; Philippidou et al, 2012; Deneris & Hobert, 2014; Allan & Thor, 2015; Zeisel et al, 2018; Domsch et al, 2019; Sugino et al, 2019; Allen et al, 2020; Reilly et al, 2020). Importantly, it has been shown just recently that each neuron class in the nematode C. elegans expresses a unique combination of homeodomain TFs, which is unambiguously associated with neuronal identities (Reilly et al, 2020; Hobert, 2021). However, it is so far unclear whether this concept extends to other organisms; whether such cell-specific combinations of homeodomain TFs also instruct later events in circuit formation; and, finally, which molecules downstream of such homeodomain TFs realize synaptic target choice and specificity at the single-cell level. Using single-cell RNA sequencing (scRNA-Seq) with high numbers of biological replicates, we demonstrate that cell-specific expression of multiple homeodomain TFs is associated with the cellular heterogeneity within differentiated MNs along the major body axes of Drosophila embryos. We furthermore show that multiple CSPs, in particular cell surface immunoglobulins (Igs), act downstream of homeodomain TFs in individual MNs and play an important role in determining specificity during the synaptic wiring phase. Knockdown and ectopic expression of homeodomain TFs induces synaptic wiring defects specific to single cells that are partly phenocopied by genetic manipulation of their putative Ig targets. Additionally, our data suggest that shared combinations of homeodomain TF are expressed in matching synaptic partners of functional neuronal circuits. Based on these findings, we propose that the development of individual neuronal circuits is genetically defined by a linked “homeo-immunoglobulin program”, which serves as one of the major determinants for complex neuronal wiring with single cell precision. Results A reference map of MNs during the synaptic wiring phase in Drosophila embryos We aimed at identifying molecules driving specificity in synaptic wiring at the single-cell level to gain a comprehensive view of the complex yet highly precise synaptic matching process. To this end, we used the Drosophila neuromuscular system as our model, as it is ideally suited to study mechanisms of synaptic specificity: first, this system is of relatively low complexity; and second, it is fully established at the end of embryogenesis with about 30-35 MNs innervating in a highly stereotypic manner 30 muscles in each abdominal hemisegment of stage 17 embryos (Landgraf et al, 1997; Hoang & Chiba, 2001; Landgraf & Thor, 2006; Kim et al, 2009; Couton et al, 2015). Evidently, selection of the proper developmental stage is critical for the comprehensive identification of cues driving the highly specific interaction of neuronal cells. This notion is based on previous studies showing that neurons diversify most on the transcriptional level when they are in the process of contacting their synaptic partners while their transcriptomes become indistinguishable upon completion of neuronal connectivity (Li et al, 2017). In the Drosophila neuromuscular system, embryonic MNs interact with their muscle partners at the end of embryonic stage 16 (Landgraf et al, 1997), suggesting motoneuronal transcriptomes to be most diverse at this developmental stage. Based on these considerations, we performed scRNA-Seq of stage 16 embryonic cells marked by the OK371-GAL4 driver (Mahr & Aberle, 2006) controlling the expression of the UAS-RFP transgene (Fig EV1A and B). This driver is based on a regulatory element controlling the expression of the presynaptic vesicular glutamate transporter (VGlut) and is active specifically in all/most MNs at late stages of embryogenesis (stage 17) (Mahr & Aberle, 2006) (Fig EV1A). In addition, the OK371-GAL4 driver is active in a few glutamatergic brain neurons (Fig EV1A), which were later excluded by restricting the analysis to Hox-expressing cells. We further confirmed that known motoneuronal subtypes are targeted by this driver in expected ratios in stage 16 embryos (Fig EV3A–D, and see below) (Mahr & Aberle, 2006). Click here to expand this figure. Figure EV1. scRNA-Seq data of embryonic Drosophila MNs are of high quality Time course of GFP expression induced by the OK371-GAL4 motoneuronal driver during embryonic stages, showing that GFP expression starts after embryonic stages 15 and is clearly detectable at stages 16 and 17 in neuronal cells. Dashed lines highlight the ventral nerve cord. Illustration of the time course shown in (A) in relation to the synaptic wiring in the embryonic neuromuscular system, highlighting that OK371-GAL4-driven transgene expression occurs at the time when the first synaptic connections are formed between MNs and muscles. Quantification of the average number of differentiated OK371 > GFP-positive MNs in stage 16 embryos (n = 3, biological replicates with two independent biological repeats, error bar denotes standard deviation). Visualization of filtering criteria for single cells (dashed blue line, see Materials and Methods). Density dot plot represents the total reads (library size) versus genes observed per cell (library quality, diversity). Each dot represents a motoneuronal cell (total of 1,536 cells). In total, n = 999 cells passed the filtering criteria indicated by the dotted lines (see Materials and Methods). Number of reads aligned to the Drosophila genome per cell for the two datasets from this study (red) and two other studies (black) profiling Drosophila neurons by Smart-Seq2. See Materials and Methods, section Data visualization for a definition of boxplot elements. Individual data points correspond to single cells (biological replicates), see legend of panel F for number of cells. Number of genes observed per cell for the two datasets from this study (red) and several other studies (black) profiling Drosophila neurons by scRNA-Seq (Li et al, 2017; Davie et al, 2018; Bageritz et al, 2019; Allen et al, 2020; Özel et al, 2021). See Materials and Methods, section Data visualization for a definition of boxplot elements. Individual data points correspond to single cells (biological replicates). Bageritz et al: n = 2,554, Allen et al: n = 33,115, Davie 10× genomics: n = 56,902, Davie CEL-seq: n = 22, Davie Smart-Seq2: n = 45, Davie modified Smart-Seq2: n = 34, this study (Motoneuron): n = 999, this study (Muscle): n = 837, Oezel et al: n = 31,018, Li et al: n = 1,842. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. scRNA-Seq data of embryonic Drosophila MNs identifies three major clusters Heatmap depicting gene expression of single motoneuronal cells (columns) after hierarchical clustering using the 20 most variably expressed genes (rows) following the method of Li et al (2017). Hierarchical clustering was performed using ward linkage on an Euclidean distance metrics. Colour code represents gene expression levels (see Materials and Methods). Three distinct clusters with similar expression patterns are labelled in grey (cluster 1), purple (cluster 2) and green (cluster 3), these clusters correspond to clusters shown in Fig 1B. Expression of three marker genes, elav (pan-neural), VGlut (glutamatergic MNs) and FasII (axon) were evaluated for each of the three clusters shown in (A). See Materials and Methods, section Data visualization for a definition of boxplot elements. Individual data points correspond to single cells (biological replicates), n = 758 (cluster 1), n = 76 (cluster 2), n = 165 (cluster 3). Bar chart shows the number of cells expressing different numbers of Hox genes. In sum, 749 of 999 cells express at least one Hox gene (~ 75% Hox gene coverage). Left panel: Multiplex HCR visualizes the expression pattern of VGlut, a general marker for glutamatergic MNs, and two key marker genes, twit and Vmat, which drive the clustering shown in (A). The dashed boxes in the upper panels, which show the lateral and dorsal view of representative embryos, are displayed at higher resolution in the lower panel. Right panel: Venn diagram showing ratios of cells labelled with these key marker genes, which are compared with ratios expected from scRNA-Seq experiments. Download figure Download PowerPoint Click here to expand this figure. Figure EV3. Detailed analysis of single cell data identifies known MN subtypes and variable processes in stage 16 embryonic MNs Left panel: ventral view of representative stage 16 and late-stage 17 embryos expressing UAS-RFP under the control of OK371-GAL4. The red dashed lines highlight the midline, the dashed boxes indicate one hemisegment. Within each hemisegment, RFP-positive cells were counted to define the average number of MNs present per abdominal hemisegment. Right panel: table depicting the average number of MNs per hemisegment counted in stage 16 and late-stage 17 embryos. Expression of known motoneuron subtype markers (Landgraf et al, 1999; Certel & Thor, 2004; Garces & Thor, 2006; Technau et al, 2014; Zarin et al, 2014; Couton et al, 2015) on n = 758 cells from the twitlow cluster. Columns correspond to single cells. Cells were assigned as dorsally projecting MNs (dMN) or ventrally projecting MNs (vMN) by computing expression scores on dMN and vMN markers. If a given marker was observed in a single cell, −log(p) was added to the respective score, where p is the total fraction of cells expressing a marker. Scatter plot comparing the fraction of MNs falling into the distinct classes according to literature (Zarin et al, 2014), and according to the assignment performed in (B). Bar chart depicting the expression of the marker genes in the different populations. Principal component analysis (PCA) of genes expressed in twitlow cells. GO term analysis for biological processes was performed on the top 10% genes with highest loadings on principal component 1, PC1 (log 10 P-value; left), principal component 2, PC2 (middle) and principal component 3, PC3 (right). GO term and SMART domain analysis was performed on the top 300 genes representing the most enriched candidates in the PCA. Dark grey indicates processes enriched among genes with positive loadings, light grey indicates processes enriched among genes with negative loadings. Together these analyses indicated that PC1 and PC2 are associated with metabolic processes, cellular differentiation and/or technical variation, while PC3 is associated with anterior/posterior patterning and synaptic processes. Principal component loadings plots highlighting homeodomain TF genes (red) as well as genes associated with the GO terms MN axon guidance (blue) and synaptic organization (green). Points with label correspond to the highest 5% of loadings. Download figure Download PowerPoint For the experiment, single RFP-expressing MNs were sorted from a pool of precisely staged embryos by fluorescence-activated cell sorting (FACS) (Fig 1A). In total, 1,536 MNs were sequenced by SMART-Seq2 (Picelli et al, 2014) from pooled embryos. After filtering based on a minimum of 500 genes observed with 10 reads each, 999 single-cell transcriptomes were retained (Fig EV1D). Thus, every biologically unique motoneuronal cell (~140 OK371-positive cells in a single embryo, Fig EV1A–C) was sequenced in approximately 7 biological replicates in our dataset. By comparing our dataset to recently published data, we confirmed that the quality of our data matches the standards in the field, in particular with regard to sequencing depth (Fig EV1E and F). A median of 1,202 unique genes were observed per cell, and a negligible fraction of 0.1% of reads mapped to the mitochondrial genome, supporting the high technical quality of the data. Abundant expression of motoneuronal marker genes like Vesicular glutamate transporter (VGlut) and embryonic lethal abnormal vision (elav) indicated successful sorting of the targeted cell population (Fig EV2B). Figure 1. scRNA-Seq identifies highly variable homeodomain TF expression in late embryonic MNs at the time of synaptic wiring Schematic drawing depicts the step-wise development of the nervous system in Drosophila, starting during embryogenesis, progressing through three larval stages and metamorphosing during pupal stage into the adult nervous system. The first connections in the neuromuscular system are formed between MNs and muscles in late Drosophila embryos (stage 16). MNs at this stage expressing UAS-RFP under the control of the motoneuronal driver OK371-GAL4 (OK371 > RFP) are color-coded along the ventral nerve cord according to different patterns of Hox gene expression. OK371-RFP-positive MNs were FACS sorted and single cells were sequenced by targeted Smart-Seq2 to enrich for Hox gene representation (HoxSeq) as spatial markers (see also Materials and Methods). t-distributed stochastic neighbour embedding (t-SNE) plot of n = 999 single-cell transcriptomes. Colours correspond to three clusters, VUM neurons (purple), twitlow (grey) and twithigh (green) MNs. Identification of highly variable genes in the twitlow cluster using the method by (Brennecke et al, 2013). Scatter plot depicts the mean expression for each gene and squared coefficient of variation across twitlow cells. The solid line indicates the fit, dashed lines the 95% confidence interval. Genes with a significantly elevated variance are shown as triangles, other genes as circles. Different gene classes are colour coded. P-values shown were retrieved by a hypergeometric test for enrichment of the respective gene class among highly variable genes. Outline of strategy to map single MNs to a position along the AP axis (see also Materials and Methods). Upper panel: Intensities of Hox protein expression along the ventral nerve cord measured by immunofluorescence (upper left panel) and co-expression patterns of Hox gene transcripts measured by scRNA-Seq (upper right panel) were used as input. Upper right panel depicts a heatmap of color-coded Hox gene expression levels; columns correspond to n = 758 single twitlow MNs. Lower panel: AP position is inferred form scRNA-Seq data by probabilistically mapping Hox gene expression pattern in each individual cell to the immunofluorescence reference data. Genes with significant variation along the AP axis were identified and clustered into 10 groups of distinct expression patterns (Materials and Methods). Heatmap shows average gene expression per cluster (rows) across single cells (columns). Asterisks indicate P-value of a hypergeometric test for enrichment of protein domains, ***P < 0.001. Left panel: ZINB-WaVE (Risso et al, 2018, 2019) was used to statistically separate gene expression variability into parts linked to AP position and parts independent thereof. Scatter plot of ZINB-WaVE loadings separates known dorsal and ventral marker genes on ZINB-WaVE component 1. Right panel: Genes encoding homeodomain TFs and genes encoding Ig domain molecules (see colour code) show high loadings on ZINB-WaVE components 1 and 3, demonstrating high variability independent of AP position. Download figure Download PowerPoint Hox genes are known to be expressed in a consecutive order along the AP axis of Drosophila. We used this property to precisely locate single-cell transcriptomes along the AP axis as further described below. To this end, we implemented a custom modification of the SMART-Seq2 protocol by adding primers targeting each Hox gene to the reverse transcription (RT) and preamplification step that permits an increased representation of the lowly expressed Hox genes as spatial markers (Fig 1A, see Materials and Methods) (Giustacchini et al, 2017; Velten et al, 2021). Despite the low expression of Hox genes in late embryonic stages, which is common to all TF encoding genes, we identified 75% of the MNs to express at least one Hox gene (Fig EV2C). To explore the molecular diversity of the MNs, we performed two independent unsupervised analyses, t-distributed neighbour embedding (tSNE) and hierarchical clustering (Figs 1B and EV2A). Both methods identified a cluster corresponding to modulator neurons (VUMs, 8% of the cells) as well as two large, yet distinct clusters of cells that differ in the expression of the marker genes rhea and target of wit (twit). VUM MNs belong to a very distinct MN subtype expressing a combination of subtype-specific marker genes, Vesicular monoamine transporter (Vmat; Fig EV2A and D), Tyramine β hydroxylase (Tbh), diacyl glycerol kinase (dgk) and the motoneuronal marker Zn finger homeodomain 1 (zfh1) (Stagg et al, 2011), which we all identified in the VUM neuron cluster. These type II glutamatergic/octopaminergic MNs exhibit modulator roles in taste responses (Sink & Whitington, 1991; Landgraf et al, 1997; Siegler & Jia, 1999; Stagg et al, 2011), while the twitlow and twithigh cluster can be assigned to the abundant glutamatergic type I MN class (Hoang & Chiba, 2001; Kim et al, 2009). In situ hybridization chain reactions (HCR) of late stage embryos localized twit transcripts in median and lateral clusters of posteriorly located MNs (Fig EV2D) (Kim & Marqués
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