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Additional file 1: Fig. S1. FACS-plot of D. melanogaster eye-antennal disc cells after live/dead cell staining. (A) Counterstaining of Propidium Iodide to label dead cells (y-axis, Q1) and Calcein violet to label live cells (x-axis, Q4). Double positive signals might indicate dying cells or incompletely separated cells (Q2). This method allows removing debris (Q3) efficiently. (B) The cell population P4 (i.e. 16,208 living cells) were isolated and used for the scRNAseq run using 10x Genomics. Fig. S2. Quality and quantity of cDNA after reverse transcription of mRNA fraction (polyA-based enrichment) and full-length cDNA amplification from cell lysate of 30 cells sorted from cell suspension of eye-antennal discs run on Fragment Analyzer (Agilent). Size distribution of all fragments shows little impact on degradation (almost no cDNA detectable below 400 bp). Fig. S3. Contribution of mitochondrial gene expression to scRNAseq and snRNAseq datasets. (A) Total amount of genes (features) over percentage of mitochondrial reads, per cell each. The dashed line indicates a threshold of 10% of reads attributed to mitochondrial genes. In scRNAseq data, approximately 14% of cells show a high (>10%) proportion of mitochondrial gene reads on the total number of reads. (B) Total amount of genes (features) over Percentage of mitochondrial reads, per cell each in snRNAseq data. The dashed line indicates a threshold of 10% of reads attributed to mitochondrial genes. In most nuclei, only a low percentage of reads is attributed to mitochondrial genes. Fig. S4. Clustering and cluster annotation for scRNAseq data. (A) The heatmap shows the score for each potential cell type (Y-axis) in each cluster (X-axis). The cell types are annotated based on the highest scoring identity in the heatmap. The clusters are grouped based on their transcriptional similarity to each other. For clusters which express an equal number of marker genes for two different identities both identities were assigned (e.g. cluster 3:Antenna|EAB). Clusters with unresolved identities (i.e. more than two equal assignments) are called “Other”. The colors of the cluster names correspond to the colors in UMAP in (B). The marker score is calculated using a matrix of published marker genes (see Additional file Table S2). (B) UMAP of scRNAseq data. The clusters were annotated based on the heatmap in (A). This UMAP is identical to the UMAP with combined cluster annotation shown in Fig. 3A. Note that the color code is not comparable to the one used in Additional file Figs. S7 and S10. Fig. S5. Fluorescence intensity curves from Bioanalyzer for fresh- and cryopreserved nuclei obtained by different nuclei extraction protocols. (A) The RNA was extracted directly from a fresh sample (36 eye-antennal discs), which was dissociated using the 10x Genomics protocol with 0.1% IGEPAL as a detergent. (B) RNA isolated from a cryopreserved sample, which was dissociated using a protocol based on Triton X-100 as a detergent and a variety of RNAse inhibitors [1]. Note that the sample was thawed for 3.5h before being frozen again. Both curves are close to the expectation of RNA isolated from D. melanogaster [2]. Fig. S6. BioAnalyzer results comparing different nuclei isolation protocols for frozen samples. Samples 1, 2, 4 and 5 were dissociated using the protocol based on Triton X-100 as a detergent and a variety of RNAse inhibitors [1]. Samples 1 and 2 were dissociated by pipetting up and down and samples 4 and 5 were dissociated using a Dounce homogenizer. Samples 7 and 8 were dissociated using the protocol “10x Genomics® Isolation of Nuclei for Single Cell RNA Sequencing” [3]. Samples 7 and 8 were dissociated using only citric acid buffer and samples 10 und 11 were dissociated using only a detergent. Note that for D. melanogaster, intense bands are expected at about 40s and weaker bands at 25s and 45-50s [2]. Each run was repeated once. Fig. S7. Clustering and cluster annotation for snRNAseq data. (A) The heatmap shows the score for each potential cell type (Y-axis) in each cluster (X-axis). The cell types are annotated based on the highest scoring identity in the heatmap. The clusters are grouped based on their transcriptional similarity to each other. For clusters which express an equal number of marker genes for two different identities both identities were assigned (e.g. cluster 15:MFurrow|SMW). Clusters with unresolved identities (i.e. more than two equal assignments) are called “Other”. The colors of the cluster names correspond to the colors in UMAP in (B). The marker score is calculated using a matrix of published marker genes (see Additional file Table S2). (B) UMAP of snRNAseq data. The clusters were annotated based on the heatmap in (A). This UMAP is identical to the UMAP with combined cluster annotation shown in Fig. 3C. Note that the color code is not comparable to the one used in Additional file Figs. S4 and S10. Fig. S8. Gene ontology enrichment analysis for genes with most variable expression. (A) Top 3000 genes unique to scRNAseq (i.e. 1520 genes). (B) Top 3000 genes unique to snRNAseq (i.e. 1520 genes). (C) Top 3000 genes shared between scRNAseq and snRNAseq (i.e. 1480 genes). See also Supplementary Table S9 for a full list of enriched GO terms. Fig. S9. Comparison of dataset specific and shared differentially expressed genes for each cell type. The radar plot shows for each cell type the percentage of cluster specific differentially expressed genes unique for the scRNAseq and snRNAseq data, respectively (red and blue lines), as well as the percentage of differentially expressed genes shared between both datasets (black line). The total number of genes fulfilling the differential expression criteria (FDR 0.05 and log2-fold change > 0.25) for each cell type is shown in brackets. Fig. S10. Clustering and cluster annotation of integrated scRNAseq and snRNAseq dataset. (A) The heatmap shows the score for each potential cell type (Y-axis) in each cluster (X-axis). The cell types are annotated based on the highest scoring identity in the heatmap. The clusters are grouped based on their transcriptional similarity to each other. For clusters which express an equal number of marker genes for two different identities both identities were assigned (e.g. cluster 14:Dorsal|Ocelli). Clusters with unresolved identities (i.e. more than two equal assignments) are called “Other”. The colors of the cluster names correspond to the colors in the UMAP in (B). The marker score is calculated using a matrix of published marker genes (see Additional file Table S2). (B) UMAP of integrated scRNAseq and snRNAseq data. Cells are colored by clusters identified based on the (A). Note that the color code in A and B is not comparable to the one used in Additional file Figs. S4 and S7. Table S1. Overview of different dissociation conditions. Samples within blocks (highlighted in grey and white) were prepared in parallel. The Flow Cytometer only provides percentages of survival because it stops after a defined number of events (i.e. ~50,000 cells) and therefore absolute numbers are not meaningful. “Pipetting” refers to the number of strokes during and after incubation. The cells obtained by experiment/block 12 were subjected to 10X Genomics scRNAseq.

Drosophila melanogaster head development represents a valuable process to study the developmental control of various organs, such as the antennae, the dorsal ocelli and the compound eyes from a common precursor, the eye-antennal imaginal disc. While the gene regulatory network underlying compound eye development has been extensively studied, the key transcription factors regulating the formation of other head structures from the same imaginal disc are largely unknown. We obtained the developmental transcriptome of the eye-antennal discs covering late patterning processes at the late 2nd larval instar stage to the onset and progression of differentiation at the end of larval development. We revealed the expression profiles of all genes expressed during eye-antennal disc development and we determined temporally co-expressed genes by hierarchical clustering. Since co-expressed genes may be regulated by common transcriptional regulators, we combined our transcriptome dataset with publicly available ChIP-seq data to identify central transcription factors that co-regulate genes during head development. Besides the identification of already known and well-described transcription factors, we show that the transcription factor Hunchback (Hb) regulates a significant number of genes that are expressed during late differentiation stages. We confirm that hb is expressed in two polyploid subperineurial glia cells (carpet cells) and a thorough functional analysis shows that loss of Hb function results in a loss of carpet cells in the eye-antennal disc. Additionally, we provide for the first time functional data indicating that carpet cells are an integral part of the blood-brain barrier. Eventually, we combined our expression data with a de novo Hb motif search to reveal stage specific putative target genes of which we find a significant number indeed expressed in carpet cells., Author summary The development of different cell types must be tightly coordinated, and the eye-antennal imaginal discs of Drosophila melanogaster represent an excellent model to study the molecular mechanisms underlying this coordination. These imaginal discs contain the anlagen of nearly all adult head structures, such as the antennae, the head cuticle, the ocelli and the compound eyes. While large scale screens have been performed to unravel the gene regulatory network underlying compound eye development, a comprehensive understanding of genome wide expression dynamics throughout head development is still missing to date. We studied the genome wide gene expression dynamics during eye-antennal disc development in D. melanogaster to identify new central regulators of the underlying gene regulatory network. Expression based gene clustering and transcription factor motif enrichment analyses revealed a central regulatory role of the transcription factor Hunchback (Hb). We confirmed that hb is expressed in two polyploid retinal subperineurial glia cells (carpet cells). Our functional analysis shows that Hb is necessary for carpet cell development and we show for the first time that the carpet cells are an integral part of the blood-brain barrier.