dataesr/scientific-paragraphs-categorization huggingface dataset:
org/asap) (David et al. , 2020). If the same cluster was annotated with two terms, the voting system allows users to vote for their preferred annotation. To ensure that cell type annotations are consistent with previous literature and databases and to allow a posteriori computational analyses at different anatomical resolutions, we linked the controlled vocabulary to the Flybase anatomy ontology  (Costa et al. , 2013). Since some cell types are present at low clustering resolution, and others appear only at higher resolution, we used the VSN pipeline to calculate different Leiden resolutions ranging from 0. 8 to 8 (fig. S3A ). Annotations were then collapsed across resolutions and the annotation with the highest number of up-votes was selected per cell. All initial annotations were performed on the Relaxed dataset, and were then exported to the Stringent dataset, where field experts verified the accuracy of the annotation transfer (Fig.
2   While single cell datasets exist for a number of fly tissues, our data yield two further advances. First, our dataset provides the first single cell transcriptomic profiling for several adult tissues, including the haltere, heart, leg, Malpighian tubule, proboscis, maxillary palp, trachea, and wing (fig. S3-S15 ). In these tissues, all major expected cell types were identified. For example, in the proboscis and maxillary palp (fig. S4A, B ), we could annotate gustatory and olfactory receptor neurons, mechanosensory neurons and several glial clusters, and all 7 olfactory receptors expressed in the maxillary palp could be detected. In the wing (fig. S5 ), we could identify four different neuronal types -gustatory receptor neurons, pheromone-sensing neurons, nociceptive neurons and mechanosensory neurons, as well as three glial clusters.
Another surprising finding was the existence of two distinct types of nuclei among main cells in the male accessory gland (fig. S14 ), a cell type previously thought to be uniform. Next, we compared the distribution of cells between 10x and Smart-seq2 for all tissues profiled by both sequencing technologies. In almost all cases, cells from two technologies were well matched from co-clustering analysis (fig. S17  and S18 ). Since Smart-seq2 cells only account for a small fraction, our previous annotations focused on 10x cells. The cell-matched co-clustering analysis allowed us to directly transfer annotations from 10x to Smart-seq2 datasets (fig. S17E ), validating the transfer using cluster-specific markers (fig. S17F ). We also identified a number of genes specifically detected using Smart-seq2, presumably because the deeper sequencing allowed a higher gene detection rate (fig.
Next, we integrated our head snRNA-seq dataset with previously published single cell RNA-seq data from the brain  (Davie et al. , 2018; Kurmangaliyev et al. , 2020; Li et al. , 2017; Özel et al. , 2021)  to automatically annotate our head cells (Fig. 3E ). Head-specific clusters made up 20% of the cells, including the antennae, photoreceptors, muscle, cone cells and cuticular cell types, while the other 80% were present in mixed clusters containing both head-and brain-derived cells covering the neuronal and glial cell types of the brain. This co-clustering across genotypes and protocols underscores the usefulness of our snRNA-seq data, as well as its quality compared to more conventional scRNA-seq data. Next, we used a neural network for optic lobe cell types and trained a support-vector machine on a whole brain sample to predict annotations per cluster that were manually checked in an additional Jamboree.
3A ), indicating that our dataset does not contain a sufficient number of cells to resolve the complexity of the central brain, which may contain hundreds or thousands of neuronal types. Subclustering analysis performed on cells within larger groups was especially useful in identifying subtypes in the combined dataset (Fig. 3F ). This separated photoreceptors into inner and outer, with the inner photoreceptors further splitting into R7 and R8 subtypes, each with pale and yellow subtypes based on rhodopsin expression. Additionally, subclustering identified dorsal rim area and ocellar photoreceptors and further split Kenyon cells into the three different KC subtypes: α/β, α'/β' and γ based on known marker genes  (Davie et al. , 2018). These case studies highlight the resolution in our dataset and the potential of using subclustering to discover rare cell types.
5 (E) Integration of a brain scRNA-seq dataset with the head snRNA-seq for label transfer. Outlined are example clusters revealed by the head snRNA-seq dataset but not by the brain scRNA-seq datasets, including epithelial cells (EPI), photoreceptors (PRs), olfactory receptor neurons (ORNs), and muscle cells (MUS). (F) Subclustering analysis reveals subtypes of photoreceptors, including inner and outer photoreceptors, with the inner photoreceptors further splitting into R7 and R8 subtypes, and mushroom body Kenyon cells, forming three distinct KC subtypes: α/β, α'/β' and γ.
, 2020; Jevitt et al. , 2020; Rust et al. , 2020) , and our FCA data on gut and ovary accurately co-clustered with these published datasets (fig. S32, S33 ). The testis has two populations of stem cells, namely the somatic cyst stem cells (CySCs) that produce cell types with supporting roles that are essential to spermatogenesis, and the germline stem cells (GSCs) that produce haploid sperm (Fig. 2C ). Trajectory inference on germ cells from the testis (Fig. 6E-F ) revealed transitions from GSCs to proliferating spermatogonia, then early spermatocytes (e. g. , marker genes include kmg and Rbp4; Fig. 2C ), followed by progressively maturing spermatocytes, early spermatids, and finally mid-to-late elongation stage spermatids. As expected, the spermatocyte stage featured a robust increase in the number of genes being transcribed (high UMI) (Fig.
One of the key challenges in large scale atlassing projects is the definition of cell types. Here we solved this by using a consensus-based voting system across multiple resolutions. An FCA cell type is thus defined as a cluster detected at any clustering resolution (Leiden, ranging from 0. 4 to 10 (50 in brain)) that could be separated by the expression of known marker genes from other clusters. Further, every annotation was manually curated by a tissue expert, leading to a high confidence dataset with over 250 annotated cell types. Still, we would like to add two notes. First, there are differences in annotation depth with some cell types only linked to broad cell types (e. g. epithelial cell), in contrast to much more detailed cell types (e. g. , different types of olfactory receptor neurons, ORNs).
For example, in the gut dataset five clusters were identified as crop cells, each with their own transcriptome profile, but without literature knowledge on how to further divide them. These shallow annotations provide information about the cell's function, and present ample opportunities for follow-up studies to further identify them based on novel marker genes and to highlight new cell types. Secondly, while many marker genes are very useful in identifying cell types, we have also detected cases where marker gene expression was not congruent with cluster expression. This can be caused by discrepancies between mRNA expression and protein, or by mistakes made in literature. These examples highlight the need and the opportunities presented by the FCA to serve as the basis for future validation.
Comparative analysis of different scRNA-seq datasets poses common challenges, including variation in genetic background, sequencing technologies, and sample preparation. To minimize these effects, we used the same wild-type w 1118 flies from one laboratory for most tissues except for tissues of which simple dissection is impossible, such as tracheal cells. Second, we used a unified snRNA-seq platform for all samples, because for many adult Drosophila cuticular tissues, such as the antenna, wing, leg and haltere, it is extremely difficult to isolate intact cells. In addition, isolation and sequencing of nuclei solves the issue associated with large multinucleated cells (e. g. , muscle), and collection of (frozen) tissues from different laboratories. In a recent study  (McLaughlin et al. , 2021) , we directly compared snRNA-seq and scRNA-seq of the same fly cell types and found that about 70-90% of transcriptomic information was preserved from snRNA-seq compared to scRNA-seq.
In addition, especially important for dynamic cell types differentiating in adult stem cell lineages, snRNA-seq offers a more immediate picture of changes in transcript expression as cells progress, separable from mRNAs purduring from later stages or stored for later usage. To further validate the snRNA-seq dataset, we compared single-nucleus FCA data with previously published scRNA-seq datasets from three tissues: brain (Fig. 3E ), gut (fig. S32 ) and ovary (fig. S33 ). In all three cases, our data not only included all previously identified cell types, but also revealed additional cell types not detected in previous studies. For example, in the ovary, our data captured late-stage germ cells while three published datasets did not. In addition, the Tabula Drosophilae data set has increased our knowledge of late-stage follicle cells and allowed identification of new marker genes that were validated using in vivo genetic reporters (fig.
S30 ). In this regard, the FCA presents the first opportunity to distinguish marker genes on different levels of specificity. In addition, our FCA data together with other atlas data (e. g. C. elegans, mouse, human) will allow cross-species studies of common cell types, such as neurons, muscle and blood cells for the generation of evolutionary lineages of cell types in the tree of life. Finally, our analysis also presents several technical novelties, including the use of Nextflow pipelines improving reproducibility (VSN, https://github. com/vib-singlecell-nf), the availability of raw and processed datasets for users to explore (https://www. flycellatlas. org), and the development of a crowd-annotation platform with voting, comments and references (SCope, https://flycellatlas. org/scope). Furthermore, our atlas is fully linked to existing Drosophila databases through the use of a common vocabulary, benefitting its use and integration in future projects.
We believe that these elements will serve as inspiration for future atlassing projects and will become indispensable for large-scale single cell experiments. All the FCA data are freely available for further analysis via multiple portals, including SCope, ASAP, and cellxgene, and can be downloaded for custom analysis using other single cell tools (all links can be found on https://www. flycellatlas. org). For most samples, 5-day adults were used with two exceptions (see below). Male and female flies were collected on day 1 and kept together for mating, and on day 5, flies were sexed and dissected. UAS-lamGFP from Bloomington Drosophila Stock Center (BDSC# 7378). UAS-unc84GFP from  (Henry et al. , 2012). 5 *For the male reproductive gland, we used 2-3 day old virgin males in order to detect active transcripts which may be not detectable in 5 day old virgin flies.
Sequencing read alignment Prior to read alignment, the raw FASTQ files were processed with the index-hopping-filter software from 10x Genomics (version 1. 0. 1) in order to remove index-hopped reads. More information about this software is available at https://support. 10xgenomics. com/docs/indexhopping-filter. A Cell Ranger (version 3. 1. 0) index was built from a pre-mRNA GTF which was derived from the Flybase version r6. 31 GTF. A complete recipe on how to build this custom pre-mRNA GTF is available here: https://github. com/FlyCellAtlas/genome_references/tree/master/flybase/r6. 31 For 10x, the filtered reads (index-hopped reads removed) were processed all the way up to the gene expression matrix using Cell Ranger (version 3. 1. 0). For Smart-seq2, reads were aligned to the Drosophila melanogaster genome (r6.
Cluster markers for each of the generated clusterings were computed from the log2 normalized expression matrix by means of scanpy. tl. rank_genes_groups (method=wilcoxon, n_genes=0). To ensure reproducibility of the 10x Genomics data processing, all the analyses from raw counts to final processed files (. loom and. h5ad) were performed using the VSN-Pipelines (https://githhub. com/vib-singlecell-nf/vsn-pipelines).
Cell filtering, decontamination and clustering: Stringent version In the previous section, we described how the Relaxed version of the data was generated. The main reason to generate a Stringent version was that we identified a significant number of cells, we called "black hole" cells, which are expressing multiple big cell type markers e. g. : grh (epithelial cell), Mhc (muscle cell), onecut (neuron). These cells likely originated from droplets that were contaminated by ambient RNA. DecontX was chosen in order to correct for this bias. Practically, the raw counts generated from the Cell Ranger pipeline were corrected using this algorithm, available in the celda R package (version 1. 4. 5, Docker image: vibsinglecellnf/celda:1. 4. 5). The corrected counts were then rounded using the R base::round function and newly generated empty cells were removed.
ASAP features and applications The ASAP platform  (David et al. , 2020)  was used to perform more detailed analyses on the datasets. In particular, ASAP was used to perform sub-clustering and additional differential expression / marker gene discovery. Since the platform also allows annotating (e. g. , a color gradient) cells according to the expression of a particular gene set (rather than a single gene), ASAP was also used to study the activity state of KEGG pathways. Finally, because ASAP allows users to share a project (or its copy) privately with a group, all FCA-related projects have been made public so that researchers can share/clone them freely, and annotated Loom files can then be directly downloaded in ASAP. Brain-Head integration (Fig. 3E ) Single cell RNA-seq dataset from Davie et al.
was downloaded from GEO (GSE107451) and processed using VSN. Next, the data was integrated with the single nucleus data from the FCA using Seurat. Data was normalized using SCT normalization  (Hafemeister and Satija, 2019)  and batch correction was performed as described  (Stuart et al. , 2019). 150 components were selected for clustering and UMAP/tSNE visualization. Annotations were added using three computational approaches. First, we transferred annotations based on co-clustering of annotated cells with new cells: if a cluster contained at least 25% of cells with the same annotation, this annotation was transferred to all cells in the cluster. Next, we used a classifier from Özel et al. (Özel et al. , 2021)  to annotate optic lobe cell types. Finally, we trained an SVM classifier on the Davie et al.
Integration of 10x Genomics and Smart-Seq2 data We integrated 10x Genomics and Smart-Seq2 data using Harmony  (Korsunsky et al. , 2019). To facilitate and improve integration, we first selected most relevant genes by performing differential gene expression on annotated 10x Genomics data (t-test; Benjamini-Hochberg corrected p-value < 0. 1). To integrate individual tissues, we used genes differentially expressed between cell types of a given tissue. To integrate entire cell atlases, we used genes differentially expressed between tissues. After selecting differentially expressed genes, we batch-corrected datasets using Harmony to remove the influence of the sequencing technology. We included an additional step of batchcorrection for those tissues in which gender specific clusters were present.
In total, we performed additional batch-correction based on the gender for 12/15 tissues. We next systematically validated integration in the following way. If the marker genes were known, we visually inspected whether marker genes are expressed at the same tSNE location for 10x Genomics and Smart-Seq2 data after integration. In case marker genes were not known, we found differentially expressed genes based on annotations of 10x Genomics data and selected 3-5 genes that are as cluster specific as possible. Finally, for each tissue and cell type we came up with the list of genes and validated whether these genes are expressed at the same location in the tSNE space for 10x Genomics and Smart-Seq2 data. Besides Harmony, we considered three other integration approaches [BBKNN  (Polański et al.
Annotating Smart-Seq2 data After integrating 10x Genomics and Smart-Seq2 data, we next aimed at transferring cell type annotations from 10x Genomics to unannotated Smart-Seq2 data. To develop an approach and quantitatively compare performance of different classification methods, we used Smart-Seq2 cells from olfactory receptor neurons (ORN)  (McLaughlin et al. , 2021)  annotated using MARS  (Brbić et al. , 2020)  and manually validated based on the marker-gene expressions. We integrated this dataset with ORN antenna 10x Genomics dataset that was annotated on the same granularity level. The classification accuracy was high (0. 88) with a linear logistic regression model and did not improve by using non-linear models. Therefore, we decided to use logistic regression as the base classifier due to its simplicity and interpretability.
Comparison of 10x Genomics and Smart-Seq2 data To compare the number of detected genes between 10x Genomics and Smart-Seq2 data (Fig. 1E ), we considered a gene detected if a single read maps to it. In particular, for 10x Genomics data we used UMI greater or equal to 1 as a threshold, while for Smart-Seq2 data we used CPM greater or equal to 1 as a threshold. Given these thresholds, we counted the number of genes detected in at least 1% of cells. To obtain examples of genes that are detected using Smart-Seq2, but not using 10x Genomics (fig. S17G ) we obtained a list of genes that are expressed in less than 20 cells in 10x Genomics data, and two to four times more cells in Smart-Seq2 data depending on a tissue. Transcription factors and cell type specificity analysis (Fig. 5A-D ) Cell's expression profiles were averaged in log2CPM space and subsequently Z-normalised per gene.
For every gene the evolutionary age was determined using the GenTree database (http://gentree. ioz. ac. cn/). Finally, sets of genes with their evolutionary age were then plotted in an upset plot using Python (UpSetPlot version 0. 4. 4). Sex-differences analysis (Fig. 6A-6D ) For the sex bias analysis, we CPM normalised the gene expression matrix. Next, we filtered out the cells from sex specific samples, i. e. , testis, ovary and male reproductive glands and also the cells which were marked to be of 'mix'ed sex or marked 'artefact' or 'unannotated' in the annotation. Since 'body' samples also contain sex specific tissues, we further removed the cells annotated as germ cells or cells assigned to other sex specific clusters. To be as stringent as possible, we further removed cells that were either (i) co-clustered in the t-SNE with these sex-specific cell types or organs, or (ii) might be improperly annotated as evidenced by co-expression of mutually exclusive cell-type specific markers.
Metabolic clustering using ASAP For probing the FCA data for an enrichment in Fatty acid synthesis (FAS) and Fatty acid degradation (FAD) metabolic genes, we first downloaded the latest KEGG assignments of genes of Drosophila melanogaster (DM) which were mapped to KEGG pathways (FAS: map00061, and FAD: map00071) on December 2020. We next used the ASAP (Automated Single-cell Analysis Pipeline) platform to probe the FCA datasets for a transcriptional enrichment of the genes of these metabolic pathways  (David et al. , 2020). ASAP generates a score from the difference of expression of the genes in the FAS or FAD gene sets as compared to background genes. In short, for each gene in the gene set, the function will take n random genes from the same expression quantile and add them to a background gene set.
The gene set score is calculated as the difference of average expression of the genes in the module score and the genes in the background, for each cell. Scores close to zero indicate a similar expression, positive scores indicate higher expression and negative scores indicate lower expression of the genes in the gene set than the background genes. This function was adapted from the AddModuleScore function from the Seurat package  (Satija et al. , 2015) , and was entirely recorded in Java. We have used the non-normalized parsed Fat body v2 data as input matrix, 24 bins, 100 background genes and set the seed to 42. We plotted the results using the visualization feature implemented in ASAP and colored the cells according to the score values  (David et al. , 2020). Finally, we used the FCA 0.
Ovary data integration (fig. S33) Four scRNA-seq datasets of the adult ovary were used for the data integration: current FCA data and three other published datasets  (Rust et al. , 2020)   (Jevitt et al. , 2020; Rust et al. , 2020; Slaidina et al. , 2020)  merged and batch corrected using Seurat v4. 0. 1. Datasets were processed with Seurat v4. 0. 1 in RStudio Version 1. 4. 1103. Batch correction was performed as described  (Stuart et al. , 2019)  4. Clustering of unannotated cell types was performed using the FindClusters command in Seurat v4. 0. 1 with a resolution factor of 0. 6. Image processing was performed with FIJI. Fly lines were ordered from BDSC: sick-Gal4 (#76195), Wnt4-Gal4 (#67449), UAS-RedStinger, UAS-Flp, Ubi-(FRT. STOP. FRT) -Stinger (#28281).
Supplementary Text Testis snRNA-seq features developmental trajectories in stem cell lineages For the following discussion, we are referring to the UMAP plot using the Relaxed dataset of the testis in Figure  2C. The 10x snRNA-seq data from adult testis plus seminal vesicle, like other dissected tissues, showed clusters representing several terminally differentiated cell types, including muscle and pigment cells from the testis sheath and a large number of nuclei representing cells in various epithelial components of the organ, such as the terminal epithelial cells and embedded late head cyst cells, which hold the head ends of elongated spermatid bundles at the base of the testis (Fig. 2C ). In addition, sample dissection carried over small numbers of tracheal and fat body cells, hemocytes, neurons, and male reproductive tract secretory cells not considered integral parts of the testis (Fig.
2C ). Unlike many other tissues, but similar to the ovary and gut, the geography of the UMAP plot of 10x snRNA-seq data from testis was dominated by continually renewing differentiating cell lineages produced by dedicated stem cells. In testis, there are two such adult stem cell-founded lineages: somatic cyst stem cells (CySCs) produce post-mitotic differentiating daughters that play signaling and key cell-biological supporting roles essential to spermatogenesis, while germline stem cells (GSCs) produce proliferating daughters that undergo a well-orchestrated series of differentiation steps to produce haploid sperms. These two stem cell types are anchored in proximity at the apical tip of the testis by physical attachment to a small cluster of epithelial-like somatic cells termed the hub, which provides close-range niche signals important for the maintenance of the two stem cell states.
The interleaved arrangement of GSCs and CySCs and their attachment to the hub ensures that immediate stem cell daughters are positioned to interact: two postmitotic early cyst cells enclose the gonialblast produced by a germ line stem cell to form a cyst, the functional unit of further co-differentiation of both the germ line and the somatic cyst cells. Despite their physical proximity and cooperation in the organ, the germ line and cyst cell lineages map to largely non-overlapping formations in the UMAP plot of 10x snRNA-seq data, consistent with their different embryological origin, cell biology, and known roles. The dynamic sequence of cell states in the germ line and cyst cell lineages is manifest in the geographical arrangement of nuclei with similar but progressing gene expression patterns in the UMAP plot of the 10X snRNA-seq data from testis (Fig.