Author response: A single-cell survey of Drosophila blood (2020)

Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Drosophila blood cells, called hemocytes, are classified into plasmatocytes, crystal cells, and lamellocytes based on the expression of a few marker genes and cell morphologies, which are inadequate to classify the complete hemocyte repertoire. Here, we used single-cell RNA sequencing (scRNA-seq) to map hemocytes across different inflammatory conditions in larvae. We resolved plasmatocytes into different states based on the expression of genes involved in cell cycle, antimicrobial response, and metabolism together with the identification of intermediate states. Further, we discovered rare subsets within crystal cells and lamellocytes that express fibroblast growth factor (FGF) ligand branchless and receptor breathless, respectively. We demonstrate that these FGF components are required for mediating effective immune responses against parasitoid wasp eggs, highlighting a novel role for FGF signaling in inter-hemocyte crosstalk. Our scRNA-seq analysis reveals the diversity of hemocytes and provides a rich resource of gene expression profiles for a systems-level understanding of their functions. Introduction The immune system forms an important layer of defense against pathogens in a wide variety of organisms including Drosophila (Banerjee et al., 2019; Mathey-Prevot and Perrimon, 1998). The chief mode of immune response in flies involves innate immunity, which is composed of diverse tissue types including fat body, gut, and blood cells called the hemocytes (Buchon et al., 2014). Hemocytes represent the myeloid-like immune cells, but so far have been considered less diverse compared to their vertebrate counterparts (Evans et al., 2003; Wood and Martin, 2017). In addition to progenitor cells or prohemocytes, three major types of hemocytes are known in Drosophila: plasmatocytes, crystal cells, and lamellocytes. Plasmatocytes are macrophage-like cells with parallels to vertebrate tissue macrophages, while crystal cells and lamellocytes perform functions analogous to clotting and granuloma formation in vertebrates (Buchon et al., 2014). Hemocytes in the larva derive from two lineages: the lymph gland and the embryonic lineage, which in the larva forms resident (sessile) clusters of hemocytes in subepidermal locations, also known as hematopoietic pockets (Gold and Brückner, 2015; Holz et al., 2003; Jung et al., 2005; Lanot et al., 2001; Makhijani et al., 2011). Prohemocytes can give rise to all mature hemocytes in the lymph gland (Banerjee et al., 2019). Likewise, the embryonic lineage, which consists of self-renewing plasmatocytes, is capable of producing crystal cells and lamellocytes during inflammatory conditions (Gold and Brückner, 2015; Leitão and Sucena, 2015; Márkus et al., 2009). Whereas plasmatocytes are important for phagocytosis and represent ~90–95% of total hemocytes, crystal cells, which constitute ~5%, are required for wound healing and melanization (Banerjee et al., 2019; Evans et al., 2003). Traditionally, the classification of hemocytes is based on two major criteria: cell morphology (Rizki, 1957; Rizki, 1962; Shrestha and Gateff, 1982) and expression of a few marker genes (Evans et al., 2003; Evans et al., 2014; Kurucz et al., 2007). The paucity of markers available to define cell types and the low-resolution of cell morphologies may have hindered the identification of rare cell types and failed to distinguish transient states. For instance, a plasmatocyte-like cell called the podocyte, which possibly corresponds to an intermediate state between plasmatocytes and lamellocytes, has been reported but its transcriptional signature remains unknown (Honti et al., 2010; Rizki, 1957; Stofanko et al., 2010). Moreover, ultrastructural and microscopic evidence has also suggested that several subsets within plasmatocytes and crystal cells exist, but they have not been characterized at the molecular level (Rizki, 1957; Shrestha and Gateff, 1982). Finally, little is known about hemocyte lineage trajectories with regards to the source cells or precursors, or about intermediate states that exist on the path to terminal differentiation of mature cell types. Hence, it is important to thoroughly characterize the molecular signatures of all the dynamic states of mature cell types in steady state and inflammatory conditions. Advances in single-cell RNA sequencing (scRNA-seq) technologies allow comprehensive characterization of complex tissues, including blood (Satija and Shalek, 2014). In particular, scRNA-seq is powerful not only for identifying cell types but also resolving cell states and their dynamic gene expression patterns that are often buried in bulk RNA measurements (Trapnell, 2015). For example, recent studies using various scRNA-seq platforms have helped identify novel subtypes within monocytes and dendritic cells (Villani et al., 2017) and activated states of T cells (Szabo et al., 2019) in human blood. Further, scRNA-seq has documented the continuous spectrum of differentiation along the hematopoietic lineage in various species (Macaulay et al., 2016; Nestorowa et al., 2016; Velten et al., 2017; Zhang et al., 2018). In addition, scRNA-seq data allows pseudotemporal ordering of cells to re-draw developmental trajectories of cellular lineages (Cao et al., 2019). Thus, scRNA-seq, in conjunction with various lineage trajectory algorithms, allows the precise characterization of 1. differentiated cell types and their subtypes, 2. transient intermediate states, 3. progenitor or precursor states, and 4. activated states, which are often influenced by mitotic, metabolic, or immune-activated gene modules (Adlung and Amit, 2018; Trapnell, 2015; Wagner et al., 2016). Here, we performed scRNA-seq of Drosophila hemocytes in unwounded, wounded, and parasitic wasp infested larvae to comprehensively distinguish mature cell types from their transient intermediate states. Our scRNA-seq analysis identifies novel marker genes to existing cell types and distinguishes activated states within plasmatocytes enriched in various genes involved in the regulation of cell cycle, metabolism, and antimicrobial response. In addition, we could precisely distinguish mature crystal cells and lamellocytes from their respective intermediate states. Interestingly, our scRNA-seq revealed the expression of fibroblast growth factor (FGF) receptor breathless (btl) and its ligand branchless (bnl), in rare subsets of lamellocytes and crystal cells, respectively, which we implicate in regulating effective immune responses against parasitoid wasp eggs in vivo. Altogether, our scRNA-seq analysis documents the diversity of hemocyte cell populations circulating in the fly blood and provides a resource of gene expression profiles of the various cell types and their states in Drosophila. Results scRNA-seq of Drosophila hemocytes Hemocyte differentiation can be induced in Drosophila larvae by mechanical wounding or oviposition by wasps such as Leptopilina boulardi (Márkus et al., 2005; Rizki and Rizki, 1992). Hence, to characterize hemocyte populations and their heterogeneity, we first performed the two immune responsive conditions: wounded and wasp 24 hr post-infested (wasp inf. 24 hr), together with unwounded control conditions (Figure 1A). Further, to mobilize the sessile hemocytes into circulation, we briefly vortexed the larvae prior to bleeding (Petraki et al., 2015). Subsequently, single hemocytes were encapsulated using microfluidics-based scRNA-seq technologies including inDrops (Klein et al., 2015), 10X Chromium (Zheng et al., 2017) or Drop-seq (Macosko et al., 2015). A total of 19,458 cells were profiled, with 3–4 replicates per condition, and obtained a median of 1010 genes and 2883 unique molecular identifiers (UMIs) per cell across all conditions (Supplementary file 1; Figure 1—figure supplement 1A,B). In order to achieve a comprehensive map of all the hemocytes profiled by the three scRNA-seq platforms, we merged all data sets. We observed notable ‘batch effects’ where cell types were being clustered according to condition, replicate, or technology (Figure 1—figure supplement 1C,D,E). Thus, we applied the Harmony batch correcting method (Korsunsky et al., 2019), which is integrated into the Seurat R package (Stuart et al., 2019). Harmony successfully integrated all three data sets, including their replicates (Figure 1B; Figure 1—figure supplement 1D’, E’), and identified a total of 17 clusters (Figure 1C). Based on known marker genes, we confidently assigned certain clusters as plasmatocytes (marked by NimC1), crystal cells (marked by lozenge [lz]), or lamellocytes (marked by atilla) (Figure 1C; Figure 1—figure supplement 1H). Of the 17 clusters, one small cluster, representing ~0.2% of the total profiled cells, did not express any of the pan-hemocyte markers such as Hemese (He) and serpent (srp) (Figure 1—figure supplement 1H; Evans et al., 2014). Hence, we labeled this cluster as non-hemocyte (non-hemo). Figure 1 with 2 supplements see all Download asset Open asset scRNA-seq of Drosophila hemocytes reveals subpopulations of plasmatocytes, crystal cells, and lamellocytes. (A) Schematic of the microfluidics-based scRNA-seq workflow. (B) t-Distributed Stochastic Neighbor Embedding (t-SNE) plot of Harmony-based batch correction and integration of unwounded (red), wounded (blue), and wasp inf. 24 hr (green) data sets. (C) Clustering of batch corrected cells from all three conditions reveals a total of 17 clusters. (D) Dot plot representing the top three genes enriched per cluster based on average expression (avg_logFC). Color gradient of the dot represents the expression level, while the size represents percentage of cells expressing any gene per cluster. Diversity of hemocyte populations and their transcriptional dynamics Plasmatocyte clusters Despite the fact that plasmatocytes constitute over 90–95% of the total hemocyte pool, subclasses within this major cell type have not been described. The majority of the clusters we identified (12/17) express Hml or NimC1 but not crystal cell or lamellocyte markers and thus we annotated them as plasmatocyte (PM) clusters: PM1-12 (Figure 1C; Figure 1—figure supplement 1H). PM1 represents the largest cluster and is enriched in rRNA genes such as 18SrRNA:CR45841 (Figure 1D). Interestingly, there is evidence in mammals that certain rRNA genes, including pre-rRNA molecules, accumulate in activated macrophages (Radzioch et al., 1987; Varesio, 1985). We determined PM2 as the cycling or self-renewing state of plasmatocytes based on the expression of genes related to cell cycle such as CycB, stg, and polo, which are markers of G2/M stages of the cell cycle (Edgar and O'Farrell, 1990; Glover, 2005; Whitfield et al., 1990). Next, we identified a group of PM clusters, PM3-5, which are enriched in several immune-induced genes, including Matrix metalloproteinase 1 (Mmp1) and Immune induced molecule 18 (IM18). Further, our scRNA-seq identified two distinct clusters, PM6 and 7, expressing several genes that encode antimicrobial peptides (AMP) (Figure 1D). PM6 highly expresses genes of the cecropin family, including CecA2 and CecC, while PM7 is enriched in additional AMPs such as Mtk, DptB, and Drs (Figure 1D). The differential expression of this broad spectrum of AMPs (Ferrandon et al., 1998; Hoffmann and Reichhart, 2002; Reichhart et al., 1992; Samakovlis et al., 1990; Tzou et al., 2002) in PM6 and 7 (collectively termed PMAMP) suggests that hemocytes can elicit a humoral immune response against a variety of pathogens. Together, we define PM3-7 as the immune-activated states of the plasmatocytes because of the expression of several immune-induced genes including the AMP-genes. In addition to these major clusters, our scRNA-seq also identified several minor clusters: PM8-12. PM8 is enriched in genes encoding peroxidase enzymes such as Prx2540-1, Prx2540-2, and CG12896, which is an uncharacterized gene highly similar to peroxiredoxin 6 (PRDX6) in humans. Although the function of these genes is not known in the context of hemocytes and immunity, peroxisomes have been reported to be necessary for phagocytosis by macrophages in both mice and Drosophila (Di Cara et al., 2017). PM9 and PM10 are enriched in the protooncogenes Abl, Sno oncogene (Snoo), and the transcriptional regulator Ultrabithorax (Ubx). PM11 and PM12 represent the smallest clusters within plasmatocytes and are defined by the expression of the uncharacterized genes CG15550 and CG6023, together with a methuselah-type receptor gene, methuselah-like 7 (mthl7) in PM11, and Cys, CG8860, and COX8 in PM12 (Figure 1D; Supplementary file 2). In order to distinguish plasmatocytes in sessile or circulating compartments, we used quantitative real time PCR (qRT-PCR) of some of the marker genes per cluster in sessile and circulating hemocytes in steady state unwounded larvae. Of all genes that we tested, only Ubx and mthl7, representing PM9-11, were relatively more enriched in sessile hemocytes compared to circulating hemocytes (Figure 1—figure supplement 1G), suggesting that most clusters may reside in both sessile and circulating compartments. Crystal cell clusters The next most abundant immune responsive cells are the crystal cells, which are the main source of two enzymes important for melanization, prophenoloxidase 1 and 2 (PPO1 and PPO2). These enzymes are critical for survival upon wounding in larvae and adults (Binggeli et al., 2014; Dudzic et al., 2019; Theopold et al., 2014). Based on the expression of PPO1 and PPO2 together with the gene encoding the Runt related transcription factor lozenge (lz), two clusters were assigned to crystal cells (CC): CC1 and CC2 (Figure 1C–D; Figure 1—figure supplement 1H). CC1 expresses low levels of PPO1 but high levels of Notch, pebbled (peb), and the enhancer of split complex gene E(spl)m3-HLH (Figure 1D). Although Notch and peb have been shown to be associated with crystal cell development (Terriente-Felix et al., 2013), expression of the Notch target gene E(spl)m3-HLH (Couturier et al., 2019) has not been reported. On the other hand, CC2 shows higher expression levels of PPO1 and PPO2 genes. Hence, we consider CC2 to represent mature crystal cells, while CC1 may represent an immature or a transient intermediate state. Lamellocyte clusters Lamellocytes represent the rarest cell type, the numbers of which dramatically increase during wounding and wasp infestation (Márkus et al., 2005; Rizki and Rizki, 1992). Based on the expression of the lamellocyte marker gene atilla (Evans et al., 2014; Kurucz et al., 2007), we assigned two clusters to lamellocytes (LM): LM1 and LM2 (Figure 1C–D; Figure 1—figure supplement 1J). LM1 is the larger cluster of the two and is enriched in atilla besides a long non-coding RNA, lncRNA:CR44316 (Figure 1D). LM2 represents a smaller cluster expressing atilla, betaTub60D, and alphaTub85E. The expression level of atilla is higher in LM2 compared to LM1 (Figure 1D), suggesting that LM2 represents mature lamellocytes, whereas LM1 may represent the lamellocyte intermediate state. Moreover, the strong expression pattern of tubulins and other cytoskeletal proteins may be important for the maintenance of structural integrity of lamellocytes and their dynamic roles in encapsulation (Rizki and Rizki, 1994). Altogether, our scRNA-seq analysis recovered all major cell types within the hemocyte repertoire including the fine-grained dissection of plasmatocytes into self-renewing or cell-cycle (PM2) and various immune-activated states (PM3-7). The functions of the newly identified genes in the rest of the plasmatocyte clusters, which are minor subpopulations except for PM1, remain to be characterized. Presumably, these subpopulations represent transient intermediates along the course of terminal differentiation or activated states of plasmatocytes and other cell types. We also identified two clusters each for crystal cells and lamellocytes, which display differential expression of their marker genes, PPO1 and atilla, respectively (Figure 1D; see Supplementary file 2 for additional marker genes). To validate our scRNA-seq data, we compared all the genes expressed in hemocytes of unwounded condition to publicly available bulk RNA-seq data sets of all larval hemocytes and crystal cells (Miller et al., 2017; Neves et al., 2016). To achieve this, we first converted our scRNA-seq data of all hemocytes and crystal cell clusters into pseudobulk RNA-seq and then compared with the published bulk RNA-seq data sets (Miller et al., 2017; Neves et al., 2016). We identified a strong correlation with spearman correlation of ~0.79 for both hemocyte and crystal cell comparisons, which reflects a high quality of our scRNA-seq data sets (Figure 1—figure supplement 2A–B). This resource can be mined using a user-friendly searchable web-tool (www.flyrnai.org/scRNA/blood/) where genes can be queried, visualized, and compared across conditions (Figure 1—figure supplement 2C). Changes in hemocyte composition and identification of a novel Mtk-like AMP In addition to identifying genes enriched in each cluster and their changes across conditions, it is possible to estimate cell fraction changes from scRNA-seq data sets. To achieve this, we first segregated the three conditions (Figure 2A–C), then calculated cell fraction changes. Whereas PM1-5, 8, and 11 are well represented in all three conditions, PM9 is negligibly detected in the wasp inf. 24 hr condition, and PM10 and 12 were majorly detected in the wounded condition. The PMAMP clusters, PM6 and 7, emerged mainly upon wounding or wasp infestation compared to unwounded controls (Figure 2A–D). Strikingly, the proportion of cecropin-enriched PM6 cluster (~5.5%) is similar to the previously observed fraction of hemocytes (~5–10%) expressing cecropin genes upon infection (Samakovlis et al., 1990). With regard to the crystal cell clusters, both CC1 and CC2 were underrepresented in wasp inf. 24 hr, consistent with previous observations that crystal cell numbers dramatically decrease following oviposition by wasps (Kacsoh and Schlenke, 2012; Figure 2A–D). Of the lamellocyte clusters, LM1 was detected in all three conditions, however, LM2 emerged only upon wounding or wasp infestation. In summary, LM2 and PMAMP are the major clusters that are represented mostly upon wounding or wasp infestation. Figure 2 with 1 supplement see all Download asset Open asset Changes in blood cell composition and identification of a novel Mtk-like AMP. (A-C) t-SNE plots of (A) Unwounded, (B) Wounded, and (C) Wasp inf. 24 hr conditions. (D) Cell fraction changes in clusters based on treatment conditions. (E) Heat map profile of the top expressed genes in cluster PM7 identifies CG43236/Mtk-like (Mtkl). Genes were ranked based on expression levels in each condition in the heat map. (F) Phylogenetic tree map constructed with the peptide sequences of all known AMPs together with Mtkl. (G) Global alignment of Mtkl and Mtk peptide sequences using Jalview protein alignment software (Waterhouse et al., 2009). Figure 2—source data 1 Source data pertaining to cell fraction bar graph of Figure 2D. https://cdn.elifesciences.org/articles/54818/elife-54818-fig2-data1-v2.xlsx Download elife-54818-fig2-data1-v2.xlsx Despite some PM clusters being represented in all three conditions, their gene expression patterns are specific to wounding or wasp inf. 24 hr. For instance, Mmp1 showed increased expression only upon wounding or wasp inf. 24 hr in PM3 compared to unwounded controls (Figure 2—figure supplement 1A). Likewise, the increased expression of GST genes such as GstE6 is specific to wounded or wasp inf. 24 hr (Figure 2—figure supplement 1B). With regards to the PM6 cluster of immune-activated state, CecA2 showed an increased expression specifically in wasp inf. 24 hr compared to wounded conditions (Figure 2—figure supplement 1C). Further, differentially expressed gene (DEG) analysis of PM7 revealed that most of the AMP genes were more enriched during wounding or wasp inf. 24 hr than in unwounded controls (Figure 2E). Of note, the AMP gene signature was unique to either wounded or wasp inf. 24 hr conditions. For example, whereas Mtk, Drs, DptB, and AttD were specific to wounded condition, CecA1, CecB, Dro, and AttA were unique to wasp inf. 24 hr (Figure 2E). These data indicate that clusters represented in all three conditions may nevertheless differ from each other in a condition-specific manner with respect to their differential gene signatures (Supplementary file 3). A survey of all top enriched genes in PM7 revealed the identification of an uncharacterized gene, CG43236, which is relatively more enriched in wounded compared to unwounded or wasp infested conditions (Figure 2E). CG43236 encodes a small peptide of 50 amino acids (aa) and its phylogenetic alignment with all known AMPs revealed that it clustered with Mtk, an antibacterial and antifungal AMP (Levashina et al., 1995; Levashina et al., 1998; Figure 2F,G). Both Mtk and CG43236 possess an Antimicrobial10 domain that is unique to the Metchnikowin family (Figure 2G; Figure 2—figure supplement 1D–D’), leading us to name CG43236 as Mtk-like (Mtkl). To validate its expression within hemocytes and whole larvae, we performed qRT-PCR in unwounded control and wounded conditions. with our scRNA-seq data (Figure Mtkl is well expressed and more enriched in hemocytes upon wounding in larvae. the of Mtkl was compared to the of CecC, Drs, and Mtk (Figure 2—figure supplement suggesting that Mtkl may be by other of immune such as or its expression is induced upon wounding in whole larvae and in fat of wasp infested larvae (Figure 2—figure supplement Interestingly, a has shown that the expression of Mtkl is in whole flies that were to various types of et al., 2018). has been as a AMP et al., 2018). Altogether, the analysis of PM7 identified a novel Mtk-like AMP. ordering of cells hemocyte lineages Plasmatocytes of the embryonic lineage reside in larval hematopoietic pockets over the course of larval the to in the system et al., 2011). The of these plasmatocytes, by differentiation from embryonic and in the larva of differentiated plasmatocytes, is well et al., 2011). In the development of differentiated crystal cells and lamellocytes from the embryonic lineage has but of from plasmatocytes et al., 2016; Leitão and Sucena, 2015; Márkus et al., 2009). In our wounding the lymph gland to the inflammatory we did not any or of lymph gland (Figure supplement suggesting that this tissue may not be the major source of differentiated cells such as lamellocytes in Hence, it is important to the of mature cell types in scRNA-seq data can be used to pseudotemporal between cell and cell lineages from precursor cells (Trapnell, 2015; et al., 2014). We of our that PM2 expresses cell cycle genes to lineage from this cell-cycle state. To using batch corrected cells, we 10X unwounded and wounded data which represents all the mature cell types including lamellocytes that only upon wounding (Figure supplement Supplementary file We used (Cao et al., 2019) and assigned PM2 as the of the (Figure Figure supplement data shows that three major lineages from the (Figure in crystal cell and the two clusters, CC1 and CC1 that CC1 represents an intermediate state (Figure the expression of PPO1 from CC1 and its level upon mature crystal cells (Figure supplement on the other in a that PM1 and other minor PM clusters (Figure the clusters of the immune-activated state together with mature lamellocytes (Figure LM1 our that LM1 corresponds to the intermediate state of lamellocytes This is by the expression of atilla, which with low levels in LM1 and higher levels in LM2 (Figure supplement also with cells of the immune-activated states, PM7 and (Figure which express Drs and GstE6 (Figure supplement Interestingly, is the only state that is enriched for the suggesting a role for in this (Figure supplement Figure with 2 supplements see all Download asset Open asset ordering of cells using blood cell (A) was used to cells over on the unwounded and wounded data sets. (B) of clusters Figure the map. major lineage were obtained from the 1 2 and (F) gene expression signature between 1 and with the in the (G) Schematic lineage from the state of plasmatocytes (PM2) to mature cell types with their Next, to into the gene expression signatures along the we the differentially expressed genes (DEG) between the source and or analysis over revealed major clusters on the expression of marker genes at the and of the along and (Figure Interestingly, the is enriched with PM marker genes such as and suggesting that precursor cells are the self-renewing Moreover, the expression of the cell cycle genes as the lineages 1 or (Figure consistent with a between cell cycle and terminal differentiation observed in vertebrates and flies and et al., 2016; et al., and 2016; and 2016). To the cell cycle terminal we expressed against one of the top enriched cell cycle genes, polo, in plasmatocytes of in a increase in the of lamellocytes compared to controls (Figure supplement suggesting that cell cycle may be required for terminal differentiation of cell types. Finally, to cells along the we the cell using which revealed that between the and of that they are all transient plasmatocyte intermediates (Figure supplement 2E). Altogether, based on we that PM2 has and can give rise to differentiated cell types and possibly other activated states within Further, our analysis the of crystal cell and lamellocyte intermediate states that their differentiated mature cell types (Figure Crystal cell distinguishes crystal cell intermediates from mature crystal cells crystal cells split into two distinct clusters and (Figure we these cells of the other clusters. We used Harmony to for batch from the platforms and conditions (Figure supplement cell revealed two distinct clusters: one with low PPO1 expression and one with high PPO1 expression (Figure the percentage of cells the mature cell shows a upon wounding or wasp infestation (Figure Figure supplement the expression level of PPO1 was similar between unwounded controls and wounded condition in both clusters. Interestingly, PPO1 expression was negligibly detected in cells and in cells in wasp infested larvae (Figure To the two crystal cell clusters as distinct populations or is a state along the course of crystal cell we used larvae to and cells with and respectively. We the where clusters of hemocytes that crystal cells