Fruit flies are widely used in the life and biomedical sciences as models of animal biology. They are small in size and easy to care for in a laboratory, making them ideal for studying how the body works. There are, however, some experiments that are difficult to perform on whole flies and it would be advantageous to use populations of fruit fly cells grown in the laboratory – known as cell cultures – instead.

Unlike studies in humans and other mammals, which – for ethical and practical reasons –heavily rely on cell cultures, few studies have used fruit fly cell cultures. Recent work has shown that having an always active version of a gene called Ras in fruit fly cells helps the cells to survive and grow in cultures, making it simpler to generate new fruit fly cell lines compared with traditional methods. However, the methods used to express activated Ras result in cell lines that can be a mixture of many different types of cell, which limits how useful they are for research.

Here, Coleman-Gosser, Hu, Raghuvanshi, Stitzinger et al. aimed to use Ras to generate a collection of cell lines from specific types of fruit fly cells in the muscle, nervous system, blood and other parts of the body. The experiments show that selectively expressing activated Ras in an individual type of cell enables them to outcompete other cells in culture to generate a cell line consisting only of the cell type of interest.

The new cell lines offer models for experiments that more closely reflect their counterparts in flies. For example, the team were able to recapitulate how fly muscles develop by treating one of the cell lines with a hormone called ecdysone, which triggered the cells to mature into active muscle cells that spontaneously contract and relax.

In the future, the new cell lines could be used for various experiments including high throughput genetic screening or testing the effects of new drugs and other compounds. The method used in this work may also be used by other researchers to generate more fruit fly cell lines.

The use of cell cultures has been important for studying biological processes that are not easily accessible in whole organisms (Klein et al., 2022). A number of advances in mammalian cell cultures, for instance, development of 3D/organoid cultures (Rossi et al., 2018), improved genome editing tools to manipulate induced pluripotent stem cells (Hockemeyer and Jaenisch, 2016), and better optimized media formulations for recombinant protein expression Ritacco et al., 2018 have further enhanced the utility of mammalian cell culture systems. These advances are accompanied by the availability of several distinct mammalian cell lines derived from different tissue types. Similarly, the use of insect cell lines also complements whole organismal studies and helped to illuminate many aspects of insect cell biology (Luhur et al., 2019) including development (Sato and Siomi, 2020), immunity (Goodman et al., 2021; Chen et al., 2021), host–pathogen relationships (Smagghe et al., 2009), in addition to biotechnological applications (Hong et al., 2022).

Fruit fly (Drosophila melanogaster) cell cultures are among the most widely used invertebrate cell cultures (Luhur et al., 2019). Drosophila cell lines are relatively homogenous, and highly scalable for both biochemical and high-throughput functional genomic analyses (Debec et al., 2016, Baum and Cherbas, 2008; Zirin et al., 2022; Mohr, 2014; Viswanatha et al., 2019). These features underlie their status as an important workhorse for scientific discovery in organismal development and as models for human disease. There are approximately 250 distinct Drosophila cell lines housed by the Drosophila Genomics Resource Center (DGRC) (Luhur et al., 2019). The majority of these cell lines, initially established by independent laboratories worldwide, were donated to the DGRC. A subset of 25 of these lines was subjected to transcriptome analysis, with the results demonstrating that approximately half of the transcripts expressed by each of these lines were unique such that even cell lines derived from the same tissue had distinct transcriptomic profiles (Cherbas et al., 2011). Furthermore, the transcriptional profiles of several imaginal disc lines analyzed were found to match profiles of cells from distinct spatial locations in the respective discs (Cherbas et al., 2011). All lines exhibited transcript profiles indicative of cell growth and cell division, and not cellular differentiation, as expected for proliferating cells (Cherbas et al., 2011). Thus, the transcriptional profiles of several Drosophila cell lines provided a platform for subsequent analyses. For instance, a few examples of the impact of this work include research into better understanding crosstalk between signaling pathways (Ammeux et al., 2016), exploring transcription factor networks (Rhee et al., 2014), establishing small RNA diversity (Wen et al., 2014), characterizing signaling pathways (Neal et al., 2019), nucleosomal organization (Martin et al., 2017) among multiple other utilities reviewed extensively (Cherbas and Gong, 2014; Luhur et al., 2019).

Over two-thirds of the D. melanogaster cell lines listed in the DGRC catalog were derived from whole embryos and the remainder are from various larval imaginal discs, the larval central nervous system, larval hemocytes, or adult ovaries. The potential of cells from these different sources to differentiate into adult cell types is not known. However, temporal transcriptional profiling of the Ecdysone response of 41 cell lines (Stoiber et al., 2016) provided evidence that cell lines exhibited varying levels of ecdysone sensitivity and potential for cellular differentiation, suggesting the possibility of developing cell-type-specific cell lines with the capacity to differentiate.

As well as having unknown cellular origins, most Drosophila cell lines arose spontaneously, and the time needed to develop a continuous cell line was often protracted. In contrast, expression of activated Ras, Ras^V12, using the Gal4-UAS system, resulted in the rapid and reproducible generation of continuous cell lines from primary embryonic cultures (Simcox et al., 2008b). The Ras method was used to develop an array of mutant cell lines by using appropriate genotypes to establish the primary cultures (Simcox et al., 2008a, Lee et al., 2015; Kahn et al., 2014; Lim et al., 2016; Nakato et al., 2019). To date all lines have been generated using ubiquitous expression of UAS-Ras with Act5C-Gal4 and therefore the cell type in a given line is unknown.

Here, we describe a second-generation version of the Ras method in which Ras^V12 expression is restricted to a lineage by using tissue-specific Gal4 drivers. This genetic ‘dissection’ provides only the targeted cells with the survival and proliferation advantage conferred by Ras^V12 expression (Simcox et al., 2008b). As we show, the approach has been successful and resulted in the generation of cell lines with glial, epithelial, and muscle characteristics. Lines generated by broad Ras^V12 expression should also include those of specific cell types and by using single-cell cloning and cell type characterization (marker gene expression and RNAseq) we identified lines with neuronal and hemocyte characteristics. Collectively, these cell lines provide in vitro models for five different cell types and are expected to be a valuable resource for high-throughput and biochemical approaches, which rely on large numbers of homogeneous cells.

Primary cultures were established from embryos in which UAS-Ras^V12 expression was restricted to glial, tracheal epithelial, and mesodermal cells using lineage-specific Gal4 drivers (Table 1, Supplementary file 1). A subset of continuous cell lines derived from each type of primary culture was analyzed with regard to cell morphology, the presence of proteins characteristic of specific cell types, and other attributes (Table 1, Supplementary file 1, Supplementary file 2; Figure 1). We also analyzed lines with neuronal- or hemocyte-like characteristics that were cloned from parental lines resulting from ubiquitous expression of UAS-Ras^V12 (Table 1, Supplementary file 1, Supplementary file 2; Figure 1). We further analyzed the cell lines by RNAseq to determine the transcriptome and signaling pathways (Figure 2 and Figure 2—figure supplements 1–3). The gene expression values (Fragments Per Kilobase per Million mapped fragments, FKPM) are provided in Supplementary file 3. The dataset (Ras cell lines) has been imported into the Drosophila Gene Expression Tool (DGET) database (https://www.flyrnai.org/tools/dget/web/), which is the bulk RNAseq data portal at Drosophila RNAi Screening Center (DRSC) (Hu et al., 2017). The TM4 package was used for making the plot in Figure 2 (Wang et al., 2017). As expected, the transcriptomes of the new cell lines are distinct from those of existing cell lines (Cherbas et al., 2011; Figure 2—figure supplement 1) and new cell lines derived from the same Gal4 driver cluster with one another (Figure 2—figure supplement 2). Moreover, comparison of differentially expressed (DE) genes with RNAseq data from single-cell RNAseq data (Li et al., 2022; Table 2) or with known cell type-associated transcription factors (Figure 2—figure supplement 3) reveals that these cells express genes characteristic of specific cell types. The results of our detailed characterization are described according to cell type in the sections below.

Figure 1

Download asset Open asset

Figure 2 with 3 supplements see all

Download asset Open asset

Glial-lineage cell lines

Repo is expressed exclusively in glial cells (Xiong et al., 1994). A repo-Gal4 driver that recapitulates Repo expression was used to express UAS-Ras^V12 (Ogienko et al., 2020; Sepp et al., 2001). This led to robust production of primary cultures however these failed to survive beyond early passages (Supplementary file 1). To counter potential cell death or modulate growth signaling, additional genotypes were tested including co-expression of UAS-transgenes encoding the P35 baculovirus cell survival factor, dsRNAs targeting tumor suppressors, or the Gal4 inhibitor Gal80^ts (Supplementary file 1). Co-expression of a UAS-brat^dsRNA or expression of tub-Gal80^ts each produced a single line of cells that could be propagated for extended passages however the latter line was difficult to maintain and eventually lost (Supplementary file 1). The repo-Gal4: UAS-brat^dsRNA; UAS-Ras^V12 (Rbr6) line has been passaged more than 50 times. The parental Rbr6 line and three clonal derivatives (Rbr6-2, Rbr6-4, and Rbr6-F9) have an elongated morphology and stained positive for Repo (Table 1; Figures 1 and 3, and Figure 3—figure supplement 1). A few cells expressed neuronal markers (Figure 3—figure supplement 1; Supplementary file 2). To induce differentiation, we gave cells two 24 hr ecdysone treatments separated by 24 hr to approximate the pulses of ecdysone during the larval to pupal transition. Cells from each of the clones survived treatment with ecdysone suggesting they are of adult type, two clones showed morphological changes and formed a network, and all continued to express Repo (Figure 3 and Figure 3—figure supplement 2).

Figure 3 with 3 supplements see all

Download asset Open asset

The results of RNAseq analysis revealed that the three Rbr6 clones have very similar expression patterns (Figure 2—figure supplement 2). In addition, their DE gene signatures are also a close match to gene signatures of glial cells as identified by single-cell RNAseq (Table 2) and to glial-associated genes reported in the literature. For example, zydeco (zyd), which encodes a potassium-dependent sodium/calcium exchanger, is upregulated in all three clones, consistent with the literature (Zwarts et al., 2015; Featherstone, 2011), and gcm2, a transcription factor, is upregulated in two clones (Figure 2—figure supplement 3). These data suggest the Rbr6 clones will be a useful in vitro source of glial cells.

Tracheal epithelium-lineage cell lines

Breathless is expressed in the tracheal epithelium and a btl-Gal4 driver was used to express UAS-Ras^V12 (Shiga et al., 1996). Patches of cells with epithelial morphology proliferated in primary cultures and several continuous lines were generated (Table 1, Supplementary file 1). We were unable to derive clones of these using dilution or selection methods, which were successful for other cell types. Correspondingly, three parental lines were examined: Btl3, Btl7, and Btl8 (Table 1). All showed expression of the epithelial marker Shotgun/E-Cadherin (Shg/Ecad) and two grew in a squamous epithelial sheet with Ecad expression at the cell periphery (Figures 1 and 4, and Figure 4—figure supplement 1). In comparison S2 did not show peripheral expression of Ecad (Figure 4). Treatment of the squamous epithelial cells (Btl3 and Btl7) with ecdysone caused aggregation and formation of large multicellular clusters (Figure 4, Figure 4—figure supplement 2).

Figure 4 with 3 supplements see all

Download asset Open asset

RNAseq data analysis comparing the top upregulated genes in the Btl cell lines with scRNAseq datasets revealed that the lines closely match the signatures of the adult trachea, a network of epithelial tubules (Table 2) and Btl3 expresses trachealess (trh) a master regulator of tracheal identity (Wilk et al., 1996; Figure 2—figure supplement 3). Overall, the morphological and molecular characteristics of the lines are consistent with an epithelial cell type of tracheal origin.

Mesodermal-lineage cell lines

The 24B-Gal4 driver is an insertion in held out wings (how) and is expressed in mesoderm and muscle cells (Brand and Perrimon, 1993; Zaffran et al., 1997). Expression of UAS-Ras^V12 with 24B-Gal4 readily produced continuous lines (Table 1, Supplementary file 1). Four clones (24B5-B8, 24B5-D8, 24BG1-F3, and 24BG1-G1) derived from two parental lines (24B5 and 25BG1) were analyzed in more detail (Table 1). The cells had a bipolar shape and expressed mesoderm markers including Twist and Mef2 (Figures 1 and 5, and Figure 5—figure supplement 1). When treated with ecdysone, cells from both parental lines and clones 24B5-B8 and 24B5-D8 elongated, fused as indicated by multinucleate cells, formed a network, and expressed Myosin heavy chain (Mhc) (Figure 5 and Figure 5—figure supplement 2). There was also extensive cell lysis. Beginning 2 days after the second ecdysone treatment, the cells began to contract spontaneously. Contraction of cells from the 24B5 parental line and the two derivative clones (24B5-B8 and 24B5-D8) was visible in real time (Videos 1 and 2), whereas contraction of parental line 24BG1 cells was much slower and visualized more clearly in time-lapse (Videos 3 and 4). The clones 24BG1-F3 and 24BG1-G1 underwent morphological change but did not express Mhc or contract (Figure 5—figure supplements 2 and 3). In later passages, the 24BG1 parental line also lost expression of Mhc and the ability to contract (Figure 5—figure supplement 2). This highlights the importance of using early passage cells and avoiding extended passaging that could alter the phenotypic (and genotypic) characteristics of the cells.

Video 1

Download asset

Video 2

Download asset

Figure 5 with 5 supplements see all

Download asset Open asset

Video 3

Download asset

Video 4

Download asset

We also attempted to derive lines from Mef2-Gal4 because Mef2 regulates muscle development and is expressed in muscle progenitors and differentiated muscle suggesting Mef2-Gal4 would be a good candidate for deriving cell lines (Bour et al., 1995; Gossett et al., 1989; Lilly et al., 1995; Ranganayakulu et al., 1995). However, only rare primary cultures had some proliferating cell patches, and none progressed to continuous lines (Supplementary file 1; Figure 5—figure supplement 4). Analysis of larvae from the cross (Mef2-Gal4/+; UAS-GFP/UAS-Ras^V12) and control larvae (Mef2-Gal4/+; UAS-GFP/+) showed that Ras^V12 expression disrupted muscle development, suggesting that the prevalent amorphous GFP-positive cells observed in primary cultures were abnormal muscle cells (Figure 5—figure supplement 4).

The RNAseq analysis for 24B-Gal4-derived cell lines, identified the cells as muscle (Table 2). 24B5-B8 cells express high levels of the transcription factors nautilus (nau) and twist (twi) (Figure 2—figure supplement 3; Figure 5—figure supplement 1; Table 2), and high levels of myoblast city (mbo), which encodes an unconventional bipartite GEF with a role in myoblast fusion (Erickson et al., 1997). The capacity of these mesoderm-derived cell lines to differentiate into active muscle shows that the cells are muscle precursors and thus should be a useful reagent to analyze muscle physiology and development.

Neuronal-like cell lines

To target neuronal cells, we expressed UAS-Ras^V12 with the pan-neural drivers scratch-Gal4 and elav-Gal4, however none of the primary cultures resulted in continuous cell lines (Supplementary file 1; Figure 6—figure supplement 1). In previous work, we made primary cultures from embryos with ubiquitous expression of UAS-Ras^V12 using the Act5C-Gal4 driver (Simcox et al., 2008b). The cells growing in these cultures included neuronal cells (Simcox et al., 2008b). Here, we used an Act5C-GeneSwitch-Gal4 driver to express UAS-Ras^V12. GeneSwitch-Gal4 is only active in the presence of the drug, RU486/mifepristone, which provides the advantage of being able to regulate Ras^V12 expression (Nicholson et al., 2008; Osterwalder et al., 2001). Several continuous lines were generated (Supplementary file 1). Clones derived from two of these (ActGSB-6 and ActGSI-2) (Table 1) were positive for the neuronal marker, HRP (horseradish peroxidase) (Figure 6, Figure 6—figure supplements 2 and 3). After differentiation with ecdysone, expression of Futsch/MAPB1 (Hummel et al., 2000) and Fas2 (Mao and Freeman, 2009) was enhanced and revealed axonal-like outgrowths from the cells (Figure 6 and Figure 6—figure supplement 3). Differentiated cells also showed enhanced expression of Elav, which is commonly used as a marker for postmitotic neurons (Figure 6 and Figure 6—figure supplement 3; Robinow and White, 1991). Elav is also expressed transiently in glial cells and proliferating neuroblasts Berger et al., 2007; however, the cells were negative for the glial marker Repo (Supplementary file 2).

Figure 6 with 4 supplements see all

Download asset Open asset

RNAseq analysis revealed that many neuronal genes are upregulated in these cell lines, including Glutamic acid decarboxylase 1 (Gad1), slowpoke (slo), 5-hydroxytryptamine (serotonin) receptor 1A (5-HT1A), Protein C kinase 53E (Pkc53E), Diuretic hormone 31 Receptor (Dh31-R), and straightjacket (stj). In addition, comparison of the top upregulated genes in these cells to marker genes from scRNAseq data identifies a cell type of neuronal origin as the best match (Table 2). The cells should be a useful source of neuronal cells.

Hemocyte-like cell line

Cells of clone ActGSI-3 derived from the ActGSI parental line (UAS-Ras^V12 expression with Act5C-GeneSwitch-Gal4; Table 1, Supplementary file 1) show characteristics of hemocytes and express the hemocyte marker Hemese (Figure 7; Kurucz et al., 2003). They are also positive for HRP, but not other neuronal markers (Figure 7—figure supplement 1). ActGSI-3 cells divide in floating clusters, contrasting with S2 cells, which are also thought to be hemocytes, that grow as single cells (Figures 1 and 7).

Figure 7 with 2 supplements see all

Download asset Open asset

RNAseq analysis demonstrated that many hemocyte genes are upregulated in these cells, including serpent (srp), Hemese (He), eater, u-shaped (ush), Cecropin A2 (CecA2), and Cecropin C (CecC). Comparison of top upregulated genes with scRNAseq data showed that the cells have a strong match to the top marker genes of hemocytes (Table 2).

Growth, karyotype, and transfection efficiency of cell lines

We determined the cell density at confluence for the cell lines (Table 3). The cells in each line grow to confluence attached to the tissue-culture surface, except ActGSI-3, which grow as floating cell clusters (Figure 8). The cells are not contact inhibited and cell clusters are formed allowing cells to grow to higher density (Figure 8). We determined the doubling time of 13 cell lines and clones using growth curves (Table 3; Figure 8—figure supplement 1). Most had doubling times within a range of approximately 20–40 hr (Table 3). The hemocyte-like clone ActGSI-3 was an outlier with a longer doubling time of 70 hr (Table 3). In cells from clones ActGSB-6, ActGSI-2, and ActGSI-3, expression of Ras^V12 is dependent on GeneSwitch Gal4, which is active only in the presence of mifepristone. In the absence of the drug the cells become quiescent (Figure 8—figure supplement 1).

Figure 8 with 1 supplement see all

Download asset Open asset

Table 3

Tissue type	Line	Confluent density (×106)*	Doubling time (hr)	Karyotype	Transfection efficiency (%)
Glial	Rbr6-2	1.8	20	8, XY	24
	Rbr6-4	2.4	20	8, XY	28
	Rbr6-F9	3.4	19	8, XY	22
Epithelial	Btl3	3.7	33	7, XY, –4	26
	Btl7	2.6	37	Abnormal tetraploid, XX, variable 4	34
	Btl8	2.7	22	Abnormal tetraploid, XX, –4	16
Mesodermal	24B5-B8	1.4	29	Abnormal tetraploid, XXY, variable 4	23
	24B5-D8	5.1	23	Abnormal tetraploid, XX, variable 4	27
	24BG1-G1	2.8	21	8, XY (some –4)	ND
	24BG1-F3	2.7	35	8, XY (some –4)	ND
Neuronal	ActGSB-6	2.9	23	7, XO	29
	ActGSI-2	8.1	27	8, XX	ND
Blood	ActGSI-3	1.9	70	Abnormal tetraploid, XX, variable 4	ND
	S2	6.2	ND	ND	53

1. *

   Confluent density in one well of a 12-well plate, 3.5 cm² surface area (average of three wells).

We determined the gross karyotype of 13 cell lines and clones. In keeping with previous findings for Ras^V12 expressing cell lines, most (8) were diploid, or near diploid (Simcox et al., 2008b; Table 3; Figure 3—figure supplement 3; Figure 4—figure supplement 3; Figure 5—figure supplement 5; Figure 6—figure supplement 4; Figure 7—figure supplement 2). Related clones had similar karyotypes, which likely indicates that parental lines may also be clonal as a result of selective pressure for cells that grow well in culture. Some lines were polyploid and common aneuploid conditions include loss of an X chromosome and varying numbers of chromosome 4 (Table 3).

Nine parental and clonal lines were transfected with an Act5C-EGFP plasmid and the fraction of GFP-positive cells was determined after 48 hr. Cells from all lines tested could be transfected. The range of efficiency was from 16% to 34% with most lines showing transfection of approximately one quarter of the cells (Table 3). Similarly treated, cells from the S2 line showed an efficiency of 53%.

Expressing activated Ras, Ras^V12, in primary cells provides a growth and survival advantage and leads to the rapid and reliable generation of continuous cell lines—the so-called Ras method (Simcox et al., 2008b). In a second-generation version of the Ras method, we found that restricting Ras^V12 expression with lineage-specific Gal4 drivers gave the targeted cells a competitive advantage and produced continuous lines with expected cell-type-specific phenotypes. With this approach we produced glial, epithelial, and muscle cell lines using the repo-, btl-, and 24B/how-Gal4 drivers, respectively.

In theory, the approach could be used to produce cell lines corresponding to any cell type for which there is an appropriate Gal4 driver. We tried to derive lines with Mef2-Gal4, a muscle master regulator gene, and the pan-neuronal driver elav-Gal4; however, no continuous lines were produced (Supplementary file 1; Figure 5—figure supplement 4 and Figure 6—figure supplement 1). In both cases, Ras^V12 expression appeared to disrupt growth of the targeted cell type. In the case of the muscle lineage, 24B/how-Gal4 was efficient at producing cell lines. The success with one and not the other muscle driver shows that in practice, it may be necessary to test multiple Gal4 lines for a given lineage. Drivers with very specific expression patterns may prove useful, including those generated by the Split Gal4 system (Luan et al., 2006). As with any tissue-culture system, the unnatural conditions of growing in vitro may select for ‘generic’ cells that survive well in culture and lose their lineage identities. This means that characterizing cell lines after generation for a battery of features (morphological, physiological, and molecular) is an essential step in assessing whether cells represent the tissue of origin expected for a given Gal4 driver.

repo-Gal4 is a pan-glial driver and many primary cultures expressing Ras^V12 with this driver reached confluence and could be passaged several times but did not produce continuous lines (Supplementary file 1). We tested different genotypes to determine if the success rate could be improved by modulation of Ras^V12 expression (co-expression of the Gal4 inhibitor Gal80ts), co-expression of the p35 baculovirus survival factor, or growth stimulation by downregulation of tumor suppressors (dsRNA for warts or brat). One line, also harboring a Gal80ts transgene, reached passage 25; however, the line was unstable and in early passages the cells variably lost Repo expression and changed morphologically. The one continuous glial line generated expresses a transgene that targets the tumor suppressor, brat (repo-Gal4; UAS-Ras^V12; UAS-brat^dsRNA). Given a single success, it is not clear if downregulation of brat contributed to derivation of the line. Moreover, there is no evidence that these genotypic variations enhanced cell line generation with other drivers, as primary cultures expressing Ras^V12 without modulation or a survival factor produced lines with similar success rates for the btl-Gal4 or 24B/how-Gal4 drivers (Supplementary file 1).

As with all types of tissue culture, best practices involve maintaining frozen aliquots of cell lines at relatively low passage numbers. Aliquots of cells from the lines and clones described here, on which RNAseq was performed, have been archived at similar passage numbers as those used for the RNAseq analysis. This will allow users to start experimentation with the lines in a known state. The importance of this is exemplified by line 24BG1, which lost the ability to contract and express the muscle protein Mhc after multiple passages (Figure 5—figure supplement 2).

The mesodermal, neuronal, and glial cells represent in vitro counterparts of the tissues of origin that can be used for studying development and physiology in an accessible and reproducible system. The mesodermal cells that differentiate into active muscle will allow investigation of muscle fusion, as the cells are multinucleate (Figure 5), as well as muscle physiology and function. For example, the cells contract spontaneously and in apparent waves (Videos 1 and 2); however, the mechanism for stimulation (if any) and regulation have not been investigated and may cast light on in vivo processes. Given a variety of cell types, it will also be interesting to examine cell form and function in co-cultures, for example, of glia and neurons.

The method and the cells will be useful for generating disease models. New lineage-specific lines could be generated in the desired mutant background by establishing primary cultures from embryos in which only the mutant genotype expresses Ras^V12 giving these cells a growth and survival advantage (Simcox et al., 2008a). Derivative lines should include those of the desired cell type and genotype. Alternatively, the existing cell lines could be edited using CRISPR, or insertion of transgenes using the attP site that most lines and clones contain (Supplementary file 1; Bateman et al., 2006; Manivannan et al., 2015).

The cells with epithelial morphology derived from the tracheal lineage (Btl3 and Btl7) will provide good models for investigating assay conditions that promote polarization and 3D cell interactions that could allow the cells to manifest a more complex tissue architecture. In keeping with this possibility, treating these cells with ecdysone to induce differentiation showed cell clumping suggestive of a multicellular structure (Figure 4—figure supplement 2).

RNAseq analysis of cells from the ActGSI-3 cell clone showed a striking similarity to hemocytes, and the cells may be a good model for studying immunity (Table 2). The cells lyse after ecdysone treatment suggesting they are of embryonic origin (Figure 7). The cells grow as floating cell clumps (Figures 1 and 7) that may recapitulate subepidermal clusters of sessile hemocytes of the larva (Leitão and Sucena, 2015; Márkus et al., 2009).

The most significantly upregulated marker genes in each cell line are significantly enriched for top marker genes from expected cell types based on the single-cell RNAseq data from Fly Cell Atlas in most cases. This indicates the potential value of these cell lines as corresponding in vitro models for studying these cell types. While the cells will prove to be valuable models, it should be noted that even those showing a clear differentiated phenotype exhibit unexpected patterns of gene expression. For example, some cells in the mesodermal clone, 24B5-B8, are positive for HRP (Figure 5—figure supplement 1; Supplementary file 2) and the two neuronal-like lines express a mesodermal marker, Twist (Figure 6—figure supplement 2; Supplementary file 2). This anomalous gene expression is likely to be an effect of Ras activation on downstream pathways and genes. Ras/MAPK has a key role in muscle cell determination (Buff et al., 1998; Carmena et al., 2002; Halfon et al., 2000) and activates downstream muscle determination genes. It will also be important to consider what genes are not expressed by a given cell line, for example, glial cell missing (gcm) is not differentially expressed in the three glial-lineage cell clones and gcm2 is differentially expressed in only two of the three clones. Further, trachealess (trh) is only differentially expressed in one of the three tracheal-lineage cell lines. Similarly, the muscle-specific transcription factors twist (twi), nautilus (nau), snail (sna), and Mef2 show variable expression in the muscle-lineage cell clones. It should also be noted that the expression patterns were determined for undifferentiated cells and expression levels could change after hormone exposure.

The cells will have value for both low- and high-throughput approaches, including genetic or compound screens for which screening in the relevant cell type will result in identifying targets that are more likely to be of physiological relevance. Most of the cells have an attP-flanked cassette (Table 1), which makes them amenable to insertion of transgenes such as reporters by Recombination Mediated Cassette Exchange (RMCE) (Bateman et al., 2006; Manivannan et al., 2015). Moreover, cells competent for RMCE can be modified by stable expression of Cas9 and then used for genome-wide CRISPR pooled screening. With this approach, a library of single guide RNAs (sgRNAs) are integrated at RMCE sites (Viswanatha et al., 2018; Viswanatha et al., 2019). This generates a pool of cells, each with a different sgRNA, that can be subjected to a screen assay. Results are identified by PCR amplification of inserted sgRNAs followed by next-generation sequencing to detect sgRNAs that are enriched or depleted in the experimental cell pool as compared with a control. To date, pooled CRISPR screens in Drosophila have only been performed in S2 cells, which have hemocyte-like features. The availability of new cell lines with muscle, glial, and epithelial characteristics will enable screens designed to interrogate biological processes specific to these cell types.

There are hundreds of Drosophila cell lines; however, the number corresponding to known cell types is low. This is due in part to the lack of a method for generating cell lines from specific tissues. We expect that the method described here, using restricted expression of Ras^V12, will be a tractable approach for investigators to generate lines of cell types of interest. Single-cell cloning followed by cell characterization (immunohistochemistry and RNAseq) also proved to be a useful method to identify cell-type-specific lines and this approach could identify additional valuable lines in the existing collection at the DGRC. In summary, we show that lineage-restricted Ras expression and cell cloning has produced a set of new cell lines that will be of immediate value for analyses in the five cell types they represent.

Sequencing data have been deposited in GEO under accession code GSE219105.

1. 1. Mariyappa D
   2. Luhur A
   3. Zelhof A
   4. Hu Y
   5. Simcox A

   (2022) NCBI Gene Expression Omnibus

   ID GSE219105. Continuous muscle, glial, epithelial, neuronal, and hemocyte cell lines for Drosophila research.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE219105

1. 1. Ammeux N
   2. Housden BE
   3. Georgiadis A
   4. Hu Y
   5. Perrimon N

   (2016) Mapping signaling pathway cross-talk in Drosophila cells

   PNAS 113:9940–9945.

   https://doi.org/10.1073/pnas.1610432113

   • PubMed
   • Google Scholar
2. 1. Bateman JR
   2. Lee AM
   3. Wu C

   (2006) Site-specific transformation of Drosophila via phiC31 integrase-mediated cassette exchange

   Genetics 173:769–777.

   https://doi.org/10.1534/genetics.106.056945

   • PubMed
   • Google Scholar
3. 1. Baum B
   2. Cherbas L

   (2008) Drosophila cell lines as model systems and as an experimental tool

   Methods in Molecular Biology 420:391–424.

   https://doi.org/10.1007/978-1-59745-583-1

   • Google Scholar
4. 1. Berger C
   2. Renner S
   3. Lüer K
   4. Technau GM

   (2007) The commonly used marker ELAV is transiently expressed in neuroblasts and glial cells in the Drosophila embryonic CNS

   Developmental Dynamics 236:3562–3568.

   https://doi.org/10.1002/dvdy.21372

   • PubMed
   • Google Scholar
5. 1. Bour BA
   2. O’Brien MA
   3. Lockwood WL
   4. Goldstein ES
   5. Bodmer R
   6. Taghert PH
   7. Abmayr SM
   8. Nguyen HT

   (1995) Drosophila MEF2, a transcription factor that is essential for myogenesis

   Genes & Development 9:730–741.

   https://doi.org/10.1101/gad.9.6.730

   • PubMed
   • Google Scholar
6. 1. Brand AH
   2. Perrimon N

   (1993) Targeted gene expression as a means of altering cell fates and generating dominant phenotypes

   Development 118:401–415.

   https://doi.org/10.1242/dev.118.2.401

   • Google Scholar
7. 1. Buff E
   2. Carmena A
   3. Gisselbrecht S
   4. Jiménez F
   5. Michelson AM

   (1998) Signalling by the Drosophila epidermal growth factor receptor is required for the specification and diversification of embryonic muscle progenitors

   Development 125:2075–2086.

   https://doi.org/10.1242/dev.125.11.2075

   • Google Scholar
8. 1. Carmena A
   2. Buff E
   3. Halfon MS
   4. Gisselbrecht S
   5. Jiménez F
   6. Baylies MK
   7. Michelson AM

   (2002) Reciprocal Regulatory Interactions between the Notch and Ras Signaling Pathways in the Drosophila Embryonic Mesoderm

   Developmental Biology 244:226–242.

   https://doi.org/10.1006/dbio.2002.0606

   • Google Scholar
9. 1. Chen D
   2. Roychowdhury-Sinha A
   3. Prakash P
   4. Lan X
   5. Fan W
   6. Goto A
   7. Hoffmann JA

   (2021) A time course transcriptomic analysis of host and injected oncogenic cells reveals new aspects of Drosophila immune defenses

   PNAS 118:e2100825118.

   https://doi.org/10.1073/pnas.2100825118

   • PubMed
   • Google Scholar
10. 1. Cherbas L
    2. Willingham A
    3. Zhang D
    4. Yang L
    5. Zou Y
    6. Eads BD
    7. Carlson JW
    8. Landolin JM
    9. Kapranov P
    10. Dumais J
    11. Samsonova A
    12. Choi J-H
    13. Roberts J
    14. Davis CA
    15. Tang H
    16. van Baren MJ
    17. Ghosh S
    18. Dobin A
    19. Bell K
    20. Lin W
    21. Langton L
    22. Duff MO
    23. Tenney AE
    24. Zaleski C
    25. Brent MR
    26. Hoskins RA
    27. Kaufman TC
    28. Andrews J
    29. Graveley BR
    30. Perrimon N
    31. Celniker SE
    32. Gingeras TR
    33. Cherbas P

    (2011) The transcriptional diversity of 25 Drosophila cell lines

    Genome Research 21:301–314.

    https://doi.org/10.1101/gr.112961.110

    • PubMed
    • Google Scholar
11. 1. Cherbas L
    2. Gong L

    (2014) Cell lines

    Methods 68:74–81.

    https://doi.org/10.1016/j.ymeth.2014.01.006

    • Google Scholar
12. 1. Debec A
    2. Megraw TL
    3. Guichet A

    (2016) Methods to Establish Drosophila Cell Lines

    Methods in Molecular Biology 1478:333–351.

    https://doi.org/10.1007/978-1-4939-6371-3

    • Google Scholar
13. 1. Dobin A
    2. Davis CA
    3. Schlesinger F
    4. Drenkow J
    5. Zaleski C
    6. Jha S
    7. Batut P
    8. Chaisson M
    9. Gingeras TR

    (2013) STAR: ultrafast universal RNA-seq aligner

    Bioinformatics 29:15–21.

    https://doi.org/10.1093/bioinformatics/bts635

    • PubMed
    • Google Scholar
14. 1. Erickson MR
    2. Galletta BJ
    3. Abmayr SM

    (1997) Drosophila myoblast city encodes a conserved protein that is essential for myoblast fusion, dorsal closure, and cytoskeletal organization

    The Journal of Cell Biology 138:589–603.

    https://doi.org/10.1083/jcb.138.3.589

    • PubMed
    • Google Scholar
15. 1. Featherstone DE

    (2011) Glial solute carrier transporters in Drosophila and mice

    Glia 59:1351–1363.

    https://doi.org/10.1002/glia.21085

    • PubMed
    • Google Scholar
16. 1. Goodman CL
    2. Kang DS
    3. Stanley D

    (2021) Cell line platforms support research into arthropod immunity

    Insects 12:738.

    https://doi.org/10.3390/insects12080738

    • PubMed
    • Google Scholar
17. 1. Gossett LA
    2. Kelvin DJ
    3. Sternberg EA
    4. Olson EN

    (1989) A new myocyte-specific enhancer-binding factor that recognizes A conserved element associated with multiple muscle-specific genes

    Molecular and Cellular Biology 9:5022–5033.

    https://doi.org/10.1128/mcb.9.11.5022-5033.1989

    • PubMed
    • Google Scholar
18. 1. Halfon MS
    2. Carmena A
    3. Gisselbrecht S
    4. Sackerson CM
    5. Jiménez F
    6. Baylies MK
    7. Michelson AM

    (2000) Ras pathway specificity is determined by the integration of multiple signal-activated and tissue-restricted transcription factors

    Cell 103:63–74.

    https://doi.org/10.1016/S0092-8674(00)00105-7

    • Google Scholar
19. 1. Hockemeyer D
    2. Jaenisch R

    (2016) Induced pluripotent stem cells meet genome editing

    Cell Stem Cell 18:573–586.

    https://doi.org/10.1016/j.stem.2016.04.013

    • Google Scholar
20. 1. Hong M
    2. Li T
    3. Xue W
    4. Zhang S
    5. Cui L
    6. Wang H
    7. Zhang Y
    8. Zhou L
    9. Gu Y
    10. Xia N
    11. Li S

    (2022) Genetic engineering of baculovirus-insect cell system to improve protein production

    Frontiers in Bioengineering and Biotechnology 10:994743.

    https://doi.org/10.3389/fbioe.2022.994743

    • PubMed
    • Google Scholar
21. 1. Housden BE
    2. Valvezan AJ
    3. Kelley C
    4. Sopko R
    5. Hu Y
    6. Roesel C
    7. Lin S
    8. Buckner M
    9. Tao R
    10. Yilmazel B
    11. Mohr SE
    12. Manning BD
    13. Perrimon N

    (2015) Identification of potential drug targets for tuberous sclerosis complex by synthetic screens combining CRISPR-based knockouts with RNAi

    Science Signaling 8:rs9.

    https://doi.org/10.1126/scisignal.aab3729

    • Google Scholar
22. 1. Hu Y
    2. Comjean A
    3. Perrimon N
    4. Mohr SE

    (2017) The Drosophila Gene Expression Tool (DGET) for expression analyses

    BMC Bioinformatics 18:98.

    https://doi.org/10.1186/s12859-017-1509-z

    • Google Scholar
23. 1. Hu Y
    2. Tattikota SG
    3. Liu Y
    4. Comjean A
    5. Gao Y
    6. Forman C
    7. Kim G
    8. Rodiger J
    9. Papatheodorou I
    10. dos Santos G
    11. Mohr SE
    12. Perrimon N

    (2021) DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species

    Computational and Structural Biotechnology Journal 19:2018–2026.

    https://doi.org/10.1016/j.csbj.2021.04.021

    • Google Scholar
24. 1. Hummel T
    2. Krukkert K
    3. Roos J
    4. Davis G
    5. Klämbt C

    (2000) Drosophila Futsch/22C10 Is a MAP1B-like Protein Required for Dendritic and Axonal Development

    Neuron 26:357–370.

    https://doi.org/10.1016/S0896-6273(00)81169-1

    • Google Scholar
25. 1. Jenkins VK
    2. Larkin A
    3. Thurmond J
    4. FlyBase Consortium

    (2022) Using FlyBase: A Database of Drosophila Genes and Genetics

    Methods in Molecular Biology 2540:1–34.

    https://doi.org/10.1007/978-1-0716-2541-5_1

    • PubMed
    • Google Scholar
26. 1. Kahn TG
    2. Stenberg P
    3. Pirrotta V
    4. Schwartz YB

    (2014) Combinatorial interactions are required for the efficient recruitment of pho repressive complex (PhoRC) to polycomb response elements

    PLOS Genetics 10:e1004495.

    https://doi.org/10.1371/journal.pgen.1004495

    • PubMed
    • Google Scholar
27. 1. Klein SG
    2. Steckbauer A
    3. Alsolami SM
    4. Arossa S
    5. Parry AJ
    6. Li M
    7. Duarte CM

    (2022) Toward best practices for controlling mammalian cell culture environments

    Frontiers in Cell and Developmental Biology 10:788808.

    https://doi.org/10.3389/fcell.2022.788808

    • PubMed
    • Google Scholar
28. 1. Kurucz E
    2. Zettervall C-J
    3. Sinka R
    4. Vilmos P
    5. Pivarcsi A
    6. Ekengren S
    7. Hegedüs Z
    8. Ando I
    9. Hultmark D

    (2003) Hemese, a hemocyte-specific transmembrane protein, affects the cellular immune response in Drosophila

    PNAS 100:2622–2627.

    https://doi.org/10.1073/pnas.0436940100

    • PubMed
    • Google Scholar
29. 1. Lee H
    2. McManus CJ
    3. Cho D-Y
    4. Eaton M
    5. Renda F
    6. Somma MP
    7. Cherbas L
    8. May G
    9. Powell S
    10. Zhang D
    11. Zhan L
    12. Resch A
    13. Andrews J
    14. Celniker SE
    15. Cherbas P
    16. Przytycka TM
    17. Gatti M
    18. Oliver B
    19. Graveley B
    20. MacAlpine D

    (2014) DNA copy number evolution in Drosophila cell lines

    Genome Biology 15:R70.

    https://doi.org/10.1186/gb-2014-15-8-r70

    • PubMed
    • Google Scholar
30. 1. Lee H-G
    2. Kahn TG
    3. Simcox A
    4. Schwartz YB
    5. Pirrotta V

    (2015) Genome-wide activities of Polycomb complexes control pervasive transcription

    Genome Research 25:1170–1181.

    https://doi.org/10.1101/gr.188920.114

    • Google Scholar
31. 1. Leitão AB
    2. Sucena É

    (2015) Drosophila sessile hemocyte clusters are true hematopoietic tissues that regulate larval blood cell differentiation

    eLife 4:e06166.

    https://doi.org/10.7554/eLife.06166

    • PubMed
    • Google Scholar
32. 1. Li H
    2. Janssens J
    3. De Waegeneer M
    4. Kolluru SS
    5. Davie K
    6. Gardeux V
    7. Saelens W
    8. David FPA
    9. Brbić M
    10. Spanier K
    11. Leskovec J
    12. McLaughlin CN
    13. Xie Q
    14. Jones RC
    15. Brueckner K
    16. Shim J
    17. Tattikota SG
    18. Schnorrer F
    19. Rust K
    20. Nystul TG
    21. Carvalho-Santos Z
    22. Ribeiro C
    23. Pal S
    24. Mahadevaraju S
    25. Przytycka TM
    26. Allen AM
    27. Goodwin SF
    28. Berry CW
    29. Fuller MT
    30. White-Cooper H
    31. Matunis EL
    32. DiNardo S
    33. Galenza A
    34. O’Brien LE
    35. Dow JAT
    36. FCA Consortium§
    37. Jasper H
    38. Oliver B
    39. Perrimon N
    40. Deplancke B
    41. Quake SR
    42. Luo L
    43. Aerts S
    44. Agarwal D
    45. Ahmed-Braimah Y
    46. Arbeitman M
    47. Ariss MM
    48. Augsburger J
    49. Ayush K
    50. Baker CC
    51. Banisch T
    52. Birker K
    53. Bodmer R
    54. Bolival B
    55. Brantley SE
    56. Brill JA
    57. Brown NC
    58. Buehner NA
    59. Cai XT
    60. Cardoso-Figueiredo R
    61. Casares F
    62. Chang A
    63. Clandinin TR
    64. Crasta S
    65. Desplan C
    66. Detweiler AM
    67. Dhakan DB
    68. Donà E
    69. Engert S
    70. Floc’hlay S
    71. George N
    72. González-Segarra AJ
    73. Groves AK
    74. Gumbin S
    75. Guo Y
    76. Harris DE
    77. Heifetz Y
    78. Holtz SL
    79. Horns F
    80. Hudry B
    81. Hung R-J
    82. Jan YN
    83. Jaszczak JS
    84. Jefferis GSXE
    85. Karkanias J
    86. Karr TL
    87. Katheder NS
    88. Kezos J
    89. Kim AA
    90. Kim SK
    91. Kockel L
    92. Konstantinides N
    93. Kornberg TB
    94. Krause HM
    95. Labott AT
    96. Laturney M
    97. Lehmann R
    98. Leinwand S
    99. Li J
    100. Li JSS
    101. Li K
    102. Li K
    103. Li L
    104. Li T
    105. Litovchenko M
    106. Liu H-H
    107. Liu Y
    108. Lu T-C
    109. Manning J
    110. Mase A
    111. Matera-Vatnick M
    112. Matias NR
    113. McDonough-Goldstein CE
    114. McGeever A
    115. McLachlan AD
    116. Moreno-Roman P
    117. Neff N
    118. Neville M
    119. Ngo S
    120. Nielsen T
    121. O’Brien CE
    122. Osumi-Sutherland D
    123. Özel MN
    124. Papatheodorou I
    125. Petkovic M
    126. Pilgrim C
    127. Pisco AO
    128. Reisenman C
    129. Sanders EN
    130. dos Santos G
    131. Scott K
    132. Sherlekar A
    133. Shiu P
    134. Sims D
    135. Sit RV
    136. Slaidina M
    137. Smith HE
    138. Sterne G
    139. Su Y-H
    140. Sutton D
    141. Tamayo M
    142. Tan M
    143. Tastekin I
    144. Treiber C
    145. Vacek D
    146. Vogler G
    147. Waddell S
    148. Wang W
    149. Wilson RI
    150. Wolfner MF
    151. Wong Y-CE
    152. Xie A
    153. Xu J
    154. Yamamoto S
    155. Yan J
    156. Yao Z
    157. Yoda K
    158. Zhu R
    159. Zinzen RP

    (2022) Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly

    Science 375:eabk2432.

    https://doi.org/10.1126/science.abk2432

    • Google Scholar
33. 1. Lilly B
    2. Zhao B
    3. Ranganayakulu G
    4. Paterson BM
    5. Schulz RA
    6. Olson EN

    (1995) Requirement of MADS Domain Transcription Factor D-MEF2 for Muscle Formation in Drosophila

    Science 267:688–693.

    https://doi.org/10.1126/science.7839146

    • Google Scholar
34. 1. Lim MYT
    2. Ng AWT
    3. Chou Y
    4. Lim TP
    5. Simcox A
    6. Tucker-Kellogg G
    7. Okamura K

    (2016) The Drosophila Dicer-1 Partner Loquacious Enhances miRNA Processing from Hairpins with Unstable Structures at the Dicing Site

    Cell Reports 15:1795–1808.

    https://doi.org/10.1016/j.celrep.2016.04.059

    • Google Scholar
35. 1. Love M
    2. Anders S
    3. Huber W

    (2014) Deseq2

    Deseq2, 3.4, https://bioconductor.statistik.tu-dortmund.de/packages/3.4/bioc/html/DESeq2.html.

    https://bioconductor.statistik.tu-dortmund.de/packages/3.4/bioc/html/DESeq2.html

    • Google Scholar
36. 1. Luan H
    2. Peabody NC
    3. Vinson CR
    4. White BH

    (2006) Refined spatial manipulation of neuronal function by combinatorial restriction of transgene expression

    Neuron 52:425–436.

    https://doi.org/10.1016/j.neuron.2006.08.028

    • PubMed
    • Google Scholar
37. 1. Luhur A
    2. Klueg KM
    3. Zelhof AC

    (2019) Generating and working with Drosophila cell cultures: Current challenges and opportunities

    Wiley Interdisciplinary Reviews. Developmental Biology 8:e339.

    https://doi.org/10.1002/wdev.339

    • PubMed
    • Google Scholar
38. 1. Manivannan SN
    2. Jacobsen TL
    3. Lyon P
    4. Selvaraj B
    5. Halpin P
    6. Simcox A

    (2015) Targeted Integration of Single-Copy Transgenes in Drosophila melanogaster Tissue-Culture Cells Using Recombination-Mediated Cassette Exchange

    Genetics 201:1319–1328.

    https://doi.org/10.1534/genetics.115.181230

    • PubMed
    • Google Scholar
39. 1. Mao Y
    2. Freeman M

    (2009) Fasciclin 2, the Drosophila orthologue of neural cell-adhesion molecule, inhibits EGF receptor signalling

    Development 136:473–481.

    https://doi.org/10.1242/dev.026054

    • PubMed
    • Google Scholar
40. 1. Márkus R
    2. Laurinyecz B
    3. Kurucz E
    4. Honti V
    5. Bajusz I
    6. Sipos B
    7. Somogyi K
    8. Kronhamn J
    9. Hultmark D
    10. Andó I

    (2009) Sessile hemocytes as a hematopoietic compartment in Drosophila melanogaster

    PNAS 106:4805–4809.

    https://doi.org/10.1073/pnas.0801766106

    • PubMed
    • Google Scholar
41. 1. Martin RL
    2. Maiorano J
    3. Beitel GJ
    4. Marko JF
    5. McVicker G
    6. Fondufe-Mittendorf YN

    (2017) A comparison of nucleosome organization in Drosophila cell lines

    PLOS ONE 12:e0178590.

    https://doi.org/10.1371/journal.pone.0178590

    • PubMed
    • Google Scholar
42. 1. Mohr SE

    (2014) RNAi screening in Drosophila cells and in vivo

    Methods 68:82–88.

    https://doi.org/10.1016/j.ymeth.2014.02.018

    • PubMed
    • Google Scholar
43. 1. Nakato E
    2. Liu X
    3. Eriksson I
    4. Yamamoto M
    5. Kinoshita-Toyoda A
    6. Toyoda H
    7. Kjellén L
    8. Li J-P
    9. Nakato H

    (2019) Establishment and characterization of Drosophila cell lines mutant for heparan sulfate modifying enzymes

    Glycobiology 29:479–489.

    https://doi.org/10.1093/glycob/cwz020

    • PubMed
    • Google Scholar
44. 1. Neal SJ
    2. Dolezal D
    3. Jusić N
    4. Pignoni F

    (2019) Drosophila ML-DmD17-c3 cells respond robustly to Dpp and exhibit complex transcriptional feedback on BMP signaling components

    BMC Developmental Biology 19:1.

    https://doi.org/10.1186/s12861-019-0181-0

    • PubMed
    • Google Scholar
45. 1. Nicholson L
    2. Singh GK
    3. Osterwalder T
    4. Roman GW
    5. Davis RL
    6. Keshishian H

    (2008) Spatial and temporal control of gene expression in Drosophila using the inducible GeneSwitch GAL4 system I Screen for larval nervous system drivers

    Genetics 178:215–234.

    https://doi.org/10.1534/genetics.107.081968

    • PubMed
    • Google Scholar
46. 1. Oda H
    2. Uemura T
    3. Harada Y
    4. Iwai Y
    5. Takeichi M

    (1994) A Drosophila homolog of cadherin associated with armadillo and essential for embryonic cell-cell adhesion

    Developmental Biology 165:716–726.

    https://doi.org/10.1006/dbio.1994.1287

    • PubMed
    • Google Scholar
47. 1. Ogienko AA
    2. Andreyeva EN
    3. Omelina ES
    4. Oshchepkova AL
    5. Pindyurin AV

    (2020) Molecular and cytological analysis of widely-used Gal4 driver lines for Drosophila neurobiology

    BMC Genetics 21:96.

    https://doi.org/10.1186/s12863-020-00895-7

    • PubMed
    • Google Scholar
48. 1. Osterwalder T
    2. Yoon KS
    3. White BH
    4. Keshishian H

    (2001) A conditional tissue-specific transgene expression system using inducible GAL4

    PNAS 98:12596–12601.

    https://doi.org/10.1073/pnas.221303298

    • PubMed
    • Google Scholar
49. 1. Ranganayakulu G
    2. Zhao B
    3. Dokidis A
    4. Molkentin JD
    5. Olson EN
    6. Schulz RA

    (1995) A series of mutations in the D-MEF2 transcription factor reveal multiple functions in larval and adult myogenesis in Drosophila

    Developmental Biology 171:169–181.

    https://doi.org/10.1006/dbio.1995.1269

    • PubMed
    • Google Scholar
50. 1. Rhee DY
    2. Cho D-Y
    3. Zhai B
    4. Slattery M
    5. Ma L
    6. Mintseris J
    7. Wong CY
    8. White KP
    9. Celniker SE
    10. Przytycka TM
    11. Gygi SP
    12. Obar RA
    13. Artavanis-Tsakonas S

    (2014) Transcription factor networks in Drosophila melanogaster

    Cell Reports 8:2031–2043.

    https://doi.org/10.1016/j.celrep.2014.08.038

    • PubMed
    • Google Scholar
51. 1. Ritacco FV
    2. Wu Y
    3. Khetan A

    (2018) Cell culture media for recombinant protein expression in Chinese hamster ovary (CHO) cells: History, key components, and optimization strategies

    Biotechnology Progress 34:1407–1426.

    https://doi.org/10.1002/btpr.2706

    • Google Scholar
52. 1. Robinow S
    2. White K

    (1991) Characterization and spatial distribution of the ELAV protein during Drosophila melanogaster development

    Journal of Neurobiology 22:443–461.

    https://doi.org/10.1002/neu.480220503

    • PubMed
    • Google Scholar
53. 1. Rossi G
    2. Manfrin A
    3. Lutolf MP

    (2018) Progress and potential in organoid research

    Nature Reviews. Genetics 19:671–687.

    https://doi.org/10.1038/s41576-018-0051-9

    • PubMed
    • Google Scholar
54. Software

    1. Roth V

    (2006) Doubling Time

    Doubling Time Computing.

    http://www.doubling-time.com/compute.php
55. 1. Sato K
    2. Siomi MC

    (2020) The piRNA pathway in Drosophila ovarian germ and somatic cells

    Proceedings of the Japan Academy. Series B, Physical and Biological Sciences 96:32–42.

    https://doi.org/10.2183/pjab.96.003

    • PubMed
    • Google Scholar
56. 1. Sepp KJ
    2. Schulte J
    3. Auld VJ

    (2001) Peripheral glia direct axon guidance across the CNS/PNS transition zone

    Developmental Biology 238:47–63.

    https://doi.org/10.1006/dbio.2001.0411

    • PubMed
    • Google Scholar
57. 1. Shiga Y
    2. Tanaka-Matakatsu M
    3. Hayashi S

    (1996) A nuclear GFP/beta-galactosidase fusion protein as A marker for morphogenesis in living Drosophila

    Development, Growth and Differentiation 38:99–106.

    https://doi.org/10.1046/j.1440-169X.1996.00012.x

    • Google Scholar
58. 1. Simcox A
    2. Mitra S
    3. Truesdell S
    4. Paul L
    5. Chen T
    6. Butchar JP
    7. Justiniano S

    (2008a) Efficient genetic method for establishing Drosophila cell lines unlocks the potential to create lines of specific genotypes

    PLOS Genetics 4:e1000142.

    https://doi.org/10.1371/journal.pgen.1000142

    • PubMed
    • Google Scholar
59. 1. Simcox AA
    2. Austin CL
    3. Jacobsen TL
    4. Jafar-Nejad H

    (2008b) Drosophila embryonic “fibroblasts”: extending mutant analysis in vitro

    Fly 2:306–309.

    https://doi.org/10.4161/fly.7427

    • PubMed
    • Google Scholar
60. 1. Simcox A

    (2013) Progress towards Drosophila epithelial cell culture

    Methods in Molecular Biology 945:1–11.

    https://doi.org/10.1007/978-1-62703-125-7_1

    • PubMed
    • Google Scholar
61. 1. Smagghe G
    2. Goodman CL
    3. Stanley D

    (2009) Insect cell culture and applications to research and pest management

    In Vitro Cellular & Developmental Biology - Animal 45:93–105.

    https://doi.org/10.1007/s11626-009-9181-x

    • Google Scholar
62. 1. Stoiber M
    2. Celniker S
    3. Cherbas L
    4. Brown B
    5. Cherbas P

    (2016) Diverse Hormone Response Networks in 41 Independent Drosophila Cell Lines

    G3: Genes, Genomes, Genetics 6:683–694.

    https://doi.org/10.1534/g3.115.023366

    • PubMed
    • Google Scholar
63. 1. Vanderploeg J
    2. Vazquez Paz LL
    3. MacMullin A
    4. Jacobs JR

    (2012) Integrins are required for cardioblast polarisation in Drosophila

    BMC Developmental Biology 12:8.

    https://doi.org/10.1186/1471-213X-12-8

    • Google Scholar
64. 1. Viswanatha R
    2. Li Z
    3. Hu Y
    4. Perrimon N

    (2018) Pooled genome-wide CRISPR screening for basal and context-specific fitness gene essentiality in Drosophila cells

    eLife 7:e36333.

    https://doi.org/10.7554/eLife.36333

    • PubMed
    • Google Scholar
65. 1. Viswanatha R
    2. Brathwaite R
    3. Hu Y
    4. Li Z
    5. Rodiger J
    6. Merckaert P
    7. Chung V
    8. Mohr SE
    9. Perrimon N

    (2019) Pooled CRISPR Screens in Drosophila Cells

    Current Protocols in Molecular Biology 129:e111.

    https://doi.org/10.1002/cpmb.111

    • PubMed
    • Google Scholar
66. 1. Wang YE
    2. Kutnetsov L
    3. Partensky A
    4. Farid J
    5. Quackenbush J

    (2017) WebMeV: a cloud platform for analyzing and visualizing cancer genomic data

    Cancer Research 77:e11–e14.

    https://doi.org/10.1158/0008-5472.CAN-17-0802

    • PubMed
    • Google Scholar
67. 1. Wen J
    2. Mohammed J
    3. Bortolamiol-Becet D
    4. Tsai H
    5. Robine N
    6. Westholm JO
    7. Ladewig E
    8. Dai Q
    9. Okamura K
    10. Flynt AS
    11. Zhang D
    12. Andrews J
    13. Cherbas L
    14. Kaufman TC
    15. Cherbas P
    16. Siepel A
    17. Lai EC

    (2014) Diversity of miRNAs, siRNAs, and piRNAs across 25 Drosophila cell lines

    Genome Research 24:1236–1250.

    https://doi.org/10.1101/gr.161554.113

    • PubMed
    • Google Scholar
68. 1. Wilk R
    2. Weizman I
    3. Shilo BZ

    (1996) trachealess encodes a bHLH-PAS protein that is an inducer of tracheal cell fates in Drosophila

    Genes & Development 10:93–102.

    https://doi.org/10.1101/gad.10.1.93

    • PubMed
    • Google Scholar
69. 1. Xiong WC
    2. Okano H
    3. Patel NH
    4. Blendy JA
    5. Montell C

    (1994) repo encodes a glial-specific homeo domain protein required in the Drosophila nervous system

    Genes & Development 8:981–994.

    https://doi.org/10.1101/gad.8.8.981

    • PubMed
    • Google Scholar
70. 1. Zaffran S
    2. Astier M
    3. and DG
    4. Sémériva M

    (1997) The held out wings (how) Drosophila gene encodes a putative RNA-binding protein involved in the control of muscular and cardiac activity

    Development 124:2087–2098.

    https://doi.org/10.1242/dev.124.10.2087

    • Google Scholar
71. 1. Zirin J
    2. Bosch J
    3. Viswanatha R
    4. Mohr SE
    5. Perrimon N

    (2022) State-of-the-art CRISPR for in vivo and cell-based studies in Drosophila

    Trends in Genetics 38:437–453.

    https://doi.org/10.1016/j.tig.2021.11.006

    • PubMed
    • Google Scholar
72. 1. Zwarts L
    2. Van Eijs F
    3. Callaerts P

    (2015) Glia in Drosophila behavior

    Journal of Comparative Physiology. A, Neuroethology, Sensory, Neural, and Behavioral Physiology 201:879–893.

    https://doi.org/10.1007/s00359-014-0952-9

    • PubMed
    • Google Scholar

Author details

1. Nikki Coleman-Gosser

   Department of Molecular Genetics, Ohio State University, Columbus, United States

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Contributed equally with

   Yanhui Hu, Shiva Raghuvanshi and Shane Stitzinger

   Competing interests

   No competing interests declared

2. Yanhui Hu

   Drosophila RNAi Screening Center and Department of Genetics, Harvard Medical School, Boston, United States

   Contribution

   Formal analysis, Writing – review and editing

   Contributed equally with

   Nikki Coleman-Gosser, Shiva Raghuvanshi and Shane Stitzinger

   Competing interests

   No competing interests declared

3. Shiva Raghuvanshi

   Department of Molecular Genetics, Ohio State University, Columbus, United States

   Contribution

   Investigation

   Contributed equally with

   Nikki Coleman-Gosser, Yanhui Hu and Shane Stitzinger

   Competing interests

   No competing interests declared

4. Shane Stitzinger

   Department of Molecular Genetics, Ohio State University, Columbus, United States

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Contributed equally with

   Nikki Coleman-Gosser, Yanhui Hu and Shiva Raghuvanshi

   Competing interests

   No competing interests declared

5. Weihang Chen

   Drosophila RNAi Screening Center and Department of Genetics, Harvard Medical School, Boston, United States

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

6. Arthur Luhur

   Drosophila Genomics Resource Center and Department of Biology, Indiana University, Bloomington, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Daniel Mariyappa

   Drosophila Genomics Resource Center and Department of Biology, Indiana University, Bloomington, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4775-1656

8. Molly Josifov

   Department of Molecular Genetics, Ohio State University, Columbus, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2899-7186

9. Andrew Zelhof

   Drosophila Genomics Resource Center and Department of Biology, Indiana University, Bloomington, United States

   Contribution

   Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

10. Stephanie E Mohr

    Drosophila RNAi Screening Center and Department of Genetics, Harvard Medical School, Boston, United States

    Contribution

    Supervision, Funding acquisition, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9639-7708

11. Norbert Perrimon

    1. Drosophila RNAi Screening Center and Department of Genetics, Harvard Medical School, Boston, United States
    2. Howard Hughes Medical Institute, Chevy Chase, United States

    Contribution

    Supervision, Funding acquisition, Writing – review and editing

    For correspondence

    perrimon@genetics.med.harvard.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-7542-472X

12. Amanda Simcox

    1. Department of Molecular Genetics, Ohio State University, Columbus, United States
    2. National Science Foundation, Alexandria, United States

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing – review and editing

    For correspondence

    simcox.1@osu.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5572-7042

• 2,124

  views

• 367

  downloads

• 1

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.