1. Cancer Biology
  2. Developmental Biology

Tumors mimic the niche to inhibit neighboring stem cell differentiation

  1. Yang Zhang
  2. Yuejia Wang
  3. Jinqiao Song
  4. Lizhong Yan
  5. Ziguang Wang
  6. Dongze Song
  7. Haojun Wang
  8. Sining Yang
  9. Liyuan Niu
  10. Chang Sun
  11. Hanning Zhang
  12. Yudi Zhao
  13. Shaowei Zhao  Is a corresponding author
  1. Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, China
  2. Nankai International Advanced Research Institute (SHENZHEN FUTIAN), China
https://doi.org/10.7554/eLife.108910.4
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  1. Yang Zhang
  2. Yuejia Wang
  3. Jinqiao Song
  4. Lizhong Yan
  5. Ziguang Wang
  6. Dongze Song
  7. Haojun Wang
  8. Sining Yang
  9. Liyuan Niu
  10. Chang Sun
  11. Hanning Zhang
  12. Yudi Zhao
  13. Shaowei Zhao
(2026)
Tumors mimic the niche to inhibit neighboring stem cell differentiation
eLife 14:RP108910.
https://doi.org/10.7554/eLife.108910.4
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eLife Assessment

This study provides important insights into how tumorous germline stem cells (GSCs) in the Drosophila melanogaster ovary can mimic niche function and suppress the differentiation of neighboring cells. The findings that GSC tumors can incorporate non-mutant cells and inhibit their differentiation are compelling and extend current understanding of stem cell-niche interactions. However, the evidence supporting the conclusion that GSC tumors produce BMP ligands to mediate this effect remains incomplete, due to concerns regarding the quality and interpretation of the HCR-FISH data.

https://doi.org/10.7554/eLife.108910.4.sa0

Abstract

Although it is well established that stem cells maintain tissue homeostasis while tumors disrupt it, the mechanisms by which tumors influence the development of nearby stem cells remain poorly understood. Using Drosophila ovaries as a model system, here we discovered that bam or bgcn mutant germline tumors inhibit the differentiation of neighboring wild-type germline stem cells (GSCs). Mechanistically, these tumor cells mimic the stem cell niche by secreting the bone morphogenetic protein (BMP) ligands Dpp and Gbb, but at reduced levels, resulting in moderate BMP signaling activation in adjacent GSCs. Such BMP signaling activation is sufficient to repress bam transcription, thereby blocking GSC differentiation. To our knowledge, this is the first example that tumors can functionally mimic a stem cell niche to inhibit the differentiation of neighboring wild-type stem cells. Similar regulatory paradigms may operate in mammalian tissues, including humans, during tumorigenesis.

Introduction

The homeostasis of many tissues in our bodies is maintained by adult stem cells, but this balance can be disrupted by tumor cells. What occurs when tumorigenesis intersects with stem cell development? To address this question, a mosaic analysis model system is essential, where wild-type stem cells develop alongside tumor cells. Drosophila offers an exceptional model for such studies, as it allows for the efficient generation of mosaic clones through various established methods (Germani et al., 2018; Pastor-Pareja and Xu, 2013).

In Drosophila ovaries, germline stem cells (GSCs) play a crucial role in sustaining normal oogenesis and maintaining fertility (Fuller and Spradling, 2007; Lin, 1997). These GSCs reside in a specialized microenvironment known as the stem cell niche (hereafter referred to as niche) (Xie and Spradling, 2000). Typically, a GSC undergoes asymmetric division, generating two distinct daughter cells: one remains in the niche to self-renew as a GSC, while the other, called a cystoblast, exits the niche and initiates differentiation. During the differentiation process, each cystoblast performs exactly four rounds of mitotic division with incomplete cytokinesis to produce 16 interconnected cystocytes, forming a germline cyst. In each germline cyst, only one germ cell is destined to become the oocyte, while the remaining 15 differentiate into nurse cells that support the development of the oocyte (Figure 1A; Fuller and Spradling, 2007; Lin, 1997). The principal niche signals are Bone morphogenetic protein (BMP) ligands, including Decapentaplegic (Dpp) and Glass bottom boat (Gbb), which are secreted by cap and terminal filament (TF) cells (Chen and McKearin, 2003a; Li et al., 2016; Song et al., 2004; Xie and Spradling, 1998; Xie and Spradling, 2000). These ligands activate BMP signaling in GSCs, leading to the transcriptional repression of bag of marbles (bam), a key gene that promotes differentiation. In contrast, BMP signaling is inactive in cystoblasts, allowing Bam to be expressed and to drive their differentiation (Chen and McKearin, 2003a; Song et al., 2004). Bam carries out this function in collaboration with its partner, Benign gonial cell neoplasm (Bgcn) (Li et al., 2009; Ohlstein et al., 2000).

Figure 1 with 3 supplements see all
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bam or bgcn mutant germline tumors inhibit the differentiation of neighboring wild-type GSCs.

(A) Schematic cartoon for early oogenesis. The red dots and branches indicate spectrosomes and fusomes, respectively. TF cell: terminal filament cell; GSC: germline stem cell. (B) Mosaic analysis strategy. The FLP recombinase triggers mitotic recombination by targeting FRT sequences. The nos>FLP method restricts FLP expression to the germline, while the hs-FLP method enables heatshock-inducible FLP expression. (C–F) Representative samples. The asterisks mark cap cells, and the arrows indicate SGCs that have exited the niche and are surrounded by bam or bgcn mutant germline tumors. Vasa, a germ cell marker, should label all germ cells. However, due to poor tumor permeability, staining often fails to detect tumorous germ cells in the central region (see Vasa panels in D–F). (G–I) Representative samples (z-stack projections). In (G), the arrowheads and arrow, respectively, mark two GSCs and one cystoblast, all containing dot-like spectrosomes, while the dotted lines delineate cystocytes with branched fusomes. In (H) and (I), the arrows denote SGCs that also contain dot-like spectrosomes, akin to GSCs and the adjacent GSC-like tumor cells. (J, K) Quantification data. bamBG is a strong loss-of-function allele of bam (Chen and McKearin, 2005). For each experiment, three independent replicates were performed, and data represent mean ± SEM. In (J), over 100 SGCs and germline cysts were quantified per replicate, and statistical significance was determined by one-way ANOVA. n.s. (P > 0.05). In (K), over 100 germaria were quantified per replicate.

GSCs mutant for bam or bgcn fail to differentiate and instead hyper-proliferate, forming a well-established Drosophila germline tumor model (Lavoie et al., 1999; McKearin and Ohlstein, 1995; McKearin and Spradling, 1990; Niki and Mahowald, 2003). Notably, these germline tumor cells competitively displace wild-type GSCs from the niche (Jin et al., 2008). The resulting displacement creates a microenvironment where wild-type GSCs are surrounded by tumor cells, providing an excellent model system to study stem cell behavior in tumor neighborhoods.

Here, we demonstrate that bam or bgcn mutant germline tumors inhibit the differentiation of neighboring wild-type GSCs by functionally mimicking the stem cell niche. This mechanism may be conserved in mammals, including humans, during tumorigenesis, where malignant cells could similarly disrupt normal stem cell development.

Results

Germline tumors inhibit the differentiation of neighboring wild-type GSCs

To generate bam or bgcn mutant germline clones, we employed either nos>FLP/FRT or hs-FLP/FRT systems that we previously established (Zhang et al., 2023b; Zhao et al., 2018). These two systems induce the expression of FLP recombinase either germline-specifically (nos-GAL4-VP16/UASz-FLP) or via heatshock (hs-FLP). The expressed FLP recombinase targets the FRT sites to mediate mitotic recombination on homologous chromosome arms, generating adjacent GFP-negative (bam or bgcn mutant) and GFP-positive (wild-type) germ cell populations (Figure 1B). Remarkably, we observed that many wild-type germ cells located outside the niche retained a GSC-like single-germ-cell (SGC) morphology (Figure 1C and D), even when encapsulated within egg chambers (Figure 1—figure supplement 1). Under normal conditions, GSCs that exit the niche differentiate into interconnected germline cysts, where germ cells are linked rather than remaining as individual, isolated cells (Fuller and Spradling, 2007; Xie and Spradling, 2000). To rule out the possibility that the SGC phenotype is an artifact caused by GFP expression, we repeated the experiments using RFP and arm-lacZ as alternative mosaic analysis markers. Consistent results were observed (Figure 1E and F), confirming that the phenotype is not attributable to GFP.

To further confirm that these SGCs exhibit GSC-like characteristics, we conducted anti-α-Spectrin immunofluorescent staining, a method that labels a germline-specific organelle known as the spectrosome in GSCs and cystoblasts, and the fusome in cystocytes. GSCs perform complete cell division, whereas cystocytes undergo incomplete cytokinesis, remaining interconnected through fusomes and ring canals. Consequently, spectrosomes appear as dot-like structures, while fusomes exhibit branched morphologies (Figure 1G; Lin et al., 1994). To accurately capture the three-dimensional (3D) architecture of spectrosomes and fusomes, we acquired z-stack images using confocal microscopy. Strikingly, these SGCs displayed dot-like spectrosomes, closely resembling those observed in wild-type GSCs and bam or bgcn mutant GSC-like tumor cells (Figure 1H and I). We also considered the possibility that SGCs might arise through the dedifferentiation of the cystocytes in germline cysts surrounded by germline tumors. If this were the case, such cystocytes would initially undergo complete cell division, leaving behind midbodies as markers of the late cytokinesis stage. When visualized by anti-α-Spectrin immunofluorescence, midbody appears as a central sphere that is slightly connected to two larger flanking structures, resembling a variant of nunchucks (Mathieu et al., 2022). Notably, in our analyses of over 50 germline cysts surrounded by bam mutant germline tumors, none contained midbodies, suggesting that dedifferentiation is unlikely to be the primary mechanism responsible for the SGC phenotype. Together, these findings indicate that bam or bgcn mutant germline tumors inhibit the differentiation of neighboring wild-type GSCs.

To quantify the SGC phenotype, which requires the presence of both germline tumors and out-of-niche wild-type germ cells, we analyzed germaria containing both. In 14-day-old fly ovaries, 70% of germaria (432/618) met this criterion. We calculated the percentage of SGCs relative to the total number of SGCs and germline cysts, considering the out-of-niche germ cells that are either fully enclosed by germline tumors (e.g. the right SGC in Figure 1I and the marked germline cyst in Figure 2G) or in contact with wild-type germ cells or somatic cells on only one side (e.g. the left SGC in Figure 1I and the germline cyst in the lower right corner of Figure 4C). Notably, the SGC phenotype was consistent across the 14-day period analyzed (Figure 1J). For either 1-, 7-, or 14-day time point, we measured the sizes of bam mutant germline clones in over 30 germaria containing these clones. To estimate 3D clone size, we counted cell numbers within the maximal 2D cross-sectional area of each clone. Clones were larger in 14-day-old flies than in either 1- or 7-day-old flies (Figure 1—figure supplement 2). Therefore, we selected the 14-day time point for all subsequent analyses to maximize experimental efficiency. In qualifying germaria, the average number of SGCs was approximately 1.5 (Figure 1K). For each biological replicate used to quantify the SGC phenotype, we counted more than 100 SGCs and germline cysts (>50 germaria analyzed). Furthermore, the SGC phenotypes induced by the nos >FLP/FRT and hs-FLP/FRT systems were indistinguishable (Figure 1—figure supplement 3). Given its simplicity and germline specificity, we primarily used the nos>FLP/FRT system in the following studies.

Figure 2
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The inhibition of SGC differentiation depends on the lack of Bam expression.

(A) Representative sample. The arrowhead marks a BamC-positive 4-cystocyte germline cyst, while the arrows indicate BamC-negative SGCs. (B) Representative sample. The asterisk denotes cap cells, and the dotted circles outline bamP-GFP-negative GSCs. The solid circle marks a bamP-GFP-positive cystoblast. The arrow and arrowhead point to bamP-GFP-negative and -positive SGCs, respectively. (C) Quantification data. 14-day-old flies were used for the analyses. CBs: cystoblasts. (D) Schematic of the experimental strategy for (E–H). In ‘with hs-bam’ flies (E, G), wild-type germ cells (both bam+/+ and bam+/-) carry the hs-bam transgene, while control ‘without hs-bam’ flies (F) lack this element in their wild-type germ cells. (E–G) Representative samples. The arrows mark SGCs with dot-like spectrosomes, while the arrowhead indicates a 4-cystocyte germline cyst containing branched fusomes. (H) Quantification data. For each experiment, three independent replicates were performed, with over 100 SGCs and germline cysts quantified per replicate. Data represent mean ± SEM, and statistical significance was determined by t test. n.s. (P > 0.05).

The inhibition of differentiation in SGCs relies on the lack of Bam expression

Given that Bam is the key factor promoting GSC differentiation (McKearin and Ohlstein, 1995; Ohlstein and McKearin, 1997), we were very curious about the expression of Bam in SGCs. At first, we assessed Bam protein levels using immunofluorescent staining with an anti-BamC antibody (McKearin and Ohlstein, 1995). Strikingly, none of the SGCs examined (n > 100) were BamC-positive (Figure 2A). Then, we analyzed bam transcription levels using a bamP-GFP reporter (Chen and McKearin, 2003b). 100% of GSCs within the niche (n = 153) were GFP-negative, while 98% of cystoblasts (n = 106) were GFP-positive (Figure 2B and C), confirming that bam transcription is associated with the initiation of GSC differentiation (McKearin and Ohlstein, 1995). Notably, 74% of SGCs (n = 132) were GFP-negative (GSC-like), while the remaining 26% were GFP-positive (cystoblast-like) (Figure 2B and C). The cystoblast-like SGCs may have already initiated their differentiation program toward becoming cystocytes. Since bam transcription initiates in cystoblasts (McKearin and Spradling, 1990) but Bam proteins accumulate predominantly in cystocytes (McKearin and Ohlstein, 1995), the Bam protein levels in these cystoblast-like SGCs are likely below the detection threshold at this early stage.

Next, we asked whether ectopic expression of Bam can drive SGCs to differentiate. To address this, we established two experimental scenarios: one with the hs-bam element and one without as the control. In the hs-bam scenario (with hs-bam), GFP-positive germ cells are wild-type (carrying hs-bam), while GFP-negative cells are bam mutant (lacking hs-bam). In the control scenario (without hs-bam), GFP-positive cells are wild-type, and GFP-negative cells are bam mutant (Figure 2D, see genotypes in Source data 1). To induce ectopic Bam expression, 12-day-old female flies were subjected to heatshock treatment, which involved heating at 37°C for 2 hr, twice daily with a 6 hr interval, and over 2 consecutive days. In the absence of heatshock treatment, the percentage of SGCs in ovaries of both genotypes showed no significant difference at either 12 or 14 days (Figure 2E–H), indicating that the hs-bam element alone, without heatshock, does not affect the phenotype. However, following heatshock treatment, the percentage of SGCs in ovaries with hs-bam was markedly reduced compared to those without hs-bam (Figure 2E–H), suggesting that ectopic Bam expression can drive SGCs to differentiate. Collectively, these results support that the differentiation defects of SGCs are due to the lack of Bam expression.

SGCs retain moderate BMP signaling activation

Within the niche, BMP signaling functions to repress bam transcription to inhibit GSC differentiation (Chen and McKearin, 2003a; Song et al., 2004). To investigate BMP signaling activation in SGCs, we employed immunofluorescent staining for pMad, a well-characterized marker of BMP signaling activity (Kai and Spradling, 2003). Surprisingly, we observed undetectable pMad levels in all SGCs examined (n > 100) (Figure 3A, B). To investigate this further, we examined the activity of Dad-lacZ, a highly sensitive BMP signaling reporter known to be activated not only in GSCs but also in cystoblasts (Kai and Spradling, 2003; Song et al., 2004). Notably, 73% of SGCs were lacZ-positive (n = 107), a proportion lower than that of GSCs within the niche, which showed 100% lacZ positivity (n = 122) (Figure 3C, D). Furthermore, when comparing Dad-lacZ expression levels exclusively in lacZ-positive cells, we found that SGCs exhibited significantly lower expression levels than GSCs within the niche (Figure 3C, E). These findings indicate that BMP signaling is activated in SGCs but at lower levels than those in GSCs within the niche.

Figure 3
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SGCs maintain lower BMP signaling levels than GSCs within the niche.

(A, B) Representative samples. The asterisks mark cap cells, arrowheads indicate pMad-positive GSCs, and arrows point to pMad-negative SGCs. (C) Representative samples. The asterisks denote cap cells, arrowheads mark Dad-lacZ-positive GSCs, and arrows highlight Dad-lacZ-positive SGCs. The dotted cycles outline one Dad-lacZ-negative SGC. (D, E) Quantification data. 14-day-old flies were used for the analyses. In (E), data represent mean ± SEM, and statistical significance was determined by t test. (F) Representative sample. The asterisk marks a cap cell, while the arrows indicate a BrdU+ GSC within the niche. (G) Representative sample. The arrow indicates a BrdU+ SGC surrounded by germline tumors. (H) Quantification data. 14-day-old flies were used for the analyses. Statistical significance was determined by chi-squared test.

Beyond maintaining Drosophila female GSCs in the niche, BMP signaling also promotes their division (Xie and Spradling, 1998). Since the activation levels of BMP signaling in SGCs were lower than those in GSCs within the niche, we hypothesized that SGCs would exhibit slower proliferation rates than GSCs. To test this hypothesis, we performed BrdU incorporation assays. The results revealed that only 4.5% of SGCs were BrdU-positive (n = 1034), a significantly lower proportion than the 7.8% observed in GSCs within the niche (n = 1337) (Figure 3F–H). These findings further corroborate the reduced activation of BMP signaling in SGCs relative to GSCs.

BMP signaling inhibits SGC differentiation

Then, we investigated whether BMP signaling functions to inhibit SGC differentiation. The BMP type II receptor Punt and the co-Smad Medea (Med) are essential for maintaining GSC stemness within the niche (Xie and Spradling, 1998). Therefore, we sought to determine whether they are also required to inhibit SGC differentiation. However, because distinguishing one versus two copies of GFP proved difficult in our germline mosaic assays, we established a genetic scenario, in which GFP+/+ RFP-/- germ cells are punt-/- or med-/-; GFP+/- RFP+/- germ cells are punt+/- bam+/- or med +/- bam+/- (similar to wild-type); and GFP-/- RFP+/+ germ cells are bam-/-. In control experiments (with no punt or med mutation), GFP+/+ RFP-/- germ cells are wild-type; GFP+/- RFP+/- germ cells are bam+/- (similar to wild-type); and GFP-/- RFP+/+ germ cells are bam-/- (Figure 4A, see genotypes in Source data 1). Strikingly, the proportion of punt-/- or med-/- SGCs relative to total SGCs was significantly lower than in controls (Figure 4B–E). Conversely, among punt-/- or med-/- germ cells meeting our established criteria for SGC phenotype quantification, germline cysts constituted a higher percentage compared to controls (Figure 4F). These results indicate that Punt and Med function to inhibit SGC differentiation.

Figure 4
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BMP signaling inhibits SGC differentiation.

(A) Schematic of the experimental strategy for (B–F). Genotypes were unambiguously distinguished using a triple-color system (red, yellow, and green). (B–D) Representative samples. The dotted cycles mark an SGC, while the solid lines outline germline cysts containing differentiating cystocytes. (E, F) Quantification data. 14-day-old flies were used for the analyses. (G) Schematic of the experimental strategy for (H–J). (H, I) Representative samples. The dotted lines mark an SGC, while the solid lines outline a germline cyst containing differentiating cystocytes. (J) Quantification data. 14-day-old flies were used for the analyses. For each experiment, three independent replicates were performed, with over 100 SGCs and germline cysts quantified per replicate. Data represent mean ± SEM, and statistical significance was determined by t test.

Mothers against dpp (Mad) is the primary transcription factor of BMP signaling, and it is also essential for GSC maintenance in Drosophila ovaries (Xie and Spradling, 1998). Unlike punt and med, which reside on the same chromosome arm (3R) as bam, mad is located on a separate chromosome arm (2L). To investigate whether Mad is required to inhibit SGC differentiation, we established a genetic scenario, in which GFP-/- germ cells are mad-/- and GFP+/+ germ cells are bam-/-. In control experiments (with no mad mutation), GFP-/- germ cells are wild-type and GFP+/+ germ cells are bam-/- (Figure 4G, see genotypes in Source data 1). Notably, mad mutation significantly decreased the SGC proportion relative to controls (Figure 4H–J). These results suggest that, like Punt and Med, Mad also plays a crucial role in suppressing SGC differentiation. Together, these findings demonstrate that BMP signaling contributes to inhibiting SGC differentiation, despite at reduced activation levels.

Germline tumors secrete Dpp and Gbb

The formation of a differentiation niche by escort cells is required for GSC differentiation and is known to be disrupted by bam mutant germline tumors (Chen et al., 2022; Kirilly et al., 2011). Although this niche disruption could contribute to the SGC phenotype, an unaddressed question is the source of the BMP ligands (Dpp and Gbb) that maintain BMP signaling activation within SGCs. Given that dpp expression has been detected in some bam mutant germline tumor cells from both in vivo and in vitro sources (Niki et al., 2006), we hypothesized that these tumor cells secrete BMP ligands to inhibit neighboring GSC differentiation. To assess the expression of dpp and gbb, we employed third-generation in situ hybridization chain reaction (HCR) (Choi et al., 2018). Successful detection was confirmed by prominent signal foci in cap and TF cells (Figure 5A and C). To enable quantitative comparison, all experiments and confocal imaging were performed under identical parameters. Signal intensity within bam mutant germline tumors and wild-type cystocytes was normalized to the signal in wild-type cap and TF cells. Strikingly, bam mutant germline tumor cells exhibited significantly elevated expression of both dpp and gbb compared to wild-type cystocytes (Figure 5A–D).

Figure 5
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Germline tumors secrete Dpp and Gbb.

(A, C) Representative samples. The asterisks denote cap/TF cells. The dotted lines highlight wild-type (WT) cystocytes, while the solid lines outline bam mutant germline tumor cells. The magenta box areas are enlarged below. (B, D) Quantification data for in situ HCR assays. 14-day-old flies were used for the analyses, and over 10 samples were quantified for each genotype. (E, F) Quantification data for RT-qPCR assays. 14-day-old flies were used for the analyses. For each experiment, three independent replicates were performed. Data represent mean ± SEM, and statistical significance in (B, D) was determined by one-way ANOVA and in (E, F) by t test.

To more sensitively assess dpp and gbb expression, we performed real-time quantitative PCR (RT-qPCR) analyses in bam or bgcn mutant ovaries, comparing samples with and without germline-specific knockdown of dpp or gbb. Detection of reduced transcript levels in knockdown conditions would confirm active expression of these genes in the respective genetic backgrounds. Consistent with the essential roles of these two genes in fly viability, ubiquitous knockdown using act-GAL4 with either dpp-RNAi or gbb-RNAi caused lethality, which also validated the efficacy of these RNAi lines. Notably, germline-specific knockdown of dpp or gbb significantly reduced their transcript levels compared to yellow (y) or white (w) knockdown controls (Figure 5E and F). Collectively, these findings demonstrate that bam or bgcn mutant germline tumors secrete the BMP ligands, albeit at lower levels than cap and TF cells.

Dpp and Gbb secreted by germline tumors are required to inhibit SGC differentiation

Finally, we investigated whether Dpp and Gbb secreted by germline tumors are required to inhibit SGC differentiation. Using a previously established double-mutant mosaic analysis strategy for two genes on different chromosomes (Zhang et al., 2024; Zhang et al., 2023b), we generated dpp bam or gbb bam double-mutant germline clones using two dpp mutant alleles, dppd6, dppd12, and one gbb allele, gbb1 (Figure 6A and B, see genotypes in Source data 1). Heterozygotes in any of these alleles did not affect GSC maintenance, germ cell differentiation, and female fly fertility (Figure 6—figure supplement 1). However, both dpp bam and gbb bam double-mutant germline tumor cells exhibited reduced proliferation rates compared to bam single-mutant controls (Figure 6—figure supplement 2), indicating that autocrine BMP signaling promotes bam mutant tumor growth. As mentioned earlier, our evaluation focused on germ cells that have exited the niche and are surrounded by germline tumors to quantify the SGC phenotype. Thus, it raises the question of whether the extent of tumor encirclement (i.e. being surrounded by more or fewer tumor cells) influences the phenotype. To investigate this, we compared the SGC phenotype in bigger and smaller bam mutant germline tumors. A total of 70 germaria containing bam mutant germline clones were analyzed using the same method described in Figure 1—figure supplement 2B. The 35 bigger and 35 smaller clones were categorized as ‘bigger’ and ‘smaller’ tumors, respectively. Strikingly, the SGC phenotype remained consistent between the two tumor groups (Figure 6—figure supplement 3), aligning with our earlier finding that this phenotype is stable over a 14-day period (Figure 1J), a timeframe sufficient for substantial germline tumor growth (Figure 1—figure supplement 2). These results suggest that direct contact between tumorous and wild-type germ cells, rather than tumor size, is the primary determinant of this phenotype.

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Dpp and Gbb secreted by germline tumors are required to inhibit SGC differentiation.

(A) Schematic of the experimental strategy for (C–F). (B) Schematic of the experimental strategy for (G–I). (C-E, G, H) Representative samples. The arrows mark SGCs containing dot-like spectrosomes, while the arrowheads denote germline cysts with differentiating cystocytes that possess branched fusomes. (F, I) Quantification data for the SGC phenotype. 14-day-old flies were used for the analyses. For each experiment, three independent replicates were performed, with over 100 SGCs and germline cysts quantified per replicate. Data represent mean ± SEM. Statistical significance in (F) was determined by one-way ANOVA and in (I) by t test.

The results above demonstrate that comparing the severity of the SGC phenotype is feasible between germ cells surrounded by smaller dpp bam or gbb bam double-mutant germline tumors and those surrounded by larger bam single-mutant germline tumors. Remarkably, both dpp bam and gbb bam double-mutant germline tumors enclosed fewer SGCs but more germline cysts than their bam single-mutant counterparts (Figure 6C–I). Thus, we concluded that the BMP ligands from directly-contacting germline tumor cells mediate the dominant inhibition of SGC differentiation. This amazingly parallels the mechanism observed in the normal stem cell niche, where only germ cells in direct contact with cap cells are maintained as GSCs (Chen and McKearin, 2003a; Song et al., 2004; Xie and Spradling, 2000).

Discussion

Our study reveals that bam or bgcn mutant germline tumors in Drosophila ovaries secrete lower levels of BMP ligands Dpp and Gbb than cap and TF cells, resulting in moderate BMP signaling activation in adjacent wild-type GSCs (called SGCs in this study). Such BMP signaling activation is sufficient to repress bam transcription, thereby blocking SGC differentiation (see our working model in Figure 7). Strikingly, this mechanism closely recapitulates the normal niche signaling program mediated by cap and TF cells (Chen and McKearin, 2003a; Song et al., 2004; Xie and Spradling, 1998; Xie and Spradling, 2000). To our knowledge, this represents the first evidence that tumor cells can functionally mimic a stem cell niche to arrest neighboring wild-type stem cells in an undifferentiated state.

Figure 7
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A working model.

bam or bgcn mutant germline tumors secrete the BMP ligands Dpp and Gbb to activate BMP signaling in out-of-niche GSCs (called SGCs in this study) to inhibit their differentiation (left panel). In contrast, dpp bam and gbb bam double-mutant germline tumors exhibit a significant loss of this differentiation-inhibiting ability (right panel).

While bam or bgcn mutant germline tumors consist of GSC-like cells expected to resemble SGCs (Lavoie et al., 1999; McKearin and Ohlstein, 1995), we found key differences in BMP signaling. Out-of-niche bgcn mutant tumor cells showed significantly lower BMP activity than neighboring SGCs, as evidenced by reduced Dad-lacZ expression (Figure 3C). Consistent with this, most of the out-of-niche bam mutant tumor cells expressed bamP-GFP, a reporter suppressed by BMP signaling (Chen and McKearin, 2003a; Song et al., 2004), whereas only 26% of SGCs were bamP-GFP-positive (Figure 2B and C). These findings suggest that SGCs are more responsive to BMP signals secreted by germline tumors than the tumors themselves. Future studies are needed to elucidate the underlying mechanisms.

In the Drosophila ovarian germarium, the cell types that express dpp remain controversial. Two major approaches have been used to detect dpp transcription: in situ hybridization (ISH) and the dpp-lacZ reporter. An early, seminal study using ISH reported strong dpp transcription in developing follicle cells, with low levels in both cap and inner sheath cells (Xie and Spradling, 2000). In contrast, using either ISH or the dpp-lacZ reporter, some studies claimed that dpp is expressed exclusively in cap cells (Luo et al., 2017; Wang and Page-McCaw, 2018). Several additional studies, also employing ISH or the dpp-lacZ reporter, detected strong dpp transcription in both cap and TF cells (Li et al., 2016; Liu et al., 2015; Zhang et al., 2023a), a pattern consistent with our in situ HCR data (Figure 5A and C). Notably, the cell type consistently identified across these studies is cap cell, the primary somatic cell comprising the stem cell niche (Xie and Spradling, 2000). These discrepancies in dpp expression patterns may arise from differences in the ISH probes and dpp enhancer elements used, and further studies are clearly needed to resolve them.

One interesting finding is that bam or bgcn mutant germline tumors secrete lower levels of BMP ligands than cap and TF cells (Figure 5A–D). This aligns with earlier microarray data showing that purified Drosophila female GSCs express minimal Dpp and Gbb (Kai et al., 2005). However, our work reveals that such BMP levels in germline tumors are functionally critical to suppress SGC differentiation (Figure 6). Unlike normal GSCs, which receive unidirectional BMP ligands from cap cells (Chen and McKearin, 2003a; Li et al., 2016; Song et al., 2004; Xie and Spradling, 2000), SGCs are often fully surrounded by bam or bgcn mutant germline tumors. This spatial advantage likely enables tumors to inhibit SGC differentiation efficiently without matching the high BMP output of cap and TF cells. Moreover, since BMP signaling is known to both inhibit normal GSC differentiation and promote their proliferation (Xie and Spradling, 1998), it should similarly stimulate SGC expansion, which is detrimental for bam or bgcn mutant germline tumors. We propose that these tumor cells finely regulate BMP secretion to balance these opposing demands: maintaining differentiation blockade of SGCs while avoiding stimulation of their excessive proliferation.

A well-established principle in oncology is that tumor aggressiveness correlates with poor differentiation, with less-differentiated tumors exhibiting enhanced transformative capacity and metastatic potential (Jögi et al., 2012; Lytle et al., 2018). In Drosophila ovaries, bam or bgcn mutant germline tumors consist of GSC-like cells that may resemble these poorly differentiated human tumors (Lavoie et al., 1999; McKearin and Ohlstein, 1995). This similarity raises the possibility that stem cell-like human tumors may similarly inhibit the differentiation of adjacent wild-type stem cells. By blocking differentiation, such tumors could deplete terminally differentiated cell populations, potentially exacerbating patient mortality. This mechanism may contribute to the heightened lethality of poorly differentiated tumors. Further investigation is needed to test this hypothesis.

The differentiation of a single GSC into a 16-cell germline cyst, comprising 15 polyploid nurse cells and 1 developing oocyte, represents a substantial metabolic investment (Fuller and Spradling, 2007; Lin, 1997). We propose that bam or bgcn mutant germline tumors block this process to divert nutrients toward their own uncontrolled growth. This phenomenon could have broad implications, as many human tissues and organs (intestine, muscle, skin, blood system, male germline, etc.) similarly depend on adult stem cells for homeostasis (Blanpain and Fuchs, 2006; Gehart and Clevers, 2019; Sousa-Victor et al., 2022; Spradling et al., 2011; Wilkinson et al., 2020). Notably, these stem cell-dependent tissues and organs are frequent sites of tumorigenesis, raising the possibility that human cancers may similarly impair neighboring stem cell differentiation to optimize nutrient allocation for malignant growth. A key limitation of our study is that the evidence is derived solely from Drosophila germline. Future work should explore whether similar regulatory paradigms operate in mammalian tissues during tumorigenesis.

Materials and methods

Fly husbandry

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Flies were raised at 25°C on standard cornmeal/molasses/agar media.

Transgenic flies

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hs-bam on chromosome 3R: The coding sequence of the bam gene, amplified from the cDNA clone, was cloned into the BglII-XbaI sites of the pCaSpeR-hs vector, while the attB sequence was inserted into the XhoI site. The resulting attB-pCaSpeR-hs-bam plasmid was then microinjected into the attP154 (Chromosome 3R, 97D2) fly strain to generate site-specific transgenic flies.

Heatshock method to induce germline clones

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To ensure developmental synchrony and maintain low-density growth, eggs within 8 hr of laying were collected for heatshock treatment. The animals (late-Larva 3/early-Pupa stage) were subjected to twice-daily heatshocks at 37°C (2 hr per session, with a 6 hr interval between the two sessions) for 6 consecutive days.

Fertility test

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For each genotype, three independent crosses were performed. Each cross vial contained two females and four w1118 (wild-type) males, all aged 3 days old. The crosses were transferred to fresh vials every 2 days, with five replicate vials quantified per genotype. After all adult flies eclosed, offspring production was assessed by counting the number of empty pupae on the vial walls.

BrdU labeling

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Ovaries were dissected in Schneider’s insect medium (SIM) and incubated in freshly prepared BrdU solution (100 μg/mL in SIM) for 5 hr at 25°C. After washing with PBS for 30 min, samples were fixed in 4% paraformaldehyde (in PBS) for 3 hr, followed by another PBS wash for 30 min. Samples were then treated with RQ1 DNase reaction solution (Promega, Madison, WI, USA) for 1 hr, washed with PBST (0.3% Triton X-100 in PBS) for 30 min, and incubated overnight at 4°C with mouse anti-BrdU antibody. Following a PBST wash for 1 hr, ovaries were incubated with goat anti-mouse 546 and DAPI (0.1 μg/mL) in PBST for 3 hr, washed again in PBST for 1 hr, and mounted in autoclaved 70% glycerol.

Immunofluorescent staining, image collection, and data processing

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Ovaries were dissected in PBS, fixed in 4% paraformaldehyde (in PBS) for 3 hr, washed with PBST for 30 min, and then incubated overnight at 4°C with primary antibodies. The rabbit anti-pMad antibody was a gift from Ed Laufer, and the rabbit anti-Vasa antibody was a gift from Zhaohui Wang (Chen et al., 2014). After washing with PBST for 1 hr, samples were incubated with Alexa Fluor-conjugated secondary antibodies and 0.1 μg/mL DAPI (in PBST) for 3 hr, followed by a final PBST wash for 1 hr. Ovaries were mounted in autoclaved 70% glycerol and imaged using a Zeiss LSM 710 confocal microscope (Carl Zeiss AG, Baden-Württemberg, Germany). Images were processed with ZEN 3.0 SR imaging software (Carl Zeiss) and Adobe Photoshop 2025. The quantification data were processed by GraphPad Prism, ImageJ, or Microsoft Excel.

In situ HCR assay

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Ovaries dissected from 14-day-old female flies were processed according to the following protocol.

  1. Fixation: Ovaries were fixed in 4% paraformaldehyde (in PBS) for 3 hr at room temperature (RT) or overnight at 4°C.

  2. Hybridization: Following fixation, samples were washed three times for 5 min each in PBST, dehydrated in methanol for 5 min, rehydrated through a methanol:PBST gradient series (3:1, 1:1, and 1:3), followed by another three 5 min PBST washes, treated with Proteinase K (10 μg/mL) for 5 min, washed again three times for 5 min each in PBST, pre-hybridized in preheated hybridization buffer (50% formamide, 5× SSC, 9 mM citric acid [pH 6.0], 0.1% Tween 20, 50 µg/mL heparin, 1× Denhardt’s solution, 10% dextran sulfate) for 30 min at 37°C, and then incubated with in situ HCR probes (0.1 μM in hybridization buffer) overnight at 37°C. For the detection of dpp and gbb, a pool of 20 in situ HCR probes targeting each mRNA was employed. The probe sequences were provided in the Key resources table.

  3. Signal amplification: The next day, samples were washed four times for 15 min each at 37°C with preheated probe wash buffer (50% formamide, 5× SSC, 9 mM citric acid, 0.1% Tween 20, 50 µg/mL heparin), followed by three 10 min washes in 5× SSCT (5× SSC, 0.1% Tween 20) at RT. After pre-hybridization in amplification buffer (5× SSC, 0.1% Tween 20, 10% sodium sulfate) for 10 min at RT, an amplification reaction was performed using heat-denatured hairpin nucleic acids (30 nM for each in amplification buffer) overnight in the dark at RT. The hairpin sequences were provided in the Key resources table.

  4. Washing and mounting: Samples were washed three times for 10 min each in 5× SSCT, followed by three 10 min washes in PBST, and then mounted in autoclaved 70% glycerol for imaging.

Quantification of the in situ HCR assay

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The 2D cross-sectional germarium images containing cap/TF regions were captured using confocal microscopy with identical parameters. For each wild-type (w1118) germarium image, the cap/TF and cystocyte regions were outlined separately; for each bam mutant (bamBG/Δ86) germarium, the entire germline region was outlined. Mean fluorescence intensities from these regions were measured using ImageJ to assess the expression levels of dpp and gbb. For both wild-type cystocytes and bam mutant germline tumor cells, these expression levels were normalized to the average levels measured in wild-type cap/TF cells. Given that nearly no background signal was observed (compare germline with empty regions in wild-type germaria in Figure 5A and C), background subtraction was not applied. Over 10 germaria were quantified for each genotype.

Real-time quantitative PCR

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Ovaries from 14-day-old flies were dissected, and total RNA was extracted using the RNeasy Micro Kit. Equal amounts of RNA were reverse-transcribed into cDNA using the HiFiScript cDNA Synthesis Kit. RT-qPCR was performed on a CFX Connect Real-Time PCR System (Bio-Rad) with ChamQ SYBR qPCR Master Mix. The PCR protocol consisted of an initial denaturation at 95°C for 30 min, followed by 40 cycles of 95°C for 10 s and 60°C for 30 s. Relative gene expression was calculated using the 2−ΔΔCT method (Livak and Schmittgen, 2001). The primers used, which were previously described (Huang et al., 2017), were listed in the Key resources table.

Appendix 1

Appendix 1—key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Genetic reagent (Drosophila melanogaster)act-GAL4FlyBase Reference Report: Tepass, 2016.9.10, P{Act-GAL4.U} insertion.
Genetic reagent (D. melanogaster)bamBG (strong loss-of-function allele)Chen and McKearin, 2005
Genetic reagent (D. melanogaster)bamΔ86 (null allele)Bloomington Drosophila Stock Center (BDSC)5427
Genetic reagent (D. melanogaster)bamP-GFPChen and McKearin, 2003b
Genetic reagent (D. melanogaster)bgcn1 (null allele)BDSC6054
Genetic reagent (D. melanogaster)bgcnMI06696 (strong loss-of-function allele)BDSC40815
Genetic reagent (D. melanogaster)Canton-SBDSC64349
Genetic reagent (D. melanogaster)Dad-lacZKai and Spradling, 2003
Genetic reagent (D. melanogaster)dppd6 (hypomorphic allele)BDSC2062
Genetic reagent (D. melanogaster)dppd12 (hypomorphic allele)BDSC2070
Genetic reagent (D. melanogaster)EGFP FRT40ABDSC5629
Genetic reagent (D. melanogaster)FRT40ABDSC1816
Genetic reagent (D. melanogaster)FRT42DBDSC1802
Genetic reagent (D. melanogaster)FRT42D EGFPBDSC5626
Genetic reagent (D. melanogaster)FRT82BBDSC86313
Genetic reagent (D. melanogaster)FRT82B arm-lacZBDSC7369
Genetic reagent (D. melanogaster)FRT82B EGFPBDSC32655
Genetic reagent (D. melanogaster)FRT82B RFPBDSC30555
Genetic reagent (D. melanogaster)gbb1 (null allele)BDSC98344
Genetic reagent (D. melanogaster)hs-bam on chromosome 3RThis paperConstruction information described in the Materials and methods section
Genetic reagent (D. melanogaster)mad12 (null allele)BDSC51301
Genetic reagent (D. melanogaster)med1 (null allele)BDSC9033
Genetic reagent (D. melanogaster)nos-GAL4-VP16BDSC4937
Genetic reagent (D. melanogaster)P{bam+}Zhang et al., 2023b
Genetic reagent (D. melanogaster)punt135 (strong loss-of-function allele)BDSC3100
Genetic reagent (D. melanogaster)UASp-dpp-RNAi-1TsingHua Fly Center (THFC)TH201500984.S
Genetic reagent (D. melanogaster)UASp-dpp-RNAi-2THFCTHU5880
Genetic reagent (D. melanogaster)UASp-FLPZhang et al., 2023b
Genetic reagent (D. melanogaster)UASp-gbb-RNAiTHFCTHU1480
Genetic reagent (D. melanogaster)UASp-GFPZhang et al., 2024
Genetic reagent (D. melanogaster)UASp-yellow-RNAiTHFCTH03150.N
Genetic reagent (D. melanogaster)UASz-FLPZhang et al., 2023b
Genetic reagent (D. melanogaster)w1118BDSC3605
AntibodyAnti-α-Spectrin (Mouse monoclonal)Developmental Studies Hybridoma Bank (DSHB)RRID:AB_528473IF (1:100)
AntibodyAnti-BamC (Mouse monoclonal)DSHBRRID:AB_10570327IF (1:5)
AntibodyAnti-β-GalDSHBRRID:AB_528101IF (1:200)
AntibodyAnti-BrdU (Mouse monoclonal)SigmaB5002IF (1:400)
AntibodyAnti-pMad (Rabbit polyclonal)Zhao et al., 2018A gift from Ed Laufer, IF (1:500)
AntibodyAnti-Vasa (Rabbit polyclonal)Chen et al., 2014A gift from Zhaohui Wang, IF (1:2000)
AntibodyAlexa Fluor 546 goat anti-mouseInvitrogenCat# A-11030IF (1:1000)
AntibodyAlexa Fluor 546 goat anti-rabbitInvitrogenCat# A11035IF (1:1000)
AntibodyGoat anti-rabbit 488ApexbioK1206IF (1:1000)
Recombinant DNA reagentbam cDNA cloneBerkeley Drosophila Genome ProjectFI05606
Recombinant DNA reagentpCaSpeR-hsDrosophila Genomics Resource CenterRRID:DGRC_1215
Recombinant DNA reagentattB-pCaSpeR-hs-bamThis paperConstruction information described in the Materials and methods section
Sequence-based reagentThe hairpin sequence for in situ HCRThis paperB1H1-594CGTAAAGGAAGACTCTTCCCGTTTGCTGCCCTCCTCGCATTCTTTCTTGAGGAGGGCAGCAAACGGGAAGAG
Sequence-based reagentThe hairpin sequence for in situ HCRThis paperB1H2-594GAGGAGGGCAGCAAACGGGAAGAGTCTTCCTTTACGCTCTTCCCGTTTGCTGCCCTCCTCAAGAAAGAATGC
Sequence-based reagentdpp probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaAGAGCATGGCCACGCTGTCCAGTTG
Sequence-based reagentdpp probe for in situ HCRThis paperGCACCACCGTACTTTGGTCGTTGAGtaGAAGAGTCTTCCTTTACG
Sequence-based reagentdpp probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaTCGTAGCCCAGAGGCGCCACAATCC
Sequence-based reagentdpp probe for in situ HCRThis paperGGGCACTTCCCGTGGCAGTAATATGtaGAAGAGTCTTCCTTTACG
Sequence-based reagentdpp probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaTCTCGGCTGCCGCTTGTTCCGGCCG
Sequence-based reagentdpp probe for in situ HCRThis paperGTCGTGGTTCTTGCGCCTCGTAGGCtaGAAGAGTCTTCCTTTACG
Sequence-based reagentdpp probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaGCTGCTTGTGCTGCCACCGCTCGTG
Sequence-based reagentdpp probe for in situ HCRThis paperCGTCGTCCGTGTAGGTGAACAGGAGtaGAAGAGTCTTCCTTTACG
Sequence-based reagentdpp probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaGGCCGGCTGGACATCGAGGCTCACC
Sequence-based reagentdpp probe for in situ HCRThis paperCTGCGGACTCGCCAGCCACCGGTCCtaGAAGAGTCTTCCTTTACG
Sequence-based reagentdpp probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaCACCTGGTAGCGCGTCCGATTCGCC
Sequence-based reagentdpp probe for in situ HCRThis paperCCCGACGCGCGTGATGTCGTAGACAtaGAAGAGTCTTCCTTTACG
Sequence-based reagentdpp probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaTGCTCTTCACGTCGAAGTGCAGCCG
Sequence-based reagentdpp probe for in situ HCRThis paperCCGCCTTCAGCTTCTCGTCGGCGGGtaGAAGAGTCTTCCTTTACG
Sequence-based reagentdpp probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaCCCATGATCTCGGCGTAGAGCTTCT
Sequence-based reagentdpp probe for in situ HCRThis paperGGGATGTTGACCGAGTCGAGCTCGTtaGAAGAGTCTTCCTTTACG
Sequence-based reagentdpp probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaAGCGCCTCCTTGCTGTAGGTGGACG
Sequence-based reagentdpp probe for in situ HCRThis paperGGGTCTGGCTTCAGCTTGTCCTTGAtaGAAGAGTCTTCCTTTACG
Sequence-based reagentdpp probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaCACGAAGATTGATTCAATCGACGAG
Sequence-based reagentdpp probe for in situ HCRThis paperGCGGTCGAGCACCAGCGTCGGCTCCtaGAAGAGTCTTCCTTTACG
Sequence-based reagentgbb probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaGGTGGTACAGAACGGGTAGTGCTCC
Sequence-based reagentgbb probe for in situ HCRThis paperTTTTCAGGTTCACATTCTCGTCGTTtaGAAGAGTCTTCCTTTACG
Sequence-based reagentgbb probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaGCGTTCATGTGCGCATTGAGCGGGA
Sequence-based reagentgbb probe for in situ HCRThis paperAGGGTCTGGACGATCGCATGGTTCGtaGAAGAGTCTTCCTTTACG
Sequence-based reagentgbb probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaGTACAGGGTCTGCATCTGGCAGCTG
Sequence-based reagentgbb probe for in situ HCRThis paperATGCCAGCCCAGATCCTTGAAGTCTtaGAAGAGTCTTCCTTTACG
Sequence-based reagentgbb probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaTGCTCCTGTGGTGGCTGCTGTGGGC
Sequence-based reagentgbb probe for in situ HCRThis paperGCTTGCGTGGATGGCTGGCGCTTCGtaGAAGAGTCTTCCTTTACG
Sequence-based reagentgbb probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaATGTCGTCCAGCTTCACCTCGCGGT
Sequence-based reagentgbb probe for in situ HCRThis paperTCGTCCACCTTGCGGTGGATCAGTCtaGAAGAGTCTTCCTTTACG
Sequence-based reagentgbb probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaGTTGAGCTCCAACCAGCCCACGTAG
Sequence-based reagentgbb probe for in situ HCRThis paperCAGCCACTCGTGCAGGCCCTCGGTCtaGAAGAGTCTTCCTTTACG
Sequence-based reagentgbb probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaACTCCCTGTTGGCGGTCAGCCACTT
Sequence-based reagentgbb probe for in situ HCRThis paperTGCCAATGGCGTATACCGTGATGGTtaGAAGAGTCTTCCTTTACG
Sequence-based reagentgbb probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaCGACGGCCGTGCTCGTGACGCAGTT
Sequence-based reagentgbb probe for in situ HCRThis paperGGCACGTTGGAGACGTCGAACCACAtaGAAGAGTCTTCCTTTACG
Sequence-based reagentgbb probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaGTTCTTCTGCTGCTCGCCCTCATCC
Sequence-based reagentgbb probe for in situ HCRThis paperGGCCCGCTTGTCCAGGTCGGTGATGtaGAAGAGTCTTCCTTTACG
Sequence-based reagentgbb probe for in situ HCRThis paperGAGGAGGGCAGCAAACGGaaTGATGCGGTGGTAGACGTCCAGCAG
Sequence-based reagentgbb probe for in situ HCRThis paperCCTGATCGCTGAGACCCTCCTCCGCtaGAAGAGTCTTCCTTTACG
Sequence-based reagentRT-qPCR primerHuang et al., 2017dpp primer-1TACCACGCCATCCACTCAAC
Sequence-based reagentRT-qPCR primerHuang et al., 2017dpp primer-2GCTCGTTACTCGATACGGCT
Sequence-based reagentRT-qPCR primerHuang et al., 2017gbb primer-1CTGGATCATCGCACCAGAGG
Sequence-based reagentRT-qPCR primerHuang et al., 2017gbb primer-2GTCTGGACGATCGCATGGTT
Sequence-based reagentRT-qPCR primerHuang et al., 2017rp49 (internal control) primer-1CACCGGATTCAAGAAGTTCC
Sequence-based reagentRT-qPCR primerHuang et al., 2017rp49 (internal control) primer-2GACAATCTCCTTGCGCTTCT
Commercial assay or kitChamQ SYBR qPCR Master MixVazymeQ311
Commercial assay or kitHiFiScript cDNA Synthesis KitCWBIOCW2569M
Commercial assay or kitRNeasy Micro KitQIAGEN74004
Software, algorithmAdobe Photoshop 2025San Jose, CA, USARRID:SCR_014199
Software, algorithmImageJNIHRRID:SCR_003070
Software, algorithmGraphPad PrismGraphPad Software, IncRRID:SCR_002798

Data availability

All genotypes are described in Source data 1, and the raw quantification data are included in Source data 2. Fly strains and plasmids are available upon request.

References

Article and author information

Author details

  1. Yang Zhang

    Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
    Contribution
    Data curation, Software, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  2. Yuejia Wang

    Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  3. Jinqiao Song

    Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  4. Lizhong Yan

    Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  5. Ziguang Wang

    Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  6. Dongze Song

    Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  7. Haojun Wang

    Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  8. Sining Yang

    Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  9. Liyuan Niu

    Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  10. Chang Sun

    Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  11. Hanning Zhang

    Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  12. Yudi Zhao

    Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  13. Shaowei Zhao

    1. Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
    2. Nankai International Advanced Research Institute (SHENZHEN FUTIAN), Shenzhen, China
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    swzhao@nankai.edu.cn
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4544-7215

Funding

National Natural Science Foundation of China (32270841)

  • Shaowei Zhao

National Natural Science Foundation of China (32070871)

  • Shaowei Zhao

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Eric Baehrecke, Michael Buszczak, Zheng Guo, Ed Laufer, Ruth Lehmann, Weiwei Liu, Rongwen Xi, Ting Xie, Zhaohui Wang, Guojie Zhang, BDGP, BDSC, DSHB, and THFC for providing antibodies, plasmids, fly strains, and technical assistance. This study was supported by grants 32270841 and 32070871 from the National Natural Science Foundation of China (NSFC) to Shaowei Zhao.

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© 2025, Zhang et al.

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  1. Yang Zhang
  2. Yuejia Wang
  3. Jinqiao Song
  4. Lizhong Yan
  5. Ziguang Wang
  6. Dongze Song
  7. Haojun Wang
  8. Sining Yang
  9. Liyuan Niu
  10. Chang Sun
  11. Hanning Zhang
  12. Yudi Zhao
  13. Shaowei Zhao
(2026)
Tumors mimic the niche to inhibit neighboring stem cell differentiation
eLife 14:RP108910.
https://doi.org/10.7554/eLife.108910.4

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