Abstract
The transfer of intact mitochondria between heterogeneous cell types has been confirmed in various settings, including cancer. However, the functional implications of mitochondria transfer on tumor biology are poorly understood. Here we show that mitochondria transfer is a prevalent phenomenon in glioblastoma (GBM), the most frequent and malignant primary brain tumor. We identified horizontal mitochondria transfer from astrocytes as a mechanism that enhances tumorigenesis in GBM. This transfer is dependent on network-forming intercellular connections between GBM cells and astrocytes, which are facilitated by growth-associated protein 43 (GAP43), a protein involved in neuron axon regeneration and astrocyte reactivity. The acquisition of astrocyte mitochondria drives an increase in mitochondrial respiration and upregulation of metabolic pathways linked to proliferation and tumorigenicity. Functionally, uptake of astrocyte mitochondria promotes cell cycle progression to proliferative G2/M phases and enhances self-renewal and tumorigenicity of GBM. Collectively, our findings reveal a host–tumor interaction that drives proliferation and self-renewal of cancer cells, providing opportunities for therapeutic development.
Subject terms: Cancer microenvironment, CNS cancer, Cancer, Cell division, Mitochondria
Watson et al. demonstrate that astrocyte mitochondria can be horizontally transferred to glioblastoma cells in a GAP43-dependent manner, leading to changes in mitochondrial respiration and metabolism that promote proliferation and tumor growth.
Main
Mitochondria are vital in cell metabolism through their role in generating ATP via oxidative phosphorylation; yet, they have also been implicated in numerous other cellular functions, including apoptotic cell death, inflammation, stem cell differentiation and autophagy1,2. After the discovery of the Warburg effect identifying conversion of glucose to lactate in the presence of oxygen to fuel cancer growth3, aerobic glycolysis has been assumed to be the main energy pathway for cancer cells4, including in glioblastoma (GBM)5, the most common primary adult brain tumor. However, recent reports suggest a more complex situation where mitochondrial respiration is also an alternative energy source6,7. The existence of multiple metabolic phenotypes is in line with the cellular heterogeneity of this disease8–10.
While this heterogeneity may arise from variation in cell-intrinsic metabolic regulation, it may also arise via interaction with the tumor microenvironment (TME), namely by transfer of metabolic signals, including mitochondria themselves, from the TME to tumor cells. In many contexts, dynamic microenvironmental interactions have been shown to be major drivers of tumor growth and therapeutic resistance in GBM11–13, while the underlying direct cell–cell communication mechanisms remain poorly understood.
Transfer of mitochondria has been demonstrated between different cell types and via different routes. Mitochondria transfer by extracellular vesicles from astrocytes to neurons after stroke has been documented in vitro and in vivo14, identifying a CD38/cADP-dependent mechanism using calcium signaling. Extracellular vesicles as a mode of mitochondria transfer has also been shown for mesenchymal stem cells15, indicating that transfer by secretion is a viable route for this organelle. In other studies, mitochondria transfer mechanisms independent of secreted particles have been proposed16–18. Intercellular membrane protrusions known as tunneling nanotubes have been implicated in organelle transport19 and more recently in mitochondria transfer16–18. By connecting cells over distances that may exceed 100 μm (refs. 20,21), tunneling nanotubes are prime avenues for intercellular cross-talk22; composed of components like actin and microtubules22, they maintain a close cytoskeletal relationship22.
Tumor microtubes (MTs) are similar structures; however, they are thicker and more stable than tunneling nanotubes and can reach lengths of more than 500 μm in vivo23. MTs have so far only been described in glioma, particularly in GBM, and have been identified as an important mode of intercellular communication, invasion and therapy resistance24–27. Growth-associated protein 43 (GAP43), a neuronal protein known for its important role in axon guidance28, is a major structural protein of MTs. Knockdown of GAP43 in GBM cells reduced MT formation and tumorigenicity in vivo24. This work highlighted the relevance of the interconnected network of GBM cells formed by GAP43+ MTs for tumor growth. In a recent study, mitochondria were reported to be exchanged in vitro among GBM cells interconnected via MTs29.
Given the reports of horizontal mitochondria transfer in the brain across distinct cell types under pathologic conditions14, we hypothesized that GBM also acquires functional mitochondria from non-malignant cells in the TME, comprising an unexplored layer of metabolic heterogeneity. Here, we show that GBM cells acquire host mitochondria from astrocytes through a contact-dependent mechanism facilitated by GAP43+ structures consistent with MTs, resulting in enhanced metabolic activity and augmented tumorigenicity. Our findings identify mitochondria transfer from the TME as a fundamental protumorigenic mechanism in GBM.
Results
GBM acquires mitochondria from the microenvironment
To assess whether non-malignant host cells could transfer mitochondria to GBM in vivo, we orthotopically implanted green fluorescent protein (GFP)-expressing syngeneic mouse GBM models (SB28 and GL261) into transgenic C57BL/6 mice expressing a mitochondria-localized mKate2 fluorophore fused to the localization peptide of cytochrome c oxidase 8 (mito::mKate2 mice30; Fig. 1a). Confocal microscopy of GBM tumors from mito::mKate2 mice revealed mKate2+ puncta within 15–60% of GFP+ GBM cells (Fig. 1b–f and Extended Data Fig. 1), demonstrating that host cell mitochondria were acquired in vivo by orthotopic GBM tumor cells. By co-staining tissue sections with wheat germ agglutinin (WGA) to highlight glycoprotein-rich structures31, we observed host mKate2+ mitochondria in transit at host–tumor interfaces along intercellular connections between GFP+ GBM cells and GFP– host cells (Extended Data Fig. 1). To determine whether mitochondria transfer occurred in the context of human GBM in vivo, we first injected a high-titer mitoDsRed lentivirus into the brain of nude rats to transduce normal brain cells with a mitochondria fluorescent tag. After 7 d, P3 GFP+ human-derived GBM stem-like cells (GSCs) were injected in the same location (Fig. 1g). High-resolution confocal images verified mitochondria transfer from stromal cells to tumor cells, supporting our mouse in vivo data (Fig. 1g). When reconstructed in three dimensions, the mitoDsRed signal was present within acceptor cells positive for GFP. We verified these results in a second human-derived GSC line GG16 transduced with mito-GFP and by immunostaining with a human-specific nestin antibody (Extended Data Fig. 2).
Having observed mitochondria transfer from the TME to mouse and human GBM models, we proceeded to interrogate the identity of the host mitochondria donor cells. GBM tumors in mouse models and humans are known to have substantial infiltration by both brain-resident glia and peripheral immune cells that transmigrate into the TME13. We established orthotopic GBM tumors in wild-type C57BL/6 mice that had first received lethal irradiation with subsequent bone marrow reconstitution from mito::mKate2 mice (mito::mKate2→WT) to restrict mito::mKate2 expression to bone marrow-derived immune cells (Extended Data Fig. 3a). Analysis of single-cell suspensions by flow cytometry indicated negligible host mitochondria transfer to GFP+ GBM cells in mito::mKate2→WT mice, while 20–60% of GFP+ GBM cells were mKate2+ in mito::mKate2 mice (Extended Data Fig. 3b,c). Taken together, these data suggest that brain-resident cells, not tumor-infiltrating immune cells, were the major donors of mitochondria to GBM cells in vivo.
To further elucidate the identity of the predominant mitochondria donor cell populations, we cocultured prevalent tumor-infiltrating cell types with GFP+ GBM cells at a 1:2 donor:recipient ratio. After 2 h, we assessed the percentage of mKate2+ cells as a marker of mitochondria transfer (schematized in Fig. 2a). Consistent with our in vivo observations, we found that brain-resident glia (astrocytes and microglia) donated significantly more mitochondria on a per-cell basis than bone marrow-derived macrophages, with astrocytes having higher transfer rates (Fig. 2b and Extended Data Fig. 4a). While further polarization of macrophages to an M2- or M1-like phenotype potentially favored increased mitochondria transfer, the degree of transfer was, on average, five- to tenfold less than that observed with the brain-resident glia in vitro (Extended Data Fig. 4b).
Given our observation that astrocytes displayed high rates of mitochondria transfer to mouse models of GBM and their previously described interconnected nature with GBM in vivo32, we focused our subsequent studies on this glial cell population. We began by coculturing either mito-mCherry or mitoDsRed immortalized human astrocytes with six different human-derived GSCs (L1, DI318, 3832, GG16, P3 and BG5) for 3–4 d to interrogate the applicability of our findings in human models of disease. As with mouse GBM models, we found that human-derived GSCs acquired mitochondria from astrocytes, with flow cytometry revealing a transfer rate of 5–40% (Fig. 2c,d and Extended Data Fig. 4c). We confirmed the internalization of astrocyte mitochondria by confocal microscopy of the GSCs, whereby the fluorescent protein-tagged mitochondria were visualized entirely within GSCs with cytoplasm prelabeled with CellTrace dye or expressing GFP (Extended Data Fig. 4d,e). We confirmed that astrocyte-to-human GBM transfer of mitochondria was relevant in vivo using the human orthotopic GBM model (P3) derived from Fig. 1g. We identified mitoDsRed+ astrocytes (glial fibrillary acidic protein (GFAP+) and GFP–) surrounding GFP+ GBM cells with transferred mitoDsRed+ mitochondria (Fig. 2e and Extended Data Fig. 4f). ImageStream analysis on a distinct set of human-derived co-culture models, where donor astrocytes were tagged with mito-GFP and GBM recipients with red-fluorescent protein (RFP), revealed similar results with internalized donor mitochondria in recipient cells (Extended Data Fig. 4g–i). Finally, we confirmed that transferred mitochondria labeled with mito-mCherry expressed the mitochondrial protein translocator of outer mitochondrial membrane 20 (TOMM20) by colocalization analysis, and the protein was distributed within the mitochondrial network of the receiving GBM cell (Extended Data Fig. 5 and Supplementary Video 1 and 2). In summary, we observed mitochondria transfer from the TME to GBM in vivo and in vitro and identified astrocytes as a major mitochondria donor.
GAP43+ MTs facilitate mitochondria transfer
We considered that mitochondria transfer could occur via secretion14 and/or cell contact24,33. To discriminate between the two mechanisms, we compared the mitochondria transfer rate of co-cultures to that of transferred donor-conditioned medium (mouse models; Extended Data Fig. 6a,b) or cells separated by a 5-μm porous transwell insert (human models; Fig. 3a). Regardless of the separation method, transfer primarily occurred when donor and recipient cells were in physical contact, and there was only low-level transfer by secretion that was near the limit of detection of our assays. Live confocal microscopy of mitochondria transfer from astrocytes to mouse GBM cells was also visualized in adjacent cells (Extended Data Fig. 6c and Supplementary Video 3). Transfer was abrogated in both mouse and human models when co-cultures were incubated at 4 °C (Extended Data Fig. 6d,e), suggesting that this was an active, energy-dependent process rather than a passive event. Taken together, these results indicate that active physical interaction of tumor and donor cells is required for effective mitochondria transfer, in line with imaging of in vivo brain tumor models (Figs. 1 and 2 and Extended Data Figs. 1 and 2).
MTs are established conduits of intercellular communication and network formation in GBM and, thus, are also potential mediators of mitochondria transfer24,32,34–36. To visualize mitochondria within the interfaces of GBM cells, we performed confocal microscopy of co-cultures immunostained for TOMM20 and actin. In addition to a perinuclear localization, mitochondria were found in MT connections between tumor cells (P3 model; Fig. 3b), consistent with a previous study reporting that mitochondrial components localize in between astrocyte and GBM cell connections in vitro37. Their intratubular location was confirmed by z stack. Importantly, confocal microscopy of mitoDsRed+ astrocytes and GFP+ P3 co-cultures immunostained with actin showed MTs with mitoDsRed+ mitochondria connecting both cell types. These were present in the vicinity of transferred mitochondria (Fig. 3c). We also found astrocytic mitoDsRed+ donor mitochondria shuttling between connected tumor acceptor cells, indicating that astrocytic mitochondria can be further exchanged within the complete network of tumor–tumor connections (Fig. 3c). After visualizing mitochondria in transit along intercellular connections containing actin filaments, we hypothesized that the actin cytoskeleton was critical in facilitating mitochondria transfer. Indeed, inhibition of actin polymerization by cytochalasin B resulted in a significant decrease in transfer rate, without an effect on cell viability (Fig. 3d and Extended Data Fig. 6f–h). By contrast, inhibition of tubulin polymerization with vincristine had no effect on mitochondria transfer (Fig. 3d).
Previous work showed that GAP43 facilitated the formation of an interconnected network of GBM cells in vivo, which enabled connexin 43-mediated propagation of calcium waves24. GAP43 is an actin-associated protein that facilitates neurite outgrowth via growth cones38,39. We visualized that GAP43 also localized to the cellular projections of GBM cells and astrocytes in co-culture (Fig. 4a). When we knocked down the expression of GAP43 in human-derived GSCs, we observed a decrease in the number of cellular projections (Fig. 4b,c and Extended Data Fig. 6i), as previously reported24,34. Importantly, we found that mitochondria transfer from astrocytes was significantly decreased when GAP43 was knocked down in GSCs alone or in both GSCs and astrocytes (Fig. 4d,e and Extended Data Fig. 6i,j). These data identify a role for GAP43 in facilitating mitochondria transfer from astrocytes to GBM.
Astrocyte mitochondria metabolically reprogram GBM cells
We hypothesized that receiving entire organelles from astrocytes would have biologically important downstream functional sequelae in recipient GBM cells. We first interrogated how mitochondria transfer affected cells transcriptionally by bulk RNA sequencing (RNA-seq) of sorted mouse GBM cells with (mKate2+) or without (mKate2−) astrocyte mitochondria acquisition from co-cultures (Extended Data Fig. 7a). Unsupervised clustering of RNA-seq data revealed that tumor cells had a distinct gene expression profile compared to astrocytes, confirming that sorted mKate2+ SB28 cells were not meaningfully contaminated by astrocytes from the co-culture (Extended Data Fig. 7b–d). Genes that were upregulated >1.5-fold in mKate2+ tumor cells compared to mKate2– tumor cells (Extended Data Fig. 7e,f and Supplementary Table 1) were enriched within pathways related to mitochondrial biology, in particular electron transport and mitochondrial organization (Extended Data Fig. 7a).
Given the central role of mitochondria in ATP production and the results of our RNA-seq analysis, we investigated whether an increase in functional mitochondria through transfer causes a measurable change in metabolic parameters, in particular oxygen consumption rate. We performed a mitochondrial stress test on GFP+ acceptor cells (P3 model) with high and low mitochondria transfer from mitoDsRed+ astrocytes compared to control cells from the same co-cultures using the Seahorse system. After measuring the basal respiration rate, we sequentially added electron transport chain inhibitors to evaluate the changes in maximal respiration capacity. Interestingly, both basal respiration and maximal respiration rate were increased in accordance with mitochondria transfer (Fig. 5a–c). When dividing the cells into metabolic subgroups based on basal respiratory and glycolytic rates, cells with the highest mitochondria transfer were more aerobic and energetic than cells with lower transfer and controls (Fig. 5d). We further characterized the metabolic profile of a panel of GSCs after co-culture with human astrocytes by adapting a previously reported metabolic flow cytometry panel40. The mitochondrial ATP synthase subunit ATP5A was among the most consistently upregulated metabolic proteins in recipient GSCs (Fig. 5e and Extended Data Fig. 8a) and in mouse models of GBM (Extended Data Fig. 8b,c). We evaluated whether these cells with higher respiratory capacity and ATP synthase levels generated more ATP. Across multiple human-derived GBM models, we verified that GBM cells that acquired mitochondria from astrocytes also had higher levels of ATP as measured by luminescence reporter assay (Fig. 5f,g). Collectively, these data show that transferred astrocyte mitochondria are bioactive and lead to augmented mitochondrial respiration and ATP production in recipient GBM cells.
Mitochondria can further influence cellular biology by modulating diverse metabolic pathways41, and there is increasing evidence that they influence intracellular phosphorylation signaling cascades42,43. We thus performed a metabolite mass spectrometry assay and 584-site protein phosphorylation array on sorted GBM cells from co-cultures with astrocytes to interrogate the functional consequence of mitochondria uptake. Metabolite enrichment analysis revealed multiple upregulated metabolic pathways in mKate2+ GBM cells shared between both GL261 and SB28 mouse models (Fig. 6a, Extended Data Fig. 8d and Supplementary Table 2). Among these pathways was amino acid and nucleotide metabolism, previously linked to proliferation, self-renewal and tumorigenicity in GBM44–46. Metabolomic analysis of the sorted human-derived L1 cells showed that mCherry+ cells have higher amounts of glutamate, α-ketoglutarate, glutathione and essential amino acids than mCherry− cells (Fig. 6b and Extended Data Fig. 8e,f). Glutamate is one of three amino acids in glutathione, a major cellular antioxidant, and glutamate metabolism is a principal route for assimilation of nitrogen in de novo nucleotide synthesis, which promotes numerous oncogenic processes in gliomas47,48. This suggests that transfer of mitochondria may be helping the GBM cells support proliferation and protect against oxidative stress. Our phosphoprotein array also revealed changes in the phosphorylation levels of numerous signaling and effector proteins in human-derived GSCs that received astrocyte mitochondria, many of which mapped to proliferation and cell cycle pathways (Fig. 6c and Supplementary Table 3). These findings are consistent with metabolic reprogramming of recipient cells beyond increased mitochondrial respiration, along with intracellular signaling with potential effects on cell cycle regulation and other processes.
Mitochondria transfer increases GBM tumorigenicity
To determine whether the metabolic and signaling changes that we observed in GBM cells that acquired astrocyte mitochondria resulted in altered cell cycle regulation, we performed cell cycle analysis in our models of mitochondria transfer by DNA staining and flow cytometry across multiple human-derived GSC models. We observed a consistent increase in the proportion of cells in the proliferative G2/M phases of the cell cycle following acquisition of astrocyte mitochondria (Fig. 7a,b and Extended Data Fig. 9a). This observation was consistent in vivo when analyzing mouse GBM models from orthotopic tumors established in mito::mKate2 mice (Fig. 7c,d). Moreover, orthotopic mouse GBM tumors originating from sorted cells that had acquired astrocyte mitochondria had a higher mitotic index (Extended Data Fig. 9b–e), suggesting that the increased proliferation phenotype was retained in vivo. Previous reports demonstrated that GSCs could take up isolated cell-free mitochondria added to cell culture medium49. Addition of astrocyte-derived mitochondria to human-derived GSCs was sufficient to recapitulate the increased proportion of cells in G2/M phases of the cell cycle (Fig. 7e–g). These data suggest that the transfer of astrocyte mitochondria impacts cell cycle regulation in human GBM models.
To further assess the impact of mitochondria acquisition on self-renewal, we performed limiting dilution sphere formation assays using sorted GBM cells with and without astrocyte mitochondria. In both human and mouse GBM models, we found that cells that acquired astrocyte mitochondria had significantly higher self-renewal capacity, represented as a higher estimated stem cell frequency (Fig. 7h and Extended Data Fig. 10a). While human-derived GSC models expressed high levels of the pluripotency transcription factor SOX2, we found that GSCs that acquired astrocyte mitochondria further upregulated another self-renewal transcription factor, OCT4 (Extended Data Fig. 10b). These findings suggest that mitochondria transfer from astrocytes consists of a previously undescribed TME interaction that promotes GBM self-renewal.
Increased proliferative capacity and self-renewal are central hallmarks of cancer; we thus hypothesized that mitochondria transfer from astrocytes promotes tumorigenicity of GBM. We tested this hypothesis by assessing the lethality (time to death or humane endpoint) and tumor initiation capacity of human-derived GBM cells with and without acquisition of human astrocyte mitochondria by orthotopic implantation in immunocompromised mice. These studies revealed that tumors led to symptomatic or lethal disease much faster when originating from GSCs that had acquired mitochondria from astrocytes and had a significantly higher in vivo tumor initiation capacity (Fig. 7i,j and Extended Data Fig. 10c). This observation was similar in mouse GBM models (Extended Data Fig. 10d,e). Thus, beyond altering the phenotype of GBM cells in vitro, mitochondria transfer from astrocytes increases the in vivo tumorigenicity of these cells in animal models.
Discussion
Organelle transfer is an increasingly recognized biological process in models of GBM35–37 and other cancers50–52. Most of the existing knowledge on mitochondria transfer in cancer relies on depicting mitochondrial exchange among tumor cells, in purely in vitro systems, and/or using tumor cells artificially depleted of mitochondria. Thus, the role of mitochondria transfer from the TME to cancer cells (and to GBM in particular) remains poorly understood. Specifically, there is a lack of understanding about the in vivo relevance of mitochondria transfer from the GBM microenvironment, the cell types involved, the mechanism of transfer and its downstream effects on cellular function and tumorigenicity in disease-relevant contexts.
We found that in the context of GBM, mitochondria transfer from the TME is a frequent in vivo event and involves brain-resident glial cell donors. Here, we focused mechanistically on astrocytes as donor cells given their abundance in the brain, and our data identify them as important mitochondrial donors. Our data support that astrocytes in the TME form physical actin-based connections with GBM cells that have strong similarity to the previously described MTs, which are network-forming connections between GBM cells. Our data also support an extension of the previously described interconnected network of GBM to non-malignant astrocytes that has recently been verified by exchange of calcium waves within this network in vivo24,32. GAP43, typically found in neuronal projections and critical to GBM growth and the tumor cell network24, mediates mitochondria transfer from astrocytes in a previously undescribed role for this actin-associated protein.
Our findings further demonstrate that mitochondria transfer from astrocytes to GBM cells drives metabolic reprogramming toward oxidative respiration with increased ATP production. Mitochondria transfer also leads to intracellular signaling via protein phosphorylation linked to cellular proliferation and cell cycle progression.
The central finding of our study is that mitochondria transfer from a non-malignant cell of the TME promotes a highly tumorigenic cell phenotype characterized by both increased proliferative capacity and self-renewal (Fig. 7k). This manifested as higher penetrance and faster lethality of orthotopic tumors in vivo. Our findings suggest that the phenotype of increased proliferation and self-renewal driven by acquisition of astrocyte mitochondria before intracranial tumor implantation is sufficient to lead to increased tumorigenicity, while lacking this phenotype at the time of experimental tumor initiation leads to lower GBM cell proliferation. Thus, mitochondria transfer and the shift to oxidative metabolism comprises a fundamental protumorigenic interaction of GBM with its microenvironment. Our study adds a mechanistic understanding to this understudied process, forming the basis of future studies, which could also have broad applicability to tumors outside the central nervous system and other pathological contexts.
Methods
Ethics statements
Human material was obtained from surgeries performed at the Haukeland University Hospital (Bergen, Norway). Written consent was obtained from individuals with procedures that were approved for the projects (project numbers 013.09 and 151825) by the Regional Ethical Committee. Animal experiments were approved by the Institutional Animal Care and Use Committee of Cleveland Clinic and local ethical committee. Animals were treated in accordance with the Norwegian Animal Act.
Human cell culture
The GSC lines P3, GG16 (provided by F. Kruyt, University of Groningen), BG5 and BG7 (ref. 15) were all derived from IDH wild-type biopsy specimens from individuals with GBM; 3832 cells were provided by J. Rich (University of Pittsburgh Medical Center) and have been described previously53. L1 cells were obtained from B. Reynolds (University of Florida) and have been described previously54. DI318 cells were obtained from the Rose Ella Burkhardt Brain Tumor Center biorepository and have been described previously (Cleveland Clinic)55.
Human-derived GBM cells were cultured in ‘complete’ Neurobasal medium (NBM): Neurobasal without phenol red (Gibco), supplemented with 2% B-27 supplement (Gibco), 1 mM sodium pyruvate (Gibco), 2 mM l-glutamine (Gibco), 1 U ml–1 penicillin + 1 μg ml–1 streptomycin (Cleveland Clinic Media Preparation Core), 20 ng ml–1 epidermal growth factor (EGF) and 20 ng ml–1 fibroblast growth factor 2 (FGF-2; R&D Systems). EGF was not added to P3 cells. Immortalized normal human astrocytes (provided by R. Pieper at University of California, San Francisco, and P. Øyvind Enger at Universitetet i Bergen) were cultured in tissue culture vessels (adherent) in neural stem cell (NSC) medium (DMEM-F12 (Media Preparation Core), 1 U ml–1 penicillin + 1 μg ml–1 streptomycin, 5% fetal bovine serum (FBS), 1% N2 supplement (Thermo Fisher Scientific), 20 ng ml–1 EGF and 20 ng ml–1 FGF-2 (R&D Systems)). For experiments involving downstream in vitro limiting dilution assays with L1 and DI318 cell lines, human-derived cells were maintained in DMEM (Cleveland Clinic Media Preparation Core) supplemented with 10% FBS (Thermo Fisher), 1 U ml–1 penicillin and 1 μg ml–1 streptomycin (Cleveland Clinic Media Preparation Core) for at least 7 d before co-culture with astrocytes.
Human-derived xenograft D456 was provided by D. Bigner (Duke University), and the JX22 cell line was provided by J. Sarkaria (Mayo Clinic). DMEM-F12 containing 10 ng ml–1 EGF, 10 ng ml–1 FGF, 1% sodium pyruvate, 2% GEM21 (Gemini Bio) and 1 U ml–1 penicillin + 1 μg ml–1 streptomycin was used to culture the human-derived xenograft lines.
Lentiviral transductions of human cells
Human astrocytes were transduced with mito-mCherry lentivirus (Takara) and selected by fluorescence-activated cell sorting. GAP43 short hairpin RNA (shRNA) and non-target (control) lentiviruses were generated in-house according to the protocol described by Tisconia et al.56. shRNA sequences and sources are described in Supplementary Table 4.
Lentiviral vectors encoding mitoDsRed (Addgene, 44386), enhanced GFP (eGFP)57, mito-GFP (Addgene, 44385) and shRNA against GAP43 (ref. 24) were prepared and titrated according to a protocol reported previously58. The mitochondria donor cells were transduced with pLV-mitoDsRed. The acceptor cells were transduced with a lentiviral eGFP vector57 or, for indicated in vivo experiments, with pLV-mito-GFP.
Human cell co-culture mitochondria transfer assessment by flow cytometry
L1, DI318 and 3832 cells were cultured in tissue culture vessels (adherent) precoated with Geltrex (Thermo Fisher Scientific; 1:250 in serum-free medium overnight) in 90% complete Neurobasal + 10% NSC medium for 4 d, unless otherwise indicated. For GAP43-knockdown experiments, co-culture time was reduced to 24 h to reduce the confounding effect of different growth rates in control versus knockdown GBM cells, and comparisons were made between co-cultures with similar astrocyte:tumor cell ratios. Growth factor-reduced Matrigel (Corning) was used as a coating reagent to attach P3, BG5, BG7 and GG16 GBM cells and normal human astrocytes.
The following inhibitors were used: cytochalasin B (Sigma, C6762) and vincristine sulfate (Sigma, V8388). An equivalent amount of vehicle (DMSO) was used as a control. Cell viability was assessed with a LIVE/DEAD Fixable Blue Dead Cell staining kit.
For viability testing of P3 in the presence of inhibitor, GBM cells were seeded at 5,000 cells per Matrigel-coated well of a 96-well plate in 150 µl of complete Neurobasal medium. After incubation with cytochalasin B for 24 h, cell proliferation reagent WST-1 (Roche, CELLPRO-RO) was added, and sample absorbance measurements at 450 nM were determined on a multiscan FC microplate photometer (Thermo Scientific).
For transwell experiments, human-derived GBM cells were plated on Geltrex-coated tissue culture wells. Under ‘contact’ conditions, human mito-mCherry astrocytes were simultaneously added to the culture well. Under ‘transwell’ conditions, an equal number of astrocytes was plated in a transwell insert (5-μm pore size, Corning), which was submerged in the culture medium of the underlying culture well.
Co-culture experiments were analyzed on a BD LSR Fortessa or BD FACS Symphony S6 (BD Biosciences) operated by BD FACSDiva software (v8.0 or v9.0). FlowJo software (BD Biosciences, v10.7.2 or 10.8.1) was used to analyze flow cytometry data. The gating strategy is described in Supplementary Fig. 1.
Mouse tumor cell maintenance and transduction
SB28 cells were a gift from H. Okada (University of California, San Francisco). GL261 cells were obtained from the Developmental Therapeutics Program, National Cancer Institute. All cell lines were treated with 1:100 MycoRemoval Agent (MP Biomedicals) after thawing and were routinely tested for Mycoplasma spp. (Lonza). Cells were maintained in RPMI 1640 (Media Preparation Core, Cleveland Clinic) supplemented with 10% FBS (Thermo Fisher Scientific) and 1 U ml–1 penicillin + 1 μg ml–1 streptomycin (Cleveland Clinic Media Preparation Core), that is, non-stem-promoting conditions. For the generation of GFP-expressing GL261 cells, parental GL261 cells were transduced with pReceiver-Lv207 (Genecopoeia) and were selected with 300 μg ml–1 hygromycin B (Invitrogen). GFP expression was confirmed by flow cytometry.
Mice
Tg(CAG-mKate2)1Poche/J (mito::mKate2; stock 032188) mice were purchased from The Jackson Laboratory and were housed in the Cleveland Clinic Biological Research Unit. Both sexes of mito::mKate2 mice were intracranially injected at 4–8 weeks of age with 10,000–20,000 SB28 or 100,000 GL261-GFP cells in 5 μl of RPMI null medium into the left cerebral hemisphere 2 mm caudal to the coronal suture and 3 mm lateral to the sagittal suture at a 90° angle with the skull to a depth of 2.5 mm using a stereotaxis apparatus (Kopf). Male NSG mice were bred in-house by the Cleveland Clinic Biological Resources Unit, and tumors were established orthotopically as described above. Mice were fed standard chow (Teklad Global 18% Protein Rodent Diet, 2913, Envigo) and filtered water ad libitum and were housed in forced/filtered air isolator cages containing up to five mice. Mice were maintained on a 12-h light/12-h dark cycle, with a temperature of 20–26 °C and humidity of 30–70%.
Bone marrow transplantation
Four-week-old male mice were treated with 11 Gy radiation in two fractions 3–4 h apart. Reconstitution was achieved by retro-orbital injection of 2 × 106 bone marrow cells from mito::mKate2 mice. Drinking water was supplemented with Sulfatrim (trimethoprim–sulfamethoxazole; Pharmaceutical Associates) during the first 10 d, and mice were monitored for an additional 6 weeks for weight loss and symptoms of infection before tumor inoculation.
Orthotopic xenograft models in nude rats
Immunodeficient nude-RNU rats of both sexes, bred in-house, were fed a diet containing standard pellets (Sniff, V1536-000), had access to water ad libitum and were housed in filtered air isolator cages (Allentown type IV (Rat 1800), HEPA filter) with a 12-h light/12-h dark cycle at 21 °C and ~45% humidity. Stereotactical implantation of tumor cells into the brain has been described previously57. To model mitochondria transfer from normal cells to tumor cells, a high-titer mitoDsRed lentivirus was injected into the brain followed by implantation of GFP+ or mito-GFP+ tumor acceptor cells after 7 d. Animals were euthanized with CO2 and perfused with 0.9% NaCl.
Immunofluorescence
For immunofluorescence analysis, brains were fixed in 4% paraformaldehyde (PFA), dehydrated in a 30% sucrose solution, embedded in optimal cutting temperature compound and snap frozen. Sectioning (10 μm thick) was performed on a Leica CM3050 S precooled to −20 °C before use. Sections were protected from light and frozen at −80 °C. For staining, tissue sections were incubated overnight at 4 °C with the following primary antibodies: monoclonal anti-GFAP, anti-human nestin, anti-F-actin, anti-GAP43, chicken anti-GFP, rabbit anti-phospho-histone H3 and rabbit anti-cleaved caspase-3. The following secondary antibodies were used: goat anti-mouse 647 at room temperature for 120 min, donkey anti-chicken Alexa Fluor 488 overnight at 4 °C and WGA Alexa Fluor 680 (4 μg ml–1 in HBSS-T with magnesium and calcium) for 1 h at room temperature.
Cells on coverslips were fixed in 4% PFA and permeabilized with 0.1% Triton X-100 (Sigma) for 20 min at room temperature. Anti-TOMM20 or anti-nestin was incubated overnight at 4 °C. Goat anti-mouse 488 or goat anti-rabbit 647 were incubated for 60 min at room temperature.
Confocal microscopy and time-lapse imaging
Confocal microscopy of stained mouse tissue sections was performed using a Leica SP8. Still-image processing and z reconstructions were completed using LasX software (version 3.3; Leica). Image analysis for estimation of mito::mKate2 transfer to GBM cells in vivo was performed using Velocity software (version 6.3; PerkinElmer). The three-dimensional (3D) segmentation algorithms (Supplementary Note) were set to minimize the (1) detection of mKate2 channel noise (using wild-type tissue sections as a negative control) and (2) identification of GFP+ cells not morphologically compatible with tumor cells, likely the result of GFP phagocytosis in the TME (using a relevant size cutoff).
SB28 cells (40,000) were cocultured with 80,000 mito::mKate2 astrocytes in a glass-bottom 35-mm dish (Mat-tek) overnight. Growth medium was replaced with phenol red-free NSC medium. Multiple full-thickness z stacks were obtained every 10 min using a Leica SP8 microscope in a 37 °C chamber supplemented with 5% CO2 and 95% humidity using a ×20/0.8-NA objective lens. Time-lapse frames were subsequently analyzed by LasX software.
L1 and DI318 cells were stained with CellTrace Green (Thermo Fisher) at 5 μM in serum-free medium at 37 °C for 30 min and washed with complete Neurobasal medium before plating with human mito-mCherry astrocytes in ibidiTread 35-mm microscopy dishes (Ibidi) that had been coated with Geltrex (Gibco). After 48 h, cells were washed gently with PBS, fixed with 4% PFA for 15 min at 4 °C and imaged by confocal microscopy.
For TOMM20 colocalization experiments, cells were plated as described above and cocultured for 72 h. Cells were then stained with primary antibody (anti-TOMM20; clone D8T4N) for 1 h at room temperature and with secondary anti-rabbit DyLight405 alpaca for 1 h at room temperature. Colocalization analysis (line profile quantification) was performed using Image-Pro Plus 10 (Media Cybernetics).
Confocal imaging for P3, BG5, BG7 and GG16 co-culture experiments was performed using a Leica TCS SP8 STED 3X (Leica Microsystems) run by LasX software (version 3.3, Leica, version LAS4.13). Image analyses were performed with ImageJ (v2.3.0/1.53f) and Imaris (v9.6).
Measurement of mitochondrial trafficking in vivo by flow cytometry
Resected tumors or the contralateral hemisphere were digested with 1 mg ml–1 collagenase IV (StemCell Technologies) and 1 mg ml–1 DNase I (Roche) for 15 min at 37 °C. Samples were strained through a 100-μm strainer (Fisherbrand) and washed with PBS. Cells were stained with a LIVE/DEAD Fixable Blue Dead Cell Stain kit (Thermo Fisher Scientific) for 10 min on ice, treated with FcR blocking reagent (Miltenyi Biotec) diluted 1:50 for 15 min on ice and stained with 1:100 APC-conjugated anti-CD11b (BioLegend, clone M1/70) for 20 min to exclude phagocytic cells. Samples were fixed overnight with a eBioscience FoxP3 transcription factor fixation kit (Thermo Fisher Scientific) and analyzed with a BD LSRII Fortessa (BD Biosciences) in PBS.
Generation of mouse astrocytes and microglia and in vitro mouse mitochondria transfer assay
Brain-resident glial cell cultures were obtained, as previously described59, by resecting the subventricular zone of brains from postnatal day 0–3 mice and culturing in NSC medium. Microglia were generated by culturing confluent monolayers of early passage (passage 2 or 3) glial cell cultures with microglia polarization medium (DMEM-F12, 1 U ml–1 penicillin + 1 μg ml–1 streptomycin, 10% FBS and 20 ng ml–1 granulocyte–macrophage colony-stimulated factor (BioLegend)). After 5 d, microglia (loosely adherent) were obtained by agitation of the culture flask on an orbital shaker for 45 min.
A total of 20,000–40,000 astrocytes and microglia were separately cultured in a 96-well flat-bottom plate in NSC medium or microglia polarization medium, respectively. Forty-eight hours later, supernatants were collected and centrifuged at 400g for 5 min to remove residual cells; these supernatants consisted of the conditioned, cell-free culture medium. Tumor cells were added at a recipient:donor ratio of 2:1 to the adherent donor cell cultures to assess total mitochondria transfer (contact dependent and independent); separately, tumor cells alone were cultured with conditioned, cell-free medium described above to assess contact-independent (secreted) mitochondria transfer. Samples were incubated for 2 h and treated with Accutase to generate single-cell suspensions. Cells were transferred to 96-well U-bottom plates to stain with LIVE/DEAD dye. Exogenous mitochondria uptake by GFP+ tumor cells was assessed with a BD LSRII Fortessa.
Generation of mouse macrophages and in vitro mitochondria uptake assay
Bone marrow from the femurs and tibiae of 4- to 8-week-old male and female mito::mKate2 mice was flushed with PBS using a 27-gauge needle. Eighty thousand cells were cultured in 24-well plates and treated with 50 ng ml–1 recombinant mouse macrophage colony-stimulating factor (BioLegend) in IMDM (Media Preparation Core) supplemented with 1 U ml–1 penicillin + 1 μg ml–1 streptomycin and 20% FBS for 6 d. Interferon-γ or interleukin-4 (50 ng ml–1; BioLegend) was added for 48 h to further induce polarization of macrophages to M1- or M2-like macrophages, respectively. Supernatants were collected and centrifuged at 400g for 5 min to remove residual cells and to generate conditioned, cell-free culture medium. Tumor cells were added at twofold abundance in technical duplicates for a 2-h incubation, as described above, to test contact-dependent versus contact-independent mitochondria transfer. Samples were incubated with Accutase for 5 min and transferred into 96-well U-bottom plates for staining with the viability dye and anti-CD11b as described above. Exogenous mitochondria uptake was analyzed in GFP+ tumor cells using a BD LSRII Fortessa.
Western blotting
Cells were washed twice with PBS and dissolved in lysis buffer (10 mM Tris-HCl (pH 7.4), 150 mM NaCl, 0.5% NP-40, 1% Triton X-100 and 1 mM EDTA) supplemented with protease and phosphatase inhibitor cocktails (Roche). Protein concentration was quantified by Bradford assay (Euromedex). Cell lysates were resuspended in Laemmli buffer (62.5 mM Tris (pH 6.8), 10% glycerol, 2.5% SDS and 2.5% β-mercaptoethanol). The primary antibody was anti-GAP43, the housekeeping protein vinculin was used as the loading control and the secondary antibody was goat anti-rabbit horseradish peroxidase (HRP; Invitrogen, 31462). Membranes were developed on a LAS 3000 (version 2.2; Fujifilm).
For GAP43 blots (Extended Data Fig. 6i,j) and stem cell transcription factors (Extended Data Fig. 10b), cells were lysed with ice-cold RIPA buffer. The following primary antibodies were used: anti-GAP43, anti-actin, anti-SOX2 and anti-OCT4. Secondary antibodies were goat anti-rabbit HRP or goat anti-mouse HRP.
Sorting of tumor cells from co-cultures
For mouse GBM models, astrocytes were collected from flasks by Accutase treatment and stained with a 1:1,000 dilution of CellTrace Violet cell proliferation dye in PBS at 37 °C for 20 min. Tumor cells and astrocytes were then cocultured at a 1:1 ratio for 48 h in NSC medium. Samples were sorted into RPMI with 20% FBS and cultured overnight in complete RPMI for subsequent functional assays.
For collection of double-positive or single-positive cells in co-cultures of P3, BG5 and GG16 GBM cells with immortalized mitoDsRed normal human astrocytes by cell sorting, 2 × 106 cells of each cell line were seeded in Neurobasal medium on Matrigel (T75 flasks). For analyzing mitochondria transfer by flow cytometry, 1.5 × 105 cells from each cell line were seeded in Neurobasal medium on Matrigel (T25 flasks). L1 and DI318 cells were sorted after 4 d of co-culture with immortalized mito-mCherry human astrocytes from Geltrex-coated flasks (seeded at 1:1 and 1:1.5 donor:recipient ratio, respectively, to adjust for differences in cell growth rate).
In vitro limiting dilution assay
Sorted tumor cells were cultured at decreasing cell densities over 12 technical replicates in complete Neurobasal medium, and the number of wells containing spheres was counted after 11–14 d. For the mouse cell line SB28, 400 to 25 cells per well of a 96-well plate were seeded; for human lines, this ranged from 100 to 1.25 cells per well. The online ELDA tool (http://bioinf.wehi.edu.au/software/elda/, 24 October 2014 version) was used to calculate stem cell frequency60.
In vivo limiting dilution/tumor initiation assays
Sorted mouse/human GBM cells with and without the acquisition of astrocyte mitochondria in vitro were counted with trypan blue using a TC-20 cell counter (Bio-Rad) and were volume adjusted to achieve decreasing cell concentrations (18,000–1,000, as indicated) for intracranial implantation. Subsequently, C57BL/6 mice (mouse GBM models) or NSG mice (human GBM models) were intracranially implanted with equal numbers of tumor cells, as described above. The identity of the implanted cells was then blinded to investigators. Mice were euthanized at humane endpoints (neurological symptoms, weight loss, poor grooming or any other sign of distress).
Seahorse assay
For sorted P3 GFP mitoDsRed+ and mitoDsRed− cells, mitochondrial respiration assays were performed using 3.0 mM oligomycin, 1.5 mM carbonyl cyanide m-chlorophenyl hydrazone (CCCP), 1.0 mM rotenone and 1.0 mM antimycin A in assay medium consisting of unbuffered, phenol red-free DMEM with 10 mM glucose, 2 mM sodium pyruvate and 4 mM l-glutamine with a pH of 7.4. Cells were incubated for 60 min in a Prep Station (Agilent) under non-CO2 conditions at 37 °C and were subsequently placed in the Seahorse Xfe96 analyzer. The chemical compounds were serially injected to manipulate the cells and create metabolic flux reports. Data were analyzed using Agilent Seahorse Wave Controler v2.6.3, IDEAS software (version 6.2; EMD Millipore).
ATP quantification
CellTiter-Glo reagent (100 µl; Promega) was added to cells and incubated on an orbital shaker at room temperature for 20–30 min. ATP levels were quantified indirectly by relative luminescence intensity measured on a Victor 3 plate reader (PerkinElmer).
Metabolic protein flow cytometry analysis
For mouse cells, the metabolic protein flow cytometry panel was adapted from Ahl et al.40. CellTrace Violet-stained astrocytes were cocultured with tumor cells overnight at a 1:1 ratio. Samples were fixed in eBioscience FoxP3 transcription factor fixation buffer for 30 min on ice and stained with the following antibodies in 1× permeabilization buffer for 30 min at room temperature: anti-argininosuccinate synthetase 1 (ASS1), anti-ATP synthase F1 subunit α (ATP5A), anti-glucose transporter 1 (GLUT1), anti-isocitrate dehydrogenase 2 (IDH2), anti-glucose 6 phosphate dehydrogenase (G6PD), anti-acetyl-CoA carboxylase (ACC1), anti-peroxiredoxin 2 (PRDX2), anti-hexokinase 1 (HK1), anti-carnitine palmitoyltransferase I (CPT1A), anti-SLC20A1, anti-mouse IgG1 (κ monoclonal), anti-mouse IgG2b (κ monoclonal) and rabbit IgG (monoclonal). Samples were washed and resuspended in 1× permeabilization buffer containing goat anti-mouse IgG H&L (Alexa Fluor 647) or donkey anti-rabbit IgG H&L (Alexa Fluor 647). After 30 min of incubation at room temperature, cells were washed with permeabilization buffer and resuspended in PBS for analysis with a BD LSRII Fortessa. Geometric mean fluorescence intensity was used to calculate expression levels after subtraction of the background levels from isotype control staining.
The same procedure was used for human cell metabolic flow, with minor modifications. Seven different human-derived cell lines were cocultured with human mito-mCherry astrocytes for 4 d. Most antibodies listed above cross-reacted with human antigens and were thus also used in these experiments, with the exception of using directly conjugated Alexa Fluor 647 antibodies for ATP5A and GLUT1.
Metabolomics analysis
Sorted mouse GBM cell pellets were resuspended in 80% ice-cold methanol for metabolite extraction and sent to an academic core facility for targeted metabolomics (Beth Israel Deaconess Mass Spectrometry Core Facility, Harvard Medical School). Data were normalized by peak count. Values of zero (below the limit of quantitation) were substituted with the approximate limit of quantitation (2,000 peak count) to facilitate fold change analysis. Metabolites enriched by >20% in mKate2+ GBM cells were put into the metabolic pathway enrichment analysis algorithm MetaboAnalyst 5.0 web tool (www.metaboanalyst.ca)61. The following analysis settings were used: HMDB and KEGG compound names; feature type = metabolites; KEGG analysis.
For human GSCs, triplicate cell pellet samples were lysed in 80:20 methanol:water at dry ice temperature. The quantity of the metabolite fraction analyzed was adjusted to the cell count. Extracts were clarified by centrifugation, dried by nitrogen blower and reconstituted in equal volumes 50:50 methanol:water. Metabolite fractions were analyzed by targeted liquid chromatography–tandem mass spectrometry via dynamic multiple reaction monitoring. An Agilent Technologies Triple Quad 6470 liquid chromatography–tandem mass spectrometry system, consisting of the 1290 Infinity II LC flexible pump (Quaternary Pump), the 1290 Infinity II multisampler, the 1290 Infinity II multicolumn thermostat with six-port valve and the 6470 triple quad mass spectrometer, was used for analysis. Agilent MassHunter Workstation software LC/MS data acquisition for 6400 Series Triple Quadrupole MS with version B.08.02 was used for compound optimization and sample data acquisition. Studies were performed in negative ion acquisition mode with ion-pairing chromatography using an Agilent ZORBAX RRHD Extend-C18, 2.1 × 150 mm, 1.8 µm and ZORBAX Extend Fast Guards for UHPLC separation. Agilent MassHunter Workstation quantitative analysis for QQQ version 10.1, build 10.1.733.0, was used to integrate and quantitate metabolite peak areas. Liquid chromatography–mass spectrometry peaks corresponding to metabolites with coefficients of variation greater than 0.5 underwent manual inspection and integration. The data were normalized to the average sum of metabolites from all the samples and were analyzed using Morpheus to generate the heat map. Metaboanalyst was used to compare metabolites and metabolic pathways enriched in mito-mCherry+ L1 cells and mito-mCherry– L1 cells.
Phosphoprotein array
Human-derived GBM cells (L1) were cocultured with human mito-mCherry astrocytes for 4 d and flow sorted to obtain mCherry+ and mCherry– GBM cells. Sorted cell pellets were flash-frozen in liquid nitrogen and sent for a commercial microarray platform, antibody-based phosphoprotein array (Phospho Explorer array, FullMoon BioSystems). The complete antibody list can be found in Supplementary Table 3. Analysis was conducted using the phosphorylation ratio of each protein for samples tested (phosphorylated protein signal divided by corresponding total protein signal). We analyzed the functional enrichment of the upregulated phosphoproteins using Enrichr62 against the Gene Ontology (GO) biological process term data set. GO terms with FDR values of <0.05 were considered significantly enriched. We summarized the enriched terms using Revigo63 to generate a graph-based view of the subdivisions of the terms.
ImageStream
A clone of HEK293T cells able to grow in serum-free medium (CSC293T) was generated and cultured as previously described64. CSC293T cells were transfected with psPAX2, pCMV-VSVG and pLYS1-Mito-GFP or pCMV-RFP using FuGENE HD transfection reagent (Promega). Viral concentration was determined using the Lenti-X qRT–PCR titration kit (Takara Bio). Immortalized normal human astrocytes were transduced with mito-GFP lentivirus and selected for cells stably expressing mito-GFP using puromycin. D456 and JX22 cells were transduced with RFP lentivirus and selected using blasticidin S (Gibco) to select for stable RFP-expressing cells. Where indicated, cells were sorted for mito-GFP positivity or RFP positivity with assistance from the Flow Cytometry Core at the University of Alabama at Birmingham.
RFP-expressing D456 or JX22 cells were cocultured at a 1:1 ratio in the presence of mito-GFP+ astrocytes for 24 h. Samples were imaged at ×40 magnification with extended depth of field. Mito-GFP was acquired on ch02, and RFP was acquired on ch04. Ch01 and ch09 were used for brightfield imaging, and ch12 was used for side scatter. Five thousand events were recorded, and relevant single-color and unstained controls were used. Data were analyzed using IDEAS software (version 6.2; EMD Millipore).
RNA-seq
mKate+ and mKate– SB28 cells and astrocytes from three distinct co-cultures (biological replicates) were sorted into multiple 1.5-ml DNA LoBind microtubes (Eppendorf), each containing 700 μl of RLT Plus lysis buffer (Qiagen) supplemented with 1% β-mercaptoethanol. RNA isolation was performed using the RNEasy Plus Micro kit (Qiagen).
RNA-seq and analysis were performed by GENEWIZ. Briefly, samples were sequenced using an Illumina HiSeq, with a 2 × 150 base pair configuration and ≥350 million raw paired-end reads. An average of 41.6 million paired-end reads was sequenced across nine samples. After Illumina universal adapters were trimmed, the reads were mapped to the Mus musculus GRCm38 reference genome using the STAR aligner v.2.5.2b. Unique gene hit counts were calculated by using featureCounts from the Subread package v.1.5.2.
For comparison of tumor cells with astrocytes, genes with an adjusted P value of <0.05 and absolute log2 (fold change) of >1 were called as differentially expressed genes (DEGs) using DESeq2. For assessment of differentially upregulated pathways in mKate+ versus mKate– SB28 cells, genes that were upregulated >1.5-fold with a count number >50 and an unadjusted P value of <0.05 (Supplementary Table 1) were plugged into https://maayanlab.cloud/Enrichr/.
Protein–protein interactions and network visualization
Differential expression analysis was performed using edgeR 3.34 (ref. 65). Genes with a count per million greater than 1 in at least two samples were used for the analysis. P values of <0.05 were considered significant. The mouse DEGs were mapped to the human homologs using the NCBI HomoloGene database (https://www.ncbi.nlm.nih.gov/homologene). We then performed the enrichment analysis using Enrichr62 for the entire set of DEGs and for the up- and downregulated genes separately.
The protein–protein interactions among the DEGs were extracted using a human protein interactome we built previously66 that contains 17,706 protein nodes and 351,444 protein–protein interaction edges. We then visualized this protein–protein interaction network using Cytoscape 3.8 (ref. 67). Genes that localize to mitochondria are indicated by a diamond node shape based on the Human MitoCarta2.0 database68.
Cell cycle analysis
Single-cell suspensions (from mouse tumors or cultured cells) were fixed in PBS with 4% PFA for 1 h on ice and washed with permeabilization buffer (Foxp3/transcription factor staining buffer set; Ebioscience). Subsequently, cells were stained with Hoechst 33342 (Thermo Fisher) at 3.33 μg ml–1 in PBS for 1 h at room temperature, washed with PBS supplemented with 2% bovine serum albumin and assayed by flow cytometry.
Mitochondria isolation
Mitochondria were isolated from confluent immortalized human astrocyte cultures expressing mCherry by using the mitochondria isolation kit for cultured cells (Thermo Fisher Scientific), as per the manufacturer’s instructions. Mitochondria were quantified by total protein using the Qubit protein assay kit (Thermo Fisher), according to the manufacturer’s protocol.
Quantification of phospho-histone H3 and cleaved caspase-3 immunofluorescence
To quantify elements within the TME, tissue was examined from three animals per biological sex per mitochondrial transfer status. For a given animal, 15 representative images were captured (5 images × 3 tumor-bearing sections). Cellular proliferation was quantified by counting each phosphorylated histone H3+ nucleus in each visual field. This count was divided by the fluorescence intensity of the GFP signal of the same field to account for variation in tumor size and cellular density to yield a mitotic index. The apoptotic cell death index was quantified by dividing the fluorescence intensity of the cleaved caspase-3 signal by the fluorescence intensity of the GFP signal in the same visual field.
MT quantifications
MT number was quantified manually using the NIH ImageJ software. DAPI and nestin-immunostained confocal images magnified ×200 were used. For each experimental condition, a minimum of 80–120 MTs showing a direct connection between two tumor cells was included for measurement. All measured data were exported into Microsoft Excel and GraphPad Prism 8.1.2 for further calculation of statistical significance.
Statistics and reproducibility
For most in vitro experiments, a minimum of three biologically independent samples was used per experimental group; power was not calculated. For in vivo experiments not involving tumor initiation capacity, sample size was determined based on minimum utilization of vertebrate animals and high expected magnitude of effect without formal power evaluation. For in vivo experiments involving tumor initiation analysis, sample size was determined by prior experience and application of this assay in stem cell biology studies, for example, in Karunanithi et al.69. No data were excluded from analyses. Key findings were replicated across time, by different institutions, using diverse models. For survival/tumor initiation studies, mice were randomized before tumor implantation, and investigators were blinded to the grouping. Data distribution was assumed to be normal, but this was not formally tested.
Data representation and analysis
Flow cytometry data were analyzed and generated using FlowJo software (BD Biosciences, v10.7.2). Graphs were generated and statistical analyses were performed using Excel (Microsoft Office, v16.52) or Prism (GraphPad, v9.2.0) software. All measurements shown represent distinct samples, unless otherwise indicated. All statistical tests are two tailed and corrected for multiple comparisons, unless otherwise indicated.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Supplementary information
Acknowledgements
We thank A. Mendelsohn for illustrations; the LRI Flow Cytometry Core (an ISAC-recognized Shared Resource Laboratory), the LRI Imaging Core, J. Peterson, A. Mulya, S. Prasad, B. Nordanger and H. S. Saed for their technical assistance and discussions; the Molecular Imaging Center and the Flow Cytometry Core Facility in Bergen, Norway, for technical support; F. Kruyt for the GG16 cell line (University of Groningen, Netherlands) and A. Andren and L. Zhang for technical support with metabolomics analysis. This work used flow cytometry and sorting equipment supported with funds from the Clinical and Translational Science Collaborative of Cleveland, UL1TR002548, from the National Center for Advancing Translational Sciences. This work used the Leica SP8 confocal microscope that was purchased with funding from National Institutes of Health SIG grant 1S10OD019972-01. Funding for this study was provided by Lerner Research Institute, Cleveland Clinic (J.D.L.), Case Comprehensive Cancer Center (J.D.L.), National Institutes of Health grant R35 NS127083 (J.D.L.), VeloSano Cancer Research Pilot Award (D.C.W., D.B. and J.D.L.), 2020 VeloSano Trainee Dream Experiment (D.C.W.), National Institutes of Health grants 5T32AI007024 and 5TL1TR002549 (D.C.W.), National Institutes of Health grant K99CA248611 (D.B.), Helse Vest fellowship grant (S.S.), University of Bergen fellowship grant (S.S.M.), The Research Council of Norway grant 325883 (S.S.M. and H.M.), Norwegian Cancer Society grant 197933 (J.H. and H.M.), National Institutes of Health grant K00CA253768 (S.A.S.), National Institutes of Health grant F30CA250254 (A.L.), National Institutes of Health grant F31CA264849 (K.K.) and National Institutes of Health grant F32CA260735 (A.J.S.).
Extended data
Source data
Author contributions
Conceptualization: D.C.W., D.B., J.D.L. and H.M. Methodology: D.C.W., D.B., S.S., S.S.M., S.A.S., J.H., M.T.A., S.E.W., A.L., P. Sravya, Y.Z., S.A.S., K.T., S.W., K.K., D.J.S., E.M.-H., G.D., A.C., G.V.R., K.J.T., K.S., T.R.L., J.V.J., L.A.R.Y., J.G., C.R.M., L.N., J.M., T.D., J.A.H., A.J.S. and P. Sajjakulnukit. Investigation: D.C.W., D.B., S.S., S.S.M., J.H., M.T.A., S.E.W., A.L., P. Sravya, Y.Z., G.D., J.S., S.A.S., K.T., S.W., K.K., D.J.S., S.D., A.C., G.V.R., L.A.R.Y., J.A.H., A.J.S., P. Sajjakulnukit, C.A.L. and D.R.W. Visualization: D.C.W., D.B., S.A.S., S.S.M., J.H., S.E.W., Y.Z., G.D., K.K., D.J.S., S.S. and P. Sravya. Funding acquisition: D.C.W., D.B., J.D.L. and H.M. Project administration: J.D.L. and H.M. Supervision: J.A.M., F.C., A.B.H., J.D.L., H.M., R.B., C.A.L. and D.R.W. Writing, original draft: D.C.W., D.B., J.D.L., S.S., S.S.M. and H.M. Writing, review and editing: D.C.W., D.B., S.S., S.S.M., J.H., S.E.W., A.L., G.D., J.S., J.A.M., Y.Z., F.C., A.B.H., J.D.L., R.B., T.D., P. Sravya, D.R.W., J.A.H. and H.M.
Peer review
Peer review information
Nature Cancer thanks Jonathan Brestoff, Luca Scorrano and Frank Winkler for their contribution to the peer review of this work.
Data availability
Sequencing files have been deposited to Gene Expression Omnibus under accession number GSE183004. Metabolic pathway analysis was based on the KEGG human metabolic pathways database (October 2019; https://www.genome.jp/kegg/pathway.html#metabolism). Protein phosphophorylation array data were mapped to pathways based on the GO biological process term data set (http://geneontology.org/). RNA-seq reads of mouse cells were mapped to the M. musculus GRCm38 reference genome (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.20/). The RNA-seq inferred protein–protein interaction network was constructed by mapping to human homologs using the NCBI HomoloGene database (https://www.ncbi.nlm.nih.gov/homologene). Genes encoding mitochondria-localizing proteins were identified with MitoCarta2.0 (https://www.broadinstitute.org/files/shared/metabolism/mitocarta/human.mitocarta2.0.html). All other data are available in the main text or the Supplementary Information. Source data are provided with this paper.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
These authors contributed equally: Dionysios C. Watson, Defne Bayik, Simon Storevik, Shannon Sherwin Moreino.
These authors jointly supervised this work: Hrvoje Miletic, Justin D. Lathia.
Contributor Information
Hrvoje Miletic, Email: hrvoje.miletic@uib.no.
Justin D. Lathia, Email: lathiaj@ccf.org
Extended data
is available for this paper at 10.1038/s43018-023-00556-5.
Supplementary information
The online version contains supplementary material available at 10.1038/s43018-023-00556-5.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Sequencing files have been deposited to Gene Expression Omnibus under accession number GSE183004. Metabolic pathway analysis was based on the KEGG human metabolic pathways database (October 2019; https://www.genome.jp/kegg/pathway.html#metabolism). Protein phosphophorylation array data were mapped to pathways based on the GO biological process term data set (http://geneontology.org/). RNA-seq reads of mouse cells were mapped to the M. musculus GRCm38 reference genome (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.20/). The RNA-seq inferred protein–protein interaction network was constructed by mapping to human homologs using the NCBI HomoloGene database (https://www.ncbi.nlm.nih.gov/homologene). Genes encoding mitochondria-localizing proteins were identified with MitoCarta2.0 (https://www.broadinstitute.org/files/shared/metabolism/mitocarta/human.mitocarta2.0.html). All other data are available in the main text or the Supplementary Information. Source data are provided with this paper.