Background: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. Methods: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. Results: The algorithm detected 49.9% of human expert-identified TNTs, counted TNTs, and calculated the number of TNTs per cell, or TNT-to-cell ratio (TCR); it detected TNTs that were not originally detected by the experts. The model had 0.41 precision, 0.26 recall, and 0.32 f-1 score on a test dataset. The predicted and true TCRs were not significantly different across the training and test datasets (p = 0.78). Conclusions: Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm.
Tumor Treating Fields (TTFields) is a novel therapeutic strategy that uses alternating electric fields to disrupt mitosis in actively dividing cells through exertion of dielectrophoretic force and dipole alignment on microtubule subunits. However, the additional effects of TTFields on cellular morphology and communication remain unclear. Tunneling nanotubes (TNTs) are ultrafine F-actin-based protrusions that facilitate intercellular communication through cell-cell contact, including efficient transport of molecular cargo that accelerate invasive potential and chemoresistance. We hypothesized that by creating dielectrophoretic force on polar actin subunits, treatment with TTFields would lead to sustained disruption or prevention of formation of MPM TNTs. TTFields (200 kHz) were applied at 0.5 or 1.0 V/cm to VAMT and MSTO MPM cell lines using the Inovitro system (Novocure). TNT index (average # of TNTs/cell) was determined at 0, 24, 48, and 72 hours of TTFields application. At the 72 hour period, TTFields were discontinued and assessment for recovery of TNT formation was performed after an additional 24 hours. Cell viability was determined by staining with NucGreen 488 dye. We also used time-lapse microscopy with concurrent application of TTFields and the chemotherapeutic agents cisplatin and pemetrexed to analyze effects on TNTs and functional cargo transfer. Application of continuous TTFields at 1.0 V/cm, but not at 0.5 V/cm, suppressed TNT formation by 48.9% in MSTO (p=0.005). This suppression was achieved at the 48-hour time point and was independent of cell proliferation. No significant differences in TNT index were noted for VAMT. Cell viability was consistently above 95% at all time points for both cell lines at the stated frequency and intensities. Cargo transfer rates were lower in experimental groups treated with TTFields and either cisplatin, pemetrexed, or both. Here, we show that treatment with TTFields suppresses formation of TNTs between MSTO cells, but not VAMT, suggesting additional factors that may determine susceptibility to TTFields treatment. Additionally, TTFields treatment of MSTO decreased the function of TNTs in these cells, as demonstrated by lower cargo transfer rates. In sum, these data identify effects of TTFields on TNTs as a novel mechanism for this therapeutic modality. Citation Format: Akshat Sarkari, Sophie Korenfeld, Katherine Ladner, Phillip Wong, Antonia Martinez, Eyal Dor-On, Moshe Giladi, Amrinder Nain, Emil Lou. Tumor treating fields induce cellular and morphologic changes including disruption of intercellular communication networks in malignant pleural mesothelioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2011.
BACKGROUND Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. METHODS We used the convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. RESULTS The U-Net model detected 73.3% of human expert-identified TNTs, counted TNTs and cells, and calculated the TNT-to-cell ratio (TCR). We obtained a precision of 0.88, recall of 0.67, and f-1 score of 0.76 on a test data set. The predicted and true TCRs were not significantly different between the training and test data sets. CONCLUSIONS In summary, we report application of an automated model generated by deep learning and trained to accurately label and detect TNTs and cells imaged in culture. Continued application and refinement of this process will provide a new approach to the analysis of TNTs, which form to connect cancer and other cells. This approach has the potential to enhance the drug screens intended to assess therapeutic efficacy of experimental agents, and to reproducibly assess TNTs as a potential biomarker of response to therapy in cancer.
Intercellular communication is critical for the development of invasive cancers. Multiple forms of intercellular communication have been well characterized, involving diffusible soluble factors or contact-dependent channels for immediately adjacent cells. Over the past 1-2 decades, the emergence of a unique form of F-actin-based cellular protrusion known as tunneling nanotubes (TNTs) has filled the niche of long-range cell-contact dependent intercellular communication that facilitates cell growth, differentiation, and in the case of invasive cancer phenotypes, a more chemoresistant phenotype. The cellular machinery of TNT-mediated transport is an area of active investigation, and microtubules have been implicated in this process as they are in other membranous protrusions. Tumor-Treating Fields (TTFields) therapy is a novel therapeutic strategy in clinical use for patients with advanced cancers, based on the principle of using low-intensity alternating electric fields to disrupt microtubules in cancer cells undergoing mitosis. Other mechanisms of action have also been demonstrated. In this study, we investigated the effects of TTFields on TNTs in malignant pleural mesothelioma (MPM) in vitro and also on the spatial transcriptomic landscape in vivo. We found that applying TTFields at 1.0 V/cm significantly suppressed TNT formation in a biphasic MPM cell line (MSTO-211H), but not in sarcomatoid MPM (VAMT). At these parameters, TTFields significantly reduced cell count in MSTO-211H, but did not significantly alter intercellular transport of mitochondria via intact TNTs. To understand how TTFields may impact expression of genes with known involvement to TNT formation and overall tumor growth, we performed spatial genomic assessment of TTFields-treated tumors from an in vivo animal model of MPM, and detected upregulation of immuno-oncologic biomarkers with simultaneous downregulation of pathways associated with cell hyperproliferation, invasion, and other critical regulators of oncogenic growth. Several molecular classes and pathways coincide with markers that we and others have found to be differentially expressed in cancer cell TNTs, including MPM specifically. In this study, we report novel cellular and molecular effects of TTFields in relation to tumor communication networks enabled by TNTs and related molecular pathways. These results position TNTs as potential therapeutic targets for TTFields-directed cancer treatment strategies; and also identify the ability of TTFields to potentially remodel the tumor microenvironment, thus enhancing response to immunotherapeutic drugs.
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