Siri Andresen

• Andresen, Siri; Outa, Amani al; Formica, Miriam; Enserink, Jorrit & Knævelsrud, Helene (2025). Improved detection of lipidated Atg8a by immunoblotting in Drosophila melanogaster cells and tissues enables precise investigation of Atg8a flux and its termination. Autophagy. ISSN 1554-8627. 21(11), p. 2497–2517. doi: 10.1080/15548627.2025.2508551. Full text in Research Archive Show summary

  Macroautophagy/autophagy is an essential intracellular catabolic process for maintaining cellularhomeostasis. In Drosophila melanogaster, Atg8a lipidation serves as a key marker for autophagy, yettraditional methods often fail to effectively detect its lipidated state. To overcome this limitation, wedeveloped a refined approach that employs N-ethylmaleimide (NEM) to inhibit Atg4, thereby pre-serving Atg8a lipidation during sample preparation both in vitro and in vivo. We determined theoptimal concentration of the autophagic inhibitors bafilomycin A1 (BafA1) and chloroquine (CQ)required for inhibition of autolysosomal degradation. Furthermore, we investigated the effects ofprolonged nutrient deprivation on autophagic flux and TORC1 signaling. Our findings not onlyvalidate the effectiveness of this new approach to monitor lipidation of Atg8a but also provideinsights into selection of autolysosomal inhibitors and nutrient-dependent regulatory roles of TORC1in Drosophila.

• Presacan, Oriana; Mesa, María Hernández; Aldea, Alexandru C.; Andresen, Siri; Outa, Amani al & Johannessen, Julie Aarmo [Show all 9 contributors for this article] (2025). Explainable AI to unveil cellular autophagy dynamics. PLOS ONE. 20(9). doi: 10.1371/journal.pone.0331045. Full text in Research Archive Show summary

  Autophagy is a fundamental intracellular renovation process vital for maintaining cellular homeostasis through the degradation and recycling of damaged components. It is implicated in numerous pathological conditions, including cancer and neurodegenerative diseases. However, its dynamic nature and complexity pose challenges for manual analysis. In this study, we present a computational pipeline that leverages advanced deep learning models to automate the analysis of autophagic processes in 6,240 fluorescence microscopy images from the CELLULAR dataset. Our framework integrates object detection, cell segmentation, classification by autophagic state, cellular tracking, and explainability methods for interpretability. We achieved optimal results using YOLOv8 for object detection with a mAP50 of 0.80, U-Net++ for segmentation with an IoU of 0.82, and a vision transformer for classification with an accuracy of 0.86. To track cells, we developed a custom algorithm capable of handling complex scenarios such as cell division and morphological changes, all without requiring annotated tracking data. To enhance transparency, we employed explainability techniques based on class activation mappings to analyze model decision-making processes and validate classification outcomes, complemented by t-SNE visualizations for deeper insights into the data. Collaboration with biology experts validated our findings, highlighting the pipeline’s potential to advance autophagy research. This study demonstrates the potential of deep learning and explainable AI to streamline biomedical research, reduce manual effort, and uncover key autophagy dynamics.

• Outa, Amani al; Hicks, Steven; Thambawita, Vajira L B; Andresen, Siri; Enserink, Jorrit Martijn & Halvorsen, Pål [Show all 8 contributors for this article] (2023). CELLULAR, A Cell Autophagy Imaging Dataset. Scientific Data. doi: 10.1038/s41597-023-02687-x. Full text in Research Archive Show summary

  Cells in living organisms are dynamic compartments that continuously respond to changes in their environment to maintain physiological homeostasis. While basal autophagy exists in cells to aid in the regular turnover of intracellular material, autophagy is also a critical cellular response to stress, such as nutritional depletion. Conversely, the deregulation of autophagy is linked to several diseases, such as cancer, and hence, autophagy constitutes a potential therapeutic target. Image analysis to follow autophagy in cells, especially on high-content screens, has proven to be a bottleneck. Machine learning (ML) algorithms have recently emerged as crucial in analyzing images to efficiently extract information, thus contributing to a better understanding of the questions at hand. This paper presents CELLULAR, an open dataset consisting of images of cells expressing the autophagy reporter mRFP-EGFP-Atg8a with cell-specific segmentation masks. Each cell is annotated into either basal autophagy, activated autophagy, or unknown. Furthermore, we introduce some preliminary experiments using the dataset that can be used as a baseline for future research.

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