Autophagy and endocytosis – interconnections and interdependencies

Autophagy and endocytosis are membrane-vesicle-based cellular pathways for degradation and recycling of intracellular and extracellular components, respectively. These pathways have a common endpoint at the lysosome, where their cargo is degraded. In addition, the two pathways intersect at different stages during vesicle formation, fusion and trafficking, and share parts of the molecular machinery. Accumulating evidence shows that autophagy is dependent upon endocytosis and vice versa. The emerging joint network of autophagy and endocytosis is of vital importance for cellular metabolism and signaling, and thus also highly relevant in disease settings. In this Review, we will discuss examples of how the autophagy machinery impacts on endocytosis and cell signaling, and highlight how endocytosis regulates the different steps in autophagy in mammalian cells. Finally, we will focus on the interplay of these pathways in the quality control of their common endpoint, the lysosome

Use of Peptide Arrays for Identification and Characterization of LIR Motifs

This is a post-peer-review, pre-copyedit version of an article published in Methods in Molecular Biology. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-1-4939-8873-0_8The mammalian ATG8 proteins (LC3A-C/GABARAP, GABARAPL1, and GABARAPL2) are small ubiquitin-like proteins critically involved in macroautophagy. Their processed C-termini are posttranslationally conjugated to a phosphatidylethanolamine moiety, enabling their insertion into the lipid bilayers of both the inner and outer membranes of the forming autophagosomes. The ATG8s bind a diverse selection of proteins including cargo receptors for selective autophagy, members of the core autophagy machinery, and other proteins involved in formation, transport, and maturation (fusion to lysosomes) of autophagosomes. Protein binding to the ATG8s is in most cases mediated by short, conserved sequence motifs known as LC3-interacting regions (LIRs). Here, we present a protocol for identifying putative LIR motifs in a whole protein sequence using peptide arrays generated by SPOT synthesis on nitrocellulose membranes. The use of two-dimensional peptide arrays allows for further identification of specific residues critical for LIR binding

Mitochondrial dynamics and quantification of mitochondria-derived vesicles in cardiomyoblasts using structured illumination microscopy

Mitochondria are essential energy-providing organelles of particular importance in energy-demanding tissue such as the heart. The production of mitochondria-derived vesicles (MDVs) is a cellular mechanism by which cells ensure a healthy pool of mitochondria. These vesicles are small and fast-moving objects not easily captured by imaging. In this work, we have tested the ability of the optical super-resolution technique 3DSIM to capture high-resolution images of MDVs. We optimized the imaging conditions both for high-speed video microscopy and fixed-cell imaging and analysis. From the 3DSIM videos, we observed an abundance of MDVs and many dynamic mitochondrial tubules. The density of MDVs in cells was compared for cells under normal growth conditions and cells during metabolic perturbation. Our results indicate a higher abundance of MDVs in H9c2 cells during glucose deprivation compared with cells under normal growth conditions. Furthermore, the results reveal a large untapped potential of 3DSIM in MDV research

Structured Illumination Microscopy of Biological Structures

Abstract of presentation held at Norwegian Electro-Optics Meeting, Henningsvær, Norway, 2-4 May 2018.Resolution in optical microscopy has long been limited to the Abbe diffraction limit, i.e. about 250 nm laterally for visible wavelengths on a very good microscope. In the last two decades several techniques have been devised to circumvent this limit: an achievement which was recognized with the 2014 Nobel Prize in Chemistry. Structured Illumination Microscopy (SIM) was the first of these techniques to become commercially available, and continues to be the only super-resolution technique which is practically compatible with living cells, while also requiring the least modification to conventional sample-labeling protocols. SIM utilizes Moiré patterns and frequency shifting to improve resolution 2X in each dimension, as well as significantly improve the contrast for the mid-range spatial frequencies. These advances have unlocked a new realm of biological inquiry: the combination of the high biochemical specificity of fluorescent probes with resolution previously only possible with electron microscopy now enables the direct study of sub-organelle colocalization and the dynamics of living cells. Here, we will present both the basics of the SIM technique as well as a sampling of its biological applications from our lab at UiT, including sub-mitochondrial localization and dynamics, sieve-like nanostructures in liver cells, and large-scale visualization of super-resolved cardiac tissue sections, as well as discuss the practical limitations and implications of this work

Virtual labeling of mitochondria in living cells using correlative imaging and physics-guided deep learning

Mitochondria play a crucial role in cellular metabolism. This paper presents a novel method to visualize mitochondria in living cells without the use of fluorescent markers. We propose a physics-guided deep learning approach for obtaining virtually labeled micrographs of mitochondria from bright-field images. We integrate a microscope’s point spread function in the learning of an adversarial neural network for improving virtual labeling. We show results (average Pearson correlation 0.86) significantly better than what was achieved by state-of-the-art (0.71) for virtual labeling of mitochondria. We also provide new insights into the virtual labeling problem and suggest additional metrics for quality assessment. The results show that our virtual labeling approach is a powerful way of segmenting and tracking individual mitochondria in bright-field images, results previously achievable only for fluorescently labeled mitochondria

High-resolution visualization and assessment of basal and OXPHOS-induced mitophagy in H9c2 cardiomyoblasts

Mitochondria are susceptible to damage resulting from their activity as energy providers. Damaged mitochondria can cause harm to the cell and thus mitochondria are subjected to elaborate quality-control mechanisms including elimination via lysosomal degradation in a process termed mitophagy. Basal mitophagy is a house-keeping mechanism fine-tuning the number of mitochondria according to the metabolic state of the cell. However, the molecular mechanisms underlying basal mitophagy remain largely elusive. In this study, we visualized and assessed the level of mitophagy in H9c2 cardiomyoblasts at basal conditions and after OXPHOS induction by galactose adaptation. We used cells with a stable expression of a pH-sensitive fluorescent mitochondrial reporter and applied state-of-the-art imaging techniques and image analysis. Our data showed a significant increase in acidic mitochondria after galactose adaptation. Using a machine-learning approach we also demonstrated increased mitochondrial fragmentation by OXPHOS induction. Furthermore, super-resolution microscopy of live cells enabled capturing of mitochondrial fragments within lysosomes as well as dynamic transfer of mitochondrial contents to lysosomes. Applying correlative light and electron microscopy we revealed the ultrastructure of the acidic mitochondria confirming their proximity to the mitochondrial network, ER and lysosomes. Finally, exploiting siRNA knockdown strategy combined with flux perturbation with lysosomal inhibitors, we demonstrated the importance of both canonical as well as non-canonical autophagy mediators in lysosomal degradation of mitochondria after OXPHOS induction. Taken together, our high-resolution imaging approaches applied on H9c2 cells provide novel insights on mitophagy during physiologically relevant conditions. The implication of redundant underlying mechanisms highlights the fundamental importance of mitophagy

Multifocus microscopy with optical sectioning and high axial resolution

Multifocus microscopy enables recording of entire volumes in a single camera 11 exposure. In dense samples, multifocus microscopy is severely hampered by background haze. 12 Here, we introduce a scalable multifocus method that incorporates optical sectioning and offers 13 improved axial resolution capabilities. In our method, a dithered oblique light-sheet scans the 14 sample volume during a single exposure, while fluorescence from each illuminated plane in the 15 sample is mapped onto a line on the camera with a multifocus optical element. A synchronized 16 rolling shutter readout realizes optical sectioning. We describe the technique theoretically and 17 verify its optical sectioning and resolution improvement capabilities. We demonstrate a 18 prototype system with a multifocus beam splitter cascade and record monolayers of endothelial 19 cells at 35 volumes per second. We furthermore image uncleared engineered human heart tissue 20 and visualize the distribution of mitochondria at high axial resolution. Our method manages to 21 capture sub-diffraction sized mitochondria-derived vesicles up to 30 µm deep into the tissue

Multifocus microscopy with optically sectioned axial superresolution

Multifocus microscopy enables recording of entire volumes in a single camera exposure. In dense samples, multifocus microscopy is severely hampered by background haze. Here, we introduce a scalable multifocus method that incorporates optical sectioning and offers axial superresolution capabilities. In our method, a dithered oblique light-sheet scans the sample volume during a single exposure, while generated fluorescence is linearised onto the camera with a multifocus optical element. A synchronised rolling shutter readout realised optical sectioning. We describe the technique theoretically and verify its optical sectioning and superresolution capabilities. We demonstrate a prototype system with a multifocus beam splitter cascade and record monolayers of endothelial cells at 35 volumes per second. We furthermore image uncleared engineered human heart tissue and visualise the distribution of mitochondria at axial superresolution. Our method manages to capture sub-diffraction sized mitochondria-derived vesicles up to 30 um deep into the tissue

The ability to form full-length intron RNA circles is a general property of nuclear group I introns

In addition to splicing, group I intron RNA is capable of an alternative two-step processing pathway that results in the formation of full-length intron circular RNA. The circularization pathway is initiated by hydrolytic cleavage at the 3′ splice site and followed by a transesterification reaction in which the intron terminal guanosine attacks the 5′ splice site presented in a structure analogous to that of the first step of splicing. The products of the reactions are full-length circular intron and unligated exons. For this reason, the circularization reaction is to the benefit of the intron at the expense of the host. The circularization pathway has distinct structural requirements that differ from those of splicing and appears to be specifically suppressed in vivo. The ability to form full-length circles is found in all types of nuclear group I introns, including those from the Tetrahymena ribosomal DNA. The biological function of the full-length circles is not known, but the fact that the circles contain the entire genetic information of the intron suggests a role in intron mobility

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

The quantitative analysis of subcellular organelles such as mitochondria in cell fluorescence microscopy images is a demanding task because of the inherent challenges in the segmentation of these small and morphologically diverse structures. In this article, we demonstrate the use of a machine learning-aided segmentation and analysis pipeline for the quantification of mitochondrial morphology in fluorescence microscopy images of fixed cells. The deep learning-based segmentation tool is trained on simulated images and eliminates the requirement for ground truth annotations for supervised deep learning. We demonstrate the utility of this tool on fluorescence microscopy images of fixed cardiomyoblasts with a stable expression of fluorescent mitochondria markers and employ specific cell culture conditions to induce changes in the mitochondrial morphology