Fluorescence microscopy and correlative brightfield videos of mitochondria and vesicles in H9c2 cardiomyoblasts.

This paper presents data acquired to study the dynamics and interactions of mitochondria and subcellular vesicles in living cardiomyoblasts. The study was motivated by the importance of mitochondrial quality control and turnover in cardiovascular health. Although fluorescence microscopy is an invaluable tool, it presents several limitations. Correlative fluorescence and brightfield images (label-free) were therefore acquired with the purpose of achieving virtual labelling via machine learning. In comparison with the fluorescence images of mitochondria, the brightfield images show vesicles and subcellular components, providing additional insights about sub-cellular components. A large part of the data contains correlative fluorescence images of lysosomes and/or endosomes over a duration of up to 400 timepoints (>30 min). The data can be reused for biological inferences about mitochondrial and vesicular morphology, dynamics, and interactions. Furthermore, virtual labelling of mitochondria or subcellular vesicles can be achieved using these datasets. Finally, the data can inspire new imaging experiments for cellular investigations or computational developments. The data is available through two large, open datasets on DataverseNO.

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.Abbreviations: ATG: autophagy related; ATG7: autophagy related 7; ATP: adenosine triphosphate; BafA1: bafilomycin A1; CLEM: correlative light and electron microscopy; EGFP: enhanced green fluorescent protein; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; OXPHOS: oxidative phosphorylation; PepA: pepstatin A; PLA: proximity ligation assay; PRKN: parkin RBR E3 ubiquitin protein ligase; RAB5A: RAB5A, member RAS oncogene family; RAB7A: RAB7A, member RAS oncogene family; RAB9A: RAB9A, member RAS oncogene family; ROS: reactive oxygen species; SIM: structured illumination microscopy; siRNA: short interfering RNA; SYNJ2BP: synaptojanin 2 binding protein; TEM: transmission electron microscopy; TOMM20: translocase of outer mitochondrial membrane 20; ULK1: unc-51 like kinase 1.

Label-free superior contrast with c-band ultra-violet extinction microscopy.

In 1934, Frits Zernike demonstrated that it is possible to exploit the sample's refractive index to obtain superior contrast images of biological cells. The refractive index contrast of a cell surrounded by media yields a change in the phase and intensity of the transmitted light wave. This change can be due to either scattering or absorption caused by the sample. Most cells are transparent at visible wavelengths, which means the imaginary component of their complex refractive index, also known as extinction coefficient k, is close to zero. Here, we explore the use of c-band ultra-violet (UVC) light for high-contrast high-resolution label-free microscopy, as k is naturally substantially higher in the UVC than at visible wavelengths. Using differential phase contrast illumination and associated processing, we achieve a 7- to 300-fold improvement in contrast compared to visible-wavelength and UVA differential interference contrast microscopy or holotomography, and quantify the extinction coefficient distribution within liver sinusoidal endothelial cells. With a resolution down to 215 nm, we are, for the first time in a far-field label-free method, able to image individual fenestrations within their sieve plates which normally requires electron or fluorescence superresolution microscopy. UVC illumination also matches the excitation peak of intrinsically fluorescent proteins and amino acids and thus allows us to utilize autofluorescence as an independent imaging modality on the same setup.

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.

Multidisciplinary biophotonics, open science, and plug & pray deep learning?

Due to its great creative potential for innovation and scientific discovery, inter- and multidisciplinary collaboration is being increasingly encouraged by institutions and funding agencies. The increased opportunities in the multidisciplinary arena also come with significant challenges like the added experimental, analytical and logistical complexity, blended with a high likelihood of miscommunications. When is multidisciplinarity worth the effort, and how can we be better collaborators? With a focus on cross-disciplinary collaborative work to answer burning questions in biology and biomedicine, this paper discusses both large challenges and opportunities with multidisciplinary biophotonics, how we can better navigate the arena of big data and artificial intelligence combined with open, reproducible science and biological discoveries.

Three-dimensional structured illumination microscopy data of mitochondria and lysosomes in cardiomyoblasts under normal and galactose-adapted conditions.

This three-dimensional structured illumination microscopy (3DSIM) dataset was generated to highlight the suitability of 3DSIM to investigate mitochondria-derived vesicles (MDVs) in H9c2 cardiomyoblasts in living or fixed cells. MDVs act as a mitochondria quality control mechanism. The cells were stably expressing the tandem-tag eGFP-mCherry-OMP25-TM (outer mitochondrial membrane) which can be used as a sensor for acidity. A part of the dataset is showing correlative imaging of lysosomes labeled using LysoTracker in fixed and living cells. The cells were cultivated in either normal or glucose-deprived medium containing galactose. The resulting 3DSIM data were of high quality and can be used to undertake a variety of studies. Interestingly, many dynamic tubules derived from mitochondria are visible in the 3DSIM videos under both glucose and galactose-adapted growth conditions. As the raw 3DSIM data, optical parameters, and reconstructed 3DSIM images are provided, the data is especially suitable for use in the development of SIM reconstruction algorithms, bioimage analysis methods, and for biological studies of mitochondria.

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.

Fluorescence fluctuation-based super-resolution microscopy using multimodal waveguided illumination.

Photonic chip-based total internal reflection fluorescence microscopy (c-TIRFM) is an emerging technology enabling a large TIRF excitation area decoupled from the detection objective. Additionally, due to the inherent multimodal nature of wide waveguides, it is a convenient platform for introducing temporal fluctuations in the illumination pattern. The fluorescence fluctuation-based nanoscopy technique multiple signal classification algorithm (MUSICAL) does not assume stochastic independence of the emitter emission and can therefore exploit fluctuations arising from other sources, as such multimodal illumination patterns. In this work, we demonstrate and verify the utilization of fluctuations in the illumination for super-resolution imaging using MUSICAL on actin in salmon keratocytes. The resolution improvement was measured to be 2.2-3.6-fold compared to the corresponding conventional images.

Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning.

Image denoising or artefact removal using deep learning is possible in the availability of supervised training dataset acquired in real experiments or synthesized using known noise models. Neither of the conditions can be fulfilled for nanoscopy (super-resolution optical microscopy) images that are generated from microscopy videos through statistical analysis techniques. Due to several physical constraints, a supervised dataset cannot be measured. Further, the non-linear spatio-temporal mixing of data and valuable statistics of fluctuations from fluorescent molecules that compete with noise statistics. Therefore, noise or artefact models in nanoscopy images cannot be explicitly learned. Here, we propose a robust and versatile simulation-supervised training approach of deep learning auto-encoder architectures for the highly challenging nanoscopy images of sub-cellular structures inside biological samples. We show the proof of concept for one nanoscopy method and investigate the scope of generalizability across structures, and nanoscopy algorithms not included during simulation-supervised training. We also investigate a variety of loss functions and learning models and discuss the limitation of existing performance metrics for nanoscopy images. We generate valuable insights for this highly challenging and unsolved problem in nanoscopy, and set the foundation for the application of deep learning problems in nanoscopy for life sciences.

Soft thresholding schemes for multiple signal classification algorithm.

Multiple signal classification algorithm (MUSICAL) exploits temporal fluctuations in fluorescence intensity to perform super-resolution microscopy by computing the value of a super-resolving indicator function across a fine sample grid. A key step in the algorithm is the separation of the measurements into signal and noise subspaces, based on a single user-specified parameter called the threshold. The resulting image is strongly sensitive to this parameter and the subjectivity arising from multiple practical factors makes it difficult to determine the right rule of selection. We address this issue by proposing soft thresholding schemes derived from a new generalized framework for indicator function design. We show that the new schemes significantly alleviate the subjectivity and sensitivity of hard thresholding while retaining the super-resolution ability. We also evaluate the trade-off between resolution and contrast and the out-of-focus light rejection using the various indicator functions. Through this, we create significant new insights into the use and further optimization of MUSICAL for a wide range of practical scenarios.

MusiJ: an ImageJ plugin for video nanoscopy.

We present an open-source implementation of the fluctuation-based nanoscopy method MUSICAL for ImageJ. This implementation improves the algorithm's computational efficiency and takes advantage of multi-threading to provide orders of magnitude faster reconstructions than the original MATLAB implementation. In addition, the plugin is capable of generating super-resolution videos from large stacks of time-lapse images via an interleaved reconstruction, thus enabling easy-to-use multi-color super-resolution imaging of dynamic systems.

A waveguide imaging platform for live-cell TIRF imaging of neurons over large fields of view.

Large fields of view (FOVs) in total internal reflection fluorescence microscopy (TIRFM) via waveguides have been shown to be highly beneficial for single molecule localisation microscopy on fixed cells [1,2] and have also been demonstrated for short-term live-imaging of robust cell types [3-5], but not yet for delicate primary neurons nor over extended periods of time. Here, we present a waveguide-based TIRFM set-up for live-cell imaging of demanding samples. Using the developed microscope, referred to as the ChipScope, we demonstrate successful culturing and imaging of fibroblasts, primary rat hippocampal neurons and axons of Xenopus retinal ganglion cells (RGCs). The high contrast and gentle illumination mode provided by TIRFM coupled with the exceptionally large excitation areas and superior illumination homogeneity offered by photonic waveguides have potential for a wide application span in neuroscience applications.

Super-condenser enables labelfree nanoscopy.

Labelfree nanoscopy encompasses optical imaging with resolution in the 100 nm range using visible wavelengths. Here, we present a labelfree nanoscopy method that combines coherent imaging techniques with waveguide microscopy to realize a super-condenser featuring maximally inclined coherent darkfield illumination with artificially stretched wave vectors due to large refractive indices of the employed Si3N4 waveguide material. We produce the required coherent plane wave illumination for Fourier ptychography over imaging areas 400 µm2 in size via adiabatically tapered single-mode waveguides and tackle the overlap constraints of the Fourier ptychography phase retrieval algorithm two-fold: firstly, the directionality of the illumination wave vector is changed sequentially via a multiplexed input structure of the waveguide chip layout and secondly, the wave vector modulus is shortend via step-wise increases of the illumination light wavelength over the visible spectrum. We test the method in simulations and in experiments and provide details on the underlying image formation theory as well as the reconstruction algorithm. While the generated Fourier ptychography reconstructions are found to be prone to image artefacts, an alternative coherent imaging method, rotating coherent scattering microscopy (ROCS), is found to be more robust against artefacts but with less achievable resolution.

Live-cell imaging of human spermatozoa using structured illumination microscopy.

Structural details of spermatozoa are interesting from the perspectives of fundamental biology and growing reproductive health problems. Studies of nanostructural details of these extremely motile cells have been limited to fixed cells, largely using electron microscopy. Here we provide the protocols for and demonstrate live-cell multi-color super-resolution imaging of human spermatozoa using structured illumination microscopy (SIM). By using patches of agarose for immobilization, we achieved four-channel 3D SIM imaging of the plasma membrane, nucleus, mitochondria and microtubulin in the same living sperm cells. We expect that high-resolution imaging of living spermatozoa will be implemented for research on fundamental cellular mechanisms together with morphological aberrations involved in male infertility for a future improved cell selection process in in vitro fertilization treatments.