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- Opstad, IS, et al.
(författare)
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Three-dimensional structured illumination microscopy data of mitochondria and lysosomes in cardiomyoblasts under normal and galactose-adapted conditions
- 2022
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Ingår i: Scientific data. - : Springer Science and Business Media LLC. - 2052-4463. ; 9:1, s. 98-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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- Sekh, AA, et al.
(författare)
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Physics-based machine learning for subcellular segmentation in living cells
- 2021
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Ingår i: NATURE MACHINE INTELLIGENCE. - : Springer Science and Business Media LLC. - 2522-5839. ; 3:12, s. 1071-1080
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Segmenting subcellular structures in living cells from fluorescence microscope images is a ground truth (GT)-deficient problem. The microscopes’ three-dimensional blurring function, finite optical resolution due to light diffraction, finite pixel resolution and the complex morphological manifestations of the structures all contribute to GT-hardness. Unsupervised segmentation approaches are quite inaccurate. Therefore, manual segmentation relying on heuristics and experience remains the preferred approach. However, this process is tedious, given the countless structures present inside a single cell, and generating analytics across a large population of cells or performing advanced artificial intelligence tasks such as tracking are greatly limited. Here we bring modelling and deep learning to a nexus for solving this GT-hard problem, improving both the accuracy and speed of subcellular segmentation. We introduce a simulation-supervision approach empowered by physics-based GT, which presents two advantages. First, the physics-based GT resolves the GT-hardness. Second, computational modelling of all the relevant physical aspects assists the deep learning models in learning to compensate, to a great extent, for the limitations of physics and the instrument. We show extensive results on the segmentation of small vesicles and mitochondria in diverse and independent living- and fixed-cell datasets. We demonstrate the adaptability of the approach across diverse microscopes through transfer learning, and illustrate biologically relevant applications of automated analytics and motion analysis.
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- Strohl, F, et al.
(författare)
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Label-free superior contrast with c-band ultra-violet extinction microscopy
- 2023
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Ingår i: Light, science & applications. - : Springer Science and Business Media LLC. - 2047-7538. ; 12:1, s. 56-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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