Measurement of changes of pH at various intracellular compartments has potential to solve questions concerning the processing of endocytosed material, regulation of the acidification process, and also acidification of vesicles destined for exocytosis. To monitor these events, the nanosized optical pH probes need to provide ratiometric signals in the optically transparent biological window, target to all relevant intracellular compartments, and to facilitate imaging at subcellular resolution without interference from the biological matrix. To meet these criteria we sensitize the surface conjugated pH sensitive indicator via an upconversion process utilizing an energy transfer from the nanoparticle to the indicator. Live cells were imaged with a scanning confocal microscope equipped with a low-energy 980 nm laser excitation, which facilitated high resolution and penetration depth into the specimen, and low phototoxicity needed for long-term imaging. Our upconversion nanoparticle resonance energy transfer based sensor with polyethylenimine-coating provides high colloidal stability, enhanced cellular uptake, and distribution across cellular compartments. This distribution was modulated with membrane integrity perturbing treatment that resulted into total loss of lysosomal compartments and a dramatic pH shift of endosomal compartments. These nanoprobes are well suited for detection of pH changes in in vitro models with high biological background fluorescence and in in vivo applications, e.g., for the bioimaging of small animal models.
The sphingolipids, sphingosine 1-phosphate (S1P) and sphingosylphosphorylcholine (SPC), can induce or inhibit cellular migration. The intermediate filament protein vimentin is an inducer of migration and a marker for epithelial-mesenchymal transition. Given that keratin intermediate filaments are regulated by SPC, with consequences for cell motility, we wanted to determine whether vimentin is also regulated by sphingolipid signalling and whether it is a determinant for sphingolipid-mediated functions. In cancer cells where S1P and SPC inhibited migration, we observed that S1P and SPC induced phosphorylation of vimentin on S71, leading to a corresponding reorganization of vimentin filaments. These effects were sphingolipid-signalling-dependent, because inhibition of either the S1P 2 receptor (also known as S1PR2) or its downstream effector Rho-associated kinase (ROCK, for which there are two isoforms ROCK1 and ROCK2) nullified the sphingolipid-induced effects on vimentin organization and S71 phosphorylation. Furthermore, the anti-migratory effect of S1P and SPC could be prevented by expressing S71-phosphorylation-deficient vimentin. In addition, we demonstrated, by using wild-type and vimentin-knockout mouse embryonic fibroblasts, that the sphingolipid-mediated inhibition of migration is dependent on vimentin. These results imply that this newly discovered sphingolipid-vimentin signalling axis exerts brake-and-throttle functions in the regulation of cell migration.
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The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.
Automated analysis of microscope images is necessitated by the increased need for high-resolution follow up of events in time. Manually finding the right images to be analyzed, or eliminated from data analysis are common day-to-day problems in microscopy research today, and the constantly growing size of image datasets does not help the matter. We propose a simple method and a software tool for sorting images within a dataset, according to their relative quality. We demonstrate the applicability of our method in finding good quality images in a STED microscope sample preparation optimization image dataset. The results are validated by comparisons to subjective opinion scores, as well as five state-of-the-art blind image quality assessment methods. We also show how our method can be applied to eliminate useless out-of-focus images in a High-Content-Screening experiment. We further evaluate the ability of our image quality ranking method to detect out-of-focus images, by extensive simulations, and by comparing its performance against previously published, well-established microscopy autofocus metrics.
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