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Principles Of Fluorescence Spectroscopy

: The unpolarized absorption and circular dichroism spectra of the fundamental vibrational transitions of the chiral molecule, 4-methyl-2-oxetanone, are calculated ab initio. Harmonic force fields are obtained using Density Functional Theory (DFT), MP2, and SCF methodologies and a 5S4P2D/3S2P (TZ2P) basis set. DFT calculations use the Local Spin Density Approximation (LSDA), BLYP, and Becke3LYP (B3LYP) density functionals. Mid-IR spectra predicted using LSDA, BLYP, and B3LYP force fields are of significantly different quality, the B3LYP force field yielding spectra in clearly superior, and overall excellent, agreement with experiment. The MP2 force field yields spectra in slightly worse agreement with experiment than the B3LYP force field. The SCF force field yields spectra in poor agreement with experiment.The basis set dependence of B3LYP force fields is also explored: the 6-31G* and TZ2P basis sets give very similar results while the 3-21G basis set yields spectra in substantially worse agreements with experiment. jg

Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields

Lipid Rafts: Elusive or Illusive?

Cellular plasma membranes are laterally heterogeneous, featuring a variety of distinct subcompartments that differ in their biophysical properties and composition. A large number of studies have focused on understanding the basis for this heterogeneity and its physiological relevance. The membrane raft hypothesis formalized a physicochemical principle for a subtype of such lateral membrane heterogeneity, in which the preferential associations between cholesterol and saturated lipids drive the formation of relatively packed (or ordered) membrane domains that selectively recruit certain lipids and proteins. Recent studies have yielded new insights into this mechanism and its relevance in vivo, owing primarily to the development of improved biochemical and biophysical technologies.

The mystery of membrane organization: composition, regulation and roles of lipid rafts

Endocytic recycling is coordinated with endocytic uptake to control the composition of the plasma membrane. Although much of our understanding of endocytic recycling has come from studies on the transferrin receptor, a protein internalized through clathrin-dependent endocytosis, increased interest in clathrin-independent endocytosis has led to the discovery of new endocytic recycling systems. Recent insights into the regulatory mechanisms that control endocytic recycling have focused on recycling through tubular carriers and the return to the cell surface of cargo that enters cells through clathrin-independent mechanisms. Recent work emphasizes the importance of regulated recycling in such diverse processes as cytokinesis, cell adhesion and morphogenesis, cell fusion, and learning and memory.

Pathways and mechanisms of endocytic recycling.

Late endosomes and lysosomes (LE/LYSs) play a central role in trafficking of endocytic cargo, secretion of exosomes, and hydrolysis of ingested proteins and lipids. Failure in such processes can lead to lysosomal storage disorders in which a particular metabolite accumulates within LE/LYSs. Analysis of endocytic trafficking relies heavily on quantitative fluorescence microscopy, but evaluation of the huge image data sets is challenging and demands computer-assisted statistical tools. Here, we describe how to use SpatTrack ( www.sdu.dk/bmb/spattrack ), an imaging toolbox, which we developed for quantification of the distribution and dynamics of endo-lysosomal cargo from fluorescence images of living cells. First, we explain how to analyze experimental images of endocytic processes in Niemann Pick C2 disease fibroblasts using SpatTrack. We demonstrate how to quantify the location of the sterol-binding protein NPC2 in LE/LYSs relative to cholesterol -rich lysosomal storage organelles (LSOs) stained with filipin. Second, we show how to simulate realistic vesicle patterns in the cell geometry using Markov Chain Monte Carlo and suitable inter-vesicle and cell-vesicle interaction potentials. Finally, we use such synthetic vesicle patterns as "ground truth" for validation of two-channel analysis tools in SpatTrack, revealing their high reliability. An improved version of SpatTrack for microscopy-based quantification of cargo transport through the endo-lysosomal system accompanies this protocol.

Quantitative Co-Localization and Pattern Analysis of Endo-Lysosomal Cargo in Subcellular Image Cytometry and Validation on Synthetic Image Sets

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ejoc.202300238

Facile Suzuki Coupling Strategy toward New Nile Red Derivatives with Improved Two‐Photon Brightness

Abstract Lipophagy is a form of autophagy by which lipid droplets (LDs) become digested to provide nutrients as a cellular response to starvation. Lipophagy is often studied in yeast, Saccharomyces cerevisiae , in which LDs become internalized into the vacuole. There is a lack of tools to quantitatively assess lipophagy in intact cells with high resolution and throughput. Here, we combine soft X-ray tomography (SXT) with fluorescence microscopy and use a deep learning computational approach to visualize and quantify lipophagy in yeast. We focus on yeast homologs of mammalian Niemann Pick type C proteins, whose dysfunction leads to Niemann Pick type C disease in humans, i.e., NPC1 (named NCR1 in yeast) and NPC2. We developed a convolutional neural network (CNN) model which classifies ring-shaped versus lipid-filled or fragmented vacuoles containing ingested LDs in fluorescence images from wild-type yeast and from cells lacking NCR1 ( Δncr1 cells) or NPC2 ( Δnpc2 cells). Using a second CNN model, which performs automated segmentation of LDs and vacuoles from high-resolution reconstructions of X-ray tomograms, we can obtain 3D renderings of LDs inside and outside of the vacuole in a fully automated manner and additionally measure droplet volume, number, and distribution. We find that cells lacking functional NPC proteins can ingest LDs into vacuoles normally but show compromised degradation of LDs and accumulation of lipid vesicles inside vacuoles. This phenotype is most severe in Δnpc2 cells. Our new method is versatile and allows for automated high-throughput 3D visualization and quantification of lipophagy in intact cells. 

https://www.biorxiv.org/content/biorxiv/early/2023/02/27/2023.02.27.530171.full.pdf

Automated quantification of lipophagy in Saccharomyces cerevisiae from fluorescence and cryo-soft X-ray microscopy data using deep learning

Background: Image segmentation in fluorescence microscopy is often based on spectral separation of fluorescent probes (color-based segmentation) or on significant intensity differences in individual image regions (intensity-based segmentation). These approaches fail, if dye fluorescence shows large spectral overlap with other employed probes or with strong cellular autofluorescence. Results: Here, a novel model-free approach is presented which determines bleaching kinetics based on dynamic mode decomposition (DMD) and uses the inferred photobleaching kinetics to distinguish different probes or dye molecules from autofluorescence. DMD is a data-driven computational method for detecting and quantifying dynamic events in complex spatiotemporal data. Here, DMD is used to determine photobleaching characteristics of a fluorescent sterol probe, dehydroergosterol (DHE), compared to that of cellular autofluorescence in the nematode Caenorhabditis elegans. It is shown that decomposition of those dynamic modes allows for precise image segmentation, thereby separating probe from autofluorescence without invoking a particular model for the bleaching process. In a second application, DMD of dye-specific photobleaching is used to separate two green-fluorescent dyes, an NBD-tagged sphingolipid and Alexa488-transferrin, thereby assigning them to different cellular compartments. Conclusions: Data-based decomposition of dynamic modes can be employed to analyze spatially varying photobleaching of fluorescent probes in cells and tissues for image segmentation, discrimination of probe from autofluorescence and image denoising. The new method should find wide application in analysis of dynamic fluorescence imaging data. 

Daniel Wüstner

Papers

Quantitative Co-Localization and Pattern Analysis of Endo-Lysosomal Cargo in Subcellular Image Cytometry and Validation on Synthetic Image Sets

Facile Suzuki Coupling Strategy toward New Nile Red Derivatives with Improved Two‐Photon Brightness

Automated quantification of lipophagy in Saccharomyces cerevisiae from fluorescence and cryo-soft X-ray microscopy data using deep learning

Image segmentation and separation of spectrally similar dyes in fluorescence microscopy by dynamic mode decomposition of photobleaching kinetics