Steffen Härtel

In fluorescence microscopy imaging, the segmentation of adjacent cell membranes within cell aggregates, multicellular samples, tissue, organs, or whole organisms remains a challenging task. The lipid bilayer is a very thin membrane when compared to the wavelength of photons in the visual spectra. Fluorescent molecules or proteins used for labelling membranes provide a limited signal intensity, and light scattering in combination with sample dynamics during in vivo imaging lead to poor or ambivalent signal patterns that hinder precise localisation of the membrane sheets. In the proximity of cells, membranes approach and distance each other. Here, the presence of membrane protrusions such as blebs; filopodia and lamellipodia; microvilli; or membrane vesicle trafficking, lead to a plurality of signal patterns, and the accurate localisation of two adjacent membranes becomes difficult. Several computational methods for membrane segmentation have been introduced. However, few of them specifically consider the accurate detection of adjacent membranes. In this article we present ALPACA (ALgorithm for Piecewise Adjacent Contour Adjustment), a novel method based on 2D piecewise parametric active contours that allows: (i) a definition of proximity for adjacent contours, (ii) a precise detection of adjacent, non‐adjacent, and overlapping contour sections, (iii) the definition of a polyline for an optimised shared contour within adjacent sections, and (iv) a solution for connecting adjacent and non‐adjacent sections under the constraint of preserving the inherent cell morphology. We show that ALPACA leads to a precise quantification of adjacent and non‐adjacent membrane zones in regular hexagons and live image sequences of cells of the parapineal organ during zebrafish embryo development. The algorithm detects and corrects adjacent, non‐adjacent, and overlapping contour sections within a selected adjacency distance d, calculates shared contour sections for neighbouring cells with minimum alterations of the contour characteristics, and presents piecewise active contour solutions, preserving the contour shape and the overall cell morphology. ALPACA quantifies adjacent contours and can improve the meshing of 3D surfaces, the determination of forces, or tracking of contours in combination with previously published algorithms. We discuss pitfalls, strengths, and limits of our approach, and present a guideline to take the best decision for varying experimental conditions for in vivo microscopy. This article is protected by copyright. All rights reserved

Optical Flow (OF) approaches for motion estimation calculate vector fields for the apparent velocities of objects in image sequences. In 1981 Horn and Schunck (HS) introduced two basic assumptions: 'brightness value constancy' and 'smooth variation' to estimate a smooth OF field over the entire image -global approach-. In parallel, Lucas and Kanade (LK) assumed constant motion patterns for image patches, estimating piecewise-homogeneous OF fields -local approach-. Several variations of these approaches exist today. Here we present the combined local-global (CLG) approach by Bruhn et al. which encompasses properties of HS-OF and LK-OF, aiming to improve the OF accuracy for small-scale variations, while delivering the HS-OF dense and smooth fields. A multiscale implementation is provided for 2D images, together with two numerical solvers: Successive Over-Relaxation and the faster Pointwise-Coupled Gauss-Seidel by Bruhn et al.. The algorithm works on gray-scale (single channel) images, with color images being converted prior to the OF computation.

Mounting evidence indicates that iron accumulation impairs brain function. We have reported previously that addition of sub-lethal concentrations of iron to primary hippocampal neurons produces Ca2+-signals and promotes cytoplasmic generation of reactive oxygen species. These Ca2+ signals, which emerge within seconds after iron addition, arise mostly from Ca2+ release through the redox-sensitive ryanodine receptor (RyR) channels present in the endoplasmic reticulum. We have reported also that addition of synaptotoxic amyloid-β oligomers to primary hippocampal neurons stimulates RyR-mediated Ca2+ release, generating long-lasting Ca2+ signals that activate Ca2+-sensitive cellular effectors and promote the disruption of the mitochondrial network. Here, we describe that 24 h incubation of primary hippocampal neurons with iron enhanced agonist-induced RyR-mediated Ca2+ release and promoted mitochondrial network fragmentation in 43% of neurons, a response significantly prevented by RyR inhibition and by the antioxidant agent N-acetyl-L-cysteine. Stimulation of RyR-mediated Ca2+ release by a RyR agonist promoted mitochondrial Ca2+ uptake in control neurons and in iron-treated neurons that displayed non-fragmented mitochondria, but not in neurons with fragmented mitochondria. Yet, the global cytoplasmic Ca2+ increase induced by the Ca2+ ionophore ionomycin prompted significant mitochondrial Ca2+ uptake in neurons with fragmented mitochondria, indicating that fragmentation did not prevent mitochondrial Ca2+ uptake but presumably decreased the functional coupling between RyR-mediated Ca2+ release and the mitochondrial Ca2+ uniporter. Taken together, our results indicate that stimulation of redox-sensitive RyR-mediated Ca2+-release by iron causes significant neuronal mitochondrial fragmentation, which presumably contributes to the impairment of neuronal function produced by iron accumulation.

Cell migration is a complex biological process that involves changes in shape and organization at the sub-cellular, cellular, and supra-cellular levels. Individual and collective cell migration can be assessed in vitro and in vivo starting from the flagellar driven movement of single sperm cells or bacteria, bacterial gliding and swarming, and amoeboid movement to the orchestrated movement of collective cell migration. One key technology to access migration phenomena is the combination of optical microscopy with image processing algorithms. This approach resolves simple motion estimation (e.g. preferred direction of migrating cells or path characteristics), but can also reveal more complex descriptors (e.g. protrusions or cellular deformations). In order to ensure an accurate quantification, the phenomena under study, their complexity, and the required level of description need to be addressed by an adequate experimental setup and processing pipeline. Here, we review typical workflows for processing starting with image acquisition, restoration (noise and artifact removal, signal enhancement), registration, analysis (object detection, segmentation and characterization) and interpretation (high level understanding). Image processing approaches for quantitative description of cell migration in 2- and 3-dimensional image series, including registration, segmentation, shape and topology description, tracking and motion fields are presented. We discuss advantages, limitations and suitability for different approaches and levels of description.

Summary: The quantification of colocalizing signals in multichannel fluorescence microscopy images depends on the reliable segmentation of corresponding regions of interest, on the selection of appropriate colocalization coefficients, and on a robust statistical criterion to discriminate true from random colocalization. Here, we introduce a confined displacement algorithm based on image correlation spectroscopy in combination with Manders colocalization coefficients M1_ROI and M2_ROI to quantify true and random colocalization of a given florescence pattern. We show that existing algorithms based on block scrambling exaggerate the randomization of fluorescent patterns with resulting inappropriately narrow probability density functions and false significance of true colocalization in terms of p values. We further confine our approach to subcellular compartments and show that true and random colocalization can be analysed for model dendrites and for GABA_B receptor subunits GABA_BR1/2 in cultured hippocampal neurons. Together, we demonstrate that the confined displacement algorithm detects true colocalization of specific fluorescence patterns down to subcellular levels.