Detection sensitivity of fluorescence lifetime imaging ophthalmoscopy for laser-induced selective damage of retinal pigment epithelium

PURPOSE

To investigate the sensitivity of fluorescence lifetime imaging ophthalmoscopy (FLIO) to detect retinal laser spots by comparative analysis with other imaging modalities.

METHODS

A diode laser with a wavelength of 514 nm was applied with pulse durations of 5.2, 12, 20, and 50 µs. The laser pulse energy was increased so that the visibility of the laser spot by slit-lamp fundus examination (SL) under the irradiator's observation covers from the subvisible to visible range immediately after irradiation. The irradiated areas were then examined by fundus color photography (FC), optical coherence tomography (OCT), fundus autofluorescence (AF), FLIO, and fluorescein angiography (FA). The visibility of a total of over 2200 laser spots was evaluated by two independent researchers, and effective dose (ED) 50 laser pulse energy values were calculated for each imaging modality and compared.

RESULTS

Among examined modalities, FA showed the lowest mean of ED50 energy value and SL the highest, that is, they had the highest and lowest sensitivity to detect retinal pigment epithalium (RPE)-selective laser spots, respectively. FLIO also detected spots significantly more sensitively than SL at most laser pulse durations and was not significantly inferior to FA. AF was also often more sensitive than SL, but the difference was slightly less significant than FLIO.

CONCLUSION

Considering its high sensitivity in detecting laser spots and previously reported potential of indicating local wound healing and metabolic changes around laser spots, FLIO may be useful as a non-invasive monitoring tool during and after minimally invasive retinal laser treatment.

Basal Gp78-dependent mitophagy promotes mitochondrial health and limits mitochondrial ROS

Mitochondria are major sources of cytotoxic reactive oxygen species (ROS), such as superoxide and hydrogen peroxide, that when uncontrolled contribute to cancer progression. Maintaining a finely tuned, healthy mitochondrial population is essential for cellular homeostasis and survival. Mitophagy, the selective elimination of mitochondria by autophagy, monitors and maintains mitochondrial health and integrity, eliminating damaged ROS-producing mitochondria. However, mechanisms underlying mitophagic control of mitochondrial homeostasis under basal conditions remain poorly understood. E3 ubiquitin ligase Gp78 is an endoplasmic reticulum membrane protein that induces mitochondrial fission and mitophagy of depolarized mitochondria. Here, we report that CRISPR/Cas9 knockout of Gp78 in HT-1080 fibrosarcoma cells increased mitochondrial volume, elevated ROS production and rendered cells resistant to carbonyl cyanide m-chlorophenyl hydrazone (CCCP)-induced mitophagy. These effects were phenocopied by knockdown of the essential autophagy protein ATG5 in wild-type HT-1080 cells. Use of the mito-Keima mitophagy probe confirmed that Gp78 promoted both basal and damage-induced mitophagy. Application of a spot detection algorithm (SPECHT) to GFP-mRFP tandem fluorescent-tagged LC3 (tfLC3)-positive autophagosomes reported elevated autophagosomal maturation in wild-type HT-1080 cells relative to Gp78 knockout cells, predominantly in proximity to mitochondria. Mitophagy inhibition by either Gp78 knockout or ATG5 knockdown reduced mitochondrial potential and increased mitochondrial ROS. Live cell analysis of tfLC3 in HT-1080 cells showed the preferential association of autophagosomes with mitochondria of reduced potential. Xenograft tumors of HT-1080 knockout cells show increased labeling for mitochondria and the cell proliferation marker Ki67 and reduced labeling for the TUNEL cell death reporter. Basal Gp78-dependent mitophagic flux is, therefore, selectively associated with reduced potential mitochondria promoting maintenance of a healthy mitochondrial population, limiting ROS production and tumor cell proliferation.

FreeClimber: automated quantification of climbing performance in Drosophila

Negative geotaxis (climbing) performance is a useful metric for quantifying Drosophila health. Manual methods to quantify climbing performance are tedious and often biased, while many available computational methods have challenging hardware or software requirements. We present an alternative: FreeClimber. This open source, Python-based platform subtracts a video's static background to improve detection for flies moving across heterogeneous backgrounds. FreeClimber calculates a cohort's velocity as the slope of the most linear portion of a mean vertical position versus time curve. It can run from a graphical user interface for optimization or a command line interface for high-throughput and automated batch processing, improving accessibility for users with different expertise. FreeClimber outputs calculated slopes, spot locations for follow-up analyses (e.g. tracking), and several visualizations and plots. We demonstrate FreeClimber's utility in a longitudinal study for endurance exercise performance in Drosophila mitonuclear genotypes using six distinct mitochondrial haplotypes paired with a common D. melanogaster nuclear background.

Quantitative Image Analysis of the Spatial Organization and Mobility of Caveolin Aggregates at the Plasma Membrane

Caveolins are integral membrane proteins that are the principal structural component of caveolae. Newly synthesized caveolin self-associates into oligomers that further assemble into higher-order structures. Imaging fluorescently labeled caveolin at the plasma membrane with total internal reflection fluorescence (TIRF) microscopy reveals a spatially heterogeneous distribution with aggregates of various sizes. In this chapter, we present a set of image-processing tools to quantify the spatial organization and mobility of caveolin aggregates seen in TIRF images. We apply a spot detection algorithm to identify punctate features on multiple length scales, and computationally estimate the area and integrated fluorescence signal of each detected feature. We then partition the original image into two disjoint sets: one containing pixels within punctae, and the other containing pixels on the rest of the plasma membrane. From these partitions, we estimate the relative fraction of caveolin that is punctate versus diffuse. Finally, we analyze the mobility of caveolin aggregates by tracking them and classify individual trajectories as diffusive or subdiffusive using a moment scaling spectrum analysis. Together, these analyses capture multiple facets of caveolin organization and dynamics. To demonstrate their utility, we quantify the distribution of fluorescent Caveolin 1 stably transfected in HeLa cells. We analyze cells at baseline and after being exposed to the anesthetic Dibucaine that is known to scramble membrane phospholipids. Our analysis shows how this perturbation dramatically alters caveolin aggregation and mobility.

Automatic Search of Spots and Color Classification in ELISPOT Assay

Accuracy of spot detection and classification plays a critical role in the analysis of ELISPOT data. Differences in staining intensities of spots and their morphological variations make it difficult developing a reliable software application. An image recognition method allowing the automatic detection and classification of round objects (spots) on ELISPOT images independently of the registration conditions was developed. The emphasis is done on objects of elliptical shape, which is typical for a wide range of spots. It can be analyzed by both monochrome and a dual-color version of our software. The method of subdivision of objects into groups is also described which is based on color attributes of spots.

Data for automated, high-throughput microscopy analysis of intracellular bacterial colonies using spot detection

Quantification of intracellular bacterial colonies is useful in strategies directed against bacterial attachment, subsequent cellular invasion and intracellular proliferation. An automated, high-throughput microscopy-method was established to quantify the number and size of intracellular bacterial colonies in infected host cells (Detection and quantification of intracellular bacterial colonies by automated, high-throughput microscopy, Ernstsen et al., 2017 [1]). The infected cells were imaged with a 10× objective and number of intracellular bacterial colonies, their size distribution and the number of cell nuclei were automatically quantified using a spot detection-tool. The spot detection-output was exported to Excel, where data analysis was performed. In this article, micrographs and spot detection data are made available to facilitate implementation of the method.

Automatic Quantification of the Number of Intracellular Compartments in Arabidopsis thaliana Root Cells

In the era of quantitative biology, it is increasingly required to quantify confocal microscopy images. If possible, quantification should be performed in an automatic way, in order to avoid bias from the experimenter, to allow the quantification of a large number of samples, and to increase reproducibility between laboratories. In this protocol, we describe procedures for automatic counting of the number of intracellular compartments in Arabidopsis root cells, which can be used for example to study endocytosis or secretory trafficking pathways and to compare membrane organization between different genotypes or treatments. While developed for Arabidopsis roots, this method can be used on other tissues, cell types and plant species.

Comparative Evaluation of Software Features and Performances

Analysis of two-dimensional gel images is a crucial step for the determination of changes in the protein expression, but at present, it still represents one of the bottlenecks in 2-DE studies. Over the years, different commercial and academic software packages have been developed for the analysis of 2-DE images. Each of these shows different advantageous characteristics in terms of quality of analysis. In this chapter, the characteristics of the different commercial software packages are compared in order to evaluate their main features and performances.

Detection and Quantification of Protein Spots by Pinnacle

Accurate spot detection and quantification is a challenging task that must be performed effectively in order to properly extract the proteomic information from two-dimensional (2-D) gel electrophoresis images. In Morris et al., Bioinformatics 24:529-536, 2008, we introduced Pinnacle, an automatic, fast, effective noncommercial package for spot detection and quantification for 2-D gel images, and subsequently we have developed a freely available gui-based interface for applying the method to a set of gels. In this chapter, we overview Pinnacle, and in a step-by-step manner we describe how to use the software to obtain spot lists and quantifications, to be used for comparative proteomic analysis.

2-DE Gel Analysis: The Spot Detection

The abundance of different proteins on a 2-DE gel is reflected by the shape, size, and intensity of the corresponding spots. Protein quantitation requires the conversion of an analog gel image into digital data, resulting into a catalog of individual spots listed as x, y positions, shape parameters, and quantitative values. So, it is possible to carry out objective comparisons of equivalent spots on different gels, determining whether a particular protein is more or less abundant in one sample compared with another. Unfortunately, spots on protein gels are not uniform in shape, size, or density, and detection, quantitation, and comparison can be challenging without intervention. Once a processed image is available, a number of different algorithms can be applied to detect and quantitate individual spots.

Three-Dimensional Spot Detection in Ratiometric Fluorescence Imaging For Measurement of Subcellular Organelles

Lysosomes are subcellular organelles playing a vital role in the endocytosis process of the cell. Lysosomal acidity is an important factor in assuring proper functioning of the enzymes within the organelle, and can be assessed by labeling the lysosomes with pH-sensitive fluorescence probes. To enhance our understanding of the acidification mechanisms, the goal of this work is to develop a method that can accurately detect and characterize the acidity of each lysosome captured in ratiometric fluorescence images. We present an algorithm that utilizes the h-dome transformation and reconciles spots detected independently from two wavelength channels. We evaluated our algorithm using simulated images for which the exact locations were known. The h-dome algorithm achieved an f-score as high as 0.890. We also computed the fluorescence ratios from lysosomes in live HeLa cell images with known lysosomal pHs. Using leave-one-out cross-validation, we demonstrated that the new algorithm was able to achieve much better pH prediction accuracy than the conventional method.