Automatic Hotspots Detection for Intracellular Calcium Analysis in Fluorescence Microscopic Videos

In recent years, life-cell imaging techniques and their software applications have become powerful tools to investigate complex biological mechanisms such as calcium signalling. In this paper, we propose an automated framework to detect areas inside cells that show changes in their calcium concentration i.e. the regions of interests or hotspots, based on videos taken after loading living mouse cardiomyocytes with fluorescent calcium reporter dyes. The proposed system allows an objective and efficient analysis through the following four key stages: (1) Pre-processing to enhance video quality, (2) First level segmentation to detect candidate hotspots based on adaptive thresholding on the frame level, (3) Second-level segmentation to fuse and identify the best hotspots from the entire video by proposing the concept of calcium fluorescence hit-ratio, and (4) Extraction of the changes of calcium fluorescence over time per hotspot. From the extracted signals, different measurements are calculated such as maximum peak amplitude, area under the curve, peak frequency, and inter-spike interval of calcium changes. The system was tested using calcium imaging data collected from Heart muscle cells. The paper argues that the automated proposal offers biologists a tool to speed up the processing time and mitigate the consequences of inter-intra observer variability

QuantEv: quantifying the spatial distribution of intracellular events

Analysis of the spatial distribution of endomembrane trafficking is fundamental to understand the mechanisms controlling cellular dynamics, cell homeostasy, and cell interaction with its external environment in normal and pathological situations. The development of automated methods to visualize and quantify the spatial distribution of intracellular events is essential to process the ever-increasing amount of data generated with modern light mi-croscopy. We present a generic and non-parametric framework to quantitatively analyze and visualize the spatio-temporal distribution of intracellular events from different conditions in fluorescence microscopy. From the spatial coordinates of intracellular features such as segmented subcellular structures or dynamic processes like vesicle trajectories, QuantEv automatically estimates weighted densities for each dimension of the 3D cylindrical coordinate system and performs a comprehensive statistical analysis from distribution distances. We apply this approach to study the spatio-temporal distribution of moving Rab6 fluorescently labeled membranes with respect to their direction of movement in cells constrained in crossbow-and disk-shaped fibronectin patterns. We also investigate the position of the generating hub of Rab11 positive membranes and the effect of actin disruption on Rab11 trafficking in coordination with cell shape. An Icy plugin and a tutorial are available athttp://icy.bioimageanalysis.org/plugin/QuantEv

Lifetime estimation on moving sub-cellular objects in frequency domain FLIM imaging

International audienceFluorescence lifetime is usually defined as the average nanosecond-scale delay between excitation and emission of fluorescence. It has been established that lifetime measurement yields numerous indications on cellular processes such as inter-protein and intra-protein mechanisms through fluorescent tagging and Förster resonance energy transfer (FRET). In this area, frequency domain fluorescence lifetime imaging microscopy (FD FLIM) is particularly well appropriate to probe a sample non-invasively and quantify these interactions in living cells. The aim is then to measure fluorescence lifetime in the sample at each location in space from fluorescence variations observed in a temporal sequence of images obtained by phase modulation of the detection signal. This leads to a sensitivity of lifetime determination to other sources of fluorescence variations such as intracellular motion. In this paper, we propose a robust statistical method for lifetime estimation on both background and small moving structures with a focus on intracellular vesicle trafficking

ACME: Automatic feature extraction for cell migration examination through intravital microscopy imaging.

Cell detection and tracking applied to in vivo fluorescence microscopy has become an essential tool in biomedicine to characterize 4D (3D space plus time) biological processes at the cellular level. Traditional approaches to cell motion analysis by microscopy imaging, although based on automatic frameworks, still require manual supervision at some points of the system. Hence, when dealing with a large amount of data, the analysis becomes incredibly time-consuming and typically yields poor biological information. In this paper, we propose a fully-automated system for segmentation, tracking and feature extraction of migrating cells within blood vessels in 4D microscopy imaging. Our system consists of a robust 3D convolutional neural network (CNN) for joint blood vessel and cell segmentation, a 3D tracking module with collision handling, and a novel method for feature extraction, which takes into account the particular geometry in the cell-vessel arrangement. Experiments on a large 4D intravital microscopy dataset show that the proposed system achieves a significantly better performance than the state-of-the-art tools for cell segmentation and tracking. Furthermore, we have designed an analytical method of cell behaviors based on the automatically extracted features, which supports the hypotheses related to leukocyte migration posed by expert biologists. This is the first time that such a comprehensive automatic analysis of immune cell migration has been performed, where the total population under study reaches hundreds of neutrophils and thousands of time instances.This work has been partially supported by the National Grant TEC2017-84395-P of the Spanish Ministry of Economy and Competitiveness, Madrid Regional Government and Universidad Carlos III de Madrid through the project SHARON-CM-UC3M, RTI2018- 095497-B-I00 from Ministerio de Ciencia e Innovación (MICINN) and HR17_00527 from Fundación La Caixa to A.H. M.M-M. is supported by the Spanish Ministry of Education, Culture and Sports FPU Grant FPU18/02825. M.P-S. is supported by a Federation of European Biochemical Societies long-term fellowship. J.S. is supported by a fellowship (PRE2019-089130) from MICINN.S

A quantitative approach for analyzing the spatio-temporal distribution of 3D intracellular events in fluorescence microscopy

International audienceAnalysis of the spatial distribution of endomembrane trafficking is fundamental to understand the mechanisms controlling cellular dynamics, cell homeostasy, and cell interaction with its external environment in normal and pathological situations. We present a semi-parametric framework to quantitatively analyze and visualize the spatio-temporal distribution of intracellular events from different conditions. From the spatial coordinates of intracellular features such as segmented subcellular structures or vesicle trajectories, QuantEv automatically estimates weighted densities that are easy to interpret and performs a comprehensive statistical analysis from distribution distances. We apply this approach to study the spatio-temporal distribution of moving Rab6 fluorescently labeled membranes with respect to their direction of movement in crossbow-and disk-shaped cells. We also investigate the position of the generating hub of Rab11-positive membranes and the effect of actin disruption on Rab11 trafficking in coordination with cell shape

Background Fluorescence Estimation and Vesicle Segmentation in Live Cell Imaging with Conditional Random Fields

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Quantitative Optical Imaging of Metabolic and Structural Biomarkers in Rodent Injury Models

The assessment of organ metabolic function using optical imaging techniques is an overgrowing field of disease diagnosis. The broad research objective of my PhD thesis is to detect quantitative biomarkers by developing and applying optical imaging and image processing tools to animal models of human diseases. To achieve this goal, I have designed and implemented an optical imaging instrument called in vivo fluorescence imager to study wound healing progress. I have also developed a 3-dimensional (3D) vascular segmentation technique that uses intrinsic fluorescence images of whole organs. Intrinsic fluorophores (autofluorescence signals) provide information about the status of cellular bioenergetics in different tissue types. Reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavin adenine dinucleotide (FAD) are two key Krebs cycle coenzymes in mitochondria, which are autofluorescent. The ratio of these two fluorophores (NADH/FAD) is used as an optical biomarker for mitochondrial redox state of the tissues. The custom-designed optical tools have enabled me to probe the metabolic state of diseases as well as structural information of the organs at different regimes (in vivo, at cryogenic temperature, and in vitro). Here are the main projects that I have conducted and significantly contributed to: 1) Fluorescent metabolic imaging. I have designed and implemented an in vivo fluorescence imaging device to study diabetic wounds in small animals. This device can monitor the dynamics of the metabolism of the skin by capturing the images of the surface fluorescence of NADH and FAD. The area of the wounds can also be monitored simultaneously. The spatiotemporal mitochondrial redox ratio changes can give information on the status of wound healing online. This device was utilized to study diabetic wounds and the effect of photo-biomodulation on the wound healing progress. I have also utilized the optical cryo-imaging system to study the three-dimensional (3D) mitochondrial redox state of kidneys, hearts, livers, and wound biopsies of the small animal models of various injuries. For example, cryo-imaging was conducted on irradiated rat hearts during ischemia-reperfusion (IR) to investigate the role of mitochondrial metabolism in the differential susceptibility to IR injury. Also, I developed a 3D image processing tool that can segment and quantify the medullary versus the cortical redox state in the kidneys of animal injury models. 2) 3D Vascular-Metabolic Imaging (VMI). I have designed VMI, an image processing algorithm that segments vascular networks from intrinsic fluorescence. VMI allows the simultaneous acquisition of vasculature and metabolism in multiple organs. I demonstrate that this technique provides the vascular network of the whole organ without the need for a contrast agent. A proof validation has performed using TdTomato fluorescence expressing endothelium. The VMI also showed convincing evidence for the “minimum work” hypothesis in the vascular network by following Murray’s law. For a proof-of-concept, I have also utilized a partial body irradiation model that VMI can provide information on radiation-induced vascular regression. 3) Time-lapse fluorescence microscopy. I have utilized fluorescence microscopy to quantify the dynamics of cellular reactive oxygen species (ROS) concentration. ROS is imaged and quantified under oxygen or metabolic stress conditions in cells in vitro. This approach enabled me to study the sensitivity of retinal endothelial cells and pericytes to stress under high glucose conditions. In short, I developed and utilized optical bio-instrumentation and image processing tools to be able to detect metabolic and vascular information about different diseases

Automatic approach for spot detection in microscopy imaging based on image processing and statistical analysis

Abstract: In biological research, fluorescence microscopy has become one of the vital tools used for observation, allowing researchers to study, visualise and image the details of intracel-lular structures which result in better understanding of biology. However, analysis of large numbers of samples is often required to draw statistically verifiable conclusions. Automated methods for analysis of microscopy image data make it possible to handle large datasets, and at the same time reduce the risk of bias imposed by manual techniques in the image analysis pipeline. This work covers automated methods for extracting quan-titative measurements from microscopy images, enabling the detection of spots resulting from different experimental conditions. The work resulted in four main significant con-tributions developed around the microscopy image analysis pipeline. Firstly, an investiga-tion into the importance of spot detection within the automated image analysis pipeline is conducted. Experimental findings show that poor spot detection adversely affected the remainder of the processing pipeline...D.Ing. (Electrical and Electronic Engineering