Background fluorescence estimation and vesicle segmentation in live cell imaging with conditional random fields

A generative model to simulate spatiotemporal dynamics of biomolecules in cells

Generators of space-time dynamics in bioimaging have become essential to build ground truth datasets for image processing algorithm evaluation such as biomolecule detectors and trackers, as well as to generate training datasets for deep learning algorithms. In this contribution, we leverage a stochastic model, called birth-death-move (BDM) point process, in order to generate joint dynamics of biomolecules in cells. This particle-based stochastic simulation method is very flexible and can be seen as a generalization of well-established standard particle-based generators. In comparison, our approach allows us: (1) to model a system of particles in motion, possibly in interaction, that can each possibly switch from a motion regime (e.g., Brownian) to another (e.g., a directed motion); (2) to take into account finely the appearance over time of new trajectories and their disappearance, these events possibly depending on the cell regions but also on the current spatial configuration of all existing particles. This flexibility enables to generate more realistic dynamics than standard particle-based simulation procedures, by for example accounting for the colocalization phenomena often observed between intracellular vesicles. We explain how to specify all characteristics of a BDM model, with many practical examples that are relevant for bioimaging applications. As an illustration, based on real fluorescence microscopy datasets, we finally calibrate our model to mimic the joint dynamics of Langerin and Rab11 proteins near the plasma membrane, including the well-known colocalization occurrence between these two types of vesicles. We show that the resulting synthetic sequences exhibit comparable features as those observed in real microscopy image sequences.

Advanced Nanotechnology Approaches as Emerging Tools in Cellular-Based Technologies

Stem cells are valuable tools in regenerative medicine because they can generate a wide variety of cell types and tissues that can be used to treat or replace damaged tissues and organs. However, challenges related to the application of stem cells in the scope of regenerative medicine have urged scientists to utilize nanomedicine as a prerequisite to circumvent some of these hurdles. Nanomedicine plays a crucial role in this process and manipulates surface biology, the fate of stem cells, and biomaterials. Many attempts have been made to modify cellular behavior and improve their regenerative ability using nano-based strategies. Notably, nanotechnology applications in regenerative medicine and cellular therapies are controversial because of ethical and legal considerations. Therefore, this review describes nanotechnology in cell-based applications and focuses on newly proposed nano-based approaches. Cutting-edge strategies to engineer biological tissues and the ethical, legal, and social considerations of nanotechnology in regenerative nanomedicine applications are also discussed.

Background-free wide-field fluorescence imaging using edge detection combined with HiLo

Fluorescence microscopy has been widely used in the field of biological imaging, but the disturbance of background noise has always been an unavoidable phenomenon. To obtain a background free image, a virtual HiLo based on edge detection (V-HiLo-ED) method for background removing is proposed, which is different from the existing popular software algorithms that obtain the background-free image by subtracting the estimated background, but the background-free image is directly reconstructed by estimating the foreground. Compared with two other popular software-based methods, the wavelet-based background and noise subtraction algorithm (WBNS) and the rolling ball algorithm (RBA), the V-HiLo-ED owns a better quality on achieving background-free imaging. Compared with hardware-based method such as HiLo method, V-HiLo-ED exhibits almost the same performance but faster speed. In combination with light sheet microscopy, the V-HiLo-ED further improves the signal-to-noise ratio of images with thick light-sheet. These experiment results indicate that the V-HiLo-ED owns the potentiality in many other image applications such as endoscopy.

VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images

This research work aims to implement an automated segmentation process to extract the endoplasmic reticulum (ER) network in fluorescence microscopy images (FMI) using pretrained convolutional neural network (CNN). The threshold level of the raw FMT is complex, and extraction of the ER network is a challenging task. Hence, an image conversion procedure is initially employed to reduce its complexity. This work employed the pretrained CNN schemes, such as VGG-UNet and VGG-SegNet, to mine the ER network from the chosen FMI test images. The proposed ER segmentation pipeline consists of the following phases; (i) clinical image collection, 16-bit to 8-bit conversion and resizing; (ii) implementation of pretrained VGG-UNet and VGG-SegNet; (iii) extraction of the binary form of ER network; (iv) comparing the mined ER with ground-truth; and (v) computation of image measures and validation. The considered FMI dataset consists of 223 test images, and image augmentation is then implemented to increase these images. The result of this scheme is then confirmed against other CNN methods, such as U-Net, SegNet, and Res-UNet. The experimental outcome confirms a segmentation accuracy of >98% with VGG-UNet and VGG-SegNet. The results of this research authenticate that the proposed pipeline can be considered to examine the clinical-grade FMI.

Three-dimensional vascular and metabolic imaging using inverted autofluorescence

SIGNIFICANCE

Three-dimensional (3D) vascular and metabolic imaging (VMI) of whole organs in rodents provides critical and important (patho)physiological information in studying animal models of vascular network.

AIM

Autofluorescence metabolic imaging has been used to evaluate mitochondrial metabolites such as nicotinamide adenine dinucleotide (NADH) and flavine adenine dinucleotide (FAD). Leveraging these autofluorescence images of whole organs of rodents, we have developed a 3D vascular segmentation technique to delineate the anatomy of the vasculature as well as mitochondrial metabolic distribution.

APPROACH

By measuring fluorescence from naturally occurring mitochondrial metabolites combined with light-absorbing properties of hemoglobin, we detected the 3D structure of the vascular tree of rodent lungs, kidneys, hearts, and livers using VMI. For lung VMI, an exogenous fluorescent dye was injected into the trachea for inflation and to separate the airways, confirming no overlap between the segmented vessels and airways.

RESULTS

The kidney vasculature from genetically engineered rats expressing endothelial-specific red fluorescent protein TdTomato confirmed a significant overlap with VMI. This approach abided by the "minimum work" hypothesis of the vascular network fitting to Murray's law. Finally, the vascular segmentation approach confirmed the vascular regression in rats, induced by ionizing radiation.

CONCLUSIONS

Simultaneous vascular and metabolic information extracted from the VMI provides quantitative diagnostic markers without the confounding effects of vascular stains, fillers, or contrast agents.

Will Nanotechnology Bring New Hope for Stem Cell Therapy?

The potential of stem cell therapy has been shown in preclinical trials for the treatment of damage and replacement of organs and degenerative diseases. After many years of research, its clinical application is limited. Currently there is not a single stem cell therapy product or procedure. Nanotechnology is an emerging field in medicine and has huge potential due to its unique characteristics such as its size, surface effects, tunnel effects, and quantum size effect. The importance of application of nanotechnology in stem cell technology and cell-based therapies has been recognized. In particular, the effects of nanotopography on stem cell differentiation, proliferation, and adhesion have become an area of intense research in tissue engineering and regenerative medicine. Despite the many opportunities that nanotechnology can create to change the fate of stem cell technology and cell therapies, it poses several risks since some nanomaterials are cytotoxic and can affect the differentiation program of stem cells and their viability. Here we review some of the advances and the prospects of nanotechnology in stem cell research and cell-based therapies and discuss the issues, obstacles, applications, and approaches with the aim of opening new avenues for further research.

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

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. 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.

Proteins are the workhorses of the body, performing a range of roles that are essential for life. Often, this requires these molecules to move from one location to another inside a cell. Scientists are interested in following an individual protein in a living cell ‘in real time’, as this helps understand what this protein does.

Scientists can track the whereabouts of a protein by ‘tagging’ it with a fluorescent molecule that emits light which can be picked up by a powerful microscope. This process is repeated many times on different samples. Finally, researchers have to analyze all the resulting images, and conduct statistical analysis to draw robust conclusions about the overall trajectories of the proteins. This process often relies on experts assessing the images, and it is therefore time-consuming and not easily scalable or applied to other experiments.

To help with this, Pécot et al. have developed QuantEV, a free algorithm that can analyze proteins’ paths within a cell, and then return statistical graphs and 3D visualizations. The program also gives access to the statistical procedure that was used, which means that different experiments can be compared.

Pécot et al. used the method to follow the Rab6 protein in cells of different shapes, and found that the conformation of the cell influences where Rab6 is located. For example, in crossbow-shaped cells, Rab6 is found more often toward the three tips of the crossbow, while its distribution is uniform in cells that look like disks. Another experiment examined where the protein Rab11 is normally placed, and how this changes when the cell’s skeleton is artificially disrupted. Both studies help to gain an insight into the behavior of the cellular structures in which Rab6 and Rab11 are embedded.

Following proteins in the cell is an increasingly popular method, and there is therefore a growing amount of data to process. QuantEV should make it easier for biologists to analyze their results, which could help them to have a better grasp on how cells work in various circumstances.

Adaptive Spot Detection With Optimal Scale Selection in Fluorescence Microscopy Images

Accurately detecting subcellular particles in fluorescence microscopy is of primary interest for further quantitative analysis such as counting, tracking, or classification. Our primary goal is to segment vesicles likely to share nearly the same size in fluorescence microscopy images. Our method termed adaptive thresholding of Laplacian of Gaussian (LoG) images with autoselected scale (ATLAS) automatically selects the optimal scale corresponding to the most frequent spot size in the image. Four criteria are proposed and compared to determine the optimal scale in a scale-space framework. Then, the segmentation stage amounts to thresholding the LoG of the intensity image. In contrast to other methods, the threshold is locally adapted given a probability of false alarm (PFA) specified by the user for the whole set of images to be processed. The local threshold is automatically derived from the PFA value and local image statistics estimated in a window whose size is not a critical parameter. We also propose a new data set for benchmarking, consisting of six collections of one hundred images each, which exploits backgrounds extracted from real microscopy images. We have carried out an extensive comparative evaluation on several data sets with ground-truth, which demonstrates that ATLAS outperforms existing methods. ATLAS does not need any fine parameter tuning and requires very low computation time. Convincing results are also reported on real total internal reflection fluorescence microscopy images.

Lifetime estimation of moving subcellular objects in frequency-domain fluorescence lifetime imaging microscopy

Fluorescence lifetime is usually defined as the average nanosecond-scale delay between excitation and emission of fluorescence. It has been established that lifetime measurements yield numerous indications on cellular processes such as interprotein and intraprotein mechanisms through fluorescent tagging and Förster resonance energy transfer. In this area, frequency-domain fluorescence lifetime imaging microscopy is particularly appropriate to probe a sample noninvasively and quantify these interactions in living cells. The aim is then to measure the 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 for both background and small moving structures with a focus on intracellular vesicle trafficking.