Performance and sensitivity evaluation of 3D spot detection methods in confocal microscopy

Multi-detector fusion and Bayesian smoothing for tracking viral and chromatin structures

Automatic tracking of viral and intracellular structures displayed as spots with varying sizes in fluorescence microscopy images is an important task to quantify cellular processes. We propose a novel probabilistic tracking approach for multiple particle tracking based on multi-detector and multi-scale data fusion as well as Bayesian smoothing. The approach integrates results from multiple detectors using a novel intensity-based covariance intersection method which takes into account information about the image intensities, positions, and uncertainties. The method ensures a consistent estimate of multiple fused particle detections and does not require an optimization step. Our probabilistic tracking approach performs data fusion of detections from classical and deep learning methods as well as exploits single-scale and multi-scale detections. In addition, we use Bayesian smoothing to fuse information of predictions from both past and future time points. We evaluated our approach using image data of the Particle Tracking Challenge and achieved state-of-the-art results or outperformed previous methods. Our method was also assessed on challenging live cell fluorescence microscopy image data of viral and cellular proteins expressed in hepatitis C virus-infected cells and chromatin structures in non-infected cells, acquired at different spatial-temporal resolutions. We found that the proposed approach outperforms existing methods.

upU-Net Approaches for Background Emission Removal in Fluorescence Microscopy

The physical process underlying microscopy imaging suffers from several issues: some of them include the blurring effect due to the Point Spread Function, the presence of Gaussian or Poisson noise, or even a mixture of these two types of perturbation. Among them, auto-fluorescence presents other artifacts in the registered image, and such fluorescence may be an important obstacle in correctly recognizing objects and organisms in the image. For example, particle tracking may suffer from the presence of this kind of perturbation. The objective of this work is to employ Deep Learning techniques, in the form of U-Nets like architectures, for background emission removal. Such fluorescence is modeled by Perlin noise, which reveals to be a suitable candidate for simulating such a phenomenon. The proposed architecture succeeds in removing the fluorescence, and at the same time, it acts as a denoiser for both Gaussian and Poisson noise. The performance of this approach is furthermore assessed on actual microscopy images and by employing the restored images for particle recognition.

A bird's-eye view of deep learning in bioimage analysis

Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird's-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.

Deep-learning method for data association in particle tracking

Motivation

Biological studies of dynamic processes in living cells often require accurate particle tracking as a first step toward quantitative analysis. Although many particle tracking methods have been developed for this purpose, they are typically based on prior assumptions about the particle dynamics, and/or they involve careful tuning of various algorithm parameters by the user for each application. This may make existing methods difficult to apply by non-expert users and to a broader range of tracking problems. Recent advances in deep-learning techniques hold great promise in eliminating these disadvantages, as they can learn how to optimally track particles from example data.

Results

Here, we present a deep-learning-based method for the data association stage of particle tracking. The proposed method uses convolutional neural networks and long short-term memory networks to extract relevant dynamics features and predict the motion of a particle and the cost of linking detected particles from one time point to the next. Comprehensive evaluations on datasets from the particle tracking challenge demonstrate the competitiveness of the proposed deep-learning method compared to the state of the art. Additional tests on real-time-lapse fluorescence microscopy images of various types of intracellular particles show the method performs comparably with human experts.

Availability and implementation

The software code implementing the proposed method as well as a description of how to obtain the test data used in the presented experiments will be available for non-commercial purposes from https://github.com/yoyohoho0221/pt_linking.

Supplementary information

Supplementary data are available at Bioinformatics online.

In vivo large-scale analysis of Drosophila neuronal calcium traces by automated tracking of single somata

How does the concerted activity of neuronal populations shape behavior? Impediments to address this question are primarily due to critical experimental barriers. An integrated perspective on large scale neural information processing requires an in vivo approach that can combine the advantages of exhaustively observing all neurons dedicated to a given type of stimulus, and simultaneously achieve a resolution that is precise enough to capture individual neuron activity. Current experimental data from in vivo observations are either restricted to a small fraction of the total number of neurons, or are based on larger brain volumes but at a low spatial and temporal resolution. Consequently, fundamental questions as to how sensory information is represented on a population scale remain unanswered. In Drosophila melanogaster, the mushroom body (MB) represents an excellent model to analyze sensory coding and memory plasticity. In this work, we present an experimental setup coupled with a dedicated computational method that provides in vivo measurements of the activity of hundreds of densely packed somata uniformly spread in the MB. We exploit spinning-disk confocal 3D imaging over time of the whole MB cell body layer in vivo while it is exposed to olfactory stimulation. Importantly, to derive individual signal from densely packed somata, we have developed a fully automated image analysis procedure that takes advantage of the specificities of our data. After anisotropy correction, our approach operates a dedicated spot detection and registration over the entire time sequence to transform trajectories to identifiable clusters. This enabled us to discard spurious detections and reconstruct missing ones in a robust way. We demonstrate that this approach outperformed existing methods in this specific context and made possible high-throughput analysis of approximately 500 single somata uniformly spread over the MB in various conditions. Applying this approach, we find that learned experiences change the population code of odor representations in the MB. After long-term memory (LTM) formation, we quantified an increase in responsive somata count and a stable single neuron signal. We predict that this method, which should further enable studying the population pattern of neuronal activity, has the potential to uncover fine details of sensory processing and memory plasticity.

Image-Based Profiling of Synaptic Connectivity in Primary Neuronal Cell Culture

Neurological disorders display a broad spectrum of clinical manifestations. Yet, at the cellular level, virtually all these diseases converge into a common phenotype of dysregulated synaptic connectivity. In dementia, synapse dysfunction precedes neurodegeneration and cognitive impairment by several years, making the synapse a crucial entry point for the development of diagnostic and therapeutic strategies. Whereas high-resolution imaging and biochemical fractionations yield detailed insight into the molecular composition of the synapse, standardized assays are required to quickly gauge synaptic connectivity across large populations of cells under a variety of experimental conditions. Such screening capabilities have now become widely accessible with the advent of high-throughput, high-content microscopy. In this review, we discuss how microscopy-based approaches can be used to extract quantitative information about synaptic connectivity in primary neurons with deep coverage. We elaborate on microscopic readouts that may serve as a proxy for morphofunctional connectivity and we critically analyze their merits and limitations. Finally, we allude to the potential of alternative culture paradigms and integrative approaches to enable comprehensive profiling of synaptic connectivity.

Automated Analysis of Intracellular Dynamic Processes

The study of intracellular dynamic processes is of fundamental importance for understanding a wide variety of diseases and developing effective drugs and therapies. Advanced fluorescence microscopy imaging systems nowadays allow the recording of virtually any type of process in space and time with super-resolved detail and with high sensitivity and specificity. The large volume and high information content of the resulting image data, and the desire to obtain objective, quantitative descriptions and biophysical models of the processes of interest, require a high level of automation in data analysis. Two key tasks in extracting biologically meaningful information about intracellular dynamics from image data are particle tracking and particle trajectory analysis. Here we present state-of-the-art software tools for these tasks and describe how to use them.

Reprogramming of Adult Peripheral Blood Cells into Human Induced Pluripotent Stem Cells as a Safe and Accessible Source of Endothelial Cells

New approaches in regenerative medicine and vasculogenesis have generated a demand for sufficient numbers of human endothelial cells (ECs). ECs and their progenitors reside on the interior surface of blood and lymphatic vessels or circulate in peripheral blood; however, their numbers are limited, and they are difficult to expand after isolation. Recent advances in human induced pluripotent stem cell (hiPSC) research have opened possible avenues to generate unlimited numbers of ECs from easily accessible cell sources, such as the peripheral blood. In this study, we reprogrammed peripheral blood mononuclear cells, human umbilical vein endothelial cells (HUVECs), and human saphenous vein endothelial cells (HSVECs) into hiPSCs and differentiated them into ECs. The phenotype profiles, functionality, and genome stability of all hiPSC-derived ECs were assessed and compared with HUVECs and HSVECs. hiPSC-derived ECs resembled their natural EC counterparts, as shown by the expression of the endothelial surface markers CD31 and CD144 and the results of the functional analysis. Higher expression of endothelial progenitor markers CD34 and kinase insert domain receptor (KDR) was measured in hiPSC-derived ECs. An analysis of phosphorylated histone H2AX (γH2AX) foci revealed that an increased number of DNA double-strand breaks upon reprogramming into pluripotent cells. However, differentiation into ECs restored a normal number of γH2AX foci. Our hiPSCs retained a normal karyotype, with the exception of the HSVEC-derived hiPSC line, which displayed mosaicism due to a gain of chromosome 1. Peripheral blood from adult donors is a suitable source for the unlimited production of patient-specific ECs through the hiPSC interstage. hiPSC-derived ECs are fully functional and comparable to natural ECs. The protocol is eligible for clinical applications in regenerative medicine, if the genomic stability of the pluripotent cell stage is closely monitored.

DNA double-strand breaks in human induced pluripotent stem cell reprogramming and long-term in vitro culturing

Background

Human induced pluripotent stem cells (hiPSCs) play roles in both disease modelling and regenerative medicine. It is critical that the genomic integrity of the cells remains intact and that the DNA repair systems are fully functional. In this article, we focused on the detection of DNA double-strand breaks (DSBs) by phosphorylated histone H2AX (known as γH2AX) and p53-binding protein 1 (53BP1) in three distinct lines of hiPSCs, their source cells, and one line of human embryonic stem cells (hESCs).

Methods

We measured spontaneously occurring DSBs throughout the process of fibroblast reprogramming and during long-term in vitro culturing. To assess the variations in the functionality of the DNA repair system among the samples, the number of DSBs induced by γ-irradiation and the decrease over time was analysed. The foci number was detected by fluorescence microscopy separately for the G1 and S/G2 cell cycle phases.

Results

We demonstrated that fibroblasts contained a low number of non-replication-related DSBs, while this number increased after reprogramming into hiPSCs and then decreased again after long-term in vitro passaging. The artificial induction of DSBs revealed that the repair mechanisms function well in the source cells and hiPSCs at low passages, but fail to recognize a substantial proportion of DSBs at high passages.

Conclusions

Our observations suggest that cellular reprogramming increases the DSB number but that the repair mechanism functions well. However, after prolonged in vitro culturing of hiPSCs, the repair capacity decreases.

Electronic supplementary material

The online version of this article (doi:10.1186/s13287-017-0522-5) contains supplementary material, which is available to authorized users.