FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis

Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ

Engineering Systems to Study Cancer Metastasis.

Metastasis causes most late relapses and fatalities from cancer. Investigating mechanisms of metastasis and designing therapies to limit metastatic disease are challenging due to difficulties studying key events that occur before clinical detection, frequently involving small numbers of cells and intercellular interactions within three metastatic compartments (primary tumor, intravascular, and metastatic sites). By combining microfluidic, tissue engineering, and molecular imaging tools to screen therapies and analyze individual steps of metastasis, we further bridge the physiological gap between standard in vitro models, animal models, and human disease. Firstly, we model how multiple cell types form and respond to chemotactic gradients that drive cell migration in a primary tumor. Using microfluidic tools to robustly pattern chemokine CXCL12 secreting cells, CXCR7+ cells that scavenge this chemokine, and CXCR4+ cells that migrate towards resulting gradients, we performed sensitivity analysis to identify functional combinations that are refractory to therapeutic inhibition. We found one high-matrix binding isoform (CXCL12-γ) to robustly promote chemotaxis even at low chemokine levels in the absence of scavenging cells and in the presence of a clinically approved inhibitor. Linking these findings to clinical physiology, we found this high-matrix-binding isoform to only be expressed in late stage breast cancer. Secondly, we combine tissue engineering and molecular imaging tools to study the response of cancer cells in 3D tissue spheroids. We designed a platform to facilitate handling and high resolution imaging of 384 well spheroids. Using this platform we developed a bone marrow spheroid model to recreate breast cancer quiescence and resistance to therapies. Using dual-colored bioluminescence imaging we simultaneously measured response of quiescent cancer and bone stromal cells to standard cytotoxic and targeted therapies. Using this strategy we identified therapeutic combinations that selectively eliminated quiescent cancer cells in vitro and entirely eliminated bone marrow metastases in mice. We also used this system to visualize metabolic gradients in bone marrow spheroids and measure cancer and stromal response to metabolic perturbations. Combining microfluidic, tissue engineering, and molecular imaging tools as described herein can improve development of better models that recreate in vivo physiology and allow development of patient-specific therapies that prevent or eliminate metastatic disease.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111509/1/spcavnar_1.pd

Remote refocusing light-sheet fluorescence microscopy for high-speed 2D and 3D imaging of calcium dynamics in cardiomyocytes

The high prevalence and poor prognosis of heart failure are two key drivers for research into cardiac electrophysiology and regeneration. Dyssynchrony in calcium release and loss of structural organization within individual cardiomyocytes (CM) has been linked to reduced contractile strength and arrhythmia. Correlating calcium dynamics and cell microstructure requires multidimensional imaging with high spatiotemporal resolution. In light-sheet fluorescence microscopy (LSFM), selective plane illumination enables fast optically sectioned imaging with lower phototoxicity, making it suitable for imaging subcellular dynamics. In this work, a custom remote refocusing LSFM system is applied to studying calcium dynamics in isolated CM, cardiac cell cultures and tissue slices. The spatial resolution of the LSFM system was modelled and experimentally characterized. Simulation of the illumination path in Zemax was used to estimate the light-sheet beam waist and confocal parameter. Automated MATLAB-based image analysis was used to quantify the optical sectioning and the 3D point spread function using Gaussian fitting of bead image intensity distributions. The results demonstrated improved and more uniform axial resolution and optical sectioning with the tighter focused beam used for axially swept light-sheet microscopy. High-speed dual-channel LSFM was used for 2D imaging of calcium dynamics in correlation with the t-tubule structure in left and right ventricle cardiomyocytes at 395 fps. The high spatio-temporal resolution enabled the characterization of calcium sparks. The use of para-nitro-blebbistatin (NBleb), a non-phototoxic, low fluorescence contraction uncoupler, allowed 2D-mapping of the spatial dyssynchrony of calcium transient development across the cell. Finally, aberration-free remote refocusing was used for high-speed volumetric imaging of calcium dynamics in human induced pluripotent stem-cell derived cardiomyocytes (hiPSC-CM) and their co-culture with adult-CM. 3D-imaging at up to 8 Hz demonstrated the synchronization of calcium transients in co-culture, with increased coupling with longer co-culture duration, uninhibited by motion uncoupling with NBleb.Open Acces

Quantitative electron microscopy for microstructural characterisation

Development of materials for high-performance applications requires accurate and useful analysis tools. In parallel with advances in electron microscopy hardware, we require analysis approaches to better understand microstructural behaviour. Such improvements in characterisation capability permit informed alloy design. New approaches to the characterisation of metallic materials are presented, primarily using signals collected from electron microscopy experiments. Electron backscatter diffraction is regularly used to investigate crystallography in the scanning electron microscope, and combined with energy-dispersive X-ray spectroscopy to simultaneusly investigate chemistry. New algorithms and analysis pipelines are developed to permit accurate and routine microstructural evaluation, leveraging a variety of machine learning approaches. This thesis investigates the structure and behaviour of Co/Ni-base superalloys, derived from V208C. Use of the presently developed techniques permits informed development of a new generation of advanced gas turbine engine materials.Open Acces

Na+/K+-ATPase α1 Identified as an Abundant Protein in the Blood-Labyrinth Barrier That Plays an Essential Role in the Barrier Integrity

BACKGROUND:The endothelial-blood/tissue barrier is critical for maintaining tissue homeostasis. The ear harbors a unique endothelial-blood/tissue barrier which we term "blood-labyrinth-barrier". This barrier is critical for maintaining inner ear homeostasis. Disruption of the blood-labyrinth-barrier is closely associated with a number of hearing disorders. Many proteins of the blood-brain-barrier and blood-retinal-barrier have been identified, leading to significant advances in understanding their tissue specific functions. In contrast, capillaries in the ear are small in volume and anatomically complex. This presents a challenge for protein analysis studies, which has resulted in limited knowledge of the molecular and functional components of the blood-labyrinth-barrier. In this study, we developed a novel method for isolation of the stria vascularis capillary from CBA/CaJ mouse cochlea and provided the first database of protein components in the blood-labyrinth barrier as well as evidence that the interaction of Na(+)/K(+)-ATPase α1 (ATP1A1) with protein kinase C eta (PKCη) and occludin is one of the mechanisms of loud sound-induced vascular permeability increase. METHODOLOGY/PRINCIPAL FINDINGS:Using a mass-spectrometry, shotgun-proteomics approach combined with a novel "sandwich-dissociation" method, more than 600 proteins from isolated stria vascularis capillaries were identified from adult CBA/CaJ mouse cochlea. The ion transporter ATP1A1 was the most abundant protein in the blood-labyrinth barrier. Pharmacological inhibition of ATP1A1 activity resulted in hyperphosphorylation of tight junction proteins such as occludin which increased the blood-labyrinth-barrier permeability. PKCη directly interacted with ATP1A1 and was an essential mediator of ATP1A1-initiated occludin phosphorylation. Moreover, this identified signaling pathway was involved in the breakdown of the blood-labyrinth-barrier resulting from loud sound trauma. CONCLUSIONS/SIGNIFICANCE:The results presented here provide a novel method for capillary isolation from the inner ear and the first database on protein components in the blood-labyrinth-barrier. Additionally, we found that ATP1A1 interaction with PKCη and occludin was involved in the integrity of the blood-labyrinth-barrier

Deep Learning Methods for Detection and Tracking of Particles in Fluorescence Microscopy Images

Studying the dynamics of sub-cellular structures such as receptors, filaments, and vesicles is a prerequisite for investigating cellular processes at the molecular level. In addition, it is important to characterize the dynamic behavior of virus structures to gain a better understanding of infection mechanisms and to develop novel drugs. To investigate the dynamics of fluorescently labeled sub-cellular and viral structures, time-lapse fluorescence microscopy is the most often used imaging technique. Due to the limited spatial resolution of microscopes caused by diffraction, these very small structures appear as bright, blurred spots, denoted as particles, in microscopy images. To draw statistically meaningful biological conclusions, a large number of such particles need to be analyzed. However, since manual analysis of fluorescent particles is very time consuming, fully automated computer-based methods are indispensable. We introduce novel deep learning methods for detection and tracking of multiple particles in fluorescence microscopy images. We propose a particle detection method based on a convolutional neural network which performs image-to-image mapping by density map regression and uses the adaptive wing loss. For particle tracking, we present a recurrent neural network that exploits past and future information in both forward and backward direction. Assignment probabilities across multiple detections as well as the probabilities for missing detections are computed jointly. To resolve tracking ambiguities using future information, several track hypotheses are propagated to later time points. In addition, we developed a novel probabilistic deep learning method for particle tracking, which is based on a recurrent neural network mimicking classical Bayesian filtering. The method includes both aleatoric and epistemic uncertainty, and provides valuable information about the reliability of the computed trajectories. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. Moreover, we developed a convolutional Long Short-Term Memory neural network for combined particle tracking and colocalization analysis in two-channel microscopy image sequences. The network determines colocalization probabilities, and colocalization information is exploited to improve tracking. Short and long-term temporal dependencies of object motion as well as image intensities are taken into account to compute assignment probabilities jointly across multiple detections. We also introduce a deep learning method for probabilistic particle detection and tracking. For particle detection, temporal information is integrated to regress a density map and determine sub-pixel particle positions. For tracking, a fully Bayesian neural network is presented that mimics classical Bayesian filtering and takes into account both aleatoric and epistemic uncertainty. Uncertainty information of individual particle detections is considered. Network training for the developed deep learning-based particle tracking methods relies only on synthetic data, avoiding the need of time-consuming manual annotation. We performed an extensive evaluation of our methods based on image data of the Particle Tracking Challenge as well as on fluorescence microscopy images displaying virus proteins of HCV and HIV, chromatin structures, and cell-surface receptors. It turned out that the methods outperform previous methods