Correction: MATtrack: A MATLAB-Based Quantitative Image Analysis Platform for Investigating Real-Time Photo-Converted Fluorescent Signals in Live Cells.
Correction: MATtrack: A MATLAB-Based Quantitative Image Analysis Platform for Investigating Real-Time Photo-Converted Fluorescent Signals in Live Cells.
Project description:Defining cellular and subcellular structures in images, referred to as cell segmentation, is an outstanding obstacle to scalable single-cell analysis of multiplex imaging data. While advances in machine learning-based segmentation have led to potentially robust solutions, such algorithms typically rely on large amounts of example annotations, known as training data. Datasets consisting of annotations which are thoroughly assessed for quality are rarely released to the public. As a result, there is a lack of widely available, annotated data suitable for benchmarking and algorithm development. To address this unmet need, we release 105,774 primarily oncological cellular annotations concentrating on tumor and immune cells using over 40 antibody markers spanning three fluorescent imaging platforms, over a dozen tissue types and across various cellular morphologies. We use readily available annotation techniques to provide a modifiable community data set with the goal of advancing cellular segmentation for the greater imaging community.
Project description:Human carboxylesterases (CESs) are serine hydrolases that are responsible for the phase I metabolism of an assortment of ester, amide, thioester, carbonate, and carbamate containing drugs. CES activity is known to be influenced by a variety of factors including single nucleotide polymorphisms, alternative splicing, and drug-drug interactions. These different factors contribute to interindividual variability of CES activity which has been demonstrated to influence clinical outcomes among people treated with CES-substrate therapeutics. Detailed exploration of the factors that influence CES activity is emerging as an important area of research. The use of fluorescent probes with live cell imaging techniques can selectively visualize the real-time activity of CESs and have the potential to be useful tools to help reveal the impacts of CES activity variations on human health. This review summarizes the properties of the five known human CESs including factors reported to or that could potentially influence their activity before discussing the design aspects and use considerations of CES fluorescent probes in general in addition to highlighting several well-characterized probes.
Project description:Background. Common manufactured depth sensors generate depth images that humans normally obtain from their eyes and hands. Various designs converting spatial data into sound have been recently proposed, speculating on their applicability as sensory substitution devices (SSDs). Objective. We tested such a design as a travel aid in a navigation task. Methods. Our portable device (MeloSee) converted 2D array of a depth image into melody in real-time. Distance from the sensor was translated into sound intensity, stereo-modulated laterally, and the pitch represented verticality. Twenty-one blindfolded young adults navigated along four different paths during two sessions separated by one-week interval. In some instances, a dual task required them to recognize a temporal pattern applied through a tactile vibrator while they navigated. Results. Participants learnt how to use the system on both new paths and on those they had already navigated from. Based on travel time and errors, performance improved from one week to the next. The dual task was achieved successfully, slightly affecting but not preventing effective navigation. Conclusions. The use of Kinect-type sensors to implement SSDs is promising, but it is restricted to indoor use and it is inefficient on too short range.
Project description:The structure-property relationship between biomineralized calcium phosphate compounds upon a fluorescent quenching-recovery platform and their distinct crystalline structure and surficial functional groups are investigated. A fluorescence-based sensing platform is shown to be viable for the sensing of 8-hydroxy-2-deoxy-guanosine in simulated systems.
Project description:Abstract: Sequencing technologies have enabled in-depth analysis of liquid biopsies in cancer, offering a minimally invasive sample collection. The most widely used material is blood which, next to circulating tumor cells and circulating tumor DNA, is the source of tumor-educated platelets (TEPs). Methods: We developed imPlatelet method which converts RNA-sequenced platelet data to images, additionally implementing biological knowledge from the Kyoto Encyclopedia of Genes and Genomes Pathway. First, we tested imPlatelet method on a cohort of 401 non-small cell lung cancer patients and 62 sarcoma patients. Next, we applied the developed tool to platelets collected from a new, independent cohort of 28 ovarian cancer patients and 30 non-cancer benign gynaecological conditions. Results: imPlatelet provided excellent discrimination between cancer cases and healthy controls, with accuracy equal to 1 in training, validation and independent datasets. When discriminating between ovarian cancer cases and benign conditions, imPlatelet reached 0.91 balanced accuracy, with sensitivity and specificity equal to 0.95 and 0.88, respectively, in an independent test set. ImPlatelet outperformed current state-of-the-art method thromboSeq in the aspects of balanced classification accuracy, the computational power needed, user experience, and execution time. Conclusions: According to our knowledge, this is the first study implementing an image-based deep learning approach combined with biological knowledge to classify human samples. Our results on classification of ovarian cancer considerably outperform previously published methods and our own alternative attempts of discrimination. We show that a deep learning image-based classifier accurately identifies cancer, despite the limited number of samples and even among non-cancer conditions which affect platelet transcriptome making the diagnosis difficult.
Project description:Single-molecule localization microscopy (SMLM) achieves super-resolution imaging beyond the diffraction limit but critically relies on the use of photo-modulatable fluorescent probes. Here we report a general strategy for constructing cell-permeable photo-modulatable organic fluorescent probes for live-cell SMLM by exploiting the remarkable cytosolic delivery ability of a cell-penetrating peptide (rR)3R2. We develop photo-modulatable organic fluorescent probes consisting of a (rR)3R2 peptide coupled to a cell-impermeable organic fluorophore and a recognition unit. Our results indicate that these organic probes are not only cell permeable but can also specifically and directly label endogenous targeted proteins. Using the probes, we obtain super-resolution images of lysosomes and endogenous F-actin under physiological conditions. We resolve the dynamics of F-actin with 10 s temporal resolution in live cells and discern fine F-actin structures with diameters of ~80 nm. These results open up new avenues in the design of fluorescent probes for live-cell super-resolution imaging.
Project description:SummaryImageJ-MATLAB is a lightweight Java library facilitating bi-directional interoperability between MATLAB and ImageJ. By defining a standard for translation between matrix and image data structures, researchers are empowered to select the best tool for their image-analysis tasks.Availability and implementationFreely available extension to ImageJ2 ( http://imagej.net/Downloads ). Installation and use instructions available at http://imagej.net/MATLAB_Scripting. Tested with ImageJ 2.0.0-rc-54 , Java 1.8.0_66 and MATLAB R2015b.Contacteliceiri@wisc.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
Project description:The fluorescence microscopy methods presently used to characterize protein motion in cells infer protein motion from indirect observables, rather than measuring protein motion directly. Operationalizing these methods requires expertise that can constitute a barrier to their broad utilization. Here, we have developed PIPE (photo-converted intensity profile expansion) to directly measure the motion of tagged proteins and quantify it using an effective diffusion coefficient. PIPE works by pulsing photo-convertible fluorescent proteins, generating a peaked fluorescence signal at the pulsed region, and analyzing the spatial expansion of the signal. We demonstrate PIPE's success in measuring accurate diffusion coefficients in silico and in vitro and compare effective diffusion coefficients of native cellular proteins and free fluorophores in vivo. We apply PIPE to measure diffusion anomality in the cell and use it to distinguish free fluorophores from native cellular proteins. PIPE's direct measurement and ease of use make it appealing for cell biologists.
Project description:Copper is indispensable in most aerobic organisms although it is toxic if unregulated as illustrated in many neurodegenerative diseases. To elucidate the mechanisms underlying copper release from cells, a membrane-targeting reporter which can compete with extracellular copper-binding molecules is highly desirable. However, engineering a reporter protein to provide both high sensitivity and selectivity for copper(ii) has been challenging, likely due to a lack of proper copper(ii)-chelating strategies within proteins. Here, we report a new genetically encoded fluorescent copper(ii) reporter by employing a copper-binding tripeptide derived from human serum albumin (HSA), which is one of the major copper-binding proteins in extracellular environments. Optimized insertion of the tripeptide into the green fluorescent protein leads to rapid fluorescence quenching (up to >85% change) upon copper-binding, while other metal ions have no effect. Furthermore, the high binding affinity of the reporter enables reliable copper detection even in the presence of competing biomolecules such as HSA and amyloid beta peptides. We also demonstrate that our reporter proteins can be used to visualize dynamic copper fluctuations on living HeLa cell surfaces.
Project description:Groundtruth is a Matlab Graphical User Interface (GUI) developed for the identification of key features and artifacts within physiological signals. The ultimate aim of this GUI is to provide a simple means of assessing the performance of new sensors. Secondary, to this is providing a means of providing marked data, enabling assessment of automated artifact rejection and feature identification algorithms. With the emergence of new wearable sensor technologies, there is an unmet need for convenient assessment of device performance, and a faster means of assessing new algorithms. The proposed GUI allows interactive marking of artifact regions as well as simultaneous interactive identification of key features, e.g., respiration peaks in respiration signals, R-peaks in Electrocardiography signals, etc. In this paper, we present the base structure of the system, together with an example of its use for two simultaneously worn respiration sensors. The respiration rates are computed for both original as well as artifact removed data and validated using Bland-Altman plots. The respiration rates computed based on the proposed GUI (after artifact removal process) demonstrated consistent results for two respiration sensors after artifact removal process. Groundtruth is customizable, and alternative processing modules are easy to add/remove. Groundtruth is intended for open-source use.