AFid: a tool for automated identification and exclusion of autofluorescent objects from microscopy images

2020 ◽  
Author(s):  
Heeva Baharlou ◽  
Nicolas P Canete ◽  
Kirstie M Bertram ◽  
Kerrie J Sandgren ◽  
Anthony L Cunningham ◽  
...  
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
Hiv Transmission ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Automated Identification ◽  
Immune Markers ◽  
Downstream Analysis ◽  
Microscopy Images ◽  
Standing Problem

Abstract Motivation Autofluorescence is a long-standing problem that has hindered the analysis of images of tissues acquired by fluorescence microscopy. Current approaches to mitigate autofluorescence in tissue are lab-based and involve either chemical treatment of sections or specialized instrumentation and software to ‘unmix’ autofluorescent signals. Importantly, these approaches are pre-emptive and there are currently no methods to deal with autofluorescence in acquired fluorescence microscopy images. Results To address this, we developed Autofluorescence Identifier (AFid). AFid identifies autofluorescent pixels as discrete objects in multi-channel images post-acquisition. These objects can then be tagged for exclusion from downstream analysis. We validated AFid using images of FFPE human colorectal tissue stained for common immune markers. Further, we demonstrate its utility for image analysis where its implementation allows the accurate measurement of HIV–Dendritic cell interactions in a colorectal explant model of HIV transmission. Therefore, AFid represents a major leap forward in the extraction of useful data from images plagued by autofluorescence by offering an approach that is easily incorporated into existing workflows and that can be used with various samples, staining panels and image acquisition methods. We have implemented AFid in ImageJ, Matlab and R to accommodate the diverse image analysis community. Availability and implementation AFid software is available at https://ellispatrick.github.io/AFid. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Digital removal of autofluorescence from microscopy images

2019 ◽  
Author(s):  
Heeva Baharlou ◽  
Nicolas P Canete ◽  
Kirstie M Bertram ◽  
Kerrie J Sandgren ◽  
Anthony L Cunningham ◽  
...  
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
Protein Expression ◽  
Supplementary Information ◽  
Link Type ◽  
Microscopy Image ◽  
Microscopy Image Analysis ◽  
Microscopy Images ◽  
Standing Problem

AbstractAutofluorescence is a long-standing problem that has hindered fluorescence microscopy image analysis. To address this, we have developed a method that identifies and removes autofluorescent signals from multi-channel images post acquisition. We demonstrate the broad utility of this algorithm in accurately assessing protein expression in situ through the removal of interfering autofluorescent signals.Availability and implementationhttps://ellispatrick.github.io/[email protected] informationSupplementary Figs. 1–13

scholarly journals YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells

2019 ◽  
Vol 35 (21) ◽  
pp. 4525-4527 ◽  
Author(s):  
Alex X Lu ◽  
Taraneh Zarin ◽  
Ian S Hsu ◽  
Alan M Moses
Keyword(s):  
Image Analysis ◽  
Web Application ◽  
Single Cells ◽  
Yeast Cells ◽  
Supplementary Information ◽  
Supplementary Data ◽  
User Friendly ◽  
Microscopy Images

Abstract Summary We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines. Availability and implementation YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation. Supplementary information Supplementary data are available at Bioinformatics online.

LOBSTER: an environment to design bioimage analysis workflows for large and complex fluorescence microscopy data

2019 ◽  
Vol 36 (8) ◽  
pp. 2634-2635 ◽  
Author(s):  
Sébastien Tosi ◽  
Lídia Bardia ◽  
Maria Jose Filgueira ◽  
Alexandre Calon ◽  
Julien Colombelli
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
Main Memory ◽  
Supplementary Information ◽  
Research Projects ◽  
Online Tutorials ◽  
Bioimage Analysis ◽  
Set Up ◽  
Microscopy Data ◽  
Microscopy Images

Abstract Summary Open source software such as ImageJ and CellProfiler greatly simplified the quantitative analysis of microscopy images but their applicability is limited by the size, dimensionality and complexity of the images under study. In contrast, software optimized for the needs of specific research projects can overcome these limitations, but they may be harder to find, set up and customize to different needs. Overall, the analysis of large, complex, microscopy images is hence still a critical bottleneck for many Life Scientists. We introduce LOBSTER (Little Objects Segmentation and Tracking Environment), an environment designed to help scientists design and customize image analysis workflows to accurately characterize biological objects from a broad range of fluorescence microscopy images, including large images exceeding workstation main memory. LOBSTER comes with a starting set of over 75 sample image analysis workflows and associated images stemming from state-of-the-art image-based research projects. Availability and implementation LOBSTER requires MATLAB (version ≥ 2015a), MATLAB Image processing toolbox, and MATLAB statistics and machine learning toolbox. Code source, online tutorials, video demonstrations, documentation and sample images are freely available from: https://sebastients.github.io. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MAJIQ-SPEL: web-tool to interrogate classical and complex splicing variations from RNA-Seq data

2017 ◽  
Vol 34 (2) ◽  
pp. 300-302 ◽  
Author(s):  
Christopher J Green ◽  
Matthew R Gazzara ◽  
Yoseph Barash
Keyword(s):  
Experimental Validation ◽  
Ucsc Genome Browser ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Web Tool ◽  
Rt Pcr ◽  
Design Algorithm ◽  
Gene Isoforms ◽  
Downstream Analysis

Abstract Summary Analysis of RNA sequencing (RNA-Seq) data have highlighted the fact that most genes undergo alternative splicing (AS) and that these patterns are tightly regulated. Many of these events are complex, resulting in numerous possible isoforms that quickly become difficult to visualize, interpret and experimentally validate. To address these challenges we developed MAJIQ-SPEL, a web-tool that takes as input local splicing variations (LSVs) quantified from RNA-Seq data and provides users with visualization and quantification of gene isoforms associated with those. Importantly, MAJIQ-SPEL is able to handle both classical (binary) and complex, non-binary, splicing variations. Using a matching primer design algorithm it also suggests to users possible primers for experimental validation by RT-PCR and displays those, along with the matching protein domains affected by the LSV, on UCSC Genome Browser for further downstream analysis. Availability and implementation Program and code will be available athttp://majiq.biociphers.org/majiq-spel. Supplementary information Supplementary data are available atBioinformatics online.

scholarly journals Intracellular Background Estimation for Quantitative Fluorescence Microscopy

Proceedings ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 22
Author(s):  
Yannis Kalaidzidis ◽  
Hernán Morales-Navarrete ◽  
Inna Kalaidzidis ◽  
Marino Zerial
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
Spatial Frequency ◽  
Background Intensity ◽  
Background Estimation ◽  
Peripheral Proteins ◽  
Abrupt Changes ◽  
Intracellular Organelles ◽  
Quantitative Fluorescence ◽  
Microscopy Images

Fluorescently targeted proteins are widely used for studies of intracellular organelles dynamic. Peripheral proteins are transiently associated with organelles and a significant fraction of them are located at the cytosol. Image analysis of peripheral proteins poses a problem on properly discriminating membrane-associated signal from the cytosolic one. In most cases, signals from organelles are compact in comparison with diffuse signal from cytosol. Commonly used methods for background estimation depend on the assumption that background and foreground signals are separable by spatial frequency filters. However, large non-stained organelles (e.g., nuclei) result in abrupt changes in the cytosol intensity and lead to errors in the background estimation. Such mistakes result in artifacts in the reconstructed foreground signal. We developed a new algorithm that estimates background intensity in fluorescence microscopy images and does not produce artifacts on the borders of nuclei.

scholarly journals Rotation equivariant and invariant neural networks for microscopy image analysis

2019 ◽  
Vol 35 (14) ◽  
pp. i530-i537 ◽  
Author(s):  
Benjamin Chidester ◽  
Tianming Zhou ◽  
Minh N Do ◽  
Jian Ma
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
High Throughput ◽  
Protein Localization ◽  
Effective Means ◽  
Image Data ◽  
Supplementary Information ◽  
Microscopy Image ◽  
Microscopy Image Analysis ◽  
Microscopy Images

Abstract Motivation Neural networks have been widely used to analyze high-throughput microscopy images. However, the performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Highly relevant to the goal of automated cell phenotyping from microscopy image data is rotation invariance. Here we consider the application of two schemes for encoding rotation equivariance and invariance in a convolutional neural network, namely, the group-equivariant CNN (G-CNN), and a new architecture with simple, efficient conic convolution, for classifying microscopy images. We additionally integrate the 2D-discrete-Fourier transform (2D-DFT) as an effective means for encoding global rotational invariance. We call our new method the Conic Convolution and DFT Network (CFNet). Results We evaluated the efficacy of CFNet and G-CNN as compared to a standard CNN for several different image classification tasks, including simulated and real microscopy images of subcellular protein localization, and demonstrated improved performance. We believe CFNet has the potential to improve many high-throughput microscopy image analysis applications. Availability and implementation Source code of CFNet is available at: https://github.com/bchidest/CFNet. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals snakePipes: facilitating flexible, scalable and integrative epigenomic analysis

2019 ◽  
Vol 35 (22) ◽  
pp. 4757-4759 ◽  
Author(s):  
Vivek Bhardwaj ◽  
Steffen Heyne ◽  
Katarzyna Sikora ◽  
Leily Rabbani ◽  
Michael Rauer ◽  
...  
Keyword(s):  
Single Cell ◽  
Source Code ◽  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
Rna Seq ◽  
Downstream Analysis ◽  
Scalable Analysis

Abstract Summary Due to the rapidly increasing scale and diversity of epigenomic data, modular and scalable analysis workflows are of wide interest. Here we present snakePipes, a workflow package for processing and downstream analysis of data from common epigenomic assays: ChIP-seq, RNA-seq, Bisulfite-seq, ATAC-seq, Hi-C and single-cell RNA-seq. snakePipes enables users to assemble variants of each workflow and to easily install and upgrade the underlying tools, via its simple command-line wrappers and yaml files. Availability and implementation snakePipes can be installed via conda: `conda install -c mpi-ie -c bioconda -c conda-forge snakePipes’. Source code (https://github.com/maxplanck-ie/snakepipes) and documentation (https://snakepipes.readthedocs.io/en/latest/) are available online. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals TRTools: a toolkit for genome-wide analysis of tandem repeats

2020 ◽  
Author(s):  
Nima Mousavi ◽  
Jonathan Margoliash ◽  
Neha Pusarla ◽  
Shubham Saini ◽  
Richard Yanicky ◽  
...  
Keyword(s):  
Quality Control ◽  
Tandem Repeats ◽  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
Genome Wide Analysis ◽  
Genome Wide ◽  
Wide Range ◽  
Downstream Analysis

Abstract Summary A rich set of tools have recently been developed for performing genome-wide genotyping of tandem repeats (TRs). However, standardized tools for downstream analysis of these results are lacking. To facilitate TR analysis applications, we present TRTools, a Python library and suite of command line tools for filtering, merging and quality control of TR genotype files. TRTools utilizes an internal harmonization module, making it compatible with outputs from a wide range of TR genotypers. Availability and implementation TRTools is freely available at https://github.com/gymreklab/TRTools. Detailed documentation is available at https://trtools.readthedocs.io. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals EpiGraph: an open-source platform to quantify epithelial organization

2019 ◽  
Author(s):  
Pablo Vicente-Munuera ◽  
Pedro Gómez-Gálvez ◽  
Robert J Tetley ◽  
Cristina Forja ◽  
Antonio Tagua ◽  
...  
Keyword(s):  
Image Analysis ◽  
Open Access ◽  
Supplementary Information ◽  
Analysis Tool ◽  
Distribution Analysis ◽  
Supplementary Data ◽  
Programming Skills ◽  
User Friendly ◽  
Degree Of Order

Abstract Summary Here we present EpiGraph, an image analysis tool that quantifies epithelial organization. Our method combines computational geometry and graph theory to measure the degree of order of any packed tissue. EpiGraph goes beyond the traditional polygon distribution analysis, capturing other organizational traits that improve the characterization of epithelia. EpiGraph can objectively compare the rearrangements of epithelial cells during development and homeostasis to quantify how the global ensemble is affected. Importantly, it has been implemented in the open-access platform Fiji. This makes EpiGraph very user friendly, with no programming skills required. Availability and implementation EpiGraph is available at https://imagej.net/EpiGraph and the code is accessible (https://github.com/ComplexOrganizationOfLivingMatter/Epigraph) under GPLv3 license. Supplementary information Supplementary data are available at Bioinformatics online.

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