scholarly journals BactMAP: an R package for integrating, analyzing and visualizing bacterial microscopy data

2019 ◽  
Author(s):  
Renske van Raaphorst ◽  
Morten Kjos ◽  
Jan-Willem Veening
Keyword(s):  
Cell Biology ◽  
R Package ◽  
Cell Segmentation ◽  
Bacterial Cells ◽  
Detection Algorithms ◽  
Spot Detection ◽  
Z Ring ◽  
Microscopy Data ◽  
Cell Microscopy ◽  
Different Sources

AbstractHigh-throughput analyses of single-cell microscopy data is a critical tool within the field of bacterial cell biology. Several programs have been developed to specifically segment bacterial cells from phase-contrast images. Together with spot and object detection algorithms, these programs offer powerful approaches to quantify observations from microscopy data, ranging from cell-to-cell genealogy to localization and movement of proteins. Most segmentation programs contain specific post-processing and plotting options, but these options vary between programs and possibilities to optimize or alter the outputs are often limited. Therefore, we developed BactMAP (Bacterial toolbox for Microscopy Analysis & Plotting), a software package that allows researchers to transform cell segmentation and spot detection data generated by different programs automatically into various plots. Furthermore, BactMAP makes it possible to perform custom analyses and change the layout of the output. Because BactMAP works independently of segmentation and detection programs, inputs from different sources can be compared within the same analysis pipeline. BactMAP runs in R, which enables the use of advanced statistical analysis tools as well as easily adjustable plot graphics in every operating system. Using BactMAP we visualize key cell cycle parameters in Bacillus subtilis and Staphylococcus aureus, and demonstrate that the DNA replication forks in Streptococcus pneumoniae dissociate and associate before splitting of the cell, after the Z-ring is formed at the new quarter positions. BactMAP is available from https://veeninglab.com/bactmap.

scholarly journals MiSiC, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Swapnesh Panigrahi ◽  
Dorothée Murat ◽  
Antoine Le Gall ◽  
Eugénie Martineau ◽  
Kelly Goldlust ◽  
...  
Keyword(s):  
Deep Learning ◽  
Bacterial Communities ◽  
Cell Biology ◽  
Imaging Modality ◽  
Interaction Model ◽  
Cell Segmentation ◽  
Bacterial Cells ◽  
Interspecies Interactions ◽  
Prey Interaction ◽  
Simple Implementation

Studies of bacterial communities, biofilms and microbiomes, are multiplying due to their impact on health and ecology. Live imaging of microbial communities requires new tools for the robust identification of bacterial cells in dense and often inter-species populations, sometimes over very large scales. Here, we developed MiSiC, a general deep-learning-based 2D segmentation method that automatically segments single bacteria in complex images of interacting bacterial communities with very little parameter adjustment, independent of the microscopy settings and imaging modality. Using a bacterial predator-prey interaction model, we demonstrate that MiSiC enables the analysis of interspecies interactions, resolving processes at subcellular scales and discriminating between species in millimeter size datasets. The simple implementation of MiSiC and the relatively low need in computing power make its use broadly accessible to fields interested in bacterial interactions and cell biology.

scholarly journals MiSiC, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities

2020 ◽  
Author(s):  
Swapnesh Panigrahi ◽  
Dorothée Murat ◽  
Antoine Le Gall ◽  
Eugénie Martineau ◽  
Kelly Goldlust ◽  
...  
Keyword(s):  
Deep Learning ◽  
Bacterial Communities ◽  
Cell Biology ◽  
Imaging Modality ◽  
Interaction Model ◽  
Cell Segmentation ◽  
Bacterial Cells ◽  
Interspecies Interactions ◽  
Wide Range ◽  
Prey Interaction

AbstractStudies of microbial communities by live imaging require new tools for the robust identification of bacterial cells in dense and often inter-species populations, sometimes over very large scales. Here, we developed MiSiC, a general deep-learning-based segmentation method that automatically segments a wide range of spatially structured bacterial communities with very little parameter adjustment, independent of the imaging modality. Using a bacterial predator-prey interaction model, we demonstrate that MiSiC enables the analysis of interspecies interactions, resolving processes at subcellular scales and discriminating between species in millimeter size datasets. The simple implementation of MiSiC and the relatively low need in computing power make its use broadly accessible to fields interested in bacterial interactions and cell biology.

scholarly journals Linking morphological and molecular sources to disentangle the case of Xylodon australis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Javier Fernández-López ◽  
M. Teresa Telleria ◽  
Margarita Dueñas ◽  
Mara Laguna-Castro ◽  
Klaus Schliep ◽  
...  
Keyword(s):  
New Zealand ◽  
Phylogenetic Analyses ◽  
Single Species ◽  
R Package ◽  
Molecular Data ◽  
Historical Collections ◽  
Close Relationship ◽  
Kinship Relations ◽  
History Of ◽  
Different Sources

AbstractThe use of different sources of evidence has been recommended in order to conduct species delimitation analyses to solve taxonomic issues. In this study, we use a maximum likelihood framework to combine morphological and molecular traits to study the case of Xylodon australis (Hymenochaetales, Basidiomycota) using the locate.yeti function from the phytools R package. Xylodon australis has been considered a single species distributed across Australia, New Zealand and Patagonia. Multi-locus phylogenetic analyses were conducted to unmask the actual diversity under X. australis as well as the kinship relations respect their relatives. To assess the taxonomic position of each clade, locate.yeti function was used to locate in a molecular phylogeny the X. australis type material for which no molecular data was available using morphological continuous traits. Two different species were distinguished under the X. australis name, one from Australia–New Zealand and other from Patagonia. In addition, a close relationship with Xylodon lenis, a species from the South East of Asia, was confirmed for the Patagonian clade. We discuss the implications of our results for the biogeographical history of this genus and we evaluate the potential of this method to be used with historical collections for which molecular data is not available.

scholarly journals 616 Preventing Biofilms on BACTIGON® Coated Camstent Urinary Catheters in Patients Undergoing Elective Colorectal Surgery: A Single Centre Pilot Study

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
C Lewis-Lloyd ◽  
J Dubern ◽  
K Kalenderski ◽  
N Halliday ◽  
M Alexander ◽  
...  
Keyword(s):  
Environmental Scanning Electron Microscopy ◽  
Environmental Scanning ◽  
Bacterial Cells ◽  
Urinary Catheters ◽  
Elective Colorectal Surgery ◽  
Mineral Compositions ◽  
Microscopy Data ◽  
Tract Infections ◽  
Biofilm Development ◽  
Hospital Acquired

Abstract Introduction Catheter associated urinary tract infections account for 40% of hospital acquired infections. They are associated with biofilms consisting of bacterial cells enmeshed in a self-generated extracellular matrix adhering to catheter surfaces. We have developed a novel polymer family that, coated onto urinary catheters, creates a “non-stick” surface preventing biofilm development. Method Prospective cohort of elective colorectal patients recruited pre-operatively, received a standard silicone (SS) or Camstent (BACTIGON®) coated urinary catheter. After removal, catheters were cut longitudinally into 3 segments. Biomass and biomineralisation were analysed using confocal fluorescence microscopy. Data were normalised by square rooting the catheter indwelling duration. Environmental scanning electron microscopy and energy dispersive x-ray spectroscopy was performed. Results Of 40 patients, 20 each received a SS or coated catheter. Between SS and coated catheters, average indwelling duration was similar and biofilm biomass was 32.068µg/cm2 (95%CI ±21.950) vs. 1.948µg/cm2 (95%CI ±2.595) (P = 0.0111). Confocal microscopy suggested a 93.93% reduction in biofilm biomass on coated catheters. Mineral compositions were different with biofilm and struvite/apatite on SS and calcium oxalate, endogenously derived, on coated catheters. Conclusions Inert BACTIGON® coated catheters appear superior at preventing biofilm formation than SS catheters. Clinical trials are needed to determine the clinical and health economic benefit of this intervention.

Detection of differentially methylated CpG sites between tumor samples with uneven tumor purities

2019 ◽  
Vol 36 (7) ◽  
pp. 2017-2024
Author(s):  
Weiwei Zhang ◽  
Ziyi Li ◽  
Nana Wei ◽  
Hua-Jun Wu ◽  
Xiaoqi Zheng
Keyword(s):  
Real Data ◽  
R Package ◽  
Differential Methylation ◽  
Least Square ◽  
Epigenetic Mechanism ◽  
Supplementary Information ◽  
Cpg Sites ◽  
Tumor Purity ◽  
Different Sources ◽  
Normal Controls

Abstract Motivation Inference of differentially methylated (DM) CpG sites between two groups of tumor samples with different geno- or pheno-types is a critical step to uncover the epigenetic mechanism of tumorigenesis, and identify biomarkers for cancer subtyping. However, as a major source of confounding factor, uneven distributions of tumor purity between two groups of tumor samples will lead to biased discovery of DM sites if not properly accounted for. Results We here propose InfiniumDM, a generalized least square model to adjust tumor purity effect for differential methylation analysis. Our method is applicable to a variety of experimental designs including with or without normal controls, different sources of normal tissue contaminations. We compared our method with conventional methods including minfi, limma and limma corrected by tumor purity using simulated datasets. Our method shows significantly better performance at different levels of differential methylation thresholds, sample sizes, mean purity deviations and so on. We also applied the proposed method to breast cancer samples from TCGA database to further evaluate its performance. Overall, both simulation and real data analyses demonstrate favorable performance over existing methods serving similar purpose. Availability and implementation InfiniumDM is a part of R package InfiniumPurify, which is freely available from GitHub (https://github.com/Xiaoqizheng/InfiniumPurify). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BactMAP: An R package for integrating, analyzing and visualizing bacterial microscopy data

2019 ◽  
Vol 113 (1) ◽  
pp. 297-308 ◽  
Author(s):  
Renske Raaphorst ◽  
Morten Kjos ◽  
Jan‐Willem Veening
Keyword(s):  
R Package ◽  
Microscopy Data

scholarly journals Treeio: An R Package for Phylogenetic Tree Input and Output with Richly Annotated and Associated Data

2019 ◽  
Vol 37 (2) ◽  
pp. 599-603 ◽  
Author(s):  
Li-Gen Wang ◽  
Tommy Tsan-Yuk Lam ◽  
Shuangbin Xu ◽  
Zehan Dai ◽  
Lang Zhou ◽  
...  
Keyword(s):  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
R Package ◽  
External Data ◽  
Input And Output ◽  
Evolutionary Context ◽  
Tree Data ◽  
Downstream Analysis ◽  
Different Sources ◽  
Associated Data

Abstract Phylogenetic trees and data are often stored in incompatible and inconsistent formats. The outputs of software tools that contain trees with analysis findings are often not compatible with each other, making it hard to integrate the results of different analyses in a comparative study. The treeio package is designed to connect phylogenetic tree input and output. It supports extracting phylogenetic trees as well as the outputs of commonly used analytical software. It can link external data to phylogenies and merge tree data obtained from different sources, enabling analyses of phylogeny-associated data from different disciplines in an evolutionary context. Treeio also supports export of a phylogenetic tree with heterogeneous-associated data to a single tree file, including BEAST compatible NEXUS and jtree formats; these facilitate data sharing as well as file format conversion for downstream analysis. The treeio package is designed to work with the tidytree and ggtree packages. Tree data can be processed using the tidy interface with tidytree and visualized by ggtree. The treeio package is released within the Bioconductor and rOpenSci projects. It is available at https://www.bioconductor.org/packages/treeio/.

scholarly journals The JCB DataViewer scales up

2012 ◽  
Vol 198 (3) ◽  
pp. 271-272 ◽  
Author(s):  
Elizabeth H. Williams ◽  
Pamela Carpentier ◽  
Tom Misteli
Keyword(s):  
Electron Microscopy ◽  
Cell Biology ◽  
Biological Imaging ◽  
Electron Microscopy Data ◽  
New Frontier ◽  
Microscopy Data

One of the major forces driving the birth of the field of cell biology was the application of electron microscopy to cells. Today, virtual nanoscopy has brought electron microscopy and the cell biology community to a new frontier in biological imaging and cell biological inquiry. The Journal of Cell Biology is pleased to announce that the JCB DataViewer is “going big” to host electron microscopy data at a whole new scale.

scholarly journals BCM3D 2.0: Accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations

2021 ◽  
Author(s):  
Ji Zhang ◽  
Yibo Wang ◽  
Eric Donarski ◽  
Andreas Gahlmann
Keyword(s):  
Image Analysis ◽  
Bacterial Cell ◽  
Single Cells ◽  
Three Dimensional ◽  
Time Lapse ◽  
Cell Segmentation ◽  
Bacterial Cells ◽  
Deep Convolutional Neural Networks ◽  
Fluorescence Images ◽  
Image Representations

Accurate detection and segmentation of single cells in three-dimensional (3D) fluorescence time-lapse images is essential for measuring individual cell behaviors in large bacterial communities called biofilms. Recent progress in machine-learning-based image analysis is providing this capability with every increasing accuracy. Leveraging the capabilities of deep convolutional neural networks (CNNs), we recently developed bacterial cell morphometry in 3D (BCM3D), an integrated image analysis pipeline that combines deep learning with conventional image analysis to detect and segment single biofilm-dwelling cells in 3D fluorescence images. While the first release of BCM3D (BCM3D 1.0) achieved state-of-the-art 3D bacterial cell segmentation accuracies, low signal-to-background ratios (SBRs) and images of very dense biofilms remained challenging. Here, we present BCM3D 2.0 to address this challenge. BCM3D 2.0 is completely complementary to the approach utilized in BCM3D 1.0. Instead of training CNNs to perform voxel classification, we trained CNNs to translate 3D fluorescence images into intermediate 3D image representations that are, when combined appropriately later, more amenable to conventional mathematical image processing than a single experimental image. Using this approach, improved segmentation results are obtained even for very low SBRs and/or high cell density biofilm images. The improved cell segmentation accuracies in turn enable improved accuracies of tracking individual cells through 3D space and time, which opens the door to investigating time-dependent phenomena in bacterial biofilms at the cellular level.

scholarly journals Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

2021 ◽  
Author(s):  
Kevin John Cutler ◽  
Carsen Stringer ◽  
Paul A Wiggins ◽  
Joseph D Mougous
Keyword(s):  
Neural Network ◽  
Bacterial Cell ◽  
Cell Biology ◽  
Independent Solution ◽  
Cell Segmentation ◽  
Great Promise ◽  
Single Cell Level ◽  
Cell Level ◽  
Bacterial Cultures ◽  
And Performance

Advances in microscopy hold great promise for allowing quantitative and precise readouts of morphological and molecular phenomena at the single cell level in bacteria. However, the potential of this approach is ultimately limited by the availability of methods to perform unbiased cell segmentation, defined as the ability to faithfully identify cells independent of their morphology or optical characteristics. In this study, we present a new algorithm, Omnipose, which accurately segments samples that present significant challenges to current algorithms, including mixed bacterial cultures, antibiotic-treated cells, and cells of extended or branched morphology. We show that Omnipose achieves generality and performance beyond leading algorithms and its predecessor, Cellpose, by virtue of unique neural network outputs such as the gradient of the distance field. Finally, we demonstrate the utility of Omnipose in the context of characterizing extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a uniquely powerful tool for answering diverse questions in bacterial cell biology.

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