Biology subjects

Mercer, J.

Publications and source records attributed to Mercer, J..

8 recordsLinked to original sources

bioRxiv2018

Fix your membrane receptor imaging: Actin cytoskeleton and CD4 membrane organization disruption by chemical fixation

Single-molecule localization microscopy (SMLM) techniques allow near molecular scale resolution (~ 20nm) as well as precise and robust analysis of protein organization at different scales. SMLM hardware, analytics and probes have been the focus of a variety of studies and are now commonly used in laboratories across the world. Protocol reliability and artefact identification are increasingly seen as important aspects of super-resolution microscopy. The reliability of these approaches thus requires in-depth evaluation so that biological findings are based on solid foundations. Here we explore how different fixation approaches that disrupt or preserve the actin cytoskeleton affect membrane protein organization. Using CD4 as a model, we show that fixation-mediated disruption of the actin cytoskeleton correlates with changes in CD4 membrane organization. We highlight how these artefacts are easy to overlook and how careful sample preparation is essential for extracting meaningful results from super-resolution microscopy.

cell biology↗

bioRxiv2018

NanoJ: a high-performance open-source super-resolution microscopy toolbox

Super-resolution microscopy has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for super-resolution microscopy designed to combine high performance and ease of use. We named it NanoJ - a reference to the popular ImageJ software it was de-veloped for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image re-construction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). We expect to expand NanoJ in the future through the development of new tools designed to improve quantitative data analysis and measure the reliability of fluorescent microscopy studies.

bioinformatics↗

bioRxiv2018

An Artificial Intelligence Workflow for Defining Host-Pathogen Interactions

For image-based infection biology, accurate unbiased quantification of host-pathogen interactions is essential, yet often performed manually or using limited enumeration employing simple image analysis algorithms based on image segmentation. Host protein recruitment to pathogens is often refractory to accurate automated assessment due to its heterogeneous nature. An intuitive intelligent image analysis program to assess host protein recruitment within general cellular pathogen defense is lacking. We present HRMAn (Host Response to Microbe Analysis), an open-source image analysis platform based on machine learning algorithms and deep learning. We show that HRMAn has the capability to learn phenotypes from the data, without relying on researcher-based assumptions. Using Toxoplasma gondii and Salmonella typhimurium we demonstrate HRMAns capacity to recognize, classify and quantify pathogen killing, replication and cellular defense responses.

microbiology↗

bioRxiv2018

Nanoscale Polarization of the Vaccinia Virus Entry Fusion Complex Drives Efficient Fusion

The spatial distribution of binding and fusion proteins on most viruses and the functional relevance of this organization remains largely unexplored. Employing super-resolution microscopy we define the nanoscale membrane architecture of the prototypic poxvirus, vaccinia. We show that binding and entry fusion complex (EFC) proteins are organized into distinct functional domains with fusion proteins polarized to the tips of virions. Repression of individual EFC components disrupted fusion protein polarization, correlating with a loss of fusion activity. Repression of vaccinia A27, a non-EFC protein implicated in fusion, revealed that disruption of EFC localization impacts virus fusion pore formation. We propose that the polarized distribution of EFCs is essential for poxvirus fusion efficiency.

microbiology↗

bioRxiv2018

Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases

Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the "Disease Module Identification DREAM Challenge", an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).

bioinformatics↗

bioRxiv2017

A unified web platform for network-based analyses of genomic data

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to quantitatively compare the signal-to-noise ratio of different networks, the biology they describe, and to identify the optimal network to interpret a particular genetic dataset. Via GeNets users can train a machine-learning model (Quack) to make such comparisons; and they can execute, store, and share analyses of genetic and RNA sequencing datasets.

genomics↗

bioRxiv2017

NanoJ-SQUIRREL: quantitative mapping and minimisation of super-resolution optical imaging artefacts

Most super-resolution microscopy methods depend on steps that contribute to the formation of image artefacts. Here we present NanoJ-SQUIRREL, an ImageJ-based analytical approach providing a quantitative assessment of super-resolution image quality. By comparing diffraction-limited images and super-resolution equivalents of the same focal volume, this approach generates a quantitative map of super-resolution defects, as well as methods for their correction. To illustrate its broad applicability to super-resolution approaches we apply our method to Localization Microscopy, STED and SIM images of a variety of in-cell structures including microtubules, poxviruses, neuronal actin rings and clathrin coated pits. We particularly focus on single-molecule localisation microscopy, where super-resolution reconstructions often feature imperfections not present in the original data. By showing the quantitative evolution of data quality over these varied sample preparation, acquisition and super-resolution methods we display the potential of NanoJ-SQUIRREL to guide optimization of superresolution imaging parameters.

biophysics↗

bioRxiv2016

Proteotype profiling unmasks a viral signaling network essential for poxvirus assembly and transcriptional competence

To orchestrate context-dependent signaling programs poxviruses encode two dual-specificity enzymes, the F10 kinase and the H1 phosphatase. These signaling mediators are essential for poxvirus production, yet their substrate profiles and systems level functions remain enigmatic. Using a phosphoproteomic screen of cells infected with wildtype, F10, and H1 mutant viruses we systematically defined the viral signaling network controlled by these enzymes. Quantitative cross-comparison revealed 33 F10 and/or H1 phosphosites within 17 viral proteins. Using this proteotype dataset to inform genotype-phenotype relationships we found that H1-deficient virions harbor a hidden hyper-cleavage phenotype driven by reversible phosphorylation of the virus protease I7 (S134). Quantitative phospho-proteotyping further revealed that the phosphorylation-dependent activity of the viral early transcription factor, A7 (Y367), underlies the transcription-deficient phenotype of H1 mutant virions. Together these results highlight the utility of combining quantitative proteotype screens with mutant viruses to uncover novel proteotype-phenotype-genotype relationships that are masked by classical genetic studies.

microbiology↗