Biology subjects

Barry, D. J.

Publications and source records attributed to Barry, D. J..

6 recordsLinked to original sources

bioRxiv2025

Enhancing Reproducibility Through Bioimage Analysis: The Significance of Effect Sizes and Controls

Bioimage analysis is a powerful tool for investigating complex biological processes, but its robustness depends on technical precision and rigorous experimental design. In particular, the use of appropriate controls and experimental repetition is critical for drawing meaningful conclusions. However, both are often used inadequately or overlooked, with "statistical significance" often prioritised, frequently obtained through the misuse or misinterpretation of statistical tests. In this study, we reanalyse publicly available image datasets to highlight the crucial role of robust experimental design in interpreting results. Our findings underscore the importance of focusing on effect sizes and biological relevance over arbitrary statistical thresholds. We also discuss the diminishing returns of increased data collection once statistical stability has been achieved. By refining control usage and emphasising effect sizes, this work aims to enhance the reproducibility and robustness of research findings. We provide open-access code to allow researchers to engage with the dataset, promoting better practices in experimental design and data interpretation.

cell biology↗

bioRxiv2023

Bridging Imaging Users to Imaging Analysis - A community survey

The "Bridging Imaging Users to Imaging Analysis" survey was conducted in 2022 by the Center for Open Bioimage Analysis (COBA), Bioimaging North America (BINA), and the Royal Microscopical Society Data Analysis in Imaging Section (RMS DAIM) to understand the needs of the imaging community. Through multi-choice and open-ended questions, the survey inquired about demographics, image analysis experiences, future needs, and suggestions on the role of tool developers and users. Participants of the survey were from diverse roles and domains of the life and physical sciences. To our knowledge, this is the first attempt to survey cross-community to bridge knowledge gaps between physical and life sciences imaging. Survey results indicate that respondents overarching needs are documentation, detailed tutorials on the usage of image analysis tools, user-friendly intuitive software, and better solutions for segmentation, ideally in a format tailored to their specific use cases. The tool creators suggested the users familiarize themselves with the fundamentals of image analysis, provide constant feedback, and report the issues faced during image analysis while the users would like more documentation and an emphasis on tool friendliness. Regardless of the computational experience, there is a strong preference for written tutorials to acquire knowledge on image analysis. We also observed that the interest in having office hours to get an expert opinion on their image analysis methods has increased over the years. In addition, the community suggests the need for a common repository for the available image analysis tools and their applications. The opinions and suggestions of the community, released here in full, will help the image analysis tool creation and education communities to design and deliver the resources accordingly.

scientific communication and education↗

bioRxiv2023

Automated staging of zebrafish embryos with KimmelNet

The zebrafish (Danio rerio), is an important biomedical model organism used in many disciplines. The phenomenon of developmental delay in zebrafish embryos has been widely reported as part of a mutant or treatment-induced phenotype. However, the detection and quantification of these delays is often achieved through manual observation with reference to staging guides, which is both time-consuming and subjective. We recently reported a machine learning-based classifier, capable of quantifying the developmental delay between two populations of zebrafish embryos. Here, we build on that work by introducing a deep learning-based model (KimmelNet) that has been trained to predict the age (hours post fertilisation) of populations of zebrafish embryos. We show that when KimmelNet is tested on 2D brightfield images of zebrafish embryos, the predictions generated agree closely with those expected from established approaches to staging. Random sampling of the test data demonstrate that KimmelNet can be used to detect developmental delay between two populations with high confidence based on as few as 100 images of each population. Finally, we show that KimmelNet generalises to previously unseen data, with limited transfer learning improving this performance significantly. With the ability to analyse tens of thousands of standard brightfield microscopy images on a timescale of minutes, we envisage that KimmelNet will be a valuable resource for the developmental biology community. Furthermore, the approach we have used could easily be adapted to generate models for other organisms.

developmental biology↗

bioRxiv2020

GIANI: open-source software for automated analysis of 3D microscopy images

The study of cellular and developmental processes in physiologically relevant three-dimensional (3D) systems facilitates an understanding of mechanisms underlying cell fate, disease and injury. While cutting-edge microscopy technologies permit the routine acquisition of 3D datasets, there is currently a limited number of open-source software packages to analyse such images. Here we describe GIANI (djpbarry.github.io/Giani), new software for the analysis of 3D images, implemented as a plugin for the popular FIJI platform. The design primarily facilitates segmentation of nuclei and cells, followed by quantification of morphology and protein expression. GIANI enables routine and reproducible batch-processing of large numbers of images and also comes with scripting and command line tools, allowing users to incorporate its functionality into their own scripts and also run GIANI on a high-performance computing cluster. We demonstrate the utility of GIANI by quantifying cell morphology and protein expression in confocal images of mouse early embryos and by segmenting nuclei from light sheet microscopy images of the flour beetle embryo. We also validate the performance of the software using simulated data. More generally, we anticipate that GIANI will be a useful tool for researchers in a variety of biomedical fields.

bioinformatics↗

bioRxiv2020

MICAL2 acts through Arp3B isoform-specific Arp2/3 complexes to destabilize branched actin networks

The Arp2/3 complex (Arp2, Arp3 and ARPC1-5) is essential to generate branched actin filament networks for many cellular processes. Human Arp3, ARPC1 and ARPC5 exist as two isoforms but the functional properties of Arp2/3 iso-complexes is largely unexplored. Here we show that Arp3B, but not Arp3 is subject to regulation by the methionine monooxygenase MICAL2, which is recruited to branched actin networks by coronin-1C. Although Arp3 and Arp3B iso-complexes promote actin assembly equally efficiently in vitro, they have different cellular properties. Arp3B turns over significantly faster than Arp3 within the network and upon its depletion actin turnover decreases. Substitution of Arp3B Met293 by Thr, the corresponding residue in Arp3 increases actin network stability, and conversely, replacing Arp3 Thr293 with Gln to mimic Met oxidation promotes network disassembly. Thus, MICAL2 regulates a subset of Arp2/3 complexes to control branched actin network disassembly.

cell biology↗

bioRxiv2019

A FIJI Macro for quantifying pattern in extracellular matrix

Diverse extracellular matrix patterns are observed in both normal and pathological tissue. However, most current tools for quantitative analysis focus on a single aspect of matrix patterning. Thus, an automated pipeline that simultaneously quantifies a broad range of metrics and enables a comprehensive description of varied matrix patterns is needed. To this end we have developed an ImageJ plugin called TWOMBLI, which stands for The Workflow Of Matrix BioLogy Informatics. TWOMBLI is designed to be quick, versatile and easy-to-use particularly for non-computational scientists. TWOMBLI can be downloaded from https://github.com/wershofe/TWOMBLI together with detailed documentation. Here we present an overview of the pipeline together with examples from a wide range of contexts where matrix patterns are generated.

cancer biology↗