Biology subjects

White, M. D.

Publications and source records attributed to White, M. D..

7 recordsLinked to original sources

bioRxiv2026

A Cre-responsive reporter quail for flexible genetic lineage tracing

Genetic lineage tracing is a powerful approach for investigating cell fate and tissue development, but versatile Cre/loxP-based tools remain limited in avian models. Here, we generated and characterised a Cre-responsive transgenic quail line that enables permanent genetic labelling of embryonic cell populations. The reporter line ubiquitously expresses nuclear-localised mCherry, which is excised following Cre-mediated recombination, enabling expression of nuclear-localised EGFP. We demonstrate that this reporter can be coupled to multiple Cre delivery strategies, providing flexible spatial and temporal control of lineage labelling. Ubiquitous or cell-type-specific Cre expression induces recombination and stable EGFP labelling that is maintained during clonal expansion. Optogenetic activation of a split Cre system enables highly spatially and temporally restricted recombination. Furthermore, lipid nanoparticle-mediated delivery of Cre mRNA enables efficient recombination in distinct embryonic cell populations, including mesodermal, neuroepithelial, neural crest, endothelial and circulating cells. This transgenic reporter quail provides a flexible platform for permanent lineage labelling without the need to generate tissue-specific Cre transgenic lines and can be readily combined with diverse Cre delivery strategies to investigate cell fate, lineage and morphogenesis in vivo.

developmental biology↗

bioRxiv2024

Genomic mosaicism reveals developmental organization of trunk neural crest-derived ganglia

The neural crest generates numerous cell types, but conflicting results leave developmental origins unresolved. Here using somatic mosaic variants as cellular barcodes, we infer embryonic clonal dynamics of trunk neural crest, focusing on the sensory and sympathetic ganglia. From three independent adult neurotypical human donors, we identified 1,278 mosaic variants using deep whole-genome sequencing, then profiled allelic fractions in 187 anatomically dissected ganglia. We found a massive rostrocaudal spread of progenitor clones specific to sensory or sympathetic ganglia, which unlike in the brain, showed robust bilateral distributions. Computational modeling suggested neural crest progenitor fate specification preceded delamination from neural tube. Single-cell multiomic analysis suggested both neurons and glia contributed to the rostrocaudal clonal organization. CRISPR barcoding in mice and live imaging in quail embryos confirmed these clonal dynamics across multiple somite levels. Our findings reveal an evolutionarily conserved clonal spread of cells populating peripheral neural ganglia. Highlights- Genetic mosaicism and real-time imaging reveal trunk neural crest cellular dynamics. - DRG or SG cells from different axial levels are more lineage-related than from the same level. - Cell fate specification of trunk neural crest progenitors occurs before neural tube delamination. - These aspects of clonal organization are evolutionarily conserved across mammals and avians.

developmental biology↗

bioRxiv2023

Host autophagy is exploited by the intracellular parasite Toxoplasma gondii to enhance amino acids levels.

Toxoplasma gondii, a widespread parasite, has the ability to infect nearly any nucleated cell in warm-blooded vertebrates. It is estimated that around 2 billion people globally have been infected by this pathogen. Although most healthy individuals can effectively control parasite replication, certain parasites may evade the immune response, establishing cysts in the brain that are refractory to the immune system and resistance to available drugs. For its chronic persistence in the brain, the parasite relies on host cells nutrients, particularly amino acids and lipids. Therefore, understanding how latent parasites persist in the brain is crucial for identifying potential drug targets against chronic forms. While shielded within parasitophorous vacuoles (PVs) or cysts, Toxoplasma exploits the host endoplasmic reticulum (ER) metabolism to sustains its persistence in the brain, resulting in host neurological alterations. In this study, we demonstrate that T. gondii disrupts the host ER homeostasis, resulting in accumulation of unfolded protein with the host ER. The host counters this stress by initiating an autophagic pathway known as ER-phagy, which breaks down unfolded proteins into amino acids, promoting their recycling. Remarkably, the persistence of latent forms in cell culture as well as behavioral changes in mice caused by the latent infection could be successfully reversed by restricting the availability of various amino acids during T. gondi infection. Our findings unveil the underlying mechanisms employed by T. gondii to exploit host ER and lysosomal pathways, enhancing nutrient levels during infection. These insights provide new strategies for the treatment of toxoplasmosis. ImportanceIntracellular parasites employ several mechanisms to manipulate the cellular environment, enabling them to persist in the host. Toxoplasma gondii, a single-celled parasite, possesses the ability to infect virtually any nucleated cell of warm-blooded vertebrates, including nearly 2 billion people worldwide. Unfortunately, existing treatments and immune responses are not entirely effective in eliminating the chronic persisting forms of the parasite. This study reveals that T. gondii induces the hosts autophagic pathway to boost amino acid levels in infected cells. The depletion of amino acids, in turn, influences the persistence of the parasites chronic forms, resulting in a reduction of neurological alterations caused by chronic infection in mice. Significantly, our investigation establishes the crucial role of host ER-phagy in the parasites persistence within the host during latent infection.

microbiology↗

bioRxiv2023

The Lifeact-EGFP Quail: A New Avian Model for Studying Actin Dynamics In Vivo

Here we report the generation of a transgenic Lifeact-EGFP quail line for the investigation of actin organization and dynamics during morphogenesis in vivo. This transgenic avian line allows for the high-resolution visualization of actin structures within the living embryo, from the subcellular filaments that guide cell shape to the supracellular assemblies that coordinate movements across tissues. The unique suitability of avian embryos to live imaging facilitates the investigation of previously intractable processes during embryogenesis. Using high-resolution live imaging approaches, we present the dynamic behaviours and morphologies of cellular protrusions in different tissue contexts. Furthermore, through the integration of live imaging with computational segmentation, we reveal the dynamics of cells undergoing apical constriction and the emergence of large-scale actin structures such as supracellular cables and rosettes within the neuroepithelium. These findings not only enhance our understanding of tissue morphogenesis but also demonstrate the utility of the Lifeact-EGFP transgenic quail as a new model system for live in vivo investigations of the actin cytoskeleton.

developmental biology↗

bioRxiv2022

Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity

Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. TRIAGE-Cluster integrates patterns of repressive chromatin deposited across diverse cell types with weighted density estimation to determine cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method that evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.

bioinformatics↗

bioRxiv2022

A Microtubule Mechanostat Enables Cells to Navigate Confined Environments

Cells migrating through complex 3D environments experience considerable physical challenges including tensile stress and compression. To move, cells need to resist these forces whilst also squeezing the large nucleus through confined spaces. This requires highly coordinated cortical contractility. Microtubules can both resist compressive forces and sequester key actomyosin regulators to ensure appropriate activation of contractile forces. Yet, how these two roles are integrated to achieve nuclear transmigration in 3D is largely unknown. Here, we demonstrate that compression triggers reinforcement of a dedicated microtubule structure at the rear of the nucleus by the mechanoresponsive recruitment of CLASPs (cytoplasmic linker-associated proteins) which dynamically strengthens and repairs the lattice. These reinforced microtubules form the mechanostat: an adaptive feedback mechanism that allows the cell to both withstand compressive force and spatiotemporally organise contractility signalling pathways. The microtubule mechanostat facilitates nuclear positioning and coordinates force production to enable the cell to pass through constrictions. Disruption of the mechanostat imbalances cortical contractility, stalling migration and ultimately resulting in catastrophic cell rupture. Our findings reveal a new role for microtubules as cellular sensors which detect and respond to compressive forces, enabling movement and ensuring survival in mechanically demanding environments. One Sentence SummaryMechanically tuned microtubules form a mechanostat to coordinate contractility and nuclear positioning in confined migration.

cell biology↗

bioRxiv2018

Machine learning to classify animal species in camera trap images: applications in ecology

1. Motion-activated cameras (\"camera traps\") are increasingly used in ecological and management studies for remotely observing wildlife and have been regarded as among the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analyzed, typically by visually observing each image, in order to extract data that can be used in ecological analyses.\n\n2. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or \"out-of-distribution\" in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model.\n\n3. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy, and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an R package (Machine Learning for Wildlife Image Classification; MLWIC) that allows the users to A) implement the trained model presented here and B) train their own model using classified images of wildlife from their studies.\n\n4. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analyzing images. We present an R package making these methods accessible to ecologists. We discuss the implications of this technology for ecology and considerations that should be addressed in future implementations of these methods.

ecology↗