Biology subjects

Mulvey, C. M.

Publications and source records attributed to Mulvey, C. M..

7 recordsLinked to original sources

bioRxiv2026

4D spatial transcriptomics reveals nodule identity emerges through stacked parallel developmental programs

Legume root nodules enable symbiotic nitrogen fixation through the development of specialized cells that accommodate nitrogen-fixing bacteria intracellularly and support bacterial nitrogenase activity. Here, we present a 4D (3D space and time) spatial transcriptomic atlas of Medicago truncatula nodules and lateral roots, revealing specialized symbiotic cell types that develop alongside a conserved lateral-root-derived program that underpins vascularization. Spatial profiling of both plant and bacterial transcripts resolves distinct transcriptional states and previously unrecognized cell states. Spatial analysis of developmental regulator mutants uncovers a cascading series of cell-type-specific programs during nodule maturation. LSH1/LSH2 are central regulators of these programs, and lsh1/lsh2 mutants exhibit collapse of hormonal gradients and nodule identity. Strikingly, loss of nodule identity collapses to a primary-root identity rather than lateral-root fate. This work reveals how tissue complexity emerges through stacked developmental programs sustained in distinct cellular compartments, allowing the emergence of cell types specialized for harboring nitrogen-fixing bacteria. HIGHLIGHTSO_LI>136,000 cells define a 4D spatial atlas of nodules and lateral roots C_LIO_LI3D spatial and dual-species analysis resolves dynamic host and rhizobial cell states C_LIO_LIA shared meristem generates 3 spatially coordinated symbiotic, non-symbiotic, and vascular cell programs C_LIO_LILSH1/LSH2 are critical for regulatory programs underlying nodule development and identity C_LIO_LINodule identity loss collapses toward a primary root-associated state, not a lateral root fate C_LI

plant biology↗

bioRxiv2025

Robust Consensus Nuclear and Cell Segmentation

Cell segmentation is a crucial step in numerous biomedical imaging endeavors; so much so that the community is flooded with publicly available, state-of-the-art segmentation techniques ready for out of the box use. Assessing the virtues and limitations of each method on a tissue sample set and then selecting the optimum method for each research objective and input image is a time consuming and exacting task that often monopolizes the resources of biologists, biochemists, immunologists and pathologists; despite not being their project primary research goal. In this work, we present a segmentation software wrapper, coined CellSampler, which runs a selection of established segmentation methods and then combines their individual segmentation masks into a single optimized mask. This, so called uber mask, selects the best of the established masks across local neighborhoods within the image, where the neighborhood size and the statistical measure which determines the qualitative term best are both chosen by the user.

bioinformatics↗

bioRxiv2021

Exploration and analysis of molecularly annotated, 3D models of breast cancer at single-cell resolution using virtual reality

A set of increasingly powerful approaches are enabling spatially resolved measurements of growing numbers of molecular features in biological samples. While important insights can be derived from the two-dimensional data that many of these technologies generate, it is clear that extending these approaches into the third and fourth dimensions will magnify their impact. Realizing biological insights from datasets where thousands to millions of cells are annotated with tens to hundreds of parameters in space will require the development of new computational and visualization strategies. Here, we describe Theia, a virtual reality-based platform, which enables exploration and analysis of either volumetric or segmented, molecularly-annotated, three-dimensional datasets, with the option to extend the analysis to time-series data. We also describe our pipeline for generating annotated 3D models of breast cancer and supply several datasets to enable users to explore the utility of Theia for understanding cancer biology in three dimensions.

cancer biology↗

bioRxiv2021

The Imaging and Molecular Annotation of Xenografts and Tumours (IMAXT) High Throughput Data and Analysis Infrastructure

With the aim of producing a 3D representation of tumours, IMAXT uses a large variety of modalities in order to acquire tumour samples and produce a map of every cell in the tumour and its host environment. With the large volume and variety of data produced in the project we develop automatic data workflows and analysis pipelines and introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located instead of bringing data to their local computers. Here we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumour models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT.

cancer biology↗

bioRxiv2019

A direct role for SNX9 in the biogenesis of filopodia

Filopodia are finger-like actin-rich protrusions that extend from the cell surface and are important for cell-cell communication and pathogen internalization. The small size and transient nature of filopodia combined with shared usage of actin regulators within cells confounds attempts to identify filopodial proteins. Here, we used phage display phenotypic screening to isolate antibodies that alter the actin morphology of filopodia-like structures in vitro. We found that all of the antibodies that cause shorter FLS interact with SNX9, an actin regulator that binds phosphoinositides during endocytosis and in invadopodia. In cells, we discover SNX9 at specialised filopodia in Xenopus development and that SNX9 is an endogenous component of filopodia that are hijacked by Chlamydia entry. We show the use of antibody technology to identify proteins used in filopodia-like structures, and a role for SNX9 in filopodia.

cell biology↗

bioRxiv2018

LOPIT-DC: A simpler approach to high-resolution spatial proteomics

Hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method for studying protein subcellular localisation in complex biological samples. As a simpler alternative we developed a second workflow named Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) which is faster and less resource-intensive. We present the most comprehensive high-resolution mass spectrometry-based human dataset to date and deliver a flexible set of subcellular proteomics protocols for sample preparation and data analysis. For the first time, we methodically compare these two different mass spectrometry-based spatial proteomics methods within the same study and also apply QSep, the first tool that objectively and robustly quantifies subcellular resolution in spatial proteomics data. Using both approaches we highlight suborganellar resolution and isoform-specific subcellular niches as well as the locations of large protein complexes and proteins involved in signalling pathways which play important roles in cancer and metabolism. Finally, we showcase an extensive analysis of the multilocalising proteome identified via both methods.

biochemistry↗

bioRxiv2018

A Bayesian Mixture Modelling Approach For Spatial Proteomics

AbstractAnalysis of the spatial sub-cellular distribution of proteins is of vital importance to fully understand context specific protein function. Some proteins can be found with a single location within a cell, but up to half of proteins may reside in multiple locations, can dynamically re-localise, or reside within an unknown functional compartment. These considerations lead to uncertainty in associating a protein to a single location. Currently, mass spectrometry (MS) based spatial proteomics relies on supervised machine learning algorithms to assign proteins to sub-cellular locations based on common gradient profiles. However, such methods fail to quantify uncertainty associated with sub-cellular class assignment. Here we reformulate the framework on which we perform statistical analysis. We propose a Bayesian generative classifier based on Gaussian mixture models to assign proteins probabilistically to sub-cellular niches, thus proteins have a probability distribution over sub-cellular locations, with Bayesian computation performed using the expectation-maximisation (EM) algorithm, as well as Markov-chain Monte-Carlo (MCMC). Our methodology allows proteome-wide uncertainty quantification, thus adding a further layer to the analysis of spatial proteomics. Our framework is flexible, allowing many different systems to be analysed and reveals new modelling opportunities for spatial proteomics. We find our methods perform competitively with current state-of-the art machine learning methods, whilst simultaneously providing more information. We highlight several examples where classification based on the support vector machine is unable to make any conclusions, while uncertainty quantification using our approach provides biologically intriguing results. To our knowledge this is the first Bayesian model of MS-based spatial proteomics data.\n\nAuthor summarySub-cellular localisation of proteins provides insights into sub-cellular biological processes. For a protein to carry out its intended function it must be localised to the correct sub-cellular environment, whether that be organelles, vesicles or any sub-cellular niche. Correct sub-cellular localisation ensures the biochemical conditions for the protein to carry out its molecular function are met, as well as being near its intended interaction partners. Therefore, mis-localisation of proteins alters cell biochemistry and can disrupt, for example, signalling pathways or inhibit the trafficking of material around the cell. The sub-cellular distribution of proteins is complicated by proteins that can reside in multiple micro-environments, or those that move dynamically within the cell. Methods that predict protein sub-cellular localisation often fail to quantify the uncertainty that arises from the complex and dynamic nature of the sub-cellular environment. Here we present a Bayesian methodology to analyse protein sub-cellular localisation. We explicitly model our data and use Bayesian inference to quantify uncertainty in our predictions. We find our method is competitive with state-of-the-art machine learning methods and additionally provides uncertainty quantification. We show that, with this additional information, we can make deeper insights into the fundamental biochemistry of the cell.

systems biology↗