Biology subjects

Masison, J.

Publications and source records attributed to Masison, J..

4 recordsLinked to original sources

bioRxiv2026

High-Throughput, Multiplex Immunofluorescence for the Computer-automated Immunophenotyping of Mycosis Fungoides

The diagnosis of Mycosis Fungoides (MF) is difficult and often delayed, exacerbated by the constraint of conventional immunohistochemistry (IHC) to analyze only one antigen per tissue section, often necessitating repeat biopsies and extensive workups. We sought to validate a high-throughput Multiplex Immunofluorescence (MIF) method, coupled with computer-automated image analysis, to generate comprehensive immunophenotyping data from a single formalin-fixed, paraffin-embedded (FFPE) biopsy. We applied an 11-biomarker MIF panel across 18 archived skin specimens (9 MF/TCR clonality positive and 9 control/TCR clonality negative). Initial validation confirmed that MIF antigen expression and spatial localization were concordant with sequential IHC-stained sections. Whole slide image stacks were analyzed using both computer-assisted and fully computer-automated pipelines. Both methods successfully delineated immunophenotypic differences. MF specimens showed a significant expansion of hematopoietic cells and proliferative T-lymphocytes compared to controls. Crucially, MF tissues also exhibited a significant increase in the percentage of atypical T-lymphocytes. Our results validate the potential of MIF to obtain comprehensive, high-dimensional diagnostic information from a single tissue section. Integration with computer-automated analysis offers a scalable, high-throughput platform that can significantly aid in the timely and accurate diagnosis of cutaneous lymphomas.

pathology↗

bioRxiv2022

Modeling the iron storage protein ferritin reveals how residual ferrihydrite iron determines initial ferritin iron sequestration kinetics

Ferritin is the primary iron storage protein in mammalian cells and is necessary for cellular iron homeostasis. Its ability to sequester iron protects cells from rising concentrations of ferrous iron limiting oxidative cell damage. The dynamics of its sequestration behavior have been studied extensively experimentally, but mostly in vitro, rather than within mammalian cells, computational modeling efforts are lacking, and several open questions remain. The importance of FT modeling is driven by the idea that understanding the kinetics of ferritin iron sequestration and how it impacts the regulation of iron metabolism in the cells that compose the main iron regulatory tissues (eg. Kupffer cells, splenic macrophages, enterocytes, erythroblasts, etc.) is central to understanding systemic iron control. The focus of the work here is establishing a model that tractably simulates the ferritin iron sequestration kinetics in a mechanistic way for incorporation into larger cell models, in addition to contributing to the understanding of ferritin iron sequestration dynamics within cells. The models parameter values were determined from published kinetic and binding experiments and the model was validated against independent data not used in its construction. Simulation results indicate that FT concentration is the most impactful on overall sequestration dynamics, while the FT iron saturation (number of iron atoms sequestered per FT cage) fine tunes the initial rates. Finally, because this model has a small number of reactions and species, was built to represent important mechanistic details of FT kinetics, and has flexibility to include subtle changes in subunit composition, we propose this model to be used as a building block for models of iron metabolism in a variety of specific cell types.

systems biology↗

bioRxiv2022

Computational modeling of macrophage iron sequestration during host defense against Aspergillus

Iron is essential to the virulence of Aspergillus species, and restricting iron availability is a critical mechanism of antimicrobial host defense. Macrophages recruited to the site of infection are at the crux of this process, employing multiple intersecting mechanisms to orchestrate iron sequestration from pathogens. To gain an integrated understanding of how this is achieved in invasive aspergillosis, we generated a transcriptomic time-series of the response of human monocyte-derived macrophages to Aspergillus and used this and the available literature to construct a mechanistic computational model of iron handling of macrophages during this infection. We found an overwhelming macrophage response beginning 2-4 hours after exposure to the fungus, which included upregulated transcription of iron import proteins transferrin receptor-1, divalent metal transporter-1, and ZIP family transporters, and downregulated transcription of the iron exporter ferroportin. The computational model, based on a discrete dynamical systems framework, consisted of 21 3-state nodes, and was validated with additional experimental data that were not used in model generation. The model accurately captures the steady state and the trajectories of most of the quantitatively measured nodes. In the experimental data, we surprisingly found that transferrin receptor-1 upregulation preceded the induction of inflammatory cytokines, a feature that deviated from model predictions. Model simulations suggested that direct induction of TfR1 after fungal recognition, independent of the Iron Regulatory Protein - Labile Iron Pool system, explains this finding. We anticipate that this model will contribute to a quantitative understanding of iron regulation as a fundamental host defense mechanism during aspergillosis. ImportanceInvasive pulmonary aspergillosis is a major cause of death among immunosuppressed individuals despite the best available therapy. Depriving the pathogen of iron is an essential component of host defense in this infection, but the mechanisms by which the host achieves this are complex. To understand how recruited macrophages mediate iron deprivation during the infection, we developed and validated a mechanistic computational model that integrates the available information in the field. The insights provided by this approach can help in designing iron modulation therapies as anti-fungal treatments.

immunology↗

bioRxiv2018

mol2sphere: Spherical Decomposition of Multi-Domain Molecules for Visualization and Coarse Grained Spatial Modeling

SummaryProteins, especially those involved in signaling pathways are composed of functional modules consisting of long strings of amino acids. These functional \"domains\" are linked together in geometric arrangements that can be rigid or flexible, depending on the nature of the linker domains. To understand the structure-function relationships in these macromolecules, it would be helpful to visualize the geometric arrangement of domains. Furthermore, accurate spatial representation of domain structure is necessary for coarse-grain models of the multi-molecular interactions that comprise signaling pathways. Here we introduce a new tool, mol2sphere, that transforms the atomistic structure of a macromolecule into a series of linked spheres corresponding to domains. mol2sphere does this with a k-means clustering algorithm. It may be used for visualization or for coarse grain modeling and simulation.\n\nAvailability and implementationmol2sphere is available as both a plugin for PyMOL and as a new feature within the SpringSaLaD modeling and simulation software. They run on all desktop platforms and are available with documentation at, respectively, https://nmrbox.org/registry/mol2sphere and http://vcell.org/ssalad. Source code is available for the PyMOL (and python) implementations on the NMRbox platform and for the SpringSaLaD implementation at https://github.com/jmasison/SpringSaLaD.

biophysics↗