Biology subjects

Inman, J. L.

Publications and source records attributed to Inman, J. L..

4 recordsLinked to original sources

bioRxiv2026

A quantitative proteomics dataset for assessment and prediction of low dose X-ray radiation exposure in mice.

Ionizing radiation induces molecular responses that may be used to estimate exposure when physical dosimeters are unavailable. Here we present two large-scale proteomics datasets generated from mouse dorsal skin punch samples collected following controlled X-ray exposures spanning multiple doses, dose rates, and post-exposure time points. Experiment 1 comprised 96 samples (including 16 reference samples) collected 6 days after exposure to 0-75 cGy delivered at either 30 or 300 cGy/min. Experiment 2 comprised 936 samples (including 236 reference samples) exposed to 0-100 cGy at either 3 or 28 cGy/min dose rates and harvested between 7 and 150 days post-exposure. All samples were processed using a standardized workflow involving automated bead-based digestion and data-independent acquisition mass spectrometry. The datasets include multiple pooled reference sample types, process controls, and system suitability standards ensuring high quality data. All data presented are available via ProteomeXchange at several levels of processing, from raw files through normalized peptide- and protein-level abundance matrices suitable for biomarker discovery and machine learning applications. This dataset will facilitate generation of new insights into the biological changes and molecular signatures resulting from X-ray exposure in mice and may also help inform future studies in humans.

biochemistry↗

bioRxiv2026

Is Protein Quantification and Physical Normalization Always Necessary in Proteomics?

Dogma suggests protein quantification is a pre-requisite to LC-MS/MS based proteomics studies. Such quantification allows a standardized ratio of sample to digestion enzyme and enables physical normalization of protein digest loaded onto the mass spectrometer for analysis. Most proteomics studies include these steps. However, there are significant costs in time, money and experimental complexity, associated with performing protein quantification and physical normalization for every sample, especially for larger studies. Proteomics data analysis pipelines typically include computational normalization strategies to compensate for unavoidable systematic biases. These strategies also have the potential to compensate for avoidable variation such as omitting sample amount normalization. Here we investigate the effects of either physically normalizing the amount of protein for each individual sample or leaving it unnormalized. Our results show the relationship between increased protein amount variation in sample input, and the variance of quantified relative abundances of peptides and proteins output after data analysis. The experiments presented here suggest that protein quantification and physical normalization steps can be omitted from some quantitative proteomic experiments without incurring an unacceptable increase in measurement variability after computational normalization has been applied. This work will enable important time and cost saving optimizations to be made to many proteomics workflows.

biochemistry↗

bioRxiv2026

Volume EM reveals three-dimensional architecture of the desmosome in epithelial cells and tissue models

Desmosomes are a type of cell-cell adhesive junction present in cardiac tissue and epithelial tissues such as the epidermis. These intercellular junctions anchor to the intermediate filament cytoskeleton, providing mechanical integrity to the tissues in which they reside. Our understanding of desmosome architecture has largely been influenced by observations of two-dimensional images obtained through conventional electron microscopy. Here, using focused ion beam scanning electron microscopy, we report the three-dimensional ultrastructure of desmosomes in A431 and S1 human mammary epithelial cells. We also reveal differences in desmosome ultrastructure at homo- and heterotypic junctions of human nasal airway epithelial cells. Quantitative analyses of these volume EM datasets reveal variations in desmosome size, shape, and organization. Importantly, we report the presence of discontinuities or "holes" within the desmosome outer dense plaque, a novel feature that is observed in either one or both halves of a desmosome. This study provides the first comprehensive description of the epithelial desmosome as a three-dimensional structure, and emphasizes the need to investigate the effects of dynamic morphogenetic processes and disease states on desmosome ultrastructure.

cell biology↗

bioRxiv2019

The microstructure of laminin-111 compensates for dystroglycan loss in functional differentiation of mammary epithelial cells

AbstractLaminin-111, an extracellular matrix (ECM) glycoprotein found in the basement membrane of mammary gland epithelia, is essential for lactation. In mammary epithelial cells, dystroglycan (Dg) is believed to be necessary for polymerization of laminin-111 into networks, thus we asked whether correct polymerization could compensate for Dg loss. Artificially polymerized laminin-111 and the laminin-glycoprotein mix Matrigel, both formed branching, spread networks with fractal dimensions from 1.7-1.8, whereas laminin-111 in media formed small aggregates without fractal properties (a fractal dimension of 2). In Dg knockout cells, either polymerized laminin-111 or Matrigel readily attached to the cell surface, whereas aggregated laminin-111 did not. In contrast, polymerized and aggregated laminin-111 bound similarly to Dg knock-ins. Both polymerized laminin-111 and Matrigel promoted cell rounding, clustering, formation of tight junctions, and expression of milk proteins, whereas aggregated Ln-1 did not attach to cells or promote functional differentiation. HighlightsO_LILaminin assembles into a fractal network when in presence of either the cell surface receptor dystroglycan or acidic glycoproteins or an acidic buffer. C_LIO_LIWhen this microstructure is recreated with an acidic treatment, laminin binds readily to dystroglycan null cells and induces functional differentiation of mammary epithelial cells. C_LI

bioengineering↗