Biology subjects

Ramsey, J. M.

Publications and source records attributed to Ramsey, J. M..

4 recordsLinked to original sources

bioRxiv2026

Performance Evaluation of a Quantitative Metabolomics Workflow Incorporating Microchip Capillary Electrophoresis, Indexed Migration Time, and Single-Point External Calibration

Capillary electrophoresis-mass spectrometry (CE-MS) offers unique analytical advantages for polar metabolite profiling but has remained underutilized in metabolomics relative to liquid chromatography-MS (LC-MS), in part due to challenges in managing migration time drift during data analysis. Here we introduce the use of indexed migration time (iMT) for easily managing this aspect of CE-MS data for metabolomics. Migration time indexing using a panel of stable isotope-labeled (SIL) amino acid reference standards, stored as an iRT database in Skyline, outperformed both uncorrected migration time and relative migration time (RMT) correction across three independent analytical batches spanning 90 samples from four biological matrices. The indexed migration time approach achieved sub-1% relative standard deviation (RSD) in migration index across batches, compared to up to ~15% RSD for uncorrected migration times. Additionally, we evaluate the use of single-point external calibration in Skyline for the purposes of metabolite quantification from complex matrices in order to ease the burden of translational metabolite quantification from metabolomics using high-resolution mass spectrometry (HRMS). Single-point external calibration using a biological matrix-based calibrator was benchmarked against a 13-point linear calibration curve across a panel of amino acids; above 1 M, greater than 95% of back-calculated concentrations fell within {+/-}20% of multi-point calibration. Application of the complete workflow to plasma, serum, urine, and NIST Standard Reference Material (SRM)-1950 demonstrated low inter-batch variability by principal components analysis, broad metabolite coverage across 126 quantifiable analytes, and strong quantitative concordance (Deming slope = 0.862, pseudo-R2 = 0.994, n = 64 analytes) with an independent comprehensive reference dataset for NIST SRM-1950. Together, these results establish a practical mCE-HRMS metabolomics workflow that bridges targeted and discovery metabolomics paradigms and lays the groundwork for single-point external calibration as a powerful tool for translational metabolomics.

molecular biology↗

bioRxiv2022

A Trypanosoma cruzi Antigen and Epitope Atlas: deep characterization of antibody specificities in Chagas Disease patients across the Americas

During an infection, the immune system produces pathogen-specific antibodies. With time, these antibody repertoires become specific to the history of infections and represent a rich source of diagnostic markers. However, the specificities of these antibodies are mostly unknown. Here, using high-density peptide arrays we examined the specificities of human antibody repertoires of Chagas disease patients. Chagas disease is a neglected disease caused by Trypanosoma cruzi, a protozoan parasite that evades immune mediated elimination and mounts long-lasting chronic infections. We describe here the first proteome-wide search for antigens and epitopes and their seroprevalence at the individual level and across human populations. In a first discovery screening of 2.84 million short peptides spanning two T. cruzi proteomes we found 3,868 distinct antigenic protein regions. Further analysis of repertoires from 71 individuals provided information on their seroprevalence and showed a large fraction of private epitopes of low seroprevalence (<20%), and novel high seroprevalence antigens. Using single-residue mutagenesis we found the core epitopes required for antibody binding for 232 of these epitopes. These datasets enable the study of the Chagas antibody repertoire at an unprecedented depth and granularity, while also providing a rich source of novel serological biomarkers. IMPACT STATEMENTThis work reveals the diversity and extent of antibody specificities in Chagas Disease and provides a wealth of well-defined antigenic markers for diagnosis and development of serological applications for this neglected infectious disease.

immunology↗

bioRxiv2022

Population genetic structure of the maize weevil, Sitophilus zeamais, in southern Mexico

The maize weevil, Sitophilus zeamais, is a ubiquitous pest of maize and other cereal crops worldwide and remains a threat to food security in subsistence communities. Few population genetic studies have been conducted on the maize weevil, but those that exist have shown that there is very little genetic differentiation between geographically dispersed populations and that it is likely the species has experienced a recent range expansion within the last few hundred years. While the previous studies found little genetic structure, they relied primarily on mitochondrial and nuclear microsatellite markers for their analyses. It is possible that more fine-scaled population genetic structure exists due to local population dynamics, the biological limits of natural species dispersal, and the isolated nature of subsistence farming communities. In contrast to previous studies, here, we utilized genome-wide single nucleotide polymorphism data to evaluate the genetic population structure of the maize weevil from the southern and coastal Mexican states of Oaxaca and Chiapas. Our study is the first to find significant genetic population structure in the maize weevil. Here, we show that although there continues to be gene flow between populations of maize weevil, that fine-scale genetic structure exists. It is possible that this structure is shaped by the movement and trade of maize by humans in the region, by geographic barriers to gene flow, or a combination of factors.

genetics↗

bioRxiv2017

Insights into the molecular changes associated with postnatal human brain development: an integrated transcriptomics and proteomics study

Healthy cortical development depends on precise regulation of transcription and translation. However, the dynamics of how proteins are expressed, function and interact across postnatal human cortical development remain poorly understood. We surveyed the proteomic landscape of 69 dorsolateral prefrontal cortex samples across seven stages of postnatal life and integrated these data with paired transcriptome data. We detected 911 proteins by liquid chromatography-mass spectrometry, and 83 were significantly associated with postnatal age (FDR p < 0.05). Network analysis identified three modules of co-regulated proteins correlated with age, including two modules with increasing expression involved in gliogenesis and NADH-metabolism and one neurogenesis-related module with decreasing expression throughout development. Integration with paired transcriptome data revealed that these age-related protein modules overlapped with RNA modules and displayed collinear developmental trajectories. Importantly, RNA expression profiles that are dynamically regulated throughout cortical development display tighter correlations with their respective translated protein expression compared to those RNA profiles that are not. Moreover, the correspondence between RNA and protein expression significantly decreases as a function of cortical aging, especially for genes involved in myelination and cytoskeleton organization. Finally, we used this data resource to elucidate the functional impact of genetic risk loci for intellectual disability, converging on gliogenesis, myelination and ATP-metabolism modules in the proteome and transcriptome. We share all data in an interactive, searchable companion website. Collectively, our findings reveal dynamic aspects of protein regulation and provide new insights into brain development, maturation and disease.

neuroscience↗