Biology subjects

Carson, R.

Publications and source records attributed to Carson, R..

4 recordsLinked to original sources

bioRxiv2024

Serum proteomics reveals high-affinity and convergent antibodies by tracking SARS-CoV2 hybrid immunity to emerging variants of concern

The rapid spread of SARS-CoV2 and continuing impact on human health has prompted the need for effective and rapid development of monoclonal antibody therapeutics. In this study, we interrogate polyclonal antibodies in serum and B cells from whole blood of three donors with SARS-CoV2 immunity to find high-affinity anti-SARS-CoV2 antibodies to escape variants. Serum IgG antibodies were selected by affinity to the receptor-binding domain (RBD) and non-RBD sites on the spike protein of Omicron subvariant B.1.1.529 from each donor. Antibodies were analyzed by bottom-up mass spectrometry, and matched to single- and bulk-cell sequenced repertoires for each donor. Antibodies observed in serum were recombinantly expressed, and characterized to assess domain binding, cross-reactivity between different variants, and capacity to inhibit RBD binding to host protein. Donors infected with early Omicron subvariants had serum antibodies with subnanomolar affinity to RBD that show binding activity to a newer Omicron subvariant BQ.1.1. The donors also showed a convergent immune response. Serum antibodies and other single- and bulk-cell sequences were similar to publicly reported anti-SARS-CoV-2 antibodies, and characterized serum antibodies had the same variant-binding and neutralization profiles as their reported public sequence. The serum antibodies analyzed were a subset of anti-SARS-CoV2 antibodies in the B cell repertoire, which demonstrates significant dynamics between the B cells and circulating antibodies in peripheral blood.

immunology↗

bioRxiv2024

Connecting the Dots: Approaching a Standardized Nomenclature for Molecular Connectivity Combining Data and Literature

PET-based connectivity computation is a molecular approach that complements fMRI-derived functional connectivity. However, the diversity of methodologies and terms employed in PET connectivity analysis has resulted in ambiguities and confounded interpretations, highlighting the need for a standardized nomenclature. Drawing parallels from other imaging modalities, we propose "molecular connectivity" as an umbrella term to characterize statistical dependencies between PET signals across brain regions at the individual level (within-subject). Like fMRI resting-state functional connectivity, "molecular connectivity" leverages temporal associations in the PET signal to derive brain network associations. Another within-subject approach evaluates regional similarities of tracer kinetics, which are unique in PET imaging, thus referred to as "kinetic connectivity". On the other hand, "molecular covariance" denotes group-level computations of covariance matrices across-subject. Further specification of the terminology can be achieved by including the employed radioligand, such as "metabolic connectivity/covariance" for [18F]FDG as well as "tau/amyloid covariance" for [18F]flutemetamol / [18F]flortaucipir. To augment these distinctions, high-temporal resolution functional [18F]FDG PET scans from 17 healthy participants were analysed with common techniques of molecular connectivity and covariance, allowing for a data-driven support of the above terminology. Our findings demonstrate that temporal band-pass filtering yields structured network organization, whereas other techniques like 3rd order polynomial fitting, spatio-temporal filtering and baseline normalization require further methodological refinement for high-temporal resolution data. Conversely, molecular covariance from across-subject data provided a simple means to estimate brain region interactions through regularized or sparse inverse covariance estimation. A standardized nomenclature in PET-based connectivity research can reduce ambiguity, enhance reproducibility, and facilitate interpretability across radiotracers and imaging modalities. Via a data-driven approach, this work provides a transparent framework for categorizing and comparing PET-derived connectivity and covariance metrics, laying the foundation for future investigations in the field.

neuroscience↗

bioRxiv2023

Rescue of Impaired Blood-Brain Barrier in Tuberous Sclerosis Complex Patient Derived Neurovascular Unit

Tuberous sclerosis complex (TSC) is a multi-system genetic disease that causes benign tumors in the brain and other vital organs. The most debilitating symptoms result from involvement of the central nervous system and lead to a multitude of severe symptoms including seizures, intellectual disability, autism, and behavioral problems. TSC is caused by heterozygous mutations of either the TSC1 or TSC2 gene. Dysregulation of mTOR kinase with its multifaceted downstream signaling alterations is central to disease pathogenesis. Although the neurological sequelae of the disease are well established, little is known about how these mutations might affect cellular components and the function of the blood-brain barrier (BBB). We generated disease-specific cell models of the BBB by leveraging human induced pluripotent stem cell and microfluidic cell culture technologies. Using these microphysiological systems, we demonstrate that the BBB generated from TSC2 heterozygous mutant cells shows increased permeability which can be rescued by wild type astrocytes and with treatment with rapamycin, an mTOR kinase inhibitor. Our results further demonstrate the utility of microphysiological systems to study human neurological disorders and advance our knowledge of the cell lineages contributing to TSC pathogenesis.

neuroscience↗

bioRxiv2023

TDP-43-stratified single-cell proteomic profiling of postmortem human spinal motor neurons reveals protein dynamics in amyotrophic lateral sclerosis

Unbiased proteomics has been employed to interrogate central nervous system (CNS) tissues (brain, spinal cord) and fluid matrices (CSF, plasma) from amyotrophic lateral sclerosis (ALS) patients; yet, a limitation of conventional bulk tissue studies is that motor neuron (MN) proteome signals may be confounded by admixed non-MN proteins. Recent advances in trace sample proteomics have enabled quantitative protein abundance datasets from single human MNs (Cong et al., 2020b). In this study, we leveraged laser capture microdissection (LCM) and nanoPOTS (Zhu et al., 2018c) single-cell mass spectrometry (MS)-based proteomics to query changes in protein expression in single MNs from postmortem ALS and control donor spinal cord tissues, leading to the identification of 2515 proteins across MNs samples (>900 per single MN) and quantitative comparison of 1870 proteins between disease groups. Furthermore, we studied the impact of enriching/stratifying MN proteome samples based on the presence and extent of immunoreactive, cytoplasmic TDP-43 inclusions, allowing identification of 3368 proteins across MNs samples and profiling of 2238 proteins across TDP-43 strata. We found extensive overlap in differential protein abundance profiles between MNs with or without obvious TDP-43 cytoplasmic inclusions that together point to early and sustained dysregulation of oxidative phosphorylation, mRNA splicing and translation, and retromer-mediated vesicular transport in ALS. Our data are the first unbiased quantification of single MN protein abundance changes associated with TDP-43 proteinopathy and begin to demonstrate the utility of pathology-stratified trace sample proteomics for understanding single-cell protein abundance changes in human neurologic diseases.

pathology↗