Biology subjects

Plowey, E. D.

Publications and source records attributed to Plowey, E. D..

9 recordsLinked to original sources

bioRxiv2026

An in vivo model of Amyloid-Related Imaging Abnormalities (ARIA) predicts ARIA incidence of anti-Aβ antibodies seen in clinical trials

Amyloid-related imaging abnormality (ARIA) is the most common adverse event associated with amyloid-beta (A{beta}) immunotherapy. The neuropathology underlying active ARIA lesions has been studied in only a few rare fatal cases. A faithful translational model of ARIA should allow a better understanding of pathogenic mechanisms and enable development of mitigation strategies. Since bapineuzumab induced the most frequent and severe forms of ARIA in humans, we assessed the ability of its murine precursor, 3D6, to induce ARIA-like lesions by applying clinically-validated MRI sequences in a mouse model with extensive amyloid deposition. Using this paradigm we documented ARIA-like changes based on their imaging features, location, and temporal evolution and established postmortem histopathologic correlates. We observed that ARIA-like lesions were ubiquitously induced with 3D6, including T2-hyperintense leptomeningeal effusions, T2-hyperintense parenchymal lesions and T2* hypointense parenchymal lesions. Histological assays demonstrated meningovascular inflammation as well as vascular mural permeation and microvascular lesions, including microhemorrhages and microinfarcts. Compared to 3D6-treated mice, animals treated with mouse analogues of aducanumab and gantenerumab showed less frequent radiologic and histopathologic ARIA-like lesions with delayed onset. Our results align well with the differential incidence of ARIA observed in humans treated with different anti-A{beta} antibodies. Collectively, our studies demonstrate that: i) anti-A{beta} antibodies chronically administered to 5xFAD mice can induce MRI lesions reminiscent of human ARIA; ii) this paradigm allows identification of transient lesions other than microhemorrhages, and (iii) the incidence and severity of ARIA-like lesions induced with three clinically tested antibodies faithfully recapitulates that seen in humans. One Sentence SummaryMRI and histological correlation in a preclinical ARIA mouse model treated with anti-A{beta} antibodies that recapitulates results seen in humans.

neuroscience↗

bioRxiv2025

Comparative snRNAseq study of C9orf72, SOD1, and sALS spinal cord

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease characterized by the loss of motor neurons, yet the cell-type specific molecular alterations within the spinal cords are not well characterized. In this study, we conducted deep molecular profiling of spinal cord tissues donated by people living with sporadic, C9orf72, and SOD1-ALS using single-nucleus RNA sequencing (snRNAseq). We observed numerous distinct gene expression patterns and enriched pathways among ALS types. However, when focusing on common features, we identified activation of stress-response and inflammatory pathways in specific microglia subtypes, as well as disrupted vesicle transport and synaptic function in a ventral inhibitory neuronal subtype. Notably, CPLX3, a SNARE regulator, was uniquely expressed in alpha-motor neurons and was commonly downregulated across all ALS types. While this study uncovers the molecular heterogeneity underlying ALS, it also highlights shared pathways within specific cell types, especially in the ventral inhibitory neurons that have been less explored in ALS research.

neuroscience↗

bioRxiv2024

Engineering anti-amyloid antibodies with transferrin receptor targeting improves safety and brain biodistribution

Although the first generation of immunotherapies for Alzheimers disease (AD) are now clinically approved, amyloid-related imaging abnormalities (ARIA) remain a major safety problem for this class of drugs. Here, we report an antibody transport vehicle (ATV) targeting the transferrin receptor (TfR) for brain delivery of amyloid beta (A{beta}) antibodies that significantly reduced ARIA-like lesions and improved plaque target engagement in a mouse model of amyloid deposition. Asymmetrical Fc mutations (ATVcisLALA) allowed the molecule to selectively retain effector function only when bound to A{beta} while mitigating TfR-related hematology liabilities. Mice treated with ATVcisLALA:A{beta} exhibited broad brain parenchymal antibody distribution; in contrast, anti-A{beta} IgG was highly enriched at arterial perivascular spaces where vascular A{beta} localizes and likely plays a role in induction of ARIA. Importantly, ATVcisLALA: A{beta} almost completely eliminated ARIA-like lesions and vascular inflammation associated with anti-A{beta} treatment. Taken together, ATVcisLALA has the potential to significantly improve both safety and efficacy of A{beta} immunotherapy through enhanced biodistribution mediated by transport across the blood-brain barrier.

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↗

bioRxiv2021

Features of peptide fragmentation spectra in single cell proteomics

The goal of proteomics is to identify and quantify the complete set of proteins in a biological sample. Single cell proteomics specializes in identification and quantitation of proteins for individual cells, often used to elucidate cellular heterogeneity. The significant reduction in ions introduced into the mass spectrometer for single cell samples could impact the features of MS2 fragmentation spectra. As all peptide identification software tools have been developed on spectra from bulk samples and the associated ion rich spectra, the potential for spectral features to change is of great interest. We characterize the differences between single cell spectra and bulk spectra by examining three fundamental spectral features that are likely to affect peptide identification performance. All features show significant changes in single cell spectra, including loss of annotated fragment ions, blurring signal and background peaks due to diminishing ion intensity and distinct fragmentation pattern compared to bulk spectra. As each of these features is a foundational part of peptide identification algorithms, it is critical to adjust algorithms to compensate for these losses.

bioinformatics↗

bioRxiv2021

Benchmarking PSM identification tools for single cell proteomics

Single cell proteomics is an emerging sub-field within proteomics with the potential to revolutionize our understanding of cellular heterogeneity and interactions. Recent efforts have largely focused on technological advancements in sample preparation, chromatography and instrumentation to enable measuring proteins present in these ultra-limited samples. Although advancements in data acquisition have rapidly improved our ability to analyze single cells, the software pipelines used in data analysis were originally written for traditional bulk samples and their performance on single cell data has not been investigated. We benchmarked five popular peptide identification tools on single cell proteomics data. We found that MetaMorpheus achieved the greatest number of peptide spectrum matches at a 1% false discovery rate. Depending on the tool, we also find that post processing machine learning can improve spectrum identification results by up to [~]40%. Although rescoring leads to a greater number of peptide spectrum matches, these new results typically are generated by 3rd party tools and have no way of being utilized by the primary pipeline for quantification. Exploration of novel metrics for machine learning algorithms will continue to improve performance.

bioinformatics↗

bioRxiv2020

Calculating sample size requirements for temporal dynamics in single cell proteomics

Single cell measurements are uniquely capable of characterizing cell-to-cell heterogeneity, and have been used to explore the large diversity of cell types and physiological functions present in tissues and other complex cell assemblies. An intriguing application of single cell proteomics is the characterization of proteome dynamics during biological transitions, like cellular differentiation or disease progression. Time course experiments, which regularly take measurements during state transitions, rely on the ability to detect dynamic trajectories in a data series. However, in a single cell proteomics experiment, cell-to-cell heterogeneity complicates the confident identification of proteome dynamics as measurement variability may be higher than expected. Therefore, a critical question for these experiments is how many data points need to be acquired during the time course to enable robust statistical analysis. We present here an analysis of the most important variables that affect statistical confidence in the detection of proteome dynamics: fold-change, measurement variability, and the number of cells measured during the time course. Importantly, we show that datasets with less than 16 measurements across the time domain suffer from low accuracy and also have a high false-positive rate. We also demonstrate how to balance competing demands in experimental design to achieve a desired result.

bioinformatics↗

bioRxiv2020

Ultrasensitive single-cell proteomics workflow identifies >1000 protein groups per mammalian cell

We report on the combination of nanodroplet sample preparation, ultra-low-flow nanoLC, high-field asymmetric ion mobility spectrometry (FAIMS), and the latest-generation Orbitrap Eclipse Tribrid mass spectrometer for greatly improved single-cell proteome profiling. FAIMS effectively filtered out singly charged ions for more effective MS analysis of multiply charged peptides, resulting in an average of 1056 protein groups identified from single HeLa cells without MS1-level feature matching. This is 2.3 times more identifications than without FAIMS and a far greater level of proteome coverage for single mammalian cells than has been previously reported for a label-free study. Differential analysis of single microdissected motor neurons and interneurons from human spinal tissue indicated a similar level of proteome coverage, and the two subpopulations of cells were readily differentiated based on single-cell label-free quantification.

systems biology↗

bioRxiv2019

CSF sTREM2 correlates with CSF tau in advancing Parkinson’s disease

Parkinsons disease (PD) is the second most common neurodegenerative disease after Alzheimers disease (AD) and affects 1% of the population above 60 years old. Although PD commonly manifests with motor symptoms, a majority of patients with PD subsequently develop cognitive impairment which often progresses to dementia, a major cause of morbidity and disability. PD is characterized by -synuclein accumulation that frequently associates with amyloid beta (A{beta}) and tau fibrils, the hallmarks of AD neuropathologic changes; this co-occurrence suggests that onset of cognitive decline in PD may be associated with appearance of pathologic A{beta} and/or tau. Recent studies have highlighted the appearance of the soluble form of the Triggering Receptor Expressed on Myeloid cells 2 (sTREM2) receptor in CSF during development of AD. Given the known association of microglial activation with advancing PD, we investigated whether CSF and/or plasma sTREM2 increased with progression to PD dementia. We examined 165 participants consisting of 17 cognitively normal elderly, 45 PD patients with no cognitive impairment, 86 with mild cognitive impairment, and 17 with dementia. Stratification of subjects by CSF A{beta} and tau levels revealed that CSF sTREM2 concentrations were elevated in PD subgroups with abnormal tau, but not A{beta}, CSF concentration. These findings indicate that CSF sTREM2 could serve as a surrogate immune biomarker of neuronal injury in PD that is associated with cognitive decline.\n\nOne sentence summaryCSF sTREM2 correlates with CSF tau in PD

neuroscience↗