Biology subjects

Guise, A. J.

Publications and source records attributed to Guise, A. J..

6 recordsLinked to original sources

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↗

bioRxiv2025

Spatial transcriptomics reveals heterogeneous cell-cell interactions among brain regions in a cuprizone model consistent with multiple sclerosis lesions

The cuprizone (CPZ) model is widely used for modeling demyelination in multiple sclerosis (MS) and for testing potential remyelination therapies. We integrated single-cell and spatial transcriptomics (ST) to fine map the spatial cellular and molecular responses during de and remyelination. ST revealed global demyelination and neuroinflammation in the brain beyond the corpus callosum, with region-specific differences. We identified oligodendroglia and microglia as two major cell types with significant transcriptomic changes in the model. Ligand receptor pairing analyses predicted growth factor and phagocytic pathway enrichment during demyelination, which is consistent with changes in MS lesions. During remyelination, while mature oligodendrocytes nearly reversed their phenotype back to the control state, microglia remained associated with the demyelination phenotype. Finally, astrocytes in the CPZ model had the greatest preservation of disease-associated modules to MS lesions, while the MOL, OPC, and microglia showed moderate to low preservation, which overall suggested that the CPZ model had moderate translatability to chronically active MS lesions.

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↗