Biology subjects

Rieck, J.

Publications and source records attributed to Rieck, J..

4 recordsLinked to original sources

bioRxiv2025

A deep, quantitative lipid atlas of extracellular vesicles across multiple cell lines

Extracellular vesicles (EVs) are subcellular particles surrounded by a lipid bilayer membrane and incorporating various additional biomolecules derived from their donor cell. In many disease contexts circulating EVs have received increasing scientific attention due to their potential diagnostic and prognostic value. Additionally, EVs have been ascribed multiple biological functions, ranging from cellular waste disposal to sophisticated, intercellular communication. Consequently, EVs involved in pathological processes may represent therapeutic targets, whereas EV-based therapeutics are being developed for targeted delivery of molecular cargoes in vivo. Detailed knowledge of the molecular content of natural EVs derived from diverse cellular origins is crucial to identify biomarkers, dissect EV functions, and optimize EV engineering for therapeutic purposes. Although the lipid composition of biological membranes has a significant impact on their biophysical and -chemical properties and may affect signaling and interactions at the molecular and cellular level, relatively little is known about the lipid composition of EV membranes. Here, we applied high resolution mass spectrometry to deeply and quantitatively characterize the lipidome of EVs isolated from a panel of malignant and non-malignant cell lines, providing a comprehensive data resource for biomarker research and EV engineering efforts. Furthermore, subset comparisons indicate striking differences between lipid profiles of EVs isolated from cells of different tissue origin, suggesting distinct membrane characteristics that could affect EV biodistribution and function in vivo.

cell biology↗

bioRxiv2023

Neural Signatures of Emotion Regulation

Emotional experience is central to a fulfilling life. Although exposure to negative experiences is inevitable, an individuals emotion regulation response may buffer against psychopathology. Identification of neural activation patterns associated with emotion regulation via an fMRI task is a promising and non-invasive means of furthering our understanding of the how the brain engages with negative experiences. Prior work has applied multivariate pattern analysis to identify signatures of response to negative emotion-inducing images; we adapt these techniques to establish novel neural signatures associated with conscious efforts to modulate emotional response. We model voxel-level activation via LASSO principal components regression and linear discriminant analysis to predict if a subject was engaged in emotion regulation and to identify brain regions which define this emotion regulation signature. We train our models using 82 participants and evaluate them on a holdout sample of 40 participants, demonstrating an accuracy up to 82.5% across three classes. Our results suggest that emotional regulation produces a unique signature that is differentiable from passive viewing of negative imagery.

neuroscience↗

bioRxiv2019

Joint contributions of cortical morphometry and white matter microstructure in healthy brain aging: A partial least squares correlation analysis

Cortical atrophy and degraded axonal health have been shown to coincide during normal aging; however, few studies have examined these measures together. To lend insight into both the regional specificity and the relative timecourse of structural degradation of these tissue compartments across the lifespan, we analyzed grey matter (GM) morphometry (cortical thickness, surface area, volume) and estimates of white matter (WM) microstructure (fractional anisotropy, mean diffusivity) using traditional univariate and more robust multivariate techniques to examine age associations in 186 healthy adults aged 20-94 years old. Univariate analysis of each tissue type revealed that negative age associations were largest in frontal grey and white matter tissue and weaker in temporal, cingulate, and occipital regions, representative of not only an anterior-to-posterior gradient, but also a medial-to-lateral gradient. Multivariate partial least squares correlation (PLSC) found the greatest covariance between GM and WM was driven by the relationship between WM metrics in the anterior corpus callosum and projections of the genu, anterior cingulum, and fornix; and with GM thickness in parietal and frontal regions. Surface area was far less susceptible to age effects and displayed less covariance with WM metrics, while regional volume covariance patterns largely mirrored those of cortical thickness. Results support a retrogenesis-like model of aging, revealing a coupled relationship between frontal and parietal GM and the underlying WM, which evidence the most protracted development and the most vulnerability during healthy aging.

neuroscience↗

bioRxiv2017

Multivariate genotypic analyses that identify specific genotypes to characterize disease and control groups in ADNI

INTRODUCTIONGenetic contributions to Alzheimers Disease (AD) are likely polygenic and not necessarily explained by uniformly applied linear and additive effects. In order to better understand the genetics of AD, we require statistical techniques to address both polygenic and possible non-additive effects.\n\nMETHODSWe used partial least squares-correspondence analysis (PLS-CA)--a method designed to detect multivariate genotypic effects. We used ADNI-1 (N = 756) as a discovery sample with two forms of PLS-CA: diagnosis-based and ApoE-based. We used ADNI-2 (N= 791) as a validation sample with a diagnosis-based PLS-CA.\n\nRESULTSWith PLS-CA we identified some expected genotypic effects (e.g., APOE/TOMM40, and APP) and a number of new effects that include, for examples, risk-associated genotypes in RBFOX1 and GPC6 and control-associated genotypes in PTPN14 and CPNE5.\n\nDISCUSSIONThrough the use of PLS-CA, we were able to detect complex (multivariate, genotypic) genetic contributions to AD, which included many non-additive and non-linear risk and possibly protective effects.

genomics↗