Proteomic Insight Into Alzheimer's Disease Pathogenesis Pathways DOI Creative Commons

Taekyung Ryu,

Kyungdo Kim, Nicholas Asiimwe

et al.

PROTEOMICS, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

ABSTRACT Alzheimer's disease (AD) is a leading cause of dementia, but the pathogenesis mechanism still elusive. Advances in proteomics have uncovered key molecular mechanisms underlying AD, revealing complex network dysregulated pathways, including amyloid metabolism, tau pathology, apolipoprotein E (APOE), protein degradation, neuroinflammation, RNA splicing, metabolic dysregulation, and cognitive resilience. This review examines recent proteomic findings from AD brain tissues biological fluids, highlighting potential biomarkers therapeutic targets. By examining landscape them, we aim to deepen our understanding support developing precision medicine strategies for more effective interventions.

Language: Английский

Gene regulatory network inference in the era of single-cell multi-omics DOI
Pau Badia-i-Mompel, Lorna Wessels, Sophia Müller‐Dott

et al.

Nature Reviews Genetics, Journal Year: 2023, Volume and Issue: 24(11), P. 739 - 754

Published: June 26, 2023

Language: Английский

Citations

179
Hallmarks of cancer stemness DOI Creative Commons

Jia-Jian Loh,

Stephanie Ma

Cell stem cell, Journal Year: 2024, Volume and Issue: 31(5), P. 617 - 639

Published: May 1, 2024

Language: Английский

Citations

60
Nanopore DNA sequencing technologies and their applications towards single-molecule proteomics DOI
Adam Dorey, Stefan Howorka

Nature Chemistry, Journal Year: 2024, Volume and Issue: 16(3), P. 314 - 334

Published: March 1, 2024

Language: Английский

Citations

38
Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases DOI Creative Commons
Paul Kießling, Christoph Kuppe

Genome Medicine, Journal Year: 2024, Volume and Issue: 16(1)

Published: Jan. 18, 2024

Abstract Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome parallel or even same tissue section. This review focuses on recent advancements spatial multi-omics technologies, including novel computational approaches. We discuss low-resolution high-resolution methods which can resolve up 10,000 individual molecules at subcellular level. By applying integrating these techniques, researchers recently gained valuable insights into molecular circuits mechanisms govern cell biology along cardiovascular disease spectrum. provide an overview current approaches, with focus integration datasets, highlighting strengths weaknesses various pipelines. These tools play crucial role analyzing interpreting facilitating discovery new findings, enhancing translational research. Despite nontrivial challenges, such need for standardization experimental setups, analysis, improved tools, application holds tremendous potential revolutionizing our understanding human processes identification biomarkers therapeutic targets. Exciting opportunities lie ahead field will likely contribute advancement personalized medicine diseases.

Language: Английский

Citations

23
Single-cell sequencing to multi-omics: technologies and applications DOI Creative Commons
Xiangyu Wu, Xin Yang,

Yunhan Dai

et al.

Biomarker Research, Journal Year: 2024, Volume and Issue: 12(1)

Published: Sept. 27, 2024

Abstract Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged one most vibrant research fields today. With optimization innovation technologies, intricate details concealed within cells are gradually unveiled. The combination scRNA-seq other multi-omics at forefront field. This involves simultaneously measuring various omics data individual cells, expanding our understanding across a broader spectrum dimensions. precisely captures aspects transcriptomes, immune repertoire, spatial information, temporal epitopes, in diverse contexts. In addition to depicting cell atlas normal or diseased tissues, it also provides cornerstone for studying differentiation development patterns, disease heterogeneity, drug resistance mechanisms, treatment strategies. Herein, we review traditional technologies outline latest advancements multi-omics. We summarize current status challenges applying biological clinical applications. Finally, discuss limitations potential strategies address them.

Language: Английский

Citations

18
Protein glycosylation in cardiovascular health and disease DOI
John C. Chatham, Rakesh P. Patel

Nature Reviews Cardiology, Journal Year: 2024, Volume and Issue: 21(8), P. 525 - 544

Published: March 18, 2024

Language: Английский

Citations

16
Enhanced sensitivity and scalability with a Chip-Tip workflow enables deep single-cell proteomics DOI Creative Commons
Zilu Ye, Pierre Sabatier, Leander van der Hoeven

et al.

Nature Methods, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 16, 2025

Single-cell proteomics (SCP) promises to revolutionize biomedicine by providing an unparalleled view of the proteome in individual cells. Here, we present a high-sensitivity SCP workflow named Chip-Tip, identifying >5,000 proteins HeLa It also facilitated direct detection post-translational modifications single cells, making need for specific modification-enrichment unnecessary. Our study demonstrates feasibility processing up 120 label-free samples per day. An optimized tissue dissociation buffer enabled effective single-cell disaggregation drug-treated cancer cell spheroids, refining overall analysis. Analyzing nondirected human-induced pluripotent stem differentiation, consistently quantified markers OCT4 and SOX2 cells lineage such as GATA4 (endoderm), HAND1 (mesoderm) MAP2 (ectoderm) different embryoid body sets benchmark sensitivity throughput, with broad applications basic biology identification type-specific therapeutic targets.

Language: Английский

Citations

7
Single Cell Untargeted Lipidomics Using Liquid Chromatography Ion Mobility-Mass Spectrometry DOI
Jin Yong Kim, Dong‐Gi Mun, Husheng Ding

et al.

Journal of Proteome Research, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 14, 2025

Advancements in technology over the years have propelled omics analysis to level of single cell resolution. Following breakthroughs transcriptomics and genomics, proteomics has recently rapidly progressed, aided by highly sensitive mass spectrometry instrumentation. However, there is currently a paucity studies methodologies for lipidomics, aside from imaging-based approaches. Profiling lipids at holds promise providing novel insights into complex heterogeneity cells various human disorders. Further, integrating lipidomics with other including proteomics, it becomes possible achieve multiomics, enabling discovery molecular signatures. We developed untargeted using nanoflow liquid chromatography-ion mobility spectrometry-mass spectrometry. To enhance lipid coverage level, method was conducted both positive negative ion modes. identified an average 161 spanning phospholipids, sphingolipids, cholesteryl esters, glycerides mode cholangiocarcinoma based on rule-based annotation. Additionally, 20 species phospholipids mode. These preliminary data demonstrate new methodology profile or low input cells.

Language: Английский

Citations

2
Rapid, One-Step Sample Processing for Label-Free Single-Cell Proteomics DOI

S. Madisyn Johnston,

Kei G. I. Webber,

Xiaofeng Xie

et al.

Journal of the American Society for Mass Spectrometry, Journal Year: 2023, Volume and Issue: 34(8), P. 1701 - 1707

Published: July 6, 2023

Sample preparation for single-cell proteomics is generally performed in a one-pot workflow with multiple dispensing and incubation steps. These hours-long processes can be labor intensive lead to long sample-to-answer times. Here we report sample method that achieves cell lysis, protein denaturation, digestion 1 h using commercially available high-temperature-stabilized proteases single reagent step. Four different one-step compositions were evaluated, the mixture providing highest proteome coverage was compared previously employed multistep workflow. The increases relative previous while minimizing input possibility of human error. We also recovery between used microfabricated glass nanowell chips injection-molded polypropylene found provided improved coverage. Combined, substrates enabled identification an average nearly 2400 proteins per standard data-dependent Orbitrap mass spectrometers. advances greatly simplify broaden accessibility no compromise terms

Language: Английский

Citations

32
Evaluating the capabilities of the Astral mass analyzer for single-cell proteomics DOI Creative Commons
Valdemaras Petrosius, Pedro Aragon-Fernandez,

Tabiwang N. Arrey

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: June 8, 2023

Abstract The complexity of human physiology arises from well-orchestrated interactions between trillions single cells in the body. While single-cell RNA sequencing (scRNA-seq) has enhanced our understanding cell diversity, gene expression alone does not fully characterize phenotypes. Additional molecular dimensions, such as proteins, are needed to define cellular states accurately. Mass spectrometry (MS)-based proteomics emerged a powerful tool for comprehensive protein analysis, including applications. However, challenges remain terms throughput and proteomic depth, order maximize biological impact by Spectrometry (scp-MS) workflows. This study leverages novel high-resolution, accurate mass (HRAM) instrument platform, consisting both an Orbitrap innovative HRAM Asymmetric Track Lossless (Astral) analyzer. Astral analyzer offers high sensitivity resolution through lossless ion transfer unique flight track design. We evaluate performance Thermo Scientific MS using Data-Independent Acquisition (DIA) assess proteome depth quantitative precision ultra-low input samples. Optimal DIA method parameters identified, we demonstrate ability cycle dynamics Human Embryonic Kidney (HEK293) cells, cancer heterogeneity primary Acute Myeloid Leukemia (AML) culture model.

Language: Английский

Citations

31