Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Large scale cerebrospinal fluid proteomics reveals biomarkers of Multiple Sclerosis

ABSTRACT: Cerebrospinal fluid (CSF) plays critical roles in clinical diagnostics, offers a window into neuropathological mechanisms and is available at scale. To enable its large-scale proteomics characterization, we developed a high-throughput mass spectrometry-based workflow based on the Orbitrap Astral. This workflow quantifies about 2100 proteins per sample at a throughput of 100 samples per day. We measured 2600 CSF samples across a discovery and replication cohort from patients with multiple sclerosis and related inflammatory CNS conditions as well as neurological controls. Analysis of CSF proteomes yielded a panel of 29 protein biomarkers to identify MS with better diagnostic performance than established clinical CSF markers (QIgG, QAlb, leukocyte count) amongst patients presenting with MS-like symptoms but without CSF-specific oligoclonal bands.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cerebrospinal Fluid

DISEASE(S): Multiple Sclerosis

SUBMITTER: Mario Oroshi

LAB HEAD: Matthias Mann

PROVIDER: PXD064570 | Pride | 2025-12-10

REPOSITORIES: Pride

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	Astral_DIA_method.zip	Other
	Astral_proteome_search_results.zip	Other
	Astral_raw_files_readme.docx	Other
	Astral_spectral_library.zip	Other
	PL01_Astral.zip	Other

Items per page:

1 - 5 of 66

Similar Datasets

Large scale cerebrospinal fluid proteomics reveals biomarkers of Multiple Sclerosis

Project description:Cerebrospinal fluid (CSF) plays critical roles in clinical diagnostics, offers a window into neuropathological mechanisms and is available at scale. To enable its large-scale proteomics characterization, we developed a high-throughput mass spectrometry-based workflow based on the Orbitrap Astral. This workflow quantifies about 2100 proteins per sample at a throughput of 100 samples per day. We measured 2600 CSF samples across a discovery and replication cohort from patients with multiple sclerosis and related inflammatory CNS conditions as well as neurological controls. Analysis of CSF proteomes yielded a panel of 29 protein biomarkers to identify MS with better diagnostic performance than established clinical CSF markers (QIgG, QAlb, leukocyte count) amongst patients presenting with MS-like symptoms but without CSF-specific oligoclonal bands. To verify this biomarker panel and provide an assay format compatible with routine diagnostics, we have built a targeted assay on the Stellar mass spectrometer. With this assay, we have remeasured the discovery and replication cohort, confirming the diagnostic performance. This repository entry pertains to the targeted assay data only, the discovery proteomics data is deposited separately, see methods section of the publication.

2025-12-10 | PXD064513 | Pride

hnRNP R promotes O-GlcNAcylation of eIF4G to facilitate axonal protein synthesis

Project description:Motoneurons critically depend on precise spatial and temporal control of translation for axon growth and the establishment and maintenance of neuromuscular connections. While defects in local translation have been implicated in the pathogenesis of motoneuron disorders, little is known about mechanisms regulating axonal protein synthesis. Here, we report that motoneurons derived from Hnrnpr knockout mice show reduced axon growth accompanied by lowered synthesis of cytoskeletal and synaptic components in axons. Mutant mice display denervated neuromuscular junctions and impaired motor behavior. In axons, hnRNP R is a component of translation initiation complexes and, through interaction with O-linked β-N-acetylglucosamine (O-GlcNAc) transferase (Ogt), modulates O-GlcNAcylation of eIF4G, thereby stimulating translation. Restoring axonal O-GlcNAc levels rescued local protein synthesis and axon growth defects of hnRNP R knockout motoneurons. Together, these findings demonstrate a function of hnRNP R in controlling the local production of key factors required for axon growth and formation of neuromuscular innervations.

2024-08-22 | PXD052115 | Pride

Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel

Project description:Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables fiveplex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.

2023-08-30 | PXD038632 | Pride

One-Tip enables comprehensive proteome coverage in minimal cells and single zygotes

Project description:We present One-Tip, a lossless proteomics methodology that seamlessly combines swift, one-pot sample preparation with narrow-window data-independent acquisition mass spectrometric analysis. With simplest sample processing, One-Tip reproducibly identifies > 9,000 proteins from ~1000 cells and ~ 6,000 proteins in a single mouse zygote with a throughput of 40 samples-per-day. This easy-to-use method expands capabilities of proteomics research by enabling greater depth, scalability and throughput covering low to high input samples.

2023-09-15 | PXD044991 | Pride

hnRNPR interactome in axons and soma of murine motoneurons

Project description:We determined the protein interaction partners of hnRNP R in murine motoneuron axons and soma by immunoprecipitation-MS. The neurons were grown in microchambers and axons and soma could be harvested individually. We performed an IP with an antibody against the murine hnRNP R.

2024-08-22 | PXD043851 | Pride

High throughput single-cell proteomics of in-vivo cells

Project description:Major advancements in single-cell proteomics (SCP) methodologies have been introduced in recent years, providig highly sensitive sample preparation methods and mass spectrometric technologies. However, most studies present limited throughput and mainly focus on the analysis of cultured cells. To enhance the depth, accuracy, and throughput of SCP for tumor analysis, we developed an automated, high-throughput pipeline that enables the analysis of 1,536 single cells in a single experiment. This approach integrates low-volume sample preparation, automated sample purification, and LC-MS analysis with the Slice-PASEF method. Integration of these methodologies into a streamlined pipeline led to a robust and reproducible identification of more than 3000 proteins per cell. We applied this pipeline to analyze tumor macrophages in a murine lung metastasis model. We identified over 1,800 proteins per cell, including key macrophage markers and ~500 differentially expressed proteins between tumor and control macrophages. PCA analysis successfully separated these populations, revealing the utility of SCP in capturing biologically relevant signals in the tumor microenvironment. Our results demonstrate a robust and scalable pipeline poised to advance single-cell proteomics in cancer research.

2025-07-09 | PXD058457 | Pride

Human adipose tissue proteomics reveal adaptations to exercise and type 2 diabetes

Project description:White adipose tissue (WAT) plays a critical role in whole-body energy homeostasis. In this study, we established the differential proteomic signatures of WAT in glucose-tolerant lean and obese individuals and patients with type 2 diabetes (T2D) and in response to 8 weeks of high intensity interval training (HIIT). We combined a high-throughput and reproducible mass spectrometry-based proteomics pipeline and identified a total of 3773 proteins. We find that a majority of regulated proteins displayed progression from lean to obese and T2D individuals and were highly associated with clinical measures of glucose homeostasis (e.g., insulin sensitivity, HbA1c) and they could, therefore, serve as potential disease biomarkers. Interestingly, HIIT induced a strong increase in WAT ferritin levels independent of group. WAT ferritin levels strongly correlated with individual insulin sensitivity. Thus, we report novel proteomic signatures of WAT related to obesity and T2D and highlight an unrecognized role of human WAT iron metabolism in exercise training adaptations.

2024-01-10 | PXD040434 | Pride

Proteome profiling of cerebrospinal fluid and machine learning reveal biomarkers of two forms of Alzheimer’s disease characterized by increased or not altered levels of tau

Project description:Alzheimer's disease (AD) is a progressive neurodegenerative disorder that impairs memory, cognition, behavior, and other cognitive functions. Despite significant advances in understanding its molecular mechanisms, a definitive cure remains elusive. However, some treatments have the potential to slow disease progression if applied before brain damage occurs. Therefore, the identification of reliable biomarkers is critical for early diagnosis of AD and effective intervention. Recent advances in proteomics and the increased accuracy of machine learning algorithms have enhanced biomarker discovery and validation. In this study, we used a newly developed proteomic pipeline to analyse cerebrospinal fluid (CSF) to profile the proteome of AD patients, which included two different subgroups based on their CSF levels of tau. Then, machine learning was used to identify proteins that best classified the two subgroups of AD patients compared to non-AD controls. The resulting model, based on few CSF proteins, demonstrated high accuracy in predicting AD and differentiating patients with elevated or normal CSF tau levels. These protein classifiers, detectable in the preclinical stages of AD, were further validated in silico using larger, publicly available proteomic datasets, confirming their potential as early diagnostic tools.

2025-08-25 | PXD063058 | Pride

Multisystem Inflammatory Syndrome in Children Disease characterization and diagnostics using mass-spectrometry-based proteomics and artificial intelligence

Project description:During the COVID-19 pandemic, Multisystem Inflammatory Syndrome in Children (MIS-C) emerged as a disease with unknown causes and no specific diagnostic test. We used artificial intelligence in a nationwide study from April 2020 to March 2022 to identify a 4-protein diagnostic signature for MIS-C. This signature had an AUC of 1.0 for distinguishing MIS-C from other febrile diseases. Our analysis also revealed intricate protein patterns, indicating that MIS-C is associated with immune dysregulation, lipid metabolism changes, increased coagulation activity, and tissue remodeling.

2024-05-14 | PXD045661 | Pride

Human plasma proteome immune response to COVID-19

Project description:In this study, we investigated immune responses in a prospective cohort of hospitalized COVID-19 patients (derivation cohort, DC; n = 126) during the spring of 2020. Plasma samples were collected within four days of admission to measure a panel of innate and complement system immune markers. From a subset of this cohort (n = 80), we performed plasma proteomics and developed a predictive survival model based on proteins differentially expressed between survivors (n = 60) and non-survivors (n = 20). Logistic regression analyses were employed to evaluate the predictive value of these biomarkers for 30-day mortality. The developed proteomic prediction model and the regression analyses were subsequently validated using an independent cohort of hospitalized COVID-19 patients (validation cohort, VC) from the autumn/winter of 2020, who received treatment with remdesivir and dexamethasone.

2025-02-18 | PXD055973 | Pride