Academic Background

Frontotemporal Dementia (FTD) is a group of progressive neurodegenerative diseases characterized primarily by behavioral changes, language impairment, or motor dysfunction. Although the incidence of FTD is lower than that of Alzheimer’s Disease (AD), it remains one of the leading causes of early-onset dementia. The molecular basis of FTD is complex, with most cases attributed to Frontotemporal Lobar Degeneration (FTLD) pathology, manifested by cellular inclusions of Tau protein, TDP-43 protein, or FET protein. Unlike AD, approximately one-third of FTD cases are hereditary, with the most common mutations occurring in the GRN, C9orf72, and MAPT genes. These mutations lead to TDP-43 proteinopathy and Tauopathy, respectively.

Currently, the diagnosis and treatment of FTD face challenges, particularly due to the lack of specific biomarkers. Therefore, identifying Cerebrospinal Fluid (CSF) protein markers associated with FTD has become a focus of research. Proteomic analysis can reveal the pathological mechanisms of different genetic subtypes of FTD and provide new biomarkers for diagnosis and prognosis.

Source of the Paper

The study was conducted by Aitana Sogorb-Esteve, Sophia Weiner, Joel Simrén, and other researchers from multiple institutions, including University College London, the University of Gothenburg, and Erasmus Medical Center Rotterdam. The paper was published on February 5, 2025, in the journal Science Translational Medicine, titled “Proteomic analysis reveals distinct cerebrospinal fluid signatures across genetic frontotemporal dementia subtypes.”

Research Process

1. Study Subjects and Sample Collection

The study utilized 238 CSF samples from the Genetic FTD Initiative (GENFI) cohort, including 107 asymptomatic mutation carriers (44 C9orf72, 38 GRN, and 25 MAPT), 55 symptomatic mutation carriers (27 C9orf72, 17 GRN, and 11 MAPT), and 76 non-carrier controls. All samples were collected via lumbar puncture and centrifuged within 2 hours to remove insoluble material and cells. The supernatants were aliquoted and stored at -80°C.

2. Sample Processing and Proteomic Analysis

Sample processing included the following steps: - Reduction and Alkylation: Proteins were reduced using tris(2-carboxyethyl)phosphine (TCEP) and dithiothreitol (DTT), followed by alkylation with iodoacetamide. - Enzymatic Digestion: Proteins were digested using trypsin to generate peptides. - TMT Labeling: Peptides were labeled using 18-plex Tandem Mass Tag (TMTpro) reagents, and samples were divided into multiple 18-plex groups. - Liquid Chromatography Separation: Peptides were separated using high-pH reversed-phase high-performance liquid chromatography (HPLC) to reduce sample complexity. - Mass Spectrometry Analysis: High-resolution mass spectrometry (Orbitrap Fusion Lumos) was used to analyze peptide relative abundances.

3. Data Analysis

Data analysis employed the following methods: - Differential Protein Abundance Analysis: Linear regression models were used to compare protein abundance differences between groups, adjusting for age and sex as covariates, with multiple testing corrected using the Benjamini-Hochberg method. - Gene Co-expression Network Analysis: Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify protein modules associated with pathology, and Gene Ontology (GO) annotation was used to determine the biological functions of the modules. - Clinical Parameter Correlation Analysis: Module eigengenes were correlated with clinical parameters (e.g., plasma NFL, FTLD-CDR-SOB scores, brain volume) to assess their prognostic value.

Key Results

1. Proteomic Differences Across Genetic FTD Subtypes

The study revealed significant differences in the CSF proteome among FTD patients with different genetic subtypes. In symptomatic mutation carriers, MAPT, GRN, and C9orf72 carriers showed significant changes in the abundance of 58, 138, and 385 proteins, respectively. These proteins were primarily involved in neuronal damage, glial responses, and synaptic function.

2. Comparison with Alzheimer’s Disease

The study also compared the FTD proteomic data with AD proteomic data. Proteins such as YWHAG, NPTXR, and FABP3 were found to be significantly altered in both FTD and AD, indicating shared downstream pathological features in neurodegeneration and neuroinflammation.

3. Proteomic Changes in Asymptomatic Mutation Carriers

The study found that the abundance of some proteins was already altered in asymptomatic mutation carriers. For example, CALB2, HK1, and PGK1 were significantly elevated in asymptomatic C9orf72 carriers, suggesting a potential link to metabolic dysregulation associated with C9orf72.

4. Protein Network Analysis

Using WGCNA, the study identified multiple protein modules associated with FTD pathology, including “core markers,” “synapse,” and “lysosome” modules. The “core markers” module was significantly correlated with disease severity and cognitive decline, while reduced abundance of the “synapse” module was associated with cognitive decline.

Conclusion

The study revealed the CSF proteomic signatures of genetic FTD patients, providing deep insights into the pathological mechanisms of the disease. The findings showed both shared and distinct proteomic changes among different genetic subtypes of FTD, which were evident even in the asymptomatic stage. The study also identified specific proteins associated with C9orf72, GRN, and MAPT mutations, offering potential biomarkers for FTD diagnosis and prognosis. Additionally, the study found shared proteomic changes between FTD and AD in neurodegeneration and neuroinflammation, suggesting common pathological mechanisms in the two diseases.

Highlights of the Study

1. Comprehensive Proteomic Analysis: The study employed untargeted mass spectrometry to systematically analyze the CSF proteome of genetic FTD patients, revealing proteomic signatures of different genetic subtypes.
2. Discovery of Early Pathological Markers: The study found that the abundance of some proteins was altered in asymptomatic mutation carriers, providing potential biomarkers for early FTD diagnosis.
3. Comparative Study with AD: The study compared FTD proteomic data with AD data, identifying shared proteomic changes in neurodegeneration and neuroinflammation.
4. Application of Protein Network Analysis: Using WGCNA, the study identified protein modules associated with FTD pathology and evaluated their correlations with clinical parameters, offering new perspectives for FTD diagnosis and prognosis.

Summary

Through comprehensive proteomic analysis, the study revealed the CSF proteomic signatures of genetic FTD, identified specific proteins associated with different genetic subtypes, and proposed potential diagnostic and prognostic biomarkers. The findings not only provide new insights into the pathological mechanisms of FTD but also offer important scientific foundations for the early diagnosis and treatment of FTD.