Multi-platform proteomic analysis of Alzheimer’s disease cerebrospinal fluid and plasma reveals network biomarkers associated with proteostasis and the matrisome

Dammer, Eric B.; Ping, Lingyan; Duong, Duc M.; Modeste, Erica; Seyfried, Nicholas T.; Lah, James J.; Levey, Aĺlan I.; Johnson, Erik C.B.

doi:10.1186/s13195-022-01113-5

Cited by 78 publications

(117 citation statements)

References 47 publications

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“…Significant correlations of these peptides to the established biomarker and cognitive measures indicate the potential of these measurements to classify or stage disease progression. The targeted SRM measurement correlations largely agree with those observed from unbiased discovery proteomics 42 and parallel reaction monitoring 21 experiments.…”

Section: Usage Notessupporting

confidence: 73%

Quantitative Mass Spectrometry Analysis of Cerebrospinal Fluid Protein Biomarkers in Alzheimer’s Disease

Watson

Dammer

Ping

et al. 2022

Preprint

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Alzheimer's disease (AD) is the most common form of dementia, with cerebrospinal fluid (CSF) β-amyloid (Aβ), total Tau, and phosphorylated Tau providing the most sensitive and specific biomarkers for diagnosis. However, these diagnostic biomarkers do not reflect the complex changes in AD brain beyond plaque and tangle pathologies. Here we report a sensitive, quantitative, and scalable targeted proteomics assay of AD biomarkers representing mainly neuronal, glial, vasculature and metabolic pathways. As quality controls (QCs), we pooled CSF from individuals having normal Aβ and Tau levels (AT-), and individuals having low Aβ and high Tau levels (AT+) to determine the coefficient of variation (CV) and fold-change of protein measurements. Additionally, we analyzed 390 CSF samples using selective reaction monitoring-based mass spectrometry (SRM-MS). Following trypsin digestion, 133 controls (cognitively normal and AT-), 127 asymptomatic (cognitively normal and AT+) and 130 symptomatic AD (cognitively impaired and AT+), and 30 pooled CSF samples were analyzed by SRM-MS using a 15-minute targeted liquid chromatography-mass spectrometry method. Isotopically labeled peptide standards were added for relative quantification by reporting the area ratios for each targeted peptide. We reproducibly detected 62 peptides from 51 proteins in all clinical samples with an average CV of approximately 13% across pools. Proteins that could best distinguish AsymAD and AD cases from controls included SMOC1, GDA, 14-3-3 proteins, and proteins involved in glucose metabolism. In contrast, proteins that could best distinguish AD from AsymAD were mainly neuronal/synaptic proteins including VGF, NPTX2, NPTXR, and SCG2. Collectively, this highlights the utility of high-throughput SRM-MS to quantify peptide biomarkers in CSF that can potentially monitor disease progression.

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Section: Usage Notessupporting

confidence: 73%

Quantitative Mass Spectrometry Analysis of Cerebrospinal Fluid Protein Biomarkers in Alzheimer’s Disease

Watson

Dammer

Ping

et al. 2022

Preprint

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“…In that study, however, increased, instead of decreased CSF levels of CLEC1B associated with progression to dementia. In serum, increased levels of TGF β -1 have been associated with AD incidence 34 , which may be in line with a recent study in CSF and plasma indicating that the direction of protein level abnormality tends to be opposite between these matrices 35 . Plasma proteomics focussed on inflammation and vascular injury have been associated with cognitive decline 11,36 , but there was no overlap with our top biomarkers in CSF.…”

Section: Discussionsupporting

confidence: 87%

“…While the number of proteins is still limited by the availability of antibodies, it is due to the antibody-based technology that translation to a panel or single assays could be feasible 13 . However, the validation of our findings, especially across different proteomics methods is challenging, since the measurements of protein abundance depend on the nature of the techniques 35 . Olink proteomics measurements are based on antibody binding, while mass spectrometry analysis provides peptide counts.…”

Section: Discussionmentioning

confidence: 97%

“…An earlier study investigated aged AD mice and reported a 50% reduction of Amyloid-β load with a modest increase in astroglial TGF β -1 production 44 . A recent multi-platform proteomic co-expression analysis of AD in CSF identified strong signals with TGF β signalling pathway 35 . Lower levels of TGF β -1 in CSF of fast decliners compared to the slow decliners suggests that lower levels of TGF β -1 might reflect a lack of neuroprotective effects of TGF β -1.…”

Section: Discussionmentioning

confidence: 99%

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Discovery of novel CSF biomarkers to predict progression in dementia using machine learning

Gogishvili

Vromen²,

Koppes

et al. 2022

Preprint

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Providing an accurate prognosis for individual dementia patients remains a challenge since they greatly differ in rates of cognitive decline. In this study, we used machine learning techniques to identify cerebrospinal fluid (CSF) biomarkers that predict the rate of cognitive decline. First, longitudinal follow-up data of 210 dementia patients were used to create fast and slow progression groups. Secondly, we trained random forest classifiers on CSF proteomic profiles and obtained a well-performing prediction model for the progression group (ROC-AUC = 0.82). As a third step, Shapley values and Gini feature importance measures were used to interpret the model performance and identify top biomarker candidates. Lastly, we explored the progression biomarker potential for each of the 20 top candidates in internal sensitivity analyses. TNFRSF4 and TGF β-1 emerged as the top markers, being lower in fast-progressing patients compared to slow-progressing patients. Proteins of which a low concentration was associated with fast progression were enriched for cell signalling and immune response pathways, which could indicate a lack of a protective response in these individuals. None of our top markers stood out as strong individual predictors of subsequent cognitive decline. This could be explained by small effect sizes per protein and biological heterogeneity among dementia patients. Taken together, this study presents a novel progression biomarker identification framework and protein leads for personalised prediction of cognitive decline in dementia.

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“…Overall, 10 external datasets were added for the correlation comparison (supplementary table 3), covering comparison between SomaScan versus immunoassays, PEA and mass spectrometry (MS). Spearman correlation coefficients were obtained; since the correlations reported in Dammer et al 23 , were Pearson correlations, the Spearman correlation coefficient was calculated to maintain the same coefficient calculation method for all studies. Raffield and colleagues reported correlations measured in different platforms; the average data that they reported were used here (Table S2-9 of reference 24 ).…”

Section: Cross-platform Comparisons Of Protein Measurementsmentioning

confidence: 99%

Analytical validation of the PROphet test for treatment decision-making guidance in metastatic non-small cell lung cancer

Sela¹,

Lahav²,

Yellin³

et al. 2023

Preprint

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The PROphet test provides a decision-making tool for first-line treatment of non-small cell lung cancer (NSCLC) patients without driver mutations. The test is based on plasma proteomics profiling using the SomaScan platform, followed by a machine learning-based analysis. During the PROphet predictor development, a set of predictive proteins was identified, termed resistance-associated proteins (RAPs). Here we set to examine the analytical validity of the SomaScan assay as the biomarker measurement method in the PROphet test, focusing either on all proteins measured using this platform, or specifically on the RAPs. Experimental precision analysis displayed a median coefficient of variation (CV) of 3.9% and 4.7% for intra-plate and inter-plate examination, respectively, when studying all proteins, with no difference between technicians. Notably, the RAPs displayed lower median CV values and lower CV range. The median accuracy rate of measurements obtained in sites was 88% and 94% for all proteins and for the RAPs, respectively, with no significant difference between technicians. Computational precision examination displayed high precision for 13 out of 14 examined samples, with median standard error of 0.1689. Cross-platform comparison between SomaScan platform and other proteomics platforms, including immunoassays, proximity extension assay (PEA) and mass spectrometry, displayed a median Spearman coefficient of 0.51 and 0.53 for all examined proteins and RAPs, with no significant difference between the two protein sets. Last, the effect of the cross-platform correlations on the prediction capabilities was examined, displaying only a minor effect of the correlation on the prediction capabilities of the PROphet predictor, with a median of 80% agreement between predictions obtained between the two examined proteomic platforms. Taken together, these results demonstrate strong analytical performance for the SomaScan technology, while the RAPs displayed improved capabilities in most of the analytical performance analyses.

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Multi-platform proteomic analysis of Alzheimer’s disease cerebrospinal fluid and plasma reveals network biomarkers associated with proteostasis and the matrisome

Cited by 78 publications

References 47 publications

Quantitative Mass Spectrometry Analysis of Cerebrospinal Fluid Protein Biomarkers in Alzheimer’s Disease

Quantitative Mass Spectrometry Analysis of Cerebrospinal Fluid Protein Biomarkers in Alzheimer’s Disease

Discovery of novel CSF biomarkers to predict progression in dementia using machine learning

Analytical validation of the PROphet test for treatment decision-making guidance in metastatic non-small cell lung cancer

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