Head-to-head comparison of aptamer- and antibody-based proteomic platforms in human cerebrospinal fluid samples from a real-world memory clinic cohort

Head-to-head comparison of aptamer- and antibody-based proteomic platforms in human cerebrospinal fluid samples from a real-world memory clinic cohort | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var 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Profile Itziar de Rojas , Clàudia Olivé , Josep Blázquez-Folch , Oscar Sotolongo-Grau , Andrea Miguel , Laura Montrreal , Pamela Martino-Adami , Asif Khan , Adelina Orellana , Yun Ju Sung , View ORCID Profile Ruth Frikke-Schmidt , Natalie Marchant , View ORCID Profile Jean Charles Lambert , Maitée Rosende-Roca , Montserrat Alegret , Maria Victoria Fernández , Marta Marquié , View ORCID Profile Sergi Valero , Lluís Tárraga , View ORCID Profile Carlos Cruchaga , Alfredo Ramírez , Mercè Boada , Bart Smets , Alfredo Cabrera-Socorro , Agustín Ruiz doi: https://doi.org/10.1101/2024.07.18.24310563 Raquel Puerta 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain 2 Universitat de Barcelona (UB) Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amanda Cano 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain 3 CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pablo García-González 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain 3 CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pablo García-González Fernando García-Gutiérrez 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site María Capdevila 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain 4 Departament de Farmacologia, Toxicologia i Química Terapèutica, Facultat de Farmàcia i Ciències de l’Alimentació, Universitat de Barcelona , Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Itziar de Rojas 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain 3 CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Itziar de Rojas Clàudia Olivé 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Josep Blázquez-Folch 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Oscar Sotolongo-Grau 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrea Miguel 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laura Montrreal 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pamela Martino-Adami 5 Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne , 50937 Cologne, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Asif Khan 6 Janssen Pharmaceutica NV , a Johnson & Johnson company Find this author on Google Scholar Find this author on PubMed Search for this author on this site Adelina Orellana 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yun Ju Sung 7 NeuroGenomics and Informatics Center, Washington University School of Medicine , St. Louis, MO, USA 8 Hope Center for Neurological Disorders , St. Louis, MO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ruth Frikke-Schmidt 9 Department of Clinical Biochemistry, Copenhagen University Hospital - Rigshospitalet , Copenhagen, Denmark 10 Department of Clinical Medicine, University of Copenhagen , Copenhagen, Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ruth Frikke-Schmidt Natalie Marchant 11 Division of Psychiatry, University College London , London, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jean Charles Lambert 12 Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement , F-59000, Lille, France 13 Inserm U1167 - Université de Lille — CHU de Lille — Institut Pasteur de Lille — LabEx DISTALZ , Lille, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jean Charles Lambert Maitée Rosende-Roca 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Montserrat Alegret 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain 3 CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria Victoria Fernández 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain 3 CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marta Marquié 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain 3 CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sergi Valero 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain 3 CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sergi Valero Lluís Tárraga 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain 3 CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Carlos Cruchaga 7 NeuroGenomics and Informatics Center, Washington University School of Medicine , St. Louis, MO, USA 8 Hope Center for Neurological Disorders , St. Louis, MO, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carlos Cruchaga Alfredo Ramírez 5 Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne , 50937 Cologne, Germany 14 Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Medical Faculty , 53127 Bonn, Germany 15 German Center for Neurodegenerative Diseases (DZNE) , 53127 Bonn, Germany 16 Department of Psychiatry and Glenn, Biggs Institute for Alzheimer’s and Neurodegenerative Diseases , 78229 San Antonio, TX, USA 17 Cluster of Excellence Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne , 50931 Cologne, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mercè Boada 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain 3 CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III , Madrid, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bart Smets 6 Janssen Pharmaceutica NV , a Johnson & Johnson company Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alfredo Cabrera-Socorro 6 Janssen Pharmaceutica NV , a Johnson & Johnson company Find this author on Google Scholar Find this author on PubMed Search for this author on this site Agustín Ruiz 1 Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya , Spain 3 CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III , Madrid, Spain 18 Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center , San Antonio, TX, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: aruiz{at}fundacioace.org Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract High-throughput proteomic platforms have a crucial role in identifying novel Alzheimer’s disease (AD) biomarkers and pathways. In this study, we evaluated the reproducibility and reliability of aptamer-based (SomaScan® 7k) and antibody-based (Olink® Explore 3k) proteomic platforms in cerebrospinal fluid (CSF) samples from the Ace Alzheimer Center Barcelona real-world cohort. Intra- and interplatform reproducibility was evaluated through correlations between two independent SomaScan® assays analyzing the same samples and between SomaScan® and Olink® results. Our 12-category metric of reproducibility combining both correlation analyses identified 2,428 highly reproducible SomaScan CSF measures, with over 600 proteins well reproduced on another proteomic platform. The association analyses among AD clinical phenotypes revealed that the significant associations mainly involved reproducible proteins. The validation of reproducibility in these novel proteomics platforms, measured using this scarce biomaterial, is essential for accurate analysis and proper interpretation of innovative results. This classification metric could enhance confidence in multiplexed proteomic platforms and improve the design of future panels. The growing interest in understanding the complex molecular mechanisms of different diseases and identifying novel biomarkers and potential drug targets has driven the development of highly multiplexed proteomic techniques. Moreover, the evolution of proteomic platforms has led to the evaluation of many analytes, enabling the simultaneous analysis of multiple samples and the development of different detection methods 1 . Mass spectrometry (MS) has been the gold standard technique in the field of proteomics, permitting the measurement of protein abundance, protein interactions and posttranslational modifications and providing crucial insights into multiple pathological mechanisms across various study areas 2 , 3 . Recently, two major affinity-based approaches capable of being used to analyze thousands of proteins have emerged as multiplex platforms 4 . Among these, the most well-known methods include immune-based techniques, such as Olink® proteomics, and aptamer-based techniques, such as the SomaScan® proteomic platform. While the Olink® platform uses antibodies labeled with oligonucleotides to detect protein abundance by proximity extension assays (PEAs), quantitative polymerase chain reaction (qPCR) and next-generation sequencing (NGS), the SomaScan platform uses modified DNA aptamers that bind to proteins and detect them by fluorescence 5 , 6 . These high-throughput multiplex proteomic techniques represent valuable improvements by reducing the costs of single assays and reducing the time consumed by simultaneously analyzing multiple analytes and samples 7 , 8 . Furthermore, most of the proteomic analyses have been conducted using blood samples (plasma and serum), mainly because of the simple accessibility of the biomaterial. Thus, several studies have compared the affinity of these proteomic techniques (immune- and aptamer-based) for plasma samples in the context of several diseases, such as ovarian cancer, cardiovascular disease, atherosclerosis risk, and chronic obstructive pulmonary disease (COPD) 9 – 13 . In addition, other authors extended their analysis to cerebrospinal fluid (CSF) samples with a reduced sample size 14 . For neurodegenerative diseases, the use of high-throughput proteomic approaches could be of special interest since they could be used to provide key information on the pathological changes occurring in the brain. Due to the inaccessibility of the brain, CSF, which is in direct contact with the central nervous system (CNS), is a well-established source of biomaterial reflecting brain protein alterations, among other outcomes. Specifically, in Alzheimer’s disease (AD), the main cause of dementia worldwide, a reduction in amyloid β 42 (Aβ42) levels and an increase in phospho-tau in threonine 181 (p-tau) in CSF have been widely described and used in memory clinics to aid in the diagnosis of these patients 15 – 17 . In this sense, proteomic profiling across the AD continuum could potentially provide insights into novel CSF biomarkers and pathways associated with disease development and would definitely yield valuable information about AD pathological alterations 18 – 21 . However, due to the novelty of these techniques, the large number of proteins included, and the relevance of the potential findings, it is extremely important to perform a high-throughput assessment to determine the effects of biological and technical variability to ensure the reliability of the available platforms. Likewise, a validation analysis to improve the experimental design and reagents used could also strengthen the usage of these innovative technologies 22 . In this sense, few studies have rigorously assessed the impact of technical variations, preanalytical factors and detection and quantification rates in CSF samples. Consistent with this concept, we aimed to compare outcomes derived from the SomaScan® 7k and Olink® Explore platforms to investigate the reproducibility and reliability of these technologies. Our study included 1,370 real-world CSF samples from the large Ace Alzheimer Center Barcelona (ACE) cohort composed of highly characterized subjects across the AD continuum 23 . An extensive characterization and comparison of these platforms were performed to critically elucidate their strengths and weaknesses and determine the gold standards of the thousands of proteins tested. Additionally, we also intended to identify the top-performing proteins for analyzing the associations between clinical phenotypes and AD core biomarkers in CSF. Results Proteomic characterization and demographics In the subset of the ACE cohort, which included 264 individuals with available proteomic information derived from three independent experiments, there was a generally greater proportion of female individuals (mean age: 71.0 ± 8.28 years), most of whom were diagnosed with MCI. Additionally, those participants were highly characterized via biochemical analysis in CSF biofluid, including albumin, total globulins, total protein levels, the Qalb and red blood cell count. In addition to those CSF biomarkers for AD, the ATN (amyloid, tau, neurodegeneration) classification 15 and the Mini-Mental State Examination (MMSE) score of the participants were also provided. Regarding the APOE locus, 29.5% of individuals were carriers of the ε4 allele, and lower MMSE scores were observed in individuals with MCI than in HCs, as we expected ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1. Demographic and clinical data of the ACE cohort (n = 264). PCA evaluation We conducted a PCA to evaluate the variability captured by these proteomic platforms by aligning them with multiple orthogonal axes (PCs). In the nonadjusted correlation analysis between PCs and phenotypes, we found multiple statistically significant correlations with clinical traits across platforms ( Fig. 1C ). However, the associations analysis, including all of these variables in the model, identified four independent components of the variance: CSF Aβ42, CSF p-tau, CSF total proteins and the sample storage duration at -80°C ( Fig. 1D ). Interestingly, the first PC (PC1) of both SomaScanA and Olink Explore were strongly associated with CSF total protein, p-tau and Aβ42 levels, suggesting that these variables are the major contributors to this fraction of explained variance. However, PC1 of the SomaScanB dataset was not significantly associated with any phenotype ( Fig. 1C and 1D ). In addition, the correlation analysis of the top PCs revealed a strong correlation between SomaScanA and Olink Explore PC1 (r = 0.682, P < 2.2E-16). In contrast, the SomaScanB PC1 exhibited a weaker negative correlation with the Olink Explore (r = -0.22, P = 8.73E-05) and SomaScanA (r = -0.144, P = 0.0120) PC1. These results suggest that PC1 for both the SomaScanA and the Olink Explore datasets are tracking a similar source of variance and that there are other factors strongly impacting the SomaScanB variance (Extended data Fig. 1 ). Other moderate correlations were found between Olink Explore PC1 and the second PC (PC2) for the SomaScan (SomaScanA: r = 0.532, P < 2.2E-16; SomaScanB: r = 0.482, P < 2.2E-16) as well as between SomaScanA PC1 and SomaScanB PC2 (r = 0.405, P = 1.81E-13) (Extended data Fig. 1 ). Download figure Open in new tab Figure 1. Principal component analysis results. a) Variance explained by the top 10 PCs. b) Percentage of cumulative variance explained by the top 300 PCs; the dashed line represents 95% of the explained variance. c) Pearson correlations of the top 5 PCs with clinical phenotypes according to the nonadjusted model. d) Linear model associations of the top 5 PCs adjusted by all clinical phenotypes that were included in the model. An asterisk (*) represents a P value lower than 0.05, two asterisks (**) represent a P value lower than 0.01, and three asterisks (***) represent a P value lower than 0.001. Moreover, to assess the effect of demographic variables, such as age at lumbar puncture (LP) and sex, on PC1 and PC2, we evaluated the PC plots according to these variables. For both the SomaScan® and Olink® Explore platforms, we observed a weak correlation between age and PCs (Extended data Fig. 2A , 2C and 2E). In the SomaScan® platform, these PCs were not found to be associated with sex (Extended data Fig. 2B and 2D ). Moreover, we found significant differences between males and females in terms of the Olink® Explore PC1 and PC2 means (P PC1 = 4.9E-04; P PC2 = 8.9E-08; Extended data Fig. 2F and 3 ). Download figure Open in new tab Figure 2. Coefficient of variation (CV) for the SomaScan and Olink Explore Platforms. a) Intra- and b) interassay CVs colored by the overlap between the SomaScan and Olink assays after removing outlier proteins at 1.5-fold IQRs. The Mann‒Whitney–Wilcoxon test was applied to explore differences across median values. The red line in the zoom plot represents the median CV for each platform. c) Percentages of intra- and interassay CVs for the SomaScan and Olink Explore platforms. Download figure Open in new tab Figure 3. Distribution of Spearman’s rho values in the correlation analysis, including the sample size of each category [n (%)]. A) Intraplatform correlation between SomaScanA and SomaScanB assays, B) Interplatform correlation between SomaScanA and Olink Explore, and C) Interplatform correlation between SomaScanB and Olink Explore platforms. We established three categories: good (rho > 0.5), moderate (0.5 > rho ≥ 0.3) and poor (rho < 0.3). Regarding the top protein loadings contributing to PC1 and PC2, we observed a reduced set of overlapping proteins in the two SomaScan® experiments. This could be due to slight differences in the experimental analysis, mainly caused by noise or reagent saturation resulting in protein level variations. The proteins ADH1A (seq.17396.23), CD031 (seq.6604.59), FCRL6 (seq.6617.12), RB1 (seq.5024.67) and ULBP.1 (seq.3081.70) were represented in both SomaScan® assays protein loadings with negative contributions to both PC1 and PC2 in the SomaScanA and SomaScanB datasets, respectively. Furthermore, no overlap was observed in the top 15 loadings between the SomaScan® and Olink® Explore platforms (Extended data Fig. 4 – 6). Download figure Open in new tab Figure 4. Volcano plots of the associations between SomaScan proteins and clinical phenotypes. Proteins with intra-assay correlations (rho ≥ 0.5) are colored light blue, and the dashed line represents the significance threshold (P value < 6.860e-06). Comparing CVs The CVs for both the SomaScan® and Olink® Explore platforms were calculated to analyze the precision of proteomic measurements using calibration samples 8 . In the intraassay CV assessment (i.e., the variability of measures within a plate), several proteins with extreme CV values were identified via the Olink® Explore platform (Extended data Fig. 7). Moreover, excluding outliers at 1.5-fold IQR, Olink® proteins that did not overlap with the SomaScan® platform had more extreme CV values with different medians than did the overlapping proteins (P < 2.22E-16). Additionally, we observed that Olink® Explore intraassay CV values had a more elongated distribution with different medians than those of the SomaScan® CV values (P < 2.22E-16), suggesting greater variability in Olink’s intraplate precision. Additionally, no significant differences were detected in the median intraassay CV when comparing overlapping and nonoverlapping SomaScan® aptamers ( Fig. 2A , Supplementary Table 1). Additionally, in the interassay CV assessment (i.e., variability between plates), the Olink® Explore platform exhibited significantly more extreme CV values with different medians than those obtained with SomaScan® (P<2.22E-16 for both overlapping and nonoverlapping proteins). Similarly, Olink proteins that did not overlap with the SomaScan® platform showed significantly more extreme interassay CV values (P=0.0011). In contrast, in the SomaScan® platform, those proteins that did not overlap with Olink® had less-extreme values compared with those that did overlap, and there was a statistically significant difference across these groups (P=0.0043) ( Fig. 2B , Supplementary Table 1). The median intra- and interassay CVs were greater for the Olink® Explore platform than for the SomaScan® platform (P<2.22E‒16). Interestingly, we consistently observed that the median intraassay CV was lower than the interassay CV for both proteomic techniques, suggesting that there is greater variability in calibration samples across multiple plates than in samples within the same plate ( Fig. 2 , Extended data Fig. 7). To assess intra- and interassay CVs, we analyzed and compared CV quantiles across proteomic platforms. According to the intraassay CV analysis, 98.6% and 68.9% of the proteins had CVs lower than 20% in the SomaScan® and Olink® Explore platforms, respectively. Conversely, interassay CV analysis revealed percentages of 93.7% and 30.0% for the respective platforms. Additionally, the median CV for the SomaScan® platform reached approximately 5% for both intra- and interassay CVs. In contrast, the Olink® Explore median CV exceeded 10% in both instances ( Fig. 2C ). These findings indicate less variability in calibration samples within the same plate compared to measures analyzed across different plates, particularly with higher CV values observed in the Olink® Explore panel. Correlations among proteomic measurements Intraplatform correlations in SomaScan A bimodal distribution with one mode at a rho of 0.1 with a reduced correlation was observed, as well as another mode with a high correlation at a rho of 0.85. This distribution suggests that a reduced number of aptamer measures are reproducible in different SomaScan® CSF experiments ( Fig. 3A ). The median Spearman rho values of the intraplatform correlation analysis was 0.302. Importantly, we identified a fraction of highly reproducible aptamers that were strongly correlated with a rho ≥ 0.5 (n = 2,428, 33.3% corresponding to 2,434 SomaScan pairs) ( Fig. 3A , Supplementary Table 2). Interplatform correlations between SomaScan and Olink proteomics When comparing the measurements of the same protein using the Olink and SomaScan techniques, we again observed a bimodal distribution in the interplatform analysis. The majority of the CSF proteins exhibited low correlation coefficients, and a reduced portion of proteins were well reproducible in the other proteomic platform. There were 713 (26.7%) and 632 (23.7%) proteins with a rho ≥ 0.5 in the Spearman correlation with the SomaScanA and SomaScanB assays, respectively (Supplementary Table 2). The median Spearman rho values of the interplatform correlation analysis were 0.097 and 0.092 for the SomaScanA and SomaScanB assays, respectively ( Fig. 3B and 3C ). Reproducibility and reliability metric Considering the established correlation categories, we integrated these results into a single metric comprising [1-9] categories for those proteomic measures that we assessed using both the SomaScan® and Olink® Explore platforms. We observed that from those 2,428 highly reproducible aptamers with a Spearman rho ≥ 0.5 (corresponding to 2,434 SomaScan-Olink pairs), only 676 proteins also correlated well with another proteomic technique (Score 1). However, multiple proteins exhibited a lack of reproducibility in another proteomic platform (Score 2: n=171 and Score 3: n=376), which should be cautiously considered when conducting analyses ( Table 2 ). Furthermore, we classified aptamer measures exclusively represented in SomaScan® into [A-C] categories. Among these, there were 1,211 additional SOMAmers that were reproducible in two independent SomaScan assays with a Spearman rho ≥ 0.5 (Score A), which could be prioritized for further analysis in CSF ( Table 2 ). View this table: View inline View popup Download powerpoint Table 2. Combination of intra- and interplatform correlation categories for creating a single reproducibility and reliability score. Based on the large number of analytes measured by the SomaScan® platform and correlation analyses, we considered these SomaScan top-performing proteins with a good intraplatform correlation (Score 1-3) and those not represented in the Olink® Explore platform (Score A) as candidates for subsequent analysis (n = 2,428). These reliable measures might provide more accurate results in proteomic analysis. PANTHER annotations For analyzing the protein classification with the PANTHER tool, we considered 6,218 (97.1%) mapped proteins from 6,402 unique UniProt IDs of SomaScan® aptamer measures and 2,872 (98.2%) unique mapped proteins from 2,925 Olink® Explore analytes. The top 20 PANTHER classifications indicated multiple significant annotations (FDR < 0.05) enriched in metabolite enzymatic conversion by transferases, hydrolases and oxidoreductases, protein modifying enzymes, and signaling processes (Extended data Fig. 8A). The molecular function and biological process annotations were significantly enriched in binding, catalytic activity (hydrolases and peptidases), signaling, response to stimulus and regulatory mechanisms (Extended data Fig. 8B and 8D). In contrast, the cellular compartment annotations were significantly related to the cell surface, periphery, nucleus, organelle and extracellular space (Extended data Fig. 8C, Supplementary Table 3). Interestingly, we observed several differences across platforms, and the Olink Explore platform was more enriched in immune proteins, including immunoglobulin receptor protein types, signaling, binding and macromolecule metabolic processes. Additionally, these proteins were related to organelle cellular components. Conversely, the SomaScan® platform was more enriched in signaling and protein modifying enzyme protein classes and metabolic processes involving transferases and kinases. These proteins are commonly found in the cytoplasm, plasma membrane and extracellular space. Both platforms had a similar proportion of proteins representing the top categories except for the cellular compartment, where SomaScan® proteins were more represented in the top categories (Extended data Fig. 8, Supplementary Table 3). Thereafter, considering reproducible SomaScan® proteins (Score 1-3 and A: n=2,428), we also explored the protein types and mechanisms involved using PANTHER. There were 2,120 (97.4%) mapped proteins corresponding to 2,177 unique UniProt IDs of SomaScan® reproducible SOMAmers. The top-ranking significant protein classes (FDR < 0.05) were enriched in defense proteins, adhesion and signaling protein classes compared to the complete set of aptamers represented in the SomaScan platform (Extended data Fig. 8E). For molecular function and biological process, reproducible proteins were related to signaling, metabolic regulation and biogenesis (FDR < 0.05) (Extended data Fig. 8F and 8H). In addition, this set of proteins was enriched in cellular compartments located at organelle, plasma membrane and nucleus locations compared to all SomaScan sets of proteins (Extended data Fig. 8G, Supplementary Table 4). Linking SomaScan Platform Protein Signatures with CSF Biological Traits, Sample Demographics, and AD Endophenotypes Interestingly, we observed a large proteomic signature of proteins that met the Bonferroni-corrected significance threshold (0.05/7,289), with P values < 6.860e-06 in the phenotype association analysis for age (n = 296 proteins), the Qalb (n = 462 proteins), CSF albumin (n = 420 proteins), CSF total globulin (n = 355 proteins) and CSF p-tau (n = 1,175 proteins) phenotypes, considering age, sex, CSF total protein levels and CSF biomarker technique (when applicable) as covariates. Other phenotypes showed a reduced proteomic signature with fewer associations, such as CSF red blood cell count (n = 107 proteins), CSF Aβ42 levels (n = 89 proteins) and sex (n = 30 proteins). Considering the Bonferroni-corrected threshold, we did not find any significant proteins associated with the MMSE score ( Fig. 4 ). However, a reduced subset of proteins was significant in five or more association analyses (n=20) (Extended Data Fig. 9, Supplementary Table 5). The majority of significantly associated proteins were classified as reliable, suggesting that these associations were valid and had strong reliability ( Fig. 4 , Supplementary Table 1). Additionally, the variance explained in LASSO models by the reproducible set of proteins was greater than that explained by the complete set for age, CSF red blood cell count, CSF Aβ42 and p-tau phenotypes. However, we observed the opposite effect for CSF albumin, CSF total globulins and the Qalb. None of the models had enough statistical power to explain the MMSE phenotype (Supplementary Table 6). Regarding sex, both models were highly similar, with an AUC of 0.9997 in the complete set (sensitivity = 0.984, specificity = 0.996) and an AUC of 0.9997 in the subset of Good proteins (sensitivity = 0.986, specificity = 0.996), respectively (Supplementary Table 7). Considering the complete set of aptamers analyzed in the SomaScan platform (n = 7,289), we found 57 aptamers overlapping between the top 500 rankings of CSF Aβ42, p-tau and MMSE, thus corresponding to 55 unique proteins ( Fig. 5A , Supplementary Table 8). Interestingly, we identified 88 aptamers corresponding to 83 unique proteins among the reproducible proteins (good; n = 2,428) in the SomaScan platform by intracorrelations, suggesting the presence of a general death signature in the CSF, as expected ( Fig. 5B , Supplementary Table 9). Notably, these differences were statistically significant, favoring the set of reproducible proteins, which exhibited a fivefold (504%) increase in overlapping hits among the selected AD endophenotypes compared to the full set of SomaScan aptamers (OR=5.04, 95% CI [3.57-7.11], P=2.11E-24). This observation was further supported by empirical statistical methods. Specifically, the bootstrapping experiment revealed a mean simulated intersection of 2.113 proteins with a standard deviation of 1.441, which represents the expected result from random chance across 10,000 iterations. These results were significantly lower than the 57 aptamers identified as overlapping between the top-ranking CSF Aβ42, p-tau and MMSE association analyses (P e < 1e-04) (Extended Data Fig. 10 left). Similarly, when we restricted the analysis to reproducible proteins with good intraplatform correlations (n = 2,428), there were 88 overlapping aptamers between the top ranking of CSF Aβ42, p-tau and MMSE score association analyses, which was also significantly greater than the mean simulated intersection of 15.6 proteins, with a standard deviation of 3.8 (P e < 1e-04) (Extended Data Fig. 10 right). As expected, the intersection between the top rankings of the three AD endophenotype associations was greater after filtering out reproducible protein measures than considering the complete set of SomaScan® proteins, most likely because the filtering of those reliable measures led to more accurate findings. Download figure Open in new tab Figure 5. Venn diagram of CSF AB42, CSF p-tau and MMSE associations and enrichment analysis using the WebGestalt tool. A) Complete set of SomaScan proteins (n = 7,289) including 57 overlapping aptamers between the top 500 rankings; and B) Reproducible SomaScan proteins with good intracorrelations (n=2,428) including 88 overlapping aptamers between the top 500 rankings. Additionally, we found that the most enriched mechanism of the top-ranking overlapping proteins in the complete SomaScan protein set was Activation of BAD and translocation to mitochondria (enrichment ratio = 148.068, FDR = 2.51E-08) ( Fig. 5A and Supplementary Tables 10 and 11). Similarly, considering the intersection of reproducible proteins associated with CSF AD biomarkers and the MMSE score ( Fig. 5B ), the most enriched mechanism was the Activation of BAD and translocation to mitochondria (enrichment ratio = 130.793, FDR = 5.86E-12), as more overlapping genes were identified in the filtered analysis than in the complete set of SomaScan proteins. The selection of reproducible proteins pointed to mechanisms such as Kinase maturation complex 1 (Enrichment Ratio = 76.637, P = 1.23E-05) and Neurexins and neuroligins (Enrichment Ratio = 26.275, P = 1.85E-04), which improved their performance compared to that of the complete set. Additionally, three mechanisms increased the representation of the reproducible proteins compared to the complete set: the EGF receptor signaling pathway (enrichment ratio = 17.060, FDR = 5.59E-05), Transcriptional regulation by TP53 (enrichment ratio = 8.063, FDR = 6.11E-05) and Regulation of protein localization to membrane (enrichment ratio = 12.470, FDR = 8.45E-05) (Supplementary Tables 12 and 13). Additionally, nonintersecting proteins were evaluated through enrichment analysis, revealing similar mechanisms represented by the CSF p-tau and MMSE rankings considering the complete set of SomaScan proteins (n = 7,289) and the reproducible proteins (Good; n = 2,428) (Extended Data Fig. 11). Discussion The use of high-throughput proteomic platforms has enabled the simultaneous evaluation of multiple analytes 24 . Considering the novelty of these techniques and the many proteins assessed, it is extremely important to perform comprehensive quality control to ensure the reliability of these measures. In this sense, comparing the performance of the SomaScan® and Olink® Explore platforms could provide essential information for understanding the strengths and limitations of these techniques by assessing the reproducibility and the impact of preanalytical factors and technical variations. To our knowledge, here we present the largest head-to-head comparison of CSF sample analysis using two of the main multiplex proteomics platforms available to date, the SomaScan® and Olink® panels. Several authors have compared different proteomic platforms, mainly in their use for analyzing plasma or serum biomarkers. Thus, our analyses provide the novelty of using the ACE cohort, which includes CSF samples obtained from extensively characterized real-world HCs and MCI patients from a memory clinic. This valuable dataset and the reported results are notably relevant for providing information about brain pathological changes occurring in a wide variety of diseases, including AD. Our analysis showed that SomaScan® measurements were more uniform in both intra- and interassay CV evaluations compared to the Olink® Explore panels. Although there is a reduced number of published studies reporting CV values in CSF SomaScan® data, our results were consistent with previous findings in plasma, in which values near 5% were reported by Gold et al 6 and other authors 8 , 25 – 28 . However, the number of sample controls or calibrators could contribute to an increase in the variability of CV observations. The Olink® Explore panels included only two pooled plasma samples in a plate, which is considerably lower than the five samples included in the SomaScan platform. In our PCA, we observed that PC1 in both SomaScan® and Olink® explained a similar proportion of the total variance in both datasets, suggesting that both of these high-throughput proteomic platforms are capable of capturing a similar fraction of explained variance, tracking a similar source of variance. These results also suggested that the SomaScanB PC1 dataset might be influenced by alternative factors beyond our analysis capacity compared to other datasets (SomaScanA or Olink Explore). Despite including a different number of proteins in these platforms, they might explain a similar proportion of the underlying biological mechanisms. Previous studies have suggested that a high proportion of CSF proteomic measures that are highly correlated with each other and form clusters, supporting the idea that PC1 could explain a large proportion of variance 29 . Additionally, we found a similar general representation of PANTHER categories, suggesting that these proteins are involved in similar molecular mechanisms and pathways despite a few differences that are exclusive to each platform. Moreover, we assessed which proteins were the major contributors to PC1 and PC2. Interestingly, we observed a reduced number of proteins represented in both the SomaScanA and SomaScanB top rankings of protein loadings, suggesting that this approach is sensitive to extreme proteomic measures and preanalytical factors that could affect the protein ordering and composition of each PC. The top-ranking Olink Explore protein loadings for PC1 and PC2 included several proteins that were mainly studied in the inflammation panel. The bimodal distribution found in both intra- and intercorrelation analyses suggested that a fraction of these aptamers did not perform proper dimensional recognition of proteins, thus making it difficult to compare multiple measurements between the two proteomic techniques. Similarly, Dammer et al. also compared SomaScan 5k with the Olink platform and MS using CSF samples. Although they used a reduced sample size (n = 36), they reported a high median rho parameter (rho ∼ 0.7) 14 . They enriched the analysis of well-correlated aptamers by selecting pairs of reagents with the same UniProt ID and the best correlation, which might have led to an overestimation of the rho parameters. In a later study, Dammer et al. increased their sample size (n = 300) and observed a median rho parameter of 0.59, suggesting that the reduced sample size and aptamer selection impacted their results 30 . Nevertheless, the presence of protein quantitative trait loci (pQTLs), posttranslational modifications and alternative splicing variants could explain why these subsets of nonreproducible aptamer measures (moderate or low correlation coefficient) created alternative proteoforms that were differentially detected in each platform. This poor correlation could also be caused by technical factors, including preanalytical factors such as sample handling and off-target binding, due to a potential lack of specificity and unspecific noise behind these protein measurements. However, Hok-A-Hin et al. assessed the effect of several preanalytical factors in the CSF proteome and reported that the majority of proteins measured in the SomaScan® and Olink® platforms remained stable after these extreme conditions 31 . Other studies using plasma proteomic data pointed to these factors impacting the performance of high-throughput analysis 10 , 32 , 33 . A wide variety of studies have described several poorly correlated plasma analytes while analyzing the correlation of aptamer-based (SomaScan® 1.1k, 1.3k and 7k) and antibody-based techniques (Olink®, Myriad-RBM multiplex panel and Mesoscale Discovery Platform) 10 , 33 . Moreover, several plasma proteomic studies have reported a bimodal distribution in the correlation between various SomaScan® (5k and 1.3k) and Olink® (Explore and 92-protein panels), suggesting that one or both platforms were potentially affected by these confounding factors 8 , 32 , 34 . These studies conducted in plasma have results in line with our findings and support the notion that bimodal distributions are related to common technical issues unrelated to any specific properties of the biofluid under study. Further studies are needed to validate these results, especially at the protein level, which would be useful for evaluating the reliability of each high-throughput proteomic measurement. Furthermore, we established a metric accounting for reproducibility and reliability. Here, we will introduce a resource focused on the reproducibility and translatability of protein markers in CSF. It will be available as a downloadable resource with unrestricted access for the scientific community. Notably, a wide variety of these proteins classified in the top 20 rankings of the reproducibility score have been studied in the context of AD pathological mechanisms or endophenotypes. These include immunoglobulin M 35 , aggrecan 36 , chitotriosidase-1 37 , haptoglobin 38 , IL1RL1 39 , CD177 40 , CRP 41 , SIRBP1 42 , VNN2 43 and carbonic anhydrases 44 . The developed classification metric will provide valuable insights for future proteomic analyses using these techniques, as well as ensuring the validity of the obtained results. Moreover, we found that the aptamers most strongly associated with clinical phenotypes and AD core biomarkers were classified into the “good” or reproducible proteomic category, suggesting that these results are genuine and reliable. Several of these significant associations have already been described in the literature, such as associations of CSF neurofilament proteins (NFL and NFH) with age 45 , 46 and myosin light chain 4 (MYL4) with sex 47 , among others 48 – 57 . Interestingly, we observed that a large proportion of CSF albumin and Qalb associations, classified as Good proteins, were negative, while the opposite effect was observed in the CSF total globulin associations where good proteins had positive associations. These findings might suggest that CSF albumin levels might interfere with the protein binding of SomaScan aptamers. It is tempting to speculate that multiple conformational changes in the proteome are mediated by albumin and affect protein detection alternatively because of the general scavenging properties of albumin, which might involve interactions with numerous aptamers. The combination of this process, proteome variability and other preanalytical factors might significantly impact SomaScan measures, leading to a weaker correlation. Therefore, further studies are needed to elucidate the potential role of albumin in aptamer binding. Additionally, we analyzed the overlap between the top-ranking proteins associated with the MMSE score and the core AD biomarkers CSF Aβ42 and p-tau. We observed a significant fivefold increase in the number of overlapping proteins with the selection of good proteins, which is much greater than what was expected from bootstrapping analyses. These results strongly support the validity of our strategy for selecting well-reproduced proteins for further analysis, highlighting the importance of extensive QC. Our analysis also had several limitations. First, the lack of publicly available technical details of these proteomic assays hampered the comprehensive evaluation of additional factors potentially impacting the assay performance. Second, both proteomic techniques used different detection methods; SomaScan® fluorescence intensity was measured via microarray hybridization, and Olink® Explore used qPCR and NGS, which could have led to differences in affinity between platforms measuring multiple proteoforms. Third, we calculated CVs using raw data prior to QC to mitigate bias from following the consensus recommendations of each technique in data curation. Thus, the presence of outlier proteomic measures might influence the CV distribution. Fourth, the preselection and enrichment of specific molecular routes and proteins performed by both platforms might have an impact on PANTHER annotations, which could in turn alter the identified subset of reliable and reproducible proteins. Finally, multiple proteins were not represented in the Olink® Explore panels and could not be validated using another platform. Further studies using several larger proteomic platforms are needed to assess this subset of proteins. Taken together, the proposed strategy was effective for evaluating the reproducibility and reliability of SomaScan® proteomics in CSF samples from a real-world cohort, providing valuable insights into protein validation and developing a metric to classify reliable proteins. Furthermore, this strategy also provides valuable information about the comparability with antibody-based proteomic platforms, which could help to validate and interpret the results obtained in other studies. Further research is needed to extensively characterize and compare these platforms to determine their strengths and weaknesses and determine the gold standard for analyzing thousands of proteins. Methods Standard protocol approvals, registrations and patient consent All protocols of the ACE cohort were approved by the Clinical Research Ethics Commission of the Hospital Clinic (Barcelona, Spain) in accordance with the current Spanish regulations in the field of biomedical research and the Declaration of Helsinki. In accordance with Spain’s Data Protection Law, all participants were informed about the study’s goals and procedures by a neurologist before signing an informed consent form. Patient privacy and data confidentiality were protected in accordance with applicable laws. Additionally, the protocols of the HARPONE project were approved by the ethics committee of the Universitat Internacional de Catalunya (Barcelona, Spain). Study participants and selection criteria A total of 1,370 CSF samples were provided by the ACE CSF cohort, which was composed of healthy control participants (HCs) and individuals diagnosed with mild cognitive impairment (MCI) or dementia. Briefly, syndromic diagnosis was established at the memory clinic of ACE (Barcelona, Spain) by a multidisciplinary group of neurologists, neuropsychologists and social workers. Individuals with control and subjective cognitive decline (SCD) with no objective evidence of cognitive impairment in the evaluation and a Clinical Dementia Rating (CDR) of 0 58 were classified as HCs. A diagnosis of MCI was given for patients with one or more impaired cognitive domains on the neuropsychological battery of ACE (NBACE) 59 , accounting for the cut-offs of impairment for age, formal education levels, and a CDR of 0.5 59 – 63 . The 2011 National Institute on Aging and Alzheimer’s Association (NIA-AA) guidelines were used to establish the AD dementia diagnosis 15 . Further information on the clinical characteristics of these individuals has been described elsewhere 23 , 64 , 65 . The LP for the assessment of CSF AD-related biomarkers was offered to (a) patients with MCI and dementia assessed at Ace’s memory clinic 64 ; (b) participants of the Fundació ACE Healthy Brain Initiative (FACEHBI) 66 in individuals with SCD; and (c) participants of the BIOFACE study with early-onset MCI 67 , 68 . We collected a CSF sample from the LP following the consensus recommendations 69 , centrifuged it (2,000xg for 10 min at 4°C), aliquoted it and stored it at -80°C. For CSF Aβ42, total tau (t-tau) and p-tau protein determination, an aliquot was defrosted on the day of the analysis at room temperature and vortexed. These protein levels were examined using a standard enzyme-linked immunosorbent assay (ELISA) kit (Innotest β-AMYLOID (1-42), Fujirebio Europe, Göteborg, Sweden) or the Lumipulse G600II automated platform (Fujirebio Inc.) 64 . Additionally, all CSF samples underwent complementary biochemical analysis. SomaScan proteomic profiling SomaScan® 7k proteomic profiling (SomaLogic Operating Co., Inc., Boulder, Colorado) was selected as the representative aptamer-based detection technology. This multiplexed proteomic technique involves the use of 50 µL of CSF per sample and modified DNA aptamers, also called SOMAmers, to measure 7,596 proteins. These SOMAmers bind to protein targets, and the abundance of these complexes was detected by using fluorescence in a conventional DNA array 6 . The protein amount was expressed in relative fluorescent units (RFU) and normalized using the adaptive normalization by maximum likelihood (ANML) method 70 . Two aliquots from each subject were run in two batches using the same SomaScan® platform (SomaScan 7k, version 4.1) and analyzed within 6 months. The first experiment included 632 samples (SomaScanA), and the second experiment included 1,370 samples (SomaScanB), with 46.1% of individuals overlapped between the two SomaScan assays (Extended Data Fig. 12A). An additional quality control step involved a log2 transformation to adjust to a normal distribution, and z scores were calculated using the R function scale with centering and scaling, applied separately to each analysis. Olink proteomic profiling Olink® proteomics (Uppsala, Sweden) was selected as the representative antibody-based detection technology. This platform used antibodies labeled with oligonucleotides to detect protein amounts by PEA, a combination of qPCR and NGS technologies 5 . However, Olink® proteomic profiling was performed on a single batch of 510 samples using the complete Olink® Explore panel, which measures a total of 2,944 proteins (Cardiometabolic, Cardiometabolic II, Inflammation, Inflammation II, Neurology, Neurology II, Oncology, Oncology II in November 2021) (Uppsala, Sweden). The results are expressed as log2 normalized protein expression (NPX) values. Additionally, we scaled the protein measures using the R function scale with centering and scaling. Overall, 22.3% and 37.1% of individuals overlapped with the SomaScanA and SomaScanB assays, respectively (Extended Data Fig. 12A). For platform comparison purposes, we selected a subset of 305 samples with available proteomic data from three assays (Olink® Explore, SomaScanA and SomaScanB). These proteomic platforms included 7,289 human aptamers from SomaScan® and 2,943 analytes from the Olink® Explore panels that passed quality control within each technique. In addition, we found 2,161 overlapping reagents between platforms, corresponding to 2,159 unique proteins according to UniProt ID, representing 26.8% of the pairs assessed (Extended Data Fig. 12B). We based our analysis on the previously published proteomic head-to-head comparison of Katz et al 8 . Protein annotation using PANTHER We used the PANTHER classification system version v17.0 71 to elucidate the subfamilies and functions of the measured proteins and annotate them by using a Homo sapiens reference gene list. A statistical overrepresentation test was performed to associate these proteins with PANTHER GO-Slim terms such as Cellular Compartment, Biological Process and Molecular Function using Fisher’s exact test with FDR correction in the PANTHER online tool. In addition, functional classification was performed to annotate the protein class of each analyte. Principal component analysis (PCA) We performed a PCA on log2-transformed scaled data from both proteomic platforms using the package tidymodels in R 4.1.1. We evaluated the percentage of variance explained by each principal component (PC), the number of PCs needed to explain 95% of the variance, and the 15 proteins with the greatest absolute contributions to the first and second PCs. The SomaScan® platform needed fewer PCs to explain 95% of the variance (N PCs SomaScanA = 218, N PCs SomaScanB = 197) than did the Olink® Explore platform (N PCs = 258) ( Fig. 1A ). Similarly, the variance explained by the first PC was slightly greater for the SomaScan® platform (SomaScanA = 20.7%, SomaScanB = 18.6%) than for the Olink® Explore panels (18.43%) ( Fig. 1B ). Additionally, we conducted Pearson correlations and linear associations between the PCs and multiple potentially noisy clinical variables and phenotypes to evaluate their contributions to the variance explained. Additionally, we conducted Pearson correlations between the top 5 PCs to assess whether they represented a similar fraction of explained variance. Finally, using the interquartile range (IQR) method, we identified and removed outlier individuals outside 1.5-fold of the IQR. We excluded 21 and 19 outlier individuals from the SomaScanA and SomaScanB datasets, respectively, from subsequent analysis. Additionally, we excluded 7 outlier individuals from the Olink® Explore dataset for further analysis. These outlier individuals did not have gross differences in clinical characteristics across all SomaScan® assays, and slight differences were found across proteomic studies in CSF biomarkers due to sample selection (Supplementary Table 14). Hence, 264 individuals were considered for subsequent analysis. Coefficient of variation (CV) Similar to Katz et al. , we used two pooled calibration samples to calculate CVs in the Olink® Explore data 8 . With respect to the SomaScan® data (SomaScanB analysis), we selected the first 2 calibration samples of each plate to consider the same number of calibrators in both proteomic platforms. Intra-assay CVs were computed for the 2 QC samples on each plate and averaged across all plates. Interassay CVs were calculated using 76 pooled samples in 39 plates from Olink® and 32 samples in 16 plates from SomaScan®. Additionally, we calculated the 10th, 25th, 75th, and 90th CV percentiles and the median to evaluate the differences between both proteomic platforms, as described elsewhere 8 . To calculate the CVs, we used the following equations: Pairing platform reagents by protein target We matched protein measurements from the SomaScan® and Olink® platforms using the UniProt identification code (UniProt ID), which links each assay with a peptide. Several SomaScan® SOMAmers measuring the same protein were identified, and in the same way, several proteins were independently measured in different Olink® Explore subpanels. Furthermore, protein complexes identified with multiple UniProt IDs were considered a match in both platforms if there was a full correspondence between the complete set of identification codes. Correlation of the matched reagents To analyze the intraplatform correlation between the two SomaScan® assays, we performed Spearman correlation analysis on overlapping individuals. We classified protein measures according to their Spearman rho coefficient: 1) Good (rho > 0.5), 2) Moderate (0.3 ≤ rho > 0.5) and Poor (rho < 0.3). Similarly, an additional analysis was performed to evaluate the interplatform correlation between protein measurements derived from both SomaScanA and SomaScanB and Olink® Explore assays. Replication of proteins in both platforms was also classified into the same categories described above. Finally, to integrate the intra- and interassay correlation analyses into a single metric accounting for reproducibility and reliability, we aggregated the established correlation categories ( Table 3 ). For each intraplatform category (good, moderate and poor), we assessed the corresponding classification for interplatform correlations, considering both available SomaScanA and SomaScanB assays. We assigned the highest reproducibility and reliability to a good intraplatform correlation with a rho ≥ 0.5 and established an additional ordering considering the interplatform correlation classification (Score 1-3). If a given protein had different interplatform classification categories for the SomaScanA and SomaScanB experiments, the category with the highest rho value was considered for the score. The same process was followed for the other intraplatform categories until a single metric was established ( Table 3 ). Thereafter, we also established an additional classification for those SomaScan® measures that were not represented in the Olink® Explore panels based on internal reproducibility (intraplatform correlation) in the SomaScan® platform ( Table 3 ). View this table: View inline View popup Download powerpoint Table 3. Intra- and interplatform correlation categories. Associations between clinical traits and CSF biomarkers Linear regression was performed to analyze associations between scaled log2-transformed protein levels measured using the SomaScan® platform and 1) clinical traits such as age, sex, CSF albumin, CSF total globulins, CSF red blood cell count, the albumin quotient (Qalb; a measure of blood–brain barrier (BBB) leakage), and the MMSE score close in time to the LP; and 2) CSF biomarkers such as CSF Aβ42 and p-tau, all of which were collected from the ACE cohort. To avoid redundancy, we did not analyze the association with t-tau levels, as it is widely known that both protein levels are highly correlated 16 , 72 . We considered sex, age, CSF total protein levels and CSF biomarker technique (when applicable) as covariates. We also performed logistic regression to analyze protein associations with sex. Additionally, we considered the reliability and reproducibility of SomaScan proteins while analyzing each phenotype association. Then, we compared the top 500 proteins, ordered by significance, associated with CSF Aβ42, CSF p-tau and the MMSE score to evaluate the overlap and characterize these overlapping proteins using the WebGestalt tool 72 considering the genome protein coding reference gene list. We conducted a chi-square test to evaluate differences in overlapping proteins between the reproducible and complete sets of SomaScan proteins. To assess variance explained by the phenotypes of interest and redundancy in the protein dataset caused by highly correlated aptamer measures, we performed a LASSO regression using 1) the complete set of SomaScan® proteins (n=7,289) and 2) reproducible proteins with rho ≥ 0.5 in the intraassay correlation (Good, n=2,428). The LASSO model is a 5-fold cross-validation method that is repeated 5 times using the Caret R package. Eighty percent of the samples were randomly selected for training the model, and the remaining 20% were selected for testing purposes. Several lambda values were analyzed to select the optimal regularization parameter. Moreover, we adopted the glmnet method, where we used the ROC metric for categorical variables and the RMSE for continuous numerical variables. Thereafter, we also performed a bootstrapping experiment to analyze the overlap between three sets of 500 randomly selected proteins (with replacement), and this process was repeated for 10,000 iterations. The aim was to compare the simulated number of overlapping proteins with the actual results obtained from the top-ranking overlap of proteins significantly associated with CSF biomarkers and MMSE and to assess potential differences in the expected overlap from random chance (simulated) and our actual results. We considered two different datasets with 1) 7,289 proteins (complete SomaScan® set) and 2) 2,428 SomaScan® proteins with rho ≥ 0.5 in the intraplatform correlation analysis. Data availability The data that support the findings of this study are publicly available from the corresponding authors upon reasonable request. Additionally, the largest preprocessed SomaScan proteomic dataset, also known as SomaScanB, has been uploaded and is publicly accessible through the Alzheimer’s Disease Data Initiative (ADDI) community. Author’s contribution RP, ACF, ACS and AR designed and conceptualized the study and interpreted the data. RP, ACF, ACS and AR contributed to data acquisition, analysis, interpreted the data and co-wrote the manuscript. PGG, FG, MC, IdR, CO, JB, OS and AM, contributed to data acquisition and interpretation. LM, PM, EA, AO, YJS, RFS, NM, JCL, MRR, MA, MVF, MM, SV, LT, CC, ARZ and MB contributed to data acquisition. AR supervised the study. MRR, MA, MVF, MM, SV, CC, ARZ, MB, BS, ACS and AR contributed to the critical revision of the paper. All authors critically revised the manuscript for important intellectual content and approved the final manuscript. Competing interests All authors declare that the research was conducted in the absence of any conflict of interest. Acknowledgements We would like to thank patients and controls who participated in this project. They were processed following standard operating procedures with the appropriate approval of the Ethical and Scientific Committee. The present work has been performed as part of the doctoral thesis of RPF at the University of Barcelona (Barcelona, Spain). Authors acknowledge the support of the Agency for Innovation and Entrepreneurship (VLAIO) grant N° PR067/21 for the HARPONE project and the ADAPTED project the EU/EFPIA Innovative Medicines Initiative Joint Undertaking Grant N° 115975. Also, the Spanish Ministry of Science and Innovation, Proyectos de Generación de Conocimiento grants PID2021-122473OA-I00, PID2021-123462OB-I00 and PID2019-106625RB-I00. ISCIII, Acción Estratégica en Salud integrated in the Spanish National R+D+I Plan and financed by ISCIII Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER “Una manera de hacer Europa”) grants PI17/01474, PI19/00335, PI22/01403 and PI22/00258. The support of CIBERNED (ISCIII) under the grants CB06/05/2004 and CB18/05/00010. The support from PREADAPT project, Joint Program for Neurodegenerative Diseases (JPND) grant N° AC19/00097, and from DESCARTES project, German Research Foundation (DFG). The support of Fundación bancaria “La Caixa”, Fundación ADEY, Fundación Echevarne and Grífols SA (GR@ACE project). ACF received support from the Instituto de Salud Carlos III (ISCIII) under the grant Sara Borrell (CD22/00125). PGG is supported by CIBERNED employment plan (CNV-304-PRF-866). IdR is supported by the ISCIII under the grant FI20/00215. References 1. ↵ Bowser , B. L. & Robinson , R. A. S . Enhanced Multiplexing Technology for Proteomics . doi: 10.1146/annurev-anchem-091622-092353 16 , 379 – 400 ( 2023 ). OpenUrl CrossRef 2. ↵ Aebersold , R. & Mann , M . Mass-spectrometric exploration of proteome structure and function . Nature 2016 537:7620 537 , 347 – 355 ( 2016 ). OpenUrl CrossRef PubMed 3. ↵ Bader , J. M. , Albrecht , V. & Mann , M . MS-Based Proteomics of Body Fluids: The End of the Beginning . 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Share Head-to-head comparison of aptamer- and antibody-based proteomic platforms in human cerebrospinal fluid samples from a real-world memory clinic cohort Raquel Puerta , Amanda Cano , Pablo García-González , Fernando García-Gutiérrez , María Capdevila , Itziar de Rojas , Clàudia Olivé , Josep Blázquez-Folch , Oscar Sotolongo-Grau , Andrea Miguel , Laura Montrreal , Pamela Martino-Adami , Asif Khan , Adelina Orellana , Yun Ju Sung , Ruth Frikke-Schmidt , Natalie Marchant , Jean Charles Lambert , Maitée Rosende-Roca , Montserrat Alegret , Maria Victoria Fernández , Marta Marquié , Sergi Valero , Lluís Tárraga , Carlos Cruchaga , Alfredo Ramírez , Mercè Boada , Bart Smets , Alfredo Cabrera-Socorro , Agustín Ruiz medRxiv 2024.07.18.24310563; doi: https://doi.org/10.1101/2024.07.18.24310563 Share This Article: Copy Citation Tools Head-to-head comparison of aptamer- and antibody-based proteomic platforms in human cerebrospinal fluid samples from a real-world memory clinic cohort Raquel Puerta , Amanda Cano , Pablo García-González , Fernando García-Gutiérrez , María Capdevila , Itziar de Rojas , Clàudia Olivé , Josep Blázquez-Folch , Oscar Sotolongo-Grau , Andrea Miguel , Laura Montrreal , Pamela Martino-Adami , Asif Khan , Adelina Orellana , Yun Ju Sung , Ruth Frikke-Schmidt , Natalie Marchant , Jean Charles Lambert , Maitée Rosende-Roca , Montserrat Alegret , Maria Victoria Fernández , Marta Marquié , Sergi Valero , Lluís Tárraga , Carlos Cruchaga , Alfredo Ramírez , Mercè Boada , Bart Smets , Alfredo Cabrera-Socorro , Agustín Ruiz medRxiv 2024.07.18.24310563; doi: https://doi.org/10.1101/2024.07.18.24310563 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neurology Subject Areas All Articles Addiction Medicine (573) Allergy and Immunology (864) Anesthesia (302) Cardiovascular Medicine (4449) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1515) Epidemiology (15240) Forensic Medicine (30) Gastroenterology (1130) Genetic and Genomic Medicine (6614) Geriatric Medicine (669) Health Economics (1000) Health Informatics (4550) Health Policy (1372) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1268) Infectious Diseases (except HIV/AIDS) (15928) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (668) Neurology (6622) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1147) Occupational and Environmental Health (957) Oncology (3343) Ophthalmology (977) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1695) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5459) Public and Global Health (9249) Radiology and Imaging (2205) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1197) Rheumatology (597) Sexual and Reproductive Health (715) Sports Medicine (530) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a02941d5be9552ad',t:'MTc3OTkyOTM0Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();