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Explore 1536

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The Explore 1536 is a high-throughput protein biomarker detection system designed for large-scale protein analysis. It utilizes Olink's proprietary Proximity Extension Assay (PEA) technology to simultaneously measure up to 1,536 protein biomarkers from a small sample volume. The Explore 1536 is capable of processing multiple samples in parallel, enabling efficient and accurate protein profiling.

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11 protocols using explore 1536

1

Olink Explore 1536 COVID-19 Protein Profiling

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Detailed description is available online (https://www.olink.com/mgh-covid-study/) (Fig 1) and has been published previously [29 (link)]. Briefly, the samples were analyzed by the Olink® Explore 1536 platform which includes measurement of the ACE2 protein. The Olink platform is based on Proximity Extension Assay (PEA) technology and has been validated previously [31 (link)]. Data generation consists of three main steps: normalization to known standard (extension control), log2-transformation, and level adjustment using the plate control. The generated data represent relative protein values, Normalized Protein eXpression (NPX), on a log2 scale where a larger number represents a higher protein level in the sample. For more information about Olink® Explore 1536, PEA and NPX, please visit www.olink.com.
Kragstrup T.W., Singh H.S., Grundberg I., Nielsen A.L., Rivellese F., Mehta A., Goldberg M.B., Filbin M.R., Qvist P, & Bibby B.M. (2021). Plasma ACE2 predicts outcome of COVID-19 in hospitalized patients. PLoS ONE, 16(6), e0252799.
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2

Proximal Extension Assay of CSF

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Proximal Extension Assays (PEA, Olink® Explore 1536) were performed by Olink Proteomics, Inc. (Boston, MA). NPC1 and non-NPC1 control CSF samples (40 µl) were randomized on a 96-well plate, sealed, frozen and shipped overnight on dry ice to Olink. Normalized protein expression (NPX) values were provided by Olink and used as input to the differential abundance analysis.
Campbell K., Cawley N.X., Luke R., Scott K.E., Johnson N., Farhat N.Y., Alexander D., Wassif C.A., Li W., Cologna S.M., Berry-Kravis E., Do A.D., Dale R.K, & Porter F.D. (2023). Identification of cerebral spinal fluid protein biomarkers in Niemann-Pick disease, type C1. Biomarker Research, 11, 14.
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3

Plasma Proteome Profiling Protocol

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Collection, processing, and storage of the discovery and replication cohorts’ samples were performed according to study protocols from the HFGP (23 (link)). Venous whole-blood samples were collected using EDTA tubes and centrifuged into plasma before being stored at –80°C. Proteomic profiling was performed in plasma samples by Olink Proteomics AB using a proximity extension assay coupled with next-generation sequencing as a readout method (84 (link)). For this study, we used the Olink Explore 1536 platform, consisting of 1,472 proteins assigned into four 384-well multiplex panels focused on inflammation, cardiometabolic, oncologic, and neurologic proteins.
Vadaq N., Zhang Y., Vos W.A., Groenendijk A.L., Blaauw M.J., van Eekeren L.E., Jacobs-Cleophas M., van de Wijer L., dos Santos J.C., Gasem M.H., Joosten L.A., Netea M.G., de Mast Q., Fu J., van der Ven A.J, & Matzaraki V. (2023). High-throughput proteomic analysis reveals systemic dysregulation in virally suppressed people living with HIV. JCI Insight, 8(11), e166166.
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4

Genetic Factors Influencing Vascular Traits

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To further elucidate the potential pathophysiological mechanisms by which risk genes contribute to VTE, we conducted PheWAS. This analysis focused on a curated list of 35 blood traits under five categories in UKB, which encompassed six coagulation factors (from UKB plasma proteins data measured using the Olink Explore 1536 platform in a subset of ~50,000 individuals), 11 immunometabolic markers, 6 liver function indicators, 4 red blood cell traits, and 8 white blood cell traits. Blood trait values outside four standard deviations from the mean were considered outliers and excluded from the analyses. Detailed information about these traits was listed in Supplementary Data 19. Associations of PheWAS traits with rare variant genes were analyzed using gene-level linear mixed models in SAIGE-GENE+, while linear models were applied for common lead SNPs. All models were adjusted for age, sex, and the first 10 PCs. The significance threshold was set at P = 7.51 × 10−5 (Bonferroni correction for 6 risk genes, 13 associated lead SNPs, and 35 traits).
He X.Y., Wu B.S., Yang L., Guo Y., Deng Y.T., Li Z.Y., Fei C.J., Liu W.S., Ge Y.J., Kang J., Feng J., Cheng W., Dong Q, & Yu J.T. (2024). Genetic associations of protein-coding variants in venous thromboembolism. Nature Communications, 15, 2819.
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5

Proteomic Profiling of UK Biobank

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Proteomic profiling on blood plasma samples of 54,273 UK Biobank participants was performed using the Olink Explore 1536 platform. A total of 1,463 distinct proteins were measured across four Olink panels: Cardiometabolic, Inflammation, Neurology, and Oncology. Extensive quality control steps, and calculation of Normalized Protein eXpression (NPX) values for each protein were performed internally at Olink’s facilities.65 We tested the association between IL32 rs76580947 and 1,463 unique protein levels in 365,495 Europeans from UK Biobank under an additive genetic model by linear regression analysis adjusting for age, gender, BMI, first 10 genomic principal components and array batch. All plasma protein values were rank-based inverse normal transformed before the analysis. We corrected the marginal associations for multiple testing using Benjamini-Hochberg method, and plasma proteins with a corrected p-value <0.05 were deemed as significant.
Sasidharan K., Caddeo A., Jamialahmadi O., Noto F.R., Tomasi M., Malvestiti F., Ciociola E., Tavaglione F., Mancina R.M., Cherubini A., Bianco C., Mirarchi A., Männistö V., Pihlajamäki J., Kärjä V., Grimaudo S., Luukkonen P.K., Qadri S., Yki-Järvinen H., Petta S., Manfrini S., Vespasiani-Gentilucci U., Bruni V., Valenti L, & Romeo S. (2024). IL32 downregulation lowers triglycerides and type I collagen in di-lineage human primary liver organoids. Cell Reports Medicine, 5(1), 101352.
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6

Plasma Proteome Profiling of COVID-19 Patients

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We used publicly available data provided by the Massachusetts General Hospital (MGH) Emergency Department COVID-19 Cohort (18 (link)) (Filbin, Goldberg, Hacohen) with Olink Proteomics (https://www.olink.com/mgh-covid-study/) and call this data the MGH cohort. Patients were classified by acuity levels A1-A5 on days 1, 4, 8, and 29 (based on the World Health Organization [WHO] ordinal outcomes scale (19 ): A1, died; A2, intubated, survived; A3, hospitalized on oxygen; A4, hospitalized without oxygen; A5, discharged). Acuitymax was defined as the maximum Acuity score from day 1 through day 29. In this study, we defined “critical” patients as those with Acuitymax = A1 or A2. In total, 1472 plasma proteins, including 1463 unique proteins (Olink® Explore 1536), were evaluated with 4 panels, including inflammation, oncology, cardiometabolic, and neurology proteins (20 ). The levels of protein were expressed as normalized protein expression value (NPX) in log2 scale. In this study, cytokines were defined as “interleukins, interferons, chemokine, colony-stimulation factors and growth factors” (21 ).
Ebihara T., Matsumoto H., Matsubara T., Togami Y., Nakao S., Matsuura H., Kojima T., Sugihara F., Okuzaki D., Hirata H., Yamamura H, & Ogura H. (2022). Cytokine Elevation in Severe COVID-19 From Longitudinal Proteomics Analysis: Comparison With Sepsis. Frontiers in Immunology, 12, 798338.
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7

Proteomic Profiling in UK Biobank

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The proteomic profiling measured 1,472 protein analytes and captured 1,463 unique proteins using the Olink Explore 1536 platform in 54,306 plasma samples in the UK Biobank. The blood samples were from a randomised subset of 46,673 UKB participants at baseline visit, 6,385 individuals at baseline selected by the UKB-PPP consortium and 1,268 individuals who participated in the COVID-19 repeat imaging study at multiple visits. No batch effects, plate effects or abnormalities in protein coefficients of variation were observed [11 (link)]. We used significant (p < 3.4 × 10−11) and independent (r2 < 0.01) single nucleotide polymorphisms (SNPs) as instruments provided by the study of Sun et al. [1 (link)]. The details of the samples and sample selection in UKB-PPP were shown in Supplementary Methods. Up to 1,361 proteins with genetic instruments available in at least one of the outcome datasets were included in the analysis. The genetic instruments for proteins used in the MR analysis are provided in Supplementary Table 1.
Zhao J.V., Yao M, & Liu Z. (2024). Using genetics and proteomics data to identify proteins causally related to COVID-19, healthspan and lifespan: a Mendelian randomization study. Aging (Albany NY), 16(7), 6384-6416.
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8

Olink-based Protein Profiling in Metastatic Melanoma

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Olink profiling of blood samples (collected using DF/HCC IRB approved Protocol 11–181) from patients with metastatic melanoma at MGH was performed as previously described21 (link). Olink Proximity Extension Assay (PEA) for high-multiplex analysis of proteins was performed as previously described57 (link). The full library (Olink ® Explore 1536) consists of 1472 proteins and 48 control assays, divided into four 384-plex panels. Four overlapping assays of IL-6, IL-8 (CXCL8), and TNF are included for quality control (QC) purposes. In the immune reaction, 2.8 mL of sample is mixed with PEA probes and incubated overnight at 4°C. Olink’s relative protein quantification unit on a log2 scale and values are calculated from the number of matched counts on the NovaSeq run. Data generation of normalised protein expression (NPX) consists of normalisation to the extension control (known standard), log2-transformation, and level adjustment using the plate control (plasma sample).
Revach O.Y., Cicerchia A.M., Shorer O., Petrova B., Anderson S., Park J., Chen L., Mehta A., Wright S.J., McNamee N., Tal-Mason A., Cattaneo G., Tiwari P., Xie H., Sweere J.M., Cheng L.C., Sigal N., Enrico E., Miljkovic M., Evans S.A., Nguyen N., Whidden M.E., Srinivasan R., Spitzer M.H., Sun Y., Sharova T., Lawless A.R., Michaud W.A., Rasmussen M.Q., Fang J., Palin C.A., Chen F., Wang X., Ferrone C.R., Lawrence D.P., Sullivan R.J., Liu D., Sachdeva U.M., Sen D.R., Flaherty K.T., Manguso R.T., Bod L., Kellis M., Boland G.M., Yizhak K., Yang J., Kanarek N., Sade-Feldman M., Hacohen N, & Jenkins R.W. (2024). Disrupting CD38-driven T cell dysfunction restores sensitivity to cancer immunotherapy. bioRxiv.
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9

Profiling Inflammation and Immunity Proteins

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We measured 92 proteins involved in inflammation and immune function using a proximity extension technique (Immune-Oncology panel, Olink, Uppsala, Sweden), as previously described18 (link). Of 1473 samples provided (across maximal 500 subjects), we first excluded 7 samples based on technical failure of proteomics. Next, we excluded 18 proteins with expression below the limit of detection or not reported in at least 25% of remaining samples. Finally, we excluded data from 10 individuals who had outlier protein values at any of the time points via principal components analysis (PCA). In this approach, the first principal component of the screening visit protein expression was applied to each timepoint (screening, baseline, 12-month, 30-month) and individuals with an absolute value PC score greater than 5 were excluded. Our analytic sample consisted of 74 proteins across a maximal 490 participants in the analysis (noted as “maximal” as not all participants had samples at every time point; Table S1). Proteomics in FHS were performed using a next-generation sequencing-based Olink platform (Explore 1536, Olink, Uppsala, Sweden)19 (link). Across the 740 FHS participant samples, on average 85% passed quality control validation, leading to a variable number of samples for any given protein.
Perry A.S., Tanriverdi K., Risitano A., Hwang S.J., Murthy V.L., Nayor M., Zhao S., Levy D., Shah R.V, & Freedman J.E. (2022). The Inflammatory Proteome, Obesity, and Medical Weight Loss and Regain in Humans. Obesity (Silver Spring, Md.), 31(1), 150-158.
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10

Olink Proteomics Predict Lung Cancer

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Olink proteomics data was generated in UKB Pharma Proteomics Project.19 (link) We analyzed the initial batch of data which was generated using the Olink Explore 1536 platform (1,463 proteins) on 43,395 European descent participants. Linkages to National disease and death registries were used to identify incident lung cancers according to ICD10 code of C34. Participants diagnosed with lung cancer prior to recruitment were excluded. Additionally, participants without Olink proteomics data from recruitment were excluded from all prediction model construction. Finally, 490 newly diagnosed lung cancer were recruited.
Li H., Du S., Dai J., Jiang Y., Li Z., Fan Q., Zhang Y., You D., Zhang R., Zhao Y., Christiani D.C., Shen S, & Chen F. (2024). Proteome-wide Mendelian randomization identifies causal plasma proteins in lung cancer. iScience, 27(2), 108985.
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