• How the proteome behaves in healthy individuals

    To achieve the goal of precision medicine, not only do different molecular profiles need to be understood in disease populations, but they must also be understood in the context of healthy populations. This especially applies to the stability of molecular profiles among healthy individuals over time, as this will clarify what qualifies as a ‘normal range’ of clinical parameters in health and disease research.

    The following study by Tebani et al. (2020) conducted a longitudinal analysis of the blood profiles from 100 healthy individuals to understand how they varied both between different individuals, and within an individual over time.

    Data collection included proteomics, transcriptomics, lipidomics, metabolomics, autoantibodies, and immune cell profiling, complemented by gut microbiota and clinical chemistry analysis. Longitudinal data was collected over 2 years to obtain a detailed, high-resolution personal omics profile for each participant. Proteomics data was obtained using Olink’s PEA technology, which investigated 794 blood plasma proteins.

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    Figure: Edited figure from Tebani et al. (2020) that shows UMPA values for different omics profiles in 91 subjects. Lines between dots represent different visits (dots) for one individual. Apart from autoantibodies, the proteome is the most stable dataset, varying only slightly between each of the 4 data collection points.

    Overall, the study revealed that the omics profiles for each individual were highly unique, but stable over time. However, some types of omics were more stable and predictable than others. Autoantibody profiles were the most stable for each participant, hardly changing over the 2-year period, followed by proteomics profiles. Transcriptomics data was the least stable over time, but highly linked to the plasma proteome.

    Other general patterns between different groups could also be observed, such as sex-based differences across the whole omics profile. Linear mixed modeling of each omic feature and clinical data also revealed that the plasma proteome has the greatest influence on clinical data parameters like BMI, body composition, heart and kidney function, and blood pressure.

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    Health should be viewed at the level of the individual, rather than being more generalized

    While the findings in this study need to be validated in a larger cohort, they do support the idea that health should be viewed at the level of the individual, rather than being more generalized. The path forward lies in developing a comprehensive longitudinal molecular patient profile, which is no small task.

    In the meantime, however, it also emphasizes the need to understand health and disease in the context of both sexes, and to avoid sex-biased interpretations. Finally, the stability of the proteomics data emphasizes its potential to empower routine lab tests by providing more biologically relevant insight when interpreting data in both translational and clinical settings. These are just a sample of how Olink and you are accelerating proteomics together.

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    Tebani et al., 2020, Integration of molecular profiles in a longitudinal wellness profiling cohort, Nature Communications.

    How the proteome behaves in healthy individuals

    Clinical research, Multiomics
    To achieve the goal of precision medicine, not only do different molecular profiles need to be understood in disease populations, but they must also be understood in the context of healthy populations.

    Key proteomics publications from 2020

    Proteomics
    Welcome to the first post of the all-new weekly Olink to Science! Our customer survey revealed that you would like to know more about the many publications, research, and other science happening at Olink, therefore this blog aims to do just that: keep you informed on the exciting science taking place with our technology.

    Protein biomarkers are crucial in early detection of cancer

    Clinical research, Oncology, Protein biomarkers
    A central premise of precision medicine is to identify biomarkers indicative of disease transitions early on. This is especially important in cancer where early treatment intervention could increase a patient’s chance of survival and reduce the probability of cancer recurrence.

    Using PEA and RNA-Seq to study disease pathology

    Clinical research, Proteomics
    The following study illustrates how transcriptomics and proteomics complement one another to clarify the pathology of a complex, and little understood disease. Atopic dermatitis (AD) is the most common chronic skin condition affecting up to 20% of children and 7-10% of adults, depending on the population.

    Olink protein biomarker panel indicates fermented foods fight inflammation

    Inflammation, Proteomics
    Could food be used to fight chronic disease?

    Study identifies proteins involved in immunotherapy response

    Oncology, Proteomics
    'Ultimately, it is all about understanding and treating patients better in the future.'

    Proteins diagnostic of lung cancer up to 5 years before disease onset

    Oncology
    An earlier Olink to Science blog post covered some amazing research that found that certain blood protein biomarkers have the potential to predict cancer up to 3 years before diagnosis. This may also be the case for lung cancer, as detailed in a recent study by Dagnino and her colleagues, where elevated levels of CDCP1 were detected in participants of a cohort who later developed the disease.

    Utilizing proteogenomics technology for novel drug target discovery

    Drug discovery & development
    High-throughput multiplexed proteomic technology is leading the way to the latest developments in pre-clinical disease analysis in drug discovery. The pharmaceutical industry is now increasing its efforts in the discovery of novel drug targets by using protein quantitative trait loci (pQTLs), which allows for a more confident inference of disease causality and associated protein regulation.

    Developing a high-performance biomarker panel for Alzheimer’s disease

    Clinical research, Neurology, Protein biomarkers
    A simple search of the term ‘scourge of Alzheimer’s Disease’ brings up over half a million website hits. A major disease, about 15% of us that reach the age of 67 to 74, and 44% of those 75 to 84 will develop AD.

    How proteomics helped diabetic kidney disease research advance

    Clinical research, Proteomics
    Dr. Krolewski and his team at the Harvard Medical School found 56 proteins to be significant in diabetic kidney disease patients. Potentially, these could serve as prognostic biomarkers for disease progression and treatment response. This is how adding proteomics to the methodologies elevated their research.