Mass spectrometry-based (MS) protein analysis of biological specimen is an effective approach for the discovery of protein biomarker candidates. Promising candidates must be confirmed and qualified with other methods in studies with large patient cohorts [1]. Since sensitivity and throughput of non-targeted MS-based approaches is limited targeted approaches like multiple reaction monitoring MS or immunoassay-based methods are applied here. Highly specific and sensitive hybrid assays using targeted MS-based protein methods in combination with immunoenrichment steps prior to MS readout increase sample throughput and sensitivity [2]. These assays, called mass spectrometry-based immunoassays, offer a suitable alternative to classical sandwich immunoassays for the quantification of proteins. Mass spectrometric immunoassays (MSIA) [3] and stable isotope standards and detection by anti-peptide antibodies (SISCAPA/iMALDI) [3,4] are two prominent types of MS-based immunoassays where detection is performed either at the protein or peptide level. In this presentation the application of these types of immunoassays in the field of protein safety biomarkers research will be highlighted. In particular, the advantages of this platform for the analysis of drug-induced kidney, vascular and liver biomarkers in preclinical and clinical species will be presented [5] .

Mass spectrometry-based (MS) protein analysis of biological specimen is an effective approach for the discovery of protein biomarker candidates. Promising candidates must be confirmed and qualified with other methods in studies with large patient cohorts [1]. Since sensitivity and throughput of non-targeted MS-based approaches is limited targeted approaches like multiple reaction monitoring MS or immunoassay-based methods are applied here. Highly specific and sensitive hybrid assays using targeted MS-based protein methods in combination with immunoenrichment steps prior to MS readout increase sample throughput and sensitivity [2]. These assays, called mass spectrometry-based immunoassays, offer a suitable alternative to classical sandwich immunoassays for the quantification of proteins. Mass spectrometric immunoassays (MSIA) [3] and stable isotope standards and detection by anti-peptide antibodies (SISCAPA/iMALDI) [3,4] are two prominent types of MS-based immunoassays where detection is performed either at the protein or peptide level. In this presentation the application of these types of immunoassays in the field of protein safety biomarkers research will be highlighted. In particular, the advantages of this platform for the analysis of drug-induced kidney, vascular and liver biomarkers in preclinical and clinical species will be presented [5] .

Mass spectrometry-based (MS) protein analysis of biological specimen is an effective approach for the discovery of protein biomarker candidates. Promising candidates must be confirmed and qualified with other methods in studies with large patient cohorts [1]. Since sensitivity and throughput of non-targeted MS-based approaches is limited targeted approaches like multiple reaction monitoring MS or immunoassay-based methods are applied here. Highly specific and sensitive hybrid assays using targeted MS-based protein methods in combination with immunoenrichment steps prior to MS readout increase sample throughput and sensitivity [2]. These assays, called mass spectrometry-based immunoassays, offer a suitable alternative to classical sandwich immunoassays for the quantification of proteins. Mass spectrometric immunoassays (MSIA) [3] and stable isotope standards and detection by anti-peptide antibodies (SISCAPA/iMALDI) [3,4] are two prominent types of MS-based immunoassays where detection is performed either at the protein or peptide level. In this presentation the application of these types of immunoassays in the field of protein safety biomarkers research will be highlighted. In particular, the advantages of this platform for the analysis of drug-induced kidney, vascular and liver biomarkers in preclinical and clinical species will be presented [5] .

The analysis of cellular signalling cascades has proven to be a valuable approach for understanding the processes that underlay basic cellular functions and it allows the detection of changes that are important during disease. The profiling of central signal transduction pathways requires the detection of changes in protein expression and differences in protein modification or activation. In recent years, mass spectrometry based analysis systems showed their power by allowing unbiased discovery approaches, whereas novel image-guided analysis systems have given detailed information on the localisation of a protein. Nevertheless, the now classical Western blot is still the most widely employed approach. In hypothesis driven research, this tool gives reliable results with good sensitivity, but is not capable of giving a broader view on the status of signalling within a cell population. Here, we present an innovative approach that uses the principles of the Western blot (protein separation by SDS gel electrophoresis; protein immobilisation on a solid support, detection by specific antibodies) and combine it with a multiplexed bead array as a readout system. The system allows the generation of hundreds of semiquantitative immunoassays from micrograms of protein and information on expression and modification of hundreds of proteins is obtained [1]. Thereby, DigiWest is capable of generating wide-ranging information on the status of cellular signaling in clinical derived cancer tissues [1, 2]. We show that such a signal network analysis from the receptor level down to transcription factors in combination with analysis of apoptosis and proliferation provides a detailed description of a tumor cell. Examples will be given that the use of oncoproteomic panel profiling is likely to strengthen the rationale for personalized therapy decisions [3].

The analysis of cellular signalling cascades has proven to be a valuable approach for understanding the processes that underlay basic cellular functions and it allows the detection of changes that are important during disease. The profiling of central signal transduction pathways requires the detection of changes in protein expression and differences in protein modification or activation. In recent years, mass spectrometry based analysis systems showed their power by allowing unbiased discovery approaches, whereas novel image-guided analysis systems have given detailed information on the localisation of a protein. Nevertheless, the now classical Western blot is still the most widely employed approach. In hypothesis driven research, this tool gives reliable results with good sensitivity, but is not capable of giving a broader view on the status of signalling within a cell population. Here, we present an innovative approach that uses the principles of the Western blot (protein separation by SDS gel electrophoresis; protein immobilisation on a solid support, detection by specific antibodies) and combine it with a multiplexed bead array as a readout system. The system allows the generation of hundreds of semiquantitative immunoassays from micrograms of protein and information on expression and modification of hundreds of proteins is obtained [1]. Thereby, DigiWest is capable of generating wide-ranging information on the status of cellular signaling in clinical derived cancer tissues [1, 2]. We show that such a signal network analysis from the receptor level down to transcription factors in combination with analysis of apoptosis and proliferation provides a detailed description of a tumor cell. Examples will be given that the use of oncoproteomic panel profiling is likely to strengthen the rationale for personalized therapy decisions [3].

The analysis of cellular signalling cascades has proven to be a valuable approach for understanding the processes that underlay basic cellular functions and it allows the detection of changes that are important during disease. The profiling of central signal transduction pathways requires the detection of changes in protein expression and differences in protein modification or activation. In recent years, mass spectrometry based analysis systems showed their power by allowing unbiased discovery approaches, whereas novel image-guided analysis systems have given detailed information on the localisation of a protein. Nevertheless, the now classical Western blot is still the most widely employed approach. In hypothesis driven research, this tool gives reliable results with good sensitivity, but is not capable of giving a broader view on the status of signalling within a cell population. Here, we present an innovative approach that uses the principles of the Western blot (protein separation by SDS gel electrophoresis; protein immobilisation on a solid support, detection by specific antibodies) and combine it with a multiplexed bead array as a readout system. The system allows the generation of hundreds of semiquantitative immunoassays from micrograms of protein and information on expression and modification of hundreds of proteins is obtained [1]. Thereby, DigiWest is capable of generating wide-ranging information on the status of cellular signaling in clinical derived cancer tissues [1, 2]. We show that such a signal network analysis from the receptor level down to transcription factors in combination with analysis of apoptosis and proliferation provides a detailed description of a tumor cell. Examples will be given that the use of oncoproteomic panel profiling is likely to strengthen the rationale for personalized therapy decisions [3].

Recent advances in proteomics technologies have enabled us to study the proteins circulating in human blood at a yet unprecedented depth and precision. This consequently provides new opportunities to study human biology at a different scale. Together with complementary data from other omics fields, as well as extensive clinical and lifestyle information, we can soon draw a more complete picture of human health and diseases. In order to unlock the full potential of these capabilities, however, there is a need to carefully assess the proteins expected in the circulation [1] and what types of bias influence the evaluation of the data [2]. We apply a variety of sensitive, multiplexed affinity proteomics systems to profile 100s of proteins circulating in 1000s of plasma samples. This includes Olink’s proximity extension assays, Luminex-based assays, automated SimplePlex ELISAs, ultrasensitive Quanterix Simoa assays, and antibody bead arrays. For antibody validation, we further use paired antibodies, dual capture assays [3], sandwich immunoassays [4], immuno-capture MS [5] and antibody-free targeted MS assays [6]. We and others also infer target binding from associating the obtained plasma protein levels with genetic variants (pQTLs) [7]. From large-scaled and systematic explorations of plasma proteomes in well characterized samples and donors, we found that a variety of different parameters affect the detected protein levels. We observed a substantial impact from pre-analytical variables (sampling, needle-to-freezer, storage time, collection date, study centre) and the need to control for batch-effects when profiling 100s of samples. Longitudinal collections are particularly attractive as resampling patients enables to determine person-specific baseline values and from there monitor individual progression over time or treatment. Traits like sex, BMI, age, medication, genetics and clinical disease definitions should also be assessed prior to integrating proteomics with other omics data types. With growing numbers of proteomics assays and data from population biobanks, new insights about the individuality of human health will emerge when the above mentioned observations have been addressed accordingly. Considering these aspects, better computational tools for patient stratification will become available, hence the data will contribute to improve health management and treatment outcomes.

Recent advances in proteomics technologies have enabled us to study the proteins circulating in human blood at a yet unprecedented depth and precision. This consequently provides new opportunities to study human biology at a different scale. Together with complementary data from other omics fields, as well as extensive clinical and lifestyle information, we can soon draw a more complete picture of human health and diseases. In order to unlock the full potential of these capabilities, however, there is a need to carefully assess the proteins expected in the circulation [1] and what types of bias influence the evaluation of the data [2]. We apply a variety of sensitive, multiplexed affinity proteomics systems to profile 100s of proteins circulating in 1000s of plasma samples. This includes Olink’s proximity extension assays, Luminex-based assays, automated SimplePlex ELISAs, ultrasensitive Quanterix Simoa assays, and antibody bead arrays. For antibody validation, we further use paired antibodies, dual capture assays [3], sandwich immunoassays [4], immuno-capture MS [5] and antibody-free targeted MS assays [6]. We and others also infer target binding from associating the obtained plasma protein levels with genetic variants (pQTLs) [7]. From large-scaled and systematic explorations of plasma proteomes in well characterized samples and donors, we found that a variety of different parameters affect the detected protein levels. We observed a substantial impact from pre-analytical variables (sampling, needle-to-freezer, storage time, collection date, study centre) and the need to control for batch-effects when profiling 100s of samples. Longitudinal collections are particularly attractive as resampling patients enables to determine person-specific baseline values and from there monitor individual progression over time or treatment. Traits like sex, BMI, age, medication, genetics and clinical disease definitions should also be assessed prior to integrating proteomics with other omics data types. With growing numbers of proteomics assays and data from population biobanks, new insights about the individuality of human health will emerge when the above mentioned observations have been addressed accordingly. Considering these aspects, better computational tools for patient stratification will become available, hence the data will contribute to improve health management and treatment outcomes.

Recent advances in proteomics technologies have enabled us to study the proteins circulating in human blood at a yet unprecedented depth and precision. This consequently provides new opportunities to study human biology at a different scale. Together with complementary data from other omics fields, as well as extensive clinical and lifestyle information, we can soon draw a more complete picture of human health and diseases. In order to unlock the full potential of these capabilities, however, there is a need to carefully assess the proteins expected in the circulation [1] and what types of bias influence the evaluation of the data [2]. We apply a variety of sensitive, multiplexed affinity proteomics systems to profile 100s of proteins circulating in 1000s of plasma samples. This includes Olink’s proximity extension assays, Luminex-based assays, automated SimplePlex ELISAs, ultrasensitive Quanterix Simoa assays, and antibody bead arrays. For antibody validation, we further use paired antibodies, dual capture assays [3], sandwich immunoassays [4], immuno-capture MS [5] and antibody-free targeted MS assays [6]. We and others also infer target binding from associating the obtained plasma protein levels with genetic variants (pQTLs) [7]. From large-scaled and systematic explorations of plasma proteomes in well characterized samples and donors, we found that a variety of different parameters affect the detected protein levels. We observed a substantial impact from pre-analytical variables (sampling, needle-to-freezer, storage time, collection date, study centre) and the need to control for batch-effects when profiling 100s of samples. Longitudinal collections are particularly attractive as resampling patients enables to determine person-specific baseline values and from there monitor individual progression over time or treatment. Traits like sex, BMI, age, medication, genetics and clinical disease definitions should also be assessed prior to integrating proteomics with other omics data types. With growing numbers of proteomics assays and data from population biobanks, new insights about the individuality of human health will emerge when the above mentioned observations have been addressed accordingly. Considering these aspects, better computational tools for patient stratification will become available, hence the data will contribute to improve health management and treatment outcomes.

Alzheimer’s disease (AD) is the most common cause of dementia. Due to the ageing populations, a dramatic increase in AD prevalence is expected over the next decades, imposing a major socio-economic burden on the societies [1]. It is known that neuronal injury starts decades before symptoms occur. Accumulation of Abeta and tau, the pathological hallmarks of AD, can already be detected in healthy older individuals, increasing the risk of AD dementia [2]. Being able to reliably identify prodromal, or even preclinical, AD would offer a promising opportunity for dementia prevention approaches. Furthermore, improved knowledge about factors that help predicting the individual response to interventions would offer tremendous value for the development of more effective treatment and prevention strategies. Successful approaches to prevent cardiovascular disease highlight the importance of accurately identifying individuals at greatest risk of developing debilitating sequelae such as heart failure [3]. However, risk stratification among early AD individuals susceptible to progressing to more advanced disease stages has yet to reach comparable levels of maturity. Biomarkers such as tau and amyloid-beta are useful if applied in overt AD [4], but their usefulness in earlier disease stages is restricted by their limited ability to reliably predict clinical deterioration. Also, biomarker ascertainment currently requires an invasive lumbar puncture or exposure to radiation during a positron emission tomography scan. These markers are therefore not easily applied in clinical trials involving large numbers of healthy volunteers [5]. Recently, highly sensitive immunoassays have become available, providing improved biomarker ascertainment even in blood specimens. This presentation will highlight the most promising new approaches and how they may lead to improved clinical research and care.

Alzheimer’s disease (AD) is the most common cause of dementia. Due to the ageing populations, a dramatic increase in AD prevalence is expected over the next decades, imposing a major socio-economic burden on the societies [1]. It is known that neuronal injury starts decades before symptoms occur. Accumulation of Abeta and tau, the pathological hallmarks of AD, can already be detected in healthy older individuals, increasing the risk of AD dementia [2]. Being able to reliably identify prodromal, or even preclinical, AD would offer a promising opportunity for dementia prevention approaches. Furthermore, improved knowledge about factors that help predicting the individual response to interventions would offer tremendous value for the development of more effective treatment and prevention strategies. Successful approaches to prevent cardiovascular disease highlight the importance of accurately identifying individuals at greatest risk of developing debilitating sequelae such as heart failure [3]. However, risk stratification among early AD individuals susceptible to progressing to more advanced disease stages has yet to reach comparable levels of maturity. Biomarkers such as tau and amyloid-beta are useful if applied in overt AD [4], but their usefulness in earlier disease stages is restricted by their limited ability to reliably predict clinical deterioration. Also, biomarker ascertainment currently requires an invasive lumbar puncture or exposure to radiation during a positron emission tomography scan. These markers are therefore not easily applied in clinical trials involving large numbers of healthy volunteers [5]. Recently, highly sensitive immunoassays have become available, providing improved biomarker ascertainment even in blood specimens. This presentation will highlight the most promising new approaches and how they may lead to improved clinical research and care.

Alzheimer’s disease (AD) is the most common cause of dementia. Due to the ageing populations, a dramatic increase in AD prevalence is expected over the next decades, imposing a major socio-economic burden on the societies [1]. It is known that neuronal injury starts decades before symptoms occur. Accumulation of Abeta and tau, the pathological hallmarks of AD, can already be detected in healthy older individuals, increasing the risk of AD dementia [2]. Being able to reliably identify prodromal, or even preclinical, AD would offer a promising opportunity for dementia prevention approaches. Furthermore, improved knowledge about factors that help predicting the individual response to interventions would offer tremendous value for the development of more effective treatment and prevention strategies. Successful approaches to prevent cardiovascular disease highlight the importance of accurately identifying individuals at greatest risk of developing debilitating sequelae such as heart failure [3]. However, risk stratification among early AD individuals susceptible to progressing to more advanced disease stages has yet to reach comparable levels of maturity. Biomarkers such as tau and amyloid-beta are useful if applied in overt AD [4], but their usefulness in earlier disease stages is restricted by their limited ability to reliably predict clinical deterioration. Also, biomarker ascertainment currently requires an invasive lumbar puncture or exposure to radiation during a positron emission tomography scan. These markers are therefore not easily applied in clinical trials involving large numbers of healthy volunteers [5]. Recently, highly sensitive immunoassays have become available, providing improved biomarker ascertainment even in blood specimens. This presentation will highlight the most promising new approaches and how they may lead to improved clinical research and care.