The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.

Metabolomic and proteomic analyses of human plasma and serum samples harbor the power to advance our understanding of disease biology. Pre-analytical factors may contribute to variability and bias in the detection of analytes, especially when multiple labs are involved, caused by sample handling, processing time, and differing operating procedures. To better understand the impact of pre-analytical factors that are relevant to implementing a unified proteomic and metabolomic approach in a clinical setting, we assessed the influence of temperature, sitting times, and centrifugation speed on the plasma and serum metabolomes and proteomes from six healthy volunteers. We used targeted metabolic profiling (497 metabolites) and data-independent acquisition (DIA) proteomics (572 proteins) on the same samples generated with well-defined pre-analytical conditions to evaluate criteria for pre-analytical SOPs for plasma and serum samples. Time and temperature showed the strongest influence on the integrity of plasma and serum proteome and metabolome. While rapid handling and low temperatures (4°C) are imperative for metabolic profiling, the analyzed proteomics data set showed variability when exposed to temperatures of 4°C for more than 2 h, highlighting the need for compromises in a combined analysis. We formalized a quality control scoring system to objectively rate sample stability and tested this score using external data sets from other pre-analytical studies. Stringent and harmonized standard operating procedures (SOPs) are required for pre-analytical sample handling when combining proteomics and metabolomics of clinical samples to yield robust and interpretable data on a longitudinal scale and across different clinics. To ensure an adequate level of practicability in a clinical routine for metabolomics and proteomics studies, we suggest keeping blood samples up to 2 h on ice (4°C) prior to snap-freezing as a compromise between stability and operability. Finally, we provide the methodology as an open-source R package allowing the systematic scoring of proteomics and metabolomics data sets to assess the stability of plasma and serum samples.

Cellular functional pathways have evolved through selection based on fitness benefits conferred through protein intra- and inter-molecular interactions that comprise all protein conformational features and protein-protein interactions, collectively referred to as the interactome. While the interactome is regulated by proteome levels, it is also regulated independently by, post translational modification, co-factor, and ligand levels, as well as local protein environmental factors, such as osmolyte concentration, pH, ionic strength, temperature and others. In modern biomedical research, cultivatable cell lines have become an indispensable tool, with selection of optimal cell lines that exhibit specific functional profiles being critical for success in many cases. While it is clear that cell lines derived from different cell types have differential proteome levels, increased understanding of large-scale functional differences requires additional information beyond abundance level measurements, including how protein conformations and interactions are altered in certain cell types to shape functional landscapes. Here, we employed quantitative in vivo protein cross-linking coupled to mass spectrometry to probe large-scale protein conformational and interaction changes among three commonly employed human cell lines, HEK293, MCF-7, and HeLa cells. Isobaric quantitative Protein Interaction Reporter (iqPIR) technologies were used to obtain quantitative values of cross-linked peptides across three cell lines. These data illustrated highly reproducible (R2 values larger than 0.8 for all biological replicates) quantitative interactome levels across multiple biological replicates. We also measured protein abundance levels in these cells using data independent acquisition quantitative proteomics methods. Combining quantitative interactome and proteomics information allowed visualization of cell type-specific interactome changes mediated by proteome level adaptations as well as independently regulated interactome changes to gain deeper insight into possible drivers of these changes. Among the biggest detected alterations in protein interactions and conformations are changes in cytoskeletal proteins, RNA-binding proteins, chromatin remodeling complexes, mitochondrial proteins, and others. Overall, these data demonstrate the utility and reproducibility of quantitative cross-linking to study systems-level interactome variations. Moreover, these results illustrate how combined quantitative interactomics and proteomics can provide unique insight on cellular functional landscapes.

Metabolomic and proteomic analyses of human plasma and serum samples harbor the power to advance our understanding of disease biology. Pre-analytical factors may contribute to variability and bias in the detection of analytes, especially when multiple labs are involved, caused by sample handling, processing time, and differing operating procedures. To better understand the impact of pre-analytical factors that are relevant to implementing a unified proteomic and metabolomic approach in a clinical setting, we assessed the influence of temperature, sitting times, and centrifugation speed on the plasma and serum metabolomes and proteomes from six healthy volunteers. We used targeted metabolic profiling (497 metabolites) and data-independent acquisition (DIA) proteomics (572 proteins) on the same samples generated with well-defined pre-analytical conditions to evaluate criteria for pre-analytical SOPs for plasma and serum samples. Time and temperature showed the strongest influence on the integrity of plasma and serum proteome and metabolome. While rapid handling and low temperatures (4°C) are imperative for metabolic profiling, the analyzed proteomics data set showed variability when exposed to temperatures of 4°C for more than 2 h, highlighting the need for compromises in a combined analysis. We formalized a quality control scoring system to objectively rate sample stability and tested this score using external data sets from other pre-analytical studies. Stringent and harmonized standard operating procedures (SOPs) are required for pre-analytical sample handling when combining proteomics and metabolomics of clinical samples to yield robust and interpretable data on a longitudinal scale and across different clinics. To ensure an adequate level of practicability in a clinical routine for metabolomics and proteomics studies, we suggest keeping blood samples up to 2 h on ice (4°C) prior to snap-freezing as a compromise between stability and operability. Finally, we provide the methodology as an open-source R package allowing the systematic scoring of proteomics and metabolomics data sets to assess the stability of plasma and serum samples.

Targeted Proteomics Market Outlook

According to our latest research, the global targeted proteomics market size stood at USD 3.26 billion in 2024, reflecting robust expansion driven by technological advancements and increasing adoption in clinical and research settings. The market is forecasted to reach USD 8.14 billion by 2033, growing at a remarkable CAGR of 10.7% during the forecast period. The primary growth factor fueling this trajectory is the surging demand for precision medicine and biomarker discovery, which is transforming the landscape of diagnostics and therapeutic development worldwide.

One of the most significant growth factors for the targeted proteomics market is the escalating focus on personalized medicine and the need for high-throughput, sensitive, and specific protein quantification technologies. Targeted proteomics, leveraging platforms such as mass spectrometry and immunoassays, allows for the precise identification and quantification of proteins in complex biological samples. This capability is crucial for the discovery and validation of disease biomarkers, which are essential for early diagnosis, prognosis, and monitoring therapeutic efficacy. The increasing prevalence of chronic and infectious diseases, coupled with the growing adoption of proteomics in oncology, neurology, and cardiovascular research, is further propelling market expansion. Pharmaceutical and biotechnology companies are heavily investing in targeted proteomics to streamline drug discovery and development processes, reduce attrition rates, and enhance the predictive power of preclinical models, thereby accelerating the translation of research findings into clinical applications.

Another pivotal driver of market growth is the rapid evolution of technology platforms and analytical tools. Continuous advancements in mass spectrometry, liquid chromatography, and bioinformatics have significantly improved the sensitivity, accuracy, and throughput of proteomic analyses. The integration of artificial intelligence and machine learning algorithms into proteomics data interpretation is enabling researchers to extract deeper insights from complex datasets, facilitating the identification of novel therapeutic targets and disease mechanisms. Additionally, the development of robust multiplex immunoassays and targeted mass spectrometry assays, such as multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM), has expanded the utility of targeted proteomics across diverse applications. These technological innovations are reducing costs, minimizing sample requirements, and shortening turnaround times, making targeted proteomics increasingly accessible to a broader range of end-users, including academic institutions, clinical laboratories, and contract research organizations.

Regulatory support and funding initiatives from governments and non-profit organizations are also contributing to the market's positive outlook. In recent years, global health agencies and research foundations have launched numerous programs to support proteomics research, particularly in the context of biomarker discovery, infectious disease surveillance, and precision oncology. These initiatives are fostering collaborations between academia, industry, and healthcare providers, resulting in the development of standardized protocols, quality control measures, and data-sharing platforms. Furthermore, the growing emphasis on translational research and the integration of multi-omics approaches, combining genomics, transcriptomics, and proteomics, are enhancing the clinical relevance of proteomics data. This ecosystem of support and collaboration is expected to sustain the momentum of market growth over the next decade.

Regionally, North America continues to dominate the targeted proteomics market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The high concentration of leading pharmaceutical companies, advanced healthcare infrastructure, and significant investments in life sciences research are the primary factors underpinning North America's leadership position. However, Asia Pacific is emerging as a high-growth region, driven by increasing research funding, expanding biotechnology sectors, and rising healthcare expenditures in countries such as China, Japan, and India. The region's large patient population and growing focus on translational medicine are creating substantial opportunities for market expansion. Europe also maintains a strong presence, benefiting from robust academic research networks and supp

Multi-omics research mainly consists of two parts: 1) association analysis based on statistical methods; 2) association analysis based on metabolic pathway analysis.(I) Association heat mapThe correlation and p-value of two omics data are calculated by Pearson Correlation analysis, and the table shown below is obtained (see "cor.csv" for details)II) Association scatter plotBy calculating the correlation of two omics data for each sample, the distribution of different samples is examined, and a linear regression equation is constructed to calculate the correlation coefficient (as shown in the figure).

Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.

Understanding the interplay of the proteome and the metabolome aids in understanding cellular phenotypes. To enable more robust inferences from such multi-omics analyses, combining proteomic and metabolomic datasets from the same sample provides major benefits by reducing technical variation between extracts during the pre-analytical phase, decreasing sample variation due to varying cellular content between aliquots, and limiting the required sample amount. We evaluated the advantages, practicality and feasibility of a single-sample workflow for combined proteome and metabolome analysis. In the workflow, termed MTBE-SP3, we combined a fully automated protein lysis and extraction protocol (autoSP3) with a semi-automated biphasic 75% EtOH/MTBE extraction for quantification of polar/non-polar metabolites. Additionally, we compared the resulting proteome of various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) between autoSP3 and MTBE-SP3. Our analysis revealed that the single-sample workflow provided similar results to those obtained from autoSP3 alone, with an 85-98% overlap of proteins detected across the different biological matrices. Additionally, it provides distinct advantages by decreasing (tissue) heterogeneity by retrieving metabolomics and proteomic data from the identical biological material, and limiting the total amount of required material. Lastly, we applied MTBE-SP3 to a lung adenocarcinoma cohort of 10 patients. Integrating the metabolic and proteomic alterations between tumour and non-tumour adjacent tissue yielded consistent data independent of the method used. This revealed mitochondrial dysfunction in tumor tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and confident parallel measurement of proteins and metabolites obtained from the same sample. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.

This system delivers a comprehensive data integration and analysis software tool that provides a snapshot of our current proteomic knowledge in a gene-centric, chromosome format and will ultimately assist in analysing normal biological function, and the study of human disease. The Proteome Browser integrates various types of protein related data from a number of data sources into a report matrix of various hierarchical data types for each gene/protein within a gene set. Within the matrix, a traffic light system is used to indicate the quality of data available for a particular data type and protein combination. The underlying contributing information is available for further analyses using drill down/through capabilities. Filtration and summary tools are also provided through the web interface.

The combined use of multiple omics methods to answer complex system biology questions is growing in biological and medical sciences, as the importance of studying interrelated biological processes in their entirety is increasingly recognized. We applied a combination of metabolomics, lipidomics and proteomics to human bone to investigate the potential of this multi-omics approach to estimate the time elapsed since death (i.e., the post-mortem interval, PMI). This “ForensOMICS” approach has the potential to improve accuracy and precision of PMI estimation of skeletonized human remains, thereby helping forensic investigators to establish the timeline of events surrounding death. Anterior midshaft tibial bone was collected from four female body donors in a fresh stage of decomposition before placement of the bodies to decompose outdoors at the human taphonomy facility managed by the Forensic Anthropological Center at Texas State (FACTS). Bone samples were again collected at selected PMIs (219, 790, 834 and 872 days). Liquid chromatography mass spectrometry (LC-MS) was used to obtain untargeted metabolomic, lipidomic and proteomic profiles from the pre- and post-placement bone samples. Multivariate analysis was used to investigate the three omics blocks by means of Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO), to identify the reduced number of markers that could effectively describe post-mortem changes and classify the individuals based on their PMI. The resulting model showed that pre-placement bone metabolome, lipidome and proteome profiles were clearly distinguishable from post-placement profiles. Metabolites associated with the pre-placement samples, suggested an extinction of the energetic metabolism and a switch towards another source of fuelling (e.g., structural proteins). We were able to identify certain biomolecules from the three groups that show excellent potential for estimation of the PMI, predominantly the biomolecules from the metabolomics block. Our findings suggest that, by targeting a combination of compounds with different post-mortem stability, in future studies we could be able to estimate both short PMIs, by using metabolites and lipids, and longer PMIs, by including more stable proteins.

Spaceflight presents a unique environment with complex stressors, including microgravity and radiation, that can influence plant physiology at molecular levels. Combining transcriptomics and proteomics approaches, this research gives insights into the coordination of transcriptome and proteome in Arabidopsis’ molecular and physiological responses to Spaceflight environmental stress. Arabidopsis seedlings were germinated and grown in microgravity (µg) aboard the International Space Station (ISS) in NASA Biological Research in Canisters -Light Emitting Diode (BRIC LED) hardware, with the ground control established on Earth. At 10 days old, seedlings were frozen in RNA-later and returned to Earth. RNA-seq transcriptomics and TMT-labeled LC-MS/MS proteomic analysis of cellular fractionates from the plant tissues suggest the alteration of the photosynthetic machinery (PSII and PSI) in spaceflight, with the plant shifting photosystem core-regulatory proteins in an organ-specific manner to adapt to the microgravity environment. An overview of the ribosome, spliceosome, and proteasome activities in spaceflight revealed a significant abundance of transcripts and proteins involved in protease binding, nuclease activities, and mRNA binding in spaceflight, while those involved in tRNA binding, exoribonuclease activity, and RNA helicase activity were less abundant in spaceflight. CELLULOSE SYNTHASES (CESA1, CESA3, CESA5, CESA7) and CELLULOSE-LIKE PROTEINS (CSLE1, CSLG3), involved in cellulose deposition and TUBULIN COFACTOR B (TFCB) had reduced abundance in spaceflight. This contrasts with the increased expression of UDP-ARABINOPYRANOSE MUTASEs, involved in the biosynthesis of cell wall non-cellulosic polysaccharides, in spaceflight. Both transcripts and proteome suggested an altered polar auxin redistribution, lipid, and ionic intracellular transportation in spaceflight. Analyses also suggest an increased metabolic energy requirement for plants in Space than on Earth, hence, the activation of several shunt metabolic pathways. This study provides novel insights, based on integrated RNA and protein data, on how plants adapt to the spaceflight environment and it is a step further at achieving sustainable crop production in Space.

Comprehensive spectral libraries are essential in sequential window acquisition of all theoretical mass spectra (SWATH-MS) based high throughput proteomic studies. Even though SWATH-MS assays provide robust quantitative proteomics data its applications to human T-cell studies are limited by the lack of a human T-cell spectral library. To address this resource gap, we generated a high-quality spectral library containing data for 3,941 unique proteins from primary human T-cells across genetically unrelated donors. SWATH-MS analysis of 18 primary T-cell samples using the new human T-cell spectral library identified and quantified 3,022 proteins at 1% FDR, whereas the larger Pan-human spectral library identified and quantified 2,794 proteins, with only 34% overlap. Combining the two libraries resulted in 4,061 proteins, covering ~50% of proteins in immune-related pathways. Overall, this data suggests DDA-MS is suited to discovery projects through to its enhanced sensitivity and SWATH-MS is suited to high-throughput projects.

Lung cancer is associated with high prevalence and mortality, and despite significant successes with targeted drugs in genomically defined subsets of lung cancer and immunotherapy, the majority of patients currently does not benefit from these therapies. Through a targeted drug screen, we found the recently approved multi-kinase inhibitor midostaurin to have potent activity in several lung cancer cells independent of its intended target, PKC, or a specific genomic marker. To determine the underlying mechanism of action we applied a layered functional proteomics approach and a new data integration method. Using chemical proteomics, we identified multiple midostaurin kinase targets in these cells. Network-based integration of these targets with quantitative tyrosine and global phosphoproteomics data using protein-protein interactions from the STRING database suggested multiple targets are relevant for the mode of action of midostaurin. Subsequent functional validation using RNA interference and selective small molecule probes showed that simultaneous inhibition of TBK1, PDK1 and AURKA was required to elicit midostaurin’s cellular effects. Immunoblot analysis of downstream signaling nodes showed that combined inhibition of these targets altered PI3K/AKT and cell cycle signaling pathways that in part converged on PLK1. Furthermore, rational combination of midostaurin with the more potent PLK1 inhibitor BI2536, which is in advanced clinical trials, elicited strong synergy. Our results demonstrate that combination of complementary functional proteomics approaches and subsequent network-based data integration can reveal novel insight into the complex mode of action of multi-kinase inhibitors, actionable targets for drug discovery and cancer vulnerabilities. Finally, we illustrate how this knowledge can be utilized for the rational design of synergistic drug combinations with high potential for clinical translation.

Targeted proteomics plays a specialized role in hypothesis-driven research where the expression of cohorts of dozens of proteins related by function, disease, co-expression, localization, or class are measured after perturbing a pathway. Moreover, a major advance in proteomics is the ability to combine many samples (up to 16) for simultaneous quantification using tandem mass tag (TMT) reagents. Here we present a pathway-centric approach for targeting protein lists selected from up to 10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We applied GoDig to investigate the impact of genetic variation on protein expression in mice fed a Western-style diet high in fat and sucrose. For selected sets of proteins of interest (e.g., kinases, lipid metabolism- and lipid droplet-associated proteins), protein abundances from mouse livers from 480 fully genotyped Diversity Outbred mice were profiled. The results revealed previously unknown protein quantitative trait loci (QTL) and established potential linkages between specific proteins and lipid homeostasis. In all, GoDig provides an integrated solution for next-generation targeted pathway proteomics.

ABSTRACT: Mass spectrometry is being used to identify protein biomarkers that can facilitate development of drug treatment. Mass spectrometry based proteomics results in complex proteomic data that is hierarchical in nature often with small sample size studies. Generalized linear models (GLM) is the most popular approach in proteomics to compare protein abundances between groups. However, GLM does not address all the complexities of proteomics data such as repeated measures and variance heterogeneity. Linear Models for Microarray Data (LIMMA) and mixed models are two approaches that can address some of these data complexities to provide better statistical estimates. We compared these three statistical models to demonstrate when each approach is the best. We evaluated these methods using a dataset of known protein abundances, Systemic Lupus Erythematosus (SLE) dataset, and simulated dataset. We found in general the mixed model findings to be a subset of GLM findings which were a subset of LIMMA findings. Regardless of peptides/PSM/Fold-change restrictions or FDR, less findings were removed from the mixed model than LIMMA since the mixed model is more likely to identify proteins with a larger fold change. Although the peptides/PSM restrictions led to less findings (but higher percentage of findings), with combined FDR the findings were the same or had a large overlap with no restriction and FDR findings. As the percentage of findings were higher with the restrictions this indicated these may be the more reliable proteins. The conclusion is that the mixed model was the most protective of the type I error with the smaller MSE while LIMMA had the better overall statistical properties.

Genomic Data Storage Solutions Market Outlook

As per our latest research, the global genomic data storage solutions market size reached USD 2.47 billion in 2024, reflecting robust demand driven by the proliferation of precision medicine and next-generation sequencing technologies. The market is projected to expand at a CAGR of 14.7% from 2025 to 2033, reaching a forecasted value of USD 8.09 billion by 2033. This remarkable growth trajectory is attributed to the exponential increase in genomic data generation, advancements in data analytics, and the urgent need for secure, scalable, and cost-effective storage solutions in the healthcare and life sciences sectors.

The growth of the genomic data storage solutions market is fundamentally propelled by the rapid advancements in sequencing technologies, which have significantly reduced the cost and time required for whole genome sequencing. This has led to an unprecedented surge in the volume of genomic data generated globally. As healthcare providers, research institutions, and pharmaceutical companies increasingly adopt genomics for disease diagnosis, personalized medicine, and drug discovery, the demand for reliable data storage infrastructures has intensified. The shift towards cloud-based solutions and hybrid storage architectures is further accelerating growth, enabling organizations to manage large-scale data efficiently while ensuring data integrity and compliance with regulatory standards.

Another critical growth factor is the rising integration of artificial intelligence and machine learning in genomics research and clinical applications. These technologies require vast amounts of high-quality genomic data, driving investments in advanced storage solutions that offer not only capacity but also seamless data accessibility and high-speed retrieval. The emergence of multi-omics data, combining genomics with proteomics, transcriptomics, and metabolomics, has further compounded the need for sophisticated storage solutions capable of handling diverse and complex datasets. This trend is particularly evident in drug discovery and precision medicine, where the ability to access and analyze integrated datasets is a key competitive advantage.

Regulatory frameworks and data security requirements also play a pivotal role in shaping the genomic data storage solutions market. As genomic data is highly sensitive and subject to stringent privacy regulations such as HIPAA, GDPR, and other local data protection laws, organizations are compelled to invest in storage solutions with robust encryption, access controls, and audit trails. The growing incidence of cyber threats and data breaches in the healthcare sector has heightened awareness about the importance of secure storage architectures, further boosting market growth. Additionally, government initiatives and funding for genomics research, especially in developed economies, are fostering the adoption of advanced data storage solutions across various end-user segments.

From a regional perspective, North America remains the largest market for genomic data storage solutions, accounting for over 38% of the global revenue in 2024. This dominance is attributed to the presence of leading genomics research institutes, high healthcare expenditure, and favorable government policies supporting genomics and precision medicine. Europe follows as the second-largest market, driven by strong investments in biotechnology and collaborative research initiatives. The Asia Pacific region is witnessing the fastest growth, with a projected CAGR of 17.2% through 2033, fueled by expanding genomics research infrastructure, increasing adoption of digital health technologies, and rising awareness about personalized medicine in countries such as China, Japan, and India.

Component Analysis

The component segment of the genomic data storage solutions market is broadly categorized into hardware, software, and services. Hardware forms the backbone of data storage infrastructure, encompassing servers, storage arrays, and high-capacity drives designed to accommodate the vast volumes of genomic data generated daily. The demand for high-performance, scalable, and energy-efficient hardware solutions is rising, particularly among large research institutions and pharmaceutical companies. These organizations require robust physical storage to ensure data integrity, rapid access, and compliance with regulatory mandates. As

Proteogenomics Market Outlook

As per our latest research, the global proteogenomics market size reached USD 1.45 billion in 2024, driven by rapid advancements in multi-omics technologies and integration of proteomics with genomics. The market is expanding at a robust CAGR of 14.2% and is forecasted to reach USD 4.05 billion by 2033. Key growth factors include rising investments in precision medicine, increasing demand for biomarker discovery, and the proliferation of high-throughput sequencing and mass spectrometry platforms that enable comprehensive proteogenomic analyses.

One of the primary growth drivers for the proteogenomics market is the accelerating adoption of precision medicine across both developed and emerging economies. As healthcare systems shift towards more individualized treatment regimens, the need for integrated multi-omics approaches, particularly those that combine proteomic and genomic data, has intensified. Proteogenomics enables researchers and clinicians to gain deeper insights into disease mechanisms at the molecular level, facilitating the identification of novel biomarkers and therapeutic targets. This capability is especially crucial in oncology, where tumor heterogeneity demands highly personalized diagnostic and treatment strategies. The increasing prevalence of chronic and complex diseases, coupled with a growing geriatric population, further amplifies the demand for advanced proteogenomic tools and services.

Technological advancements in mass spectrometry, next-generation sequencing (NGS), and bioinformatics have dramatically enhanced the sensitivity, accuracy, and throughput of proteogenomic workflows. These innovations have lowered the barriers to entry for academic and clinical laboratories, enabling broader adoption of proteogenomics in both research and clinical settings. The integration of artificial intelligence (AI) and machine learning algorithms into data analysis platforms has also played a pivotal role in accelerating data interpretation, improving the reliability of protein variant identification, and streamlining the workflow from sample preparation to actionable insights. Furthermore, collaborations between academic institutions, government agencies, and industry players are fostering the development of standardized protocols and databases, which are critical for the reproducibility and scalability of proteogenomic studies.

Another significant growth factor is the expanding range of applications for proteogenomics beyond oncology, including infectious diseases, cardiovascular conditions, and neurodegenerative disorders. Pharmaceutical and biotechnology companies are increasingly leveraging proteogenomic approaches in drug discovery and development pipelines to identify novel drug targets, predict drug response, and minimize adverse effects. The ability to correlate genomic alterations with protein expression profiles enables a more holistic understanding of disease biology, paving the way for the development of safer and more effective therapeutics. Additionally, the growing use of proteogenomics in clinical diagnostics and personalized medicine is expected to drive market expansion, as healthcare providers seek more precise tools for early detection and monitoring of diseases.

Proteomic Biomarkers are increasingly becoming a focal point in the field of proteogenomics, offering significant potential for advancing personalized medicine. These biomarkers, which are proteins that can indicate the presence or progression of a disease, are crucial for developing targeted therapies and diagnostic tools. The integration of proteomic biomarkers with genomic data enhances the ability to identify disease-specific signatures, thus improving the precision of diagnostic and therapeutic strategies. In oncology, for instance, proteomic biomarkers are pivotal in understanding tumor biology and heterogeneity, enabling the design of more effective treatment regimens. As research in this area progresses, the identification and validation of novel proteomic biomarkers are expected to drive significant advancements in the proteogenomics market.

From a regional perspective, North America continues to dominate the global proteogenomics market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The regionÂ’s leadership is attributed to a well-established healthcare infrastructure, significant investments in biomedica

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been ten attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137), the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669), and the tenth sweep was conducted in 2020-24 when the respondents were aged 60-64 (held under SN 9412).

A Secure Access version of the NCDS is available under SN 9413, containing detailed sensitive variables not available under Safeguarded access (currently only sweep 10 data). Variables include uncommon health conditions (including age at diagnosis), full employment codes and income/finance details, and specific life circumstances (e.g. pregnancy details, year/age of emigration from GB).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under Safeguarded Licence (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Multi-omics Data and Risk Scores Data (GN 33592)
Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004 and are available under SL SN 9254. Metabolomics analyses were conducted on respondents of sweep 10 and are available under SL SN 9411. Polygenic indices are available under SL SN 9439. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

National Child Development Study: Proteomics: Special Licence, 2002-2004
Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004. This will substantially enhance NCDS and catalyse a step change in our understanding of the relationship between exposures from birth to midlife and their consequences for multiple physical and mental health disorders. It will provide high-dimensional biological information on these individuals during early midlife (aged 42 to 44), prior to the onset of most chronic disease, and at an age that is underrepresented in most cohorts, including UK Biobank (UKB).

Embedding this technology within NCDS with linkage to existing genetics and biomarker data, repeat measures of social and biomedical exposures, and pre-clinical and clinical disease outcomes will drive a major uptake in NCDS data use, including by large-scale international academic consortia aiming to understand the determinants of healthy ageing.

Trapped Ion Mobility Proteomics Market Outlook

According to our latest research, the global Trapped Ion Mobility Proteomics market size reached USD 1.37 billion in 2024, reflecting robust expansion fueled by technological advancements and increasing demand for high-throughput proteomic analysis. The market is expected to grow at a remarkable CAGR of 15.2% during the forecast period, reaching a projected value of USD 4.01 billion by 2033. This impressive growth trajectory is primarily driven by the rising adoption of advanced proteomics platforms in clinical diagnostics, drug discovery, and biomarker research, as well as ongoing innovation in ion mobility spectrometry technologies.

The Trapped Ion Mobility Proteomics market is experiencing significant growth due to the increasing prevalence of chronic diseases and the urgent need for precise, high-throughput protein analysis in biomedical research. The ability of trapped ion mobility spectrometry (TIMS) to enhance the resolution and sensitivity of mass spectrometry workflows has enabled researchers to detect low-abundance proteins and post-translational modifications with unprecedented accuracy. This has proven invaluable in clinical diagnostics and personalized medicine, where early disease detection and targeted therapy selection are critical. Additionally, the integration of TIMS with liquid chromatography-mass spectrometry (LC-MS) platforms has streamlined proteomic workflows, reducing analysis time and improving data quality, further propelling market growth.

Another key driver for the Trapped Ion Mobility Proteomics market is the surge in pharmaceutical and biotechnology R&D investments, particularly in drug discovery and biomarker validation. The pharmaceutical industry is increasingly leveraging TIMS-based proteomics to accelerate target identification, mechanism-of-action studies, and toxicity profiling. This technology enables comprehensive proteome coverage and multiplexed quantification, allowing researchers to analyze complex biological samples more efficiently. Furthermore, collaborations between proteomics technology providers and academic institutions have fostered innovation, leading to the development of next-generation instruments and consumables tailored for specific research applications. The growing recognition of proteomics as a cornerstone of systems biology and precision medicine continues to expand the market’s reach across multiple sectors.

The market is also benefiting from advancements in data analysis software and cloud-based bioinformatics solutions, which have addressed the challenges associated with large-scale proteomics data interpretation. Enhanced software tools now offer automated workflows, advanced statistical analysis, and seamless integration with laboratory information management systems (LIMS), enabling researchers to derive actionable insights from complex datasets. Additionally, the proliferation of open-access proteomics databases and the adoption of artificial intelligence (AI) in data mining have further democratized access to proteomics research, supporting broader market adoption. These technological trends, combined with favorable government funding for life sciences research, are expected to sustain the market’s upward momentum throughout the forecast period.

Regionally, North America currently holds the largest share of the Trapped Ion Mobility Proteomics market, accounting for approximately 38% of the global revenue in 2024. This dominance is attributed to the presence of leading proteomics technology providers, well-established healthcare infrastructure, and substantial investments in biomedical research and development. Europe follows closely, driven by robust government support for life sciences innovation and a strong network of academic research institutions. Meanwhile, the Asia Pacific region is emerging as a high-growth market, propelled by increasing healthcare expenditure, expanding pharmaceutical manufacturing capabilities, and rising awareness of precision medicine. These regional dynamics are shaping the competitive landscape and fostering global market expansion.

Product Type Analysis

Within the Trapped Ion Mobility Proteomics market, the product type segment is broadly categorized into instruments, consumables, software, and services. Instruments represent the largest revenue share, as they form the backbone of proteomics workflows, enabling high-resolution separation and analysis

Protein-Protein, Genetic, and Chemical Interactions for Galan JA (2011):Proteomic studies of Syk-interacting proteins using a novel amine-specific isotope tag and GFP nanotrap. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Green fluorescent protein (GFP) and variants have become powerful tools to study protein localization, interactions, and dynamics. We present here a mass spectrometry-based proteomics strategy to examine protein-protein interactions using anti-GFP single-chain antibody V(H)H in a combination with a novel stable isotopic labeling reagent, isotope tag on amino groups (iTAG). We demonstrate that the single-chain V(H)H (GFP nanotrap) allows us to identify interacting partners of the Syk protein-tyrosine kinase bearing a GFP epitope tag with high efficiency and high specificity. Interacting proteins identified include CrkL, BLNK, α- and β-tubulin, Csk, RanBP5 and DJ-1. The iTAG reagents were prepared with simple procedures and characterized with high accuracy in the determination of peptides in model peptide mixtures and as well as in complex mixture. Applications of the iTAG method and GFP nanotrap to an analysis of the nucleocytoplasmic trafficking of Syk led to the identification of location-specific associations between Syk and multiple proteins. While the results reveal that the new quantitative proteomic strategy is generally applicable to integrate protein interaction data with subcellular localization, extra caution should be taken in evaluating the results obtained by such affinity purification strategies as many interactions appear to occur following cell lysis.