An overview of proteomic and metabolomic technologies and their application to pregnancy research


  • RP Horgan,

    1. The Maternal and Fetal Health Research Centre, The University of Manchester, St Mary’s Hospital, Whitworth Park, Manchester, UK
    2. Manchester Centre for Integrative Systems Biology/Bioanalytical Sciences Group, School of Chemistry, The Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK
    3. The Department of Obstetrics and Gynaecology, University College Cork, Cork University Maternity Hospital, Wilton, Cork, Eire
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  • OH Clancy,

    1. The Maternal and Fetal Health Research Centre, The University of Manchester, St Mary’s Hospital, Whitworth Park, Manchester, UK
    2. Manchester Centre for Integrative Systems Biology/Bioanalytical Sciences Group, School of Chemistry, The Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK
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  • JE Myers,

    1. The Maternal and Fetal Health Research Centre, The University of Manchester, St Mary’s Hospital, Whitworth Park, Manchester, UK
    2. Manchester Centre for Integrative Systems Biology/Bioanalytical Sciences Group, School of Chemistry, The Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK
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  • PN Baker

    Corresponding author
    1. The Maternal and Fetal Health Research Centre, The University of Manchester, St Mary’s Hospital, Whitworth Park, Manchester, UK
    2. Manchester Centre for Integrative Systems Biology/Bioanalytical Sciences Group, School of Chemistry, The Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK
      Prof. PN Baker, The Maternal and Fetal Health Research Group, Faculty of Medical and Human Sciences, School of Clinical and Laboratory Sciences, The University of Manchester, Research Floor, Saint Mary’s Hospital, Hathersage Road, Manchester, M13 0JH, UK. Email
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Prof. PN Baker, The Maternal and Fetal Health Research Group, Faculty of Medical and Human Sciences, School of Clinical and Laboratory Sciences, The University of Manchester, Research Floor, Saint Mary’s Hospital, Hathersage Road, Manchester, M13 0JH, UK. Email


‘Omic’ technologies represent a strategy towards high-throughput, simultaneous analysis of thousands of biological molecules. Their development has been accelerated in the post-genomic era since these molecules represent the interaction of genes and environment or the ‘functional genome’. Omic domains are of particular interest in the search for predictive disease biomarkers and have additional relevance in understanding pathophysiology and the development of molecularly targeted therapeutics. This review examines the fields of proteomics and metabolomics in the context of obstetrics and gynaecology, including a discussion of methodology, challenges, potential applications and current research.


‘Omic technologies’ are primarily aimed at the universal detection of gene products (transcripts, proteins and metabolites) present in a specific biological sample in a non-targeted and nonbiased manner. Genomics, particularly since the completion of the human genome sequence, has emphasised the demand for discovering the function of genes, thus accelerating the development of further ‘omic’ strategies (Figure 1). These approaches demonstrate an ‘integrated’ rather than a ‘reductionist’ approach to the investigation of biological and pathological molecular pathways, which can largely be achieved in a large-scale, high-throughput manner.1 The potential of these strategies to improve understanding of disease currently holds the interest of global medical science. Research in obstetrics and gynaecology is currently taking advantage of these possibilities, and this review provides an overview of the fields of proteomics and metabolomics and their potential application to pregnancy research.

Figure 1.

Functional levels and their interaction. The flow of biological information is not only bidirectional, from genome to metabolome, but also in reverse.



The proteome is defined as the set of all expressed proteins in a cell, tissue or organism.2 The proteome is dynamic, with expression, turnover and post-translational modifications varying in response to external and internal influences during development, aging, health and disease.1,2 The ultimate goal of proteomics is to characterise information flow, within the cell and the organism, through protein pathways and networks,3 with an eventual aim to understand the functional relevance of proteins under these conditions.4 Although the term ‘proteomics’ was coined in the early 1990s, the origins stem back to the 1970s and 1980s, with the discovery of analytical biochemical techniques for protein separation. Proteomic technologies further developed following Nobel Prize winning developments of ionisation techniques for mass spectrometric protein analysis.


The definition of metabolomics is still in flux, and hence, different researchers provide slightly different definitions. However, metabolomics can generally be defined as the study of global metabolite profiles in a system (cell, tissue or organism) under a given set of conditions.5 Although the metabolome is primarily a product of metabolism, it also includes other interesting metabolites obtained from the external environment (food nutrients, drugs, lifestyle choices, etc.) or symbiotic relationships (e.g. gut microflora). Metabolism is maintained so as to regulate growth, energy supply and other requirements for survival and production of future generations; failure or absence of a metabolic process can result in cessation of the process and/or cell death. Like proteomics, metabolomics has evolved along with technology for the past 30 years.6,7 However, metabolomics is really considered a post-genomic science, with the term metabolomics first being used in 1998.8 Metabolomic data have so far been generated from a wide variety of organisms, including microbes, plants and mammals. For more detailed and specific definitions, see Table 1.

Table 1.  Omics-related definitions
Systems biology
Relatively new approach to biological research focusing on the systematic study of complex interactions in biological systems using integration models. The ultimate aim of ‘systems biology’ was to understand whole systems, for example complex cellular pathways, by studying the effect of altered external factors, for example a change in oxygen tension, on the genome, transcriptome, proteome and metabolome simultaneously
The large-scale study of proteins, including their structure and function within a cell/system/organism. A named coined as an analogy with the ‘genome’—the study of genes
The set of all expressed proteins in a cell, tissue or organism
An analytical technique measuring the mass-to-charge (m/z) ratio of charged particles
Shotgun proteomics
Identifying proteins in a digested complex mixture using high-performance liquid chromatography and tandem MS
‘Bottom-up’ proteomics
Method to identify proteins through proteolytic digestion to peptide components prior to mass spectrometric analysis
‘Top-down’ proteomics
Method to identify proteins directly without proteolytic digestion. Uses ion trap MS and electron capture dissociation, and electron transfer dissociation rather than collision-induced dissociation fragmentation methods
Peptide mass fingerprinting
An analytical technique for protein identification using the observed mass-to-charge ratios of peptides in conjunction with databases of known and/or theoretical peptide components of digested proteins
Tandem MS
Multiple steps of MS using ion fragmentation to facilitate identification or de novo sequencing of peptides
Quantitative proteomics
In addition to protein identification, these techniques aim to provide quantitative information on the relative differences in protein abundance between samples
Phosphoprotomics/glycoproteomics/structural/interactional/cellular proteomics
Related proteomic techniques that specifically analyse post-translational modifications and functional changes
The study of global metabolite profiles in a system (cell, tissue or organism) under a given set of conditions
Total quantitative collection of low-molecular-weight compounds (metabolites) present in a cell or organism that participates in metabolic reactions required for growth, maintenance and normal function. It also includes those metabolites taken in from external environments or symbiotic relationships
Metabolic profiling/metabolite profiling (untargeted analysis)
Analysis to identify and quantify metabolites related through similar chemistries or metabolic pathways
Metabolite target analysis
Quantitative determination of one or a few metabolites related to a specific metabolic pathway; this approach requires extensive sample preparation
A measure of the fingerprint of biochemical perturbations caused by disease, drugs and toxins; some would say that metabonomics and metabolomics are the same, and the terms are occasionally used interchangeably
Metabolic fingerprinting
Classification of samples according to biological differences or origin without quantification and metabolite identification
Metabolic footprinting
Analysis of the extracellular metabolome of microbial cultures, plants and mammals. Blood and urine are examples of metabolic footprints

The clinical applications of omic strategies

There are many reasons to study the ‘functional genome’. As many of the proteins and peptides found in body fluids undergo disease-specific changes,2 one of the most rapidly growing areas of biomedical research is biomarker discovery.9 A biomarker is a measurable indicator of a specific biological state used to determine whether someone has, or is at risk of developing, a particular disease. It can also determine disease severity,10 and assess therapeutic response.11 Molecular biomarkers can be found in several molecular domains including the genome, transcriptome, proteome and metabolome.10 Omic strategies lend themselves to biomarker discovery as they investigate multiple molecules simultaneously. This is advantageous as it could potentially lead to the discovery of a ‘panel’ of markers. Panels are likely to be necessary to provide sufficient sensitivity and specificity for a robust clinical application.10–12 Increasingly, examples of the use of proteomic and metabolomic investigation for the discovery of biomarkers indicative of disease,13–18 drug intervention or toxicity,19 or environmental stress20 can be found in the literature. The search for biomarkers using omic techniques can further contribute to a molecular understanding of pathophysiological molecular mechanisms.21 Eventually, it may be possible to combine the functional and structural information discovered using omic techniques to develop preventative and/or interventional therapeutic strategies.

Omics in pregnancy research

At booking, approximately 80% of pregnancies are currently considered to be ‘low risk’ for the development of pregnancy complications such as pre-eclampsia, fetal growth restriction (FGR), preterm labour and stillbirth, with the remainder being defined as ‘high risk’. Paradoxically, we have become so expert in looking after our high-risk women that large studies in Dublin,22 Belfast23 and Nottingham (D. James, pers. comm.) have demonstrated that the perinatal mortality rate is now higher in the apparent low-risk pregnancy than in the high-risk pregnancy. This highlights the need for the development of a reliable and valid screening test. Such a test would lead to increased surveillance and targeted care for women with ‘real’ risk of disease as well as facilitate the development of clinical trials by encouraging the involvement of women whose pregnancies are in this category. The eventual aim was to reduce morbidity and mortality and improve care for all pregnancies.

Although it is well established that the origins of pre-eclampsia and FGR are in early pregnancy and that the placenta plays an integral role in pregnancy outcome, the exact aetiologies of these multifactorial diseases remain poorly defined. Analytical methods devised to detect specific changes or investigate single molecules miss a wide range of other proteins or metabolites that may be significantly important. Investigation of human body fluids and tissues using proteomic and metabolomic techniques will help us to study current candidate molecules alongside novel ones, aiding our understanding of the molecular nature of these pregnancy complications. Indeed, the validity of this indirect approach has successfully been demonstrated to be of value in pre-eclampsia.24

Comparing omic approaches

As detailed above, omic strategies allow the simultaneous analysis of many thousands of molecules. Although the interactions between the ‘omes’ mean that omic strategies are complementary, there are differences in terms of their molecular properties, and therefore, the technologies required for preparation and analysis. All molecular domains are worth investigating, however, in the context of limited resources, the decision towards which molecular level to investigate will depend on many factors including the objectives of the investigation and expertise and equipment available.

The largest omic domain is the proteome; current estimates are >100 000 different human proteins25,26 in comparison with >30 000 genes ( and around 3000 metabolites.27 While there is much information to gain from proteomic investigations, the large domain makes the task complex. The proteome is a dynamic reflection of both the genes and the environment and is thought to hold special promise for biomarker discovery because proteins are most likely to be ubiquitously affected in disease and disease response.10 This is reflected in the many protein disease biomarkers already available (e.g. prostate-specific antigen and troponin T). Discovery within the proteome may also be considered the most directly translatable to guided molecular therapeutics.28,29

The metabolome is the final downstream product of gene transcription, and therefore, changes in the metabolome are amplified relative to changes in the transcriptome and the proteome.30 Additionally, as the downstream product, the metabolome is closest to the phenotype of the biological system studied. Metabolomic technology is nonspecific, as a given metabolite, unlike a transcript or protein is the same in every organism that contains it, making analysis universal. However, although the metabolome contains the smallest domain, it is more diverse than other omes; it contains many different biological molecules such as amino acids, peptides, lipids, carbohydrates and nucleotides, making it more physically and chemically complex with both low- and high-molecular-weight polar metabolites.

Both the genome and the transcriptome have many applications for specific areas such as genetic diseases and oncology and may also have wider appeal. However, discussion of these methodologies is beyond the remit of this article.

Omic experimental design

Most proteomic and metabolomic experiments are hypothesis generating; no hypothesis is known or prescribed, but all data are acquired and analysed to define a hypothesis that is tested in further targeted experimental work. For example, in disease biomarker studies following the identification of a biomarker, a more accurate targeted method or assay is used to measure the single protein/peptide/metabolite accurately and selectively.31

Experimental design is critical to proteomic and metabolomic experiments. Elements that require careful consideration include the use of suitable biological samples, the technical/analytical variation and biological variation. Most biomarker discovery studies have a case–control or case–cohort design; samples from individuals with disease are compared with matched normal control samples or with samples from a general population pool. To reduce bias and confounding variables within samples, adequate numbers, effective demographic sampling and stringent entry and exclusion criteria need to be met. Sample size is dependent on a number of factors such as the biological organism being studied, level of environmental or biological control and the resources available but most importantly the ability to draw valid statistical conclusions. For the greatest predictive power, large numbers of biological specimens are required, and so, the collection of clinical data and biological samples in large biobanks is becoming a necessity.

The question of what type of sample should be used depends on the purpose of the investigation. In terms of biomarker discovery, plasma is the obvious candidate as the ultimate goal is usually a blood test.10 Plasma is rich in proteins and metabolites containing traces of what has been encountered during constant perfusion throughout the body.3,11,12 However, biomarkers are likely to occur in low relative abundance, leaked from damaged tissues and be massively diluted in the circulation.10 It is possible to investigate biological fluids increasingly proximal to the disease process, including urine, cerebrospinal, lymphatic and interstitial fluid, diseased tissue and ‘model systems’ to raise the relative abundance of candidate biomarkers.10

Of equal importance to the quality of experimental and control samples are the protocols in place to guarantee identical sample collection and adequate storage conditions to ensure reproducibility. Much validation work has already been performed (e.g. Manchester with the UK Biobank32).

Sample preparation for omics

Sample preparation for omic experiments is imperative. The primary considerations in a proteomic experiment are protein concentration, sample purification, protein digestion plus affinity capture and sample fractionation (using gel-based or chromatography techniques) to reduce the complexity of the target fluid. Emphasis is placed differently upon these steps, depending on the biological sample being used. For example, urine provides different analytical challenges to plasma, including preparation techniques such as ultrafiltration and precipitation, necessary to remove salts and concentrate urinary proteins. Different problems may arise for each fluid, such as interindividual variation in urinary protein concentration being compounded in diseases such as pre-eclampsia. Although this may not be of concern when sampling during early gestation, it is an issue that requires consideration.

Like with proteomics, the degree of sample preparation for metabolomic experiments depends on the type of sample. For example, analysis of the extracellular metabolome requires minimal sample preparation, whereas metabolomic fingerprinting or analysis of the intracellular metabolome requires extensive sample preparation involving quenching and extraction of metabolites. Metabolomic samples also require fractionation prior to analysis and often utilize similar fractionation approaches to proteomics. These fractionation techniques exploit the different chemical/physical properties of molecules and enable the separation of proteins/peptides/metabolites in liquid or gas phase.

As with sample collection and storage, sample preparation for metabolomics and proteomics should be standardised and reproducible.

Analytical techniques

Mass spectrometry (MS) is the most commonly used method for the investigation/identification of analytes in both proteomics and metabolomics. MS operates to create ions from neutral proteins, peptides or metabolites, which are subsequently separated according to their mass-to-charge ratio (m/z) and detected to create a mass spectrum. The mass spectrum is characteristic of the molecular mass and/or structure of the metabolite. MS is summarised in Table 2; however, important overall qualities include instrument sensitivity, resolution, mass accuracy, dynamic range and throughput. Each analytical technique offers different advantages and limitations; therefore, there has to be a trade-off between the technique and the experimental objectives (Figure 2).

Table 2.  MS and additional techniques used in metabolomics and proteomics
A MS is made up of two main components—an ionisation source and a mass analyser. Electrospray ionisation (ESI)33 and matrix-assisted laser desorption/ionisation (MALDI)34 are the two most commonly used techniques to transfer molecules into the gas phase and ionise them (produce an electrically charged protein/peptide/metabolite) prior to mass separation. With MALDI, the protein or peptide samples are co-crystallised under acidic conditions on a metallic plate with an organic molecule or ‘matrix’ and transferred into the gas phase while being ionised with a rapid laser fire.35 ESI involves the spraying of sample for ionisation, dissolved in an acidic solution, through a fine needle at high voltage towards the entrance of the MS.35 The mass analyser then utilises the electric charge of the particulates for their separation by speed and/or direction, dependent on the intrinsic m/z ratio of the ion. The types of ion mass separation include: time-of-flight (ToF), quadrupole electric fields (Q), ion trap (IT), fourier transform ion cyclotron resonance (FT-ICR) and the Orbitrap36–39
MS can be used to compare samples by producing peptide ‘mass fingerprints’ and/or definitive identification of peptides using tandem MS (MS/MS)37
Peptide ‘mass fingerprinting’ (PMF) can be used to identify a peptide, or peptides, from a simple mixture of proteins digested with trypsin. The set of masses obtained (typically with a MALDI ToF instrument) are then compared with theoretically expected tryptic peptides from proteins in a database. Sophisticated scoring algorithms based on number of theoretical peptides detected, and coverage of protein sequence, can calculate confidence in the protein match36
MS/MS is the coupling of two or more mass analysers together (typically an ESI ToF or a MALDI ToF/ToF instrument) to allow the selection and additional fragmentation of a precurser peptide ion.1,36 This is generally through collision with gas like nitrogen or helium (collision-induced dissociation) to produce unique peptide product ions, which can be detected in a mass analyser.1,10,36 This permits the amino acid sequencing of the precurser peptide ion either manually or using automated computer software.1,36 Bioinformatic databases can then be used to match up the m/z and sequence information to both known and theoretical peptides and their respective proteins.1,36 MS/MS is generally more specific and discriminating than PMF and can be used with more complex samples36
Alternative proteomic strategies include protein microarrays (similar to DNA microarrays)40 and surface-enhanced laser desorption/ionisation (similar to MALDI but using a target with affinity properties)41
As mentioned in the text, metabolomics requires additional techniques to MS, definitions are provided below
Nuclear magnetic resonance (NMR): Subatomic particles (electrons, protons and neutrons) spin on their axes. Each separate resonance observed in an NMR spectrum is specific to a particular compound. This technique requires little or no sample preparation, is highly reproducible and is capable of analysing liquid and solid samples directly. The major disadvantage of nuclear magnetic resonance spectometry (NMRS) is its relatively low sensitivity, which necessitates larger volumes of sample
Direct injection mass spectrometry (DIMS) provides a rapid, high-throughput analysis of samples. The typical analysis time is 1–3 minutes, which is much quicker than gas or liquid chromatography. High mass accuracy allows metabolite identification by accurate mass measurements. While DIMS has a high mass accuracy, it does not differentiate between different isomers that can limit metabolite identification
Fourier transform infrared spectroscopy (FT-IR) involves molecules excited by an infrared beam giving an infrared absorbance spectrum that represents a metabolic footprint. FT-IR is a high throughput, reproducible, which requires minimal sample preparation but usually does not identify the metabolites
Figure 2.

Trade-off between the technique and the experimental objectives.70 DIMS, direct injection mass spectrometry; GC, gas chromatography; HPLC, high-performance liquid chromatography; IR, infra red; NMR, nuclear magnetic resonance; UPLC, ultra performance liquid chromatography.

Relative quantification is an important part of both proteomic and metabolomic analyses, enabling the comparison of the abundance of each molecule between two or more samples. Although MS is not intrinsically quantitative, several techniques for this have been developed.1,35 In proteomics, differential image gel electrophoresis uses fluorescent tags to improve relative quantification measures in gel-based techniques,42 and strategies of isotopic (ICAT™) and isobaric (iTRAQ™) labelling have been used along with tandem MS.43–46 However, the limited number of labelling reagents restricts the numbers of samples that can be compared as well as complicating interexperimental comparisons. Therefore, label-free proteomic quantification, as can currently be achieved in metabolomics, would represent a significant advance.45

Quantification in metabolomics is dependent on the metabolomic approach and technology used. Metabolite quantification is limited in metabolic profiling, but once metabolites of interest are detected, targeted analyses can be used to give absolute quantification and identification of one or a few metabolites.47

Data analysis

The front-end analyses of samples for omic experiments only comprise a relatively small part of the overall process. Given the enormous amount of data generated in these studies, sophisticated bioinformatics are vital to their success. In proteomics, the properties of many thousands of ions are recorded in a single experiment, and complex algorithms are used to match these observational data to a theoretical database to enable protein identification and/or quantification.

In metabolomics, raw data require transformation to a suitable format prior to processing. The methods available for analysis comprise various statistical techniques including univariate and multivariate analyses, supervised and unsupervised learning tools and system-based analyses. Dedicated statisticians and bioinfomaticians need to be involved in this process. The aim of these strategies was to find data patterns which provide useful biological information that can be used to generate further hypotheses for testing. Broadhurst and Kell48 highlight the potential pitfalls related to statistical analysis of metabolic data and related experiments, which include bias, inadequate sample size, excessive false discovery due to multiple hypothesis testing and overfitting (usually due to inadequate validation). These areas of experimental design, sample preparation, analytical techniques and data analysis are covered in greater detail in a number of review articles.5,25,48,49

Current application to pregnancy research

Proteomic strategies have been used to investigate early pregnancy and trophoblast responses to altered oxygen tensions using in vitro models, highlighting potential roles for annexin II50 and neurokinin B.51 It is hypothesised that placental dysfunction in intrauterine growth restriction (IUGR) and pre-eclampsia occurs as a result of reduced oxygen (O2) delivery, and some features of placental pathology seen in IUGR and pre-eclampsia can be reproduced by exposing placental explants to relative hypoxia (i.e. 1% O2).52 Work within our group has examined the proteomic profiles of culture media generated by these placental explants cultured in hypoxic and normoxic conditions and demonstrated expressional differences between several secreted and extracellular matrix proteins. This ongoing research highlights the potential use of in vitro models in the study of pathological pregnancies. These models enable parallel proteomic and metabolomic investigation, and preliminary work has demonstrated that several metabolites also differed in the culture medium from placental explants cultured in 1% compared with 6% and 20% O2.53

MS-based approaches have also been used to directly investigate pre-eclampsia 54–56 and preterm labour.57,58 For this purpose, biological samples from primary cultured trophoblast,55 placental membranes,57 amniotic fluid,56,59 maternal blood54 and vaginal fluid58 have been analysed using several different techniques. To date, the majority of research has focused on pre-eclampsia; this has included observations demonstrating altered levels of ficolins (associated with the induction of innate immunity) in the plasma of women with established disease,54 the differential expression of seven proteins from placental trophoblasts between women with normotensive pregnancies and women with pre-eclampsia55 and the increase of the monomeric form of transthyretin (a transport protein for thyroxine and retinol-binding protein) in the amniotic fluid of five women with pre-eclampsia in comparison to women with normal outcome pregnancies,56 In relation to preterm labour, the differential expression of 11 proteins identified in placental tissue and membranes between women with preterm labour and those with normal outcome pregnancies has been observed,57 and 28 proteins identified in cervical–vaginal fluid demonstrate differences in comparisons of women experiencing preterm labour with those progressing to term.58 Although these studies demonstrate the applicability of omic techniques to pregnancy complications, so far, limited conclusions can be drawn as time-of-disease samples have been used, and future work needs to concentrate on samples taken earlier in pregnancy.

Similar technologies are being used in reproductive oncology for predictive diagnostics, with ovarian cancer as a particular target. One of the most recent advances has been the identification of an autoantibody S100A7 that is significantly elevated in the plasma of women with both early- and late-stage ovarian cancer.60 This has not yet been shown to increase the sensitivity and specificity of CA125, although this may be due to the need for larger scale clinical investigation.60

Although proteomics often produces exciting and novel findings, it is paramount that premature conclusions are not drawn from proteomic data that have not been validated in alternative clinical samples and/or corroborated using traditional molecular techniques. Petricoin et al.61 reported the discovery of distinguishing proteomic patterns in the serum of women with and without neoplastic ovarian disease, promising significant advances in cancer screening management. However, these results were not found to be reproducible, likely to be due to the use of a technique with poor sensitivity, in addition to inadequate statistical analysis.

In terms of metabolomics, profiling of amniotic fluid has been used to identify women at risk of preterm delivery and women with intraamniotic infection. Metabolomic profiling was able to identify women as belonging to the correct clinical group with a 96.3% precision (53/55),62 although commentary on methodology is difficult as this is only published as an abstract. Kenny et al. have demonstrated the use of metabolomic technology in pre-eclampsia.24 These data pertained to an anonymous metabolomic screen performed on plasma obtained from the Genetics Of Pre-EClampsia (GOPEC) archive.63 In a small pilot study, 87 plasma samples from women with established pre-eclampsia and 87 normal pregnant controls were analysed using gas chromatography coupled to an electron impact time-of-flight mass spectrometer. Application of genetic programming suggested that three variables could discriminate the samples from women with pre-eclampsia from controls, with 100% sensitivity and 98% specificity. In a subsequent study of 40 plasma samples (20 cases of pre-eclampsia and 20 controls) using ultra-performance liquid chromatography-orbitrap-MS (UPLC–LTQ-Orbitrap-MS), eight metabolites that appeared in the previous patient cohort were identified as being significant (P < 0.01) discriminatory biomarkers. The chemical identities of these eight metabolites were established.64 Turner et al.65 used1 H-nuclear magnetic resonance spectroscopy to establish the metabolic profile for pre-eclampsia and to identify biomarkers that would permit a distinction between women with a normal pregnancy and those suffering from pre-eclampsia. Eleven cases and controls were used, and the plasma samples were taken at the time of disease. Metabolic profiling has revealed that both lipid and ketone body concentrations are lower in women with pre-eclampsia. Metabolomics has also been used in some areas of reproductive medicine using embryo culture medium or follicular fluid as a predictor of pregnancy outcome.66,67

Proteomics and metabolomic use in obstetrics and gynaecology is still in its ‘infancy’. Although few biomarkers have been validated from these strategies so far, it is encouraging that many studies have made efforts to increase our understanding of molecular mechanisms, characterise domains and discover candidates for disease biomarkers involved in pregnancy and pregnancy complications.

Future strategies

In Manchester, we are currently investigating several strategies towards biomarker discovery for pregnancy complications. Our primary objective is the identification of differences between the plasma proteome of women with pre-eclampsia and women with healthy pregnancy outcomes; with extension of these strategies into the investigation of preterm labour. We are exploring urine as a potential fluid for biomarker discovery in pre-eclampsia, as a reflection of the plasma proteome, and of diseased glomerular function. During the course of this investigation, we also hope to be able to contribute towards a dynamic standard urinary proteome of normal pregnancy as well as clarifying analytical and biological variability between samples. From a metabolomic stance, the human metabolome [Human Serum Metabolome (HUSERMET) project in Manchester;] is a work in progress, and as there is currently no literature on the metabolome of ‘normal’ pregnancy, ongoing work aims to detail this metabolic footprint.

Large scale biomarker discovery will not be achieved without collaboration between research groups to collect the numbers of samples necessary for both the discovery and the validation of potential biomarkers. The global Screening for Pregnancy Endpoints (SCOPE) study ( represents a collaboration of many international leading obstetricians and scientists who are seeking to develop novel effective tools for the early prediction of which first-time mothers are at high risk of the three major complications of late pregnancy: pre-eclampsia, spontaneous preterm birth and IUGR. The SCOPE study is establishing a unique pregnancy biobank for scientific investigation to develop tests that predict these conditions. Stringent control mechanisms and standard operating procedures are in place for all aspects of the study to ensure sample quality. An internet database allows sample tracking, and an extensive clinical database combined with proteomic/metabolomic data may provide the means for predictive screening tests to be developed. Over 3000 women have thus far been recruited into the study in Australasia and Europe; UK centres in London, Manchester and Leeds are currently recruiting, as is the maternity unit in Cork, Ireland.

Following the discovery of potential biomarkers, the next essential step is to validate those markers prospectively in different populations and sample sets. It may be necessary to assimilate new markers with acknowledged clinical, ultrasound and biochemical tests that individually lack the required sensitivity and specificity for population screening.68 Without this commitment to further investigation, on an organised international scale,69 our goals of predictive diagnostics and targeted therapeutics will not be realised.


There continue to be many challenges associated with omic studies. The technology is still evolving, and the human proteome and metabolome is still a work in progress. Pregnancy is a unique physiological state, and the conditions of pre-eclampsia, IUGR and spontaneous preterm delivery are extremely heterogeneous. Carefully designed experiments using standard protocols, followed by appropriate analytical techniques and statistical analyses will help to address many of these challenges, with the potential to generate reliable validated metabolomic and proteomic data to answer important biological questions. Biomarker discovery has stimulated much interest, and as many diseases are a result of metabolic disorders, it makes sense to measure the metabolites and proteins involved on a global scale. Identifying key metabolite/protein markers will likely lead to a greater understanding of the disease process; this will further serve to generate new hypotheses that can be used to develop therapeutic intervention and drug discovery. Accepting that the technical and statistical challenges can be overcome in the near future, omic strategies have the potential to translate bench top research to real clinical benefits.

Conflict of interest


Contribution of authorship

O.H.C. and R.P.H. contributed equally in writing this review. J.E.M. and P.N.B. contributed in discussion and editing.

Details of ethics approval

Not applicable.


R.P.H. is a Clinical Research Fellow funded by the Health Research Board and the Health Service Executive of Ireland. O.H.C. is currently working towards a Masters in Research and is funded by the University of Manchester and NHS Bursaries. The Maternal and Fetal Research Centre in Manchester is funded in part by Tommy’s The Baby Charity.