Metabolomics core facility
Metabolomics, or metabonomics, refers to the measurement of metabolites (compounds with a molecular mass < 1000 daltons) in biological materials such as blood, urine, cells.
About the Facility
The Metabolomics Core Facility at the University of Surrey has built, over more than a decade, one of the UK’s most extensive targeted metabolomics platforms specifically optimised for time-series data and chronobiology research. Under the academic leadership of Professor Debra J. Skene and with analytical expertise led by Dr Namrata Roy Chowdhury, the facility currently provides absolute quantification of up to 1,019 metabolites across a broad range of biochemical classes, in human and animal biofluids and tissues.
Following extensive evaluation and comparison of LC-MS/MS untargeted and targeted metabolomics platforms over many years, we have adopted targeted metabolomics as our primary analytical strategy. Targeted approaches deliver superior sensitivity, reproducibility, and absolute quantification — qualities that are essential for detecting the subtle temporal metabolite rhythms that characterise chronobiology research. This enables direct cross-study and cross-cohort comparisons, and for understanding how lifestyle factors (sleep, meals), environmental perturbations (shift work), and disease conditions (diabetes, liver disease) shape the diurnal, ultradian, circadian, and seasonal profiles of circulating metabolites.
What makes our platform stand out
✅ A decade of temporal metabolomics expertise: Unlike single time-point metabolomics, we capture dynamic metabolic changes across 24 hours and beyond. We understand how metabolite profiles change across the day, and how lifestyle factors such as sleep deprivation, meal timing, shift work, and disease state alter these rhythms.
✅ Absolute quantification with proven reproducibility: All measurements are in absolute units (μmol/L or nmol/L), not relative abundance. Internal standards are incorporated for every sample. Our inter-batch CVs are low, ensuring that data generated here reflects true biological variation, not analytical noise.
✅ Minimal sample volume: As little as 10–20 μL per sample
✅ Multi-matrix validation: Validated across plasma, serum, urine, interstitial fluid, faeces, and multiple animal models including mice, rats, sheep, and voles.
✅ Beyond data — we interpret: We do not simply deliver raw concentrations. Our team guides you through pattern recognition, pathway enrichment analysis, rhythmicity detection, and meaningful biological interpretation. Full analytical report support is provided.
Research areas
We routinely investigate 24-hour metabolite rhythms and circadian biology, and have contributed to landmark studies in the following areas:
- Circadian rhythms and endogenous time-of-day metabolite variation
- Sleep deprivation and acute sleep loss effects on the metabolome
- Shift work, circadian misalignment, and metabolic consequences
- Meal timing, food anticipation, and chrono-nutrition
- Seasonal metabolite changes
- Metabolic diseases — obesity, type 2 diabetes, cirrhosis, Huntington’s disease, Parkinson’s disease
- COVID-19 multi-omics and metabolomics
- Animal models — mice, rats, sheep (Huntington’s disease model), voles.
Collaborations
We work closely with the Chronobiology group (Professor Debra J. Skene, Dr Daan van der Veen), the Chrono-nutrition group (Prof Jonathan Johnston), and the Proteomics team (Dr Sneha Pinto, Dr Cecile Frampas, Dr Yashwanth Subbannayya). We also maintain many active international collaborations with groups across Europe, North America, Brazil and beyond (see publications below).
Research funding (2012–present)
- Total income: £4,966K
- Funders: BBSRC (5 grants), Wellcome, MRC Newton, NIH (US)
| Period | Funder | Project | Value (fEC) |
|---|---|---|---|
| 2024–2027 | BBSRC | Meal timing and energy restriction as regulators of central and peripheral human rhythms (Johnston PI, van der Veen, Skene) | £1,251,522 |
| 2022–2024 | Wellcome | U-RHYTHM: A powerful research tool for studies on human rhythms in health and disease (Lightman PI; Skene, Surrey PI) | £309,883 to Surrey |
| 2020–2023 | BBSRC/UKRI COVID-19 | Molecular mapping of SARS-CoV-2 and the host response with multiomics mass spectrometry (Barran PI; Skene Co-I) | £489,210 to Surrey |
| 2019–2023 | BBSRC | Physiological anticipation of meal time in humans (Johnston PI, van der Veen, Skene) | £810,047 |
| 2016–2021 | NIH (US) | Hacking epidemics: unlocking the drivers of transmission seasonality (Martinez-Bakker PI; Skene Co-I) | £333,360 to Surrey |
| 2015–2016 | MRC Newton | Sleep deprivation in Parkinson’s disease: Brazil–UK research network (Skene PI) | £49,857 |
| 2012–2015 | BBSRC | Effect of circadian clock time of day and sleep on the human metabolome (Skene PI) | £758,973 |
| 2011–2014 | BBSRC | Food entrainment of the human circadian timing system (Johnston PI, Skene) | £962,717 |
Contact
For enquiries about analytical services, study design support, collaboration, or access to our chrono-metabolomics dataset, please contact Dr Namrata Roy Chowdhury, Professor Debra J. Skene or Dr Daan van der Veen in the first instance.
- Address: School of Biosciences, University of Surrey, Guildford, Surrey GU2 7XH
- Telephone: +44 (0)1483 686700
Professor Debra Skene
Academic Lead (Metabolomics)
Dr Namrata Chowdhury
Analytical Expertise / Enquiries
Dr Daan Van Der Veen
Section Head (Chronobiology)
Publications (Metabolomics at Surrey, 2012–present)
All publications from the Surrey Metabolomics group.
33. Lewis H-M., Isherwood C.M., Mani A.R., Middleton B., Morgan M.Y., Skene D.J. and Montagnese S. Loss, amplification or mistiming of the daily rhythms of metabolic markers in patients with cirrhosis. JHEP Reports 2025; doi: 10.1016/j.jhepr.2025.101720
32. Spick M., Isherwood C.M., Gethings L.A., Hughes C.J., Daly M., Hassanin H., Van der Veen D.R., Skene D.J. and Johnston J.D. Challenges and opportunities for statistical power and biomarker identification arising from rhythmic variation in proteomics. NPJ Biol Timing Sleep (2025) 2(1):3. doi: 10.1038/s44323-024-00020-2.
31. Harding B.N., Espinosa A., Castaño-Vinyals G., Pozo O.J., Skene D.J., et al. Identification of predictors of shift work adaptation and its association with immune, hormonal and metabolite biomarkers. J Pineal Res. (2024) Nov;76(8):e70017. doi: 10.1111/jpi.70017.
30. Bello A.T., Sarafian M.H., Wimborne E.A., Middleton B., Revell V.L., Raynaud F.I., Chowdhury N.R., van der Veen D.R., Skene D.J. and Swann J.R. Exposing 24-hour cycles in bile acids of male humans. Nat Commun. (2024) Nov 19;15(1):10014. doi: 10.1038/s41467-024-53673-9.
29. McDermott J.E., Jacobs J.M. et al., Skene D.J., Gaddameedhi S. and Dongen H.P.A.V. Molecular-level dysregulation of insulin pathways and inflammatory processes in peripheral blood mononuclear cells by circadian misalignment. J Proteome Res. (2024) 23, 1547–1558. doi: 10.1021/acs.jproteome.3c00418.
28. Bonomo R., Canta A., Chiorazzi A., et al., Van der Veen D.R., Marmiroli P., Skene D.J. and Cavaletti G. Effect of age on metabolomic changes in a model of paclitaxel-induced peripheral neurotoxicity. J Peripher Nerv Syst. (2024) Mar;29(1):58–71. doi: 10.1111/jns.12609.
27. Onoja A., von Gerichten J., Lewis H.M., Bailey M.J., Skene D.J., Geifman N. and Spick M. Meta-analysis of COVID-19 metabolomics identifies variations in robustness of biomarkers. Int J Mol Sci. (2023) Sep 21;24(18):14371. doi: 10.3390/ijms241814371.
26. Woelders T., Revell V.L., Middleton B., Ackermann K., Kayser M., Raynaud F.I., Skene D.J. and Hut R.A. Machine learning estimation of human body time using metabolomic profiling. Proc Natl Acad Sci USA (2023) 120(18):e2212685120. doi: 10.1073/pnas.2212685120.
25. Psomas A., Chowdhury N.R., Middleton B., Winsky-Sommerer R., Skene D.J., Gerkema M.P. and van der Veen D.R. Co-expression of diurnal and ultradian rhythms in the plasma metabolome of common voles (Microtus arvalis). FASEB J. (2023) Apr;37(4):e22827. doi: 10.1096/fj.202201585R.
24. Spick M., Hancox T.P.M., Chowdhury N.R., Middleton B., Skene D.J. and Morton A.J. Metabolomic analysis of plasma in Huntington's Disease transgenic sheep reveals progressive circadian rhythm dysregulation. J Huntingtons Dis. (2023) 12(1):31–42. doi: 10.3233/JHD-220552.
23. Spick M., Campbell A., et al., Dunn-Walters D., Skene D.J., Geifman N., Whetton A.D. and Bailey M.J. Multi-omics reveals mechanisms of partial modulation of COVID-19 dysregulation by glucocorticoid treatment. Int J Mol Sci. 2022 Oct 11;23(20):12079. doi: 10.3390/ijms232012079.
22. Harding B.N., Skene D.J., Espinosa A., Middleton B., Castaño-Vinyals G., et al. Metabolic profiling of night shift work — The HORMONIT study. Chronobiol Int. 2022 Nov;39(11):1508–1516. doi: 10.1080/07420528.2022.2131562.
21. Frampas C.F., Longman K., Spick M., et al., Skene D.J., Trivedi D., Pitt A., Hollywood K., Barran P. and Bailey M.J. Untargeted saliva metabolomics by LC-MS reveals COVID-19 severity. PLoS One 2022;17(9):e0274967. doi: 10.1371/journal.pone.0274967.
20. Lewis H.M., Liu Y., Frampas C.F., et al., Skene D.J. and Bailey M.J. Targeted metabolomics shows biomarkers for COVID-19 are dependent on collection wave. Metabolites 2022;12(8):713. doi: 10.3390/metabo12080713.
19. Spick M., Lewis H.M., Frampas C.F., et al., Barran P.E., Skene D.J. and Bailey M.J. An integrated analysis and comparison of serum, saliva and sebum for COVID-19 metabolomics. Sci Rep. 2022;12(1):11867. doi: 10.1038/s41598-022-16123-4.
18. Dauvilliers Y., Barateau L., Middleton B., Van Der Veen D. and Skene D.J. Metabolomics signature of patients with narcolepsy. Neurology 2022;98(5):e493–e505. doi: 10.1212/WNL.0000000000013128.
17. Prior K.F., Middleton B., Owolabi A.T.Y., et al., Blackman M.J., Skene D.J. and Reece S.E. Synchrony between daily rhythms of malaria parasites and hosts is driven by an essential amino acid. Wellcome Open Res. 2021;6:186. doi: 10.12688/wellcomeopenres.16894.2.
16. Hancox T.P.M., Skene D.J., Dallmann R. and Dunn W.B. Tick-Tock consider the clock: the influence of circadian and external cycles on time of day variation in the human metabolome — a review. Metabolites (2021) 11(5):328. doi: 10.3390/metabo11050328.
15. Bonomo R., Cavaletti G. and Skene D.J. Metabolomics markers in neurology: current knowledge and future perspectives. Expert Rev Neurother. (2020) 20:725–738. doi: 10.1080/14737175.2020.1782746.
14. Morton A.J., Middleton B., Rudiger S., Bawden C.S., Kuchel T.R. and Skene D.J. Increased plasma melatonin in presymptomatic Huntington Disease sheep: compensatory neuroprotection in a neurodegenerative disease? J Pineal Res. (2020) 68:e12624. doi: 10.1111/jpi.12624.
13. Honma A., Revell V.L., Gunn P.J., Davies S.K., Middleton B., Raynaud F.I. and Skene D.J. Effect of acute total sleep deprivation on plasma melatonin, cortisol and metabolite rhythms in females. Eur J Neurosci. (2020) 51:366–378. doi: 10.1111/ejn.14411.
12. Fagotti J., Targa A.D.S., Rodrigues L.S., Noseda A.C.D., Dorieux F.W.C., Scarante F.F., Ilkiw J.L., Louzada F.M., Chowdhury N.R., Van der Veen D.R., Middleton B., Pennings J.L.A., Swann J.R., Skene D.J. and Lima M.M.S. Chronic sleep restriction in the rotenone Parkinson's disease model in rats reveals peripheral early-phase biomarkers. Sci Rep. (2019) 9:1898. doi: 10.1038/s41598-018-37657-6.
11. Diessler S., Jan M., Emmenegger Y., et al., Middleton B., Skene D.J., Ibberson M., et al. and Franken P. A systems genetics resource and analysis of sleep regulation in the mouse. PLoS Biol. (2018) 16:e2005750. doi: 10.1371/journal.pbio.2005750.
10. Skene D.J., Skornyakov E., Chowdhury N.R., Gajula R.P., Middleton B., Satterfield B.C., Porter K., Van Dongen H.P.A. and Gaddameedhi S. Separation of circadian- and behavior-driven metabolite rhythms in humans provides a window on peripheral oscillators and metabolism. Proc Natl Acad Sci USA (2018) 115:7825–7830. doi: 10.1073/pnas.1801183115.
9. Hughes M.E., Abruzzi K.C., Allada R., et al., Skene D.J., et al. and Hogenesch J.B. Guidelines for genome-scale analysis of biological rhythms. J Biol Rhythms (2017) 32:380–393. doi: 10.1177/0748730417728663.
8. Marini S., Santangeli O., Saarelainen P., Middleton B., Chowdhury N., Skene D.J., Costa R., Porkka-Heiskanen T. and Montagnese S. Abnormalities in the polysomnographic, adenosine and metabolic response to sleep deprivation in an animal model of hyperammonemia. Front Physiol. (2017) 8:636. doi: 10.3389/fphys.2017.00636.
7. Isherwood C.M., Van der Veen D.R., Johnston J.D. and Skene D.J. 24-hour rhythmicity of circulating metabolites: effect of body mass and type 2 diabetes. FASEB J. (2017) 31:5557–5567. doi: 10.1096/fj.201700323R.
6. Lech K., Liu F., Davies S.K., Ackermann K., Ang J.E., et al., Skene D.J. and Kayser M. Investigation of metabolites for estimating blood deposition time. Int J Legal Med. (2018) 132:25–32. doi: 10.1007/s00414-017-1638-y.
5. Ang J.E., Pal A., Asad Y.J., et al., Revell V., Skene D.J., et al., Workman P., Banerji U. and Raynaud F.I. Modulation of plasma metabolite biomarkers of MAPK pathway with MEK inhibitor RO4987655. Mol Cancer Ther (2017) 16:2315–2323. doi: 10.1158/1535-7163.MCT-16-0881.
4. Skene D.J., Middleton B., Fraser C.K., Pennings J.L.A., Kuchel T.R., Rudiger S., Bawden S. and Morton A.J. Metabolic profiling of presymptomatic Huntington's disease sheep reveals novel biomarkers. Sci Rep. (2017) 7:43030. doi: 10.1038/srep43030.
3. Giskeødegård G.F., Davies S.K., Revell V.L., Keun H. and Skene D.J. Diurnal rhythms in the human urine metabolome during sleep and total sleep deprivation. Sci Rep. (2015) 5:14843. doi: 10.1038/srep14843.
2. Davies S.K., Ang J.E., Revell V.L., Holmes B., Mann A., Robertson F.P., Cui N., Middleton B., Ackermann K., Kayser M., Thumser A.E., Raynaud F.I. and Skene D.J. Effect of sleep deprivation on the human metabolome. Proc Natl Acad Sci USA (2014) 111:10761–10766. doi: 10.1073/pnas.1402663111.
1. Ang J.E., Revell V., Mann A., Mäntele S., Otway D.T., Johnston J.D., Thumser A.E., Skene D.J. and Raynaud F. Identification of human plasma metabolites exhibiting time-of-day variation using an untargeted LC-MS metabolomics approach. Chronobiol Int. (2012) 29:868–881. doi: 10.3109/07420528.2012.699122.