Minimally Invasive Biospecimen Collection for Exposome Research in Children’s Health


Purpose of Review

The advent of low-volume biosampling and novel biomarker matrices offers non- or minimally invasive approaches to sampling in children. These new technologies, combined with advancements in mass spectrometry that provide high sensitivity, robust measurements of low-concentration exposures, facilitate the application of untargeted metabolomics in children’s exposome research. Here, we review emerging sampling technologies for alternative biomatrices—dried capillary blood, interstitial fluid, saliva, teeth, and hair—and highlight recent applications of these samplers to drive discovery in population-based exposure research.

Recent Findings

Biosampling and biomarker technologies demonstrate potential to directly measure exposures during key developmental time periods. While saliva is the most traditional of the reported biomatrices, each technology has key advantages and disadvantages. For example, hair and teeth provide retrospective analysis of past exposures, and dried capillary blood provides quantitative measurements of systemic exposures that can be more readily compared with traditional venous blood measurements. Importantly, all technologies can or have the potential to be used at home, increasing the convenience and parental support for children’s biosampling.


This review describes emerging sample collection technologies that hold promise for children’s exposome studies. While applications in metabolomics are still limited, these novel matrices are poised to facilitate longitudinal exposome studies to discover key exposures and windows of susceptibility affecting children’s health.

This is a preview of subscription content, log in to check access.

Fig. 1


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.

    Anthony JC, Eaton WW, Henderson AS. Looking to the future in psychiatric epidemiology. Epidemiol Rev. 1995;17:240–2.

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Cheng ATA, Cooper B. Genome and envirome: their roles and interaction in psychiatric epidemiology. BJP. 2001;178:f1.

    Article  Google Scholar 

  3. 3.

    Chitty M. -Omes and -omics glossary & taxonomy 2019. (accessed January 10, 2020).

  4. 4.

    Wild CP. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular rpidemiology. Cancer Epidemiol Biomark Prev. 2005;14:1847–50.

    CAS  Article  Google Scholar 

  5. 5.

    Wild CP. The exposome: from concept to utility. Int J Epidemiol. 2012;41:24–32.

    Article  PubMed  Google Scholar 

  6. 6.

    Miller GW, Jones DP. The nature of nurture: refining the definition of the exposome. Toxicol Sci. 2014;137:1–2.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Niedzwiecki MM, Walker DI, Vermeulen R, Chadeau-Hyam M, Jones DP, Miller GW. The exposome: molecules to populations. Annu Rev Pharmacol Toxicol. 2019;59:107–27.

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Athersuch TJ, Keun HC. Metabolic profiling in human exposome studies. Mutagenesis. 2015;30:755–62.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Portrait F, Teeuwiszen E, Deeg D. Early life undernutrition and chronic diseases at older ages: the effects of the Dutch famine on cardiovascular diseases and diabetes. Soc Sci Med. 2011;73:711–8.

    Article  PubMed  Google Scholar 

  10. 10.

    Falconi A, Gemmill A, Dahl RE, Catalano R. Adolescent experience predicts longevity: evidence from historical epidemiology. J Dev Orig Health Dis. 2014;5:171–7.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Teo SM, Tang HHF, Mok D, Judd LM, Watts SC, Pham K, et al. Airway microbiota dynamics uncover a critical window for interplay of pathogenic bacteria and allergy in childhood respiratory disease. Cell Host Microbe. 2018;24:341–352.e5.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Horton MK, Hsu L, Claus Henn B, Margolis A, Austin C, Svensson K, et al. Dentine biomarkers of prenatal and early childhood exposure to manganese, zinc and lead and childhood behavior. Environ Int. 2018;121:148–58.

  13. 13.

    Wright RO. Environment, susceptibility windows, development and child health. Curr Opin Pediatr. 2017;29:211–7.

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Goodpaster AM, Ramadas EH, Kennedy MA. Potential effect of diaper and cotton ball contamination on NMR- and LC/MS-based metabonomics studies of urine from newborn babies. Anal Chem. 2011;83:896–902.

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Patel SR, Bryan P, Spooner N, Timmerman P, Wickremsinhe E. Microsampling for quantitative bioanalysis, an industry update: output from an AAPS/EBF survey. Bioanalysis. 2019;11:619–28.

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Koulman A, Prentice P, Wong MCY, Matthews L, Bond NJ, Eiden M, et al. The development and validation of a fast and robust dried blood spot based lipid profiling method to study infant metabolism. Metabolomics. 2014;10:1018–25.

  17. 17.

    Guthrie R, Susi A. A simple phenylalanine method for detecting phylketomuria in large populations of newborn infants. Pediatrics. 1963;32:338–43.

    CAS  PubMed  Google Scholar 

  18. 18.

    Petrick LM, Schiffman C, Edmands WMB, Yano Y, Perttula K, Whitehead T, et al. Metabolomics of neonatal blood spots reveal distinct phenotypes of pediatric acute lymphoblastic leukemia and potential effects of early-life nutrition. Cancer Lett. 2019;452:71–8.

  19. 19.

    • Petrick L, Edmands W, Schiffman C, Grigoryan H, Perttula K, Yano Y, et al. An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies. Metabolomics. 2017;13. application of untargeted metabolomics in archived newborn dried blood spots to identify neonatal predictors of later acute lymphoblastic leukemia diagnosis.

  20. 20.

    Peck HR, Timko DM, Landmark JD, Stickle DF. A survey of apparent blood volumes and sample geometries among filter paper bloodspot samples submitted for lead screening. Clin Chim Acta. 2009;400:103–6.

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Hall EM, Flores SR, De Jesús VR. Influence of hematocrit and total-spot volume on performance characteristics of dried blood spots for newborn screening. Int J Neonatal Screen. 2015;1:69–78.

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Zhang X, Ding Y, Zhang Y, Xing J, Dai Y, Yuan E. Age- and sex-specific reference intervals for hematologic analytes in Chinese children. Int J Lab Hematol. 2019;41:331–7.

    Article  PubMed  Google Scholar 

  23. 23.

    Jopling J, Henry E, Wiedmeier SE, Christensen RD. Reference ranges for hematocrit and blood hemoglobin concentration during the neonatal period: data from a multihospital health care system. Pediatrics. 2009;123:e333–7.

    Article  PubMed  Google Scholar 

  24. 24.

    Adeli K, Raizman JE, Chen Y, Higgins V, Nieuwesteeg M, Abdelhaleem M, et al. Complex biological profile of hematologic markers across pediatric, adult, and geriatric ages: establishment of robust pediatric and adult reference intervals on the basis of the Canadian Health Measures Survey. Clin Chem. 2015;61:1075–86.

  25. 25.

    •• Protti M, Mandrioli R, Mercolini L. Tutorial: volumetric absorptive microsampling (VAMS). Anal Chim Acta. 2019:1046, 32–7. review of VAMS technology including suggested workflow for collection and pretreatment, and summary of published procedures and performances.

  26. 26.

    Velghe S, Delahaye L, Stove CP. Is the hematocrit still an issue in quantitative dried blood spot analysis? J Pharm Biomed Anal. 2019;163:188–96.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    • Nys G, MGM K, Servais A-C, Fillet M. Beyond dried blood spot: current microsampling techniques in the context of biomedical applications. TrAC Trends Anal Chem. 2017;97:326–32. of microsampling technologies available for capillary whole blood with an emphasis on applications in pre-clinical drug development and clinical studies.

    CAS  Article  Google Scholar 

  28. 28.

    Kim J-H, Woenker T, Adamec J, Regnier FE. Simple, miniaturized blood plasma extraction method. Anal Chem. 2013;85:11501–8.

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Heussner K, Rauh M, Cordasic N, Menendez-Castro C, Huebner H, Ruebner M, et al. Adhesive blood microsampling systems for steroid measurement via LC-MS/MS in the rat. Steroids. 2017;120:1–6.

  30. 30.

    Medical TS and hemaPEN. Trajan Scientific and Medical n.d. (accessed December 31, 2019).

  31. 31.

    HemaXis Micro Blood Sampling – HemaXis Micro Blood Sampling n.d. (accessed December 31, 2019).

  32. 32.

    Beck O, Kenan Modén N, Seferaj S, Lenk G, Helander A. Study of measurement of the alcohol biomarker phosphatidylethanol (PEth) in dried blood spot (DBS) samples and application of a volumetric DBS device. Clin Chim Acta. 2018;479:38–42.

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Kovač J, Panic G, Neodo A, Meister I, Coulibaly JT, Schulz JD, et al. Evaluation of a novel micro-sampling device, Mitra™, in comparison to dried blood spots, for analysis of praziquantel in Schistosoma haematobium-infected children in rural Côte d’Ivoire. J Pharm Biomed Anal. 2018;151:339–46.

  34. 34.

    Koponen J, Rudge J, Kushon S, Kiviranta H. Novel volumetric adsorptive microsampling technique for determination of perfluorinated compounds in blood. Anal Biochem. 2018;545:49–53.

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    De Kesel PMM, Lambert WE, Stove CP. Does volumetric absorptive microsampling eliminate the hematocrit bias for caffeine and paraxanthine in dried blood samples? A comparative study. Anal Chim Acta. 2015;881:65–73.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Kok MGM, Nix C, Nys G, Fillet M. Targeted metabolomics of whole blood using volumetric absorptive microsampling. Talanta. 2019;197:49–58.

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Cala MP, Meesters RJ. Comparative study on microsampling techniques in metabolic fingerprinting studies applying gas chromatography-MS analysis. Bioanalysis. 2017;9:1329–40.

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Volani C, Caprioli G, Calderisi G, Sigurdsson BB, Rainer J, Gentilini I, et al. Pre-analytic evaluation of volumetric absorptive microsampling and integration in a mass spectrometry-based metabolomics workflow. Anal Bioanal Chem. 2017;409:6263–76.

  39. 39.

    Blicharz TM, Gong P, Bunner BM, Chu LL, Leonard KM, Wakefield JA, et al. Microneedle-based device for the one-step painless collection of capillary blood samples. Nature Biomedical Engineering. 2018;2:151–7.

  40. 40.

    Catala A, Culp-Hill R, Nemkov T, D’Alessandro A. Quantitative metabolomics comparison of traditional blood draws and TAP capillary blood collection. Metabolomics. 2018;14:100.

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Wiig H, Swartz MA. Interstitial fluid and lymph formation and transport: physiological regulation and roles in inflammation and cancer. Physiol Rev. 2012;92:1005–60.

    CAS  Article  PubMed  Google Scholar 

  42. 42.

    Celis JE, Gromov P, Cabezon T, Moreira JMA, Ambartsumian N, Sandelin K, et al. Proteomic characterization of the interstitial fluid perfusing the breast tumor microenvironment: a novel resource for biomarker and therapeutic target discovery. Mol Cell Proteomics. 2004;3:327–44.

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Zhang J, Hao N, Liu W, Lu M, Sun L, Chen N, et al. In-depth proteomic analysis of tissue interstitial fluid for hepatocellular carcinoma serum biomarker discovery. Br J Cancer. 2017;117:1676–84.

  44. 44.

    Sun W, Xing B, Guo L, Liu Z, Mu J, Sun L, et al. Quantitative proteomics analysis of tissue interstitial fluid for identification of novel serum candidate diagnostic marker for hepatocellular carcinoma. Sci Rep. 2016;6:1–8.

  45. 45.

    Hsu C-W, Chang K-P, Huang Y, Liu H-P, Hsueh P-C, Gu P-W, et al. Proteomic profiling of paired interstitial fluids reveals dysregulated pathways and salivary NID1 as a biomarker of oral cavity squamous cell carcinoma. Mol Cell Proteomics. 2019;18:1939–49.

  46. 46.

    Wiig H, Reed RK, Tenstad O. Interstitial fluid pressure, composition of interstitium, and interstitial exclusion of albumin in hypothyroid rats. Am J Phys Heart Circ Phys. 2000;278:H1627–39.

    CAS  Article  Google Scholar 

  47. 47.

    Wawrzyniak R, Kosnowska A, Macioszek S, Bartoszewski R, Markuszewski MJ. New plasma preparation approach to enrich metabolome coverage in untargeted metabolomics: plasma protein bound hydrophobic metabolite release with proteinase K. Sci Rep. 2018;8:1–10.

    CAS  Article  Google Scholar 

  48. 48.

    Donnelly RF, Mooney K, Caffarel-Salvador E, Torrisi BM, Eltayib E, McElnay JC. Microneedle-mediated minimally invasive patient monitoring. Ther Drug Monit. 2014;36:10–7.

    Article  PubMed  Google Scholar 

  49. 49.

    Samant PP, Prausnitz MR. Mechanisms of sampling interstitial fluid from skin using a microneedle patch. PNAS. 2018;115:4583–8.

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    Kiistala U. Suction blister device for separation of viable epidermis from dermis*. J Investig Dermatol. 1968;50:129–37.

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    Pieber T, Birngruber T, Bodenlenz M, Höfferer C, Mautner S, Tiffner K, et al. Open flow microperfusion: an alternative method to microdialysis? In: Müller M, editor. Microdialysis in drug development. New York, NY: Springer; 2013. p. 283–302.

  52. 52.

    •• Niedzwiecki MM, Samant P, Walker DI, Tran V, Jones DP, Prausnitz MR, et al. Human suction blister fluid composition determined using high-resolution metabolomics. Anal Chem. 2018;90:3786–92. demonstration of the blister patch technology to profile metabolites in human subjects.

  53. 53.

    Nilsson AK, Sjöbom U, Christenson K, Hellström A. Lipid profiling of suction blister fluid: comparison of lipids in interstitial fluid and plasma. Lipids Health Dis. 2019;18:164.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Zhang J, Bhattacharyya S, Hickner RC, Light AR, Lambert CJ, Gale BK, et al. Skeletal muscle interstitial fluid metabolomics at rest and associated with an exercise bout: application in rats and humans. Am J Physiol Endocrinol Metabol. 2018;316:E43–53.

  55. 55.

    Sullivan MR, Danai LV, Lewis CA, Chan SH, Gui DY, Kunchok T, et al. Quantification of microenvironmental metabolites in murine cancers reveals determinants of tumor nutrient availability. ELife. 2019;8:ee44235.

  56. 56.

    Humphrey SP, Williamson RT. A review of saliva: normal composition, flow, and function. J Prosthet Dent. 2001;85:162–9.

    CAS  Article  PubMed  Google Scholar 

  57. 57.

    Mandel ID. Salivary diagnosis: promises, promises. Ann N Y Acad Sci. 1993;694:1–10.

    CAS  Article  PubMed  Google Scholar 

  58. 58.

    Dame ZT, Aziat F, Mandal R, Krishnamurthy R, Bouatra S, Borzouie S, et al. The human saliva metabolome. Metabolomics. 2015;11:1864–83.

  59. 59.

    Hassaneen M, Maron JL. Salivary diagnostics in pediatrics: applicability, translatability, and limitations. Front Public Health. 2017;5:83.

    Article  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Navazesh M, SKS K. Measuring salivary flow: challenges and opportunities. J Am Dent Assoc. 2008;139:35S–40S.

    Article  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Troisi J, Belmonte F, Bisogno A, Pierri L, Colucci A, Scala G, et al. Metabolomic salivary signature of pediatric obesity related liver disease and metabolic syndrome. Nutrients. 2019;11.

  62. 62.

    Figueira J, Gouveia-Figueira S, Öhman C, Lif Holgerson P, Nording ML, Öhman A. Metabolite quantification by NMR and LC-MS/MS reveals differences between unstimulated, stimulated, and pure parotid saliva. J Pharm Biomed Anal. 2017;140:295–300.

    CAS  Article  PubMed  Google Scholar 

  63. 63.

    • Pereira JL, Duarte D, Carneiro TJ, Ferreira S, Cunha B, Soares D, et al. Saliva NMR metabolomics: analytical issues in pediatric oral health research. Oral Dis. 2019;25:1545–54. evaluation of saliva collection devices and saliva stimulation on NMR metabolite profiles in children.

    Article  PubMed  Google Scholar 

  64. 64.

    Dallmann R, Viola AU, Tarokh L, Cajochen C, Brown SA. The human circadian metabolome. PNAS. 2012;109:2625–9.

    Article  PubMed  Google Scholar 

  65. 65.

    Kawanishi N, Hoshi N, Masahiro S, Enomoto A, Ota S, Kaneko M, et al. Effects of inter-day and intra-day variation on salivary metabolomic profiles. Clin Chim Acta. 2019;489:41–8.

  66. 66.

    Shin H-S, Kim J-G, Shin Y-J, Jee SH. Sensitive and simple method for the determination of nicotine and cotinine in human urine, plasma and saliva by gas chromatography–mass spectrometry. J Chromatogr B. 2002;769:177–83.

    CAS  Article  Google Scholar 

  67. 67.

    Bessonneau V, Pawliszyn J, Rappaport SM. The saliva exposome for monitoring of individuals’ health trajectories. Environ Health Perspect. 2017;125:077014.

    Article  PubMed  PubMed Central  Google Scholar 

  68. 68.

    de Oliveira LRP, Martins C, Fidalgo TKS, Freitas-Fernandes LB, de Oliveira TR, Soares AL, et al. Salivary metabolite fingerprint of type 1 diabetes in young children. J Proteome Res. 2016;15:2491–9.

    CAS  Article  PubMed  Google Scholar 

  69. 69.

    de Oliveira DN, Lima EO, Melo CFOR, Delafiori J, Guerreiro TM, Rodrigues RGM, et al. Inflammation markers in the saliva of infants born from Zika-infected mothers: exploring potential mechanisms of microcephaly during fetal development. Sci Rep. 2019;9:1–7.

  70. 70.

    Ladva CN, Golan R, Greenwald R, Yu T, Sarnat SE, Flanders WD, et al. Metabolomic profiles of plasma, exhaled breath condensate, and saliva are correlated with potential for air toxics detection. J Breath Res. 2017;12:016008.

  71. 71.

    Ch R, Singh AK, Pathak MK, Singh A, Kesavachandran CN, Bihari V, et al. Saliva and urine metabolic profiling reveals altered amino acid and energy metabolism in male farmers exposed to pesticides in Madhya Pradesh state, India. Chemosphere. 2019;226:636–44.

  72. 72.

    Oral Anatomy, Histology and embryology - 4th Edition n.d. (accessed January 9, 2020).

  73. 73.

    Sabel N, Johansson C, Kühnisch J, Robertson A, Steiniger F, Norén JG, et al. Neonatal lines in the enamel of primary teeth—a morphological and scanning electron microscopic investigation. Arch Oral Biol. 2008;53:954–63.

  74. 74.

    Arora M, Austin C. Teeth as a biomarker of past chemical exposure. Curr Opin Pediatr. 2013;25:261–7.

    CAS  Article  PubMed  Google Scholar 

  75. 75.

    Ewers U, Brockhaus A, Winneke G, Freier I, Jermann E, Krämer U. Lead in deciduous teeth of children living in a non-ferrous smelter area and a rural area of the FRG. Int Arch Occup Environ Health. 1982;50:139–51.

    CAS  Article  PubMed  Google Scholar 

  76. 76.

    Needleman HL, Tuncay OC, Shapiro IM. Lead levels in deciduous teeth of urban and suburban american children. Nature. 1972;235:111–2.

    CAS  Article  PubMed  Google Scholar 

  77. 77.

    Gulson B, Wilson D. History of lead exposure in children revealed from isotopic analyses of teeth. Arch Environ Health. 1994;49:279–83.

    CAS  Article  PubMed  Google Scholar 

  78. 78.

    Arora M, Bradman A, Austin C, Vedar M, Holland N, Eskenazi B, et al. Determining fetal manganese exposure from mantle dentine of deciduous teeth. Environ Sci Technol. 2012;46:5118–25.

  79. 79.

    Arora M, Kennedy BJ, Elhlou S, Pearson NJ, Walker DM, Bayl P, et al. Spatial distribution of lead in human primary teeth as a biomarker of pre- and neonatal lead exposure. Sci Total Environ. 2006;371:55–62.

  80. 80.

    Austin C, Smith TM, Bradman A, Hinde K, Joannes-Boyau R, Bishop D, et al. Barium distributions in teeth reveal early-life dietary transitions in primates. Nature. 2013;498:216–9.

  81. 81.

    Cattaneo C, Gigli F, Lodi F, Grandi M. The detection of morphine and codeine in human teeth: an aid in the identification and study of human skeletal remains. J Forensic Odontostomatol. 2003;21:1–5.

    CAS  PubMed  Google Scholar 

  82. 82.

    Jan J, Vrbic V. Polychlorinated biphenyls cause developmental enamel defects in children. Caries Res. 2000;34:469–73.

    CAS  Article  PubMed  Google Scholar 

  83. 83.

    Schüssl Y, Pelz K, Kempf J, Otten J-E. Concentrations of amoxicillin and clindamycin in teeth following a single dose of oral medication. Clin Oral Investig. 2014;18:35–40.

    Article  PubMed  Google Scholar 

  84. 84.

    Camann DE, Schultz ST, Yau AY, Heilbrun LP, Zuniga MM, Palmer RF, et al. Acetaminophen, pesticide, and diethylhexyl phthalate metabolites, anandamide, and fatty acids in deciduous molars: potential biomarkers of perinatal exposure. J Expo Sci Environ Epidemiol. 2013;23:190–6.

  85. 85.

    Zeren C, Keten A, Çelik S, Damlar I, Daglıoglu N, Çeliker A, et al. Demonstration of ethyl glucuronide in dental tissue samples by liquid chromatography/electro-spray tandem mass spectrometry. J Forensic Legal Med. 2013;20:706–10.

  86. 86.

    Pascual JA, Diaz D, Segura J, Garcia-Algar Ó, Vall O, Zuccaro P, et al. A simple and reliable method for the determination of nicotine and cotinine in teeth by gas chromatography/mass spectrometry. Rapid Commun Mass Spectrom. 2003;17:2853–5.

    CAS  Article  PubMed  Google Scholar 

  87. 87.

    Marchei E, Joya X, Garcia-Algar O, Vall O, Pacifici R, Pichini S. Ultrasensitive detection of nicotine and cotinine in teeth by high-performance liquid chromatography/tandem mass spectrometry. Rapid Commun Mass Spectrom. 2008;22:2609–12.

    CAS  Article  PubMed  Google Scholar 

  88. 88.

    Garcia-Algar O, Vall O, Segura J, Pascual JA, Diaz D, Mutnoz L, et al. Nicotine concentrations in deciduous teeth and cumulative exposure to tobacco smoke during childhood. JAMA. 2003;290:196–7.

  89. 89.

    Jan J, Vrecl M, Pogačnik A, Gašperšič D. Bioconcentration of lipophilic organochlorines in ovine dentine. Arch Oral Biol. 2001;46:1111–6.

    CAS  Article  PubMed  Google Scholar 

  90. 90.

    Jan J, Uršič M, Vrecl M. Levels and distribution of organochlorine pollutants in primary dental tissues and bone of lamb. Environ Toxicol Pharmacol. 2013;36:1040–5.

    CAS  Article  PubMed  Google Scholar 

  91. 91.

    Andra SS, Austin C, Wright RO, Arora M. Reconstructing pre-natal and early childhood exposure to multi-class organic chemicals using teeth: towards a retrospective temporal exposome. Environ Int. 2015;83:137–45. first untargeted metabolomics profiling of deciduous teeth measured 12,000 independent signals in the trimester specific dentine, including bisphenol A, tobacco metabolites, and phthalates.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Kempson IM, Lombi E. Hair analysis as a biomonitor for toxicology, disease and health status. Chem Soc Rev. 2011;40:3915–40.

    CAS  Article  PubMed  Google Scholar 

  93. 93.

    Hsu J-Y, Ho H-H, Liao P-C. The potential use of diisononyl phthalate metabolites hair as biomarkers to assess long-term exposure demonstrated by a rat model. Chemosphere. 2015;118:219–28.

    CAS  Article  PubMed  Google Scholar 

  94. 94.

    Delplancke TDJ, de Seymour JV, Tong C, Sulek K, Xia Y, Zhang H, et al. Analysis of sequential hair segments reflects changes in the metabolome across the trimesters of pregnancy. Sci Rep. 2018;8:1–12.

  95. 95.

    Akiyama M, Matsuo I, Shimizu H. Formation of cornified cell envelope in human hair follicle development. Br J Dermatol. 2002;146:968–76.

    CAS  Article  PubMed  Google Scholar 

  96. 96.

    Garcia-Bournissen F, Rokach B, Karaskov T, Koren G. Methamphetamine detection in maternal and neonatal hair: implications for fetal safety. Arch Dis Child Fetal Neonatal Ed. 2007;92:F351–5.

    CAS  Article  PubMed  Google Scholar 

  97. 97.

    Hernández AF, Lozano-Paniagua D, González-Alzaga B, Kavvalakis MP, Tzatzarakis MN, López-Flores I, et al. Biomonitoring of common organophosphate metabolites in hair and urine of children from an agricultural community. Environ Int. 2019;131:104997.

  98. 98.

    Sulek K, Han T-L, Villas-Boas SG, Wishart DS, Soh S-E, Kwek K, et al. Hair metabolomics: identification of fetal compromise provides proof of concept for biomarker discovery. Theranostics. 2014;4:953–9.

  99. 99.

    Palazzi P, Hardy EM, Appenzeller BMR. Biomonitoring of children exposure to urban pollution and environmental tobacco smoke with hair analysis – a pilot study on children living in Paris and Yeu Island, France. Sci Total Environ. 2019;665:864–72.

    CAS  Article  PubMed  Google Scholar 

  100. 100.

    Karzi V, Tzatzarakis MN, Vakonaki E, Alegakis T, Katsikantami I, Sifakis S, et al. Biomonitoring of bisphenol a, triclosan and perfluorooctanoic acid in hair samples of children and adults. J Appl Toxicol. 2018;38:1144–52.

  101. 101.

    Rashaid AHB, de Harrington PB, Jackson GP. Profiling amino acids of Jordanian scalp hair as a tool for diabetes mellitus diagnosis: a pilot study. Anal Chem. 2015;87:7078–84.

    CAS  Article  PubMed  Google Scholar 

  102. 102.

    Xie P, Wang T, Yin G, Yan Y, Xiao L, Li Q, et al. Metabonomic study of biochemical changes in human hair of heroin abusers by liquid chromatography coupled with ion trap-time of flight mass spectrometry. J Mol Neurosci. 2016;58:93–101.

  103. 103.

    Jung H-J, Kim SJ, Lee W-Y, Chung BC, Choi MH. Gas chromatography/mass spectrometry based hair steroid profiling may reveal pathogenesis in hair follicles of the scalp. Rapid Commun Mass Spectrom. 2011;25:1184–92.

    CAS  Article  PubMed  Google Scholar 

  104. 104.

    Chen X, de Seymour JV, Han T-L, Xia Y, Chen C, Zhang T, et al. Metabolomic biomarkers and novel dietary factors associated with gestational diabetes in China. Metabolomics. 2018;14:149.

  105. 105.

    He X, de Seymour JV, Sulek K, Qi H, Zhang H, Han T-L, et al. Maternal hair metabolome analysis identifies a potential marker of lipid peroxidation in gestational diabetes mellitus. Acta Diabetol. 2016;53:119–22.

  106. 106.

    Grund B, Marvin L, Rochat B. Quantitative performance of a quadrupole-orbitrap-MS in targeted LC–MS determinations of small molecules. J Pharm Biomed Anal. 2016;124:48–56.

    CAS  Article  PubMed  Google Scholar 

  107. 107.

    Rochat B. From targeted quantification to untargeted metabolomics: why LC-high-resolution-MS will become a key instrument in clinical labs. TrAC Trends Anal Chem. 2016;84:151–64.

    CAS  Article  Google Scholar 

Download references


The authors are supported by the National Institute of Environmental Health Sciences grants 2U2CES026561-02 (LP, MA), 1U2CES030859-01 (LP, MA, MN), P30ES23515 (LP, MA, MN), R21ES030882-01 (LP), R01ES031117 (LP), and R01ES026033 (MA).

Author information



Corresponding author

Correspondence to Lauren M. Petrick.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Early Life Environmental Health

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Petrick, L.M., Arora, M. & Niedzwiecki, M.M. Minimally Invasive Biospecimen Collection for Exposome Research in Children’s Health. Curr Envir Health Rpt (2020).

Download citation


  • Microsamplers
  • Untargeted metabolomics
  • Pediatric
  • Exposome