Hansmeier et al. settings. The relative precursor mass window was set to 200 (full width half mass). The MS/ MS acquisition of selected precursors was set to a maximum of 2,500 shots/spectrum with a fixed relative laser power of 4,200. Protein identification and pathway map­ ping. The combined MS/MS spectra were searched using ProteinPilot™ Software v3.0 (version 3.01 prior to July 2009; Applied Biosystems/MDS SCIEX) with the imple- mented Paragon and the Pro Group pro- cessing algorithm against the human subset of the National Center for Biotechnology Information (Bethesda, MD, USA) non- redundant protein database (downloaded on 7 January 2008) and the UniProt human proteome database (version from 20 March 2009). Peptide and protein identification was carried out with ProteinPilotTM. Most search parameters are not user-adjustable but con- fer to the molecular and cellular proteom- ics guidelines (Carr et al. 2004). Adjustable search parameters included cysteine modifica- tion by iodoacetamide, methionine oxidation, tryptic digestion, and thorough search with biological modifications ID focus. Additional information on the ProteinPilotTM algorithm is found in Shilov et al. (2007). Because the samples were obtained according to normal clinical procedures and settings, they may include proteins that were at least partially digested, proteolysed, or degraded during sample handling (Richter et al. 1999). To address this issue and also to identify partially digested proteins, additional database searches were performed allowing nonspecific diges- tion as search parameter. This search strategy was successfully employed for the generation of a plasma proteome reference map (Adkins et al. 2002; Chan et al. 2004; Richter et al. 1999). Protein identification was based on ProtScore unused score criteria (Pro Group Algorithm, ProteinPilotTM software; Applied Biosystems/MDS SCIEX). Only proteins identified with Protscore ≥ 1.3 and at least one unique peptide with ≥ 95% confidence were used for further analysis. False discovery rate (FDR) was estimated by a search against a randomized decoy database using the same parameters as the original search (Elias et al. 2005) as well as using the MAYU approach (Reiter et al. 2009). Blast2GO software was used to extract gene ontology (GO) information for each pro- tein (Götz et al. 2008) for functional char- acterization. The resulting list was curated manually. Pathway information for the iden- tified proteins was obtained by Basic Local Alignment Search Tool (BLAST searches of the proteins against the curated Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Aoki-Kinoshita and Kanehisa 2007). Supporting information. Beyond the results presented here and in the Supplemental M a t e r i a l ( h t t p : / / d x . d o i . o r g / 1 0 . 1 2 8 9 / ehp.1104190), the additional information (EHP_Appendix 1–4 and the spectral data folder) is available on our homepage http:// labs.biodesign.asu.edu/halden/publications/ (Hansmeier et al. 2012) and at http://pro- teomecommons.org (ProteomeCommons.org 2012) project UCB proteome. Appendix 1 contains the extended version of the list of all identified UCB proteins and their descriptions. Appendix 2 lists those proteins that are shared between the UCB proteome and the proteome reported for adult blood. Appendix 3 lists UCB proteins assigned to GO category “multicellular organismal development” which are unique for UCB or shared with adult blood. Appendix 4 shows a detailed overview of UCB proteins and their respective KEGG pathways involved. Results An important step forward for the evaluation of the diagnostic potential of the UCB proteome as a source of biomarkers is the comprehensive analysis of its protein composition, ideally with minimal bias from individual health histories. Therefore, we created pooled UCB composite samples from the THREE cohort study, which showed U.S.-representative exposure levels to a diverse range of environmental pollut- ants, including PFOS, PFOA, organochlorine pesticides, permethrin, and PCBs, as well as heavy metals (see “Materials and Methods” and Cord blood proteome: Adult blood proteome: Cord blood proteome: Adult blood proteome: 1,210 proteins 95% conﬁdence, this study 915 2,030 104 42 149 526 1,212 2,809 proteins 95% conﬁdence, Omenn et al. 2005 99% conﬁdence, Schenk et al. 2008 1,929 proteins 99% conﬁdence, Farrah et al. 2011 843 proteins 99% conﬁdence, this study 619 120 555 74 30 2,060 1,224 2,809 proteins 95% conﬁdence, Omenn et al. 2005 99% conﬁdence, Schenk et al. 2008 1,929 proteins 99% conﬁdence, Farrah et al. 2011 Figure 1. Comparison of the 95% (A) and 99% (B) confidence UCB proteome data sets (this study) with data sets generated from individual adult blood proteome studies (Omenn et al. 2005; Schenk et al. 2008) and a meta-analysis of high-quality data sets from the last 6 years (Farrah et al. 2011). Apelberg et al. 2007b; Herbstman et al. 2007; Neta et al. 2011; Wells et al. 2011). Using a total of 240 μL of the composite sample as starting material, we acquired a total of 62,686 MS/MS spectra from 12 different LC-MALDI- TOF/TOF runs. Altogether 1,210 non- redundant human proteins were identified with ≥ 95% confidence [see Supplemental Material, Table 1 (http://dx.doi.org/10.1289/ ehp.1104190), and Appendix 1, Hansmeier et al. 2012]. The associated FDR were esti- mated to be 5.1% using a naïve target-decoy search and 0.049 [~ 0.002 PSM (peptide spec- trum match) FDR] using MAYU. A subset of 843 proteins was identified at the ≥ 99% con- fidence level (~ 0.009 protein FDR, ~ 0.0005 PSM FDR, MAYU). Compared with the previously published UCB proteome (Colquhoun et al. 2009) generated using the same search algorithm and confidence cutoff at ≥ 95%, our data set represents an approximately 6-fold increase in the number of identified UCB proteins to date. We further compared the UCB to a nonredundant list of plasma proteins of adults using published data (Omenn et al. 2005; Schenk et al. 2008). In addition, we compared our data with a recently published high-quality data set obtained by the meta- analysis of 91 experiments (Farrah et al. 2011). In total we found 295 proteins shared between the UCB and adult proteomes (Figure 1A; see also Appendix 2, Hansmeier et al. 2012). The number of shared proteins is reduced slightly to 224 when only UCB proteins iden- tified with 99% confidence are considered (Figure 1B). Thus, a subgroup of blood pro- teins exists that can be consistently identified regardless of the methods and samples used. The detected UCB proteome covered a large range of molecular weights (4.7–3,880 kDa, as calculated from the sequence of the pro- teins). About 44.6% of the detected proteins are classified as extracellular [see Supplemental Material, Figure 1 (http://dx.doi.org/10.1289/ ehp.1104190)]. Further, 33.3% and 22.1% of the identified proteins were predicted to be intracellular or membrane UCB proteins, respectively. Similar distributions of extracel- lular, intracellular, and membrane proteins were observed in adult blood of healthy donors (Schenk et al. 2008). The presence of these pro- teins in blood is generally believed to be the result of cell lyses, tissue leakage or shedding from cell surfaces, and subsequent releases in the bloodstream (Adkins et al. 2002; Chan et al. 2004; Schenk et al. 2008). In general, protein biomarkers are most useful if their functions are known and thus can be mechanically linked to the molecu- lar basis of adverse health effects. Functional assignments of the identified UCB proteins according to GO classification (Figure 2) revealed that a plurality of UCB proteins 766 ovlume 120 | number 5 | May 2012 • Environmental Health Perspectives