# Annotating DHA Metabolites Interfering with the Quantification of Emerging Perfluoroalkyl Ether Carboxylic Acids

Yuyan Ji, Yitao Pan, Jean-Marie Galano, Ángel Sánchez-Illana, Xinchen Miao, Fengfeng Dong, Camille Oger, Thierry Durand, Jiayin Dai

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Yuyan Ji, Yitao Pan, Jean-Marie Galano, Ángel Sánchez-Illana, Xinchen Miao, et al. Annotating DHA Metabolites Interfering with the Quantification of Emerging Perfluoroalkyl Ether Carboxylic Acids. Environmental Science and Technology Letters, 2025, 12 (3), pp.320-326. 10.1021/acs.estlett.4c01029. [https://doi.org/10.1021/acs.estlett.4c01029](https://doi.org/10.1021/acs.estlett.4c01029)

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Distributed under a Creative Commons Attribution 4.0 International License
Annotating DHA Metabolites Interfering with the Quantification of
Emerging Perfluoroalkyl Ether Carboxylic Acids

Yuyan Ji¹, Yitao Pan¹*, Jean-Marie Galano², Ángel Sánchez-Illana³, Xinchen Miao¹, Fengfeng Dong¹,

Camille Oger², Thierry Durand², and Jiayin Dai¹

¹State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

²Institut des Biomolécules Max Mousseron (IBMM), Pôle Chimie Balard Recherche, UMR 5247, Université de Montpellier, CNRS, ENSCM, Montpellier, 340093, France.

³Department of Analytical Chemistry, University of Valencia, Burjassot, 46100, Spain.

*Correspondence author: Yitao Pan, State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China. E-mail: panyitao@sjtu.edu.cn

**Competing financial interests:** The authors declare no conflicts of interest.
1

<figure><img src="image_2.png" /></figure>

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Aquatic animals

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LC-MS/MS analysis

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TOC

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16,19,20-trihydroxy-bis α dihomo-TrXA<sub>5</sub>

Interference

C₄H₅O₂⁻, 377 > 85

<figure><img src="image_7.png" /></figure>

CF₃O⁻, 377 > 85

Target PFAS

PFO4DA

2

3

4
5 **ABSTRACT**

Quantitative analyses of per- and polyfluoroalkyl substances (PFAS) commonly rely on low-resolution targeted tandem mass spectrometry (MS/MS) due to its high sensitivity and relatively low operational threshold. Perfluoro-3,5,7,9-butaoxidecanoic acid (PFO4DA), an important constituent of the perfluoropolyether carboxylic acid (PFECA) family, has been widely detected in biotic and abiotic matrices. Nevertheless, marked interference has been observed in the MS/MS transition for PFO4DA (377 > 85) in biological samples, particularly in aquatic organisms, resulting in substantial overestimation of concentrations, reaching up to 66 ng/g. In this study, non-targeted molecular networking strategies, combined with the spectral simulation tools SIRIUS and QCxMS, were applied to identify the source of interference, which was confirmed as an oxygenated metabolite of docosahexaenoic acid (DHA) based on fragmentation patterns matching reference standards. The study further clarified that fatty acids generated the fragment ion [C₄H₅O₂]⁻ (85.0296 Da), which overlapped with the [CF₃O]⁻ (84.9907 Da) ion of PFECA compounds under low-resolution conditions, causing signal interference. Analysis of river water and fish samples revealed that this interference had led to a 24-fold overestimation of PFO4DA bioaccumulation in aquatic organisms. These findings are essential for improving the selectivity of environmental exposure assessments for PFECA compounds.

22

23 **Keywords:** per- and polyfluoroalkyl substances, perfluoro-3,5,7,9-butaoxidecanoic acid, tandem
24 mass spectrometry, high-resolution mass spectrometry, interference, oxylipin, docosahexaenoic acid.

25 **Synopsis:** The study employed various non-targeted tools to identify DHA metabolites interfering
26 with PFECA quantification and reported corrected bioaccumulation potential for PFO4DA.

27 INTRODUCTION

28 Per- and polyfluoroalkyl substances (PFAS) represent a diverse class of chemicals characterized by 29 the presence of at least one fully fluorinated methyl or methylene carbon atom.¹ Their exceptional 30 hydrophobic, oleophobic, and thermal stability properties have led to their extensive use in industrial 31 applications and consumer products.²,³ However, these same properties contribute to their 32 environmental persistence and raise serious toxicological concerns, driving research into their 33 detection in environmental matrices and their potential impact on wildlife and human health.⁴⁻⁶

34 Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) analysis using low 35 resolution triple quadrupole mass spectrometry (MS) remains the predominant tool for PFAS 36 quantification. Compared to high-resolution MS, low-resolution MS offers superior sensitivity and 37 precision for targeted analysis and is favored for its lower operational complexity.⁷,⁸ Nevertheless, its 38 limited resolution poses significant analytical challenges when analyzing complex biological matrices, 39 resulting in the inability to effectively differentiate ions with similar m/z ratios, potentially 40 introducing biases in quantification.⁹ Bangma et al. compiled the PFAS Interferents List 41 (doi:10.18434/mds2-3040) to emphasize the analytical challenges posed by bile acids and fatty acids 42 that interfere with PFOS, PFHxS, and PFBA detection in biological samples, such as serum and 43 placenta, highlighting liquid chromatography as a solution for resolving overlapping peaks and 44 mitigating interference.⁹⁻¹¹ The PFAS Interferents List is a publicly available and continually updated 45 repository for those working on PFAS low resolution analysis. While progress has been made in 46 addressing interference for legacy PFAS, less is known about emerging PFAS compounds. Many of 47 these compounds, not included in current interference databases, are potentially susceptible to 48 analytical biases due to their distinct structural features. The prevalence of these compounds, their 49 susceptibility to interference, and strategies to mitigate such challenges remain poorly understood, 50 necessitating further investigation into their detection and characterization.

51 Perfluoropolyether carboxylic acids (PFECA), introduced as novel alternatives to PFOA, have been
extensively detected in environmental and biological matrices.<sup>12-14</sup> Despite their widespread detection, emerging evidence has cast doubt on the concentration accuracy of certain PFECAs. In 2020, Kotlarz et al. reported concentrations of several PFECAs in human serum, but later revised these values after identifying matrix interference during the detection of PFO5DoA.<sup>15</sup> Similarly, during our 2023 study on the biomagnification of PFECAs in the marine food web, we noted considerable asymmetry in chromatographic peaks during the PFO4DA quantification transition (377 > 85) and unusually elevated concentrations of PFO4DA in aquatic organisms, ranging from 2.93 to 80.54 ng/g. These findings indicated the presence of interference, necessitating the exclusion of compromised data from our analysis.<sup>16,17</sup> Subsequent studies have confirmed the presence of similar interference across diverse biological matrices. Given the emerging profile of PFO4DA as a PFAS of concern, particularly due to its potential for bioaccumulation, achieving accurate quantification in biological samples is crucial.<sup>16,17</sup> Building on this, this study sought to identify the source and chemical nature of the PFO4DA interference using high-resolution MS and analysis of its MS/MS fragmentation pathways. These findings pave the way for developing further strategies to enhance the selectivity of PFO4DA quantification and serve as cautionary notes for future studies on similar matrices.

## METHODS AND MATERIALS

**Quantitative assessment by low resolution LC-MS/MS.** A total of 117 biological samples, including plant, animal, and human samples, along with two river surface water samples, were analyzed in this study (details in Table S1) using previously established methods.<sup>16,17</sup> The PFO4DA standard was synthesized in-house, with method provided in SI. Quantitation of PFO4DA was performed using ultra-high-performance liquid chromatography (UHPLC, Exion LC, SCIEX, MA, USA) coupled with triple quadrupole MS (QTRAP 6500 plus, SCIEX, MA, USA). Additional parameters pertaining to the LC gradient and MS parameters are presented in Table S2.

**High-resolution LC-MS/MS analysis.** High-resolution MS data were obtained using the Vanquish Flex UHPLC system coupled with Orbitrap Exploris 120 MS (ThermoFisher, MA, USA).

Chromatographic conditions were identical to those used in quantitative analysis, employing the same
78 column and mobile phase conditions. Further details on instrumentation are provided in Table S3.

79 **Data Processing and Analysis.** Structural investigation of suspect spectra was conducted through
80 multiple approaches, including the Metfrag Web Tool,<sup>18</sup> QCxMS,<sup>19,20</sup> and SIRIUS,<sup>21</sup> running on
81 several databases, including PubChem,<sup>22</sup> MassBank (massbank.eu),<sup>23</sup> HMDB (hmdb.ca),<sup>24</sup> and GNPS
82 (gnps.ucsd.edu).<sup>25</sup> The maximum mass error threshold for both parent ion searches and fragment
candidate analysis were set to 5 ppm. Additionally, seven F<sub>4t</sub>-NeuroP standards were chemically
synthesized at the IBMM laboratory.<sup>26-28</sup> Each standard solution was diluted to a concentration of 1
µg/mL with methanol and analyzed under the same parameters detailed in Table S3. Retention times
85 (RT) and MS/MS spectra for these standards were obtained under the same experimental conditions as
86 the samples (Table S4). The parameters used for molecular networking analysis are detailed in Table
87 S5, and all the raw and processed data are available in the Zenodo dataset (DOI:
10.5281/zenodo.14226855).
88
89

90 RESULTS AND DISCUSSION

91 Under high-resolution MS, both PFO4DA standards and biological samples demonstrated
92 symmetrical chromatographic peaks, with an RT of 5.48 min (Figure S1 and Figure S2). To simulate
93 the chromatographic conditions observed under low-resolution MS, all presumptive peaks within an
94 RT range of 5.18–5.78 min and a mass deviation of ±0.5 Da (m/z range 376.4525–377.4525) were
95 extracted from two aquatic animal samples (TS1 and TS2), which exhibited unusually high PFO4DA
96 signal intensities. With this RT and mass range, the strongest signal in both samples corresponded to a
97 MS1 chromatographic peak of m/z 377.2335, displaying a peak shape consistent with the 377 > 85
98 transition observed under low-resolution MS (Figure S1).
99

The MS2 spectra of m/z 377.2335 at 5.43 min (Figure S3) confirmed the presence of a high intensity
fragment at m/z 85.0296, and its contribution to the chromatographic peaks associated with the 377 >
85 transition in low-resolution MS. Notably, the chemical interferent, represented by a
chromatographic peak at 5.47 min, coincided with the RT of the expected PFO4DA peak. In samples
derived from aquatic organisms, the intensity of all peaks at m/z 377.2333 and 5.47 min reached 9E5,
100
101
102
103
approximately 10-fold higher than that observed in the 1 ppm PFO4DA standard solution (2.71E4).

Initial investigation into the chemical interferent using Metfrag and Sirius v6.07 indicated a molecular formula of C₂₂H₃₄O₅ (100% ZODIAC score),²⁹ indicating a high likelihood toward an oxylipin metabolite or diterpenoid. To further explore this, the high-resolution MS-MS experiments were optimized to enhance oxylipin fragmentation behavior, following the most recent protocols for Orbitrap instruments.³⁰ These optimized conditions improved MS2 spectral coverage, revealing molecular ions associated with the 377.233 > 85.029 transition in both samples (Figure S3). Notably, at RT of 5.47 min, molecular ions exhibited MS2 spectra with four prominent fragments: m/z 85.0296, m/z 153.0923, m/z 193.1236, and m/z 233.1547. Further annotation using CSI:FingerID in Sirius v6.07 highlighted a strong likelihood of the interferent being derived from docosahexaenoic acid (DHA),³¹ with oxylipins ranking among the top 10 candidates. However, library matching confirmed that the chemical interferent was not a neuroprostane derivative (Figure S4-S10). Molecular networking has recently demonstrated exceptional capability in the dereplication and annotation of oxylipins and their non-enzymatic oxidation products, collectively known as non-enzymatic oxidized polyunsaturated fatty acids (NEO-PUFAs).³⁰,³² Molecular networking analysis was conducted using the GNPS (https://gnps.ucsd.edu)²⁵ on the two test samples in addition to the MS2 data generated from the in-vitro oxidation of DHA with a free radical generator from the Elloumi et. al.³⁰ (details in Table S5).

Direct matching of the chromatographic feature at RT 5.43 min from samples TS1 and TS2 with nodes in the DHA in vitro oxidation dataset suggested DHA as the biosynthetic precursor from this potential enzymatic or non-enzymatic metabolite (Figure S11). The second candidate identified by Sirius corresponded to a trioxilin from the 5-series (derived from of DHA) (TrXA₅), first reported in 1997 and annotated by gas chromatography-electron ionization MS-MS (GC-EI-MS-MS) after derivatization in the rat pineal gland.³³,³⁴ However, no MS2 data under negative collision-induced dissociation (CID) were reported.

Interestingly, Sirius fragment analysis identified the m/z 85.0296 ion (anion C₄H₅O₂, 1.1 ppm) as
fragment of the omega-terminal, specifically CH(CH₂)₂COO-. If accurate, it would be recurrent in
oxylipins, and it is not. Another plausible explanation is that the C₄H₅O₂ anion arises as
4-hydroxybuta-1,3-dien-1-olate, generated from the fragmentation of pent-3-ene-1,2,5-triol unit
characteristic of the TrXA derivatives. Two HRMS/MS data of TrXA could be retrieved from the
GNPS library; 9S,12S,13S-trihydroxy-10E,15Z-octadecadienoic acid (9(S),12(S),13(S)-TriHODE)
and 9S,10S,13S-trihydroxy-11E-octadecenoic acid (9(S),10(S),13(S)-TriHOME (Figure S13), the
latter only showing m/z 85.0286 (10 ppm) as a main fragment.

To experimentally validate this hypothesis, a commercially available mixture of two hepoxilin A3 (HXA₃) ester epimers was hydrolyzed under conditions provided by the supplier (Cayman, MI, USA).
This reaction was designed to generate HXA3 free acid while simultaneously inducing epoxide ring opening to yield the required pent-3-ene-1,2,5-triol with two adjacent allylic functions (Figure 1a).
High-resolution LC-MS-MS under identical conditions confirmed HXA₃ as the major product at m/z 335.222 (Figure S12). Two additional features at m/z 353.22 (+16 amu, 10% relative intensity to HXA3) exhibited MS2 spectra containing two prominent fragment ions: m/z 127.0764 (C₇H₁₁O₂, an omega-terminal fingerprint also present in HXA₃) and m/z 85.0296 (not present in HXA₃). This result is in favor of the production of trioxilin A3 (TrXA₃), a well-documented compound, albeit lacking MS2 data in negative CID mode, and established m/z 85.0296 as a distinct fingerprint for the pent-3-ene-1,2,5-triol unit in trioxilin of the A type with at least one double bond in allylic position of the triol unit.

Further validation of the origin of the m/z 85.0296 fragment was performed using quantum-chemistry
CID-MS with QCxMS software.¹⁹ Oxylipins are particularly reluctant at providing pertinent in silico
MSMS simulation, probably due to the so-called mobile-proton effect as the pertinent molecular ions
(deprotomers) during the CID process may be delicate to ascertain.³⁵,³⁶ Initial fragmentation modeling
was performed with TrXA3 structures, affording reasonable MSMS fragments and confirming the
expected origin of the m/z 85.0296 ion (Figure 1a, and Figure S14) as 4-hydroxybuta-1,3-dien-1-olate.
Hence, simulations also revealed that reversing the sequence of the triol unit produced similar
fragmentation behaviors. Subsequently simulations of all possible 10 isomers of the 5-series (TrXA₅)
were attempted with random attribution of the stereocenters (Figure 1b). All simulations did generate m/z 85.0296 with high statistics (indistinctively of the several deprotomers per TrXA₅) clearly confirming it as a fingerprint. Two of the TrXA₅ derived from the hydroperoxide precursor 13- and 17-HpDHA seem pertinent as candidates as the four primary fragments could be retrieved in high intensity (albeit the m/z 193 for the former, and m/z 153 for the latter). MS3 investigation revealed that the four main fragments are not consecutive ions. A breakthrough came from the detailed insights into the fragmentation pathways leading to the four main fragments with QCxMS software showing that m/z 153.0923 to the contrary of m/z 193.1234 and m/z 233.1547 are from two different part of the molecule in the case of TrXA5 (originating from 13- and 17-HpDHA (Figure S15). Indeed, a second molecular network between chemical interferent spectra and an oxylipin analog of +2 Da revealed that +2 Da is also added to the three main fragments (except m/z 85.02; Figure S16). This finding has to rule out the TrXA₅ from 13- and 17-HpDHA as the chemical interferent because +2 Da should not have added to m/z 153.0923 fragment. If one would conclude on our study on the fragmentation pathways leading to the main fragments in all simulations and the second molecular network, the outcome would be that the chemical interferent is from 16- and 20-HpDHA. As the best of our knowledge, only they could lead to such additional +2 Da fragments from the same part of the molecule. One explanation for the failure in quantum simulation for those remaining TrXA₅ structures is accounting for their particular isomerization potential (three skipped diene units) resulting in wrong initial deprotomers or excessive isomerization before fragmentation. We then proposed the chemical interferent structure be either 16,17,20-trihydroxy bis α dihomo-TrXA₅ or 16,19,20-trihydroxy-trihydroxy bis α dihomo-TrXA₅ without possible further chemical distinction at present.

179
<figure><img src="image_8.png" /><figcaption>**Figure 1.** (a) Saponification of HXA3 ester and MSMS main fragments of free HXA3. (b) List of the 10 TrXA5 simulated by quantum chemical mass spectrometry with QCxMS, and two most probable structures of the chemical interferent.</figcaption></figure>

Analysis of high- and low-resolution MS data revealed that interference was exclusively detected in animal-derived samples, with varying levels observed across livestock, poultry, and human cord blood. Aquatic organisms exhibited particularly pronounced interference intensities. In contrast, no interference was detected in vegetables, milk, eggs, or breast milk, suggesting that the interfering substance may be an animal-specific metabolic product. This hypothesis aligns with the likely involvement of DHA as a precursor. Given that the m/z 85 fragment is highly susceptible to
interference by the aforementioned fatty acids under low-resolution MS conditions, the m/z 151 fragment (CF<sub>3</sub>OCF<sub>2</sub>O-) was selected as the quantification fragment. By comparing the intensity ratios of the two transitions, the extent of interference across different samples was estimated (Figure 2a). Notably, aquatic organisms exhibited the most pronounced interference, with deviations from actual concentrations ranging from 4.02- to 150.68-fold. Absolute deviations in concentration ranged from 2.18 to 65.69 ng/g. To account for the interference, bioaccumulation factor (BAF) values were recalculated for river water samples from two locations and 10 crucian carp specimens (Table S6). The corrected BAF values, presented in Figure 2b, were significantly lower following interference elimination, decreasing from 206.80 L/kg to 8.75 L/kg. These findings underscore the substantial impact of interfering substances on overestimating the bioconcentration potential of PFO4DA, highlighting the importance of refining analytical methods to achieve accurate quantification.

<figure><img src="image_9.png" /><figcaption>**Figure 2.** (a) Extent of interference in PFO4DA concentrations across environmental samples. Con.(85) and Con.(151) represent the concentrations quantified using the transitions 377 > 85 and 377 > 151, respectively. (b) Corrected BAF values of PFO4DA and other PFAS after adjusting for interference. Boxes drawn with dashed lines represent data prior to interference elimination. (PFMOAA, PFO2HxA, PFO3OA, and PFO5DoA are homologs of PFO4DA, differing by CF<sub>2</sub>O units, with carbon chain lengths ranging from 2 to 6) Fatty acid metabolites are a well-documented source of interference in PFAS detection. For instance, 3-oxo-dodecanoic acid has been shown to interfere with the 213 > 169 transition of PFBA in placental samples.<sup>10</sup> Similarly, potential interference has been identified in PFPeA detection in shellfish and cocoa mix samples, with endogenous steroid sulfates disrupting PFHxS detection in human serum and</figcaption></figure>
taurodeoxycholic acid interfering with PFOS detection in biological tissues and eggs.<sup>9,11,37,38</sup> Our study focused on PFO4DA, a member of the PFECA class, which includes structurally related congeners such as PFMOAA, PFO2HxA, and PFO3OA. These PFECA compounds, differentiated by CF<sub>2</sub> or OCF<sub>2</sub> units, represent an emerging and significant subclass of PFAS, driving extensive biomonitoring efforts due to their environmental prevalence.<sup>212</sup>

The primary fragment observed for PFECAs in MS analysis is [CF<sub>3</sub> O]-, corresponding to the m/z 85 ion, with no other significant fragments exceeding a relative intensity of 10% (Figure S3).

Furthermore, [CF<sub>3</sub> O]- is not capable of further fragmentation within the mass spectrometer, making the m/z 85 fragment the predominant marker for quantifying PFECAs. However, the trihydroxy unsaturated structural motif identified in this study fragments to produce [C<sub>4</sub> H<sub>5</sub> O<sub>2</sub>]-, introducing substantial interference during low-resolution MS analysis. This study highlighted the potential for similar fatty acid structures to disrupt the quantification of other PFECAs. By clarifying the fragmentation mechanisms responsible for this interference, the findings offer critical guidance for researchers quantifying PFECAs in environmental matrices. The study also emphasizes the need for caution when interpreting data, as such interference could lead to significant overestimations of PFAS levels. Although alternative mass transitions and high-resolution MS can mitigate interference, both approaches often reduce sensitivity compared to conventional detection methods. Consequently, further refinement of analytical techniques is essential to enhance PFECA quantification. It is important to note that when selecting alternative transitions for quantifying PFECA, transitions 377>153 and 377>193 should be avoided, as they are also present in interfering substances.

Conversely, these transitions can be utilized to monitor whether the quantification process is affected by interference.<sup>217</sup>

This study identified potential interference in the PFO4DA transition during biological sample analysis, likely attributed to a non-enzymatic oxygenated metabolite of DHA. The fragmentation mechanisms of the interfering structure were elucidated, enabling a deeper understanding of its impact on quantification. As a prominent member of the PFECA class, PFO4DA represents a significant environmental contaminant, with potential health risks associated with high exposure levels. As such,<sup>238</sup>
239

precise quantification is essential for evaluating toxicological risks, characterizing environmental
behaviors, and conducting health risk assessments. The findings and methodologies presented in this
study provide a critical framework for identifying and mitigating quantification issues associated with
PFECAs in biological matrices. By addressing potential sources of interference, this work aims to
improve the accuracy of PFAS quantification, reduce the likelihood of overestimation, and enhance
the reliability of future research in this field.

240

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245

**Supporting Information**

246

Information on raw files of tested samples, detailed instrumental analysis methods, relevant MS/MS
spectra and details of the molecular networking approach can be found in the Supporting Information.

247

248

**Acknowledgments**

249

This study was supported by the National Key Research and Development Program of China (2022YFC3105700) and National Natural Science Foundation of China (22322607, 22276124). JMG would like to express our gratitude to the National Research Agency, under the acronym TA-BOOm [ANR-20-CE44-0001], for funding and supporting molecular networking work, as well as to LabCom OxyBleu-MARI [ANR-22-LCV1-0007] and CNRS for the 80|Prime project [INSB-INC-80PRIME]. ASI acknowledges the JDC2022-049354-I grant funded by MCIN/AEI/10.13039/501100011033 and "European Union NextGenerationEU/PRTR", and the CIGE/2022/107 research project from Generalitat Valenciana, Conselleria d'Educació, Cultura, Universitats i Ocupació. We gratefully acknowledge the Servei d'Informàtica of the Universitat de València for providing access to the LluisVives v2 HPC cluster and their technical support.

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