# Blood Sample Preparation for Human Chemical Exposomics: Insights from Large-Scale Applications

Jade Chaker, Eva Gorrochategui Matas, Cécile Chevrier, Mario Campone, Jean-Sébastien Frénel, Frédéric Bigot, Vincent Bessonneau, Arthur David

► To cite this version:

Jade Chaker, Eva Gorrochategui Matas, Cécile Chevrier, Mario Campone, Jean-Sébastien Frénel, et al. Blood Sample Preparation for Human Chemical Exposomics: Insights from Large-Scale Applications. Environmental Science and Technology, 2025, 59 (40), pp.21421-21430. 10.1021/acs.est.5c01686. hal-05298699

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Blood sample preparation for human chemical exposomics: insights from large-scale applications

Jade Chaker¹†, Eva Gorrochategui Matas¹†, Cécile Chevrier¹, Mario Campone²,³, Jean-Sébastien Frenel²,³, Frédéric Bigot⁴, Vincent Bessonneau¹, Arthur David¹*

¹ Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, 35000, Rennes, France

² Medical oncology department, ICO Integrated Center for Oncology, Bd J. Monod, 44805, Nantes, Saint Herblain, France

³CRCINA, INSERM, CNRS, Université d'Angers, Université de Nantes, 44035, Nantes, France

⁴Medical oncology department, ICO Integrated Center for Oncology, 15 Rue André Boquel, 49055, Angers, France

† These authors contributed equally to the work

* Corresponding author

To whom correspondence should be addressed:

Tel: +33 299022885

email: arthur.david@ehesp.fr
Abstract

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) enables profiling of hundreds of externally-derived chemicals and their metabolites in human biofluids to capture the internal chemical exposome. Nevertheless, developing sensitive and robust high-throughput methodologies to scale up exposomics remains challenging. We compared two validated sample preparation methods (SPMs) based on protein precipitation (PPT) and PPT combined with phospholipid removal (PLR) in two cohorts using blood plasma (n=109) and serum (n=75) to assess their impact on LC-HRMS repeatability and chemical coverage. Additional filtration on PLR plates allowed the injection of extracts twice concentrated without affecting LC-HRMS repeatability. The comparison of chemical coverage between SPMs was then assessed using 184 compounds (including a large variety of exogenous chemicals) annotated with a suspect screening strategy. Most chemicals (78-86%) were detected with both SPMs, but a substantial fraction showed preferential (> 2-fold, 37-50%) or exclusive (14-22%) detection with one SPM, reflecting differences in matrix effects and/or recoveries. Low-abundance chemicals were more sensitive to the choice of SPM, though no predictive trends related to physico-chemical properties emerged, likely due to higher analytical uncertainty. Overall, both SPMs provided acceptable chemical coverage for large-scale exposomics. Nonetheless, the compound-dependent nature of detection highlights the value of applying complementary SPMs when feasible.

Keywords: exposomics, LC-HRMS, sample preparation, protein precipitation, phospholipid removal, human blood.

Synopsis: PPT, with or without additional filtration, provide satisfactory LC-HRMS repeatability in cohort studies, and a substantial overlap for exposomics coverage
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## Introduction

Environmental chemical pollution is a major and growing global concern.¹ Several hundreds of thousands of chemicals are currently present on the market,² and many of these have been detected in humans (e.g., pesticides, plasticizers, personal care products),³ and can negatively affect our health by contributing to a number of chronic diseases.¹,⁴ Large human biomonitoring programs and epidemiological studies have started to map the human internal chemical exposome (i.e., the chemical substances and their transformation products in contact with human matrices⁵,⁶) using mostly low-resolution tandem mass spectrometry (MS/MS).³,⁷ Although MS/MS methods can provide robust quantitative data at a large scale, they are limited when it comes to identifying emerging and complex mixtures.⁵ To counter this, non-targeted analysis (NTA) and suspect screening (SS) using high-resolution mass spectrometry (HRMS) can be used to profile hundreds of externally-derived chemicals and their transformation products in human biological samples.⁵, ⁸ Nevertheless, important limitations remain (e.g., sensitivity and repeatability issues, coverage of the chemical space) and have to be overcome for their applications at large scale.⁵, ⁸, ⁹

Blood, as plasma or serum, is usually the most commonly available human biofluid for retrospective cohorts.¹⁰ Several studies have already demonstrated its relevance to detect a wide range of persistent and non-persistent exogenous chemicals for exposomics purposes.¹¹, ¹² One of the main challenges associated to LC-HRMS exposomics using blood samples regards the sensitivity needed to detect trace levels of non-persistent chemicals and their transformation products.⁵, ¹¹, ¹³ NTA of blood samples (plasma or serum) can rely on non-selective methods for sample preparation such as protein precipitation (PPT) using organic solvents to avoid losses of key substances.¹⁴⁻¹⁷ However, analytical interferences with highly abundant components of the blood may result in poor detection of the components of the internal chemical exposome.⁵, ¹¹ Alternative strategies for sample preparation based on a filtration step to remove specifically proteins and (lyso)-phospholipids have been proposed to decrease interferences, inject higher volumes of extracts and improve the detection of low abundant exogenous chemicals.¹², ¹⁵, ¹⁸⁻²⁰ Solid-phase extraction (SPE) methods, assisted liquid extraction (SLE) and QuECHERS (quick, easy, cheap, effective, rugged, and safe) methods have also been tested to improve the coverage of the chemical exposome.¹², ¹⁴ Recent reviews⁸, ¹⁴, ¹⁶ have compared advantages and limitations of sample preparation strategies for HRMS-based exposome research, but no compromise has emerged so far, even though it seems clear that harmonized quality assurance (QA)/ quality control (QC) protocols, and minimum reporting standards should be implemented for HRMS chemical exposomics.¹⁶, ²¹, ²²

Since exposome research usually relies upon large-scale investigations in cohort studies, method development for sample preparation must consider the need for high-throughput and robust analytical methodologies in addition to sensitivity.¹⁴ Recently, we developed a sample preparation workflow dedicated to large-scale HRMS exposomics applications that combine in series both PPT and phospholipid removal (PLR) preparations for blood plasma and serum.¹² PPT extracts can be injected alone or after filtration on PLR plates. This workflow uses a minimum of blood plasma or serum (50-100 µL) and only few hundreds of organic solvent (200-400 µL) per sample, and can be easily automatized using robots for the PPT and 96-well plates PLR for large cohort analyses. This sample preparation strategy was selected after performing a comprehensive evaluation of twelve sample preparation methods (including different PLR, SPE, and SLE methods) using
several quantitative and qualitative criteria adapted to NTA/SS.¹² While most tested sample preparation strategies offered acceptable performances for recoveries and detection frequencies of low abundant chemicals, PLR and PPT methods were selected over SPE-based methods since they were more repeatable and less time consuming (which may be attributable to the higher complexity of SPE protocols).

In our previous study,¹² we mainly focused on comparing and validating sample preparation for chemical exposomics, and the selected sample preparation methods (SPMs) were only tested on a small subset of blood plasma/serum samples. In this study, we aimed to apply this two-step sample preparation workflow on two larger cohorts using either blood plasma (n=109) or blood serum (n=75) to assess the impact of each sample preparation on LC-HRMS analytical repeatability and chemical coverage. A set of Quality assurance (QA) and quality control (QC) criteria developed for non-targeted measurements (including those developed within the European Human Biomonitoring Initiative (HBM4EU) project) was used to assess analytical repeatability.²³ Chemical profiles generated using a suspect screening workflow allowed to annotate 184 chemicals with a wide range of physico-chemical properties and large inter-individual abundances. This dataset was then used to assess the impact of SPM on chemical coverage in both cohorts. Finally, we explored future SPMs strategies in the context of large-scale exposomics applications considering various factors and the improving sensitivity of LC-HRMS platforms.

# Methods

## 2.1. Biological samples

### 2.1.1. Pelagie cohort

The mother-child Pelagie cohort²⁴ is a longitudinal study that was set up to assess the impact of prenatal exposure to chemical contaminants on pregnancy outcomes and child health. The study enrolled 3,421 women from Brittany (France) from the general population during early pregnancy between 2002 and 2006. The children were followed up at birth and at the ages of 2, 6 and 12 years. For this study, blood serum samples were obtained from 75 male adolescents (12 years follow up). Written informed consent was obtained from parents/legal guardians for all participants aged under 18. The study has been approved by the Advisory Committee on Information Processing in Health Research (CCTIRS; 2015; no. 15.326bis), the Committee for the Protection of Persons (CPP; 2015; no.15/23-985) and the French National Commission for Information Technology and Civil Liberties (CNIL; 2002, 2015; no. 915420/2015-456).

### 2.1.2. Epicure cohort

The Epicure cohort²⁵, ²⁶ is a prospective study of metastatic breast cancer (BC) patients from Nantes (ICO cancer center) with three phenotypic subgroups, including triple negative breast cancer. This cohort was registered on May 21, 2019 with *ClinicalTrials.gov identifier (NCT number)*: NCT03958136. All participants signed an informed consent form, and the cohort was designed in accordance with the Declaration of Helsinki. For this study we selected 109 human blood plasma samples from Epicure cohort obtained from 22-79-year-old women, collected between 2019 and 2022.

## 2.2. Materials and reagents
173 A description of the materials and reagents used in this study is provided in SI.

## 2.3. Sample preparation

175 A two-step sample preparation method described and validated in Chaker et al. (2022)¹² was used
176 to prepare serum and plasma samples. Briefly, PPT was performed using a 4:1 (v:v) cold methanol
177 to sample ratio. After leaving samples at -20 °C for 1 h to improve protein removal, centrifugation
178 was performed at 4 °C and 17,000 g for 20 min. Supernatants were then collected and separated
179 in two equal volumes. The first half was evaporated to dryness under vacuum and recovered in
180 90:10 (v:v) ultrapure water to acetonitrile mixture as to obtain a sample dilution factor of 1 (i.e. no
181 concentration). This first extract constituted samples labelled as “PPT samples”. The second half
182 of supernatants were then further purified using Phree protein and phospholipid removal (PLR)
183 plates (Phenomenex). After evaporation to dryness under vacuum, a 99:1 (v:v) acetonitrile to
184 formic acid mixture was added to ultrapure water using a 4:1 (v:v) ratio. Homogenized samples
185 were then placed on the plate and drawn through it under vacuum. Plates were rinsed using 100
186 µL of the 99:1 (v:v) acetonitrile to formic acid mixture. Extracts were evaporated to dryness under
187 vacuum and recovered in 90:10 (v/v) ultrapure water to acetonitrile mixture as to obtain a sample
188 concentration factor of 2 (i.e. twice the concentration compared to PPT). This second extract
189 constituted samples labeled as “PLR samples”. Hence for both cohorts, extracts from PPT
190 combined with PLR were twice as concentrated as PPT ones. These concentration factors were
191 determined after visual inspection of extracts and observations of analytical shifts in retention
192 time (Rt) and sensitivity for internal standards (ISTD) (list in Table S1) during injections of the
193 composite QC samples and the first samples for the PPT batch (data not shown).

## 2.4. Data acquisition and analytical QAQC procedures

### 2.4.1. Pelagie cohort (X500R)

196 Pelagie extracts were analyzed using an Exon UHPLC system coupled to a X500R Q-TOF system
197 (Sciex technologies). Two µL of extracts were loaded and separated on an Acquity UHPLC HSS-T3
198 column (1.0 mm × 150 mm × 1.8 µm) maintained at 40 °C (Waters Technologies) as described in
199 Chaker et al.¹² Extracts were analyzed using full scan experiments (50–1100 Da) in both – and + ESI
200 modes and additional injections using the data dependent acquisition (DDA) were performed to
201 obtain MS/MS data. Additional information regarding the chromatographic separation, source
202 parameters and specific QC procedures is provided in the **SI**.

203 **2.4.2. Epicure cohort (timsTOF)**

204 Epicure extracts were analyzed using an Elute UHPLC coupled to the TimsTOF Pro 2 system
205 (Bruker Daltonics). Two µL of extracts were loaded and separated on an Acquity UHPLC HSS-T3
206 column (2.1 mm × 150 mm × 1.8 µm) maintained at 40 °C (Waters Technologies). Extracts were
207 analyzed using the DDA-PASEF (Parallel Accumulation Serial Fragmentation) data acquisition
208 mode in both – and + ESI modes. Additional information regarding chromatographic separation, MS
209 parameters and specific QC procedures is provided in the **SI**.

210 **2.4.3. QAQC procedures and assessment of LC-HRMS repeatability**

211 For both cohorts, batch assignment and injection order were randomized. One workup blank
212 sample (i.e., extraction with HPLC grade water instead of sample) per analytical batch was
213 prepared and injected to monitor for background contaminants. Blank subtraction based on the
214 area was performed when the annotated compounds could be detected in the blanks. Solvent
215 blanks (i.e. acetonitrile/ultrapure water 90:10 (v/v)) were also injected to ensure that there was no
carryover in the UHPLC system that might affect adjacent results in analytical runs. A composite QC (i.e., pool of all experimental samples) was injected every 5 (Pelagie) - 8 (Epicure) samples throughout all batches to assess analytical drift and repeatability. For the Pelagie cohort, a mix of 21 ISTD were systematically spiked in QCs and samples to assess the analytical repeatability (see SI and 3.1.1 for more details). For the Epicure cohort (injected more recently), additional QC materials were prepared and injected based on HBM4EU guidance.²³ These QC materials included non-spiked QCs, low level spiked-QCs (non-labelled standards spiked at 5 ng/mL) and high level spiked-QCs (same mix standards spiked at 50 ng/mL) that were injected every 8 samples to monitor analytical repeatability (see SI and 3.2.2 for more details on QAQC criteria).

## 2.5. Data processing and suspect screening

### 2.5.1. Non-targeted data processing and suspect screening workflow

#### i. Pelagie samples

The data processing workflow (i.e. software and parameters) was previously optimized and validated to detect low-abundant chemicals in blood plasma and serum samples.¹³ Briefly, raw LC-HRMS data acquired in full scan mode was processed using AB SCIEX vendor software MarkerView v.1.3. Main parameter values were set as follows: minimum noise threshold of 10, minimum intensity of 20 counts, *m/z* tolerance of 10 ppm, Rt tolerance of 2 min, minimum Rt of 1 min, no isotope filtering. The Scannotation software²⁷ was used to perform an automatized pre-annotation, by scoring proximity of *m/z*, Rt (experimental and predicted) and isotopic pattern between suspects and features. The suspect screening was performed using the 6000-compound Scannotation library (https://github.com/scannotation/Scannotation_software) mainly comprised of exogenous compounds, such as food intake biomarkers, pharmaceuticals, industrial pollutants, or pesticides and their metabolites.²⁷ Additionally, compounds annotated in Epicure samples were looked for manually in raw and processed data from Pelagie samples using the same approach to avoid false negatives and increase comparability of findings.

#### ii. Epicure samples

Raw LC-HRMS data were processed using Bruker Metaboscape v.2023b software for quality calibration, peak extraction, filtering, and preliminary annotation. Main parameters were set as follows: intensity threshold 150 counts, minimum 4D peak size 100 points, *m/z* range 50-1300 Da and Rt range 0.2-22 min. *m/z* features were extracted from each individual sample and then aligned with the rest of experimental samples in the batch to create a *m/z*-intensity table for downstream data analysis. Then, preliminary data annotation was performed through database matching using the online MS-DIAL metabolomics spectral kit in both ESI+/- containing MS/MS and ion mobility collision cross section (CCS) values. The annotation parameters were set as follows: *m/z* < 10 ppm, mSigma < 250 and MS/MS Score > 800 (highly confident). The annotation quality (AQ) score provided by Metaboscape was used to filter potential false positive annotations, by considering 5 indicators: i) precursor mass deviation, ii) retention time deviation, iii) isotopic pattern quality, iv) MS/MS score and v) CCS. Likewise, compounds annotated in Pelagie samples were looked for manually in raw and processed data from Epicure samples to avoid false negatives and increase comparability of findings. In this study, only compounds annotated with AQ score > 6 were included in the list of preliminary annotations, which resulted in 148 compounds. Additional information on individual annotation is available Table S2.
For all annotated compounds, a thorough manual curation was performed by checking MS/MS spectra, Rt, *m/z* values and isotopic patterns in the raw data using either Sciex OS or Bruker DataAnalysis v.6.0 software to provide confidence levels according to Schymanksi et al. (2014).²⁸

CCS values were used as additional diagnostic evidence during the annotation step but not considered for reporting the annotation confidence levels. Compounds with confidence levels ranging from 1-2b were systematically reported, and three level 4 compounds (no MS/MS data) were reported with tentative names and structures based on their distinctive isotopic chlorine patterns and their matches with different predicted Rt. Diagnostic evidence for the three compounds annotated with confidence level 4 is presented in **Figure S1A**.

### 2.5.2. Manual integration using targeted data processing

To ensure reliable relative comparisons between methods and cohorts, manual peak integration for all annotated compounds was achieved using open-source software Skyline daily v.24.1.²⁹ for Pelagie samples and Bruker TASQ v.2023b for Epicure samples. Values were normalized by the total ion chromatogram value to obtain the final data matrix for further statistical analysis.

## 2.6. Assessing chemical coverage: statistical analyses and visualization

Visualization was done using the package *ggplot2*, available in R. All representations of area fold change (FC) values are made with a logarithmic scale to obtain a symmetric representation.

Annotated features' detection frequencies were computed for each cohort. Area FC between methods were computed for each sample as Area in PPT+PLR extract / Area in PPT extract, and median values were determined for each compound and each cohort. For statistical analyses, compounds were separated into three groups, depending on whether they were mostly recovered in PPT extracts (FC < 0.5), in PPT+PLR extracts (FC > 2), or indiscriminately/in both (0.5 ≤ FC ≤ 2).

Unpaired t-tests were performed to determine parameters explaining significant differences between these groups. For some statistical analyses, compounds were further separated into two groups depending on the presence of common polar functional groups. "COXR" compounds refer to compounds containing at least one of these functions: carboxylic acid, carboxylic ester, thioester, and amide.
## 288 Results and discussion

### 289 3.1. Assessing LC-HRMS analytical repeatability for cohorts: PPT
290 versus combination of PPT and PLR filtration

291 A total of 75 (Pelagie cohort) and 109 (Epicure cohort) blood serum and plasma samples, 292 respectively, were prepared using our two-steps sample preparation workflow. PPT extracts were 293 injected alone or after filtration on PLR plates. LC-HRMS analytical repeatability was assessed 294 independently for the two cohorts since injections were made on different LC-HRMS 295 instrumentations and blood matrices (serum vs plasma). Samples from Epicure were injected 296 more recently and benefited from additional QC materials prepared according to Vitale et al.²³

307 3.1.1. Assessing LC-HRMS analytical repeatability in sera from Pelagie cohort

298 For each SPM, the 75 samples were separated in three 25-sample batches and injected on the 299 Sciex LC-ESI-HRMS along with blanks and all composite QCs (injected 11 times per batch: first 5 300 injections for system equilibration, last 6 to assess analytical drift between samples). 301 Assessment of analytical repeatability was performed on selected analytes (i.e. spiked ISTDs in 302 QCs and samples) and using all features obtained from QC samples according to criteria from 303 Want et al. (2010).³⁰

304 The coefficient of variation (CV) of the area of all ISTDs spiked in QCs and all samples were 305 calculated for each SPM. Satisfactory results were observed for both methods since the median 306 area CV were of 16.0% and 14.3% in QCs and in all samples when prepared by PPT+PLR, and of 307 18.2% and 14.9% in QCs and in all samples when prepared by PPT only (Fig. 1A). These values, 308 while all under the 20% threshold, were nevertheless indicative of a general tendency for batch- 309 dependent variability, especially towards the last batches. Besides analytical sensitivity, the CVs 310 of Rt for all ISTDs were computed in QCs and determined to be satisfactory, i.e. median under the 311 10% threshold for both SPM on all batches (1.5% and 9.6% in PPT and PPT+PLR samples 312 respectively).

313 The repeatability of the analytical sensitivity was also evaluated on all features of all injected QCs 314 using the criteria proposed by Want et al. (2010),³⁰ by verifying that over 80% of QC features 315 common to at least 80% of QC samples presented area CV values under 30%. This parameter was 316 assessed at 82% and 83% for PPT+PLR and PPT, respectively, which indicates satisfactory 317 repeatability in both cases.

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<figure><img src="image_4.png" /><figcaption>Figure 1. Assessment of LC-HRMS analytical repeatability in the Pelagie cohort (A) and in the Epicure cohort (B) using a set of QAQC criteria adapted for non-targeted measurements (including guidance for HBM4EU for the Epicure cohort). Scales are established based on tolerance criteria; outer edges represent best performance.</figcaption></figure>

### 3.1.2. Assessing LC-HRMS analytical repeatability in plasma from Epicure cohort

Repeatability tests based on Rt and sensitivity shifts were first performed on standards spiked in plasma extracts to determine the acceptable number of samples at the batch level on the more recent Bruker LC-ESI-IMS-HRMS platform (data not shown). In the end, the 109 samples from Epicure were separated into two 54- and 55-sample batches for each SPM and injected along with blanks and all composite QC samples prepared according to HBM4EU guidance (non-spiked QCs as well as low- and high-level spiking). Signal intensities, mass accuracy, and retention times of selected QA/QC standards were monitored in these QCs injected every 8 samples and compared to the setting acceptance thresholds determined in the HBM4EU study for repeatability and accuracy.²³

Results showed high repeatability of the collected data (Fig. 1B); the RSDs of the signal intensities were typically below 15% in QCs and samples, with good stability of the chromatographic separation (retention time drift within 15 s intrabatch and 30 s interbatch), excellent mass accuracy (median error < 1 ppm) and CCS accuracy (median error <0.4%). These results are all within the setting acceptance thresholds determined in HBM4EU,¹⁶ even if it should be noted that the matrix was different (i.e., urine for HBM4EU). Rt accuracy observed in HBM4EU was superior (i.e. generally under 5 s both intra- and interbatch), although the values obtained in this application are still within the Rt correction range of most Rt correction algorithms. Ion mobility was not used in HBM4EU, but the CCS accuracy in the application was on par or even superior to other works from the literature which report deviations usually around 1%.³¹

### 3.1.3. Lessons on LC-HRMS repeatability from both cohorts

Overall, we demonstrate that satisfactory results based on a set of QAQC criteria adapted to non-targeted analyses can be achieved using both SPM protocols. We also demonstrate that extracts from SPM combining PPT and PLR can be injected twice as concentrated as PPT extracts without affecting analytical LC-HRMS analytical repeatability, which can be important to consider when looking for gain in sensitivity. More discussions related to the different matrices (plasma vs serum) are provided in 3.2.3, however, no clear trend could be observed here.
## 3.2. Chemical coverage: overlaps and specificities between PPT and combined PPT and PLR preparations

Comparison of chemical coverage between SPM using a wide variety of spiked standards was performed previously (as well as determination of recoveries, matrix effects and extraction variabilities).¹² Here, the chemical coverage comparison was performed on non-spiked compounds to assess the detection of chemicals and their associated FC in cohort samples. After generating a representative annotation dataset for both cohorts using a suspect screening strategy, overlaps and specificities of each SPM are discussed conjointly to assess the potential impact of the higher concentration of PLR extracts on chemical coverage, and dissimilarities that could be explained by the cohort differences are then considered.

### 3.2.1. Chemical coverage for Epicure and Pelagie cohorts

In total, 141 exogenous chemicals from different classes such as environmental pollutants, food derivatives, personal care products, pharmaceuticals and gut microbiota metabolites could be annotated in HRMS datasets from both cohorts (Fig. 2). Environmental pollutants detected included 16 pesticides (herbicides, insecticides, fungicides and metabolites), several PFASs (PFOS, PFHxS, PFHpxS, PFNA, PFOA, PFDA, PFUnA and 1-hydroperfluoroheptane), phthalates, organophosphorus flame retardants (i.e., tris(2-butoxyethyl)phosphate, triethylphosphate and diphenylphosphate), and industrial products such as surfactants (see Table S3 for more details). Besides environmental pollutants, food components such as artificial sweeteners (i.e., aspartame, acesulfame, sucralose, cyclamate), natural stimulants (i.e., caffeine), essential amino acids from food (i.e., tryptophan), isoflavones found in soy (i.e., genistein disulfate), naturally occurring alkaloids (i.e., 1-methylxanthine) and chemical derived from cacao (theobromine) could be detected. Some personal products annotated included chemicals used in skin care products for their depigmenting, antistatic, antimicrobial and preservative properties. The gut microbiota metabolites annotated included metabolites from dietary amino acids such as L-tryptophan (i.e., indoxyl sulfate) and L-tyrosine (i.e., p-cresol sulfate).

In addition to exogenous chemicals, some endogenous metabolites were also included to widen the range of physico-chemical properties covered, and to account for the fact that these metabolites are often reported in exposomics research to study the effect of environmental factors (see Fig. 2). These metabolites include lysophosphatidylcholines (LPC) and lysophosphatidylethanolamines (LPE), lipid species such as, omega-3 fatty acids (i.e., EPA, DHA), mono and poly-unsaturated fatty acids, steroids (i.e., cortisol), bile-related components (i.e., bilirubin, biliverdin), amino acids and derivatives (i.e., tyrosine, phenylalanine).

Hence, this annotation step allowed to efficiently annotate a large diversity of exogenous and endogenous chemicals with a wide range of physico-chemical properties (128.0029 < monoisotopic mass (Da) < 810.4859; -0.2 < logP < 9.1) and inter-individual abundances (e.g. up to 15,000-fold for theobromine in Pelagie individuals). Many of these annotated chemicals such as the PFAS, plasticizers, parabens or food components are commonly detected in human blood, which made them representative of the human internal chemical exposome for the SPM comparison.
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Number of detected and annotated compounds in the two cohorts (i.e., Pelagie and Epicure) grouped by families: environmental pollutants (blue), gut microbiota metabolites (purple), food (yellow), personal care (black), pharmaceuticals (red) and endogenous (green). Hatched areas in the figure represent the percentage of common annotations between the two cohorts.</figcaption></figure>

### 3.2.2. Chemical coverage with PPT versus PPT combined with PLR

FC of median area were computed for all annotated chemicals detected in extracts from both SPM for the two cohorts (see **Fig. 3**). For improved accuracy, all annotated features were manually re-integrated to provide reliable FC. No correction factors were applied between SPMs (combined PPT and PLR are twice concentrated) to account for the real-life adjustments needed to obtain the optimal LC-HRMS performances for each SPM.

Overall, similar trends were observed in the two cohorts based on these FC criteria. In both plasma and sera, the larger share of chemicals could be detected in extracts originating from both methods (78-86% of chemicals were detected with PPT and PPT+PLR in both cohorts, **Fig. 3**), which is consistent with previous results¹² (71-91%). As observed previously¹², some chemicals could be detected with a preference in terms of abundance (FC> 2 for 37-50% of annotated chemicals) for one or the other method, whereas only a small group of annotated chemicals were exclusively detected in one SPM (14-22%).

Several hypotheses were explored to explain the differences in chemical coverage observed between the two SPM. Although acceptable recoveries were previously observed for this PLR plate for many chemicals (70-120%)¹², it was also noticed that PLR plates can disadvantage the recoveries of chemicals with functional groups such as thiophosphates (e.g., chlorpyrifos and diazinon) and carboxylic acids.¹² This is consistent with an improved detection of some carboxylic acid PFAS and pharmaceutical drugs such as valsartan, palbociclib, bardoxolone methyl and clobazam observed in PPT extracts here. However, other chemicals such as sulfonamide PFAS metabolite NMeFOSAA, and some herbicides and insecticides parents or metabolites (e.g., bromoxynil, tritosulfuron, dinoterb, atrazine-desethyl, triflumuron, fipronil and fipronil sulfone) and various industrial additives were overall favored by PPT, without any trends observed towards polarity, mass, or presence of any chemical function (**Fig. S4**). The assumption that some non-polar compounds containing polar COXR functions may be more retained by PLR plates could be tested with recovery assays using an array of compounds from multiple classes with presence
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Modification of matrix effects could also explain FC differences between SPMs since previous studies demonstrated that PLR filtrations can significantly decrease matrix effects for spiked chemicals compared to PPT.¹² This reduced matrix effect may help to explain the improved detection in PLR extracts of several contaminants usually present at low levels in blood such as phthalates (R-terephthalate, benzylbutylphthalate, di(ethylhexyl)phthalate and dioctylphthalate), some herbicides (ioxynil and terbuthylazine), a transformation product of 4-chlorophenol and the cosmetic preservative isopropylparaben. However, no significant trends between SPMs related to the median area could be found in either Pelagie or Epicure, even though we observed that compounds with lower abundances were more likely to be impacted by one or the other SPM (see Fig. S3).

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We observed some similarities between our results and those from Sdougkou et al.²⁰ regarding chemical coverage. In their study, a SPM based on PLR HybridSPE Phospholipid cartridges (removing both protein and phospholipid) was compared with PPT for LC-HRMS exposomics applications. Using pooled plasma, they reported that both SPMs enabled detection of a similar range of non-spiked analytes (24) and spiked internal standards (34), while some compounds were preferentially detected with one method (e.g., acesulfame for PPT or perfluorooctane sulfonamide for the exposomics SPM). In contrast, we did not observe the same consistent trend of signal enhancement for spiked and unspiked non-phospholipid chemicals with PLR, despite also injecting more concentrated PLR extracts. This discrepancy could be explained by differences in recovery between PLR methods (and their optimizations) or the selection of monitored features. It may also reflect study design, our comparison was based on median FC across a large number of blood samples with high variability in chemical concentrations and interferences, whereas Sdougkou et al.²⁰ used triplicate injections of pooled plasma. Ultimately, results from NTA/SS studies should be compared cautiously as they usually rely on different SPMs and may pursue different objectives (e.g., high throughput for scaling up versus sensitivity for deep characterization).

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In conclusion, we found that both SPMs largely overlapped in the chemical space they covered, yet some compounds were detected preferentially or exclusively with one method. Differences in detection likely arose from variations in matrix effects and/or recoveries associated with PLR plates. Low-abundance compounds were particularly sensitive to the choice of SPM, although no consistent trends were observed with respect to polarity, mass, or functional groups. This highlights the challenge of predicting recoveries for individual compounds, especially under real-life conditions where substantial inter-individual variability in chemical concentrations and interferences is common. We acknowledge that our findings are constrained by the relatively limited number of compounds successfully annotated. Nonetheless, they support the recommendation to apply complementary SPMs whenever possible to enhance detection of the internal chemical exposome.
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>
**Figure 3.** A] Comparison of areas of annotated compounds in samples extracted by PPT only and PLR extracts in Pelagie serum and Epicure plasma samples. Logged values of FC are presented on the x-axis, where -∞ and +∞ represent the exclusive detection in samples prepared with PPT and PLR, respectively. Bar color intensity indicates frequency of detection (FD), from 0 to 100%. No correction factors were applied between SPMs to account for the real-life adjustments needed to obtain the optimal performance for each SPM. B] % of annotated compounds in the two cohorts for PLR vs PPT. Three different cases are represented: i) green-shaded area with compounds only detected in one SPM; ii) orange-shaded area with compounds predominantly detected in one SPM and iii) blue-shaded area with compounds indiscriminately detected in both SPM.

### 3.2.3. Similarities and dissimilarities between cohorts

A total of 84 compounds (out of 184) were conjointly detected in both cohorts (hatched area in Fig 1). The differences in chemical exposures observed between cohorts are explained by several factors including the sex, age the period of sample collection, and the pharmaceutical treatments for the women who suffer from breast cancer. Of these 84 shared chemicals, 9 were removed due to low detection frequency (i.e. <10%) in at least one of the cohorts. Another 7 compounds were removed due to very low intensities resulting in high FC uncertainty, bringing the final list to 68 compounds.

We then compared FC values observed for these shared chemicals between cohorts, and we observed that 50% of these compounds behaved similarly in both cohorts (areas 1 and 2 in Fig. S5). Another 40% of compounds (area 3 in Fig. S5) presented non-contradictory trends, meaning that one cohort established an indiscriminate detection of the compound (0.5 < FC < 2), and the other showed a favoring of either SPM. Lastly, 8 compounds (10%, area 4 in Fig. S5) presented contradicting trends (i.e., the favored SPM was opposed). One possible explanation for these contradictory trends is that 4 of these 8 compounds (i.e. 3-ketocholanic acid, cinnamaldehyde, ibuprofen, and proline-hydroxyproline) were among the 20% less abundant in at least two of the four datasets (combination of method and cohort), thus leading to higher uncertainty in FC evaluation. Moreover, one additional compound, cocamidopropyl betaine, was detected in only 11% of Pelagie samples, which may limit the representativity of the median area for this cohort. The last three compounds (cortisone, propionylcarnitine, and pantothenate) are all favored by PPT in Epicure and slightly favored by PLR in Pelagie, with FC values ranging between 2 and 2.5. This closeness to the cutoff value of 2 could suggest that the inconsistency between cohorts is partly due to analytical uncertainty.

Matrices (plasma vs serum) could also be involved in some of the differences observed between cohorts. Nevertheless, it should be noted that we previously observed opposing trends regarding their impact of signal-to-noise ratio (S/N) for spiked chemicals, method detection limit or LC-HRMS repeatability.¹² This is consistent with previous metabolomics work reporting varying trends depending on both considered metabolite and application.³²⁻³⁴ We observed a more pronounced impact of the SPM (i.e., favoring towards one given SPM) for several chemicals in Epicure (plasma), however, because different instruments were used, and that previous studies report sometimes chemical dependent trends, no trends related to the matrix can be highlighted for these results.

In the end, more similarities than dissimilarities could be observed between cohorts. Dissimilarities are more likely to be due to higher analytical uncertainty related to chemical abundances, explained by a combination of inter-individual variability, differences in LC-HRMS
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sensitivity and/or matrix effects, which underlines once again the difficulty to predict chemical recoveries with either SPMs in some real-life conditions.

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## 3.3 Perspectives for future large-scale applications

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Several SPMs based on filtration plates/cartridges designed to remove proteins and lyso-phospholipids have been tested over the years for LC-HRMS chemical exposomics.<sup>12, 19, 20</sup> They usually present the advantage to allow the injection of more concentrated extracts without impacting analytical repeatability, to decrease overall matrix effects, and can improve the detection of some non-phospholipid features.<sup>12, 19, 20</sup> Encouraging the injection of more concentrated extracts with more efficient clean-up could be particularly advantageous for chemical exposomics when coupled with the use of significantly more sensitive nanoflow-nanoLC systems (whom narrow bore columns, emitters and connections are prone to blockages) as previously demonstrated.<sup>15, 19, 35</sup> Downsides of PLR methods for large-scale exposomics include a reduced throughput since this filtration step is often performed after a PPT step (particularly important for 96-well plates with smaller amounts of sorbent), a higher consumption of solvent for pre-washing (e.g., to remove background analytes<sup>20</sup>) and rinsing the filters (due to dead volumes and possible retention of chemicals of interest<sup>12, 19</sup>). Nevertheless, using 96-well plates as we do here to reduce solvent consumption combined with the use of robots for automatization can significantly mitigate some of these drawbacks for large-scale applications.

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A recent study by Gu et al.<sup>14, 36</sup> also provides interesting perspectives to increase the throughput of chemical exposomics SPM based on SPE protocols. Their HLB SPE protocol based on 96-well plate skips the evaporation and reconstitution step (eluates were diluted before injection) which can significantly reduce the time needed for the sample preparation as well as reducing SPE variability by reducing the complexity of SPE steps. They concluded that their PPT workflow offers wider coverage of annotated features (which was in part explained by the loss of polar acidic compounds during the SPE process due to weak retention by the sorbent), but that comparable results were obtained for many annotated compounds.

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In the same line than the developments proposed by Gu et al.<sup>36</sup>, improvement of PPT protocols could involve to skip the evaporation step and inject PPT supernatants after dilution of organic supernatants. Combined with the use of robots for automatic distribution of solvents and centrifugation, as well as the continuous improvement of LC-HRMS sensitivity (and the inputs from ion mobility), this SPM would offer the advantage of reducing the time to perform sample preparation, reduce solvent consumption (especially if lower amounts of samples are used) and contamination from plates/sorbents. This would also offer the advantage of reducing the injections of residual proteins and (lyso-)phospholipids (that can only be eluted completely using isopropanol) and would therefore contribute to increase LC columns stability and lifetime for PPT methods.

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Several strategies are currently available for LC-HRMS-based chemical exposomics, but selecting the most appropriate approach remains challenging. Selection can nevertheless be guided by quantitative and qualitative criteria such as detection frequency, recoveries, repeatability, matrix effects, detection limits, throughput, and ease of use, as demonstrated in our previous work<sup>12</sup> and other recent studies<sup>20, 36</sup>. In this study, we confirmed earlier findings on the chemical coverage of PPT and Phree PLR, while emphasizing the compound-dependent, and thus difficult-to-predict, nature of detection when accounting for large inter-individual variability in chemical concentrations and interferences at the cohort level. Finally, continuous improvements
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in SPMs, together with advances in LC-HRMS sensitivity, offer promising opportunities to enhance
exposomics workflows.

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## Supporting Information

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- SI_Tables.xlsx: List of isotopically labeled standards added in samples to verify repeatability (Table S1), Annotation parameters for the annotated features in Pelagie and Epicure cohorts (Table S2), list of annotations with the FC values, log (FC) and frequency of detection (Table S3), percentage of annotated compounds categorized by fold change (i.e., area ratio of compounds detected in PPT versus PLR) detected in Pelagie serum samples and Epicure plasma samples (Table S4), and percentage of annotated compounds (excluding endogenous compounds) categorized by fold change (i.e., area ratio of compounds detected in PPT versus PLR) detected in Pelagie serum samples and Epicure plasma samples (Table S5).

- SI_Figures.docx: Materials and reagents (Text S1), Data acquisition and quality assurance procedures (Text S2), Diagnostic proof for compound annotation (Figure S1), Impact of retention index and presence of COXR functional groups on the favored sample preparation method (Figure S2), Box plots representing links between annotated compounds classified by the favored extraction technique and their median area (Figure S3), Exploring links between compounds properties and impact of sample preparation (Figure S4), comparison of fold changes of common compounds between cohorts (Figure S5).

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## Acknowledgment

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J.C., E.G., V.B. and A.D. acknowledge the National French Research Infrastructure France Exposome. We thank the European Regional Development Fund and Britanny region (Contrat Plan Etat Region, project Exposome, AIDEN 106201) for the funding of the Bruker timsTOF Pro 2. We thank Christine Monfort for her support. J.C. was funded by the Réseau Doctoral en Santé Publique and E.G and this research was partly funded by a grant from the Brittany region (n°22000939/00002914).

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