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An Improved Dried Blood Spot-Based Metabolomics Analysis by Post Column Infused-Internal Standard Assisted Liquid Chromatography-Electrospray Ionization Mass Spectrometry Method Divyabharathi Chepyala, Han-Chun Kuo, Kang-Yi Su, Hsiao-Wei Liao, SanYuan Wang, Surendhar Reddy Chepyala, Lin-Chau Chang, and Ching-Hua Kuo Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.9b02050 • Publication Date (Web): 30 Jul 2019 Downloaded from pubs.acs.org on July 31, 2019

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Analytical Chemistry

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An Improved Dried Blood Spot-Based Metabolomics Analysis by Post Column Infused-Internal Standard Assisted Liquid Chromatography-Electrospray Ionization Mass Spectrometry Method

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Divyabharathi Chepyala, †,‡,§ Han-Chun Kuo,‡,§ Kang-Yi Su, ‖ ,¶ Hsiao-Wei Liao,†,‡ San-Yuan Wang,⊥ Surendhar Reddy Chepyala,# Lin-Chau Chang, *,† and Ching-Hua Kuo *,†,‡,§

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†

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‡

School of Pharmacy, College of Medicine, National Taiwan University, Taipei 10050, Taiwan

The Metabolomics Core Laboratory, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei 10055, Taiwan

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‖

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¶

Department of Laboratory Medicine, National Taiwan University Hospital, Taiwan

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§

Department of Pharmacy, National Taiwan University Hospital, Taipei 10051, Taiwan

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⊥

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#

Institute of Chemistry, Academia Sinica, Taipei 115, Taiwan

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§

These authors contributed equally.

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*Corresponding authors:

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1. Prof. Ching-Hua Kuo

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School of Pharmacy, College of Medicine, National Taiwan University, No. 33, Linsen S. Rd., Zhongzheng Dist., Taipei City 10050, Taiwan

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Tel.: +886-2-33668766; Fax: +886-2-23919098

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E-mail: [email protected]

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2. Dr. Lin-Chau Chang

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School of Pharmacy, College of Medicine, National Taiwan University, No. 33, Linsen S. Rd., Zhongzheng Dist., Taipei City 10050, Taiwan

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Tel.: +886-2-33668197; Fax: +886-2-23919098

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E-mail: [email protected]

Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taiwan

Master Program in Clinical Pharmacogenomics and Pharmacoproteomics, College of Pharmacy, Taipei Medical University, Taipei 11031, Taiwan

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ABSTRACT: Dried blood spots (DBSs) have gained increasing attention recently with their growing importance in precision medicine. DBS-based metabolomics analysis provides a powerful tool for investigating new biomarkers. Until now, very few studies have discussed measures for improving analytical accuracy with consideration of the special characteristics of DBSs. The present study proposed a post column infused-internal standard (PCI-IS) assisted strategy to improve data quality for DBS-based metabolomics studies. An efficient sample preparation protocol with 80% acetonitrile as the extraction solvent was first established to improve the metabolite recovery. The PCI-IS assisted LC-ESI-MS method was used to simultaneously estimate the blood volume and correct the signal change caused by ion source contamination and the matrix effect to evaluate the spot volume effect and hematocrit (Hct) variation effect on target metabolites. D8-phenylalanine was selected as the single PCI-IS to correct the matrix effect. For calibration of errors caused by the blood volume difference, 75% of the test metabolites showed good correlation (r2≥0.9) between the spot volume and the signal intensity after PCI-IS correction compared to less than 50% metabolites with good correlation before calibration. The spot volume was further calibrated by the same PCI-IS. Investigation of the Hct variation effect on target metabolites revealed that it affected the concentrations of metabolites in the DBS samples depending on their abundance in the red blood cell (RBC) or plasma; it is essential to pre-investigate the distribution of metabolites in blood to minimize the comparison bias in metabolomics studies. Finally, the PCIIS assisted method was applied to study acetaminophen-induced liver toxicity. The results indicated that the proposed PCI-IS strategy could effectively remove analytical errors and improve the data quality, which would make the DBS-based metabolomics more feasible in real world applications.

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Abstract graphic:

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Over the past decades, metabolomics has been developed as one of the important omics sciences, as it allows the estimation of a wide range of metabolites to generate new insights in disease diagnosis, investigation of physiological status, interpretation of the pathways of a disease state, and finding new biomarkers.1-3 The common samples of biofluids used for metabolomics studies are plasma or serum, urine, and recently, dried blood spots (DBSs).4-7 Metabolomics studies developed using plasma or serum may lack information about whole-blood metabolites, such as metabolites involved in red blood cell (RBC) energy metabolism. Moreover, their more invasive sample collection procedure and stringent storage conditions emphasize the growing importance of DBS sampling techniques in metabolomics studies.

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The DBS sampling technique was introduced in 1960s to screen the inborn errors of metabolism in neonates.8 It has been used extensively in newborn screening as it has numerous advantages including a minimally invasive collection procedure using a finger or heel prick, low sample volume, as well as easy sample handling and shipping. Newborn screening tests in developed countries have been performed via DBS sampling techniques. 9 Other additional advantages, including feasibility of patient self-sampling with moderate training, stability of photosensitive compounds, and low biohazard risks, 10-14 increased the applicability of the DBS sampling technique in adult care in various fields such as therapeutic drug monitoring, 14 pharmacokinetics,15 genomics,16 proteomics,17,18 lipidomics,19,20 and metabolomics.7,21-25 In addition, DBS eliminates the effects of pre-analytical factors in metabolomics studies such as timerelated requirement of sample centrifugation and processing, types of sample collection tubes. Due to the growing interest in using DBS strategy for metabolomics studies, the recent review in biological samples for metabolomics research by the metabolomics society initiative has included DBS as the blood products in addition to the conventional sample types such as serum and plasma.26 Despite the growing importance of DBS in metabolomics studies, this sampling technique still has some challenges in real world applications. Till now, few studies assessed the challenges associated with the DBS sampling technique in metabolomics studies. 23

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The most widely discussed challenges associated with DBS sampling techniques are the effects of hematocrit (Hct) variation and spotted blood volumes. Hct represents the volume percentage of RBC, which affects DBS analytical results as it varies largely between individuals.27 The individual difference in Hct values influences the blood viscosity, plasma and blood distribution and results in variations in spot formation, spot size, drying time, and homogeneity leading to analytical uncertainty.28 Several methods including potassium concentration measurements and reflectance-based methods have been proposed to estimate Hct levels.23,29-31 Additionally, using whole spot analysis could also mitigate the analytical variation caused by Hct variation and could improve the method sensitivity. 32 Abu-Rabie et al. introduced an internal standard spray technique to calibrate Hct-based recovery bias.33-35 Successful nullification of Hctbased assay bias has been demonstrated in their studies, but the selected application examples were mainly focused on pharmaceutical analysis with specific drug targets.33-35 For DBS-based metabolomics studies, Hct variation may also affect the measurement of endogenous metabolite concentrations depending on the intracellular and extracellular distribution, which may result in 3 ACS Paragon Plus Environment


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biased comparisons in DBS-based metabolomics studies. However, Hct effects in DBS-based metabolomics studies have not been discussed in previous studies.

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Blood volume variation is another frequently discussed challenge associated with DBS sampling techniques. In addition to using Mitra® or volumetric absorptive microsampling to control sampling volume,36 a post column infused-internal standard (PCI-IS) method was recently developed to estimate the blood volume of the DBS. 37 The developed method estimated the blood volume by measuring the total salts in blood samples by the PCI-IS. The developed method could facilitate the whole spot analysis.37 Moreover, the PCI-IS strategy could additionally correct matrix effect caused errors of liquid chromatography-electrospray ionization mass spectrometry (LC-ESIMS). The efficiency of the PCI-IS coupled with LC-ESI-MS for matrix effect correction for amino acids, hormones, and small molecules in biological samples has been demonstrated previously. 3740 Therefore, the PCI-IS assisted LC-ESI-MS method could enable the simultaneous estimation of the blood volume on DBS cards and correction of the matrix effect for DBS-based metabolomics studies as matrix effect differences between each test sample would be more severe due to spot volume and Hct variations.

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Although DBS-based metabolomics has become increasingly important, previous studies have neither specifically discussed the potential bias of this sampling method in metabolomics studies nor provided solutions to overcome the sampling-related bias. The present study proposed a PCI-IS assisted LC-ESI-MS method to improve the data quality of DBS-based metabolomics studies. We tried six extraction solvents to compare their efficiency in the extraction of the maximum reproducible metabolite features from DBS samples for metabolomics profiling. The most efficient extraction solvent was further used in the subsequent metabolite analysis. To examine the critical factors including the effects of Hct variation and spotted blood volumes which hamper the wider application of DBS-based metabolomics, we selected 20 target metabolites with varied characteristics. We used whole spot analysis to ameliorate the analytical variation caused by Hct variation in DBS samples and proposed a PCI-IS method to simultaneously estimate the blood volume and correct the matrix effect to evaluate the spot volume effect and Hct variation effect on target metabolites. Finally, the PCI-IS assisted method was applied to study acetaminophen (APAP)-induced liver toxicity. The proposed PCI-IS strategy could improve the data quality of DBS-based metabolomics studies and benefit various clinical research applications.

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EXPERIMENTAL SECTION

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Chemicals and Materials. Hexakis (1H,1H,3H-perfluoropropoxy2,2-difluoroethoxy)phosphazene (HKP) was purchased from Apollo (Apollo, Graham, NC, USA). Acetonitrile (ACN) (LC/MS grade) and ethanol (EtOH) (LC/MS grade) were purchased from J.T.Baker (J.T.Baker, Phillipsburg, NJ, USA). Water (MS grade) and methanol (MeOH) (MS grade) were bought from Scharlau (Scharlau, Sentmenat, Barcelona, Spain). Formic acid, acetaminophen standard powder and tert-butyl methyl ether (MTBE) were purchased from Sigma (Sigma, St. Louis, MO, USA). Acetone was obtained from Avantor Performance Materials (Center Valley, PA, USA). Whatman 903 Protein Saver cards (Whatman, Maidstone, UK) were used for spotting the blood samples 4 ACS Paragon Plus Environment

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(DBS samples). Hybrid solid phase extraction (SPE) cartridges were purchased from Sigma (Sigma, St. Louis, MO, USA). The manual puncher was purchased from a local store, and the diameter of the hole was 6 mm.

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Untargeted Metabolomics Analysis. Metabolic profiling was performed on Agilent 1290 UHPLC system (Agilent Technologies, Santa Clara, CA, USA) combined with Bruker maXis QTOF (Bruker Daltonics, Bremen, Germany). LC separation conditions were similar with the PCIIS assisted LC-ESI-MS analysis. ESI mass parameters for both positive and negative ionization detection modes were 500 V end plate offset, 4500 V capillary voltage, 200 °C drying gas temperature, 12 L/min drying gas flow, and 30 psi nebulizer pressure. The mass spectrometer was calibrated with 10 mM sodium formate before daily use. During analysis, HKP was used as reference mass to correct the mass accuracy in both positive and negative ionization modes.

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PCI-IS Assisted LC-ESI-MS Analysis. PCI-IS assisted LC-ESI-MS analysis was performed on an Agilent 1260 quaternary solvent pump (Agilent Technologies, Santa Clara, CA, USA) and Agilent 1290 UHPLC system (Agilent Technologies, Santa Clara, CA, USA) coupled with 6540-QTOF (Agilent Technologies, Santa Clara, CA, USA). An Agilent 1260 quaternary solvent pump was applied for the post-column infusion of the PCI-IS. D8-phenylalanine as the PCI-IS was dissolved in ACN at 1 μg/mL and was introduced into the ESI interface at a flow rate of 0.1 mL/min.

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An Acquity HSS T3 column (2.1 × 100 mm, 1.8 μm, Waters, Milford, MA, USA) was used for metabolite separation. The compositions of mobile phases A and B were 0.1% formic acid (FA) in deionized water (DI), and 0.1% FA in ACN, respectively. A gradient with a flow rate of 300 μL/min was used to separate the metabolites and consisted of 0−1.5 min, 2% B; 1.5−9 min, 2% to 50% B; 9−14 min, 50% to 95% B; and 14−17 min, 95% B. The column re-equilibration time was 3 min. The sampler and column oven were maintained at 4 and 40 °C, respectively. The sample injection volume was set at 5 μL. A Jet Stream electrospray ion source with capillary voltage of 4 kV in positive mode and 3.5 kV in negative mode was used for sample ionization. MS parameters were set as follows: dry gas temperature, 325 °C; dry gas ﬂow, 5 L/min; nebulizer, 40 psi; sheath gas temperature, 325 °C; sheath gas ﬂow, 10 L/min; and fragmentor, 120 V. The scan range was m/z 50 − 1700. The MS acquisition was performed using the full scan mode. The retention times of all the metabolites and their pathways are summarized in Table 1.

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Table 1. Monitoring ion, retention time and metabolic pathways of target metabolites. No.

Metabolite

Monitoring ion

Retention time (min)

Pathways

1

Carnosine

[+H]

0.82

Histidine metabolism

2

Serine

[+H]

0.83

Glycine, serine and methionine metabolism

3

Alanine

[+H]

0.84

Alanine, aspartate and glutamate metabolism



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4

Glutamine

[+H]

0.85

Nucleic acid metabolism

5

Taurine

[+H]

0.85

6

Dimethylglycine

[+H]

0.86

Glycine, serine, betaine and methionine metabolism

Taurine metabolism Bile acid biosynthesis

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Betaine

[+H]

0.87

Glycine, serine, betaine and methionine metabolism

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Creatine

[+H]

0.95

Glycine and serine metabolism

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dGMP

[+H]

0.96

Purine metabolism

10

Acetylcarnitine

[+H]

1.39

Beta oxidation of very long chain fatty acids

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Methionine

[+H]

1.42

Betaine and methionine metabolism

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Uric acid

[-H]

1.50

Purine metabolism

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Isoleucine

[+H]

1.98

Branched chain amino acids (BCAAs) metabolism

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Phenylalanine

[+H]

3.84

Phenylalanine pathway

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Gamma-glutamyl-leucine (gamma-glu-leu)

[+H]

4.89

Protein catabolism

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Hippuric acid

[-H]

5.66

Phenylalanine pathway

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Taurocholic acid (TCA)

[-H]

9.05

Bile acid biosynthesis

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Glycocholic acid (GCA)

[-H]

9.64


19

Taurochenodeoxycholic acid (TCDCA)

[-H]

10.15


20

Glycochenodeoxycholic acid (GCDCA)

[-H]

10.87


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DBS Sample Preparation Procedure. The blood samples collected in EDTA tubes were used to spot on DBS cards within 15 min of blood collection. The protocol has been approved by the Institutional Review Board at the National Taiwan University Hospital (201412115RINB). Twenty microliters of the blood sample was spotted onto a DBS card, and the spotted cards were air-dried for 2 h. After drying, the whole blood spot was cut into a clean Eppendorf tube using a 6-mm manual puncher by punching several times. Then, 300 μL of 80% ACN was added to extract the cut blood spot using ultrasonication for 30 min. After sonication, the samples were centrifuged for 5 min at 15000 rcf. Two hundred and seventy microliters of the supernatant from each sample were evaporated under nitrogen gas. The dried samples were stored at −20 °C until the analysis. Just before the sample analysis, the dried samples were reconstituted with 150 μL of 50% MeOH. The reconstituted samples were vortexed and then filtered through the 0.2 μm regenerated cellulose membrane (Sartorius, Göttingen, Germany) and analyzed by UHPLC-ESI-MS system.

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PCI-IS Method for the Estimation of Blood Volume on DBS Cards and Matrix Effect Correction. The concept of the PCI-IS method for blood volume estimation and matrix effect correction was described previously.37-41 For the estimation of the blood volume and evaluation of the calibration performance of blood volume estimation, blood samples from 3 volunteers were used to generate the DBSs with varying volumes ranging from 10 to 50 μL for the calibration curve. Another 15 DBSs from 3 volunteers at volumes of 10, 30, and 50 µl in triplicate were used as the validation set (n=3). After air drying for two hours, all the samples were prepared by using the sample preparation protocol described previously. After sample analysis, the PCI-IS signal intensity at the first ion suppression zone (from 0.74 to 0.83 min) was used to construct the calibration curve at different volumes. The generated calibration curve was used to estimate the blood volume of the DBS, and the estimated blood volume was used to correct the intensity difference of target metabolites. For correction of the matrix effect-caused errors and lipid accumulation-caused signal change, analyte signal intensities at each time point were divided by the PCI-IS signal intensity to generate the adjusted chromatogram. Peak area of each target metabolite was calculated using the PCI-IS adjusted chromatogram, and was used for studying matrix effect-caused errors, Hct effect and acetaminophen-induced liver toxicity.

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Preparation of DBS samples for the Evaluation of the Hct Variation Effect. Fresh blood samples collected from a single volunteer in EDTA tubes were used to create the blood models with different Hct levels. The collected blood samples were transferred into clean Eppendorf tubes and centrifuged at 4 °C for 5 min at 3000 rcf to obtain an upper plasma layer and a lower cellular layer. By adding or removing plasma, we generated blood models with different Hct levels. In the present study, we created 6 blood models with different Hct levels ranging from 25 to 75% using each blood sample (n=3). Twenty microliters from each blood model with a specific Hct level was used as liquid blood samples and kept in ice for 2 h. At the same time, another 20 µL of blood from each blood model with a specific Hct level was spotted onto the DBS card and air-dried for 2 h. After 2 h, all the liquid blood and DBS samples with different Hct levels were prepared using the sample preparation method described in the previous section. After sample analyses by PCI-IS assisted LC-ESI-MS, the differences in signal intensity of each metabolite due to Hct variation effect were evaluated by comparing the intensity of each target metabolite obtained from samples with different Hct levels (low to high). 7 ACS Paragon Plus Environment


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Animal Treatment and Sample Collection All animal studies were approved by the Institutional Animal Care and Use Committee (IACUC, No. 20120515 & 20190112) of National Taiwan University College of Medicine. Mice had free access to food and water and were housed at a temperature of 22±1 °C with 50±2% humidity and a 12 h light/12 h dark schedule. For acetaminophen (APAP) treatments, ten C57BL/6 adult male mice (6-8 weeks old, average weight of 20 g) were randomly assigned into the treatment group (n=5) or the control group (n=5). The treatment procedure was based on the previous study.42 Briefly, APAP was made fresh by dissolving in normal saline at 20 mg/ml. After a 16-h fasting, mice were injected i.p. with APAP solution at 500 mg/kg of body weight. Blood was drawn from tail vein to make DBS for further analysis.

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Blood samples of each mouse were spotted on the DBS cards (n=2), and the blood volume was within the range of 15 to 35 µL. After air drying for two hours, all the samples were prepared by using the sample preparation protocol described previously followed by the analysis using UHPLC-ESI-MS system.

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Data Analysis. Untargeted metabolomics data analysis was done by using Bruker Data Analysis software (Version 4.1 (build 359)). Molecular features were extracted from the raw data files with set criteria including a signal-to-noise ratio of 3 (S/N=3), inclusion of adducts and lock mass calibration. The extracted molecular features were exported to the comma-separated values (csv) format using Bruker Data Analysis software. To select the best extraction solvent for metabolomics analysis using DBS samples, we further filtered the molecular features from csv files using Microsoft Excel 2007. The filtration of reproducible molecular features from each extraction solvent was done in a step-by-step manner. In step 1, reproducible features from 3 replicate runs were extracted using the parameters including retention time (RT) window < 0.2 min, mass accuracy tolerance < 0.05 Da, and peak area RSD < 25%; in step 2, the reproducible features from 3 DBSs were extracted using the similar parameters used in step 1.

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For the PCI-IS data analysis, all the extracted ion chromatograms obtained from the Agilent QTOF system were exported as .xls files. The raw .xls file containing compound m/z, RT and signal intensity, and another Excel file containing information of target compound m/z and retention time window for each metabolite were uploaded to R version 3.4.4 and R Studio version 1.0.136 for data analysis. The comma separated value (csv) files obtained from R were further processed with Microsoft Excel 2016. The information in processed csv file included target compound m/z and the area ratio of target analyte to the PCI-IS.

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RESULTS

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The overall experimental design is shown in Figure 1. Six different extraction protocols were evaluated with untargeted metabolomics analysis based on the numbers of metabolite features extracted from the DBS samples and their reproducibilities. The reproducible extraction protocol with the highest number of metabolite features was further used for targeted metabolite analysis. The PCI-IS strategy was then used to estimate the blood volume on DBS cards and correct the matrix effect to evaluate spot volume effect and Hct variation effect on target metabolites. 8 ACS Paragon Plus Environment

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Figure 1. Schematic representation of the study design. (A) Sample extraction optimization during untargeted metabolomics analysis using LC-QTOF. (B) PCI-IS strategy for blood volume estimation of samples on DBS cards and calibration of blood volume difference-caused errors. (C) Evaluation of Hct variation effect on target metabolites.

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Method Optimization

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Optimization of the Extraction Method. Untargeted metabolomics analysis is a broad scale technique, which requires fast and reproducible sample preparation methods. Moreover, the preparation protocol should be able to extract a wide range of metabolites. Therefore, we tested six solvent compositions including ACN (80%), MeOH (55%), MeOH (100%), MeOH (33.3%)ACN (33.3%)-acetone (33.3%), MeOH (50%)-EtOH (50%), and MeOH (50%)-MTBE (50%). The solvent selection criteria were based on the properties of solvents from polar to nonpolar and previous metabolomics methods developed for plasma samples. 23,43,44 Each organic solvent-based protein precipitation method was evaluated in terms of protein-removal efficiency, reproducibility, and the number of metabolite features obtained from DBS samples. The number of extracted metabolite features and extraction repeatability were summarized in Table 2.

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Table 2. Extraction efficiency and repeatability of six extraction solvents evaluated by untargeted metabolomics analysis. Extraction solvent

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Positive mode

Negative mode

Ma

M25b

Ma

M25b

55% MeOH

1006

510

231

97

80% ACN

1574

1093

357

274

MeOH

1366

954

331

258

MeOH-ACNAcetone

1275

864

264

215

MeOH-EtOH

1365

925

306

223

a

: Extracted metabolite features from 3 replicate DBS samples. b: Extracted metabolite features from 3 replicate DBS samples with the RSD ≤25%.

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The results showed that ACN (80%) was able to extract the highest number of metabolite features among the extraction solvents tested. In contrast, MeOH (55%) showed the lowest number of extracted metabolites. Previous studies have indicated that the water content in the extraction solvent could improve the extraction efficiency by cell lysis and could facilitate the extraction of polar or ionic metabolites from DBS samples. 45 Since ACN is one of the strongest organic solvents for protein precipitation in biological samples46 and aids cell lysis through the water content, ACN (80%) extracted the highest number of reproducible metabolite features in the analyses using positive and negative ionization methods. Although the solvent strength of MeOH (33.3%)-ACN (33.3%)-acetone (33.3%), MeOH (50%)-EtOH (50%), or MeOH (50%)-MTBE (50%) solvents was higher, their overall extraction efficiencies were lower in comparison with ACN (80%). Therefore, ACN (80%) was used as the extraction solvent.

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Optimization of LC-MS Method. For simultaneous estimation of blood volume and measurement of metabolites in DBS-based metabolomics studies, the LC conditions should satisfy the requirements for both purposes. Since the concept of the PCI-IS used for blood volume estimation relied on the salt-induced signal suppression zone, it is important to start the gradient with low solvent strength to achieve the best signal suppression zone for blood volume estimation. A gradient profile using 0.1% FA in DI and ACN with the HSS T3 column was applied for both positive and negative modes for metabolite profiling. The results indicated that a gradient starting at 2% B with a constant flow for 1.5 min could attain best signal suppression zone. The low starting solvent strength additionally improved the retention of the polar metabolites.

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When DBS samples are used, only a small amount of blood (

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