Comprehensive Targeted and Non-Targeted Analysis of Various Indoor Dust Samples Using LC-HRMS with Ion Mobility Mullin L.1,2, DiLorenzo R.A.3, Simmons D.B.D4, Jobst K.5, Ladak A.1, Plumb R.1, Diamond M.6, Reiner E.5, Rosnack Kenneth1 1Waters Corporation, 34 Maple Street, Milford, MA 01757,USA 2 MTM Research Centre, Örebro University, 701 82 Örebro Sweden 3Department of Physiology and Experimental Medicine, The Hospital for Sick Children, Mouse Imaging Centre, Toronto, ON, Canada 4McMaster University, Department of Chemistry, Hamilton, ON, Canada 5Ontario Ministry of Environment and Climate Change, Laboratory Services Branch, Etobicoke, ON, Canada 6University of Toronto, Toronto, ON, Canada   2018

Overview Introduction to samples and analytes LC-HRMS-Ion Mobility system and analysis approach Targeted Screening List Results Non-Targeted Results Conclusions Overview of points

Introduction to Study Samples Data Independent Acquisition (DIA) eWaste (4), Canadian Household dust and Canadian eWaste Facility Data Independent Acquisition (DIA) Accurate mass measurement Ion mobility separation (IMS) Targeted and non-targeted data processing approach Dust provides an excellent cross section of indoor pollution exposure. Various studies have focused on GC analyses of dust extracts for classic BFRs and POPs, as well as LC studies looking at PFASs, PPCPs, pesticides and the potential for new OPFRs etc. Here, we performed an analysis of eWastes, household and eWaste facility dusts using a novel approach incorporating ion mobility seperation post ionization, and data independent acquisition using Qtof MS. Both targeted and non-targeted data processing approaches will be discussed.

LC Method TDCPP TDCPP contamination LC System: Waters ACQUITY I-Class (with Isolator Column in flow path) Column: ACQUITY UPLC BEH C18 2.1 x 50 mm, 1.7 µm Column Temp: 65 ˚C Sample Temp: 4 ˚C Flow Rate: 0.450 mL/min. Mobile Phase A: 2mM ammonium acetate in 98:2 water:MeOH Mobile Phase B: 2mM ammonium acetate in MeOH Total Run Time: 8.5 min. Gradient: Min. Flow Rate %A %B Initial 0.650 90.0 10.0 0.5 0.650 90.0 10.0 5.10 0.650 0 100 6.60 0.650 0 100 6.70 0.650 90.0 10.0 8.50 0.650 90.0 10.0 TDCPP TDCPP contamination 100 ppb 10 ppb no spike The LC method utilized was a short gradient and a BEH C18 isolator column was incorporated into the mobile phase flow path prior to injection. This approach is typically used for PFASs analysis in order to retain any system or lab contamination from teflon components, but also was found to help in the same manner by retaining OPFR contamination which is ubiquitous in many labs. Shown here is the analyte peak spiked into the vial appearing at 3.85 min., while the system contamination elutes after at 4.5 due to being retained on the isolator column.

DIA HRMS-IMS conditions: HDMSE Acquisition Settings Ionisation Mode: ESI negative and positive polarity Capillary Voltage (kV): 1.0 (positive mode); 0.5 (negative mode) Collision Energy (LE): 3 eV Collision Energy (HE ramp): 20-55 eV Scan Time: 0.25 sec Acquisition Range: 50-1000 m/z Drift Gas: N2 Size Shape Charge Collisional Cross Section (CCS) Unit = Å2 Acquisition was performed on the Waters Vion IMS Qtof, schematic shown here. ESI in both positive and negative polarities was performed. Following ionization and ion focussing, the analyte ions travel through a N2 gas filled cell with a direct current applied resulting in the creation of a high electric field through which the ions have multiple collisions. This results in separation of ions based on their size, shape, charge and charge distribution. The time it takes for a molecule to travel through the mobility cell is referred to as the drift time; a calibration is applied to the mobility separations, resulting in the conversion of DT to collisional cross section, or the rotational average of the ions shape. The molecules are then subjected to alternating high and low collision energy states prior to entering through the high-field pusher into the time of flight mass analyzer, resulting in accurate mass measurements for the intact molecule and it’s fragments. Data was collected across the mass range of 50-1000 m/z.

Ion Mobility Spectrometry (IMS) Large Extended Small Compact Slide courtesy of Severine Goscinny, ISP-WIV, Belgium

Drift Time Alignment of Spectra Low CE High CE Ion mobility separation, in addition to providing DT and thus CCS measurements, also affords the ability further align spectra. The example here is for V6, a known OPFR. Shown below is the high and low CE data for V6 identified in eWastes dust. The spectra is quite dirty, esp. in the high energy data, as these are all the peaks present at the given RT of V6. When we view the peaks from the perspective of the drift cell, we can see that V6 has a drift time window which could be used to exclude those spectra, as shown in the next advance. The resulting view of the spectra when we select to remove those peaks outside of the drift time range is shown here. It is for both the low and high CE data, as fragmentation occurs after mobility separation.

Analysis Considerations and Approach QC and blank injections ensure quality of data Sample analysis Randomized replicate injections MVA Targeted search using library ESI- CCS input in “Adduct” entry In order to ensure the quality and accuracy of the data produced, QC injections of known solvent standards are ran at the beginning and end of the sample set. Both solvent and extraction blanks were also analyzed to ensure IDs were not the product of contamination. The samples were ran in randomized replicats of 4, and the power analysis for the ESI- data set is shown here, indicating that a statistically relevant number of repeat injections was achieved for the features observed. A MVA analysis was performed to look for non-targeted/unknown analytes, as well as a targeted search using solvent standard created databases with fragment ion information, RT in some cases, and CCS.

Targeted Results Targeted results ESI+ Search List: 20 QC pesticides (+ Pesticides CCS Library Screen of 640) 10 OFPRs and plasticizers (+2 just structures—no detection info) 9 QC standards mix 5 pharmaceuticals/drugs (acetominophen, reserpine, verapamil, terfenadine and caffeine) 2 veterinary druges (sulfadimethoxine and sulfaguanidine) 2 peptides (leu enk and val-tyr-val) ESI- Search List: 13 PFASs 4 pesticides 6 QC standards mix: 2 pharmacetical (acetominophen and reserpine) 2 veterinary drugs (sulfadimethoxine and sulfaguanidine)

Identification Criteria Mass Error < +/-5 ppm CCS delta (%) < +/- 2.0 Fragment Ion confirmation XIC Fragment Ion confirmation For the targeted processing, confident IDs required mass accurate measurements within 5ppm mass error, CCS delta of 2% from the library values, fragment ion/XIC confirmation, and isotope fidelity where halogenated.

ESI+ Confident IDs Summary of confident IDs in ESI+ data. A small subset of OPFRs, PPCPs were screened for. Aside from minor variations in OPFRs observed across the samples, caffeine is observed in every sample except for the eWaste facility, and acetominophen (which we’ll come back to later) was observed only in the household dust sample.

ESI- Confident IDs Tentative IDs Summary of ESI- data, which had a database of PFASs and small number of pesticides screened for. PFOS, PFBS and PFDS were observeed in all eWaste/eWaste facility samples, while only PFBS and PFOS were observed in the household dust—as tentative IDs, meanining there was no fragment ion confirmation (but RT and CCS and exact mass). One of the other tentative IDs was the pesticide Diuron, which despite no fragment ion or RT confirmation had CCS match and isotope distribution match, reflecting a double chlorinated molecule.

Non-targeted Results Concurrent to processing the data against a target library, non-targeted processing results follow.

Principle Component Analysis: ESI- Canadian eWaste facility Dust eWastes Canadian eWaste facility BDE extraction Extraction Blanks and Master Mix CS5 Canadian Household Dust Focused Question: Compare Canadian eWaste facility dust to Canadian Household Dust. Using principal component analysis, samples are grouped according to detected ion variation; each square is an injection, and each sample type is color coded. Clustering of samples is an important indicator of the quality of the measurements, and remember the sample injections are randomized so as to eliminate a potential bias. The house dust sample is showing a far distance from the other samples, indicating a strong degree of difference from the others. The eWaste facility is also on it’s own, but close to the technical replicates of eWastes, all four of which are grouping together as we would expect. An additional extraction approach of the eWaste facility dust is also separate. The various extraction blanks and Master Mix 5 are also in the same region to one another, again indicating a quality of measurement as these will likely not contain the statistically relevant ions in this experiment.

S-Plot: Comparing Canadian eWaste Facility and Household Markers Confidence eWaste Facility markers Importance

Database searching These significant compounds (markers) were then subjected to a ChemSpider database search: KEGG Chemspiderman PubChem EPA DSSTox EPA Toxcast 1000s of compounds to be searched and structures compared to the fragmentation pattern

Proposed ID summary: ESI+ eWaste Facility Increased relative abundance of various organophosphate molecules Additional to target screening library Decreased or not present in household dust Shared abundance with eWaste replicates Common fragments with targeted OPFRs recognized

Additional OPFR Example

Proposed ID summary: ESI- eWaste Facility BDE Extraction eWaste facility: TBBP-A and additional –Br compound

Canadian Household:compounds similar in trend to Acetominophen 203 markers

Carbendazim Confirmation Standard confirmation

Conclusions and Future Work IMS provides additional dimension to HRMS data set and adds additional measurement based on compound properties Non-targeted data acquisition approach is a powerful tool for rapid assessment of sample variation with respect to contaminant profiling Future Work CCS modeling Potentially useful for where analytical standards don’t exist?

Acknowledgements and Thank You! References: Ouyang X. et al. Chemosphere 166 (2017) 431-437 Bjorklund JA, Thuresson K, deWit CA (2009) Env. Sci. and Tech. 43 (2009) 2276-2281 Abdallah M. et al. Env. Int. 94 (2016) 235-250 Bijlsma A. et al. Anal. Chem. 89 (2017) 6583-6589 Acknowledgements: Dimple Shah and Gareth Cleland (Waters), Linh Ngyugen (Dept. of Physical and Environmental Sciences, University of Toronto at Scarborough), Victoria Arrandale (Dalla Lana School of Public Health, University of Toronto), Marta Venier and William Stubbins (Indiana University), Liisa Jantunen (Environment and Climate Change Canada), Lisa Melymuk (RECETOX, Masaryk University, Czech Republic)

Thank you for your attention ? Any questions www.waters.com