Examining our chemical exposure through suspect and non-target screening (NTS)

Are Sæle Bruvold
aresb@hi.no

University of Bergen and Institute of Marine Research

Contents

  • Background
    • LC-MS and HR-MS
    • Target methods
  • Non-target and suspect screening
    • How
    • What: Identifying unknowns
    • How much: Quantifying unknowns
    • Why: Applications

Background

Our chemical exposure

(1)

  • Produce and dispose >100*106 tons of hazardous chemicals/ year (1)

  • End up in consumer products, landfills, WWTPs \(\rightarrow\) environment

  • Pollution result in >10x more deaths than all wars and violence combined

Our chemical exposure 2

Klassekampen, Sept. 28th and Oct 12th
  • 1 out of 3 Norwegian children have \(\Sigma 4\)PFAS exceeding safe levels (2)
  • Almost all exceed safe levels for BPA
  • Severely polluted hotspots in Norway as elsewhere

\(\rightarrow\) Regulations: REACH, Stockholm/Basel/Rotterdam conventions, …


\(\rightarrow\) Monitoring

Target analysis

Source: sjomatdata.hi.no

Many, emerging and changing targets..

  • Too many: Focused methods w/ standards limits number of targets

  • Emerging: Don’t know about most contaminants, and even the ones we know are not monitored. 7 million PFAS (3,4).

  • Transformation products also often persistent and toxic:

Changing targets

(5)

Enter the chemical space

Non-target and suspect screening (NTS)

Screening: […] methods that indicates whether (target) analytes are present above or below a threshold; and, fast acquisition of semi-quantitative data […](6).


NTS uses accurate mass and spectral information from HR-MS for identification of a wide array of compounds without focused methods or reference standards.

Non-target and suspect screening (NTS)

Adapted/inspired by (7)

Non-target and suspect screening (NTS)

Adapted/inspired by (7)

NTS: How to?

Back to basics: Target QqQ-LC-MS

(8)

Liquid chromatography–mass spectrometry (LC-MS)

Extraction \(\rightarrow\) separation \(\rightarrow\) ionization \(\rightarrow\) fragmentation and mass analysis

Back to basics: Target QqQ-LC-MS 2

(8)

IMR method 349 for PFAS

Extraction: freeze dried material with methanol in ultrasonic bath

Ionization: C8F17SO3H \(\rightarrow\) C8F17SO3- @ 498.9 m/z

Fragmentation: C8F17SO3- \(\rightarrow\) SO3F- @ 99.1 m/z + SO3- @ 80.0 m/z

  • Quadropole 1: 498.9 m/z
  • Quadropole 2:
    • 99.1 m/z: Quantifier (via signal to concentration calibration)
    • 80.0 m/z: Qualifier (Ratio to 99.1 as quality control)

Back to basics: QqQ-LC-MS

(8)

NTS: a generic HR-MS method

(8)
  • Contamination measures
  • More parallels and blanks
  • Generic extraction and separation
  • Ionization (POS + NEG)

NTS: a generic HR-MS method

(8)

NTS: a generic HR-MS method

The mass of an atomic nucleus is always lower than the mass of its constituents (\(m_{proton}\) and \(m_{neutron}\)) due to the nuclear binding energy (9).


  • \(M_{H^1} = 1.0078\) Da
  • \(M_{C^{12}} \equiv 12.0000\) Da
  • \(M_{O^{16}} = 15.9949\) Da
  • Stronger binding \(\Rightarrow\) lower mass


\(M_{H_2O_3} - M_{C_4H_2} =\)

2x1.0078 + 3x15.9949 -

(4x12.0000 + 2x1.0078) =

0.015

NTS: a generic HR-MS method

(8)

# possible formulae versus mass accuracy (1 ppm < 0.001 Da) (9)
  • High mass accuracy
    • “low res” QqQ-MS: ~ 0.7 Da
    • HR-MS: <0.005Da (at 1000 Da)
  • High technology demands
    • Orbitrap/TOF/FTICR
    • Data and CPU-intensive

NTS: The what?

What: Strategy

Detective work based on: accurate mass, isotope patterns, adducts, retention time, homologue series, library hits, MS2/MSn fragmentation or metadata (context/ consumption/ commercial relevance)

  1. Suspect screening: Do we find compound X?
  2. True NTS: Peak picking and prioritization:

“Volcano plot” for peak prioritization based on association with sample(s) of interest - a contaminated site

What: Accurate mass

Peak at: 498.9300+-0.0010

Candidates:

C8H8F4O18S, C14H9FO15S2, C12H11F3O12S3, C17H18F2OS7, C18H12O9S4, C11H17F5O4S6, C11F16O2S, C10H13F5O9S4, C19H16O4S6, C16H14F2O6S5, C13H15F3O7S5, C14H2F14S2, C8HF17O3S, C14H16F4O3S6, C15H13FO10S4, C20H17FS7, C22HF9S2, C9H12F4O13S3, C16F12O3S, C13H5FO20, C12H18F6S7

What: Isotope patterns

Isotope pattern, from chemcalc.org

Knowledge of isotope abundance: \(\rightarrow\) Singly charged molecule AND fits isotope “fingerprint” of C8H8F4O18S, C8HF17O3S, C11F16O2S, C13H5FO20 match!

What: Homologue series

DEF: group of compounds sharing a similar structure and a common repeating unit (e.g. CH2 or CF2)

- Open Babel Depiction F F S O O HO F F F F F F F

- Open Babel Depiction F S O O OH F F F F F F F F F F

- Open Babel Depiction F S O O OH F F F F F F F F F F F F F F

- Open Babel Depiction F F S O O OH F F F F F F F F F F F F F F F

In HR-MS-world: \(M_{defect} = M_{nominal} – M_{exact}\)
\(\rightarrow M_{defect}(\textrm{CF}_2) = 50-49.99681 = 0.00319\)

≈ Difference in BINDING ENERGY vs C12

(10)

What: Homologue series 2

  • Mass defect plots of homologue series give linear relationship and constant difference in m/z.
  • Longer homologues -> increased RT in reverse phase chromatography.

What: Fragmentation

  • Data dependent acquisition (DDA) - selecting specific m/z based on acquired MS1 data
  • Data independent acquisition (DIA) - All/predefined m/z independent of data

What: Fragmentation 2

Allows identification by:

  • Knowing minimum numbers in chemical formula
  • Expert knowledge using MS2 / MSn
  • in-silico MS2 interpretation
  • Libraries:

What: Structural and spectral libraries

Structural (+in-silico)

  • PubChem
  • PubChemLite
  • ChemSpider
  • CompTox
  • NORMAN-SLE

Spectral

  • mzCloud
  • METLIN
  • MassBank
  • MoNA
  • NIST/EPA/NIH
  • Wiley registry

What: Getting confident

(11);(12)

What: Getting confident 2

(11);(12)

NTS: How much?

All things are poison, and nothing is without poison; only the dose permits something not to be poisonous.

-Paracelsus, 16th century

How much: Signal response varies

Response factors (mass-to-signal) based on standards (courtesy of A. Ali)

  • Sample extraction
  • Sample ionization efficiency
  • Matrix: sample/ eluent composition

How much: Strategies

Strategies for semi-quantification (13):

  • Areas
  • Closest matching standard:
    • Homologue series / fragments
    • Structural similarity
      • e.g. via molecular descriptors
      • chromatographic similarity
  • Predicting ionization efficiency:

How much: Machine learning

Semiquantification model performance, black line denotes ideal fit (14)

Predicting response factors based on molecular descriptors and eluent characteristics (14):

  • Average error using only signal area: 526-fold
  • Average error (random forest): 5.4-fold
  • Consistent across matrices and instruments:
    • \(\rightarrow\) Model improvement very feasible

The big picture: applications

He who chases 1060 rabbits catches some

Fig from (15)

Regulating/monitoring compound groups

  • PFAS ban?
    • Number of documented PFAS: ~7 million (16).
  • Bisphenols: “BPA free”:

Early discovery of emerging contaminants: can we catch up?

  • ~700 new compounds introduced per year in the US (17)
  • Last 5 years ~1600+ new structures discovered by NTS

Figure 1: Number of NTS publications (from Web of Science)

The future?

The future …is the past!

  • Retrospective analysis:
    • Analyzing old data with new methods/ knowledge
    • NORMAN Digital Sample Freezing Platform (DSFP) (18)
  • Combining targeted analysis with NTS?

In summary

  • Chemical exposure pose health risks to everyone
  • Most are unknown, but NTS can help identify them
  • Chemicals differ in exact mass and why we need HR
  • Strategies for identification:
    • Accurate mass, isotope patterns, homologue series, fragments and libraries
  • Semi-quantification: similarity to standards or machine learning
  • Applications:
    • Monitoring many or groups of compounds
    • Discovering new compounds or contaminants
    • Time travel: retrospective analysis

References

1.
Naidu, R. et al. Chemical pollution: A growing peril and potential catastrophic risk to humanity. Environment International 156, 106616 (2021).
2.
Paulsen, M. M. & Thomsen, C. Miljøgifter i norske barn Resultater fra miljøbiobanken 2023. (Folkehelseinstituttet, 2023).
3.
Secretariat of the Basel, Rotterdam and Stockholm conventions. Guidance on preparing inventories of PFOS, PFOA and PFHxS. (2023).
4.
European Chemicals Agency. ANNEX XV RESTRICTION REPORT – Per- and polyfluoroalkyl substances (PFASs). (2023).
5.
Butt, C. M., Muir, D. C. G. & Mabury, S. A. Biotransformation pathways of fluorotelomer‐based polyfluoroalkyl substances: A review. Enviro Toxic and Chemistry 33, 243–267 (2014).
6.
Muñoz-Olivas, R. Screening analysis. TrAC Trends in Analytical Chemistry 23, 203–216 (2004).
7.
Pourchet, M. et al. Suspect and non-targeted screening of chemicals of emerging concern for human biomonitoring, environmental health studies and support to risk assessment: From promises to challenges and harmonisation issues. Environment International 139, 105545 (2020).
8.
Thomas, S. N., French, D., Jannetto, P. J., Rappold, B. A. & Clarke, W. A. Liquid chromatography–tandem mass spectrometry for clinical diagnostics. Nat Rev Methods Primers 2, 1–14 (2022).
9.
Niessen, W. M. A. Interpretation of MS-MS Mass Spectra of Drugs and Pesticides.
10.
Merel, S. Critical assessment of the Kendrick mass defect analysis as an innovative approach to process high resolution mass spectrometry data for environmental applications. Chemosphere 313, 137443 (2023).
11.
Schymanski, E. L. et al. Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence. Environ. Sci. Technol. 48, 2097–2098 (2014).
12.
Charbonnet, J. A. et al. Communicating Confidence of Per- and Polyfluoroalkyl Substance Identification via High-Resolution Mass Spectrometry. Environ. Sci. Technol. Lett. 9, 473–481 (2022).
13.
Malm, L. et al. Guide to Semi-Quantitative Non-Targeted Screening Using LC/ESI/HRMS. Molecules 26, 3524 (2021).
14.
Liigand, J. et al. Quantification for non-targeted LC/MS screening without standard substances. Sci Rep 10, 5808 (2020).
15.
Hollender, J. et al. NORMAN guidance on suspect and non-target screening in environmental monitoring. Environ Sci Eur 35, 75 (2023).
16.
Samanipour, S. et al. Exploring the Chemical Space of the Exposome: How Far Have We Gone? (2024). doi:10.26434/chemrxiv-2024-1l0x0
17.
Samanipour, S. et al. Exploring the Chemical Space of the Exposome: How Far Have We Gone? JACS Au 4, 2412–2425 (2024).
18.
Alygizakis, N. A. et al. NORMAN digital sample freezing platform: A European virtual platform to exchange liquid chromatography high resolution-mass spectrometry data and screen suspects in “digitally frozen” environmental samples. TrAC Trends in Analytical Chemistry 115, 129–137 (2019).