I teach primarily in the Master's program at ACES, I'm the course leader for Toxicology for Environmental Scientists (MI7015) and I co-lead Research Trends in Toxicology (MI8016). I also lecture in the following courses:

Large Scale Challenges to the Climate and Environment

Introduction to Environmental Chemistry

My research program focuses on the chemical exposome and on the development of methods to measure it. We call the application of these methods chemical exposomics, and our work combines elements of analytical chemistry and informatics to understand the complex mixture of contaminants in our bodies or in the environment. Through toxicology, bioinformatics and epidemiological studies we furthermore aim to understand the impacts that these exposures can have on health. 

Since 2018, my Stockholm University research group has worked primarily at Science for Life Laboratory (SciLifeLab) where we have ultrahigh resolution mass spectrometers and specialized clean labs to characterize complex mixtures of chemicals in air, water, and human biofluids. Our target and nontarget chemical exposomics methods are now offered as a service to Swedish and international researchers through the National Facility for Exposomics. The surrounding research environment at SciLifeLab specializes in genomics, epigenomics, proteomics, functional biology, bioimaging, and biostatistics, creating great possibilities to investigate our questions using best-available biomolecular technologies and integrative methods.

A selection from Stockholm University publication database

• Phospholipid Removal for Enhanced Chemical Exposomics in Human Plasma

  2023. Kalliroi Sdougkou (et al.). Environmental Science and Technology 57, 10173-10184

  Article

  Chemical exposomics in human plasma wasenhanced by an optimizedphospholipid removal step that increased targeted method sensitivitywhile also revealing >13,000 new molecular features by LC-HRMSnon-targetedacquisition. The challenge of chemical exposomics in human plasmais the 1000-foldconcentration gap between endogenous substances and environmentalpollutants. Phospholipids are the major endogenous small moleculesin plasma, thus we validated a chemical exposomics protocol with anoptimized phospholipid-removal step prior to targeted and non-targetedliquid chromatography high-resolution mass spectrometry. Increasedinjection volume with negligible matrix effect permitted sensitivemulticlass targeted analysis of 77 priority analytes; median MLOQ= 0.05 ng/mL for 200 & mu;L plasma.In non-targeted acquisition, mean total signal intensities of non-phospholipidswere enhanced 6-fold in positive (max 28-fold) and 4-fold in negativemode (max 58-fold) compared to a control method without phospholipidremoval. Moreover, 109 and 28% more non-phospholipid molecular featureswere detected by exposomics in positive and negative mode, respectively,allowing new substances to be annotated that were non-detectable withoutphospholipid removal. In individual adult plasma (100 & mu;L, n = 34), 28 analytes were detected and quantified among10 chemical classes, and quantitation of per- and polyfluoroalkylsubstances (PFAS) was externally validated by independent targetedanalysis. Retrospective discovery and semi-quantification of PFAS-precursorswas demonstrated, and widespread fenuron exposure is reported in plasmafor the first time. The new exposomics method is complementary tometabolomics protocols, relies on open science resources, and canbe scaled to support large studies of the exposome.

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• Bypassing the Identification: MS2Quant for Concentration Estimations of Chemicals Detected with Nontarget LC-HRMS from MS2 Data

  2023. Helen Sepman (et al.). Analytical Chemistry 95 (33), 12329-12338

  Article

  Nontarget analysis by liquid chromatography-high-resolutionmass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation(MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI: FingerID software. The root mean square errors of the training and test sets were 0.55(3.5x) and 0.80 (6.3x) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2x to 6x. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4x, ageometric mean of 4.5x, and a median of 4.0x. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5x, a geometricmean of 5.6x, and a median of 5.2x on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. TheMS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.

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• Exposures to perfluoroalkyl substances and asthma phenotypes in childhood: an investigation of the COPSAC2010 cohort

  2023. Astrid Sevelsted (et al.). EBioMedicine 94

  Article

  Background Exposure to perfluoroalkyl substances may affect offspring immune development and thereby increase risk of childhood asthma, but the underlying mechanisms and asthma phenotype affected by such exposure is unknown.

  Methods In the Danish COPSAC2010 cohort of 738 unselected pregnant women and their children plasma PFOS and PFOA concentrations were semi-quantified by untargeted metabolomics analyses and calibrated using a targeted pipeline in mothers (gestation week 24 and 1 week postpartum) and children (age 1/2 , 11/2 and 6 years). We examined associations between pregnancy and childhood PFOS and PFOA exposure and childhood infections, asthma, allergic sensitization, atopic dermatitis, and lung function measures, and studied potential mechanisms by integrating data on systemic low-grade inflammation (hs-CRP), functional immune responses, and epigenetics.

  Findings Higher maternal PFOS and PFOA exposure during pregnancy showed association with a non-atopic asthma phenotype by age 6, a protection against sensitization, and no association with atopic asthma or lung function, or atopic dermatitis. The effect was primarily driven by prenatal exposure. There was no association with infection proneness, low-grade inflammation, altered immune responses or epigenetic changes.

  Interpretations Prenatal exposure to PFOS and PFOA, but not childhood exposure, specifically increased the risk of low prevalent non-atopic asthma, whereas there was no effect on atopic asthma, lung function, or atopic dermatitis. 

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• Silicone Foam for Passive Sampling and Nontarget Analysis of Air

  2023. Stefano Papazian (et al.). Environmental Science and Technology Letters 10 (11), 989-997

  Article

  The airborne chemical exposome is a dynamic complex mixture of gases and particles, and despite clear links to chronic disease and premature death, its molecular composition and variability remains largely uncharacterized. To overcome this, we aimed to pair nontarget analysis by high-resolution mass spectrometry (HRMS) with an inexpensive and stable passive sampling media for airborne gases and particles. To this end, we synthesized silicone (polydimethylsiloxane; PDMS) foam disks resulting in a low cost (0.02$/disk) and ultraclean material suitable for analysis by gas or liquid chromatography (GC/LC)HRMS. When tested for indoor passive sampling over 1-3 months, alongside a PDMS sheet, PDMS foam accumulated many nonpolar gas phase environmental contaminants (e.g., polychlorinated biphenyls), and a surprisingly complex mixture of larger polar substances (e.g., oxygen, nitrogen and sulfur-containing) that were absent from the PDMS sheet, suggesting sampling of the particulate phase. The airborne molecular discovery potential was further demonstrated using an open-science LC-HRMS workflow integrating molecular networks and in silico structural predictions tailored on PubChemLite for Exposomics, which revealed series of known and unknown substances, including aromatic nitrophenols and sulfonyls. Future studies may benefit from implementing PDMS foam as wearable or stationary passive samplers to support advances in understanding exposure and contaminant sources in the indoor, outdoor, and personal airborne exposomes.

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• MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS

  2022. Pilleriin Peets (et al.). Environmental Science and Technology 56 (22), 15508-15517

  Article

  To achieve water quality objectives of the zero pollution action plan in Europe, rapid methods are needed to identify the presence of toxic substances in complex water samples. However, only a small fraction of chemicals detected with nontarget high-resolution mass spectrometry can be identified, and fewer have ecotoxicological data available. We hypothesized that ecotoxicological data could be predicted for unknown molecular features in data-rich high-resolution mass spectrometry (HRMS) spectra, thereby circumventing time-consuming steps of molecular identification and rapidly flagging molecules of potentially high toxicity in complex samples. Here, we present MS2Tox, a machine learning method, to predict the toxicity of unidentified chemicals based on high-resolution accurate mass tandem mass spectra (MS2). The MS2Tox model for fish toxicity was trained and tested on 647 lethal concentration (LC50) values from the CompTox database and validated for 219 chemicals and 420 MS2 spectra from MassBank. The root mean square error (RMSE) of MS2Tox predictions was below 0.89 log-mM, while the experimental repeatability of LC50 values in CompTox was 0.44 log-mM. MS2Tox allowed accurate prediction of fish LC50 values for 22 chemicals detected in water samples, and empirical evidence suggested the right directionality for another 68 chemicals. Moreover, by incorporating structural information, e.g., the presence of carbonyl-benzene, amide moieties, or hydroxyl groups, MS2Tox outperforms baseline models that use only the exact mass or logKOW. 

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• Nontarget mass spectrometry and in silico molecular characterization of air pollution from the Indian subcontinent

  2022. Stefano Papazian (et al.). Communications Earth & Environment 3 (1)

  Article

  A combination of high-resolution mass spectrometry and computational molecular characterization techniques can structurally annotate up to 17% of organic compounds in fine particulate matter in highly polluted air sampled in the Maldives. Fine particulate-matter is an important component of air pollution that impacts health and climate, and which delivers anthropogenic contaminants to remote global regions. The complex composition of organic molecules in atmospheric particulates is poorly constrained, but has important implications for understanding pollutant sources, climate-aerosol interactions, and health risks of air pollution exposure. Here, comprehensive nontarget high-resolution mass spectrometry was combined with in silico structural prediction to achieve greater molecular-level insight for fine particulate samples (n = 40) collected at a remote receptor site in the Maldives during January to April 2018. Spectral database matching identified 0.5% of 60,030 molecular features observed, while a conservative computational workflow enabled structural annotation of 17% of organic structures among the remaining molecular dark matter. Compared to clean air from the southern Indian Ocean, molecular structures from highly-polluted regions were dominated by organic nitrogen compounds, many with computed physicochemical properties of high toxicological and climate relevance. We conclude that combining nontarget analysis with computational mass spectrometry can advance molecular-level understanding of the sources and impacts of polluted air.

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Show all publications by Jonathan Martin at Stockholm University