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Mass Spectrometry-Based Visualization of Molecules Associated with Human Habitats Daniel Petras, Louis-Félix Nothias, Robert A. Quinn, Theodore Alexandrov, Nuno Bandeira, Amina Bouslimani, Gabriel Castro-Falcón, Liangyu Chen, Tam Dang, Dimitrios J. Floros, Vivian Y. H. Hook, Neha Garg, Nicole Hoffner, Yike Jiang, Clifford A Kapono, Irina Koester, Rob Knight, Christopher A. Leber, Tiejun Ling, Tal Luzzatto-Knaan, Laura-Isobel McCall, Aaron Philip McGrath, Michael J Meehan, Jonathan K Merritt, Robert H. Mills, Jamie Morton, Sonia Podvin, Ivan Protsyuk, Trevor Purdy, Kendall Satterfield, Stephen Searles, Sahil Shah, Sarah Shires, Dana Steffen, Margot White, Jelena Todoric, Robert Tuttle, Aneta Wojnicz, Valerie Sapp, Fernando Vargas, Jin Yang, Chao Zhang, and Pieter C. Dorrestein Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b03456 • Publication Date (Web): 12 Oct 2016 Downloaded from http://pubs.acs.org on October 13, 2016

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Analytical Chemistry

Todoric, Jelena; University of California San Diego Tuttle, Robert; University of California San Diego Wojnicz, Aneta; University of California San Diego Sapp, Valerie; University of California San Diego Vargas, Fernando; University of California San Diego Yang, Jin; University of California San Diego Zhang, Chao; University of California San Diego Dorrestein, Pieter; University of California, San Diego, Skaggs School of Pharmacy

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Mass Spectrometry-Based Visualization of Molecules Associated with Human Habitats Daniel Petras1,2,#, Louis-Félix Nothias1,2,#, Robert A. Quinn1,2,#, Theodore Alexandrov2,16,17, Nuno Bandeira2,18, Amina Bouslimani2, Gabriel Castro-Falcón4, Liangyu Chen1, Tam Dang2,11, Dimitrios J Floros1,3, Vivian Hook2, Neha Garg1,2, Nicole Hoffner 5, Yike Jiang10, Clifford A. Kapono3, Irina Koester4, Rob Knight18,19,20, Christopher A Leber4, Tie-Jun Ling1,2,14 ,Tal LuzzattoKnaan1,2, Laura-Isobel McCall2, Aaron P. McGrath2, Michael J. Meehan1,2, Jonathan K. Merritt5, Robert H. Mills8, Jamie Morton18, Sonia Podvin2, Ivan Protsyuk16, Trevor Purdy4, Kendall Satterfield8,15, Stephen Searles6,8 Sahil Shah5,7, Sarah Shires2,8, Dana Steffen8, Margot White4, Jelena Todoric15, Robert Tuttle4, Aneta Wojnicz12, Valerie Sapp8, Fernando Vargas10, Jin Yang2, Chao Zhang9,13, Pieter C. Dorrestein1,2,20,* 1)

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UCSD Collaborative Mass Spectrometry Innovation Center, 9500 Gilman Drive, La Jolla, CA 92093, USA UCSD Skaggs School of Pharmacy and Pharmaceutical Sciences, 9500 Gilman Drive, La Jolla, CA 92093, USA UCSD Chemistry and Biochemistry, 9500 Gilman Drive, La Jolla, CA 92093, USA Scripps Institution of Oceanography, 9500 Gilman Drive, La Jolla, CA 92093, USA UCSD Neurosciences Graduate Program, 9500 Gilman Drive, La Jolla, CA 92093, USA UCSD Department of Pathology, 9500 Gilman Drive, La Jolla, CA 92093, USA UCSD School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA UCSD Biomedical Sciences Graduate Program, 9500 Gilman Drive, La Jolla, CA 92093, USA UCSD Bioengineering Undergraduate Program, 9500 Gilman Drive, La Jolla, CA 92093, USA UCSD Biological Sciences Graduate Program, 9500 Gilman Drive, La Jolla, CA 92093, USA TU Berlin, Institut für Chemie, Strasse des 17. Juni 124, 10623 Berlin, Germany Facultad de Medicina de la Universidad Autónoma de Madrid. Calle del Arzobispo Morcillo 4. 28029 Madrid, Spain UCSD Mathematics Undergraduate Program, 9500 Gilman Drive, La Jolla, CA 92093, USA State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, 130 West Changjiang Rd. Hefei 230036, P. R. China UCSD Department of Pharmacology, 9500 Gilman Drive, La Jolla, CA 92093, USA Structural and Computational Biology, EMBL, Meyerhofstr. 1, 69117 Heidelberg, Germany SCiLS GmbH, Fahrenheitstr. 1, 28359 Bremen, Germany UCSD Department of Computer Science, 9500 Gilman Drive, La Jolla, CA 92093, USA UCSD Department of Pediatrics, 9500 Gilman Drive, La Jolla, CA 92093, USA UCSD center for Microbiome Innovation, 9500 Gilman Drive, La Jolla, CA 92093, USA


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ABSTRACT The cars we drive, the homes we live in, the restaurants we visit, and the labs and offices we work in are all a part of the modern human habitat. Remarkably, little is known about the diversity of chemicals present in these environments and to what degree molecules from our bodies influence the built environment that surrounds us and vice versa. We therefore set out to visualize the chemical diversity of five built human habitats together with their occupants, to provide a snapshot of the various molecules to which humans are exposed on a daily basis. The molecular inventory was obtained through untargeted LC-MS/MS analysis of samples from each human habitat and from the people that occupy those habitats. Mapping MS-derived data onto 3D models of the environments showed that frequently touched surfaces, such as handles (e.g. door, bicycle), resemble the molecular fingerprint of the human skin more closely than other surfaces that are less frequently in direct contact with humans (e.g. wall, bicycle frame). Approximately 50% of the MS/MS spectra detected were shared between people and the environment. Personal care products, plasticizers, cleaning supplies, food, food additives and even medications that were found to be a part of the human habitat. The annotations indicate that significant transfer of chemicals takes place between us and our built environment. The workflows applied here will lay the foundation for future studies of molecular distributions in medical, forensic, architectural, space exploration and environmental applications. INTRODUCTION Humans interact, at the chemical level, with our surrounding built habitats. These habitats, in turn, influence the chemicals we find on our skin. Exposome research, a field that investigates human exposure to chemicals, has traditionally had a strong emphasis on harmful molecules.1-4 These harmful molecules include phthalates commonly used to make plastics, the disinfectant triclosan found in many sanitizers, and polybrominated phenyl ethers used as flame-retardants that have been reported to affect fertility estrogen response and the nervous system.5-7 Locard’s exchange principle, coined in the 1930s, states that “every contact leaves a trace”, suggesting that we leave our chemical mark on the human habitat.8 While much emphasis has been placed on indoor air quality,9-11 an untargeted examination of the types of molecules present in human habitats and human transportation, and whether they are of human, industrial, or environmental origin has yet to be thoroughly investigated. This has resulted in a paucity of chemical information about the human environment, preventing informed decisions for the design of human-occupied buildings or transportation. Mass spectrometry is a sensitive approach that reveals the chemical composition of a sample and can be employed to profile their spatial distribution, often referred to as imaging



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mass spectrometry (IMS). This technique has become fairly common, but largely limited to small (0.5-5 cm) biological samples.12-14 The spatial resolution achieved in IMS is in the 1-100 µm range, depending on the ionization method used (such as MALDI, DESI and nanoDESI),15 and resolution can be increased up to 30 nm with SIMS.16 Furthermore, IMS can be performed in three dimensions (3D-IMS) by analyzing adjacent sample sections and then computationally reconstructing the spatial distribution of the detected features into a 3D image.17,18 The distribution of chemicals on larger spatial scales has not been routinely performed, but can be a powerful method for revealing how molecules partition over surfaces or across environments. Recently, 3D-surface-IMS at the scale of cm to meters was applied to study the chemistry of human skin.19 This study revealed intricate relationships between human metabolites, microbial residents of human skin, and personal care products applied to the skin surface. This study employed ‘3D molecular cartography’, a method that translates mass spectrometry features, generated by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), onto a 3D model.19 The other key challenge in analyzing the human environment is the enormous number and diversity of molecules detectable with modern mass spectrometers. For example, any inhabited built environment would be expected to contain wood, metal, plastic, soil, fabric and human skin, all very different sample types representing a diverse chemical repertoire. Each sample type will include its own set of molecules. Although many metabolomic workflows have been developed in the past few years,20-23 mostly targeted towards human clinical samples, few tools can process and visualize the volume of data and the complexity of the different types of samples expected to be present in the human environment. A strategy to circumvent this challenge is using MS driven visualization strategies, such as MS/MS molecular networking. This method visualizes the relatedness between all chemicals detected by mass spectrometry on a spectrum-by-spectrum basis.24 Molecular networking has now been implemented as a crowdsourced analysis infrastructure called Global Natural Product Social molecular networking (GNPS, at http://gnps.ucsd.edu).25 This infrastructure enables visualization of MS/MS spectra, annotation with reference MS/MS libraries, and provides a measure of their relative similarity to produce a map of all chemicals detected in a single experiment or across a large number of experiments. Here, we take a snapshot of some of the chemicals, detected as ions, we encounter in the human habitat and modes of transport. The mass spectrometry detectable chemicals are compared between sample types with multivariate statistical analysis, and then visualized by molecular networking and 3D molecular cartography. Finally, we assessed how much the molecules in the built environments are associated with the individuals occupying that environment. EXPERIMENTAL SECTION A summary of the experimental protocol is provided next, an extended experimental procedure, including detailed LC-MS/MS settings and data processing parameters, can be found in the Supporting Information of this article. In summary, 3D models of the bicycles, the social event environment and the water fountains were generated using 3D capture tools or represented by an existing digital 3D object. These were either the software 123D Catch (http://www.123dapp.com/catch) or an iPad


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equipped with a Structure sensor model ST01 3D scanner (http://structure.io/). A free 3D model of the vehicle was acquired from 3DCADBrowser.com (http://www.3dcadbrowser.com/). The sampled area of the two-bedroom apartment was recorded using a measuring tape, and the 3D model was constructed using SketchUp (http://www.sketchup.com/). After 3D image generation the three human habitats and two modes of transportation as well as body parts of human occupants and non-occupants (UCSD IRB approval number 130537X) were swabbed with ethanol soaked cotton swabs. The sample locations of all environments and human subjects can be seen in the supplemental information. Afterwards the swabs were extracted in 0.5 mL ethanol water mixture (1/1, v/v) in 96 deep-well plates according to Bouslimani et al.19 After extraction, the samples were dried and re-dissolved in 100 µL methanol/H2O (1/1, v/v) including 0.1 µg/mL amitriptyline as an internal standard. For nontargeted LC-MS/MS analysis, the samples were analyzed on a Vanquish ultra-high performance liquid chromatography (UPLC) system coupled to a Q-Exactive hybrid quadrupole orbital ion trap (Thermo Fisher Scientific, Bremen, Germany). For the chromatographic separation, a C18 core-shell column (Kinetex, 50 x 2 mm, 1.8 um particle size, 100 A pore size, Phenomenex, Torrance, USA) with a flowrate of 0.5 mL/min (solvent A: H2O + 0.1 % formic acid (FA), solvent B: acetonitrile (ACN) + 0.1 % FA) was used. After injection, the samples were eluted during a linear gradient from 0-0.5 min, 5 % B, 0.5-4 min 5-50 % B, 4-5 min 50-99 % B, followed by a 2 min washout phase at 99% B and a 2 min re-equilibration phase at 5 % B. MS/MS spectra were acquired in data dependent acquisition (DDA) mode. Both MS1 survey scans (150-1500 m/z) and up to five MS/MS scans of the most abundant ions per duty cycle were measured with a resolution (R) of 17,500 in positive mode. Raw MS/MS datasets were converted to mzXML files using MSConvert.26 Molecular networking and MS/MS database based dereplication was performed using GNPS (http://gnps.ucsd.edu).25 MS1 features were extracted from the LC-MS/MS data using Optimus (version May 2016) which used OpenMS for feature finding.27 PCoA plots of MS1 features were generated with the Bray-Curtis dissimilarity metric using the in-house tool ClusterApp and visualized in EMPeror28. For molecular 3D mapping, sample coordinates of the 3D models were obtained through geomagic (http://www.geomagic.com/en/) or (meshlab http://meshlab.sourceforge.net/). Finally, ion intensities were mapped onto the 3D models using ‘ili, a spatial visualization tool still in development by our laboratories (https://ilitoolbox.github.io/). RESULTS AND DISCUSSION Sampling and Statistical Analysis. We set out to assess the chemistry associated with the human habitat and modes of transport. We therefore sampled two bicycles, the environment where a social event is held weekly, a car, an apartment, a water fountain in a research building and 19 human subjects associated with these environments. The social event environment and the water fountain were not routinely visited by any of the volunteers. However, for this study several volunteers interacted with those environments using their hands to see if molecules from the habitat could be detected on the hands afterwards in order to establish directionality of transfer of molecules between humans and their environment. To assess the overall chemical similarity between the samples, the m/z value, retention time (RT) and intensity of each feature were displayed in a matrix which was subjected to



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multivariate statistical comparison using the Bray-Curtis dissimilarity index. Here, the distance between samples (shown as spheres) was visualized as principal coordinate analysis (PCoA) plot. The sample relationships revealed by PCoA identified the expected separation between human samples and personal objects associated with environmental samples (Figure 1a), but not between male or female subjects (Figure 1b). Although most environment samples separated from human samples, several habitat surfaces were more similar to human samples (Figures 1a-c). Many of these were surfaces that receive significant contact with human skin, such as a guitar, bicycle handlebar, foosball table handles, and parts of a pool table (Figure 1c). This demonstrates that humans have a major impact on the chemistry of the surfaces they touch. Different human habitats and modes of transport also differ in their chemistry, particularly the car samples (Group 3, Figure 1e), and some clustering were observed by individual, indicating that person specific signatures were also detected in this data set (Figure 1f). Finally, a random forests classifier was used to determine which metabolites best differentiate human from environmental samples based on their abundances. It was found that the most differential annotated metabolite between human and environment was tryptophan, consistent with the biotic and abiotic nature of the samples.

Figure 1: The relationships of the mass spectrometry derived chemical data from the human habitat and the volunteers as visualized using the first three principal coordinates from a BrayCurtis dissimilarity matrix. a) All data in this study highlighted by samples of human and environmental origin. b) Same PCoA, but only data from the environment and the hands of the volunteers are visualized and further colored by gender. c) Same plot as b), but samples from guitar, bicycle handle, foosball handle, and pool table are enlarged to aid visualization of these sample types that have extensive interactions with human hands. d) Color resolution of samples from different human body locations. e)


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Environment data only, color coded by the five different human habitats sampled. f) Human data only, color-coded according to the 19 volunteers.

Molecular Networking and Annotation of Detected Molecules. To provide molecular annotations, we examined the data at the MS/MS level by molecular networking.24 Molecular networking compares the similarity of all MS/MS spectra through spectral alignment and then visualizes these relationships as networks. In total, 1,655,109 of the 2,157,908 spectra passed filtering. The global network is shown in Figure 2a. The precursor masses of ions that are included ranged from m/z 170 to 1,200 (Supporting Figure 3). These formed 49,669 merged consensus spectra (nodes) of which most had a mass below m/z 400 (Supporting Figure 4). Rarefaction analysis showed the diversity of MS/MS spectra sampled using our methods has reached saturation (Supporting Figure 5). These molecular networks were made up of 3,743 clusters of related spectra, and 18674 self-looped nodes that do not show spectral similarity to other MS/MS spectra within the dataset.29 Clusters of nodes indicated that the chemicals detected shared a high degree of structural similarity. These relationships can include molecules separated by a methyl group, oxygen or a double bond, MS/MS spectra of molecules with similar substructures (e.g. glycosylations or fatty acid tails), different adducts when the fragmentation is similar and even source fragment ions. Indeed, most of the mass differences in the network here had mass shift of 2, 14, 16, 18, 28, 44, and 88 m/z units, consistent with mass shifts associated with oxidation, methylation, dimethylation or formylation, water addition, polymer or addition of CO2, but also mass shifts consistent with the addition of amino acids or sugars were also observed (Supporting Figure 6). Color-coding the networks based on the specific human habitat revealed that each environment had a unique set of MS/MS spectra (and therefore unique molecules), but there was also a significant number of MS/MS spectra in common, where more than 50% of the MS/MS spectra were found in two or more environments. The distribution of nodes in the molecular networks among the environment samples and the people associated with each environment is shown in Supporting Figure 7. This revealed that the largest percentage of shared MS/MS spectra (~75%) between people and habitat was found in the social gathering environment, while the least number of spectra were shared between the water fountain and the volunteers (~10%) that were a part of that habitat study. With molecular networking, one can also add MS/MS reference libraries to provide annotations. For an annotation to be considered, we used a strict scoring scheme. The two key considerations for the annotations matching were considered. First, a minimum of 6 ions were required to match; second, the cosine score had to be greater than 0.7. With those scoring parameters, the GNPS community reported these parameters to provide 1% incorrect annotations, 4% insufficient information to assess if correct or not, 4% of annotations that are likely isomers or the correct annotation and 91% was deemed correctly annotated.25 In this work, the reference library consisted of about 220,000 reference spectra from the GNPS community,25 all three MassBanks (Japan http://www.massbank.jp/?lang=en, Massbank Europe http://massbank.eu/MassBank/ and North America http://mona.fiehnlab.ucdavis.edu/),30 ReSpect,31 HMDB,32 Dereplicator,33 CASMI challenge data (http://www.casmicontest.org/2016/index.shtml),34 EMBL metabolomics library, NIST 2014 (http://www.sisweb.com/software/ms/nist.htm) and some additional reference spectra, including those provided by companies (http://gnps.ucsd.edu/ProteoSAFe/libraries.jsp). Annotations



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obtained with precursor and MS/MS matching, as was done here, are considered level two annotations according to the 2007 metabolomics standards initiative.35 However, all annotations should be considered to be putative annotations. For all annotations obtained in GNPS and discussed in this paper, the data was manually inspected against the reference MS/MS spectrum to ensure that at least the precursor mass (75 % of nodes) remains unknown. People and their habitat exchange chemicals, but each source had its own distinct chemical fingerprint. The data suggests that some environmental sites are likely impacted by the frequency of human interactions. The human-environment shared molecular signature was so significant that some of the data from samples of the human habitat more strongly resembled human skin molecular signatures than other samples of non-human objects. These chemical signatures can be used to understand how molecules are transferred and migrate within the human habitat. All MS/MS datasets come from very different sample types, each of which contains unique but high chemical diversity. When such diversity is encountered, current algorithms of spectral alignment and downstream data analysis can be used to formulate a hypothesis based on the 3D images, but requires careful manual assessment of the signals of interest. One should especially consider that due to potential differences of surface extraction efficiencies and non-linear behavior of the mass spectrometer (for example du to concentration extremes or ion suppression effects), ion intensities should be considered as semi-quantitative. We therefore presented the extracted ion maps in this study in log scale to minimize misinterpretation. Furthermore, because the majority of MS/MS spectra were not annotated, it is clear that spectral annotation is a primary bottleneck for untargeted mass spectrometry experiments, including our study. To overcome this, chemical databases such as the human metabolome data bank,41 drugbank,42 food data bank (http://foodb.ca/), the toxic exposome database (http://www.t3db.ca/) require further development to improve the analysis of our chemical environment. In combination with 3D molecular cartography expanded data resources would facilitate the spatial understanding of chemicals present in the human habitat, which could aid architects and engineers in designing vehicles and buildings in the future. There are significant applications for the methods used in this study in forensic science. It is clear from this work that drugs and molecules consumed by an individual can be passed to the built environment they find themselves in. Much like the microbiome field within microbiology, which has demonstrated that we leave a microbial signature on things we touch,43 the chemical tracking demonstrated here can also indicate the likely presence of an individual in a room or a particular transport vehicle. Moreover, similar infrastructure could be applied to problems in clinical medicine, the military and astrochemistry. From a methodological standpoint, 3D molecular cartography in combination with molecular networking proved to be a unique imaging mass spectrometry technique, which could be employed to explore chemicals from the surface of inorganic matter and of biological system at an unprecedented macroscale. ASSOCIATED CONTENT Supporting Information is available free of charge on the ACS Publications website. AUTHOR CONTRIBUTIONS PCD created the idea of the project. NB, PCD, TA, IP, VH provided the guidance on analysis and interpretation of the data.


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PCD, LIM, DP, TLK, LFN, NG, CZ, CAL wrote the paper. AB, GCF, NG, CAL, JT, TLK, LIM, DP, DJS, JKM, SES, LKS, IK, MEW, RHM, AWP, TNP, LYC, YJ, FV, RNT, APM, SCS,SS, JY, AW collected samples. MJM, LFN, DP collected the mass spectrometry data. DP, RAQ, CZ, VS, JM and RK performed statistical analysis. AB, GCF, TLK, LIM, LFN, DF, DP, NG, JKM, FV, SS, SCS, CAL created 3D images. AB, LIM, LFN, NG, DP, DJS, DF, IK, SP, TLK, TNP, CAL, RHM, CZ, RNT, SES, SCS, APM, SS, TD, VS, JY performed molecular networking and analysis of the networks. TA, IP designed and developed Optimus and `ili. AUTHOR INFORMATION # These authors contributed equally * To whom correspondence should be addressed [email protected] ACKNOWLEDGEMENTS This project is supported in part by the Sloan foundation and the European Union’s Horizon2020 program (grant 634402). GCF thanks Scripps Institution of Oceanography for a San Diego Fellowship (2T32GM067550-10) as part of The Training Program in Marine Biotechnology (NIH GM067550) and the Howard Hughes Medical Institute for a Gilliam Fellowship. The work described in this paper was a part of a UCSD course on system wide mass spectrometry. The majority of authors are faculty or students of that course. We thank Mr. Koby Arbely and Mrs. Lingli Zhu for their help with constructing the 3D model of the apartment and Aaron W. Puri University of Washington Dept. of Chemical Engineering for participating is sample collection during his visit to UCSD. Finally, thanks to Kathleen Dorrestein for reading the manuscript prior to submission. REFERENCES (1) In Use of Metabolomics to Advance Research on Environmental Exposures and the Human Exposome: Workshop in Brief: Washington (DC), 2016. (2) Nieuwenhuijsen, M. J. Environ Health 2016, 15 Suppl 1, 38. (3) Putignani, L.; Dallapiccola, B. J Proteomics 2016. in press. doi:10.1016/j.jprot.2016.04.033 (4) Siroux, V.; Agier, L.; Slama, R. Eur Respir Rev 2016, 25, 124-129. (5) Kuriyama, S. N.; Talsness, C. E.; Grote, K.; Chahoud, I. Environ Health Perspect 2005, 113, 149-154. (6) Whyatt, R. M.; Liu, X.; Rauh, V. A.; Calafat, A. M.; Just, A. C.; Hoepner, L.; Diaz, D.; Quinn, J.; Adibi, J.; Perera, F. P.; Factor-Litvak, P. Environ Health Perspect 2012, 120, 290-295. (7) Stoker, T. E.; Gibson, E. K.; Zorrilla, L. M. Toxicol Sci 2010, 117, 45-53. (8) Locard, E. The American journal of police science 1930, 1, 276-298. (9) Zhou, L.; Hiltscher, M.; Gruber, D.; Puttmann, W. Environ Sci Pollut Res Int 2016. in press. doi: 10.1007/s11356-016-6902-z (10) Fu, M.; Fernandez, E.; Martinez-Sanchez, J. M.; San Emeterio, N.; Quiros, N.; Sureda, X.; Ballbe, M.; Munoz, G.; Riccobene, A.; Centrich, F.; Salto, E.; Lopez, M. J. Environ Res 2016, 148, 421-428. (11) Singh, A.; Kamal, R.; Mudiam, M. K.; Gupta, M. K.; Satyanarayana, G. N.; Bihari, V.; Shukla, N.; Khan, A. H.; Kesavachandran, C. N. PLoS One 2016, 11, e0148641. (12) Tao, S.; Lu, X.; Levac, N.; Bateman, A. P.; Nguyen, T. B.; Bones, D. L.; Nizkorodov, S. A.; Laskin, J.; Laskin, A.; Yang, X. Environ Sci Technol 2014, 48, 10993-11001.



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