Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites.
Direct sampling of building wastewater has the potential to enable "precision public health" observations and interventions. Temporal sampling offers additional dynamic information that can be used to increase the informational content of individual metabolic "features", but few...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1008001 |
| _version_ | 1818578141640130560 |
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| author | Ethan D Evans Chengzhen Dai Siavash Isazadeh Shinkyu Park Carlo Ratti Eric J Alm |
| author_facet | Ethan D Evans Chengzhen Dai Siavash Isazadeh Shinkyu Park Carlo Ratti Eric J Alm |
| author_sort | Ethan D Evans |
| collection | DOAJ |
| description | Direct sampling of building wastewater has the potential to enable "precision public health" observations and interventions. Temporal sampling offers additional dynamic information that can be used to increase the informational content of individual metabolic "features", but few studies have focused on high-resolution sampling. Here, we sampled three spatially close buildings, revealing individual metabolomics features, retention time (rt) and mass-to-charge ratio (mz) pairs, that often possess similar stationary statistical properties, as expected from aggregate sampling. However, the temporal profiles of features-providing orthogonal information to physicochemical properties-illustrate that many possess different feature temporal dynamics (fTDs) across buildings, with large and unpredictable single day deviations from the mean. Internal to a building, numerous and seemingly unrelated features, with mz and rt differences up to hundreds of Daltons and seconds, display highly correlated fTDs, suggesting non-obvious feature relationships. Data-driven building classification achieves high sensitivity and specificity, and extracts building-identifying features found to possess unique dynamics. Analysis of fTDs from many short-duration samples allows for tailored community monitoring with applicability in public health studies. |
| first_indexed | 2024-12-16T06:41:06Z |
| format | Article |
| id | doaj.art-36ad151afe7943c486d47c9f69676b83 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-16T06:41:06Z |
| publishDate | 2020-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-36ad151afe7943c486d47c9f69676b832022-12-21T22:40:41ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-06-01166e100800110.1371/journal.pcbi.1008001Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites.Ethan D EvansChengzhen DaiSiavash IsazadehShinkyu ParkCarlo RattiEric J AlmDirect sampling of building wastewater has the potential to enable "precision public health" observations and interventions. Temporal sampling offers additional dynamic information that can be used to increase the informational content of individual metabolic "features", but few studies have focused on high-resolution sampling. Here, we sampled three spatially close buildings, revealing individual metabolomics features, retention time (rt) and mass-to-charge ratio (mz) pairs, that often possess similar stationary statistical properties, as expected from aggregate sampling. However, the temporal profiles of features-providing orthogonal information to physicochemical properties-illustrate that many possess different feature temporal dynamics (fTDs) across buildings, with large and unpredictable single day deviations from the mean. Internal to a building, numerous and seemingly unrelated features, with mz and rt differences up to hundreds of Daltons and seconds, display highly correlated fTDs, suggesting non-obvious feature relationships. Data-driven building classification achieves high sensitivity and specificity, and extracts building-identifying features found to possess unique dynamics. Analysis of fTDs from many short-duration samples allows for tailored community monitoring with applicability in public health studies.https://doi.org/10.1371/journal.pcbi.1008001 |
| spellingShingle | Ethan D Evans Chengzhen Dai Siavash Isazadeh Shinkyu Park Carlo Ratti Eric J Alm Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites. PLoS Computational Biology |
| title | Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites. |
| title_full | Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites. |
| title_fullStr | Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites. |
| title_full_unstemmed | Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites. |
| title_short | Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites. |
| title_sort | longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites |
| url | https://doi.org/10.1371/journal.pcbi.1008001 |
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