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Combining time-resolved transcriptomics and proteomics data for Adverse Outcome Pathway refinement in ecotoxicology

Cite this dataset

Bakker, Ruben et al. (2023). Combining time-resolved transcriptomics and proteomics data for Adverse Outcome Pathway refinement in ecotoxicology [Dataset]. Dryad.


Conventional Environmental Risk Assessment (ERA) of pesticide pollution is based on soil concentrations and apical endpoints, such as the reproduction of test organisms, but disregards information along the organismal response cascade leading to an adverse outcome. The Adverse Outcome Pathway (AOP) framework, on the other hand, facilitates the use of response information at any level of biological organization. Transcriptomic and proteomic data can provide thousands of data points on the response to toxic exposure. Combining multiple omics data types is necessary for a comprehensive overview of the response cascade and, therefore, AOP development. However, it is unclear if transcript and protein responses are synchronized in time or time-lagged. To understand if analysis of multi-omics data obtained at the same timepoint reveals one synchronized response cascade, we studied time-resolved shifts in gene transcript and protein abundance in the springtail Folsomia candida, a soil ecotoxicological model, after exposure to the neonicotinoid insecticide imidacloprid. We analyzed transcriptome and proteome data every 12 hours up to 72 hours after onset of exposure. The most pronounced shift in both transcript and protein abundances was observed after 48 hours of exposure. Moreover, cross-correlation analyses indicate that most genes displayed the highest correlation between transcript and protein abundances without a time-lag. This demonstrates that a combined analysis of transcriptomic and proteomic data can be used for AOP improvement. This data will promote the development of biomarkers for neonicotinoid insecticide pollution in soils or chemicals with a similar mechanism of action.


Please refer to a complete description of the methods to our peer-reviewed article. 

This upload is only for the proteomics data.

For test soil, natural LUFA2.2. was used with or without imidacloprid. RNA and protein were sent to other facilities for further processing. Pools of 70 Folsomia candida were exposed to test soil and harvested every 12 hours for a total of 72 hours. The protein and RNA fractions from these animals were isolated using a TriZol-based method. 

Proteomic data: 

Shotgun LC-MS2 (Thermo-Fisher Orbitrap), searchGUI with msgf+*, PeptideShaker*, label-free quantification moFF*, further analysis with R-Msnbase, R-limma, R-MSqRob.

* these steps have been performed on the EU Galaxy server.


Transcriptomics data have been uploaded to the NCBI GEO database (GSE220513) and Zenodo.

Usage notes

All analysis is based on R and python. In particular, R is used for all downstream analyses. A Zenodo upload has been made of the code.


Dutch Research Council, Award: 15494