In the last decades there is an increase demand to clearly define the effects of pollutants in the ecosystems and living organisms, especially those without regulation. Contrarily to this raising of environmental awareness, chemical toxicity is addressed by animal testing. To avoid this particular paradox, and to incorporate the 3R’s principle in toxicology, tissue engineering is being used to develop more innovative approaches to test toxicological effects and identify the risk assessment of potentially harmful chemicals present in the environment. The use of different scaffolds, materials and gel-based matrices to grow and maintain the cells are the main cores on the tissue bioengineering.

However, although some determinations in these systems are already developed and rutinary analyzed (cell viability, oxidative stress…), any multi-omics approach is conducted, and only scarce applications on individual omics analysis (some proteomics studies or targeted transcriptomics by PCR) can be found. Two main problems can be encountered when facing omics analysis using organ-on-chip: 1) the obtention of the embedded cells in the matrix maintaining intact their integrity to, afterwards, extract the molecules of interest; and 2) the limited amount of sample to perform different omics analysis (especially critical in metabolomics).

Here, we developed and optimized the protocols to analyze, under a multi-omics perspective (metabolomics, proteomics and transcriptomics), samples derived from a gelatin methacryloyl-based matrix (GelMA) for a New Approach Methodology (NAM) platform in the assessment of toxicology without the use of animal models. The optimization was performed not only for the analytical methodologies, but also for the cell harvesting and the extraction protocols to obtain the maximum coverage of metabolites, proteins and RNA.

The first step was to test different enzymatic and non-enzymatic commercial methods to harvest cells from GelMA hydrogel. The enzymatic methods based on collagenase were the most suitable for a high-throughput sample preparation in terms of speed, easiness, and low interference with all the omics analysis.

Secondly, extraction protocols were selected to maximize the number of molecules and avoid their degradation. In this case, the simplest extraction procedures were the most suitable. Thus, a commercial kit to purify RNA (transcriptomics), tryptic digestion and isobaric TMT labelling (proteomics) and methanolic extraction of metabolites (metabolomics) were used.

Finally, the methodologies adopted to analyze the samples were RNA-seq by Illumina platform for transcriptomics, nanoLC-Orbitrap-MS for proteomics and LC-QqQ/QTOF-MS for metabolomics.

The above methodologies detected around 20000 genes, 500 proteins and 200 metabolites involved on glycolysis, Krebs cycle, biosynthesis of amino acids, lipogenesis/lipolysis, calcium signaling pathway, cardiac contraction and cell adhesion, among others.