LIMS & Lab Automation


Automating for Multi-omics Workfl ows for drug discovery and toxicology


Nigel Skinner, Andrew Alliance


Effective translational research requires effective and robust multi-omics data integration, in order to ensure complete and correct description of the mechanisms of complex disease such as cancer. This is very much about ‘the sample’ as we have to analyse it using different workfl ows and sampling methodologies. For example, a next gen sequencing workfl ow that obtains sequence data is very different to an LC/MS metabolomics workfl ow obtaining metabolite data.


It isn’t only translational research that benefi ts from such ‘multi-omics’ workfl ows but the aptly named rapidly evolving fi eld of ‘sytems toxicology’.


We are now well into the 21st Century and have already seen many truly pivotal advances in the life sciences. Our understanding of disease, at the molecular level, has benefi tted exponentially. Rather as we once mapped the world’s oceans, we continue to construct ever more detailed ‘maps’ of disease. As these maps become more detailed, we see ‘pathways’ that describe the mechanism of specifi c diseases, be it neurodegenerative, cancer, cardiovascular or other. As a consequence, we have developed complex methods for culturing cells and reconstructed tissues, through measurements of cellular changes at a molecular level (so-called ‘omics technologies) to vastly improved computing capacity that can be applied to make sense of the huge volumes of data generated from multiple omics approaches (e.g., genomics, transcriptomics, proteomics, and metabolomics), as is the motivation of the human microbiome project.


Metabolomics experiments require fast sample processing times, demanding the same of transcriptomic and proteomic studies, in order to ensure consistency. Moreover, metabolites are also particularly sensitive to environmental infl uences (diurnal cycles, heat, humidity, diet, age, developmental stage and social interactions) and the tracking of sample meta-data is very important to mitigate the effects of environmental confounders, and to facilitate transparency and reproducibility.


As stated at the start, it is ‘all about the sample’ and the integration of omics data strongly depends on rigorous and consistent sample prep, and sample variation over pretty short periods of time. This demands accurate tracking of each step of the sample prep conducted in the different omics workfl ows, including the multitude of basic, yet critical liquid handling steps, involving anything from serial dilutions to plate normalisation.


Robust, repeatable sample prep, requires rigorous adherence to protocol in order to obviate the risk of contamination, ease of method transfer, and most important, the ability to digitally capture a complete and accurate record of every single step of the sample preparation, including labware used, pipette calibration data, reagent prep temperatures, tube or microplate agitation speeds, and much more, in order to facilitate the translation of omics data sets upon completion of each analytical workfl ow.


High-quality multi-omics studies require:


• proper experimental design • thoughtful selection, preparation, and storage of appropriate biological samples • careful collection of quantitative multi-omics data and associated meta-data • better tools for integration and interpretation of the data, • agreed minimum standards for multi-omics methods and meta-data,


Figure 1. Multi-omics data integration for the human microbiome project


High-quality multi-omics studies require: - proper experimental design - thoughtful selection, preparation, and storage of appropriate biological samples - careful collection of quantitative multi-omics data and associated meta-data - better tools for integration and interpretation of the data, - agreed minimum standards for multi-omics methods and meta-data,


Ideally such a study should involve multi-omics data being generated from the same sample(s) though this can be more challenging due to limitations in sample access, biomass, and cost. A good example of this is the fact that formalin-fi xed paraffi n- embedded (FFPE) tissues are compatible with genomic studies but not with transcriptomic or, until recently, proteomic studies, due to the fact that formalin does not stop RNA degradation and paraffi n can interfere with MS performance thus affecting both proteomic and metabolomic assays.


There is a case to be made for basing multi-omics experimental design requirements on that used for metabolomics. Metabolomics experiments are highly compatible with a wide range of biological samples including blood, serum, plasma, cells, cell culture and tissues; which also happen to be preferred for transcriptomic, genomic and proteomic studies; assuming a consistent sample storage protocol.


Ideally such a study should involve multi-omics data being generated from the same sample(s) though this can be more challenging due to limitations in sample access, biomass, and cost. A good example of this is the fact that formalin-fi xed paraffi n- embedded (FFPE) tissues are compatible with genomic studies but not with transcriptomic or, until recently, proteomic studies, due to the fact that formalin does not stop RNA degradation and paraffi n can interfere with MS performance thus affecting both proteomic and metabolomic assays.


The current Covid-19 pandemixc presenets a very pertinent and ‘current’ example of the importance of multi-omics and pathway-based approache sin pursuit of a better understanding of its mechanism and identifi cation of ways in which its effects can be mitigated. To date, much effort has been expended in better understanding the pathogenicity of SARS-CoV-2. A unique aspect of the disease has been its ability to act all over the body rather than being limited to the respiratory tract, including causing strokes in otherwise healthy, younger patients. Especially dangerous is the occurrence of a ‘cytokine storm’ in some patients, 7 to 10 days following the onset of infection.


The data gathered, thus far, by the international scientifi c community, detail the genomes and mutations of SARS-CoV-2 variants across different locations; the structure of the viral proteins; their host targets in human cells; the transcriptomics changes in infected cells; cell or tissue-level differences in the blood or in the body of COVID-19 patients; and human genomic information from patients. It has been suggested that the only way to understand this data is by taking a ‘systems approach’ that goes beyond individual actions, to connections, causes and consequences.


Ultimately, it is ‘all about the sample’ and the integration of omics data strongly depends on rigorous and consistent sample preparation protocols, in order to reduce sample variation over short periods of time. This demands accurate tracking of each step of the sample prep conducted in the different omics workfl ows, including the multitude of basic, yet critical liquid handling steps, involving anything from serial dilutions to plate normalisation.


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