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Review
. 2024 Jul 15;4(7):100820.
doi: 10.1016/j.crmeth.2024.100820. Epub 2024 Jul 9.

A practical introduction to holo-omics

Iñaki Odriozola  1 , Jacob A Rasmussen  1 , M Thomas P Gilbert  2 , Morten T Limborg  1 , Antton Alberdi  3
Affiliations
Review

A practical introduction to holo-omics

Iñaki Odriozola et al. Cell Rep Methods. .

Abstract

Holo-omics refers to the joint study of non-targeted molecular data layers from host-microbiota systems or holobionts, which is increasingly employed to disentangle the complex interactions between the elements that compose them. We navigate through the generation, analysis, and integration of omics data, focusing on the commonalities and main differences to generate and analyze the various types of omics, with a special focus on optimizing data generation and integration. We advocate for careful generation and distillation of data, followed by independent exploration and analyses of the single omic layers to obtain a better understanding of the study system, before the integration of multiple omic layers in a final model is attempted. We highlight critical decision points to achieve this aim and flag the main challenges to address complex biological questions regarding the integrative study of host-microbiota relationships.

Keywords: CP: Genetics; CP: Microbiology.

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Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Figures

Figure 1
Figure 1
Schematic representation of the host-microbiota multi-omic landscape (A) Each of the two holobionts is represented by multiple omic layers belonging to the host and microbiota domains. The metabolomic layer is left in between as it represents the molecular link between host and microbiome metabolisms and because it is often impossible to trace back the domain of origin. (B) The host domain is depicted with a single box per holobiont representing the host organisms, whereas the microbiota domain is represented by multiple boxes, each representing a different microorganism. Note that all microorganisms present in the entire study system have been represented in both holobionts, mirroring the type of data (which includes zeros) that are used for statistical analyses. (C) These microorganisms might have different levels of phylogenetic or functional similarities, which can be accounted for in the statistical analyses. (D and E) Genomes are shown as dark lines containing multiple genes represented in different colors. Note that although the host genome (D) is represented with a single line, microbial genomes (E) display different numbers of genomes, representing different microbial abundances. (F) Expressed genes and translated proteins are shown as different amounts of lines and circles that represent their quantitative nature.
Figure 2
Figure 2
Overview of data generation and analysis pathways for host genomics and microbial metagenomics (A) The datatype to be generated and the procedures to be followed depend on prior data availability and the sample type employed. Procedures dealing with host-only, microbe-only, or combined data are shown in different colors. (B) Examples of statistical analyses that can be carried out using host-only, microbe-only, and combined information, after filtration, distillation, and transformation of the data.
Figure 3
Figure 3
Overview of data generation and analysis pathways for host transcriptomics and microbial metatranscriptomics (A) The datatype to be generated and the procedures to be followed depend on prior data availability and sample type employed. Procedures dealing with host-only, microbe-only, or combined data are shown in different colors. (B) Examples of statistical analyses that can be carried out using host-only, microbe-only, and combined information, after filtration, distillation, and transformation of the data.
Figure 4
Figure 4
Overview of data generation and analysis pathways for metabolomics (A) The datatype to be generated and the procedures to be followed depend on prior data availability and the sample type employed. Procedures dealing with host-only, microbe-only, or combined data are shown in different colors. (B) Examples of statistical analyses that can be carried out using metabolomic data.
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