An evaluation of the National Institutes of Health grants portfolio: identifying opportunities and challenges for multi-omics research that leverage metabolomics data

Abstract

Background Through the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment. Aim of review To understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data. Key scientific concepts of review We observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.

MultiomicsTracks96: A high throughput PIXUL-Matrix-based toolbox to profile frozen and FFPE tissues multiomes

“…Here, we present same sample 8 omic datasets from four different organs, a degree of multiplicity not feasible for single-cell studies in the foreseeable future, let alone from FFPEs. When it comes to epigenetics, epitranscriptomics, or proteomics, only a few out of many known nucleic acids and protein modifications have been assayed thus far in multi-omics studies (1,127). In this regard, extending multiome measurements to include several molecular classes within any given omics data type (e.g., phospho- and glycoproteins for proteomics, or multiple DNA or RNA modifications) is quite feasible for bulk but not for single-cell assays.…”

“…Still, multi-omics is a nascent discipline in which most studies have included only two omics datasets (1). For instance, in a 2022 review of the NIH multi-omics grant portfolio, which included metabolomics, only 7% and 2% of projects consisted of four and five omics approaches, respectively (127). Our study measures 8 different omic datasets, including, for the first time, epitranscriptome profiles.…”

Routine omics collection is a golden opportunity for European human research in space and analog environments

“…Epigenomics is shown to span several omic data types to demonstrate that environmental exposures and activity occurring at the genomic, transcriptomic, and proteomic levels influence epigenomic status. Adapted from Yu et al 49 under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). missions may be valuable.…”

NMR-Based Chromatography Readouts: Indispensable Tools to “Translate” Analytical Features into Molecular Structures

“…The fundamental question of whether the chosen holistic approach of “omics” makes sense at all in a particular research setting is often not asked. On the contrary, it is assumed that if one axis (e.g., proteomics) is not sufficient, systems biology questions can only be adequately addressed by combining the individual stages of the mechanistic gene-phen cascade [ 53 ]. However, this question of meaning always arises, regardless of the chosen field of investigation, when a research approach tends to confuse or fail to separate irrelevant technical process noise from relevant biological process noise.…”