Why Conduct Multi-Omics Research

Multi-omics encompasses various disciplines such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics. In contrast to single-omics approaches, multi-omics enables a comprehensive analysis of biological processes across multiple layers, thereby facilitating a systemic understanding of biological phenomena and mechanisms. Phenotypic traits often result from the interplay of multiple factors. For instance, the dwarfism trait in rice is influenced by a complex network of hormones, including gibberellins, brassinosteroids, and strigolactones. The biosynthesis and catabolic processes of these hormones involve numerous biological pathways and the regulatory expression of various genes within these pathways. Any perturbation in one of these pathways can potentially impact the phenotypic trait of plant height. Therefore, a comprehensive analysis of multi-omics data is crucial for uncovering the causes of phenotypic variation.

The scope of multi-omics research is continually expanding beyond traditional omics associated with the central dogma, such as genomics, transcriptomics, and proteomics, to include areas such as epigenomics, immunomics, regulomics, and modificomics. The dynamic changes in these omics are intricately linked to developmental stages and responses to environmental stress, allowing for gene expression regulation without altering the DNA sequence.

The application of multi-omics signifies the advent of the big data era in life sciences, transitioning research methodologies from hypothesis-driven to data-driven paradigms. Traditional hypothesis-driven research may be limited by methodological constraints, such as only detecting genomic variations, thereby failing to capture meaningful data when phenotypic changes are due to epigenetic rather than genomic variations. In contrast, data-driven multi-omics approaches can collect data from different biological layers and perform comprehensive analyses through data integration, thereby uncovering more evidence. This approach permits preliminary hypothesis validation based on associative data, such as directly identifying strong correlations between epigenomic variations and traits, supported by proteomic and metabolomic data, thus increasing the likelihood of successful hypothesis verification.

Fig. 1 Multiple omics data types and approaches.

Classification of Multi-Omics

Concept and Classification of Multi-Omics

In the grand schema of biological sciences, the concept of omics represents a revolutionary advancement. It builds upon fundamental biological knowledge and leverages big data, computational technologies, and bioinformatics analyses to perform multi-layered, multi-perspective qualitative and quantitative analyses of organisms, thereby uncovering the mysteries of life forms. Omics approaches examine organisms holistically, studying the comprehensive set of their constituent parts. The term "omics" derives from the Greek root "ome," symbolizing the systemic aggregation of various entities. The concept of omics encompasses several critical fields, including but not limited to:

Genomics: Focuses on the study of the entire set of genes within an organism and their interactions.

Transcriptomics: Aims to elucidate the types and quantities of all transcriptional products within an organism.

Proteomics: Investigates the expression, structure, and function of all proteins in an organism.

Metabolomics: Analyzes the types and quantities of all metabolites within an organism.

Epigenomics: Studies structural changes in the genome, the regulation of epigenetic signals, and their functional implications.

Lipidomics: Explores the lipid molecules within an organism.

Glycomics: Concentrates on the study of carbohydrate molecules within an organism.

Immunomics: Focuses on the molecules related to an organism's immune system.

Radiomics: Utilizes imaging technologies to analyze the characteristics of organisms.

Ultrasomics: Employs ultrasound technology to investigate the features of organisms.

Genomics

The genome comprises the entirety of an organism's genetic material, with genes representing the fundamental units of hereditary information. Genomics aims to elucidate the composition, structure, function, and interactions of the genome, as well as the role of the genome in the development, growth, functionality, and evolution of the organism. The term "genomics" was first proposed by American geneticist Thomas H. Roderick in 1986. As a branch of biology, genomics emerged with the advancements in molecular biology and genetics, and it was significantly propelled by the successful completion of the Human Genome Project, which provided a draft sequence of the human genome. The rapid development of various detection technologies has further driven research in genomics.

Classification of Genomics

1. Metagenomics: Involves extracting all microbial DNA directly from environmental samples to construct metagenomic libraries, utilizing genomic strategies to study the genetic composition and community functions of all microorganisms in the samples.

2. Structural Genomics: Focuses on determining the composition and location of genes through gene mapping and nucleotide sequence analysis. Structural genomics seeks to elucidate the three-dimensional structures of all proteins encoded by a given genome.

3. Functional Genomics: Utilizes information and products from structural genomics to comprehensively analyze gene functions at the genome or system level.

4. Comparative Genomics: Involves comparing known gene features and genome structures using genome mapping and sequencing technologies to understand gene functions, expression mechanisms, and the phylogenetic relationships between different species.

5. Mutational Genomics: Studies mutations occurring in human DNA or genomes, examining their implications and effects.

Primary Research Areas in Genomics

1. Genome Structure: Investigates the composition of the genome, including DNA sequences, gene locations and quantities, and chromosomal structures.

2. Genome Function: Explores gene functions, including how genes encode proteins, regulate physiological processes, and their roles in diseases.

3. Comparative Genomics: Compares genomes of different species to understand their evolutionary relationships and biological differences.

4. Genomic Variation: Studies variations within genomes, such as single nucleotide polymorphisms (SNPs), insertions and deletions (InDels), copy number variations (CNVs), and how these variations influence phenotypes and disease risks.

5. Genomic Technologies: Develops and applies new genomic technologies, such as next-generation sequencing (NGS) and gene editing techniques (e.g., CRISPR-Cas9), to more efficiently analyze and manipulate genomes.

6. Analysis and Interpretation of Genomic Data: Utilizes bioinformatics tools and methodologies to analyze genomic data and extract valuable biological insights.

Transcriptomics

Transcriptomics is the study of the complete set of coding and non-coding RNA molecules transcribed in a cell or a population of cells. The human genome comprises approximately 3 billion base pairs, of which around 50,000 genes are transcribed into messenger RNA (mRNA) molecules. However, only about 40% of these mRNA molecules are translated into proteins, indicating that a significant portion of the transcribed RNA performs essential non-coding functions. This represents a crucial area of investigation within transcriptomics.

The primary objective of transcriptomics is to acquire the total mRNA transcribed in a particular tissue under specific conditions. By analyzing the differences in gene expression at the mRNA level, the relationship between gene expression and phenotype can be elucidated. This analysis can reveal changes in gene expression levels in response to external stimuli or artificial interventions. Additionally, transcriptomics can be employed to identify differential expression patterns across various cell types, tissues, differentiation stages, or disease states.

The transcriptome specifically refers to the complete set of messenger RNA (mRNA) molecules present in a cell or tissue. High-throughput sequencing technologies are employed to sequence complementary DNA (cDNA) libraries, which are generated by reverse transcribing all mRNA present in the sample. The analysis of the resultant reads allows for the determination of transcript structure and quantification of expression levels.

Other RNA Sequencing

Non-coding RNA (ncRNA) sequencing encompasses the analysis of various RNA species, including long non-coding RNA (lncRNA), circular RNA (circRNA), and small RNA. Similar to mRNA sequencing, ncRNA sequencing involves the design of differential comparison groups to identify differentially expressed RNAs and enriched pathways, as well as to explore regulatory mechanisms through target gene prediction.

lncRNA are transcripts longer than 200 nucleotides with limited coding potential. Sequencing of lncRNA involves precise identification and target gene prediction in samples with a reference genome, along with the analysis of mRNA within the sequencing data.

Small RNA molecules, typically endogenous non-coding RNAs less than 200 nucleotides in length, are often between 18-30 nucleotides. The most extensively studied small RNAs are microRNAs (miRNAs), which play critical roles in post-transcriptional regulation of gene expression in both plants and animals.

circRNA are a unique class of ncRNAs characterized by covalently closed loop structures. These molecules are conserved and exhibit tissue-specific expression, and they are implicated in various physiological and pathological processes.

The whole transcriptome approach involves sequencing all RNA species transcribed in a tissue or cell under specific conditions or at a particular time. Whole transcriptome studies encompass the analysis of both mRNA and ncRNA, facilitating the exploration of potential regulatory networks among diverse RNA molecules.

Proteomics

Proteomics is the scientific discipline dedicated to the comprehensive study of the proteome, which encompasses the entire set of proteins expressed by a cell, tissue, or organism. This field focuses on various aspects of proteins, including their expression levels, post-translational modifications, and protein-protein interactions. The term "proteomics" is derived from a combination of "protein" and "genomics," signifying the study of the entire complement of proteins encoded by the genome.

The objective of proteomics is to achieve a large-scale and holistic understanding of protein-related processes, such as disease pathogenesis and cellular metabolism. This encompasses not only the identification and quantification of proteins but also the elucidation of their functional roles and regulatory mechanisms. Proteomics aims to provide a detailed and integrative view of protein dynamics and interactions within the biological system.

Fig. 2 A schematic workflow of proteomics (Yang and Han 2016)

The primary objective of proteomics is to obtain a comprehensive profile of all proteins present in a sample at a given time. This includes determining the sequences and expression levels of proteins, which serve as the executors of gene function. Proteomics facilitates the investigation of changes in protein expression levels in response to external stimuli or experimental treatments. By analyzing these alterations, insights can be gained into the underlying mechanisms of biological processes and the impact of various conditions on protein dynamics.

Metabolomics

Metabolomics, an emerging discipline following genomics and proteomics, aims to qualitatively and quantitatively analyze the dynamic responses of endogenous metabolites to external stimuli, physiological and pathological changes, and genetic mutations. This field primarily focuses on small molecules with a relative molecular mass ≤1000 Da, such as organic acids, amino acids, nucleotides, sugars, lipids, and vitamins.

Metabolomics can be categorized into three main types based on research characteristics: untargeted metabolomics, targeted metabolomics, and widely targeted metabolomics.

Untargeted Metabolomics

This approach involves the unbiased detection of all detectable metabolite molecules in a sample using techniques such as liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR). It aims to capture the dynamic changes of small molecule metabolites in cells, tissues, organs, or organisms before and after exposure to stimuli or perturbations. Subsequent bioinformatics analysis is employed to identify differential metabolites, perform pathway analysis, and elucidate the physiological mechanisms underlying these changes.

Targeted Metabolomics

This method necessitates the use of standard compounds for the metabolites of interest. It involves the construction of standard curves for the target substances to facilitate absolute quantitative analysis of these metabolites.

Widely Targeted Metabolomics

This approach serves as an intermediary between untargeted and targeted metabolomics. It leverages public databases and partial standard mass spectrometry spectra to establish a local database. The ion information detected by mass spectrometry is matched with the corresponding ions of metabolites in the local database, thereby significantly reducing the number of metabolites requiring detection and enhancing the identification and quantification accuracy of the metabolites within the database.

Each type of metabolomics has its specific application scenarios. Currently, untargeted metabolomics is the most widely utilized, being instrumental in the screening of disease-related biomarkers and the investigation of metabolic changes in conjunction with various omics technologies.

Fig. 3 Summary of the metabolomics analysis workflow.

Epigenomics

Epigenomics emerged gradually in the 1980s as a discipline to explain many biological phenomena that do not adhere to classical Mendelian genetic principles. Epigenetics refers to heritable changes in gene function that occur without alterations in the DNA sequence itself. These changes ultimately influence phenotype expression.

Various epigenetic phenomena are known, including DNA methylation, genomic imprinting, maternal effects, gene silencing, nucleolar dominance, activation of dormant transposons, and RNA editing. These mechanisms illustrate how gene expression can be regulated beyond the direct sequence of DNA, impacting cellular differentiation, development, and response to environmental stimuli. Epigenetic modifications play a crucial role in understanding complex biological processes and their implications in health and disease.

Lipidomics

Lipids are hydrophobic or amphipathic small molecules. As of November 10, 2023, the most authoritative lipid database, LIPID MAPS (https://www.lipidmaps.org/), contains 48,179 lipid structures categorized into eight classes: fatty acids, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, and polyketides.

Lipidomics, a subfield of metabolomics, systematically analyzes and identifies lipids and their interacting molecules within organisms, tissues, or cells. It aims to reveal the metabolic processes of lipids and their implications in the physiological and pathological processes of cells, organs, and organisms. Advances in chromatography, mass spectrometry, and other analytical techniques have positioned lipidomics as a crucial tool in studying human diseases and physiological processes.

The development of lipidomics analysis techniques and deeper research into lipid metabolism have highlighted the close association between lipid metabolism abnormalities and various physiological disorders. Lipids play pivotal roles in the onset, progression, and treatment of diseases. Moreover, lipid stability in clinical metabolic profiling of blood samples surpasses that of most metabolites, making lipids highly sought after as potential biomarkers in diseases such as cancer, infectious diseases, and neurology.

A typical workflow in lipidomics includes sample collection, lipid extraction, metabolite detection, and data analysis. The Lipidomics Standards Initiative (LSI, https://lipidomicstandards.org/) provides researchers with comprehensive guidelines covering sample collection, lipid extraction, MS analysis, lipid identification, and quantification. These standards facilitate rigorous and standardized lipidomic research methodologies across the scientific community.

Fig. 4 General Workflow of Lipidomics (Ashley V. Ward et al,. 2021)

Glycomics

Glycomics refers to the systematic collection, analysis, and utilization of biological data related to glycan structures, investigating their compositions and functions at the omics level. Emerging after genomics and proteomics, glycomics employs key technologies such as liquid chromatography-mass spectrometry (LC-MS). LC-MS deciphers different glycoprotein subtypes from glycosylated modifications, providing structural insights into glycoconjugates. High-throughput proteomics, offering unbiased results on cellular proteomes, is currently extensively applied in glycomics research.

Fig. 5 Glycomics/glycoproteomics strategies (Yoshimi Haga et al,. 2022)

Immunomics

Immunomics encompasses three main research areas: immunogenomics, immunoproteomics, and immunoinformatics. It emphasizes leveraging bioinformatics, microarrays, systems biology, structural biology, high-throughput screening, and other technologies based on genomics and proteomics research. These technologies enable extensive studies on the molecular mechanisms of immune systems and immune response, facilitating the discovery of new immune-related molecular functions. Proteomics, aiming to separate and identify proteins constituting biological entities on a large scale with high efficiency, represents a novel research field. Immunoproteomics, arising alongside proteomics, plays a crucial role in the rapid advancement of immunology. Immunomics focuses on studying the comprehensive set of molecules related to immunity, their target molecules, and functions.

Radiomics

Radiomics is an interdisciplinary research field at the intersection of computer science and medicine, involving the quantitative extraction of imaging features from medical images. Statistical and machine learning methods are used to identify the most valuable radiomic features. These features often reflect tissue and lesion characteristics such as heterogeneity and shape, which can be used alone or in combination with demographic, histological, genomic, or proteomic data to analyze clinical information. The general workflow of radiomics includes feature computation and selection, dimensionality reduction, and data processing.

Ultrasoundomics

Ultrasoundomics is an advanced ultrasound medical data analysis method in the era of artificial intelligence. As a branch of radiomics, it aims to automate data feature analysis and high-throughput feature extraction from ultrasound images using data feature algorithms. This field utilizes deep learning or machine learning analysis methods to establish systems for intelligent clinical decision-making.

Ultrasoundomics originates from the concept of "omics" in computational biology, guiding disciplines such as genomics, transcriptomics, and proteomics. In the medical field, ultrasoundomics applies ultrasound image data to medical diagnosis and treatment based on image analysis. By collecting three-dimensional ultrasound data from different angles within human tissues using ultrasound imaging devices, extensive analysis and classification of these data reveal tissue structures and functional characteristics within organs, achieving more accurate medical diagnostics.

References

1. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017
2. Al-Sulaiti, H.; Almaliti, J.; Naman, C.B.; Al Thani, A.A.; Yassine, H.M. Metabolomics Approaches for the Diagnosis, Treatment, and Better Disease Management of Viral Infections. Metabolites 2023