Multiomics Analysis
Multiomics Analysis - Related Videos
07:47
Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker
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2023
Here, a protocol is presented in which multiple bioinformatic tools are combined to study the biological functions of TMEM200A in cancer. In addition, we also experimentally validate the bioinformatics predictions.
09:09
Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics
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Cited by 6 •
2022
Here, we present a protocol for isolating nuclei from flash-frozen, archived liver tissues for single-nucleus RNA-seq, ATAC-seq, and joint multiomics (RNA-seq and ATAC-seq).
11:00
Microfluidic Chip for Axonal Injury Models Construction and Enabling Multi-Omics Analysis
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2025
This protocol describes a microfluidic system modeling neuronal metabolic dynamics post-axonal injury, enabling imaging, multi-omics analysis, and mechanistic studies of intrinsic metabolic remodeling.
07:59
High-Quality Brain and Bone Marrow Nuclei Preparation for Single Nuclei Multiome Assays
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Cited by 1 •
2023
The success of single-cell/single-nuclei transcriptomics and multi-omics largely depends on the quality of cells/nuclei. Therefore, isolating cells/nuclei from tissue and their purification must be highly standardized. This protocol describes the preparation of nuclei from the brain and bone marrow for downstream single-nuclei multiome assay.
03:49
Optimized Nuclei Isolation from Fresh and Frozen Solid Tumor Specimens for Multiome Sequencing
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Cited by 4 •
2023
The protocol provides a reliable and optimized approach to the isolation of nuclei from solid tumor specimens for multiome sequencing using the 10x Genomics platform, including recommendations for tissue dissociation conditions, cryopreservation of single-cell suspensions, and assessment of isolated nuclei.
FAQs
Genomics records genetic variation, transcriptomics reflects gene expression, proteomics captures proteins and their activity, and metabolomics measures metabolite changes. Examining these layers together allows analysts to connect upstream molecular differences with downstream biochemical consequences. This complementarity can expose relationships that remain ambiguous when one dataset is interpreted without the others.
Computational and statistical methods make datasets from different omics layers suitable for joint interpretation. They align molecular features across datasets, identify correlations among those features, and help trace patterns from genetic variation through expression, protein activity, and metabolite changes. Their role is essential because biological meaning emerges from relationships among measurements, not simply from collecting more measurements.
Correlations connect molecular features that occur together across datasets. In Multiomics Analysis, these relationships can link genetic variation with changes in gene expression, protein activity, or metabolites. Examining coordinated patterns helps investigators interpret regulatory pathways, enzyme-related effects, cellular responses, and disease-associated signatures within the broader biochemical behavior of a biological system.
A single molecular layer may show that a feature changes without showing how that change relates to other molecular events. Integrating complementary datasets can connect altered genes with expression patterns, protein activity, and metabolite states. These cross-layer relationships provide a stronger basis for investigating regulatory pathways and biochemical mechanisms than isolated observations.
An analysis begins with high-throughput measurements from selected molecular layers. Computational and statistical procedures then align molecular features across datasets, search for correlations, and examine relationships extending from genetic variation to gene expression, protein activity, and metabolites. The integrated results can then be interpreted in the context of the biological system and the question under study.
In biochemistry, the approach can clarify regulatory pathways, enzyme function, cellular responses, and disease-associated molecular signatures. Relating molecular changes across layers helps investigators examine whether altered genetic information corresponds with changes in expression, protein activity, or metabolite behavior. These insights support systems biology, biomarker discovery, and research directed toward more targeted therapeutic strategies.