Multi-Omics Analysis Unveils the Hidden Patterns of Gene Expression Control

Exploring cell's regulatory logic, revealing how organisms achieve precise control over their molecular machinery.

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In a previous post Connecting the Dots: A Guide to Multi-Omics Data I explored the mutual information shared between different omics data types, the biological processes and stimuli that trigger changes in these measurements, and the timescales on which these changes occur. I also examined potential cause-and-effect relationships among the different types of measurements.

Then, in a more recent article Statistical Analysis in High-Throughout Biology: Why Context Matters I wrote about how the properties of different biomolecules – from RNA to phosphorylated proteins – demand tailored analytical approaches that reflect their underlying biology. Today’s article will be a bit less abstract, instead focusing on some practical visualizations we can use when working with multi-omics data.

This article was inspired by a recent project, where the team I was working with performed bulk RNA-sequencing and mass-spectrometry based proteomics analysis on micro dissected tissue. Statistical analysis of these paired measurements revealed three types of patterns, each of which are interested in their own right:

1. Genes where RNA and protein levels change in concert, suggesting direct regulation;

2. Genes where RNA levels increase or decreases while protein levels remain stable, which speaks to post-transcriptional control; and

3. Genes where protein levels change independently while RNA levels remain stable, pointing to protein-level regulation.

These patterns serve as a window into the cell's regulatory logic, revealing how organisms achieve precise control over their molecular machinery. To better visualize these processes, we can use an gene-protein scatter plot, which plots changes in gene expression (RNA levels) on the x-axis against changes in protein levels on the y-axis. In the plot below, we can see thousands of genes-protein pairs plotted simultaneously (the logarithmic scale helps us visualize both small and large changes, with positive values indicating increases in the gene/protein over time and negative values showing decreases in the gene/protein over time). a

The position each dot relative to both axes tells reveals some insights into how gene expression and protein levels are changing relative to one another, but it’s the color coding that brings the regulatory patterns into sharp focus:

• Purple dots represent genes with "concordant" changes – cases where RNA and protein levels rise or fall together. These often represent the cell's most direct regulatory responses, where increased need for a protein is met with increased production of both RNA and protein. The perfect correlation (1.00) among these changes suggests tight coordination between transcription and translation.

• Blue dots highlight genes with "RNA-specific" changes, where RNA levels change significantly while protein levels remain relatively constant. This pattern often reveals sophisticated cellular control mechanisms. For example, post-transcriptional regulation might be actively maintaining protein levels despite changes in RNA abundance. These cases remind us that measuring RNA levels alone doesn't tell the complete regulatory story.

• Red dots indicate "protein-specific" changes – cases where protein levels shift without corresponding RNA changes. These often represent the cell's rapid response mechanisms, where protein modifications, stability changes, or targeted degradation allow quick adjustments without waiting for transcriptional changes. This pattern reminds us that cells have multiple regulatory levers they can pull to achieve their goals.

This type of analysis serves multiple purposes in modern molecular biology. First, it helps identify which regulatory mechanisms might be at play for different genes, guiding further experimental investigation. Second, it reveals the relative prevalence of different regulatory strategies, showing how cells balance rapid protein-level responses with longer-term transcriptional changes. Finally, it can highlight unexpected patterns that lead to new discoveries about cellular regulation.

By applying this analytical approach across different conditions, cell types, and organisms, we can build a deeper understanding of cellular regulation. This knowledge has practical applications ranging from drug development to biotechnology, helping predict how cells will respond to various interventions and pointing the way toward new discoveries about how cells control their molecular machinery.