Choosing the right omics type begins with a question: Which molecular layer or layers hold the answer to the biological phenomenon I am observing?
The conventional layers remain the backbone for biological discovery:
The genome: What could happen.
The transcriptome: What a cell is trying to do.
The proteome: What is actually being done.
The metabolome: The functional endpoint closest to phenotype.
A single omics layer cannot reveal the whole state of the biological system, as these layers are not completely interchangeable. For example, the relationship between mRNA and protein abundance is complex, non-linear, and varies considerably from protein to protein, so for most genes the two correlate only moderately.
The risk: If your question is functional, transcriptomics alone will quietly mislead you (Samih et al., 2025).
Deciding which layer holds the most informative biological information is just the first of three questions. The other two decide at what resolution you measure it and what you read out – abundance or function. This is where modern experimental design is heading.
Resolution: Bulk, Single-cell or Spatial
The same molecular layer can be measured at very different resolutions, and the choice should follow your biology.
Bulk omics average signal across millions of cells. They remain the most cost-effective, deepest, and most reproducible option, and they are ideal for population-level questions, discovery screens, and well-mixed samples. Their limitation is structural – they average away cellular heterogeneity, so a rare but important subpopulation can vanish into the mean.
Single-cell omics recover that heterogeneity. Single-cell RNA-seq is mature, but the frontier is that the other layers are now catching up. In single-cell proteomics, mass-spectrometry workflows have advanced rapidly, now identifying up to 4,000 protein groups per single mammalian cell. Single-cell metabolomics is earlier-stage, but mass-spectrometry approaches are pushing toward comprehensive metabolite coverage at single-cell resolution (Ctortecka et al., 2024), (Aldridge & Teichmann et al., 2020).
Spatial omics keep molecules in their native tissue location. Spatial approaches combine molecular profiling with imaging to capture both the expression signatures and the physical positions of cells within a tissue, enabling the study of how cells relate to their surroundings. This too now spans all three modalities – spatial transcriptomics, proteomics and metabolomics (Loach et al., 2025).
The takeaway: The resolution decision is a question about your hypothesis – is cellular heterogeneity or tissue architecture the thing you’re studying, or is it noise you can safely average over?
Readout: Abundance/Presence vs. Function
Most omics measure how much of something is present. A growing and arguably more decision-relevant family – functional omics – measures what those molecules are doing. This is the axis most often skipped, and it is frequently where the biological answer lives.
Functional genomics asks what a gene does, not just whether it is expressed. CRISPR perturbation screens with single-cell readouts (Perturb-seq) connect genotype to phenotype at scale. The field is now adding spatial and multimodal readouts – Perturb-Multimodal, for example, builds large-scale genotype-phenotype maps in intact tissue by pairing pooled in vivo CRISPR screens with imaging and sequencing (Saunders et al., 2025), (Replogle et al., 2022).
Functional proteomics goes beyond protein abundance to engagement and activity. Knowing how much of a protein is present says nothing about what it binds or whether it is catalytically active. Two complementary stability-proteomics methods address this directly, both used for target deconvolution:
- The Proteome Integral Solubility Alteration (PISA) assay streamlines thermal proteome profiling by integrating protein solubility across a temperature range into a single measurement, sharply increasing throughput for proteome-wide drug-target identification (Gaetani et al., 2019).
- The Peptide-centric Local Stability Assay (PELSA) uses disruptive trypsinization to amplify ligand-induced local stability shifts, identifying target proteins and their binding regions without chemically modifying the ligand (Li et al., 2025).
Metabolomics sits naturally at this functional end of the spectrum too, since metabolite levels are the readout closest to actual cellular activity and phenotype.
More Omics Is Not The Goal
Choosing your omics is no longer a one-dimensional pick. It is a three-axis decision framework:
The molecular layer: genome → transcriptome → proteome → metabolome.
The resolution: bulk → single-cell → spatial.
The readout: abundance/presence → function/engagement.
More omics is not the goal. A single assay placed correctly on these axes routinely outperforms a pile of mismatched data, and multi-omic integration only pays off when each added layer answers a genuinely distinct question.
Written by: Karolis Krinickis
Cover image credits: Garun Studios / Adobe Stock
