For years, epitranscriptomics has been treated as a discovery-stage curiosity — fascinating in a well-funded lab with abundant sample, impractical almost everywhere else. The reason isn’t the biology. It’s the tools.

LC-MS and NGS-based modification profiling were built for a world where sample is plentiful. Most translational research doesn’t live in that world. Patient biopsies, rare cell populations, early-timepoint samples — these are exactly the sample types where modification data would matter most, and exactly the sample types standard workflows can’t handle.

This creates a quiet selection bias. The studies that get done are the ones with enough material and enough budget for mass spec or sequencing — not necessarily the ones asking the most important biological questions. A promising modification-associated biomarker candidate can sit unvalidated for years simply because no lab could spare the microgram-level input a bulk profiling method demands.

The fix isn’t a better version of the same approach. It’s rethinking what “enough sample” means. Workflows that report absolute, site-specific modification status from a single nanogram of input change which questions are answerable — not just which ones are cheaper to ask.

If your research has been shaped around what your samples can’t do, that’s worth revisiting.