This early-stage company with promising science but limited resources faced the same reality many startups encounter: They did not have the LC-MS instrumentation or experience required for high-quality proteomics analysis.

Building this capability in-house was not a viable option because it would have required considerable capital investment in equipment and the expertise was missing on their team. This predicament represents a widespread challenge for growing biotechs that are constantly trying to balance scientific progress with financial sustainability. Proteomics data are vital for understanding drug mechanisms and optimizing candidates; however, the infrastructure required to generate these data often remains out of reach until later funding stages.

This company needed to measure global and targeted proteomics changes in mutant mouse models, which required sophisticated analytical approaches. This is especially true when focusing on protein translation at the three-prime end (3′-end), the region affected by a premature stop codon in muscular dystrophy. Its potential progress hinged on acquiring reliable proteomics data.

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