Mass spectrometry (MS) has become the leading protein identification technique. Current mass spectrometry methods rely on tandem MS/MS detection, where a prepared peptide digestion sample is injected into the mass spectrometer and analyzed by its mass (MS1), followed by fragmentation and a second round of analysis of the fragments (MS2). MS/MS is limited in its detection sensitivity due to low protein abundance, and the need for serial fragmentation of each peptide in question. These challenges limit the applications of MS in small sample amounts and in detecting low expression proteins, such as regulatory proteins, which are often seriously under-represented in the current MS data.

Researchers in the Kirschner Lab have developed a suite of biochemical and computational methods to address the problem of mass degeneracy. By combining biochemical approaches such as amino-acid specific isotopic labeling, Edman degradation, and computational methods such as super-resolution feature analysis and machine learning algorithms, they were able to significantly increase the amount of information extracted from MS1 scans, which allows accurate peptide identification from MS1 alone. Compared with tandem MS/MS approaches, methods developed by the Kirschner Lab provide increased levels of depth of coverage and detection sensitivity.

With further implementation and optimization, these methods could eventually provide mass spectrometry with the high sensitivity and robustness that is critical for applications such as single-cell proteomics and analysis of ultra-small clinical samples.