With the advancement of mass spectrometry, large-scale bottom-up proteomics has become common. However, it is still extremely difficult to elucidate in detail of proteoforms, i.e., the structural changes caused by the shift of various post-translational modification sites and splicing in biological proteins, using only bottom-up shotgun analysis at the peptide level. The two-dimensional electrophoresis method, which attracted much attention and was widely used in the early years of proteomics, is still the most useful method to easily and visually separate nearly 10,000 proteins from biological tissues, cells, body fluids, etc. However, it is difficult to perform because it requires techniques to separated proteins quantitatively with high reproducibility, and it is time-consuming, skill-consuming, and thus the use of 2D electrophoresis for proteomics has gradually declined. Against this background, we have previously succeeded in developing a fully automated 2D electrophoresis system (Auto2D), which is simple and easy to use with high resolution, sensitivity, and reproducibility in a short time, and proposed to apply it to proteoform analysis using top-down proteomics, which has recently attracted attention.

In this presentation, we will show some successful experimental results obtained by using Auto-2D, with tumor samples including cranial nerve tumors such as neurofibroma, malignant peripheral schwannoma, drug-resistant malignant gliomas, glioma stem cells, and treatment-resistant prostate cancer, in order to identify their target proteins and molecules. In addition, the post translational modifications such as phosphorylation, glycosylation, proteolysis, alternative splicing, and other changes in the target molecule proteoforms involved in malignant transformation and drug resistance have been successfully identified. We present how the proteoforms identified with these strategies using Auto2D are useful for the development of drugs and clinical markers, and will discuss future perspectives of the top-down proteomics.