A multidimensional approach to an in-depth proteomics analysis of transcriptional regulators in neuroblastoma cells

Qing Li, Mohit Raja Jain, Wei Chen, Hong Li

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


The dynamic regulation of transcriptional events is fundamental to many aspects of neuronal cell functions. However, proteomics methods have not been routinely used in global neuroproteomics analyses of transcriptional regulators because they are much less abundant than the "house-keeping" proteins in cells and tissues. Recent improvements in both biochemical preparations of nuclear proteins and detection sensitivities of proteomics technologies have made the global analysis of nuclear transcriptional regulators possible. We report here an optimised neuroproteomic method for the analysis of transcriptional regulators in the nuclear extracts of SHSY-5Y neuroblastoma cells by combining an improved nuclear protein extraction procedure with multidimensional peptide separation approaches. We found that rigorous removal of cytoplasmic proteins and solubilisation of DNA-associated proteins improved the number of nuclear proteins identified. Furthermore, we discovered that multidimensional peptide separations by either strong cation exchange (SCX) chromatography or electrostatic repulsion-hydrophilic interaction chromatography (ERLIC) analysis detected more than 1800 nuclear proteins, which constitutes one of the largest datasets of nuclear proteins reported for a neuronal cell. Thus, in-depth analysis of transcriptional regulators for studying neurological diseases are increasingly feasible.

Original languageEnglish (US)
Pages (from-to)118-127
Number of pages10
JournalJournal of Neuroscience Methods
Issue number2
StatePublished - Jun 5 2013

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)


  • Electrostatic repulsion-hydrophilic interaction chromatographic (ERLIC)
  • Neuroblastoma
  • Neuroproteomics
  • SHSY-5Y cell
  • Strong cation exchange (SCX)
  • Transcription factor


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