Cell Surface Glycoprotein Technology Development for Small Sample Sizes


Over the past decade, our laboratory and collaborators have applied a powerful discovery-driven approach termed Cell-Surface-Capture (CSC) to identify cell surface proteins to develop new markers for immunophenotyping, immunotherapy, and discovering signalling pathways involved in immune and cardiac development and disease. CSC is an antibody-independent strategy where N-glycoproteins are selectively labelled, enriched and identified by mass spectrometry (MS). Although we have successfully applied CSC to identify new cell surface markers for heart, liver and pluripotent stem cells, it classically requires a large amount of source material to obtain high quality data (e.g. 100 million cells to identify 400-600 N-glycoproteins).

To overcome these limitations, we have set out to develop a new version of the CSC that can be applied to limited sample sizes. Funded in 2016 by an R01 award from NHLBI, we have recently combined automated liquid handling and alternative enrichment strategies to develop a microscale version of CSC (µCSC). An automated liquid handling workstation is used to complete sample handling, reducing manual intervention and benefiting reproducibility. Adaptation of magnetic streptavidin beads for sample enrichment avoids filter-based separations and greatly reduces sample losses. To date, based on our technological advancements, we are now able to identify >400 N-glycoproteins from just 1-2 mg of total peptide (1-8 million primary human cells depending on cell type) !

Overall, this new µCSC technology enables cell type specific studies not previously possible. We are using this new technology to develop cell surface markers for stem cell derived cardiomyocytes, mapping the human heart surfaceome in a cell-type specific approach, studying advanced heart failure, and for developing new insights into human development and disease related to blood cancers, viral infection, and pancreatic cells.

The Cell Surface Protein Atlas:
Bausch-Fluck et al., PLoS One, 2015

Studies that have used the Atlas to identify markers:
Mallanna, et al., Stem Cell Reports, 2016