Brendan Floyd (Stanford, CA / US), Nicholas Till (Stanford, CA / US), Elizabeth Schmidt (Stanford, CA / US), Jonathan Yang (Stanford, CA / US), Pinyu Liao (Stanford, CA / US), Carolyn Bertozzi (Stanford, CA / US)
The cell surface is a highly heterogenous environment that harbours proteins critical for immune cell activation, cell-cell interactions, and cellular signaling among many other tasks. . Despite the many critical roles of proteins found on the cell surface, the importance of protein arrangement on the cell surface has only recently come to be recognized. Methods such as high-resolution fluorescence microscopy and proximity labeling enable studies of protein arrangement on the cell surface. These methods have currently not been used to systematically analyze the arrangement of the entire cell surface proteome. In this study a combination of low- and high-resolution proximity labeling methods along with an optimized DIA mass spectrometry workflow were used to systematically map the spatial distribution of the cell surface proteome on human T- and B-lymphoblast cell lines. Antibodies for over 80 surface proteins were tested and used to bind surface proteins of interest and collect a proximity labeling dataset that encompasses the entire surface proteome. Network analysis and a high accuracy machine learning classifier allowed for the determination of cell surface protein colocalization and the identification of functional clusters on the cell surface. These clusters include regions of "unconventional" cell surface proteins previously unstudied. Overall, this study illuminates the arrangement of the cell surface proteome which provides insight for informed cell surface engineering among other goals.