4.8 Article

Highly multiplexed and quantitative cell-surface protein profiling using genetically barcoded antibodies

Publisher

NATL ACAD SCIENCES
DOI: 10.1073/pnas.1721899115

Keywords

phage display; NGS; cell surface proteomics; biomarkers; leukemia

Funding

  1. NIH [S10 OD018174, R01CA191018, P41CA196276]
  2. Harry and Dianna Hind Professorship in Pharmaceutical Sciences
  3. Canadian Institutes of Health Research [365646, 342551]
  4. NATIONAL CANCER INSTITUTE [R01CA213138, P41CA196276, R01CA191018] Funding Source: NIH RePORTER
  5. NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [T32GM064337, R35GM122451] Funding Source: NIH RePORTER
  6. OFFICE OF THE DIRECTOR, NATIONAL INSTITUTES OF HEALTH [S10OD018174] Funding Source: NIH RePORTER

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Human cells express thousands of different surface proteins that can be used for cell classification, or to distinguish healthy and disease conditions. A method capable of profiling a substantial fraction of the surface proteome simultaneously and inexpensively would enable more accurate and complete classification of cell states. We present a highly multiplexed and quantitative surface proteomic method using genetically barcoded antibodies called phage-antibody next-generation sequencing (PhaNGS). Using 144 preselected antibodies displayed on filamentous phage (Fab-phage) against 44 receptor targets, we assess changes in B cell surface proteins after the development of drug resistance in a patient with acute lymphoblastic leukemia (ALL) and in adaptation to oncogene expression in a Myc-inducible Burkitt lymphoma model. We further show PhaNGS can be applied at the single-cell level. Our results reveal that a common set of proteins including FLT3, NCR3LG1, and ROR1 dominate the response to similar oncogenic perturbations in B cells. Linking high-affinity, selective, genetically encoded binders to NGS enables direct and highly multiplexed protein detection, comparable to RNA-sequencing for mRNA. PhaNGS has the potential to profile a substantial fraction of the surface proteome simultaneously and inexpensively to enable more accurate and complete classification of cell states.

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