Document text
Principal Investigator: Nir Hacohen
Organization: BROAD INSTITUTE, INC.
Fiscal Year: 2024
Award: $210,516
Funding agency: National Cancer Institute
The development of scalable, cost-effective antibody libraries for detecting proteins (or other molecules) has
greatly lagged the development of methods for detecting (e.g. by hybridization or sequencing) specific RNA or
DNA sequences. If we were able to engineer antibodies against diverse targets and create large-scale libraries
of barcoded antibodies that could be detected by nucleic acid hybridization or sequencing, many applications
would be enabled to empower the cancer research community, including high-dimensional cell cytometry (similar
to CITE-seq), spatially-indexed sequencing (e.g. using Slide-seq, MERFISH, etc), in situ sequencing to detect
antibodies bound to targets (similar to CODEX) and targeted proteomics (such as the Olink proximity extension
assay). These applications currently rely on laborious and expensive barcoding of individual (commercial)
antibodies, followed by pooling for parallel detection. Here, we propose to develop a novel strategy to discover,
engineer and apply synthetic VHH-domain nanobodies against hundreds to thousands of human protein targets
simultaneously, thus enabling creation of sequence-defined antibodies and enabling rapid production of pools of
barcoded-antibodies at large scale by any investigator. Our proposal builds novel methods that leverage our
recently developed cell-free platform for producing large libraries of distinct nanobodies (with an input library of
1011-1012 complexity) that are barcoded with their encoding RNA by ribosome display. We will develop methods
to use these libraries for parallel selection of nanobodies that bind each of hundreds (to thousands) of cell surface
proteins (i.e. a many-to-many screen) in cancer cells. Our approach combines cell-free nanobody engineering,
ectopic ORF expression or CRISPR/Cas9 knockout, with single cell sequencing to achieve generation of
nanobodies against a large number of targets in parallel. Importantly, by analyzing many targets at once, our
approach for the first time validates nanobody specificity at scale by measuring off-target binding systematically
for each nanobody to ensure binding specificity. We envision that the resulting methods for discovering and
using nanobodies against cancer cell surface molecules would enable highly multiplexed single cell and spatial
tissue proteomics. Building on the proposed proof of principle, one can envision the platform enabling any lab to
develop sequence-defined novel detection reagents and highly multiplexed libraries of detection reagents
against conventional and unconventional targets across many projects of high value to cancer research.
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