Back

Robust and scalable single-molecule protein sequencing with fluorosequencing

Mapes, J. H.; Stover, J.; Stout, H. D.; Folsom, T. M.; Babcock, E.; Loudwig, S.; Martin, C.; Austin, M.; Howdieshell, C. J.; Simpson, Z. B.; Blom, T.; Weaver, D.; Winkler, D.; Velden, K. V.; Ossareh, P. M.; Beierle, J. M.; Somekh, T.; Bardo, A. M.; Anslyn, E. V.; Marcotte, E. M.; Swaminathan, J.

2023-09-16 molecular biology
10.1101/2023.09.15.558007 bioRxiv
Show abstract

The need to accurately survey proteins and their modifications with ever higher sensitivities, particularly in clinical settings with limited samples, is spurring development of new single molecule proteomics technologies. Fluorosequencing is one such highly parallelized single molecule peptide sequencing platform, based on determining the sequence positions of select amino acid types within peptides to enable their identification and quantification from a reference database. Here, we describe substantial improvements to fluorosequencing, including identifying fluorophores compatible with the sequencing chemistry, mitigating dye-dye interactions through the use of extended polyproline linkers, and developing an end-to-end workflow for sample preparation and sequencing. We demonstrate by fluorosequencing peptides in mixtures and identifying a target neoantigen from a database of decoy MHC peptides, highlighting the potential of the technology for high sensitivity clinical applications.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.