Abstract

Single-molecule techniques for protein sequencing are making headway towards single-cell proteomics and are projected to propel our understanding of cellular biology and disease. Yet, single cell proteomics presents a substantial unmet challenge due to the unavailability of protein amplification techniques, and the vast dynamic-range of protein expression in cells. Here, we describe and computationally investigate the feasibility of a novel approach for single-protein identification using tri-color fluorescence and plasmonic-nanopore devices. Comprehensive computer simulations of denatured protein translocation processes through the nanopores show that the tri-color fluorescence time-traces retain sufficient information to permit pattern-recognition algorithms to correctly identify the vast majority of proteins in the human proteome. Importantly, even when taking into account realistic experimental conditions, which restrict the spatial and temporal resolutions as well as the labeling efficiency, and add substantial noise, a deep-learning protein classifier achieves 97% whole-proteome accuracies. Applying our approach for protein datasets of clinical relevancy, such as the plasma proteome or cytokine panels, we obtain ~98% correct protein identification. This study suggests the feasibility of a method for accurate and high-throughput protein identification, which is highly versatile and applicable.

Highlights

  • Modern DNA sequencing techniques have revolutionized genomics [1], but extending these methods to routine proteome analysis, and to single-cell proteomics, remains a global unmet challenge

  • Macromolecules identification methods are central for most biological and biomedical studies, and while the field of genomics advanced to single-molecule resolution, the proteomic field still relies on bulk and costly techniques

  • We describe a solution for single protein identification, based on the analysis of optical traces obtained from fluorescentlylabeled proteins threaded through a nanopore and processed by a pattern recognition algorithm

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Summary

Introduction

Modern DNA sequencing techniques have revolutionized genomics [1], but extending these methods to routine proteome analysis, and to single-cell proteomics, remains a global unmet challenge. This is attributed to the fundamental complexity of the proteome: protein expression level spans several orders of magnitude, from a single copy to tens of thousands of copies per cell; and the total number of proteins in each cell is staggering [2]. Protein sequencing techniques, such as mass-spectrometry, have not reached single-molecule resolution, and rely on bulk averaging from hundreds of cells or more [3]. To date profiling of the entire proteome of individual cells remains the ultimate challenge in proteomics [7]

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