Abstract
Large-scale single-cell analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems, but have largely been limited to RNA-based technologies. Here we present a comprehensive benchmarked experimental and computational workflow, which establishes global single-cell mass spectrometry-based proteomics as a tool for large-scale single-cell analyses. By exploiting a primary leukemia model system, we demonstrate both through pre-enrichment of cell populations and through a non-enriched unbiased approach that our workflow enables the exploration of cellular heterogeneity within this aberrant developmental hierarchy. Our approach is capable of consistently quantifying ~1000 proteins per cell across thousands of individual cells using limited instrument time. Furthermore, we develop a computational workflow (SCeptre) that effectively normalizes the data, integrates available FACS data and facilitates downstream analysis. The approach presented here lays a foundation for implementing global single-cell proteomics studies across the world.
Highlights
Large-scale single-cell analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems, but have largely been limited to RNA-based technologies
Since multiplexed scMS data present challenges for computational data analysis and should ideally be processed in a streamlined and reproducible manner, we develop SCeptre (Single Cell proteomics readout of expression); a python package tightly integrated with Scanpy[40], that enables quality control, normalization of batch effects and biological interrogation of multiplexed scMS data
Given the ease by which its distinct subpopulations can be isolated, we reasoned that the OCIAML8227 model system was ideal for the development and showcasing of an easy-implementable scMS approach (Fig. 1b)
Summary
Large-scale single-cell analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems, but have largely been limited to RNA-based technologies. Detection of proteins in single-cells was first enabled by antibody-based technologies like Western blot or flow and mass cytometry; these methods depend on the availability of high-quality antibodies and are inherently limited in their multiplexing capacity[23]. Budnik and colleagues originally proposed the use of isobaric labeling for single-cell proteomics, called ScoPE-MS24 and the development was continued with ScoPE225 Their goal was to increase throughput of single-cell measurements through multiplexing, and to make use of a carrier channel to provide more peptide copies (200-cell equivalent), and ions for peptide identification in addition to the ions in the low abundant single-cell channels; a similar strategy to other low-input sample measurements in the past[26]. Comprehensive evaluations of multiplexed scMS using an isobaric carrier[29,35,36] have further demonstrated the feasibility of the approach, concluded on the tradeoffs of increasing the level of signal boosting with the isobaric carrier, and indicated the importance of estimating the reliability of protein quantification when implementing the method
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