Label-free single-cell proteomics


Recently, Matthias Mann and colleagues published a preprint (doi: 10.1101/2020.12.22.423933v1) reporting a label-free mass-spectrometry method for single-cell proteomics. Many colleagues asked me what I think about the preprint, and I summarized a few comments in the peer review below. I did not examine all aspects of the work, but I hope my comments are useful:

Dear Matthias and colleagues,

I found your preprint interesting, especially as it focuses on an area that recently has received much attention. Methods for single-cell protein analysis by label-free mass-spectrometry have made significant gains over the last few years, and the method that you report looks promising. Below, I suggest how it might be improved further and benchmarked more rigorously.

To analyze single Hela cells, you combined the recently reported diaPASEF method with Evosep LC and timsTOF improvements developed in collaboration with Bruker. This is a logical next step and sounds like a good approach for label-free MS. The method quantifies about 1000 proteins per Hela cell, a coverage comparable to published DDA label-free methods (doi: 10.1039/D0SC03636F) and reported by the Aebersold group for a DIA method performed on a Lumos instrument (data presented at the third Single-Cell Proteomics Conference). This is a good coverage, though given the advantages of diaPASEF and the timsTOF improvements, there is potential for even better performance. I look forward to exploring the raw data.

The major advantage of your label-free MS approach is its speed. It is faster than previously reported label-free single-cell proteomics methods, which allowed you to analyze over 400 single Hela cells, generating the largest label-free dataset to date. This increased speed is a major advance for label-free single-cell proteomics. The speed (and thus throughput) can be increased further based on multiplexing using the isobaric carrier approach.  

You combine Hela data from single-cell MS analysis with Hela data from two scRNA-seq methods. This is good, and I think such joint analysis of protein and RNA should be an integral part of analyzing single-cell MS proteomics data. The results shown in Fig. 5A,B are straightforward to interpret and indicate that your method compares favorably to scRNA-seq in terms of reproducibility and missing data. The interpretation of Fig. 5A, B is more confounded by systematic biases. Both mass-spec and sequencing have significant biases, such as sequence-specific biases and peptide-specific ionization propensities. These biases contribute to estimates of absolute abundances (doi: 10.1038/nmeth.2031, 10.1038/nbt.2957) and might contribute to the variance captured by PC2 in Fig. 5C, and thus may alter your conclusion.

I have possible suggestions:  

— Benchmark the accuracy of relative quantification. Ideally, this can be done by measuring protein abundance in single cells by an independent method (such as fluorescent proteins measured by a FACS sorter) and comparing the measurements to the MS estimates. You may possibly choose other methods, such as spiked in protein/peptide standards. Benchmarks of accuracy (rather than merely reproducibility) would strengthen your study. 

— Order the unperturbed Hela cells by the cell division cycle (CDC) phase and display the abundances of the periodic proteins.

— Provide more discussion positioning your work in the context of the field and other approaches, in terms of technology, depth of coverage, throughput, and biological applications.

Nikolai Slavov

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