Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments

Abstract

Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition and data analysis. We expect that broadly accepted community guidelines and standardized metrics will enhance rigor, data quality and alignment between laboratories. Here we propose best practices, quality controls and data-reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics. Resources and discussion forums are available at https://single-cell.net/guidelines .

Fig. 1 ∣. Emerging applications of single-cell proteomics by MS.

Single-cell proteomic measurements can define cell type and cell state clusters, support pseudotime inference, link protein levels to functional phenotypes, such as phagocytic activity, quantify protein covariation and apply it to study protein complexes^,,, analyze protein conformations and quantify protein modifications, such as phosphorylation and proteolysis^,,. Furthermore, integrating protein and RNA measurements from the same biological systems (as in refs.^,) allows inferring transcriptional and post-translational regulation^, and investigating the covariation of transcription factors and downstream target transcripts. Dim, dimension; PC, principal component.

Fig. 2 ∣. Evaluating and interpreting single-cell proteomic data.

a, Quantitative accuracy of protein ratios between samples A and B measured by label-free DIA analysis relative to the corresponding mixing ratios denoted by dotted lines. Some proteins are quantified with high precision but low accuracy (for example, ribosomal protein L8 (RPL8)), while others are quantified with high accuracy and low precision (for example, RelA). E. coli, Escherichia coli. The proteomes of T cells and monocytes correlate strongly (b) despite the fact that many proteins are differentially abundant between the two cell types (c). Data for b,c are from Specht et al.. d, Extracted ion chromatograms (XIC) from single-cell MS measurements by plexDIA for a peptide from the high mobility group protein A1 (HMGA1). Such data allow quantifying peptides at both MS¹ and MS² levels, which can be used to evaluate the consistency and reliability of the quantification. This example data from Derks et al. show that relative levels estimated from precursors (peach color) agree with the relative levels estimated from the corresponding summed-up fragments (green color). At both MS¹ and MS² levels, three estimates are obtained based on the three scans closest to the elution peak apex. The fold changes are between pancreatic ductal adenocarcinoma (PDAC) and monocyte (U-937) cells. e, Different dimensionality-reduction methods approximate the data in different ways. We simulated three-dimensional data for three cell states, where one cell state (green) progressively diverges to two distinct cell states (blue and red, top left). Projecting the data to two dimensions loses information. Specifically, PCA loses the non-linear cycling effect and mixes early (green) and intermediate (gray) cells, t-SNE does not correctly capture the distances between the three populations, and diffusion maps do not capture the noise in the data and compress the early state cells. DC1 and DC2 correspond to diffusion components 1 and 2. The code for this simulation is available at github.com/SlavovLab/SCP_recommendations.

Fig. 3 ∣. Suggested descriptors of single-cell proteomic samples.

Metadata should include the experimental design table with rows corresponding to single cells and columns corresponding to the required and optional features listed here (an example is provided as source data). Attributes provided in parentheses are given as examples or for clarification. The green shading highlights required descriptors, while gray shading includes a non-exhaustive list of optional descriptors, which may also include spatial (for example, position in tissues) and temporal information for the cells when available. The descriptors (and their units, when relevant) should be documented in the experiment’s dedicated README file.