Abstract
Cellular heterogeneity is important to biological processes, including cancer1,2 and development3. However, proteome heterogeneity is largely unexplored because of the limitations of existing methods for quantifying protein levels in single cells. To alleviate these limitations, we developed Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS), and validated its ability to identify distinct human cancer cell types based on their proteomes. We used SCoPE-MS to quantify over a thousand proteins in differentiating mouse embryonic stem (ES) cells. The single-cell proteomes enabled us to deconstruct cell populations and infer protein abundance relationships. Comparison between single-cell proteomes and transcriptomes indicated coordinated mRNA and protein covariation. Yet many genes exhibited functionally concerted and distinct regulatory patterns at the mRNA and the protein levels, suggesting that post-transcriptional regulatory mechanisms contribute to proteome remodeling during lineage specification, especially for developmental genes. SCoPE-MS is broadly applicable to measuring proteome configurations of single cells and linking them to functional phenotypes, such as cell type and differentiation potentials.
Cellular systems, such as tissues, cancers, and cell cultures, consist of a variety of cells with distinct molecular and functional properties. Characterizing such cellular differences is key to understanding normal physiology, combating cancer recurrence1,2, and enhancing targeted differentiation for regenerative therapies3; it demands quantifying the proteomes of single cells.
However, quantifying proteins in single mammalian cells remains confined to fluorescent imaging and antibodies. Fluorescent proteins have proved tremendously useful but are limited to quantifying only a few proteins per cell and sometimes introduce artifacts4. Multiple methods for quantifying proteins in single cells have been recently developed, including single-cell Western blots5, CyTOF6, and Proseek Multiplex, an immunoassay readout by RT-PCR7. These methods enabled quantifying up to a few dozen endogenous proteins but their throughput and accuracy are limited by the availability of highly-specific antibodies that bind their cognate proteins stoichiometrically.
We aimed to overcome these limitations by developing a high-throughput method for Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) that can quantify thousands of proteins in single mammalian cells. To develop SCoPE-MS, we resolved two major challenges: (i) delivering the proteome of a mammalian cell to a MS instrument with minimal protein losses and (ii) simultaneously identifying and quantifying peptides from single-cell samples. To overcome the first challenge, we manually picked live single cells under a microscope and lysed them mechanically (by Covaris sonication in glass microtubes) in phosphate-buffered saline, Fig. 1a. This method was chosen to obviate chemicals that may undermine peptide separation and ionization or sample cleanup that may incur significant losses. The proteins from each cell lysate were quickly denatured at 90 °C and digested with trypsin at 45 °C overnight, Fig. 1a; see Methods for full experimental details.
(a) Conceptual diagram and work flow of SCoPE-MS. Individually picked live cells are lysed by sonication, the proteins in the lysates are digested with trypsin, the resulting peptides labeled with TMT labels, combined and analyzed by LC-MS/MS (Orbitrap Elite). (b) Design of control experiments used to test the ability of SCoPE-MS to distinguish U-937 cells from Jurkat cells. (c) Unsupervised principal component (PC) analysis using data for all quantified proteins from the experiments described in panel (b) stratifies the proteomes of single cancer cells by cell type. (d) Distributions of protein levels across single U-937 and Jurkat cells indicate cell-type-specific protein abundances.
To overcome the second challenge, we made novel use of tandem mass tags (TMT). This technology was developed for multiplexing8, which affords cost-effective high-throughput. Even more crucial to our application, TMT allows quantifying the level of each TMT-labeled peptide in each sample while identifying its sequence from the total peptide amount pooled across all samples8. SCoPE-MS capitalizes on this capability by augmenting each single-cell set with a sample comprised of ~ 100 − 200 carrier cells that provide enough ions for peptide sequence identification, Fig. 1a. Increasing the number of carrier cells increases peptide identification rates but decreases quantitative precision. The carrier cells also help with the first challenge by reducing losses from single cells, since most of the peptides sticking to tips and tube walls originate from the carrier cells. Thus, the carrier cells help overcome the two major challenges.
Quantification of TMT-labeled peptides relies on reporter ions (RI) whose levels reflect both peptide abundances and noise contributions, such as coisolation interference and background noise8,9. To evaluate the contribution of background noise to single-cell RI quantification, we estimated the signal-to-noise ratio (SNR), Extended Data Fig. 1. The estimates indicated that RI intensities are proportional to the amount of labeled single-cell proteomes, and very low for channels left empty. These data suggest that the signal measured in single cells exceeds the background noise by 10-fold or more. As an added SNR control for every TMT set, SCoPE-MS leaves the 130N channel empty, thus simultaneously avoiding isotopic cross-contamination from the carrier cells in channel 131 and having a channel that reflects the background noise.
To evaluate the ability of SCoPE-MS to distinguish different cell types, we prepared two label-swapped and interlaced TMT sets with alternating single Jurkat and U-937 cells, two blood cancer cell lines with average cell diameter of only 11 µm (Fig. 1b). The levels of all 583 proteins quantified in single cells were projected onto their principle components (PC). The three-dimensional projections of single-cell proteomes clustered by cell type (Fig. 1c), suggesting that SCoPE-MS can identify cell types based on their proteomes. Next, we identified proteins whose levels vary less within a cell type than between cell types. Among the 117 proteins showing such trends at FDR < 2%, we plotted the distributions for seven in Fig. 1d. Some of these proteins are expected to be cell type specific, such as the higher abundance of Complement C3 in the U-937 cells, which are myeloid lineage precursors for macrophages. The consistency of protein fold-changes between Jurkat and U-937 cells is also reflected in the positive correlations among fold-changes estimated from different cells and TMT channels, Extended Data Fig. 2.
Identifying protein covariation and cell clusters
Next, we quantified single-cell proteome heterogeneity and dynamics during ES cell differentiation. To initiate differentiation, we withdrew leukemia inhibitor factor (LIF) from ES cell cultures and transitioned to suspension culture; LIF withdrawal results in complex and highly heterogeneous differentiation of epiblast lineages in embryoid bodies (EB). We used SCoPE-MS to quantify over a thousand proteins at FDR = 1 % (Extended Data Fig. 3a) and their pair-wise correlations (averaging across single cells) in days 3, 5, and 8 after LIF withdrawal (Fig. 2a). Cells from different days were processed together to minimize batch biases10.
(a) Clustergrams of pairwise protein-protein correlations in cells differentiating for 3, 5, and 8 days after LIF withdrawal. The correlation vectors were hierarchically clustered based on the cosine of the angles between them. (b) The proteomes of all single EB cells were projected onto their PCs, and the marker of each cell color-coded by day. The single-cell proteomes cluster by day, a trend also reflected in the distributions of PC 1 loadings by day, Extended Data Fig. 2. (c, d) The proteomes of cells differentiating for 8 days were projected onto their PCs, and the marker of each cell color-coded based on the normalized levels of all proteins from the indicated gene-ontology groups.
We first explored protein covariation as reflected in the overrepresentation of functionally related proteins within highly coherent clusters of protein-protein correlations, Fig. 2a. The large clusters on all days are enriched for proteins with biosynthetic functions. This covariation is consistent with the possibility of heterogeneous and asynchronous slowing of cell growth as cells differentiate. The smaller clusters correspond to lineage-specific proteins and more specialized functions.
Next, we projected the proteomes of single cells from all days (190 cells) onto their PCs, Fig. 2b. The projections cluster by date; indeed, PC 1 loading correlate to the days post LIF withdrawal, Extended Data Fig. 3b. The small clusters of lineage-specific genes (Fig. 2a) suggest that we have quantified proteomes of distinct cell states; thus we attempted to identify cell clusters by projecting the EB proteomes onto their PCs and identifying sets of proteins that are concertedly regulated in each cluster, Fig. 2c,d. The projection resulted in clusters of cells, whose identity is suggested by the dominant proteins in the singular vectors. We identified biological functions over-represented11 within the distribution of PC loadings and colorcoded each cell based on the average levels of proteins annotated to these functions. The PCs do not correlate to missing data, indicating that our experimental design has overcome challenges common to high-throughput single-cell data10; see Methods. These results suggest that SCoPE-MS data can meaningfully classify cell identity for cells from complex and highly heterogeneous populations.
Coordinated mRNA and protein covariation in single cells
Klein et al.12 recently quantified mRNA heterogeneity during ES differentiation, and we used their inDrop data to simultaneously analyze mRNA and protein covariation and to directly test whether genes coexpressed at the mRNA level are also coexpressed at the protein level. To this end, we computed all pairwise correlations between RNAs (Fig. 3a) and proteins (Fig. 3b) for all genes quantified at both levels in cells undergoing differentiation for 7 and 8 days. Clustering hierarchically the correlation matrices results in 3 clusters of genes. To compare these clusters, we computed the pairwise Jaccard coefficients, defined as the number of genes present in both classes divided by the number of genes present in either class, i.e., intersection/union). The results (Fig. 3c) indicate that the largest (green) cluster is 55 % identical and the medium (blue) cluster is 33 % identical. This cluster stability is also reflected in a positive correlation between corresponding mRNA and protein correlations, Fig. 3d. The magnitude of this correlation is comparable to protein-mRNA correlations from bulk datasets11,13 and testifies strongly for the quantitative accuracy of both in-Drop and SCoPE-MS.
(a) Clustergram of pairwise correlations between mRNAs with 2.5 or more reads per cell as quantified by inDrop in single EB cells12. (b) Clustergram of pairwise correlations between proteins quantified by SCoPE-MS in 12 or more single EB cells. (c) The overlap between corresponding RNA from (a) and protein clusters from (b) indicates similar clustering patterns. (d) Protein-protein correlations correlate to their corresponding mRNA-mRNA correlations. Only genes with significant mRNA-mRNA correlations were used for this analysis. (e) The concordance between corresponding mRNA and protein correlations (computed as the correlation between between corresponding correlations14) is high for ribosomal proteins (RPL and RPS) and lower for developmental genes; distribution medians are marked with red pluses. Only the subset of genes quantified at both RNA and protein levels were used for all panels.
Having established a good overall concordance between mRNA and protein covariation, we next explored whether and how much this concordance varies between genes with different biological functions. The covariation concordance of a gene was estimated as the similarity of its mRNA and protein correlations, i.e., the correlation between the corresponding correlation vectors14. The median concordance of ribosomal proteins (RP) of both the 60S (RPL) and 40S (RPS) is significantly higher than for all genes, Fig. 3e. This result indicates that RPL and RPS genes have significantly (p < 10−20) more similar gene-gene correlations at the mRNA and the protein levels than the other quantified genes. Some RPs correlate less well to the remaining RPs (Extended Data Fig. 4), which may reflect lineage specific ribosome remodeling, but this possibility needs to be evaluated more directly with isolated ribosomes15. In contrast to RPs, genes functioning in tissue morphogenesis, proteolysis, and development have significantly (p < 10−3) lower concordance at the mRNA and protein level than all genes, Fig. 3e.
Discussion
The power of MS proteomics had been circumscribed to bulk samples. Indeed, the TMT manufacturer recommends 100 µg of protein per channel, almost 106 more than the protein content of a typical mammalian cell. SCoPE-MS bridged this gap by efficient sample preparation and the use of carrier cells. These innovations open the gates to further improvements (e.g., increased multiplexing) that will make single-cell MS proteomics increasingly powerful.
SCoPE-MS enabled us to classify cells and explore the relationship between mRNA and protein levels in single mammalian cells. This first foray into single mammalian proteomes demonstrates that mRNA covariaion is predictive of protein covariaion even in single cells. It further establishes the promise of SCoPE-MS to quantitatively characterize single-cell gene regulation and classify cell types.
Acknowledgments
We thank S. Semrau, M. Jovanovic, R. Zubarev, and members of the Slavov laboratory for discussions and constructive comments, as well as the Harvard University FAS Science Operations for supporting this research project. This work was funded by startup funds from Northeastern University and a New Innovator Award from the NIGMS from the National Institutes of Health to N.S. under Award Number DP2GM123497.
Competing Interests
The authors declare that they have no competing financial interests.
Contributions
B.B., and N.S. conceived the research. B.B., E.L. and N.S. performed experiments and collected data; N.S. analyzed the data and wrote the manuscript.
The raw MS data have been deposited in MassIVE (ID: MSV000080489) and in the ProteomeX change (ID: 0000398). Supplemental website can be found at: http://www.northeastern.edu/slavovlab/datawebs.htm
References
