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Defining the carrier proteome limit for single-cell proteomics

Abstract

Single-cell proteomics by mass spectrometry (SCoPE-MS) is a recently introduced method to quantify multiplexed single-cell proteomes. While this technique has generated great excitement, the underlying technologies (isobaric labeling and mass spectrometry (MS)) have technical limitations with the potential to affect data quality and biological interpretation. These limitations are particularly relevant when a carrier proteome, a sample added at 25–500× the amount of a single-cell proteome, is used to enable peptide identifications. Here we perform controlled experiments with increasing carrier proteome amounts and evaluate quantitative accuracy, as it relates to mass analyzer dynamic range, multiplexing level and number of ions sampled. We demonstrate that an increase in carrier proteome level requires a concomitant increase in the number of ions sampled to maintain quantitative accuracy. Lastly, we introduce Single-Cell Proteomics Companion (SCPCompanion), a software tool that enables rapid evaluation of single-cell proteomic data and recommends instrument and data analysis parameters for improved data quality.

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Fig. 1: Characterization of isobaric label dynamic range and simulation of SCoPE-MS variability.
Fig. 2: Defining the carrier proteome effect in SCoPE-MS.
Fig. 3: Carrier proteome effect on the measurement of non-carrier ions.
Fig. 4: Effect of the carrier proteome on depth and biological interpretation.

Data availability

All raw mass spectrometry files and peptide and protein identification results are available at the PRIDE repository (PXD016573) and the MassIVE repository (MSV000085959). Publicly available SCP-MS datasets can be found at MassIVE under the following IDs: MSV000082841 for Specht et al.17 and MSV000084110 for Dou et al.8.

Code availability

SCPCompanion is a Windows desktop application written in the C# programming language and can be downloaded as a standalone program (https://doi.org/10.5281/zenodo.4089376 or https://github.com/scp-ms/SCPCompanion) or as source code (https://github.com/scp-ms/SCPCompanionSource).

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Acknowledgements

We acknowledge J. Krumm and D. P. Zolg for performing preliminary proof-of-concept studies that inspired many of the experiments described here. C.-Y.L acknowledges funding obtained from the Postdoctoral Research Abroad Program of the Ministry of Science and Technology of Taiwan (grant no. 108-2917-I564-034). B.K. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program, grant agreement no. 833710.

Author information

Authors and Affiliations

Authors

Contributions

T.K.C. performed experiments. C.-Y.L., F.P.B., A.M. and C.M.R. performed experiments and analyzed data. B.K. and C.M.R. conceived experiments, guided analyses and wrote the paper.

Corresponding authors

Correspondence to Bernhard Kuster or Christopher M. Rose.

Ethics declarations

Competing interests

C.M.R. and T.K.C. are currently employees and shareholders of Genentech, Inc., a member of the Roche Group. B.K. is a founder and shareholder of OmicScouts and msAId; he has no operational role in either company.

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Peer review information Allison Doerr was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Characterization of 1:1 standard peptide mixture.

a, The measured carrier proteome ratio vs. the number of ions (summed signal-to-noise [s/n] of all reporter ions) for each quantified spectrum. The carrier proteome ratio was calculated by dividing the sum of carrier proteome channels (TMT126 & TMT127c) by all other channels. All spectrum from (a) were ordered by descending number of ions (summed s/n) and the top 50% of the data (highest s/n) was used to create a distribution of carrier proteome ratios. The carrier proteome ratios were derived from the apex of the distribution. b, The peptide, SAAGAFGPELSR, was analyzed at 10 points across its elution. At each point ions were sampled at three AGC targets (1 × 104, 1 × 105, and 1 × 106). Individual measurements across the peak for each AGC target. For each spectrum the raw s/n is plotted. The scan(s) with the minimum CV are boxed in red.

Extended Data Fig. 2 Characterization of 1:2 standard peptide mixtures.

a, The measured carrier proteome ratio vs. the number of ions (summed signal-to-noise [s/n] of all reporter ions) for each quantified spectrum. The carrier proteome ratio was calculated by dividing the sum of carrier proteome channels (TMT126 & TMT127c) by all other channels. All spectrum from (a) were ordered by descending number of ions (summed s/n) and the top 50% of the data (highest s/n) was used to create a distribution of carrier proteome ratios. The carrier proteome ratios were derived from the apex of the distribution. b-d) Analysis of a sample mixed at 1:2 without background interference. b, Sample preparation schematic. c, For each quantitative FT-MSn, the fold change and number of recorded reporter ions (sum signal-to-noise, sum s/n) was calculated. Measurements were grouped into bins of 20 s/n and the median fold change was plotted as a function of the minimum s/n value of each bin. d, For each quantitative FT-MSn, the relative error and number of recorded reporter ions (sum signal-to-noise, sum s/n) was calculated. Measurements were grouped into bins of 20 s/n and the median fold change was plotted as a function of the minimum s/n value of each bin. e-g, Analysis of a sample mixed at 1:2 with background interference. d, Sample preparation schematic. e, Same analysis as (b) for both MS2 and targeted SPS-MS3 analysis. f, Same analysis as in (c) for both MS2 and targeted SPS-MS3 analysis.

Extended Data Fig. 3 Characterization of HeLa/K562 carrier level dilutions and public SCP data.

a, The measured carrier proteome ratio vs. the number of ions (sum s/n) for each quantified spectrum (scatter plot). All spectrum from (a) were ordered by descending number of ions (sum s/n) and the top 50% of the data (highest s/n) was used to create a distribution of carrier proteome ratios (density plot). The carrier proteome ratios were derived from the median of the distribution. b, Analysis of quantitative data relating to peptide identifications for data from Duo et al. (NanoPots) and Specht et al. (SCoPE-MS). Signal-to-noise—Density plot of the summed s/n for all reporter ions extracted from peptide spectral matches (PSMs) in external single cell proteomics experiments. Ion flux (s/n per ms) – Density plot of the ion flux as extracted from PSMs in external single cell proteomics experiments. ‘Ion flux’ represents the number of ions (s/n) measured per millisecond (ms) of injection time. This measurement provides a readout on the complexity of the sample and the sensitivity of the mass spectrometer. c, For each identification, the CV within cell type was calculated and the number of ‘single cell’ ions (quant s/n) within all reporter channels was recorded. Measurements were grouped into bins of 20 quant s/n and the median CV of each bin was plotted as a function of the number of ions. Data were separated by cell type. d, Same analysis as (c), but omitting channel 127n from the analysis.

Extended Data Fig. 4 Investigating the dynamic range of the Orbitrap.

a–c, Scan sequence to test the dynamic range of the Orbitrap. a, Ions were injected into the mass spectrometer and a single peptide species was isolated (1). Peptide ions were then sent to the ion-routing-multipole (IRM) for fragmentation by HCD (2). Fragment ions were transferred to the dual ion trap for MS3 isolation (3). b, Mass analysis that included the carrier channel was accomplished by setting ion trap isolation for MS3 from 125.13 mz – 133.13 mz, performing isolation (4), and transferring the resulting ions to the Orbitrap for mass analysis (5). c, Mass analysis that excluded the carrier channel was accomplished by setting ion trap isolation for MS3 from 126.63 mz – 134.63 mz, performing isolation (4), and transferring the resulting ions to the Orbitrap for mass analysis (5). d, Instrument method for dynamic range experiments. (A) MS1 scan properties. (B) MS2 scan properties. (C) Targeted mass filter. (D) Include MS3 scan properties. (E) Exclude MS3 scan properties.

Extended Data Fig. 5 Characterizing the dynamic range and multiplexing level with increasing carrier levels.

a,b, Raw summed signal-to-noise (s/n) values for reporter ions when the carrier channel is included or excluded from mass analysis. For all box plots n = 4,200 and the box represents the inner quartile range, the line represents the median, and the whiskers represent 1.5x the quartile range. a, Distributions of s/n for carrier channels (126 and 127c). Distributions of s/n values within carrier channels demonstrate channel 126 was not detected in the Orbitrap for Exclude analyses. Channel 127c (13C isotope of channel 126) demonstrates lower intensity – likely due to its proximity to the low end of the ion trap isolation window. b, Distributions of s/n for HeLa (blue) and K562 (orange) within Include and Exclude analyses. For quantitative channels, 127n demonstrates consistently lower signal in the Exclude analyses, likely due to its proximity to the low mass of the ion trap isolation window. c, The number ‘single cell’ ions (quant s/n) in Include and Exclude scans as a function of the total number of ions sampled (sum s/n) in paired include scans. d, HeLa and K562 proteomes were mixed in 6, 8, 10, 12, and 14-plex samples containing 5x, 15x, 60x, and 250x mixtures. For each identification, the CV within cell type was calculated and the number of ions (sum s/n) within all reporter channels was recorded. Measurements were grouped into bins of 150 s/n and the median CV of each bin was plotted as a function of the number of ions (sum s/n).

Extended Data Fig. 6 Further analysis of the carrier proteome effect within complex samples diluted to single cell levels.

Four HeLa and Four K562 proteomes were combined into 8-plex samples with carrier levels of 20x (orange) and 100x (teal) and then diluted to single cell levels (30–400 pg on column for each sample). All samples were analyzed with three instrument methods varying ion sampling and maximum injection time parameters - low (5 × 104 AGC, 150 ms max IT), common (1 × 105 AGC, 300 ms max IT), and high (5 × 105 AGC, 750 ms max IT). a, Box plot of MS2 injection time (ms) for all spectrum in each analysis. For all box plots the box represents the inner quartile range, the line represents the median, and the whiskers represent 1.5x the quartile range. b, The percentage of spectrum that reached the maximum injection time for each of the analyses. c, Box plots summarizing the coefficient of variation (CV) for the HeLa and K562 samples for each carrier and dilution level before and after applying quant s/n filter of 125 quant s/n to the 20x and 100x samples. For all box plots the box represents the inner quartile range, the line represents the median, and the whiskers represent 1.5x the quartile range. d, The number of proteins quantified for each carrier and dilution level before and after applying quant s/n filter of 125 quant s/n to the 20x and 100x samples. For these calculations, PSMs mapping to contaminants and decoy peptides were removed before quantitative data was summed to the protein level. For the quant s/n filtered data, the filtering was performed at the PSM level before summation to the protein level.

Extended Data Fig. 7 Comparison of samples at single cell levels vs. a bulk level sample.

For all analyses pre and post quant s/n filtering, the log2ratio of K562/HeLa is plotted vs. protein ratios derived from a 5x carrier proteome, 12.5 ng/channel sample. The Pearson correlation coefficients are depicted in Fig. 3e.

Extended Data Fig. 8 Principle component analysis of 20x and 100x carrier level samples at single cell amounts.

All data displayed here were collected with high instrument parameters (5 × 105 AGC, 750 ms max IT). a, PCA of 20x carrier proteome samples at ~30–400 pg on column pre and post quant s/n filtering. b, PCA of 100x carrier proteome samples at ~30–400 pg on column pre and post quant s/n filtering.

Extended Data Fig. 9 Single cell proteomics companion as a tool for characterization of SCP data and suggestion of analysis parameters.

a, Graphical user interface (GUI) for SCPCompanion. The main view of SCP companion allows users to input experimental design and set parameters such as quant s/n cutoff adjustment, max injection time adjustment, s/n per quantitative channel, and number of blanks in the ‘sample’ channels. Once a ‘.Raw’ file is analyzed the viewer will display histograms of the quant s/n and quant ion flux. b, SCPCompanion suggested maximum injection times for data presented in Fig. 4. c-e, SCPCompanion was used to analyze publically available SCP data. c,d, Calculations from the analysis of data from Dou. et al. for increasing boost levels (b) and biological samples (c). e, Calculations from the analysis of data from Specht. et al. for 20 randomly sampled raw files.

Extended Data Fig. 10 Characterization of dynamic range for isobaric labels with isotopic correction and missing value analysis.

a, For all data, reporter ion signals were corrected using these isotopic correction factors. b, HeLa tryptic digests were labeled with TMT and pooled together in defined ratios starting from a 1:1^n series in the first pool up to a 1:10^n series in the 10th pool. Observed ratios relative to the 131 carrier channel were calculated and log-transformed. The median (dot) and median absolute deviation (MAD, whisker) of the ratio distributions of the top N intensity-based peptides (N = 10: magenta, N = 100: blue, N = 1000: teal, All: purple) were compared against their theoretical log ratio. c, HeLa tryptic digests were labeled with TMT and pooled together in defined ratios starting from a 1:1^n series in the first pool up to a 1:10^n series in the 10th pool. Here, every diluted channel (teal) was separated by an empty channel (magenta). The median (dot) and median absolute deviation (MAD, whisker) of the ratio distributions of the top N intensity-based peptides (N = 10: magenta, N = 100: blue, N = 1000: teal, All: purple) were compared against their theoretical log ratio. d, Quantitative data from Fig. 1 were analyzed to determine the percentage of missing values for each dilution within the ten dilution series mixtures.

Supplementary information

Supplementary Information

Supplementary Notes 1–16.

Reporting Summary

Supplementary Dataset 1

Data relating to the dilution experiments in Fig. 1b,c.

Supplementary Dataset 2

Data relating to the Pierce retention-time peptide analysis presented in Fig. 2.

Supplementary Dataset 3

Data relating to the HeLa and K562 carrier proteome level dilution (Fig. 3a,b), dynamic range (Fig. 3f) and multiplexing (Fig. 3g) experiments.

Supplementary Dataset 4

Data related to the analysis of HeLa and K562 samples for sample amounts at or below single-cell levels. Data are provided before and after quant SNR filtering at a value of 125. For statistical tests (Fig. 4d), proteins were deemed significant if P ≤ 0.05 (Bonferroni-corrected two-tailed t-test assuming unequal variance).

Supplementary Dataset 5

Analysis of external data from Specht et al. with MassIVE identifier MSV000082841 and from Dou et al. with MassIVE identifier MSV000084110. These data are presented in Extended Data Fig. 3d–f.

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Cheung, T.K., Lee, CY., Bayer, F.P. et al. Defining the carrier proteome limit for single-cell proteomics. Nat Methods 18, 76–83 (2021). https://doi.org/10.1038/s41592-020-01002-5

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