Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.
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Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition
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We thank A. Makarov, E. Gordon and D. Perlman for discussions and constructive comments. This work was funded by a New Innovator Award from the National Institute of General Medical Sciences from the National Institutes of Health to N.S. under award DP2GM123497, an Allen Distinguished Investigator award through the Paul G. Allen Frontiers Group to N.S. and a Seed Networks Award from CZI CZF2019-002424 to N.S. This work received further support from The Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001134); the UK Medical Research Council (FC001134); and the Wellcome Trust (FC001134 and IA 200829/Z/16/Z), as well as the European Research Council (SyG 951475 to M.R.). The work was further supported by the German Federal Ministry of Education and Research (BMBF), as part of the National Research Node ‘Mass Spectrometry in Systems Medicine’ (MSCoresys), under grant agreements 031L0220A to M.R. and 161L0221 to V.D.
M.W. is an employee of Bruker Corporation, which manufactures timsTOF SCP. The authors declare that they have no other competing financial interests.
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The plexDIA module in DIA-NN starts the data processing by splitting the input spectral library or a sequence database into multiple channels, wherein query precursor ions are generated for each of the label states. In addition, a decoy channel is generated by considering a ‘decoy’ label with a higher mass than the actual labels, for example, +12 for mTRAQ. A preliminary precursor ion identification step is then carried out, wherein a best matching peak group is found, as in label-free search, for all of the precursors, from all the channels. These peak groups are scored by the regular neural network-based classifier implemented in DIA-NN. The most confident match is then selected, across all the non-decoy channels, for each charged peptide. DIA-NN then assumes that this most confident channel pinpoints the correct retention time of the peptide. In the process we refer to as ‘translation of identifications’, DIA-NN re-extracts the signals at this retention time for the other channels, regardless of whether these have been successfully matched to some peak groups during the previous step. Scoring of these re-extracted peak groups using the previously trained neural network classifier leads to the assignment of ‘translated q-values’, which reflect the confidence level in these identifications if they were made independently from translation, and can be used for downstream data filtering. As each plexDIA acquisition measures multiple samples, DIA-NN calculates ‘channel q-values’ that reflect the confidence in the precursors being present in specific channels. This is achieved using a target-decoy method as explained in Methods. Finally, DIA-NN also takes advantage of the presence of multiple channels when quantifying precursors. Here, DIA-NN calculates the ratios between different channels using the signal ratios for selected fragment ions at the elution apex, thus minimizing the influence of any interfering signals. The ‘translated’ quantities are then calculated for all the channels except the most confident one, by dividing the quantity in the latter by the respective ratio.
Extended Data Fig. 2 plexDIA analysis of proteins present only in one samples but missing from another.
We sought to test identification propagation by plexDIA for the case when proteins are present only in some samples and not in others. To do so, we prepared a standard in which one sample (labeled with mTRAQ, 0) had both 0.3 µg E. coli and 0.3 µg S. cerevisiae while another (labeled with mTRAQ Δ4) had only 0.3 µg S. cerevisiae. The combined set was analyzed by plexDIA using the V1 method. (a) Distributions of raw MS1 precursor intensity for E. coli and S. cerevisiae precursors at channel-q-value < 0.01. (b) Distributions of raw MS2 quantification of precursors filtered for channel-q-value < 0.01. The red asterisks correspond to the means of the distributions.
(a) Number of distinct precursors identified from 60 min active gradient runs for plexDIA, LF-DIA, and shotgun-DDA of mTRAQ at 1 % FDR. The DIA analysis used the V2 method, an MS2-optimized data acquisition cycle shown in Fig. 1e. Triplicates of each sample were analyzed (except sample C of LF-DIA, duplicates are analyzed), and the results displayed as mean; error bars correspond to standard error. (b) Total number of protein data points for plexDIA, LF-DIA and mTRAQ DDA at 1 % global protein FDR, (n = 3). (c) Venn-diagrams of each replicate for plexDIA and LF-DIA display protein groups quantified across samples A, B and C. The mean number of proteins groups intersected across samples A, B and C is 7,923 for plexDIA and 8,318 for LF-DIA. (d) We compute pairwise Jaccard indices to compare pairwise data completeness between plexDIA, LF-DIA and shotgun DDA for mTRAQ. All data were analyzed using match between runs. (e) Distributions of missing data between pairs of runs of either the same sample (that is, replicate injections) or between different samples.
Extended Data Fig. 4 Comparison of proteomic overlap between our runs to a high-quality DIA dataset Navarro et al.
DIA runs (including raw data from Navarro et al.) were searched with DIA-NN using match between runs. Results indicate that the data completeness from LF-DIA in this study is comparable to other high quality LF-DIA datasets.
As demonstrated with the MS1-optimized method in Fig. 3 of the main text, here we show quantitative accuracy of plexDIA using MS2-optimized data acquisition—specifically, we only show data from the second run of a triplicate set. (a) The number of protein groups quantified in both samples A and B is shown with barplots. plexDIA quantified 7,610 PGs, LF-DIA 9,387 PGs, and intersected between plexDIA and LF-DIA was 5,967 PGs. These 5,967 PGs were plotted to compare quantitative accuracy between plexDIA and LF-DIA for in-common protein groups. To improve visibility, the scatter plot x and y axes were set to display data points between 0.25% and 99.75% range. (b) Same as (a), but for samples A and C; human proteins were excluded because they compare two different human cell types. (c) Same as (b), but for samples B and C. (d) Absolute protein ratio errors were calculated for samples A/B, A/C and B/C and combined to compare ratio errors for samples within a plexDIA run (for example, run2 A / run2 B) to samples across runs (for example, run1 A /run2 B) with plexDIA. (e) Absolute precursor ratio errors were calculated for samples A/B, A/C and B/C and combined to compare MS2-quantified ratio errors for C-terminal lysine precursors and C-terminal arginine precursors. Boxplots: The box defines the 25th and 75th percentiles and the median is marked by a solid line. Outliers are marked as individual dots outside the whiskers. All data shown are from (n = 1) representative replicate.
Similar to main Fig. 3, we display the results from the other replicates for a total of (n = 3) replicates. (a) Figures are the same as shown in Fig. 3 of the main text with the exception that this shows the first replicate of plexDIA and the first replicate of samples A, B and C for LF-DIA. (b) Same as (a), but for the third replicate of plexDIA and LF-DIA, samples A, B and C. Boxplots: The box defines the 25th and 75th percentiles and the median is marked by a solid line. Outliers are marked as individual dots outside the whiskers. Panel a shows data from (n = 1) replicate and panel b shows data from another (n = 1) replicate for a total of (n = 2) replicates shown in this figure.
Extended Data Fig. 7 Quantitative accuracy and repeatability across different plexDIA sets and labels.
(a) Relative protein levels between samples A, B and C estimated from samples analyzed in different plexDIA sets, that is, out-of-set quantification. The quantitative accuracy between sets (and thus runs) is comparable to the set accuracy shown in Fig. 3. The display is the same as shown in main Fig. 3, but the protein ratios are estimated across runs (for example, run 1 A / run 2 B); LF-DIA is showing protein ratios for the 2nd replicate of samples A, B and C. (b) Same as (a), but for samples A and C; H. sapiens proteins were not analyzed because they are from distinct cell types. (c) Same as (b) but for samples B and C. All data shown in panels a–c are from (n = 1) representative replicate. Boxplots: The box defines the 25th and 75th percentiles and the median is marked by a solid line. Outliers are marked as individual dots outside the whiskers. (d) Quantitative repeatability of plexDIA across across different labels. Protein CVs were estimated for the same samples labeled with the same label (as in main Fig. 4) or for the same sample labeled with different labels in different runs for example, run 1, Δ0, sample A & run 2, Δ4, sample A & run 3, Δ8, sample A. Both distributions contains CV for the same set of (n = 15,158) sample-specific protein data points per condition (Same Labels or Different Labels). The median CV when using the same label was 0.110 while the label swap had a median CV of 0.148.
Percent of precursors with no MS1-level quantitation per single cell or negative control. Single cells were required to have <60% missing data to be included in downstream analysis.
Rather than colors corresponding to a cell type as performed in Fig. 6p, here colors correspond to which mTRAQ label was used to tag the single cells. This is performed to check whether labeling-induced biases affect clustering of single-cells; here there appears to be little to no effect.
Distribution of relative protein abundance of each species across labels. The Δ0, Δ4 and Δ8 samples were pooled and used for quantitative benchmarking of plexDIA.
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Derks, J., Leduc, A., Wallmann, G. et al. Increasing the throughput of sensitive proteomics by plexDIA. Nat Biotechnol 41, 50–59 (2023). https://doi.org/10.1038/s41587-022-01389-w
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