Combining Data Independent Acquisition With Spike-In SILAC (DIA-SiS) Improves Proteome Coverage and Quantification

Abstract

Data-independent acquisition (DIA) is increasingly preferred over data-dependent acquisition due to its higher throughput and fewer missing values. Whereas data-dependent acquisition often uses stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mass differential tags for relative and absolute quantification and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins in vivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios and certain high-throughput experiments. Spike-in SILAC (SiS) methods use an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA-SiS, leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed DIA-SiS and rigorously assessed its performance with mixed-species benchmark samples on bulk and single cell-like amount level. We demonstrate that DIA-SiS substantially improves proteome coverage and quantification compared to label-free approaches and reduces incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate, and comprehensive proteome profiling.

Keywords: DIA; SILAC; data independent acquisition; multispecies benchmark; spike-in.

Graphical abstract

Fig. 1

Benchmark experiment: Design and protein coverage. A, experimental design of the mixed species benchmark, amounts in ng. B, mean number of identified Escherichia coli and human HL-60 proteins over all four technical replicates per dilution step sample (standard deviation is indicated by whiskers). The different colors correspond to different E. coli dilutions. C, number of proteins identified in one, two, three, or all four replicates (increasing opacity). Numbers indicate the percentage of human or E. coli protein groups detected across all four replicates.

Fig. 2

DIA-SiS allows reliable across-sample quantification. Only proteins with both LFQ and SILAC ratios and no missing values across all replicates are shown. A, the bars indicate the number of across-sample protein ratios quantified with both LFQ and DIA-SiS. B, both DIA-LFQ and DIA-SiS capture the expected across-sample ratios, with a clearer intensity-dependent precision for DIA-SiS. The global protein group abundance is plotted against the mean across-sample protein ratios. Dashed lines indicate expected ratios. C, density plots corresponding to (B). D, DIA-SiS improves the detection of differentially abundant proteins. The volcano plots show the log2FCs on the x axis and the -log10 p-values on the y axis. The dashed lines indicate cutoffs (p-value = 0.01, absolute log2FC = 1), blue: human proteins, red: Escherichia coli proteins. E, precision-recall curves based on (D) of S2 versus S3 for log2FCs (left) and p-values (right) for DIA-LFQ (green) and DIA-SiS (purple). DIA, data-independent acquisition; DIA-SiS, DIA with spike-in SILAC; FC, fold change; LFQ, label-free quantification; SILAC, stable isotope labeling by amino acids in cell culture.

Fig. 3

"Requantify" further reduces missing values in DIA-SiS. A, the number of proteins quantified across samples can be increased by only requiring confident identification of heavy channel precursors (“requantify” option). Mean number of across sample ratios (without missing values) using DIA-SiS, requiring the light as well as heavy channel to pass filters (higher opacity) and additional ratios obtained when using "requantify" (lower opacity). B, “requantified“ DIA-SiS ratios still capture the correct trend. The log global abundance is plotted against the mean log2FCs of protein groups (blue: human proteins, red: Escherichia coli proteins). C, density plots corresponding to (B); D–G, comparison of a sample containing human and E. coli proteins to sample without E. coli shows that DIA-SiS reduces missing values, especially with "requantify" enabled. D, cumulative Venn diagrams of E. coli (upper, red/orange) and human (lower, blue) proteins quantified in four technical replicates. E, DIA-SiS also reduces missing values for human proteins, although they were equally abundant in both samples. Cumulative bar charts for the human proteins illustrated in (D) indicate the number of ratios that could be computed between samples. F, rescued human proteins cover a broad abundance range. The cumulative distribution shows the log2 global intensity of those proteins that are missing in LFQ but could be rescued with DIA-SiS and "requantify". The violin indicates the intensity of the entirety of proteins found with DIA-SiS and "requantify". G, across-sample ratios obtained using SILAC + "requantify" correctly capture the global trend (blue: human, red: E. coli). SILAC, stable isotope labeling by amino acids in cell culture; DIA, data-independent acquisition; DIA-SiS, DIA with spike-in SILAC; LFQ, label-free quantification.

Fig. 4

DIA-SiS boosts IDs for single cell-like amounts. For this analysis, the samples from Fig. 1 A were further diluted to single cell-like amounts (supplemental Fig. S9). The lowest dilution contains 300 pg human and 300 pg E. coli. The samples have been measured without spike-in (LFQ) or with 2×, 5×, and 20× the amount of spike-in as sample (increasing opacity). Across sample ratios have been calculated for proteins detected across all four replicates. A, number of proteins quantified with each approach. B, distribution of across-sample ratios (log2FC) of intersecting proteins showing the ratio compression with increasing spike-in amounts. Numbers indicate the number of proteins per comparison. C, volcano plots of all proteins found with LFQ and 5× SILAC as well as the 5× exclusive ones. Numbers indicate the number of E. coli (red) and human (blue) proteins with a log2FC <= −1 and a p-value <0.01 (within the green rectangle). DIA, data-independent acquisition; DIA-SiS, DIA with spike-in SILAC; LFQ, label-free quantification; SILAC, stable isotope labeling by amino acids in cell culture.

Fig. 5

Application of DIA-SiS (with “requantify”) to formalin-fixed paraffin-embedded (FFPE) head and neck squamous cell carcinoma (HNSCC) samples. Two FFPE tissue samples (FFPE1 and FFPE2) were compared using different input amounts: 300 ng, 50 ng and 50 ng + 250 ng of the super-SILAC spike-in reference with “requantify” (50 ng + “requantify”). A, number of across-sample ratios obtained. Reducing the input from 300 to 50 ng reduces the number of proteins that can be quantified. Adding the spike-in recovers most of the proteins lost in the low input sample. B, volcano plots for differentially abundant proteins in the FFPE1 versus FFPE2 sample for the different input amounts. Significantly differentially abundant proteins (turquoise and orange) were defined based on the 300 ng input (p-value ≤ 0.01) and colored accordingly in the other samples. The number of proteins in each subset is indicated. C, the correlation of the log2FCs of the differentially abundant protein between the 300 ng input and the 50 ng + ref. input is high. The number of proteins in the quadrants is indicated. SILAC, stable isotope labeling by amino acids in cell culture; DIA, data-independent acquisition; DIA-SiS, DIA with spike-in SILAC; FFPE, formalin-fixed paraffin-embedded.