Development of multiplexed-DIA and a dream of Tornado-DIA

Processing multiple samples in parallel increases the throughput of the analysis. This could be achieved by increasing the data acquisition capability in multiplexed analyses, which unfortunately confronts with the design of DIA-MS (1). Commonly-used reporter ion-based approaches, such as tandem mass tag (TMT) (2) or isobaric tags for relative and absolute quantification (iTRAQ) (3), rely on sampling MS2 reporter ions to partition the signals from different samples, which are co-fragmented in a DIA-MS/MS spectra. Metabolic labeling approaches, such as the stable isotope labeling of amino acids in cell culture (SILAC) (4), could double or triple the complexity of the DIA-MS/MS spectra but may lead to a biased quantification when the light-heavy pairs are split in different MS2 windows (5).

Multiplexing the metabolic way

Efforts are underway enable multiplexed DIA. The metabolic labeling approaches are used when the samples can be cultured (e.g., cells) or fed (e.g., mice). The Coon lab expanded the use of neutron-encoded (NeuCode) SILAC, which incorporates minor mass differences (≤36 mDa), to DIA (NeuCoDIA) for duplex analyses (5). Similar techniques include MdFDIA(6) and mdDiLeu (7), allowing a four-plex analysis. However, several applications do not consider the aforementioned distribution issue and directly integrate SILAC with DIA for comparative investigations of protein dynamics (8-10). Their data analysis strategies are library-based and rely on the targeted extraction of light and heavy peptides. A thorough evaluation of the relative quantification provided by this method is required before it can be applied to other biological studies.

Multiplexing the chemical way

For biomedical samples that cannot be cell-cultured, the chemical labeling with isobaric tags is recommended and would enable higher throughput and more comparable relative quantfication. A handful of methods have been delivered during the past years. The Ac-AG (acetyl-alanine-glycine) tag approach relies on low collision energy to generate reporter ions for the MS2-based quantification and high collision energy to generate backbone fragment ions for the MS2-based identification of peptides (11). The Ac-IP (acetyl-isoleucine-proline) tag approach produces MS1 and MS2 spectra profiles similar to those generated by the TMT-based approach and requires deconvolution algorithms for the MS1-based quantification (12). These two methods could allow triplex analyses. A third type of reporter-ion tag, called mass defect-based carbonyl activated tags (mdCATs), allows eight-plex analyses and is the samples are differentiated by as little as 5.8 mDa or as much as 41.9 mDa per tag (12). However, this method, which could generate highly complex MS2 spectra, can only be analyzed under high-resolution (500k) conditions: the MS2 spectra generated by this method are so complex that only high-resolution MS could analyze them, which inevitably lead to slow analysis speed leading to decreasing of throughput from the time dimension. Nevertheless these proof-of-concept chemical techniques show great promise for multiplexed DIA-MS.


Couple of years ago I proposed an idea of “Tornado-DIA” (Figure 1) that takes advantage of multiple labeling technologies to boost the number of analyzed samples in one MS experiment, by estimation over one hundred could be achieved. With the advance of new mass spectrometers carrying extremely high scanning speed (>200 Hz) & resolution, as well as the development of more and more tags / labels for selection, it’s getting more practical to achieve DIA-based hyper-throughput omics.

Figure 1 Schematic sketch of Tornado-DIA.


1.     Zhang S, Di Y, Yao J, Wang Y, Shu H, Yan G, et al. Mass defect-based carbonyl activated tags (mdCATs) for multiplex data-independent acquisition proteome quantification. Chem Commun (Camb). 2021;57(6):737-40.

2.     Dayon L, Hainard A, Licker V, Turck N, Kuhn K, Hochstrasser DF, et al. Relative quantification of proteins in human cerebrospinal fluids by MS/MS using 6-plex isobaric tags. Anal Chem. 2008;80(8):2921-31.

3.     Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics. 2004;3(12):1154-69.

4.     Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics. 2002;1(5):376-86.

5.     Minogue CE, Hebert AS, Rensvold JW, Westphall MS, Pagliarini DJ, Coon JJ. Multiplexed quantification for data-independent acquisition. Analytical chemistry. 2015;87(5):2570-5.

6.     Di Y, Zhang Y, Zhang L, Tao T, Lu H. MdFDIA: A Mass Defect Based Four-Plex Data-Independent Acquisition Strategy for Proteome Quantification. Anal Chem. 2017;89(19):10248-55.

7.     Zhong X, Frost DC, Yu Q, Li M, Gu TJ, Li L. Mass Defect-Based DiLeu Tagging for Multiplexed Data-Independent Acquisition. Anal Chem. 2020;92(16):11119-26.

8.     Pino LK, Baeza J, Lauman R, Schilling B, Garcia BA. Improved SILAC Quantification with Data-Independent Acquisition to Investigate Bortezomib-Induced Protein Degradation. Journal of proteome research. 2021;20(4):1918-27.

9.     Wu C, Ba Q, Lu D, Li W, Salovska B, Hou P, et al. Global and Site-Specific Effect of Phosphorylation on Protein Turnover. Developmental Cell. 2021;56(1):111-24.e6.

10.   Salovska B, Zhu H, Gandhi T, Frank M, Li W, Rosenberger G, et al. Isoform-resolved correlation analysis between mRNA abundance regulation and protein level degradation. Mol Syst Biol. 2020;16(3):e9170-e.

11.   Tian X, de Vries MP, Permentier HP, Bischoff R. A Versatile Isobaric Tag Enables Proteome Quantification in Data-Dependent and Data-Independent Acquisition Modes. Analytical chemistry. 2020;92(24):16149-57.

12.   Tian X, de Vries MP, Permentier HP, Bischoff R. The Isotopic Ac-IP Tag Enables Multiplexed Proteome Quantification in Data-Independent Acquisition Mode. Analytical chemistry. 2021;93(23):8196-202.

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Processing multiple samples in parallel increases the throughput of the analysis. This could be a