DIA and ion mobility buy coverage by removing competitive selection, not by separating better

Question: How do data-independent acquisition and ion mobility mass spectrometry improve peptide detection coverage in bottom-up proteomics compared with data-dependent acquisition?

Answer

Taken together, these papers support reading the coverage advantage of DIA and ion mobility as the product of two separable mechanisms rather than as a fixed ranking of acquisition modes. DIA removes intensity-ranked, stochastic precursor selection, so which peptides get fragmented becomes a designed property of the method — window scheme, duty cycle, and gradient — rather than an emergent consequence of signal competition among co-eluting ions, which yields deeper identifications in several direct comparisons [6] [7] [21]. Ion mobility contributes on a second axis: it compresses the precursor cloud in the gas phase so that isolation can be designed around it, and in the quadrupole-free limit it replaces isolation altogether, recovering ions that filtered selection would have discarded [19] [20]. Critically, when DDA and narrow-window DIA are parameter-matched on fast instrumentation the identification gap narrows markedly, and both methods remain far from saturation, indicating that a substantial part of the historical "DIA is deeper" effect is incomplete sampling on both sides rather than an intrinsic property of the acquisition scheme [11]. The defensible position is therefore mechanism-specific: DIA and ion mobility raise coverage to the extent that they raise ion utilization and sampling completeness per unit time, and the gain dissolves where those quantities are equalized.

DIA converts coverage from a stochastic outcome into a designed parameter

Direct comparisons show DIA delivering more quantifiable proteins than DDA on the same samples, with dia-PASEF detecting 4,229 quantifiable proteins against 2,817 for a DDA strategy and mean geometric CVs of 55.6% versus 203.1% [6]. The same study found more differentially expressed proteins per pairwise comparison and broader pathway coverage with dia-PASEF [6]. In a standardized cultivated-meat bottom-up workflow, DIA provided deeper proteome coverage than DDA with higher precision, roughly 6,500 versus 5,000 identifications [7]. DIA-PASEF on a fungal system increased protein identifications by more than 140% relative to prior studies while cutting analysis time more than five-fold [21]. The mechanism is explicitly invoked in peptidomics: DIA has shown strong ability to profile low-abundance ions, which most available DIA tools fail to exploit for endogenous peptides lacking defined enzymatic cleavage [16]. What DIA does not buy is exhaustive sampling — across extensive replicate analyses both DDA and narrow-window DIA continued to identify new peptides and proteins without reaching saturation, and peptide-level non-overlap between the two was substantial [11].

The coverage ceiling is set by mass-spectrometric sampling efficiency, not chromatographic resolution

Testing five stationary phases across eight column lengths under DIA at 200 Hz, all C18 and C8 phases achieved over 150,000 precursor and approximately 9,000 protein group identifications regardless of column length, leading the authors to conclude that the bottleneck has shifted from chromatographic resolution to mass-spectrometric sampling efficiency [8]. Gains at the instrument level follow the same logic: parallelized ion pre-accumulation raised scan rates above 70 Hz on hybrid Orbitraps and notably enhanced peptide and protein group identifications for short gradients [13], while a next-generation Astral platform achieved near-complete coverage of >12,000 proteins in 2.7 hours and >8,400 proteins in a 300 samples-per-day method [12]. An ion-calibration framework converting arbitrary intensity to ions per second showed a prototype instrument sampled 30% more ions per peptide, and that this ion-beam-utilization increase, not identification counts, drove the sensitivity and precision improvement [14]. Scanning DIA combined with ion trapping increased identified precursors by 30–40% [26]. On the analysis side, modelling isolation-window effects on fragment isotopes to exploit narrow windows improved peptide coverage by up to 10% [23].

Ion mobility raises coverage through ion utilization and precursor-space compression

The strongest mechanism-level evidence is quadrupole-free mobility-aligned fragmentation: replacing m/z filtering with high-resolution ion mobility isolation avoids ion losses of up to 99%, achieves near-100% ion utilization, and yielded roughly six-fold more protein group identifications than standard DDA on the same QTOF instrument, and over 100-fold improvement in low-load workflows [19]. That study also quantified low-abundance peptides undetectable by DDA and resolved co-eluting isobars and isomers before fragmentation, eliminating chimeric spectra [19]. The underlying rationale is stated bluntly elsewhere: quadrupole filters throw away up to 99% of useful ion signal and cap fragmentation speed, whereas mobility-based isolation can generate fragmentation spectra upward of 500 Hz [25]. Where mobility is used to shape rather than replace isolation, diagonal-PASEF methods that continuously cover the observed diagonal precursor distribution improved identifications and quantitative precision over dia-PASEF [20]. Combining capillary electrophoresis with diaPASEF identified 1,035 proteins from approximately 200 pg of digest [18], and on a timsUltra platform tissue type proved as important as gradient length and sample amount in determining coverage, with the benefit of more tissue sampling being both gradient- and dynamic-range-dependent [2].

Mobility's selectivity and its transmission are in direct tension

Lowering FAIMS resolution broadened the compensation-voltage window, increased ion transmission, and improved peptide identifications from HeLa digests by up to 18%, with a weaker but beneficial effect in single-cell samples and improved quantitative precision at low load [10]. That result is a transmission win purchased by giving up selectivity. The complementary strategy uses mobility as a fractionation dimension: FAIMS-based gas-phase fractionation identified 1,278 cross-links covering 213 protein–protein interactions from HeLa lysate without any in-solution fractionation, starting from less than 20 µg of sample [22]. Mobility-resolved data also enables identification features orthogonal to m/z and retention time: large-scale CCS datasets show a bimodal distribution for 3+ peptides traceable to basic-site positioning and helical versus globular conformation, which the authors argue will improve integration of CCS into identification pipelines [17]. A practical caveat accompanies resolution tuning: the same cell type separated into distinct populations in PCA depending solely on FAIMS resolution, so acquisition settings shift the proteomic fingerprint [10].

DIA's coverage advantage is co-produced by the search side and can invert against multiplexed DDA

Library and search strategy materially determine realized coverage: training deep learning models directly on DIA data improved fragment intensity prediction and peptide detection relative to pretrained DDA-derived models [24], and a library-free engine with in silico libraries exceeded prior peptide coverage by up to 10% [23]. Post-processing choices had drastic impacts on identification rates, quantitative precision and accuracy, and false quantitative rate, particularly at low sample amounts [9]. DIA also carries its own missing-value burden, motivating retention-time-boundary imputation that lowered the peptide limit of quantification [4]. Most consequentially, for static proteomes an isobaric TMT-DDA approach provided deeper and more precise coverage than label-free DIA, while DIA was more accurate but with reduced dynamic range and more missing values [1]; a separate comparison on an Astral with FAIMS found DIA and TMT both quantifying over 10,000 proteins with comparable depth [15].

Conflicts and gaps

The literature disagrees on whether DIA's advantage survives parameter matching: one study reports DDA and narrow-window DIA differing only modestly in analytical performance once parameter-matched, with data completeness only modestly better for DIA [11], whereas other comparisons report large DIA-vs-DDA depth gaps under routine conditions [6] [7]. Whether DIA reduces or increases missingness is unresolved: one report frames DIA as more accurate but with more missing values and reduced dynamic range [1], another reports far lower CVs for DIA [6], and a third treats DIA missingness as a problem requiring dedicated imputation [4]. Cross-study DDA/DIA agreement is tissue-dependent — good correlation in liver and colon but weak in lung and pancreas — with no mechanistic explanation offered [5]. No retrieved paper decomposes the DIA gain into exhaustive sampling versus MS1-based detection versus library quality. Peptide-level proteotypicity — the intrinsic probability a peptide is detectable at all — is treated as a separate, setup-specific property requiring empirical assessment [3], and no study crosses proteotypicity with acquisition mode.

What would change the answer

A parameter-matched comparison of DDA, dia-PASEF, diagonal-PASEF, and quadrupole-free mobility-aligned fragmentation across a dilution series of defined multi-species mixtures, reporting ions-per-peptide and ions-per-second normalized metrics alongside identification counts, would establish how much of the coverage difference is ion utilization rather than sampling design [14] [19] [20]. Extending the replicate-saturation design that revealed no plateau for either DDA or narrow-window DIA [11] to mobility-enabled platforms would estimate the recoverable peptide population and thus bound how much headroom remains. Controlled quantitative experiments on defined mixtures crossing acquisition mode with search and post-processing strategy would separate acquisition gains from analysis gains [9]. Finally, a mobility-resolution titration measuring ion transmission and identification yield jointly — rather than reporting identifications at a single FAIMS or TIMS setting — would resolve whether the selectivity-versus-transmission trade-off has an instrument-independent optimum [10].

References

[1] Parmar, B., Liu, Y., Ghezellou, P., Muench, C.. "Evaluation of deep and dynamic proteomic screening strategies at sub-50Hz scan rate and without automation.". bioRxiv. 2026-04-20T00:00:00.000Z. URL: https://doi.org/10.64898/2026.04.15.718630

[2] Klingeberg, M., Krisp, C., Fritzsche, S., Schallenberg, S., Hornburg, D., Coscia, F.. "An ultrasensitive spatial tissue proteomics workflow exceeding 100 proteomes per day". bioRxiv. 2025-06-05T00:00:00.000Z. URL: https://doi.org/10.1101/2025.06.02.657389

[3] Butenko, I. O., Kitsilovskaya, N. A., Vakaryuk, A. V., Lazareva, A. A., Gremyacheva, V. D., Kovalenko, A. V., Lebedeva, A. A., Baraboshkin, N. M., Chudinov, I. K., Khchoian, A. G., Kurylova, O. V., Gorbunov, K. S., Pavlenko, A., Kozhemyakin, G. L., Fedorov, O. V., Ilina, E., Govorun, V. M.. "Direct empirical in-house assessment of peptide proteotypicity for targeted proteomics". bioRxiv. 2026-02-23T00:00:00.000Z. URL: https://doi.org/10.64898/2026.02.22.699713

[4] Harris, L. J., Riffle, M., Shulman, N., Fondrie, W. E., Wu, C. C., Johnson Erickson, D. P., Morimoto, A., Shaver, B., Stein, T., Cao, N., Ford, E., Noble, W. S., MacCoss, M. J.. "Improved quantitation in data-independent acquisition proteomics via retention time boundary imputation". bioRxiv. 2026-04-03T00:00:00.000Z. URL: https://doi.org/10.1101/2025.05.27.656394

[5] Prakash, A., Collins, A., Vilmovsky, L., Fexova, S., Vizcaino, J. A., Jones, A. R.. "Integrated View of Baseline Protein Expression in Human Tissues using public Data Independent Acquisition datasets". bioRxiv. 2024-11-19T00:00:00.000Z. URL: https://doi.org/10.1101/2024.09.16.613191

[6] Hernandez, M., Saldivia, P., Latapiat, V., Antilef, B., Nourdin, G., Castro, F., Vargas, C., Koch, E.. "A dia-PASEF workflow improves proteome coverage and quantitative performance in repeated-measures analysis of cervicovaginal fluid from a preconception cohort (EARLY-PREG)". medRxiv. 2026-10-01T00:00:00.000Z. URL: https://doi.org/10.1101/2025.05.19.25327800

[7] Palma, J., Leblanc, C. C., Kusters, R., Kamgang Nzekoue, A. F.. "Proteomics for cultivated meat: the importance of Analytical Standardization". bioRxiv. 2026-03-25T00:00:00.000Z. URL: https://doi.org/10.64898/2026.03.23.713501

[8] Schebesta, A.-S., Korff, K., Itang, E. C. M., Albrecht, V., Geyer, P. E., Mueller-Reif, J. B.. "Exploring chromatographic dimensions for state-of-the-art proteomics applications". bioRxiv. 2025-08-17T00:00:00.000Z. URL: https://doi.org/10.1101/2025.08.16.670650

[9] Sidgwick, F., Frey, A., Porter, A., Palmowski, P., Trost, M.. "Recommendations for Quantitative Data-Independent Acquisition (DIA) Proteomics using Controlled Quantitative Experiments (CQEs)". bioRxiv. 2025-09-23T00:00:00.000Z. URL: https://doi.org/10.1101/2025.09.22.677725

[10] Hoch, D. G., Matzinger, M., Mechtler, K.. "Low-resolution FAIMS for increased peptide coverage in low-load and single-cell proteomics". bioRxiv. 2025-08-27T00:00:00.000Z. URL: https://doi.org/10.1101/2025.08.22.671812

[11] O'Sullivan, N., Bayer, F. P., Mogler, C., Kuster, B.. "High-Speed Mass Spectrometers diminish the difference between Data-Dependent and Data-Independent Acquisition Proteomics". bioRxiv. 2026-05-28T00:00:00.000Z. URL: https://doi.org/10.64898/2026.05.26.727836

[12] Guzman, U. H., Rykar, M., Hendriks, I. A., Stewart, H., Denisov, E., Hagedorn, B., Petzoldt, J., Kreutzmann, A., Mueller, Y., Arrey, T. N., Colonius, I., Oestergaard, O., Koenig, C., Kraegenbring, J., Fort, K. L., Couzijn, E., Hauschild, J.-P., Hermanson, D., Zabrouskov, V., Hock, C., Damoc, E., Olsen, J. V.. "Higher-Throughput Proteome Profiling Enabled by Parallelized Pre-Accumulation and Optimized Ion Processing in the Orbitrap Astral Zoom Mass Spectrometer". bioRxiv. 2025-07-19T00:00:00.000Z. URL: https://doi.org/10.1101/2025.07.14.664789

[13] Harking, F., Guzman, U. H., Kraegenbring, J., STEWART, H., Ayzikov, K., Koch, H., Fort, K. L., Harder, A., Olsen, J. V.. "Enhancing tandem MS sensitivity and peptide identification via ion pre-accumulation in an Orbitrap mass spectrometer". bioRxiv. 2025-02-25T00:00:00.000Z. URL: https://doi.org/10.1101/2025.02.20.639277

[14] Hsu, C., Shulman, N., Stewart, H., Petzoldt, J., Pashkova, A., Plubell, D., Denisov, E., Hagedorn, B., Damoc, E., MacLean, B. X., Remes, P. M., Canterbury, J. D., Makarov, A., Hock, C., Zabrouskov, V., Wu, C. C., MacCoss, M. J.. "Evaluation of a prototype Orbitrap Astral Zoom mass spectrometer for quantitative proteomics - Beyond identification lists". bioRxiv. 2025-07-30T00:00:00.000Z. URL: https://doi.org/10.1101/2025.05.30.657132

[15] Liu, X., Dawson, S. L., Gygi, S. P., Paulo, J. A.. "Isobaric Tagging and Data Independent Acquisition as Complementary Strategies for Proteome Profiling on an Orbitrap Astral Mass Spectrometer". bioRxiv. 2024-12-20T00:00:00.000Z. URL: https://doi.org/10.1101/2024.12.17.628765

[16] Fields, L., Wu, W., Dang, T. C., Ibarra, A. E., Gray, M., Li, L.. "Unlocking the Neuropeptidome using a Novel Endogenous Peptidomics Framework". bioRxiv. 2025-06-15T00:00:00.000Z. URL: https://doi.org/10.1101/2025.06.12.659356

[17] Xu, A. M., Szoelloesi, D., Grubmuller, H., Regev, O.. "Analysis of Large Peptide Collisional Cross Section Dataset Reveals Structural Origin of Bimodal Behavior". bioRxiv. 2025-09-16T00:00:00.000Z. URL: https://doi.org/10.1101/2025.09.10.675470

[18] Shen, B., Pade, L. R., Zhou, F., Nemes, P.. "Subcellular Mass Spectrometry Reveals Proteome Remodeling in an Asymmetrically Dividing (Frog) Embryonic Stem Cell". bioRxiv. 2025-05-12T00:00:00.000Z. URL: https://doi.org/10.1101/2025.05.12.652541

[19] Rorrer, L., Deng, L., Royer, L., Uribe, I., Orsburn, B., Bernhardt, O., Gandhi, T., Reiter, L., DeBord, D.. "A Parallel Accumulation-Mobility Aligned Fragmentation Strategy Utilizing High-Resolution Ion Mobility for High Performance Proteomics Analysis". bioRxiv. 2026-02-11T00:00:00.000Z. URL: https://doi.org/10.64898/2026.02.09.704896

[20] Below, C. R., Bernhardt, O. M., Kaspar-Schoenefeld, S., Willems, S., Coronado-Barrientos, E., Karemaker, I. D., Streckenbach, B., Raess, L., Schaer, S., Trede, D., Krieger, J. R., Gandhi, T., Bruderer, R. M., Reiter, L.. "Enhanced Identifications and Quantification through Retention Time Down-Sampling in Fast-Cycling diagonal-PASEF Methods". bioRxiv. 2025-04-26T00:00:00.000Z. URL: https://doi.org/10.1101/2025.04.23.650190

[21] Lee, J., West, O. K., Huso, W., Doan, A. G., Gray, K. J., Edwards, H., Tran, J. T., Carman, D. R., Betenbaugh, M., Srivastava, R., Harris, S., Marten, M. R.. "DIA-PASEF Proteomic Profiling Reveals MpkA-Dependent Iron Stress Responses and Siderophore Biosynthesis in Aspergillus nidulans". bioRxiv. 2025-07-06T00:00:00.000Z. URL: https://doi.org/10.1101/2025.07.03.662993

[22] Gizardin-Fredon, H., Terrosu, S., Pichard, S., Korkut, D., Braun, C., Poterszman, A., Charenton, C., Cianferani, S.. "FAIMS-GPF XL-MS: crosslinking-mass spectrometry based on gas-phase fractionation". bioRxiv. 2025-05-28T00:00:00.000Z. URL: https://doi.org/10.1101/2025.05.23.655748

[23] Wamsley, N. T., Wilkerson, E. M., Major, M. B., Goldfarb, D.. "Pioneer and Altimeter: Fast Analysis of DIA Proteomics Data Optimized for Narrow Isolation Windows". bioRxiv. 2026-09-10T00:00:00.000Z. URL: https://doi.org/10.64898/2026.02.16.706201

[24] Wen, B., Hsu, C., Shteynberg, D., Zeng, W.-F., Riffle, M., Chang, A., Mudge, M., Nunn, B. L., MacLean, B. X., Berg, M. D., Villen, J., MacCoss, M. J., Noble, W. S.. "Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics". bioRxiv. 2025-08-04T00:00:00.000Z. URL: https://doi.org/10.1101/2024.10.15.618504

[25] DeBord, D., Rorrer, L., Deng, L., Strathmann, F.. "Abandoning the Quadrupole for Mass Spectrometry Fragmentation Analysis: Towards 1,000 Hz Speeds with 100% Ion Utilization Using High Resolution Ion Mobility Precursor Isolation". bioRxiv. 2024-10-22T00:00:00.000Z. URL: https://doi.org/10.1101/2024.10.18.619158

[26] Sinn, L. R., Wang, Z., Alvarez, C., Chelur, A., Batruch, I., Pribil, P., Ludwig, D., Tate, S., Castro-Perez, J., Messner, C., Demichev, V., Ralser, M.. "Performance Characteristics of Zeno Trap Scanning DIA for Sensitive and Quantitative Proteomics at High Throughput". bioRxiv. 2025-05-10T00:00:00.000Z. URL: https://doi.org/10.1101/2025.05.06.652368

How this survey was made

This survey answers one question: “How do data-independent acquisition and ion mobility mass spectrometry improve peptide detection coverage in bottom-up proteomics compared with data-dependent acquisition?”

We keep our own collection of recent preprints and refresh it every day from bioRxiv, medRxiv, arXiv, PsyArXiv, SocArXiv and ChemRxiv. For each survey, we search that collection for recent work and keep the strongest matches: papers that use the same terms as the question, and papers on the same topic in different words.

The text was written from the titles and abstracts of those papers; the full texts were not read. Statements that draw on a particular paper are marked with a number in brackets. The reference list just above is the 26 papers whose work this text draws on, chosen from the 103 papers those searches returned; the rest of the shortlist is not printed. It is not a complete review of everything published on the question.

Treat this as a starting point and a reading list, not a peer-reviewed review. The searches behind this survey ran on October 21, 2026 and covered work from the previous two years.