Company

Advancing proteomics
since 2017

Originally developed at the University of Cambridge, DIA-NN rapidly became a leading tool for proteomics. Since 2024, Aptila Biotech has built it into an enterprise software suite.

Leadership Team

Our story

From research to DIA-NN Enterprise

Chapter one

Academic research

  1. 2017

    A research project at the University of Cambridge

    DIA-NN set out to make deep, reliable proteomics possible with fast chromatographic gradients.

  2. 2020

    Published in Nature Methods

    It drove the rapid adoption of DIA-NN and has over 4000 citations (Google Scholar, September 2026).

    The original paper

  3. The following years

    A basis for major technology advances

    plexDIA (multiplexing), CysQuant (cysteine oxidation) and QuantUMS (quantification with quality scores) are developed based on DIA-NN.

    plexDIA CysQuant QuantUMS
    QuantUMS quantification and qualityConceptual diagram of MS1 and MS2 evidence entering the dataset-adapted QuantUMS method. Relative quantities and quantitative quality measures are separate outputs. Signal intensities, bar lengths and quality-marker positions are illustrative, with no experimental scale or performance claim. MS1 + MS2 evidenceLearned weightingQuantities and quality MS1Isotope seriesMS2Fragment ionsQuantUMSDataset-adaptedQuantityQuality score

Chapter two

Aptila Biotech

  1. 2024

    Aptila Biotech is founded

    Aptila was spun off from Charité to commercialise and advance the development of DIA-NN.

  2. 2024

    quantms: DIA-NN in the cloud

    The open-source, Nextflow-based quantms platform, published in Nature Methods, parallelises DIA-NN across compute nodes for large-scale analyses. Its DIA workflow is now developed as quantmsdiann.

    quantms

  3. 2025

    DIA-NN Enterprise, InfinDIA and label-free DDA

    DIA-NN Enterprise launched in January, followed later that year by the introduction of rapid InfinDIA searches and label-free DDA support.

    InfinDIA in immunopeptidomics
    InfinDIA for large peptide search spacesConceptual illustration of peptide queries against the special InfinDIA data representation. Selected candidate peptides proceed to the regular DIA-NN search. Highlighted example sequences reappear in the candidate selection; they do not represent final identifications. The illustrative sequence field and three displayed examples represent neither a library size nor a candidate count. The fine organised field is a visual abstraction, not a literal description of the internal representation or a speed measurement. Example applications include non-specific digests and immunopeptidomics. Peptide queriesVLSPADKTVDLEELKASFAAGLQFPVYEAPQLLKTYASVQTMKDLGTIPNLESAKLVEDAGHFTPGASQYVLNQELVFKDAWGLSTAPYIFDEVKQLATVMQGTPESLLGTPEVQAFDVNQSKLPAELPAGWTSVQYLVKDNASAPFQTLGVESVEKPLNAFMGTLSDQAVYAPQNVKLEKDLVETGSATQALFVPENNGLKQSTAVEPVADYTLQPQLATVGESGVEASLQNFLQPTDAVKEAVKELTQGSFSNQLPAVEGILGFVFTLQEGVPTLNAVLDQSKAPEAEPTVLQNSNKVQALDGTDFGQVELAPSVLPAKQTEEVQTLKADGLAPVNQTESADQELVTKPGTPQNLAVEQVKADLSETVLENQTGPATGLAPVQKEQPSVNALETAVEGQTLKPKLSEYQAVPKTLQESVAPVQGELNATP Non-specific digests ImmunopeptidomicsInfinDIAINDEXCandidates AAGLQFPVY GILGFVFTL KLSEYQAVPDIA-NN search

  4. 2025

    Parallel chromatography with SynchroSep-MS

    SynchroSep-MS uses DIA-NN to process data acquired on multiple LC columns simultaneously connected to a single mass spectrometer.

    SynchroSep-MS

  5. 2026

    In-depth statistical analysis and visualisation

    DIA-NN 2.5 identifies up to 70% more proteins and adds extensive statistical capabilities.

    DIA-NN 2.5
    PathwaysSchematic pathway groups and a selected pathway. Connections and node colours are illustrative, not an inferred biological network or a statistical result. Selected pathway
    ProteinsIllustrative regulation patterns across proteins within a pathway. One protein row is selected for deeper examination. The colours are schematic; no experimental matrix or significance value is claimed. Selected protein

  6. 2026

    Depth and quantitative control for DDA

    DIA-NN 2.6 pairs deep DDA identification with MS1-quality filtering that facilitates relative quantification accuracy.

    DDA quantification accuracy
    DDA quantitative accuracy after MS1-level quality filteringMeasured precursor log2 ratio distributions from the ProteoBench DDA benchmark, analysed with DIA-NN 2.6 after MS1-level quality filtering. Dashed lines mark expected ratios: human zero, yeast one, E. coli minus two. Full experimental conditions and filters are available in the linked application note. ProteoBench DDAE. coli · 1:4Human · 1:1Yeast · 2:1MS1 QUALITY FILTERINGAccurate DDA quantification−4−2024Precursor abundance ratio · log₂(A:B)Density A:B = mixture A / mixture B · dashed lines: expected ratios

  7. Today

    Available from Aptila and Thermo Fisher Scientific

    Licences are available for industry and academic institutions.

    Purchasing options

Direction

Depth, speed and quantitative reliability

01

Depth

Deep protein identification in DIA and DDA, directly from protein sequences or with empirical spectral libraries.

02

Speed

DIA-NN can be configured for exceptional speed of processing. Automated pipelines scale across workstations, clusters and the cloud.

03

Quantitative reliability

QuantUMS adapts quantification algorithms to each experiment and scores the quality of each quantity.

Leadership Team

Justus Grossmann

Justus Grossmann

Founder and Managing Director
Clara Correia-Melo

Clara Correia-Melo

Founder and Head of Strategy
Vadim Demichev

Vadim Demichev

Founder and Head of Research

DIA-NN Enterprise

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