Unlocking Biological Insight with Biognosys Software: A 2025 Publication Overview

Proteomics has become one of the most powerful lenses for understanding how biology actually works. While genomics and transcriptomics reveal what could happen, proteomics shows what does—the proteins driving physiology, disease, and therapeutic response. In 2025, researchers across disciplines relied on Spectronaut^® and SpectroDive^TM to deliver deep, quantitative, and highly reproducible protein insights.

 

 

 

 

What stands out in this year’s publications is the remarkable analysis depth across different applications: from protein turnover, mechanistic pathway analysis, clinical biomarker discovery, to multi-omics integration, and chemoproteomic target identification. Whether working with multiplexed SILAC samples, FFPE tissues, complex PTM landscapes, or covalent fragment libraries, scientists relied on DIA and targeted workflows to obtain data they could trust—rich in depth, stable across runs, and directly actionable.

 

This blog explores how the Biognosys software ecosystem transforms complex proteomics data into confident biological discovery.

 

 

From Precursor Abundance to plex-DIA Protein Turnover Profiling

 

Quantifying protein turnover with multiplexed DIA (plex-DIA) presents several challenges: overlapping isotopic envelopes, low-abundance heavy channels, interference-driven ratio distortion, and dependence on large spectral libraries. Recent studies demonstrate how Spectronaut overcomes these obstacles to deliver high-precision protein turnover measurements.

 

 

Spectronaut features channel-specific identification and filtering to enhance the accuracy of multiplexed DIA (plex-DIA) quantification by adding two channel-specific q-value filtering modes designed to control false discoveries more stringently in labelled experiments. (© Salovska, B., et al., 2025, Fig. 1)

 

 

In bulk pSILAC experiments, Yansheng Liu’s team applied directDIA with the advanced Labeled (LBL) workflow featuring channel-aware scoring and channel-specific FDR control to quantify 2–3 SILAC channels and uncover coordinated protein degradation pathways associated with cisplatin resistance and aneuploidy.¹

 

In other study, Jesper Olsen’s group used Spectronaut to quantify single-cell SILAC ratios, revealing turnover-resolved insights into cell-cycle regulation, stem-cell differentiation, and drug response that cannot be captured by transcriptomics alone.²

 

These publications showcase Spectronaut as a powerful tool for plex-DIA protein turnover studies, driving high-confidence biological discoveries from complex multi-channel designs to single-cell proteome dynamics.

 

Relevant publications:
• A robust multiplex-DIA workflow profiles protein turnover regulations associated with cisplatin resistance and aneuploidy.
• Global analysis of protein turnover dynamics in single cells.

 

 

From Proteomics to Multi-omics

 

Modern biological research increasingly begins with protein-centered measurements, integrating additional omics layers—particularly regulatory and genomic information—to contextualize protein function and pathway activity. Four recent studies demonstrate how Spectronaut-powered DIA proteomics forms the core of multi-omics frameworks that unify proteome dynamics with transcriptomic, genomic, PTM-level, and metabolic biology.

 

A specific study in localized clear-cell renal cell carcinoma (ccRCC) provides a clear example of how protein-centric profiling can sharpen risk stratification. In the Bio-miR study, Gámez-Pozo, Fresno Vara and colleagues applied Spectronaut-enabled DIA proteomics to FFPE tumors and integrated pathway-level protein signatures with microRNA profiles and RNA-seq, demonstrating how proteomic rewiring clarifies the functional consequences of the Bio-miR risk signature.³

 

Multi-omics approach scheme used to characterize mouse brain aging. (© Marino, A., et al., 2025, Fig. 1)

 

 

A similar protein-first logic underpins recent advances in aging research. Alessandro Ori’s group combined deep DIA-based global proteomics with ubiquitylome, acetylome, and phosphoproteome measurements, alongside RNA-seq and protein turnover analyses, uncovering proteostasis deterioration patterns that remain largely invisible at the transcript level.⁴

 

Protein-level readouts have also proven essential for dissecting genome maintenance mechanisms. In the context of the DNA damage response, Jacob E. Corn’s consortium paired ubiquitin-remnant DIA proteomics with genome-wide CRISPRi functional genomics, ChIP–seq, and replication timing maps, enabling mechanistic links between ubiquitin signaling dynamics, structural genomic fragility, and synthetic-lethal interactions.⁵

 

Extending this paradigm to clinical heterogeneity, Zong-Ren Wang and colleagues anchored a comprehensive prostate cancer study on DIA-based global proteomics and phosphoproteomics. By integrating these data with whole-exome sequencing, bulk and single-cell RNA-seq, pathway-linked metabolomics, and spatial immunofluorescence, they identified NANS-driven sialic acid metabolism as a subtype-specific therapeutic vulnerability.⁶

 

These studies demonstrate how Spectronaut-enabled DIA proteomics provides a robust foundation for multi-omics discovery, generating high-quality protein and PTM measurements that, when integrated with complementary omics layers, support coherent biological models driving mechanistic insight and therapeutic innovation.

 

Relevant publications:
• A prognostic microRNA-based signature for localized clear cell renal cell carcinoma: the Bio-miR study.
• Aging and diet alter the protein ubiquitylation landscape in the mouse brain.
• Comprehensive interrogation of synthetic lethality in the DNA damage response.
• Integrated proteogenomic characterization of localized prostate cancer identifies biological insights and subtype-specific therapeutic strategies.

 

 

From Chemoproteomics to Drug and Target Discovery

 

Chemoproteomics has rapidly evolved into a cornerstone technology for discovering ligandable hotspots, validating mechanistic targets, and accelerating the development of chemical probes and therapeutics. Two recent studies demonstrate how DIA-based proteomics, powered by Spectronaut, is transforming this field.

 

Jacob T. Bush and colleagues established a high-throughput, label-free competitive chemoproteomics platform that integrates SP4 plate-based preparation, rapid timsTOF diaPASEF acquisition, and Spectronaut quantification to profile ~30,000 cysteine-contain­ing peptides and screen 80 covalent fragments, revealing more than 400 selective ligand–protein interactions across diverse protein families, including many lacking known small-molecule ligands.⁷

 

 

HT-LFQ chemoproteomic workflow for cysteinome analysis. (© Biggs, G.S., 2025, Fig.1)

 

 

In another study, the State Key Laboratory of Membrane Biology applied activity-based protein profiling combined with DIA-MS (DIA-ABPP) to map redox-sensitive cysteine networks across mitochondria in multiple tissues, showing that ROMO1 overexpression protects the mitochondrial cysteinome from age-associated oxidation and restores organelle function—thereby highlighting previously unrecognized redox-regulatory nodes with therapeutic potential.⁸

 

These publications illustrate how Spectronaut supports chemoproteomics studies, delivering deep site-level coverage, high data completeness, and robust quantification. This enables researchers to move from proteome-wide covalent engagement to validated targets and mechanistic insights that guide next-generation drug discovery.

 

Relevant publications:

• Robust proteome profiling of cysteine-reactive fragments using label-free chemoproteomics.
• ROMO1 overexpression protects the mitochondrial cysteinome from oxidations in aging.

 

 

From Discovery to Validation

 

Modern proteomics increasingly relies on a two-tier workflow in which DIA discovery provides comprehensive, unbiased quantification, followed by targeted proteomics for high-precision validation. Two recent studies demonstrate how Spectronaut and SpectroDive uniquely enable this discovery-to-validation pipeline across both fundamental biology and translational medicine.

 

In the killifish brain aging study, Leibniz Institute on Aging–Fritz Lipmann Institute (FLI) leveraged Spectronaut’s Pulsar generated spectral libraries, FDR control, PTM analysis, and flexible normalization to integrate whole-proteome, organelle, insoluble fractions, and PTM datasets, uncovering a conserved mechanism in which translation-elongation pausing drives selective loss of RNA- and DNA-binding proteins. Targeted assays built and quantified in SpectroDive validated these DIA-derived mechanistic signatures with high specificity.⁹

 

In another study, Liver Research Center at Beijing Friendship Hospital applied diaPASEF and Spectronaut to profile serum proteomes from chronic hepatitis B patients before and after therapy, resolving fibrosis-regression signatures and deriving machine-learning–driven short- and long-term predictive protein panels. These markers were subsequently validated by PRM-PASEF using SpectroDive, where robust peak picking, precise retention-time alignment, and heavy-peptide integration enabled consistent quantification of analytical panels across validation cohorts.¹⁰

 

Across both studies, the Biognosys software ecosystem enabled researchers to move seamlessly from large-scale, discovery approach to precise, reproducible biomarker verification. These publications highlight how Spectronaut together with SpectroDive power scientists to translate complex proteomic questions into validated biological targets and clinically actionable protein panels.

 

Relevant publications:

• Altered translation elongation contributes to key hallmarks of aging in the killifish brain.
• Serological proteomic characterization for monitoring liver fibrosis regression in chronic hepatitis B patients on treatment.

 

 

Final Thoughts: The Expanding Frontier of Proteomics-Enabled Biology

 

Across turnover biology, mechanistic discovery, clinical translation, multi-omics mapping, and drug-target identification, 2025 studies consistently demonstrated the power of Spectronaut and SpectroDive to deliver depth, reproducibility, and actionable insight. Despite the diversity of biological questions and experimental designs, from multiplexed SILAC and PTM-resolved proteomics to serum biomarker discovery and covalent ligand screening, the same analytical backbone enabled researchers to generate high-quality, interpretable protein data with confidence.

 

Together, these studies highlight a cutting-edge proteomics ecosystem where DIA and targeted mass spectrometry, powered by Biognosys software, offer a reliable and flexible toolkit for uncovering mechanistic insights across a wide range of research areas.

 

 

Curious about our software packages? Connect with our expert team to book a demo and see how our solutions can work for you.

 

Let’s Connect

 

Looking for additional insights? Explore a curated selection of 2025 publications in proteomics and metabolomics from our Biognosys Group partners, PreOmics and biocrates.

 

 

References

 

1. Salovska, B., Li, W., Bernhardt, O.M., Germain, P.-L., Wang, Q., Gandhi, T., Reiter, L., and Liu, Y. (2025). A robust multiplex-DIA workflow profiles protein turnover regulations associated with cisplatin resistance and aneuploidy. Nature Communications 16, 5034. 10.1038/s41467-025-60319-x.
2. Sabatier, P., Lechner, M., Guzmán, U.H., Beusch, C.M., Zeng, X., Wang, L., Izaguirre, F., Seth, A., Gritsenko, O., Rodin, S., et al. (2025). Global analysis of protein turnover dynamics in single cells. Cell 188, 2433–2450.e2421. 10.1016/j.cell.2025.03.002.
3. Pinto-Marín, Á., Trilla-Fuertes, L., Miranda Poma, J., Vasudev, N.S., García-Fernández, E., López-Vacas, R., Miranda, N., Wilson, M., López-Camacho, E., Pertejo, A., et al. (2025). A prognostic microRNA-based signature for localized clear cell renal cell carcinoma: the Bio-miR study. British Journal of Cancer 133, 155–167. 10.1038/s41416-025-03008-2.
4. Marino, A., Di Fraia, D., Panfilova, D., Sahu, A.K., Minetti, A., Omrani, O., Cirri, E., and Ori, A. (2025). Aging and diet alter the protein ubiquitylation landscape in the mouse brain. Nature Communications 16, 5266. 10.1038/s41467-025-60542-6.
5. Fielden, J., Siegner, S.M., Gallagher, D.N., Schröder, M.S., Dello Stritto, M.R., Lam, S., Kobel, L., Schlapansky, M.F., Jackson, S.P., Cejka, P., et al. (2025). Comprehensive interrogation of synthetic lethality in the DNA damage response. Nature 640, 1093–1102. 10.1038/s41586-025-08815-4.
6. Ou, W., Zhang, X.-X., Li, B., Tuo, Y., Lin, R.-X., Liu, P.-F., Guo, J.-P., Un, H.-C., Li, M.-H., Lei, J.-H., et al. (2025). Integrated proteogenomic characterization of localized prostate cancer identifies biological insights and subtype-specific therapeutic strategies. Nature Communications 16, 3189. 10.1038/s41467-025-58569-w.
7. Biggs, G.S., Cawood, E.E., Vuorinen, A., McCarthy, W.J., Wilders, H., Riziotis, I.G., van der Zouwen, A.J., Pettinger, J., Nightingale, L., Chen, P., et al. (2025). Robust proteome profiling of cysteine-reactive fragments using label-free chemoproteomics. Nat Commun 16, 73. 10.1038/s41467-024-55057-5.
8. Xu, F., Huang, H., Peng, K., Jian, C., Wu, H., Jing, Z., Qiu, S., Chen, Y., Liu, K., Fu, L., et al. (2025). ROMO1 overexpression protects the mitochondrial cysteinome from oxidations in aging. Nat Commun 16, 5133. 10.1038/s41467-025-60503-z.
9. Di Fraia, D., Marino, A., Lee, J.H., Kelmer Sacramento, E., Baumgart, M., Bagnoli, S., Balla, T., Schalk, F., Kamrad, S., Guan, R., et al. (2025). Altered translation elongation contributes to key hallmarks of aging in the killifish brain. Science 389, eadk3079. 10.1126/science.adk3079.
10. Zhang, M., Chen, S., Wu, X., Zhou, J., Wang, B., Meng, T., Hua, R., Sun, Y., You, H., and Chen, W. (2025). Serological proteomic characterization for monitoring liver fibrosis regression in chronic hepatitis B patients on treatment. Nat Commun 16, 7714. 10.1038/s41467-025-63006-z.