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Open research questions in Advanced Proteomics Techniques and Applications

46 unresolved questions extracted from the limitations and future-work sections of 224 Advanced Proteomics Techniques and Applications papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Nasal fluid collected at the brain-nose interface (BNI) may provide a minimally invasive window into central nervous system (CNS) biology, but its suitability for deep, reproducible proteomics remains unclear.

    Proteomic Profiling of Nasal Fluid from the Brain-Nose Interface using Mass Spectrometry · 2026 · DOI

  • While synchro-PASEF has demonstrated competitive identification depth for global protein abundance samples compared to conventional dia-PASEF, its performance for phosphoproteomics - where the precursor ion cloud is characteristically broader and bimodally distributed - has not been evaluated.

    Systematic optimization and benchmarking of synchro-PASEF for high-throughput phosphoproteome profiling · 2026 · DOI

  • throughout limited Data availability statement The data analyzed in this study is subject to the following licenses/restrictions: The datasets analyzed in this study are not Frontiers in Cardiovascular Medicine 08 frontiersin.

    Identification of a plasma proteomic signature associated with sudden cardiac death risk in the UK biobank · 2026 · DOI

  • This approach overcomes a key limitation of existing MS-based MRD assays by enabling peptide target discovery in patients without archived serum but with available BM sequencing.

    Enabling blood-based mass spectrometry MRD detection in multiple myeloma without archived serum samples. · 2026 · DOI

  • Since the advent of proteomics back in 1990s, there has been a sustained effort in this field to profile the proteome at the single-cell level. In 2025 Ye et al.11 achieved label-free quantification of >6,500 proteins in a single cell, reaching the protein coverage approaching conventional bulk proteomics. Nevertheless, the application of SCP remains largely confined to cell lines with limited studies on clinical samples. A major limitation is the insufficient analytical throughput, which restricts scalability. Together, current SCP methodologies constitute a flexible experimental toolkit and the choice of platform necessitates carefully weighing the trade-offs between throughput, depth, spatial resolution, and reproducibility. Early discovery and translational studies benefit from scalable, high-throughput approaches, whereas detailed interrogation of tumor evolution, signaling plasticity, and therapeutic resistance relies on deep, high-resolution single-cell proteomic profiling. Recognizing and explicitly aligning these trade-offs with biological questions is essential for the effective application and continued maturation of SCP. In addition, although PTMs are now detectable in single cells without enrichment, the detection depth remains inadequate for comprehensive profiling and advancing PTM studies at single-cell resolution demands significantly more sensitive technologies. Therefore, breakthroughs in detection sensitivity and analytical throughput are essential to expand the biomedical applications of SCP. Another challenge for SCP is data accuracy, completeness, and reproducibility among experiments. AI-based tools are expected to facilitate signal denoising, missing value imputation, and biological interpretation. The AI virtual cell (AIVC) concept integrates prior knowledge, structural information, and dynamic states into predictive multimodal models for in silico simulation. Domain-specific large language models trained on extensive biological datasets promise to accelerate SCP by supporting cross-omics knowledge transfer, literature-data integration, and end-to-end workflows from protein function prediction to therapeutic design165,166. Future progress will require the convergence of SCP with other omics for comprehensive cell-state modeling and the development of standardized, large-scale SCP databases. Coordinated advances in technology, data resources, and AI modeling will drive SCP toward dynamic multi-scale proteome analysis to transform basic research and clinical translation. Cancer Biol Med Vol xx, No x Month 2026 19 Despite progress in single-cell proteomics, the clinical translation faces persistent practical barriers. Most SCP strategies require cell suspensions and thus often depend on fresh samples.

    Evolution in single-cell proteomics drives new frontiers in cancer biology and precision medicine · 2026 · DOI

  • The breast cancer organoid drug treatment experiments assessed pharmacologic interactions for 72 hours with barasertib combined with doxorubicin or carboplatin, but did not include long-term resistance evolution studies or repeated drug exposure conditions that simulate clinical scenarios of acquired chemotherapy resistance.

    Proteogenomic decoding of chemotherapy resistance in patients with triple-negative breast cancer · 2026 · DOI

  • The proteogenomic analysis integrated mRNA, protein, and phosphoprotein expression data, but did not incorporate genomic mutation data, copy number alterations, or epigenetic modifications; the contribution of these additional molecular layers to the identified chemotherapy resistance phenotypes remains unexplored.

    Proteogenomic decoding of chemotherapy resistance in patients with triple-negative breast cancer · 2026 · DOI

  • Logistic regression was used to develop a predictive model for non-pathological complete response (non-pCR) using selected proteogenomic biomarkers, but the paper does not report cross-validation performance metrics, feature importance rankings, or comparison with machine learning approaches (random forest, gradient boosting, neural networks) that might improve predictive accuracy for chemotherapy resistance in TNBC.

    Proteogenomic decoding of chemotherapy resistance in patients with triple-negative breast cancer · 2026 · DOI

  • The study tested paclitaxel, doxorubicin, and carboplatin in combination with GRK2 or Aurora B inhibitors, but did not systematically evaluate whether the identified proteogenomic resistance mechanisms are specific to these chemotherapy agents or translate to other drug classes used in TNBC treatment (e.g., platinum-taxane combinations, immunotherapy combinations).

    Proteogenomic decoding of chemotherapy resistance in patients with triple-negative breast cancer · 2026 · DOI

  • The NMF clustering approach selected k=5 clusters based on cophenetic correlation coefficients, dispersion, and consensus stability metrics, but the paper does not evaluate whether alternative clustering algorithms (hierarchical clustering, k-means, or graph-based methods) would yield different proteogenomic subtypes or affect the identification of chemotherapy resistance signatures in triple-negative breast cancer.

    Proteogenomic decoding of chemotherapy resistance in patients with triple-negative breast cancer · 2026 · DOI

  • The reproducibility and stability of proteomic profiles across different sample collection, storage, and pre-analytical conditions in multiplex assays are not systematically characterized, limiting the clinical implementation of quantitative protein mass spectrometry tests for routine diagnostics.

    Next-generation proteomics in medical laboratories: metrologically sound quantitative protein tests vs. innovative and personalized proteome patterns · 2026 · DOI

  • The multiplex apolipoprotein panel demonstrated improved cardiovascular event prediction, but the paper does not specify the exact proteoform variants, post-translational modifications, or quantitative thresholds of these apolipoproteins needed to stratify patients for targeted PCSK9 inhibitor therapy in routine clinical practice.

    Next-generation proteomics in medical laboratories: metrologically sound quantitative protein tests vs. innovative and personalized proteome patterns · 2026 · DOI

  • Aptamer-based proteomic platforms (e.g., SOMAscan) and antibody-based methods show discordant results in biomarker discovery, but there is no systematic comparison protocol established to reconcile these differences when selecting proteins for targeted liquid chromatography-tandem mass spectrometry validation in clinical settings.

    Next-generation proteomics in medical laboratories: metrologically sound quantitative protein tests vs. innovative and personalized proteome patterns · 2026 · DOI

  • Top-down proteomics has emerged as a technology for proteoform characterization, but the paper does not specify which disease states, protein families, or biomarker applications would benefit most from top-down versus bottom-up mass spectrometry approaches in routine clinical laboratories.

    Next-generation proteomics in medical laboratories: metrologically sound quantitative protein tests vs. innovative and personalized proteome patterns · 2026 · DOI

  • Proteoform analysis for clinical diagnostics has not been systematically evaluated across multiple mass spectrometry platforms and clinical laboratories to establish standardized measurands and global harmonization protocols. The paper emphasizes proteoforms as the next proteomics currency but lacks specification of which post-translational modifications should be routinely measured in clinical settings.

    Next-generation proteomics in medical laboratories: metrologically sound quantitative protein tests vs. innovative and personalized proteome patterns · 2026 · DOI

  • DIA has evolved into a next-generation strategy for highthroughput quantitative proteomics. As reviewed here, recent advances in DIA data acquisition schemes and informatic approaches and tools have substantially enhanced the coverage, accuracy and speed of DIA-based proteomics. In regard to instrumentation favoring DIA data acquisition, both scanning quadrupole and ion mobility spectrometry, when coupled with different mass spectrometers, have shown great promises for high-sensitivity and high-speed DIA proteomics. We anticipate future innovations in overlappingwindow DIA (including scanning-quadrupole-based) and PASEF-enhanced DIA would further drive DIA towards complete sampling of both the precursor ion and fragment ion beams. Meanwhile, the development of these data acquisition methods would increase data complexity and provoke new challenges to DIA data compression and analysis. In this review, we classify different DIA software tools based on their analysis strategies. For widely used sequence- and library-based searches, we further designate two approaches to describe how peaks are grouped for scoring. Combining analysis strategies and peak grouping approaches can be complementary to the peptide-centric analysis (94) in specifying workflows implemented in various tools. For instance, among the peptide-centric tools, PECAN combines a sequence-based search with a spectrum-first approach, while OpenSWATH and DIA-NN conduct a library-based search in a chromatogram-first manner. For tools employing a librarybased search with a spectrum-first approach, the spectrumcentric MSPLIT-DIA differs from the “combination-centric” Specter. Alternative to the peak grouping step on which we differentiate the sequence-/library-based searches in our review, the workflow specification could be further refined based on the SSM scores, scoring models, and basic scoring units listed in Tables 2–5. While the repertoire of analysis tools continues expanding, each software package works most efficiently in its own ecosystem. The intermediate and final outputs provided by different software are in distinct formats, complicating integration or re-processing of results from diverse software. Currently, the Skyline ecosystem (233) is the primary platform that can utilize various data sources and integrate results from multiple software packages. Notably, ongoing efforts towards standardizing file formats seek to enhance transparency and flexibility in DIA data analysis (234–237). For instance, the mzTab format (238) provides well-defined records for identi- fied peptides, scores, PTM localization and confidence, linkages between identifications and MS spectra, etc. This format, supported by ProteomeXchange (239), allows querying peptide identifications from raw MS data, and has been used in A Survey of Acquisition and Analysis of DIA Data in 2023 software tools like MaxDIA.

    Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023 · 2024 · DOI

  • Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023 Ronghui Lou1,2,* and Wenqing Shui1,2,* Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation–enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, librarybased search, de novo sequencing, and sequencingindependent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics. Mass spectrometry (MS)-based bottom-up proteomics has become one of the most powerful technologies for large-scale profiling of the proteome composition and dynamic regulation in diverse biological systems and clinical specimens (1–3). Owing to significant advances in both MS instruments and informatic pipelines, current bottom-up proteomics has attained a coverage of the expressed protein-coded genes at a depth comparable to transcriptomics yet provided additional insights into protein posttranslational modification and protein complex assembly (4–8). In discovery-oriented bottom-up proteomics, two widely adopted data acquisition strategies, namely data-dependent in the way of isolating precursor acquisition (DDA) and data-independent acquisition (DIA), mainly differ ions for fragmentation and subsequent MS2 spectra acquisition. Briefly, a DDA experimental scheme typically comprises the selection, accumulation, and fragmentation of precursor ions based on real-time analysis of data/signals from an MS1 survey scan. In contrast, the mass spectrometer in DIA experiments cycles through a predefined set of precursor isolation windows within which all the precursor ions are simultaneously fragmented, obviating the need for real-time precursor selection.

    Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023 · 2024 · DOI

  • For the vast majority of phosphorylation events, it is not yet known which of the more than 300 protein serine/threonine (Ser/Thr) kinases encoded in the human genome are responsible 3 .

    An atlas of substrate specificities for the human serine/threonine kinome · 2023 · DOI

  • Whether the baseline serum concentrations of these proteins differ systematically by sex and by self-reported race in healthy individuals has not been tested across a broad clinical panel on a single standardized platform.

    Sex and race shape serum protein biomarkers and bias diagnostic reference intervals · 2026 · DOI

  • However, in-depth proteomic characterisation of axons has been limited by the difficulty of isolating pure axonal material in sufficient quantities for conventional mass spectrometry analysis.

    Deep axonal proteomics of human iPSC-derived neurons by microfluidic separation and DIA-MS · 2026 · DOI

  • To demonstrate the potential of this approach, we applied it to the choroid plexus (ChP), a highly specialized but understudied brain structure whose spatial molecular organization remains poorly characterized.

    A Robust and Generalizable Low-Input Spatial Proteomics Workflow Enabling Deep Proteome Coverage · 2026 · DOI

  • MotivationCentral human metabolism powers cellular processes, yet its dysregulation in disease remains poorly understood.

    Curating MitoCore: A Standardized Small-Scale Human Metabolic Model as Platform for Proteomics Integration and Disease Modeling · 2026 · DOI

  • Spontaneous labour onset is a precisely timed physiological transition that determines outcomes for millions of pregnancies annually, yet its upstream molecular triggers remain unknown.

    The molecular triggers of human labour: a longitudinal plasma proteomics study · 2026 · DOI

  • Blood-based proteomic profiling is now widely applied in neurodegenerative and neuroinflammatory disease, yet the choice between serum and plasma remains poorly characterised for high-multiplex platforms.

    Matrix matters: head-to-head concordance of serum and plasma for NULISAseq CNS Disease Panel · 2026 · DOI

  • Conse- quently, qualitative labels alone are insufficient to describe the predominant fraction association and quantitative sig- nificance of individual SMPs.

    Quantitative Proteomic Profiling of Pinctada fucata Shell Nacre Defines a Solubility-Based Type Classification of Shell Matrix Proteins · 2026 · DOI

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46 open questions have been extracted from the limitations and future-work passages of 224 Advanced Proteomics Techniques and Applications papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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