The research focused on the Bama miniature pig, a small breed from Guangxi in southern China prized both for its excellent meat quality and its growing role as a biomedical model for human research. The team collected samples from two anatomically distinct muscles at six time points: embryonic days 57, 73, and 90, and postnatal days 1, 28, and 120. The longissimus dorsi, a back muscle dominated by glycolytic type IIB fibers, represents the fast-twitch archetype, while the semitendinosus, rich in oxidative type I fibers, embodies the slow-twitch phenotype. This dual-region, dual-time design allowed the researchers to separate the effects of anatomy from the effects of developmental time—a distinction that many earlier studies, which sampled only single muscles or single stages, could not make.

Technically, the proteomic work was ambitious. The team employed non-targeted data-independent acquisition mass spectrometry, or nDIA, applied for the first time to porcine embryonic skeletal muscle fibers. Using an Orbitrap Astral mass spectrometer with 300 DIA windows and high-resolution scanning, they identified an average of 6,118 proteins per sample, with a range spanning roughly 3,500 to 7,400. The transcriptomic side relied on Illumina NovaSeq 6000 sequencing, capturing an average of 13,354 genes per sample. Together, these datasets covered the vast majority of the expressed muscle proteome and transcriptome, giving the researchers an unusually complete view of how gene activity and protein abundance change in parallel—and, crucially, where they diverge.

The most striking pattern to emerge was temporal rather than spatial. Across developmental stages, the researchers identified 3,509 differentially abundant proteins, dwarfing the 272 proteins that differed between the two muscle regions at any given stage. Principal component analysis showed that fetal samples from E57 through E90 clustered tightly together, with correlation coefficients between adjacent fetal stages reaching 0.86. But after birth, the samples separated sharply along the developmental timeline, and the correlation between E90 and P1 dropped to just 0.59—the lowest of any adjacent pair. Birth, in other words, marks a molecular discontinuity, a wholesale reprogramming of the muscle proteome that no other transition in the study approached in magnitude.

Within that postnatal reprogramming, one window stood out. Between postnatal days 1 and 28, the semitendinosus gained more newly expressed proteins than the longissimus dorsi, and the two regions showed significant differences in both protein abundance and the biological processes those proteins supported. A heatmap of mitochondrial respiratory complex proteins revealed that regional differences in oxidative machinery first appeared at P28 and reached their peak at P120. Because oxidative fibers carry far more mitochondrial content than glycolytic fibers, this divergence in mitochondrial protein abundance is a direct molecular signature of the slow-versus-fast fiber split. The finding aligns with older histological work from 1972 showing that pig oxidative and glycolytic fibers become distinguishable between 21 and 28 days after birth—but the new study supplies the protein-level detail that histology alone could never provide.

The integrated analysis also exposed a deep layer of post-transcriptional regulation. Classifying genes by how their RNA and protein levels changed together, the researchers found that more than 90 percent fell into categories showing minimal or discordant changes between the two levels. Most tellingly, the proportion of Type 2 genes—those with substantial protein-level changes but minimal RNA-level changes—jumped from under 10 percent during fetal stages to 12.8 percent at P1, 23 percent at P28, and 27.7 percent at P120. At P28, these Type 2 genes were enriched in ribosome biogenesis, lysosome function, and basal transcription factors, and gene set enrichment analysis linked them to fatty acid metabolism, a pathway known to run at higher activity in slow fibers. At P120, Type 5 genes, whose RNA and protein levels move in opposite directions, were enriched in the mTOR signaling pathway and lipoic acid metabolism. The message is clear: RNA sequencing alone would miss much of what actually drives muscle fiber specialization.

From the protein domain analysis came the study’s headline discovery. The serine/threonine kinase domain S_TKc was enriched in specific abundance trends in both muscles, but the proteins carrying it showed region-specific temporal patterns. One of these, NEK3—Never In Mitosis Gene A-Related Kinase 3—appeared as a differentially abundant protein between the two muscles at P1 with a striking log2 fold change exceeding 10, yet its RNA levels remained identical between regions throughout development. That discrepancy pointed to post-transcriptional control, and it is precisely the kind of regulatory mechanism that only a combined proteomic-transcriptomic approach can catch. The team used AlphaFold3 to model NEK3’s structure, then moved to the laboratory to test its function.

The functional experiments delivered a consistent verdict. When the researchers overexpressed NEK3 in mouse C2C12 myoblasts, MYHC expression dropped significantly, confirmed by both immunofluorescence and quantitative PCR. Western blotting showed that NEK3 overexpression suppressed MYH4, a fast-twitch fiber marker, while boosting MYH7 and PGC-1α, both hallmarks of slow oxidative fibers. The team then repeated the experiments in porcine embryonic muscle progenitor cells isolated from Bama pigs at 35 days post-coitum—a more physiologically relevant system—and observed the same pattern: fewer myotubes formed, and fast-twitch markers declined. NEK3, in short, acts as a conserved negative regulator of fast-twitch muscle fiber differentiation, and its transient, protein-only appearance at P1 suggests it helps orchestrate the fiber-type decisions made in the critical days after birth.

A second regulator, NCOA2, added another layer of nuance. This nuclear receptor coactivator, known to promote myogenic differentiation through the MEF2C pathway, showed distinct temporal patterns in the two muscles. In the fast-twitch longissimus dorsi, NCOA2 abundance began declining at E73 and vanished by P28; in the slow-twitch semitendinosus, the decline started later, at E90, and did not reach zero until P120. Because most fetal pig muscle fibers start life with slow-twitch characteristics, the researchers interpret NCOA2 as a molecular brake on the slow-to-fast transition: its early disappearance in the longissimus dorsi licenses that muscle to become fast, while its persistence in the semitendinosus preserves slow fiber identity deep into maturity.

Finally, the team widened the lens across species. Comparing 1,869 single-copy orthologous genes among pig, human, and mouse skeletal muscle at weaning-equivalent stages—postnatal day 28 in pigs, day 20 in mice, and 7 months in humans—they found that pig-specific high-expression clusters were enriched in skeletal muscle organ development, circulatory system development, and heart development, while pig-specific low-expression clusters involved autophagy and insulin receptor signaling. Intriguingly, genes tied to lung development were also elevated in pigs at this stage, hinting at a coordinated developmental program in which maturing respiratory muscles provide the mechanical forces that drive lung maturation. For pig farmers, the P28 window coincides with weaning, the most economically sensitive moment in production, suggesting that management strategies protecting muscle development during this period could pay dividends in both meat quality and animal health. For biomedical researchers, the study offers a molecular roadmap of muscle maturation in one of the most important animal models of human physiology—and a reminder that the proteins, not just the genes, tell the fuller story.

Subject of Research: Dynamic changes in muscle fiber types across developmental stages of porcine skeletal muscle, analyzed by integrated proteomics and transcriptomics

Article Title: Integrated proteomics and transcriptomics analysis of dynamic changes in muscle fiber types in different regions of porcine skeletal muscle

Article References: Integrated proteomics and transcriptomics analysis of dynamic changes in muscle fiber types in different regions of porcine skeletal muscle. (n.d.). https://doi.org/10.1007/s44307-025-00080-w

Image Credits: AI Generated

DOI: 10.1007/s44307-025-00080-w

Keywords: skeletal muscle, muscle fiber types, proteomics, transcriptomics, Bama miniature pig, NEK3, NCOA2, mitochondria, myogenesis, meat quality, data-independent acquisition, postnatal development

The heart of that reinvention lies in how mass spectrometrists acquire their data. For years, the field relied on data-dependent acquisition, or DDA, in which the instrument selects only the most abundant precursor ions for fragmentation in any given cycle. DDA surveys are inherently biased toward abundant proteins and suffer from stochastic sampling, producing notorious gaps in which the same peptide may be detected in one sample and missing from the next. The review describes a decisive shift toward data-independent acquisition, or DIA, in which the instrument fragments all ions within sequential isolation windows, along with targeted parallel reaction monitoring, or PRM, on high-resolution instruments. The result is superior proteome coverage, markedly enhanced quantitative accuracy, and a substantial reduction in missing values, three properties that matter enormously when the goal is to compare protein abundance across dozens or hundreds of patient specimens.

Hardware evolution matched the acquisition-mode revolution. Kulyyassov catalogs a series of instruments and platforms that have fundamentally reshaped the experimental pipeline: automated sample preparation systems that reduce hands-on variability, high-throughput liquid chromatography platforms such as the Evosep One and micro-pillar array columns known as µPAC that compress separation times while preserving chromatographic resolution, and ion-mobility separations embodied in the timsTOF family with trapped ion mobility spectrometry and parallel accumulation serial fragmentation, or PASEF, as well as cyclic ion mobility geometries that add an extra dimension of gas-phase separation before mass analysis. Next-generation mass analyzers, including the Orbitrap Astral and ZenoTOF, push sensitivity and scanning speed to levels unimaginable a decade ago. Together, these components allow deep, reproducible proteomic profiling from limited clinical material such as a few microliters of plasma or cerebrospinal fluid, something earlier workflows could only approximate.

In animal models of ischemic stroke, these advances have yielded a far richer catalogue of the proteins that rise and fall as injury unfolds. Studies built on DIA and targeted workflows have tracked dynamic protein changes in brain tissue, urine, and extracellular vesicles, the tiny membrane-bound packages that cells release into biofluids and that increasingly serve as molecular couriers of brain injury. The review links these protein signatures to well-established pathophysiological axes: neuroinflammation, oxidative stress, glutamate excitotoxic signaling, and endogenous neuroprotective responses. Because animal experiments permit time-course sampling and controlled reperfusion, typically through middle cerebral artery occlusion models with or without reperfusion, they allow researchers to watch proteome-wide perturbations evolve from the first minutes of vessel blockage through days of tissue repair, generating hypotheses that can then be tested against human specimens.

Human studies, meanwhile, have exploited the full span of accessible sample types. The review surveys proteomic investigations of plasma, serum, cerebrospinal fluid, urine, sputum, and even the thrombi retrieved during mechanical thrombectomy, each offering a different window on the ischemic cascade. From this literature, a set of promising diagnostic biomarkers has crystallized. Some panels can distinguish ischemic stroke from intracerebral hemorrhage, a distinction that is clinically urgent because the two conditions demand opposite treatments and cannot always be separated before imaging. Other candidate markers predict recurrence risk or track treatment response, including response to recombinant tissue plasminogen activator, the mainstay thrombolytic therapy whose narrow therapeutic window makes rapid, reliable decision tools especially valuable.

One recurring theme of the review is the importance of post-translational modifications, the chemical tags such as phosphorylation, acetylation, and lactylation that change a protein’s function without changing its abundance. The abbreviation list alone hints at this frontier, with categories for differentially expressed lysine lactylation sites and for up- and down-regulated modification sites. Lactylation, in particular, connects protein regulation to cellular metabolism and the lactate accumulation that follows oxygen deprivation, making it an attractive reporter of ischemic stress. Detecting PTMs reproducibly requires enrichment strategies, careful fragmentation, and informatics capable of scoring modified peptides at scale, and the review frames PTM analysis as a critical dimension of modern stroke proteomics rather than a specialist afterthought.

Data processing has matured in parallel with the instrumentation, and the review gives bioinformatics its due as an equal pillar of the field. Tools such as DIA-NN and Spectronaut, purpose-built for interrogating large DIA datasets against spectral libraries or library-free predictions, now make it routine to quantify thousands of proteins with statistical rigor, while Skyline serves as the workbench for designing and validating targeted PRM assays. The review also evaluates partial least squares discriminant analysis and related machine-learning approaches that turn protein abundance matrices into classification models for diagnosis or prognosis. Increasingly, the emphasis falls on multi-omics integration, weaving proteomic measurements together with genomic, transcriptomic, and metabolomic layers so that a single perturbed pathway can be seen from several angles at once.

The translational payoff is beginning to look tangible. Biomarker panels capable of differentiating ischemic from hemorrhagic stroke, stratifying recurrence risk, or monitoring response to reperfusion therapy could ultimately shorten the interval between emergency arrival and definitive treatment, the single most important determinant of neurological outcome. Extracellular vesicle proteomics offers a route to biomarkers that cross the blood-brain barrier, since vesicles shed by injured neurons and glia carry cargo into peripheral blood that no freely circulating protein can match. Thrombus proteomics, likewise, provides a molecular autopsy of the clot itself, revealing which pathways produced the embolus and potentially guiding secondary prevention. None of these applications has yet reached routine clinical deployment, but the review positions them as the near-term frontier rather than distant speculation.

Kulyyassov is candid about the obstacles that stand between discovery and the clinic. Translating these findings into precision diagnostics and personalized therapies, the review concludes, will require continued standardization of pre-analytical and analytical procedures, rigorous validation in large and diverse patient cohorts, and closer integration with other omics technologies. The history of proteomics is littered with biomarkers that dazzled in small studies and dissolved at scale, and the quantitative accuracy that DIA and PRM deliver is a necessary but not sufficient remedy. Standard operating procedures for sample handling, consensus metrics for assay performance, and independent multicenter replication remain prerequisites before any protein panel can sit beside computed tomography in a stroke unit.

What the decade of work summarized in this review ultimately demonstrates is that proteomics has matured from a descriptive exercise into a quantitative, reproducible, and clinically oriented discipline. The convergence of data-independent acquisition, ion-mobility-enhanced mass analyzers, automated sample preparation, and robust software has made it feasible to profile the protein landscape of ischemic stroke with a depth and consistency that were out of reach in 2015. As the author notes, these advances have significantly deepened mechanistic understanding of the disease and accelerated biomarker discovery, even as the harder work of validation and standardization continues. For a condition that kills and disables millions each year, the prospect of blood-based protein diagnostics built on this decade of engineering progress is one of the more quietly consequential stories in modern stroke research.

Subject of Research: Advances in LC-MS/MS proteomics technology for ischemic stroke biomarker discovery

Article Title: Proteomics in ischemic stroke: a decade of advances in LC-MS/MS technology

Article References: Kulyyassov, A. (2026). Proteomics in ischemic stroke: a decade of advances in LC-MS/MS technology. Clinical Proteomics. https://doi.org/10.1186/s12014-026-09636-x

Image Credits: AI Generated

DOI: 10.1186/s12014-026-09636-x

Keywords: ischemic stroke, proteomics, LC-MS/MS, data-independent acquisition, mass spectrometry, biomarkers, cerebrospinal fluid, extracellular vesicles, post-translational modifications, DIA-NN, neuroinflammation, liquid chromatography

The research, led by Yeongshin Kim, Junho Park, Dongyoon Shin and Youngsoo Kim of CHA University in Seongnam, South Korea, set out to answer a deceptively simple question: do the elaborate plasma preparation platforms that have been benchmarked on cutting-edge mass spectrometers behave the same way when run on more accessible liquid chromatography-mass spectrometry, or LC-MS, instrumentation? The answer matters because most published evaluations of these platforms have relied on next-generation instruments with exceptional sensitivity and speed, leaving a genuine gap in knowledge for the many laboratories around the world that operate older or more modest equipment. If platform performance were fundamentally tied to top-tier hardware, the democratization of plasma biomarker discovery would stall; if not, the field could expand dramatically.

To address the question, the team processed a commercial pooled plasma sample through seven different preparation strategies. These included neat plasma with no treatment at all, serving as the baseline; the Multiple Affinity Removal System Human 14, known as MARS14, an antibody-based column that strips out the fourteen most abundant plasma proteins; and perchloric acid precipitation with neutralization, a chemical approach that preferentially precipitates abundant proteins while leaving many low-abundance species in solution. Alongside these depletion methods, the researchers tested four enrichment platforms: ENRICH-iST, Mag-Net, Proteonano and Proteograph XT. Each of these uses a different physical or chemical principle, from nanoparticle surfaces to specialized capture chemistries, to concentrate the dilute population of low-abundance proteins that standard workflows miss.

All samples were then analyzed in data-independent acquisition, or DIA, mode, a mass spectrometry strategy that systematically fragments all ions within defined mass windows rather than selecting individual precursors. DIA has become the workhorse of modern quantitative proteomics because it produces highly reproducible measurements across large sample cohorts, reducing the stochastic missing values that plague older data-dependent approaches. The choice of DIA was itself significant: it is the acquisition mode most commonly implemented on accessible instrumentation, so any conclusions drawn from the study would apply directly to the laboratories the researchers hoped to reach.

The results were striking. Untreated neat plasma yielded an average of just 777 identified proteins, a figure that captures the brutal reality of plasma’s dynamic range problem. The two depletion platforms performed substantially better, identifying between 1,278 and 1,468 proteins on average. But the enrichment platforms left the depletion approaches behind, with coverage ranging from 1,891 to a remarkable 6,060 proteins. Proteograph XT, the nanoparticle-based platform, delivered the deepest coverage of all and achieved the lowest missing value rate in the study, at just 1.9 percent, meaning that nearly every protein it detected was quantified consistently across the analysis rather than appearing and disappearing between runs.

Yet the study’s most important finding may be the one that complicates the simple narrative that more proteins equals better science. The researchers observed that greater proteome coverage did not always correlate with better quantitative precision. A platform that identifies thousands of additional proteins may do so at the cost of noisier measurements, and a coefficient of variation that looks acceptable for one platform may be unacceptable for another. Because the ultimate goal of plasma proteomics is reliable quantification, particularly when comparing patient cohorts to find proteins that distinguish disease from health, precision matters as much as depth. The study makes clear that platform choice significantly affected both the abundance profiles of the resulting datasets and the coverage of clinically relevant proteins, meaning that laboratories must match their preparation strategy to their biological question rather than simply chasing the largest protein count.

This platform-specific reshaping of the plasma proteome is a phenomenon that has been noted in previous work but never before systematically confirmed on accessible instrumentation. Each preparation method imposes its own bias: antibody-based depletion removes not only its targets but also proteins that travel bound to them, such as carrier proteins shuttling hormones and metabolites; chemical precipitation can lose proteins that co-precipitate with the abundant fraction; and nanoparticle enrichment selects for proteins with affinity for particular surface chemistries, so different particles fish out different, partially overlapping slices of the proteome. The consequence is that two laboratories studying the same plasma pool with different platforms may report substantially different protein abundance profiles, a fact that has major implications for reproducibility and for the design of multi-center biomarker studies.

Crucially, when the researchers compared their results with recent benchmarks generated on next-generation mass spectrometers, the platform-specific differences they observed reproduced those earlier findings. This is the study’s central vindication: the accessible LC-MS setups captured the same key distinctions among preparation platforms that the flagship instruments had revealed. In other words, the relative ranking of platforms, the patterns of proteome reshaping and the qualitative conclusions about which strategies best expose the low-abundance proteome all held true on standard equipment. What the newest instruments add is primarily depth and throughput, not a fundamentally different picture of how the platforms behave.

The practical implications extend well beyond methodological housekeeping. Plasma biomarker discovery has accelerated enormously in recent years, fueled by large-scale efforts such as the Human Plasma Proteome Project and by growing interest in early cancer detection, neurodegenerative disease monitoring and cardiovascular risk prediction. But much of that progress has been concentrated in well-funded centers equipped with the latest triple-TOF or Orbitrap Astral class instruments. If the platform evaluation framework demonstrated here holds, hospital-affiliated laboratories, research institutes in lower-resource settings and clinical translation teams can now participate meaningfully in plasma proteomics using instrumentation they already own. The study’s authors frame this as supporting the broader adoption of plasma proteomics in settings where next-generation instrumentation is not routinely available, and the data appear to justify that framing.

There remain caveats that the field will need to keep in view. The study used a single commercial pooled plasma sample, an excellent control for technical comparison but not a substitute for testing platforms across real patient cohorts with their biological variability, pre-analytical noise and disease-specific matrix effects. The evaluation also reflects one DIA acquisition strategy on one class of accessible instrument, and other configurations could shift the absolute numbers, even if the relative platform behavior is likely to persist. And the finding that coverage and precision can trade off against each other means that no single platform emerges as a universal winner; instead, the study provides something arguably more useful, a rigorous, reproducible map of what each of seven platforms delivers on hardware that most laboratories can access. As plasma proteomics moves from discovery science toward clinical application, that kind of practical, instrument-agnostic evidence may prove to be exactly what the field needs to turn an information-rich body fluid into a routine diagnostic resource.

Subject of Research: Comparative evaluation of plasma preparation platforms for proteomics using accessible liquid chromatography-mass spectrometry instrumentation

Article Title: Comprehensive evaluation of plasma proteomics platforms toward practical applications using accessible mass spectrometry instrumentation

Article References: Kim, Y., Park, J., Shin, D., & Kim, Y. (2026). Comprehensive evaluation of plasma proteomics platforms toward practical applications using accessible mass spectrometry instrumentation. Clinical Proteomics. https://doi.org/10.1186/s12014-026-09631-2

Image Credits: AI Generated

DOI: 10.1186/s12014-026-09631-2

Keywords: plasma proteomics, mass spectrometry, biomarker discovery, data-independent acquisition, protein depletion, nanoparticle enrichment, Proteograph XT, MARS14, LC-MS, proteome coverage, quantitative precision, Clinical Proteomics

The research emerged from the Copenhagen Prospective Personalized Oncology trial, known as CoPPO, an ongoing precision oncology study in which patients with advanced cancer undergo molecular profiling to guide therapy selection. In this setting, metastatic biopsies are routinely processed using the Qiagen AllPrep system, a commercial kit that simultaneously extracts DNA and RNA from a single sample. During that extraction, proteins are separated from the nucleic acids and end up in a flowthrough fraction that is normally discarded. The team, led by Filip Mundt and including renowned proteomics pioneer Matthias Mann, systematically asked whether this residual protein fraction could serve as a reliable starting material for mass spectrometry-based proteomics, and the answer turned out to be a resounding yes.

The technical challenge was considerable. Protein recovered from extraction flowthroughs is diluted in chemical buffers designed for nucleic acid work, and it must be recovered, concentrated, and cleaned up before it can be digested into peptides and analyzed by liquid chromatography coupled to mass spectrometry. The researchers developed and optimized procedures for each of these steps, including protein precipitation, enzymatic digestion, and peptide cleanup, and they evaluated multiple mass spectrometry acquisition strategies to determine which would deliver the best performance from this unconventional source material. Both data-dependent acquisition, which favors depth of identification, and data-independent acquisition, which favors quantitative consistency across samples, were tested in the context of real clinical biopsy specimens rather than idealized reference material.

The results were striking. Using optimized single-shot workflows, the team consistently generated deep proteomes from the AllPrep-derived material, quantifying more than 10,000 protein groups in data-independent acquisition analyses. That depth of coverage, from material that would otherwise have been discarded, places the quality of these measurements on par with dedicated proteomics workflows that consume precious additional tissue. Perhaps even more importantly for clinical applications, the proteomes retained tissue-associated biological signatures, meaning the protein profiles faithfully reflected the biology of the tumors from which the biopsies were taken rather than artifacts introduced by the extraction process.

One of the most clinically significant findings concerns compatibility with existing genomic diagnostics. The researchers found that the proteomes derived from the flowthrough material quantified protein products corresponding to approximately 71 percent of the genes represented on the FoundationOne CDx panel, a widely used clinical genomic profiling assay. This overlap means that the protein data can be directly interpreted alongside the genomic results that oncologists already rely on, providing a functional layer of information on top of the genetic alterations detected in the same specimen. Where genomics reveals which genes are mutated or amplified, proteomics reveals whether those changes are actually reflected in the abundance of the proteins they encode, and whether downstream signaling pathways are active.

Beyond total protein abundance, the team extended the workflow to phosphoproteomics, the analysis of phosphorylation marks that act as molecular switches on proteins and report on the activity of signaling pathways. Phosphoproteomic analyses of the recovered material identified more than 10,000 phosphorylation sites, including many associated with key oncogenic signaling pathways driven by AKT1, BRAF, and EGFR, proteins that sit at the heart of some of the most commonly targeted pathways in modern cancer therapy. This is particularly valuable in precision oncology, where a tumor may harbor a mutation in a druggable gene, yet the downstream pathway may or may not be activated. Phosphoproteomics offers a direct readout of that functional state, information that genomic sequencing alone cannot provide.

Practical considerations for clinical deployment were also addressed head-on. The researchers assessed sample stability during long-term storage and found that protein integrity was preserved in samples kept at minus 80 degrees Celsius for up to five years. This finding has immediate operational implications, because it means that flowthrough fractions recovered during routine DNA and RNA extraction can simply be archived in existing freezers, creating a retrospective proteomic resource from biopsies processed years earlier. Hospitals would not need to change their current extraction protocols or acquire new tissue; they would only need to stop discarding the protein fraction and store it instead.

Scalability, often the Achilles heel of clinical proteomics, was another focus of the study. Mass spectrometry throughput has historically been a bottleneck for large-scale clinical studies, but the team implemented short liquid chromatography gradients that enabled high-throughput analysis of up to 60 proteomes per day without compromising data quality. At that rate, a single instrument could theoretically process thousands of patient samples per year, bringing proteomic profiling within reach of the kind of scale that genomic sequencing pipelines already achieve routinely. The analytical reproducibility of the workflow was also evaluated, confirming that measurements were consistent enough to support the comparisons that clinical decision-making would require.

The implications for precision oncology are substantial. Current molecular tumor boards typically work from genomic and transcriptomic data, which describe the blueprint and the transcriptional activity of a tumor but say little about the actual effector molecules, the proteins, that carry out cellular processes and respond to drugs. Proteins are, as the authors note, the active effectors of cellular signaling, and integrating proteomic and phosphoproteomic measurements with genomic data has become an important objective in translational oncology. By demonstrating that this integration can be achieved from a single biopsy already being processed in routine clinical workflows, the study removes one of the biggest practical obstacles: the need for extra tissue that patients often cannot spare and that clinics often cannot obtain.

The Copenhagen team frames the work as a framework for integrated proteogenomic studies in precision oncology, and the phrase is apt. What they have built is not merely a new laboratory technique but a bridge between the molecular diagnostics that hospitals already perform and the deeper functional characterization that proteomics can add. As mass spectrometry instruments become faster and more sensitive, and as workflows like this one demonstrate that clinical material streams can feed them without added burden on patients, the vision of truly multi-layered molecular profiling, spanning genome, transcriptome, proteome, and phosphoproteome from a single needle biopsy, moves closer to routine reality. For patients with advanced cancer, whose treatment options often hinge on the quality and completeness of the molecular information available, that could mean better-matched therapies chosen from a far richer picture of what their tumor is actually doing.

Subject of Research: Recovery of protein from DNA/RNA extraction flowthroughs for scalable clinical proteomics and integrated proteogenomic profiling of cancer biopsies

Article Title: Scalable clinical proteomics from DNA/RNA extraction flowthroughs enables integrated proteogenomic profiling from a single cancer biopsy

Article References: Mundt, F., Bach Nielsen, A., Wang, J., Kerzel Duel, J., Westmose Yde, C., Amnitzbøll Eriksen, M., Lassen, U., Cilius Nielsen, F., Rohrberg, K., & Mann, M. (2026). Scalable clinical proteomics from DNA/RNA extraction flowthroughs enables integrated proteogenomic profiling from a single cancer biopsy. Clinical Proteomics. https://doi.org/10.1186/s12014-026-09632-1

Image Credits: AI Generated

DOI: 10.1186/s12014-026-09632-1

Keywords: proteomics, precision oncology, mass spectrometry, phosphoproteomics, cancer biopsy, proteogenomics, DNA/RNA extraction, clinical proteomics, CoPPO trial, FoundationOne CDx, data-independent acquisition, signaling pathways

Mass spectrometry-based amyloid typing has earned a reputation as the gold standard for identifying the amyloidogenic protein in tissue deposits. By shredding laser-microdissected amyloid plaques into peptides and reading their mass spectra, laboratories can distinguish among more than thirty amyloid types with a specificity that immunohistochemistry and immunofluorescence simply cannot match. Yet the technique has remained confined largely to a handful of reference laboratories because it depends on nanoflow liquid chromatography, in which solvents are pushed through capillaries with inner diameters measured in tens of micrometers. Nanoflow systems deliver exquisite sensitivity, but they come with steep up-front costs, demanding maintenance, long column equilibration times, and a level of operational complexity that deters routine clinical adoption.

The Stanford team, led by Morgan W. Mann and Fangjun Chen with senior authors Megan L. Troxell and Ruben Y. Luo, set out to re-engineer the workflow so that it could run on the high-flow liquid chromatography systems already humming away in hospital laboratories. Their solution rests on two complementary innovations: a sample preparation method called suspension trapping, known commercially as S-Trap, and an acquisition strategy known as data-independent acquisition, or DIA. Together, these techniques allow the analysis of microdissected amyloid plaques using conventional-flow chromatography coupled to tandem mass spectrometry, without sacrificing the diagnostic accuracy that clinicians rely upon.

Suspension trapping is the quieter revolution of the two. In traditional proteomics sample preparation, proteins extracted from tiny tissue fragments must be digested into peptides through a series of solution exchanges, buffer removals, and cleanups that consume time and lose precious material at every step. The S-Trap approach instead traps proteins on a quartz filter matrix as they precipitate out of an acidic methanol-containing buffer. Digestion enzymes then pass through the trap, generating peptides that are eluted in a single, efficient step. The method is fast, robust, and minimizes sample loss, which matters enormously when the starting material consists of a few thousand cells harvested by laser capture microdissection from a formalin-fixed paraffin-embedded tissue section.

Data-independent acquisition addresses the other half of the problem. In classical data-dependent acquisition, the mass spectrometer selects the most intense ions in each scan for fragmentation, a stochastic process that can miss low-abundance peptides and produces data that are tedious to align across runs. DIA, by contrast, fragments all ions within successive wide mass windows, systematically recording fragment spectra for essentially everything in the sample. The resulting data are reconstructed computationally against spectral libraries, yielding consistent and quantitative peptide identifications. Because DIA tolerates the higher flow rates, wider chromatographic peaks, and greater sample loads of high-flow chromatography, it made the leap away from nanoflow feasible rather than merely aspirational.

To translate raw proteomic data into a diagnosis, the researchers also developed a custom selection heuristic that sifts through the hundreds of proteins detected in each plaque and identifies the most likely amyloidogenic culprit. Amyloid deposits are not pure; they sweep up serum proteins, immunoglobulin fragments, and extracellular matrix components as they form. Distinguishing the genuine amyloidogenic protein from innocent bystanders requires weighing total protein abundance, the presence of type-specific signature peptides, and the biological plausibility of each candidate. The heuristic automates this judgment, reducing an expert curation task that could take hours into a reproducible computational step suitable for a busy clinical workflow.

The performance data are striking. Working with laser capture microdissected plaques from 47 patient samples, 10 from cardiac biopsies and 37 from renal biopsies, the team split the material into a training set of 25 samples and an independent testing set of 22. The high-flow LC-DIA-MS/MS method identified the amyloidogenic protein in approximately 96 percent of plaques in both sets, a success rate comparable to established nanoflow approaches. The two inaccuracies observed across the cohort both traced to difficulties in identifying immunoglobulin lambda proteins, once in a specimen obtained after treatment had altered the composition of the deposits and once in a plaque containing two distinct amyloid types simultaneously. These edge cases, the authors note, define the boundaries of the method rather than undermining its general utility.

Reducing the sample preparation workflow to under eight hours carries practical significance beyond convenience. Amyloidosis is increasingly recognized as an urgent diagnosis; cardiac involvement can progress rapidly, and new therapies such as transthyretin stabilizers and silencing agents work best when started early. Every hour shaved from the analytical pipeline shortens the time between biopsy and answer. Equally important, the shift to high-flow chromatography means the method can run on instruments of the kind already deployed for clinical chemistry assays, drug monitoring, and newborn screening in hospital laboratories. That compatibility could transform amyloid typing from a rarefied referral test into an accessible diagnostic performed locally, at lower cost and with faster turnaround.

The study, published in Clinical Proteomics, was conducted on remnant patient specimens under approved institutional review board protocols and funded through the Stanford University Department of Pathology’s Test Development Program, underscoring its explicitly clinical orientation. The authors acknowledge one commercial entanglement: a collaborative research relationship between one investigator and Thermo Fisher Scientific, which provided technical support. While the method will still require validation in other laboratories and across broader panels of amyloid types, the combination of suspension trapping, data-independent acquisition, and high-flow chromatography represents a credible path toward democratizing molecular amyloid typing, offering patients everywhere the prospect of a faster, more precise answer to the question their treatment ultimately depends on.

Subject of Research: A high-flow liquid chromatography-tandem mass spectrometry method for typing amyloidosis in clinical laboratories

Article Title: Suspension trapping and data-independent acquisition enable high-flow liquid chromatography-tandem mass spectrometry-based amyloidosis typing in clinical laboratories

Article References: Mann, M. W., Chen, F., Zhu, C., Lu, C., Liang, B., Kambham, N., Troxell, M. L., & Luo, R. Y. (2026). Suspension trapping and data-independent acquisition enable high-flow liquid chromatography-tandem mass spectrometry-based amyloidosis typing in clinical laboratories. Clinical Proteomics. https://doi.org/10.1186/s12014-026-09628-x

Image Credits: AI Generated

DOI: 10.1186/s12014-026-09628-x

Keywords: amyloidosis, mass spectrometry, proteomics, data-independent acquisition, suspension trapping, liquid chromatography, clinical diagnostics, laser capture microdissection, AL amyloidosis, ATTR amyloidosis, tandem mass spectrometry, clinical proteomics