The investigators sampled sites associated with mussel farming, wild-mussel harvesting, intensive fishing, protected areas and anthropogenic activities. Sampling took place from March through December 2021 and included locations in northern and southern Bulgaria and the Varna area. The study was an observational assessment of plankton samples, not a clinical investigation or a direct evaluation of toxin levels in food products.

The 21 samples were collected with a conical plankton net with a 40-centimetre outer diameter and 20-micrometre mesh size. Each net haul was taken vertically from one to five metres below the surface. Immediately after collection, net-haul concentrates were adjusted to a volume of 500–1,000 millilitres with 20-micrometre-filtered seawater. After centrifugation at 4,000 times gravitational acceleration for 10 minutes at 10°C, the cell pellets were stored at minus 20°C.

For extraction, the pellets were suspended in 1,000 microlitres of 100% methanol. The suspensions were sonicated for 15 minutes at 40 hertz, centrifuged again for 10 minutes at 4,000 times gravitational acceleration, and filtered through 0.45-micrometre syringe filters. The resulting filtrates were transferred to chromatographic vials and kept at minus 20°C pending analysis. Measurements were performed with a liquid chromatograph coupled to a triple-quadrupole mass spectrometer.

The analytical programme targeted domoic acid and 12 lipophilic toxins: goniodomin A, okadaic acid, dinophysistoxins 1 and 2, pectenotoxin-2, pectenotoxin-2-sulfenic acid, its epimer, yessotoxin, hydroxy-yessotoxin, azaspiracid-1, spirolide-1 and gymnodimine A. Procedural blanks and certified reference material were included in quality control, while detection limits were established using a 3-to-1 signal-to-noise ratio. Results were expressed as nanograms per net tow.

Domoic acid was detected in three samples, representing 14% of the 21-sample collection. The measured values were 170.94 ng/NT in sample ME6, 138.48 ng/NT in ME15 and 13.38 ng/NT in ME58. These positive samples were collected in spring and autumn from areas designated as intensive-fishing sites or as having anthropogenic activities.

Pectenotoxin-2 was detected in sample ME9 at 9.23 ng/NT and sample ME76 at 6.51 ng/NT. The paper described these two positive samples as 10% of the total, while two of 21 samples correspond to approximately 9%. The samples came from an area associated with anthropogenic activities and a protected area, respectively, in spring and autumn. No targeted toxins were detected in the samples collected at mussel farms or in the one sample collected from a protected area.

Among the other compounds examined, the study reported no detections in 2021. This included yessotoxins, which had been registered in a small number of samples during 2016–2018, and spirolide-1, which had previously been recorded in summer and autumn 2018. The authors suggested that the rapid release of yessotoxins into the environment may make them difficult to determine in plankton samples, while the absence of spirolide-1 might be associated with low abundance or absence of its linked producer, Alexandrium ostenfeldii. These were possible explanations rather than measured causal results in the 2021 samples.

At least one measured toxin was detected in 33% of samples from areas with anthropogenic activities and in 50% of samples from intensive-fishing areas. Comparisons with earlier investigations in the same general region were made despite different sample numbers between years. Domoic acid had been detected in 57% of samples in 2016, 41% in 2017 and 17% in 2018, but in none in 2019. Pectenotoxin-2 was detected in 86% of samples in 2016, 48% in 2017, 47% in 2018 and 67% in 2019.

The dataset is limited by its small size, the selected sampling locations and the coverage of only one year. The number of samples also differed from earlier investigation periods, so the comparisons cannot establish a sustained trend or show whether the 2021 patterns extended to unsampled places and times. The authors interpreted the results, including values below detection limits, as indicating that the abundance of marine biotoxins was not alarming and that good-quality mussel meat might be expected. They recommended continued monitoring of harmful phytoplankton and biotoxins to support timely responses to harmful algal blooms and their potential consequences.

Subject of Research: Occurrence of selected marine biotoxins in Bulgarian Black Sea coastal plankton samples collected in 2021

Article Title: Occurrence of marine biotoxins on Bulgarian Black Sea coastal waters in 2021

Article References: Peteva, Z. V., Georgieva, S. K., Knock, B., Max, T., Stancheva, M. D., & Valkova, S. (2023). Occurrence of marine biotoxins on Bulgarian Black Sea coastal waters in 2021. BioRisk, 20, 71-81. https://doi.org/10.3897/biorisk.20.97557

Image Credits: AI Generated

DOI: 10.3897/biorisk.20.97557

Keywords: marine biotoxins, Bulgarian Black Sea, domoic acid, pectenotoxin-2, plankton monitoring, coastal waters, liquid chromatography, mass spectrometry, harmful algal blooms, shellfish poisoning, Occurrence, marine

The two active ingredients occupy distinct pharmacological territories. Brexpiprazole, chemically 7-[4-[4-(1-benzothiophen-4-yl)piperazin-1-yl]butoxy]-1H-quinolin-2-one, is an atypical antipsychotic classified as a serotonin-dopamine activity modulator. It partially activates D2 dopamine and serotonin 1A receptors and binds strongly to serotonin, dopamine, and alpha-adrenergic receptors; the United States Food and Drug Administration approved it on July 10, 2015. Escitalopram oxalate, structurally (1S)-1-[3-(dimethylamino)propyl]-1-(4-fluorophenyl)-3H-2-benzofuran-5-carbonitrile oxalate, belongs to the selective serotonin reuptake inhibitor class and treats depression and anxiety by increasing serotonergic activity in the brain. The combination of the two agents was approved in 2014 and has been studied in clinical trials for mood disorders, which makes reliable analytical tools for the pair increasingly important.

To build the method, the team used a Shimadzu semi-automatic liquid chromatograph equipped with a photodiode array detector and Win Chrome software, injecting samples through a microliter syringe onto a reversed-phase C18 column measuring 250 millimeters by 4.6 millimeters with five-micron particles. Raw materials were supplied as gift samples by Virdev Pharma of Palsana and Sun Pharmaceutical Industries of Panoli, and the researchers prepared a synthetic mixture mimicking a combined dosage form at a ratio of 40 micrograms per milliliter of brexpiprazole to 120 micrograms per milliliter of escitalopram oxalate. Because no commercial product exists, the synthetic mixture served as the practical test bed for the entire validation exercise.

Optimization of the chromatographic conditions revealed that a mobile phase of methanol and phosphate buffer, adjusted to pH 4.0 with orthophosphoric acid and mixed in a 55:45 volume ratio, delivered the best performance. Elution ran in isocratic mode at a flow rate of one milliliter per minute, with detection at 214 nanometers and a total run time of ten minutes. Under these conditions brexpiprazole eluted at 2.7 minutes and escitalopram at 7.0 minutes in the reported system suitability work, with symmetrical peak shapes, theoretical plate counts above 2000, and tailing factors at or below 2.0. The separation logic is rooted in the chemistry of the two molecules: both carry basic functional groups and exist predominantly in protonated form at pH 4.0, but brexpiprazole is considerably more lipophilic, with a log P of roughly 4.7 to 5.0 compared with 3.5 to 4.0 for escitalopram. That difference governs how strongly each analyte interacts with the nonpolar C18 stationary phase and therefore dictates the elution order.

Validation followed the International Council for Harmonization guideline Q2(R2), the current global standard for analytical procedure validation. Linearity was assessed at five concentration levels with six replicates each, spanning 0.5 to 3 micrograms per milliliter for brexpiprazole and 5 to 30 micrograms per milliliter for escitalopram oxalate. Calibration curves returned correlation coefficients exceeding 0.999 for both drugs, indicating an exceptionally straight relationship between peak area and concentration across the working ranges. Accuracy was confirmed through standard addition and recovery studies at 80, 100, and 120 percent spiking levels, with satisfactory recoveries demonstrating that common formulation excipients do not interfere with quantification.

Precision, the method’s ability to produce consistent results, was evaluated in both intraday and interday experiments. Three standard solutions of different concentrations were injected repeatedly within a single day and again across three consecutive days, and the relative standard deviation stayed below 2.0 percent throughout. Specificity was demonstrated by analyzing blank mobile phase and sample solutions, with no interfering peaks appearing at the retention times of either analyte. Sensitivity limits were calculated from the standard deviation of the response and the slope of the calibration curve using the ICH equations LOD equals 3.3 times sigma over S and LOQ equals 10 times sigma over S, yielding low detection and quantification limits that confirm the method’s suitability for trace-level work. Robustness testing deliberately introduced small variations in flow rate, mobile phase composition, pH, and detection wavelength, while ruggedness was probed through changes of analyst and column, with retention times remaining stable in all cases.

When the optimized method was applied to the synthetic mixture containing 2 micrograms per milliliter of brexpiprazole and 20 micrograms per milliliter of escitalopram oxalate, the assay results were striking: 99.67 percent recovery for brexpiprazole with a relative standard deviation of 0.308 percent, and 99.94 percent for escitalopram oxalate with a relative standard deviation of 0.227 percent. These figures indicate that the procedure measures the actual content of a mixture with near-perfect fidelity and minimal scatter, exactly the profile a quality control laboratory needs when certifying batches of a finished product.

What distinguishes this study from a routine validation exercise is its systematic greenness assessment. The authors evaluated the method with three complementary tools drawn from Green Analytical Chemistry. The AGREE metric, which scores a procedure against the twelve principles of green analytical chemistry, returned 0.71, indicating good compliance. The Blue Applicability Grade Index, which weighs practical applicability alongside sustainability, scored 72.5, and the Complex GAPI pictogram, which accounts for reagent hazards, energy consumption, and waste generation across the whole procedure, scored 81. The environmental arithmetic is straightforward: each ten-minute analysis consumes roughly ten milliliters of mobile phase, split into 5.5 milliliters of methanol and 4.5 milliliters of water, and generates only about ten milliliters of waste. The simple methanol-water system avoids more hazardous solvents entirely, and the short run time keeps energy demand low.

The team also extended the evaluation into White Analytical Chemistry, a framework that mirrors the twelve green principles but adds two further pillars: red for analytical performance and blue for practical and economic factors. By reporting the arithmetic means for the red, green, and blue sections separately, the authors acknowledged that a truly sustainable method must be green without sacrificing accuracy, precision, or cost-effectiveness. Their conclusion was that the method is acceptably green and suitable for sustainable routine applications without compromising performance, with only minor limitations remaining.

The practical significance of the work lies in its readiness for routine use. Because the method is the first created for rapid analysis of this binary mixture at a single wavelength, and because it passed every ICH Q2(R2) validation parameter with strong margins, pharmaceutical laboratories could adopt it directly for stability studies, formulation development, and batch release testing of any future brexpiprazole-escitalopram combination product. The combination of a ten-minute run time, modest solvent consumption, sub-2-percent variability, and recoveries near 100 percent represents a template for how modern analytical chemistry can serve drug development while shrinking its own environmental footprint, a balance that regulators and manufacturers alike are increasingly demanding.

Subject of Research: Development and greenness assessment of a validated RP-HPLC method for simultaneous estimation of brexpiprazole and escitalopram oxalate

Article Title: Development, validation, and greenness assessment of a green RP-HPLC method for rapid simultaneous estimation of brexpiprazole and escitalopram oxalate in combined dosage form

Article References: Solanki, D., & Dedania, Z. (2026). Development, validation, and greenness assessment of a green RP-HPLC method for rapid simultaneous estimation of brexpiprazole and escitalopram oxalate in combined dosage form. Discover Chemistry, 3(1), Article 444. https://doi.org/10.1007/s44371-026-00887-z

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00887-z

Keywords: RP-HPLC, brexpiprazole, escitalopram oxalate, method validation, ICH Q2(R2), green analytical chemistry, AGREE metric, pharmaceutical analysis, liquid chromatography, antidepressants, quality control, sustainable chemistry

The significance of the advance lies in what it replaces. Until now, researchers attempting to gauge melanin in soils had to rely on DNA-based approaches, which estimate only the genetic potential for melanin production within the microbial community. Such methods can reveal which organisms carry the genes for melanin biosynthesis, but they say nothing about whether the pigment is actually produced, deposited, and preserved in the soil matrix. Because gene presence and pigment abundance can diverge dramatically under different environmental conditions, DNA-based evidence remains indirect. For a field trying to build quantitative models of carbon stabilization, that gap has been a fundamental constraint.

Melanin itself is a chemically formidable class of pigments. In fungi, these dark, high-molecular-weight polymers are built from phenolic and indolic precursors and form the structural backbone of cell walls in many species, particularly the melanized (dematiaceous) fungi that dominate leaf litter and soil surfaces. The same chemical recalcitrance that makes melanin resistant to degradation by enzymes and oxidants in living fungi persists after cell death, meaning fungal necromass rich in melanin should, in principle, resist microbial attack and persist in soil far longer than more labile organic matter. This is the mechanistic logic behind the hypothesis that melanized fungal residues contribute disproportionately to the stable fraction of soil organic carbon.

Testing that logic requires numbers, and generating those numbers required the Finnish team to first rule out the obvious measurement routes. The researchers evaluated spectroscopic and spectrophotometric assays, techniques that measure how molecules absorb or interact with light and are often the fastest, cheapest options for pigment quantification. Both approaches proved insufficiently specific for soil work. The problem is interference: soils contain a dense cocktail of chemically related compounds — humic substances, tannins, lignin fragments, and other aromatic polymers — whose optical signatures overlap with those of melanin. In a matrix as complex as soil, a spectrophotometric signal attributed to melanin could just as easily reflect any of these look-alike molecules, rendering the measurement unreliable.

The solution the authors introduce is chromatographic separation. Liquid chromatography physically resolves the components of a soil extract before detection, allowing melanin-derived compounds to be distinguished from the chemically related interferents that defeated the spectroscopic methods. With separation achieved, the method enables quantification of the major fungal melanin types present in soil samples, converting what was previously a qualitative suspicion into a measurable concentration. The framework, the authors write, allows longstanding hypotheses regarding melanized fungal necromass and soil carbon stabilization to be tested mechanistically rather than inferred from gene surveys or circumstantial evidence.

Applying the method to real soils produced a striking result: soil melanin concentrations were strongly correlated with total soil carbon. That correlation supports a central role for fungal melanin in long-term soil carbon stabilization, the authors report. In other words, soils that accumulate more of this fungal pigment also tend to hold more carbon overall, consistent with the idea that melanin-rich necromass is not merely a passive ingredient of soil organic matter but a structurally important one. While a correlation across soils does not by itself prove causation, it provides exactly the kind of quantitative, direct evidence that the field has lacked and that mechanistic models of carbon cycling require.

The broader context makes this finding timely. Global carbon models increasingly recognize that the persistence of soil organic carbon depends less on the intrinsic recalcitrance of plant litter alone and more on the microbial products that get incorporated into soil — a pool in which dead microbial cells, or necromass, feature prominently. Fungi, with their chitinous and often melanized cell walls, are major contributors to this necromass pool. If a substantial fraction of stable soil carbon is built from melanized fungal residues, then factors that shift fungal community composition — land-use change, nitrogen deposition, warming, altered precipitation — could have cascading effects on carbon residence times that current models do not capture. A direct measurement tool for melanin makes it possible to begin quantifying those effects.

The methodological rigor behind the study reflects a careful, multi-stage validation process. The authors document that they systematically tested the simpler optical approaches before committing to chromatography, and the paper’s transparent peer review file and reporting summary, published alongside the article, lay out the analytical details for scrutiny. The work was supported by the Research Council of Finland under several grants, and the team acknowledges collaborators who provided soil samples from sites including an agricultural location in Spain, alongside colleagues at Luke and the University of Helsinki who collected samples and assisted with laboratory work. The article is open access under a Creative Commons Attribution 4.0 license, making the method immediately available to research groups worldwide.

What comes next is the scientific payoff of having a working measurement. With a validated assay in hand, researchers can now ask how melanin concentrations vary across climate zones, vegetation types, and land-management regimes; whether melanin abundance predicts carbon persistence under warming experiments; and how quickly melanized necromass accumulates or decomposes under different disturbance histories. The method could also help resolve debates about the relative importance of plant-derived versus microbial-derived stable carbon, a question with direct implications for whether soils will continue to function as carbon sinks as the climate changes. Because the authors show that melanin tracks total soil carbon, melanin measurements may eventually serve as a diagnostic indicator of a soil’s capacity for long-term carbon storage.

For a measurement that began as a methodological frustration — two standard assay families failing against the chemical noise of real soil — the outcome is unusually consequential. Soil carbon science has spent decades refining its understanding of plant inputs, mineral associations, and aggregate protection, but the fungal contribution has remained partly hidden behind indirect proxies. By making fungal melanin directly countable in soil, Adamczyk and Adamczyk have converted a long-standing hypothesis into an empirically tractable research program, and their strong correlation between melanin and total soil carbon offers the first direct quantitative support for the idea that the dark pigments of dead fungi are among the quiet architects of Earth’s largest terrestrial carbon reservoir.

Subject of Research: A liquid chromatography method for quantifying fungal melanin in soil and its relationship to soil carbon stabilization

Article Title: A method quantifying soil melanin to understand fungal impacts on soil carbon stability

Article References: Adamczyk, S., & Adamczyk, B. (2026). A method quantifying soil melanin to understand fungal impacts on soil carbon stability. Nature Communications. https://doi.org/10.1038/s41467-026-78616-4

Image Credits: AI Generated

DOI: 10.1038/s41467-026-78616-4

Keywords: soil carbon, fungal melanin, soil organic matter, fungal necromass, liquid chromatography, carbon stabilization, soil microbiology, carbon cycle, analytical chemistry, Nature Communications, soil science, melanized fungi

The principle behind the ELSD is elegantly simple. In liquid chromatography, a separated analyte dissolved in a mobile phase arrives at the detector, where the eluent is nebulised into a fine spray. The droplets pass through a heated evaporator tube that strips away the solvent, leaving behind dry analyte particles. A laser beam then illuminates the particle stream, and the scattered light provides the detection signal. Because the response depends on scattering rather than on any optical absorption by the analyte itself, the detector can handle an enormous range of compounds. But the signal strength ultimately hinges on how many particles there are, how large they are, and what shape they have taken after drying, and until now no one had measured those quantities directly inside a working instrument.

The Cambridge team chose three analytes with deliberately contrasting properties: caffeine, which serves as the standard calibration sample in Agilent ELSD instruments; dextran, a large-molecular-weight water-soluble polymer representative of the macromolecules the detector is often asked to analyse; and citric acid, a small non-cyclic molecule with low volatility. Aqueous solutions of each were prepared at concentrations of 0.125, 0.25, 0.5, and 1 grams per litre, sonicated to ensure complete dissolution, and fed continuously into an Agilent 1290 Infinity II ELSD. Steady-state operation, rather than the conventional pulsed injections from a chromatography column, was essential because the particle sizing instruments and electron microscopy required far longer sampling timescales than a transient pulse could provide.

Reconstructing the initial droplet distribution posed a formidable measurement challenge. Water evaporates so rapidly that no single instrument can capture the full size range in situ. The researchers therefore combined two complementary techniques. A phase Doppler particle analyser, or PDPA, provided rapid, spatially resolved measurements of droplets larger than roughly two micrometres, sampled just one millimetre from the nebuliser tip along the spray centreline. Below that threshold, an aerodynamic aerosol classifier paired with a condensation particle counter covered the gap between about 200 nanometres and two micrometres, though it required sampling through tubes and could not resolve individual positions in the spray. To bridge the two regimes, the team used dioctyl sebacate, a low-vapour-pressure oil with negligible evaporation at room temperature, as a non-evaporating surrogate liquid measurable by both instruments simultaneously.

By comparing the well-mixed PDPA and classifier measurements across the overlapping size range, the researchers derived diameter-dependent loss and transfer functions that corrected for gravitational settling in the liquid trap and diffusional and inertial losses in the sampling lines. The result was a reconstructed log-normal droplet distribution for the oil surrogate with a mode near 450 nanometres and a geometric standard deviation of 1.08. Applying established correlations for twin-fluid coaxial pneumatic nebulisers, which relate the Sauter mean diameter to the Reynolds, Weber, and Ohnesorge numbers of the liquid and gas flows, the team then translated this distribution to water. Because water has a higher surface tension than the oil, more energy is needed to shatter the liquid jet, and the predicted water droplet mode shifted upward to about 550 nanometres, with a correspondingly lower peak number concentration to conserve the liquid volume flow.

Downstream of the evaporator, the dried particles were characterised with a scanning mobility particle sizer operated over a size range of 8 to 232 nanometres, with each condition repeated three times for reproducibility. The measurements revealed clear differences among the analytes. Citric acid produced the lowest overall concentrations and the smallest mean diameters, consistent with its high effective density, while dextran behaved remarkably similarly to caffeine despite its much larger molecular weight, hinting at comparable effective densities after drying. In every case, the peak of the distribution grew with analyte concentration, and the distributions were skewed toward larger sizes rather than perfectly log-normal, a signature of size-dependent impaction at the instrument’s Y junction and labyrinth diffuser cartridge, which strip out the largest droplets before they reach the evaporator.

A simple mass-conservation calculation, equating the analyte mass in an initial droplet to the mass of a final spherical particle at bulk density, provided a sanity check on the measurements. For a concentration of 1 gram per litre, the model predicted final mode diameters of 46 nanometres for citric acid and 52 nanometres for caffeine, which could be compared against measured modes of 35 and 65 nanometres respectively. The discrepancies proved informative in both directions. The under-prediction for caffeine pointed to a lower effective particle density than the bulk crystal value, a conclusion borne out by microscopy showing that dried caffeine particles are far from spherical. The over-prediction for citric acid suggested that dissolved analyte may lower the surface tension of the solution, producing smaller initial droplets than the pure-water reconstruction assumes.

Scanning electron microscopy of particles collected on carbon-coated transmission electron microscopy grids, platinum-coated for conductivity and imaged at 5 kilovolts, exposed striking morphological diversity. Caffeine formed elongated, needle-like crystalline particles, echoing literature reports of caffeine crystallising into needles in bulk, a shape with potentially significant consequences for how the particles scatter the detector’s laser light. Dextran yielded approximately spherical particles spanning a wide range of diameters, frequently agglomerated into larger clusters, matching previous observations of dried dextran. Citric acid, unexpectedly, produced roughly spherical, non-agglomerated particles that preferentially collected around the holes of the grids, suggesting liquid-like surface properties during deposition. This contrasts sharply with bulk drying studies, in which citric acid forms crystals with multi-layer agglomerate networks, indicating that crystallisation within atomised droplets follows a different path than crystallisation in a bulk solution.

The study establishes, for the first time, a full experimental picture of particle formation and evolution inside an ELSD, from atomisation through evaporation to detection, and provides the benchmark data against which a companion paper validates a numerical model of aerosol transport, evaporation, and scattering in the instrument. Because the light-scattering signal depends directly on particle size, number, and morphology, the findings explain why detector response varies across analytes and open a route to optimising instrument geometry and operating conditions for specific applications. The authors suggest that the combined experimental and modelling framework could enable improved detector designs and more robust analytical methods across pharmaceutical, biomaterials, and food science research, turning a long-standing black box into a quantitatively understood instrument.

Subject of Research: Experimental characterisation of droplet drying and particle formation inside an evaporative light scattering detector

Article Title: Size distribution and particle morphology of analytes dried through the evaporative light scattering detector – Part 1

Article References: Bertani, F., Hassim, J., & Hochgreb, S. (2026). Size distribution and particle morphology of analytes dried through the evaporative light scattering detector – Part 1. Aerosol Research, 4(2), 311-323. https://doi.org/10.5194/ar-4-311-2026

Image Credits: AI Generated

DOI: 10.5194/ar-4-311-2026

Keywords: evaporative light scattering detector, aerosol science, particle size distribution, nebulisation, spray drying, phase Doppler particle analysis, aerodynamic aerosol classifier, scanning electron microscopy, caffeine, dextran, citric acid, liquid chromatography

To appreciate why this matters, consider what liquid chromatography actually does. It is the workhorse separation technology of the life sciences, used in millions of studies to tease apart the thousands of metabolites, drugs, lipids and toxins hiding inside complex biological samples. When chromatography is coupled to mass spectrometry, the time a compound needs to traverse the column — its retention time — carries precious information about its identity, reflecting properties such as polarity. But that time is not a fixed property of the molecule. It depends on the column, the solvents, the gradient, the temperature, the flow rate and even the plumbing of the instrument. Change any of these, and retention times can shift dramatically, even between instruments that are nominally identical.

This variability has crippled generations of machine-learning models. Hundreds of publications have attacked the problem, yet the standard workflow remains painfully circular: measure a few hundred compounds on one specific platform, train a model on that data, and hope it never encounters a different setup. The current best-performing methods use transfer learning, pretraining a neural network on a large dataset and then fine-tuning it on hundreds of authentic standards measured on the target system. Without that expensive fine-tuning step, these models are useless. As the authors put it in their paper, truly transferable prediction — requiring no training data from the target chromatographic system — has remained an open challenge.

The Jena-led team’s insight is elegant: stop trying to predict the time itself. Retention times may swing wildly between setups, but the order in which compounds elute is far more conserved. A longer capillary between the column and the mass spectrometer will delay every compound equally, scrambling the times while leaving the sequence untouched. The researchers therefore split the problem in two. In the first step, a machine-learning model predicts a retention order index, a real-valued number that respects elution order: if compound A exits the column before compound B, its index must be smaller. In the second step, these indices are mapped to actual retention times using a simple polynomial anchored to a handful of confidently identified compounds in the target dataset.

The technical machinery behind step one is a graph neural network built on a directed message-passing architecture. Each molecule is encoded as a graph, with atoms as nodes and bonds as edges, and the network distills this into a 512-dimensional embedding. Crucially, the chromatographic setup is encoded too, as a 13-dimensional vector combining the mobile phase pH with twelve parameters describing the column’s chemistry — six hydrophobic subtraction model parameters and six Tanaka parameters, which quantify properties like hydrophobicity, steric selectivity and hydrogen bonding capacity. Because the model learns from these continuous descriptors rather than a one-hot column identifier, it can generalize to columns it has never encountered. Training uses a Siamese architecture that feeds in pairs of compounds measured under identical conditions, penalizing the network whenever its predicted indices contradict the observed elution order.

Evaluated on 171 curated reversed-phase datasets from the RepoRT repository, using cross-validation splits that excluded entire chromatographic conditions from training, the condition-aware model achieved an error rate of 4.89 percent in predicting which of two compounds elutes first. That may sound like a modest improvement over the 5.61 percent achieved by an otherwise identical model blind to chromatographic conditions, but the authors show this comparison is misleading. Most compound pairs never change order under any reasonable conditions, so they dominate the statistics. When the analysis focused on setup-characteristic pairs — those that genuinely deviate from the consensus order — accuracy jumped from 40.1 to 60.5 percent, a leap that no setup-agnostic model could achieve, since such models essentially learn the majority vote and would get these tricky pairs wrong almost by definition.

The second step is where the method becomes genuinely practical. For any target dataset, roughly 30 compounds need to be identified with high confidence — for example through spectral library search — to serve as anchors. A polynomial of degree two, fitted robustly with median regression to weed out misidentified anchors, then maps predicted indices to retention times. Astonishingly, as few as 15 anchors suffice, and they do not even need to come from diverse compound classes. In a demonstration on human fecal samples, the team used just 19 N-alkylpyridinium 3-sulfonate standards measured in a separate run, because the structural similarity of these standards and their tiny number made any fine-tuning approach impossible. The method still delivered, resolving ambiguous bile acid matches, distinguishing diastereomers of cholic acid, and elevating nine spectral library hits to ‘almost level 1’ identifications — the gold standard of metabolite annotation, previously reserved for in-house libraries.

Head-to-head against DeepGCN-RT and RT-Transformer, the best pretrain/fine-tune methods available, the 2-step approach held its own under generous evaluation splits and then decisively pulled ahead under a realistic split that prevents models from exploiting near-identical structures in the training data. On five of six benchmark datasets, 2-step outperformed both competitors — despite never having seen the target datasets, while the rivals had been fine-tuned on them. Even more striking, the method improves as more training data accumulate, whereas pretrain/fine-tune methods plateau. Under a ‘maximum challenge’ evaluation that removed both similar datasets and all target compounds from training, performance dropped only slightly, and the method still beat the competition.

Beyond the algorithm itself, the study delivers a systematic map of what actually scrambles elution order. Analyzing thousands of dataset pairs from RepoRT, the researchers found that the column’s stationary phase exerts the largest influence, followed by mobile phase pH and solvent composition, with temperature playing a smaller, compound-dependent role and flow rate largely irrelevant. They conclude that just five parameters — column, mobile phase, gradient, temperature and flow rate — form a sufficient minimum metadata set for preserving the reuse value of retention data. Their plea to the community is concrete: deposit reference LC-MS datasets with this metadata, and column vendors should publish hydrophobic subtraction and Tanaka parameters so that transferable prediction can flourish. Hydrophilic interaction chromatography, with its far more diverse separation mechanisms, remains the next frontier. If the authors are right that huge improvements lie ahead, the humble retention time may finally take its place alongside mass spectra as a first-class citizen in the molecular identification toolkit.

Subject of Research: Transferable machine-learning prediction of small-molecule liquid chromatography retention times

Article Title: Times are changing but order matters: transferable prediction of small-molecule liquid chromatography retention times

Article References: Kretschmer, F., Harrieder, E.-M., Witting, M., & Böcker, S. (2026). Times are changing but order matters: transferable prediction of small-molecule liquid chromatography retention times. Nature Methods. https://doi.org/10.1038/s41592-026-03243-2

Image Credits: AI Generated

DOI: 10.1038/s41592-026-03243-2

Keywords: liquid chromatography, retention time prediction, machine learning, graph neural network, metabolomics, mass spectrometry, retention order, reversed-phase chromatography, small molecules, compound identification, RepoRT, transfer learning

The technique at the heart of the work is reverse-phase high-performance liquid chromatography, or RP-HPLC, a workhorse of pharmaceutical quality control. In essence, the method pushes a dissolved sample through a narrow column packed with microscopic silica beads coated in a nonpolar layer. The drug molecules interact with this stationary phase while being carried along by a liquid mobile phase, and because different compounds interact with different strengths, they separate and emerge at distinct times. A detector then registers each compound as a peak, and the size of the paracetamol peak reveals how much of the drug is present. The sensitivity, precision, and reproducibility of RP-HPLC have made it the default choice for verifying the content of pharmaceutical formulations, including the ubiquitous paracetamol tablet.

What makes the new study notable is not the mere existence of another paracetamol assay, as the authors themselves acknowledge, but the systematic way the method was constructed. Conventional method development typically proceeds one variable at a time: adjust the solvent mixture, run the sample, adjust the pH, run it again. This trial-and-error process is slow, and worse, it can miss interactions between variables, situations where changing two parameters together produces effects that neither produces alone. Analytical Quality by Design, or AQbD, flips the script. It begins with an Analytical Target Profile, a formal statement of what the method must achieve, and then uses risk assessment and designed experiments to map out how every critical parameter influences performance. The result is not just a working method but a deep, quantitative understanding of why it works.

The researchers, led by Kranti Satpute of Dayanand College of Pharmacy in Latur, Maharashtra, began by defining the attributes their method needed: adequate retention of the paracetamol peak, good peak symmetry, high chromatographic efficiency, a reproducible signal, and clean separation from any tablet excipients or impurities. They then ranked potential method variables by risk, classifying each as low, medium, or high priority. This triage focused their experimental effort on the parameters most likely to disrupt the analysis, a philosophy borrowed directly from quality risk management frameworks used across the pharmaceutical industry. Variables deemed low risk, such as those unlikely to affect retention or peak shape, were set aside, while high-risk candidates were earmarked for systematic investigation.

For the optimization itself, the team turned to a Central Composite Design, a form of Response Surface Methodology that examines multiple factors simultaneously across five levels each. Three chromatographic parameters were varied: the concentration of organic modifier in the mobile phase, the pH of that phase, and the flow rate of the pump. The ranges, spanning 5 to 25 percent organic modifier, pH 6.3 to 6.7, and flow rates of 0.8 to 1.2 milliliters per minute, were grounded in preliminary experiments. The design tracked three responses: retention time, peak area, and theoretical plate count, the last being a measure of column efficiency. Analysis of variance then revealed which factors and interactions were statistically significant, something a one-factor-at-a-time approach could never expose.

The statistics delivered a clear verdict. Retention time was strongly influenced by organic modifier concentration, with a p-value of 0.0001, and by flow rate, at p equal to 0.0004, while pH turned out to be statistically irrelevant to retention, with a p-value of 0.9917. Flow rate also significantly affected peak area and theoretical plate count. Three-dimensional response surface plots visualized these relationships, showing retention time falling as organic modifier increased while peak area and plate counts remained comparatively stable across the studied ranges. This kind of visual and statistical map of method behavior is precisely the knowledge that conventional approaches leave on the table, and it gave the team a rational basis for deciding which parameters must be tightly controlled during routine use.

The final method settled on a mobile phase of water, acetonitrile, and methanol in an 80:15:5 ratio, adjusted to pH 6.5 with triethylamine, flowing at 1.0 milliliter per minute through an Agilent TC-C18 column, with ultraviolet detection at 254 nanometers, the wavelength where paracetamol absorbs most strongly. Under these conditions, paracetamol eluted as a sharp, symmetrical peak at roughly 5.4 minutes, within a total run time of just seven minutes. Crucially, the design of experiments also defined a Method Operable Design Region, a design space within which the method is guaranteed to perform acceptably even if conditions drift slightly. Instead of a single fragile set of settings, quality control laboratories gain a flexible operating window with assured performance, a concept regulators increasingly favor under ICH Q14.

Validation followed the latest ICH Q2(R2) guidelines, and the numbers were impressive. Linearity held across concentrations from 18 to 42 micrograms per milliliter with a correlation coefficient of 0.9995, about as close to a perfect straight line as analytical chemistry allows. Repeatability produced a relative standard deviation of just 0.66 percent, and intermediate precision, tested across different days, analysts, and instruments, came in at 1.75 percent. Accuracy experiments at 80, 100, and 120 percent of the target concentration yielded recoveries between 100.03 and 102.85 percent, with an overall mean recovery of 101.58 percent and a relative standard deviation of 0.70 percent, comfortably within the accepted 97 to 103 percent window. Specificity testing confirmed that blank, placebo, and impurity-spiked samples produced no interfering peaks at the paracetamol retention time, and photodiode array assessment supported peak purity.

Robustness testing added a final layer of confidence, showing that the method tolerated small deliberate variations in conditions within the established design space, though the authors caution that flow rate and organic modifier concentration deserve strict control given their outsized influence on retention. An updated risk assessment confirmed that controls such as instrument calibration and proper material handling had reduced residual risks to a minimum. The practical upshot is a method that is simple, fast, accurate, and, perhaps most importantly, self-documenting: a laboratory adopting it inherits not just a recipe but a scientifically justified understanding of which knobs matter and how much they can be turned. For a drug taken by hundreds of millions of people daily, that transparency is more than academic elegance.

The broader significance of the study lies in its demonstration of a lifecycle mindset for analytical procedures. Rather than treating method development as a one-off exercise capped by a validation report, the AQbD framework embeds risk management, multivariate statistics, and control strategies into the very fabric of the method, aligning with the quality-by-design principles now sweeping through pharmaceutical manufacturing. As regulators worldwide push for analytical flexibility backed by scientific justification, studies like this one, applying Central Composite Design to an everyday drug, show how even the most routine quality control test can be rebuilt as a model of modern, data-driven science. The humble paracetamol tablet, it turns out, is a fitting stage for that quiet revolution.

Subject of Research: Development and validation of an AQbD-based RP-HPLC method for quantifying paracetamol in pharmaceutical tablets

Article Title: Analytical quality by design driven RP HPLC method development and validation for quantification of paracetamol in pharmaceutical tablets

Article References: Satpute, K., Avhad, A., Syed, S. M., Sonwane, S., Yelmate, A., Shetkar, B., Birajdar, M., & Lohiya, G. (2026). Analytical quality by design driven RP HPLC method development and validation for quantification of paracetamol in pharmaceutical tablets. Discover Chemistry, 3(1), Article 565. https://doi.org/10.1007/s44371-026-01007-7

Image Credits: AI Generated

DOI: 10.1007/s44371-026-01007-7

Keywords: paracetamol, RP-HPLC, Analytical Quality by Design, Central Composite Design, method validation, pharmaceutical analysis, liquid chromatography, ICH Q2(R2), design space, quality control, response surface methodology, tablets

The core problem the technology addresses is one of resolution. Biological samples such as serum contain tens of thousands of metabolites and lipids spanning an enormous range of chemical polarities and concentrations. When these compounds are separated by liquid chromatography and fed into a mass spectrometer, many arrive at the detector at nearly the same moment and with nearly identical mass-to-charge ratios. Lipids are especially troublesome: a species such as the phospholipid PC 34:2 exists in several structurally distinct isomeric forms with identical masses, and lipids from entirely different classes can share mass values so closely that their signals overlap. To make matters worse, molecules can form multiple adducts with sodium, potassium, or hydrogen ions, and the naturally occurring heavy isotopes of carbon, nitrogen, sulphur, and oxygen create additional peaks that can masquerade as entirely different compounds. The result is a forest of ambiguous signals in which confident identification, the foundation of biomarker discovery, becomes extraordinarily difficult.

Time-of-flight mass spectrometry has been a mainstay of high-resolution analysis for decades. The principle is elegant: ions are accelerated into a vacuum tube, and because heavier ions travel more slowly, their arrival times at a detector reveal their masses. Resolution improves with the length of the flight path, since ions of slightly different mass have more time and distance to separate. Classically, this meant building longer flight tubes, an approach that quickly becomes impractical in a laboratory and, crucially, degrades signal as ions travel farther. An alternative is to bounce ions back and forth between electrostatic mirrors called reflectrons, but conventional reflectrons contain wire grids that scatter and lose ions with each reflection. Over a handful of bounces the loss is tolerable; over dozens it becomes fatal to sensitivity and mass accuracy.

The multi-reflecting time-of-flight platform, or MRT, overcomes this limitation with gridless reflectron mirrors. Ions are reflected repeatedly through a series of periodic focusing lenses, extending the effective flight path to 47 metres inside an instrument of ordinary laboratory size while maintaining signal transmission. The result is a mass resolution exceeding 200,000 full width at half maximum, combined with sub-part-per-million mass accuracy and acquisition rates of up to 30 spectra per second. That combination of speed and resolution is what sets the platform apart. Fourier-transform instruments such as Orbitraps and ion cyclotron resonance analysers can achieve comparable or higher resolution, but they typically require scan times of around one second, which is far too slow to adequately sample the narrow two-to-three-second chromatographic peaks produced by modern ultra-high-performance liquid chromatography. The MRT, by contrast, collected 28 data points across a six-second peak in the demonstration study, providing the peak definition needed for both qualitative identification and quantitative analysis.

To put the instrument through its paces, the researchers analysed serum samples from eight patients recruited into the Manchester Allergy, Respiratory, and Thoracic Surgery Biobank COVID-19 cohort. The patients spanned a range of disease severity, from mild and convalescent infections to severe, active disease requiring intensive care, and were classified using the Manchester COVID-19 Severity Score, a clinical tool based on the level of respiratory intervention each patient required. Lipids and polar metabolites were extracted from the serum using a methyl-tert-butyl ether protocol and separated on an amide column over a rapid ten-minute gradient, with the mass spectrometer acquiring data in both positive and negative electrospray ionisation modes using data-independent acquisition.

The technical payoff was most vivid in the lipid region of the spectra. At a chromatographic peak eluting at 1.5 minutes, the instrument recorded four major ions. The base peak at mass-to-charge ratio 758.57019 was annotated as the phospholipid PC 34:2, but the next most intense peak, nominally at 760.58, resolved into two distinct signals separated by less than 0.01 daltons. One was the doubly carbon-13-labelled isotope of PC 34:2; the other was the monoisotopic peak of a co-eluting lipid, PC 34:1. The measured resolution of this region was 224,770, and simulations showed that even a conventional instrument operating at 100,000 resolution could not have separated the two. Similar deconvolution was achieved for the potassium adducts of the same lipids, where heavy and light isotopes of potassium and carbon combined to produce a tangle of overlapping signals that only the fine isotopic detail captured by the MRT could untangle. Because each element carries a characteristic isotope abundance pattern, this fine structure acts as a molecular fingerprint, providing an independent check on elemental composition that goes far beyond mass matching alone.

Mass accuracy proved equally transformative for compound annotation. Conventional quadrupole time-of-flight instruments typically report mass errors of one to three parts per million, and Orbitrap platforms around one to two parts per million at high resolution. The MRT delivered errors between 21 and 134 parts per billion across annotated compounds, roughly an order of magnitude tighter. This matters practically because database searching scales with the mass tolerance applied: a feature measured at 597.28739 returned nine candidate identifications when searched at two parts per million but only two when the filter was tightened to half a part per million, corresponding to isomeric forms of taurochenodeoxycholate sulphate. Narrower search spaces mean fewer false positives and far less manual curation, a persistent burden in large-scale metabolomics.

Applied to the patient cohort, the platform revealed metabolic differences associated with disease severity. Patients with severe, active infections showed broadly similar serum lipidome profiles, generally with lower feature abundance than a convalescent patient and one with mild disease, whose profiles clustered together. One severely ill patient stood apart with 152 unique mass features, compared with an average of 37.5 for the other patients. Among the annotated features in this individual were taurochenodeoxycholate sulphate and glycoursodeoxycholic acid, both bile acid derivatives. Sulphated bile acids have previously been reported as elevated in severe COVID-19 and linked to sepsis and renal dysfunction, and the patient’s clinical record of kidney failure and hypercholesterolemia was consistent with these findings. The same patient’s serum contained iohexol, an iodinated contrast agent normally cleared within 24 hours, which the researchers attributed to a recent CT scan compounded by renal failure delaying excretion. Perhaps most intriguingly, a feature was annotated as sphingofungin A, a sphingoid metabolite of the fungus Aspergillus fumigatus, the most common cause of COVID-19-associated pulmonary aspergillosis, a dangerous opportunistic infection estimated to affect roughly one in three mechanically ventilated patients. The patient had been ventilated, though the authors caution that the diagnosis could not be confirmed retrospectively.

The authors are careful to frame the work as a demonstration rather than a definitive clinical study. With only eight patients, one of whom was excluded on technical grounds, the findings cannot establish biomarkers or explain mechanisms, and the team acknowledges that the challenges of much larger metabolomics studies remain to be addressed. Several of the study’s authors are employed by Waters Corporation, the instrument’s manufacturer, a conflict of interest disclosed in the paper. Nevertheless, the proof of principle is compelling: the MRT platform resolved overlapping lipid species that would have gone undetected on conventional time-of-flight or electrostatic trap instruments, matched its annotations to the patients’ documented clinical histories, and did so at acquisition speeds compatible with the high-throughput analysis that well-powered clinical and epidemiological cohorts demand. If the technology scales as its developers hope, it could sharpen the entire pipeline of biomarker discovery, from the first chromatographic separation to the final confident assignment of a molecule’s identity, turning the chemical noise of human blood into interpretable clinical signal.

Subject of Research: High-resolution multi-reflecting time-of-flight mass spectrometry for metabolite and lipid identification in COVID-19 patient serum

Article Title: Multi-reflecting time-of-flight mass spectrometry enables high-accuracy metabolite identification in a demonstration study using serum samples from COVID-19 patients

Article References: King, A., Matthews, E., Maker, G., Trengove, R., Shareef, S., Fowler, S., Felton, T., Simpson, A., Palmer, M., Gethings, L. A., Plumb, R. S., & Mills, E. N. C. (2026). Multi-reflecting time-of-flight mass spectrometry enables high-accuracy metabolite identification in a demonstration study using serum samples from COVID-19 patients. Metabolomics, 22(5), Article 168. https://doi.org/10.1007/s11306-026-02539-w

Image Credits: AI Generated

DOI: 10.1007/s11306-026-02539-w

Keywords: metabolomics, lipidomics, mass spectrometry, time-of-flight, COVID-19, biomarkers, bile acids, isotopic fine structure, liquid chromatography, serum, renal dysfunction, aspergillosis

Liquid chromatography is a standard technique used to separate mixtures of substances. As a mixture passes through a separation column, individual molecules are retained to varying degrees based on their chemical properties. Consequently, they elute from the column at different times. This elution time, known as the retention time, serves as a key indicator for identifying which substance is present in a sample. However, accurately predicting this time has historically been difficult because it depends heavily on specific experimental conditions, including the type of column, solvent, gradient, pH, temperature, and even minor technical variations in the apparatus.

Prof. Dr. Sebastian Böcker, a bioinformatician at the University of Jena, explained that previous predictive models often reached their limits due to this sensitivity to experimental setup. For instance, replacing a tube in the apparatus with one that is slightly longer can significantly shift the measured times. As a result, earlier models typically required extensive training or fine-tuning using data from the exact measurement system on which they were to make predictions. This meant researchers had to measure numerous standard substances before the model could be used effectively, a process that is time-consuming, expensive, and often impractical for many applications.

The new approach, developed by Böcker’s team, overcomes these limitations by focusing on the most commonly used type of liquid chromatography, known as reversed-phase mode. Unlike previous methods that are tied to specific hardware, this new method can make predictions for new systems and unknown molecules without prior system-specific training. The method operates in two distinct steps, allowing it to generalize across different experimental setups while maintaining high predictive accuracy.

In the first step of the process, a machine learning model calculates a retention order index for a given molecule. This index does not directly represent a time value but instead describes the molecule’s position within the expected sequence of retention order. By focusing on the relative order rather than absolute times, the model can account for the inherent variability in experimental conditions. This step establishes a framework for how molecules are likely to separate from one another, independent of the specific hardware used.

In the second step, the calculated retention order index is converted into specific retention times using a small number of known reference points. This conversion allows the method to translate the relative order into practical, measurable times for a specific experimental setup. Fleming Kretschmer, a co-first author of the paper who worked on the method during his doctorate, emphasized that this two-step process is a key innovation. He noted that their method outperforms other approaches that require extensive training on the target system, enabling precise predictions out-of-the-box even for new systems.

The development of this tool, named “2-step,” represents a significant advancement in computational modeling for analytical chemistry. The method is available as a software package and a web application, making it accessible to researchers who may not have extensive programming expertise. According to the researchers, the tool is designed to be integrated into existing analytical programs, which could enable laboratories working with liquid chromatography and mass spectrometry to use it automatically in their workflows. This integration could streamline the process of identifying unknown molecules in complex samples.

The implications of this new method extend across several fields of research, including drug discovery, natural product research, and environmental analysis. In natural product research, scientists often seek substances from bacteria, fungi, or plants that could serve as new antibiotics, cancer drugs, or active compounds against other illnesses. The ability to predict retention times more reliably helps researchers confirm whether a measured retention time matches a presumed chemical structure, thereby aiding in the identification of potential therapeutic agents.

Similarly, in environmental analysis, food chemistry, and pharmaceutical research, the question of what is contained in a complex sample is paramount. The new method provides a robust way to address this question by offering reliable predictions that do not depend on extensive prior calibration for each new system. This could reduce the time and cost associated with sample analysis, allowing researchers to focus more on interpreting the biological or chemical significance of the findings rather than on the technical aspects of sample preparation and calibration.

The study highlights the potential of artificial intelligence to solve complex problems in analytical chemistry by leveraging machine learning to handle the variability inherent in experimental setups. By decoupling the prediction of retention order from the specific experimental conditions, the researchers have created a tool that is both flexible and accurate. As the field of metabolomics continues to grow, with an increasing number of small molecules being analyzed, methods like “2-step” will likely play a crucial role in enabling the rapid and accurate identification of these compounds in diverse biological and environmental contexts.

Subject of Research: Biology

Article Title: When does the molecule emerge from the column?

Article References: When does the molecule emerge from the column?. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Artificial Intelligence, Analytical Chemistry, Liquid Chromatography, Metabolomics, Machine Learning, University of Jena, Nature Methods, does, molecule, emerge, column, scientific research

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

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