Analytica Chimica Acta

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impacts on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and the data quality, dominate detection performance. Increases in spectral noises and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol%. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying a ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

Integrating antibody-based imaging with mass spectrometry imaging (MSI) on the same formalin-fixed paraffin-embedded (FFPE) tissue section offers powerful opportunities for multimodal spatial analysis but remains analytically challenging due to cross-platform chemical and physical interference. In particular, chemically aggressive on-tissue derivatization strategies required for isomer-resolved glycan MSI may compromise downstream antibody detection. Here, we systematically evaluate the analytical compatibility and acquisition order of imaging mass cytometry (IMC) and sialic-acid-linkage-resolving N-glycan MALDI-MSI using an Amidation-Activation-X-Linkage (AAXL) derivatization strategy on the same FFPE tissue section. Two same-section workflows were compared: AAXL-MALDI MSI followed by IMC (MALDI-first) and IMC followed by AAXL-MALDI MSI (IMC-first). We find that AAXL-first processing results in severe and widespread loss of IMC anti-body signal across epithelial, immune, and nuclear markers, rendering subsequent antibody-based analysis unreliable. In contrast, IMC-first acquisition preserves quantitative antibody performance while maintaining spatial glycan distributions, relative abundance structure, and isomer-specific signal integrity in downstream AAXL-MALDI MSI. Using high-precision co-registration, we further demonstrate that IMC-first sequencing enables analytically robust integration of IMC and MSI data at both domain and pixel levels. These results establish IMC-first acquisition as the preferred same-section strategy for workflows combining antibody imaging with chemically intensive, isomer-resolved glycan MSI and provide generalizable guidance for the design of multimodal spatial mass spectrometry experiments.

Cultivated meat production requires robust and validated analytical methods for comprehensive characterization. While transcriptomics-based approaches establish the foundational profile of molecular analysis, proteomics provides additional resolution that further enhances scientific certainty in both product development and safety characterization. However, the industry adoption of proteomics is currently hindered by technical complexity and a critical lack of analytical standardization, which leads to significant workflow-dependent variations in proteome coverage. To address this gap, we investigated the influence of key workflow steps (digestion, cleanup, LC-MS conditions) on the proteome profile of cultivated duck biomass. We compared five bottom-up sample preparation protocols - two traditional in-solution options (urea and SDC-based protocols), two device-based approaches (PreOmics iST and EasyPep kits), and an innovative protocol (SPEED), and demonstrated that device-based protocols offered the highest peptide yield and proteome coverage. However, optimization allowed cost-effective in-solution methods to achieve comparable performance. Specifically, an optimal digestion time of 3 hours at 37{degrees}C and the use of polymer-based desalting columns significantly enhanced protein identification ([~]4500 - 5000 IDs). Moreover, data independent acquisition (DIA) provided deeper proteome coverage than data dependent acquisition (DDA) with higher precision ([~]6500 vs 5000 IDs). The validated Standard Operating Procedures presented here establish a standardized framework for bulk bottom-up proteomics in cultivated meat, facilitating the generation of reliable and comparable data required for robust multi-omics characterization. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/713501v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@5b61b8org.highwire.dtl.DTLVardef@16c7e65org.highwire.dtl.DTLVardef@1de21d2org.highwire.dtl.DTLVardef@7e984a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIComplexity and non-standardization limit MS-proteomics use in cultivated meat (CM). C_LIO_LICM protein profile varies with sample prep, LC-MS, and data processing pipeline. C_LIO_LIDevice-based and optimized cost-effective protocols offer a high proteome coverage. C_LIO_LIProteomics can complement transcriptomics for a comprehensive CM characterization. C_LIO_LIProposed standardized methods ensure reliable data for future regulatory submissions. C_LI

Polymeric 3D printing of microfluidic devices for biosensing is an appealing fabrication alternative for rapid manufacturing of biosensing devices with complex geometry in a streamlined, repeatable and cost-effective manner without the need for expensive instrumentation such as those employed in photochemical etching and soft lithography. Hybrid 3D printed paper-based microfluidics is an emerging area which harnesses the unique properties of both, merging the construction of microfluidic structures and the inherent capillary-driven flow within paper substrates. In this work, we have fabricated hydrophobic barriers by 3D printing a single layer of machinable wax, thermoplastic polyurethane, polylactic acid and polypropylene directly on chromatography paper to create open microchannels and determine the most suitable material. Characterization of each open microchannel using the four materials revealed polypropylene as the most reliable material with high hydrophobic barrier integrity and resolution. Polypropylene achieved functional microchannels with a resolution of 621 {+/-} 33{micro}m, hydrophobic barrier integrity of (93.75 {+/-} 9.16%), wicking speed of 0.38mm/s and optimal hydrophilicity of channels (51.4 {+/-} 8.36 {degrees}) with minimal embedding during thermal curing. To demonstrate proof of principle, a fluorescence assay demonstrating the formation of a dimeric g-quadruplex structure from a g-rich sequence which significantly enhances fluorescence of thioflavin T was implemented.

Phytohormones are key players in the regulation of plant development and metabolism. The different phytohormone classes comprise numerous chemically very diverse compounds, which are often present at very low concentrations. The chemical properties of phytohormones range from acidic to basic and from polar to non-polar. Furthermore, concentration varies strongly among different phytohormones, between plant species, tissues and developmental stages. Challenges often arise when only small amounts of plant material are available and when plant species are investigated in which the phytohormone profile has not yet been characterized. To establish a method for comprehensive phytohormone analysis we addressed these challenges by choosing and optimizing a suitable extraction method followed by optimized HPLC separation. We compared the most widely-used mass spectrometric detection methods, multiple reaction monitoring (MRM) on a triple quad instrument with high-resolution mass spectrometry (HRMS) on a Q-TOF instrument, and discuss the advantages of both methods and their limitations. O_LIWe compared various methods described in literature for the extraction of six phytohormone classes by liquid-liquid extraction and solid phase extraction purification and describe our optimizations to the selected method. C_LIO_LIWe optimized HPLC separation for 50 different phytohormones. C_LIO_LIWe evaluated the application of MRM and HRMS detection strategies. C_LI

Capillary zone electrophoresis (CZE)-mass spectrometry (MS) has been proposed as a powerful analytical tool for bottom-up, top-down, and native proteomics (multi-level proteomics) decades ago to analyze complex biological samples at the levels of peptides (bottom-up), proteoforms (top-down), and complexoforms (native). However, its broad adoption has been impeded by the limited robustness and reproducibility. Here, we present multi-level proteomics data from nearly 170 CZE-MS runs ([~]170 hours of instrument time), demonstrating qualitatively (i.e., the number of identified peptides and proteoforms, the number of detected complexoforms, and their migration time) and quantitatively (i.e., peptide, proteoform, and complexoform intensity) reproducible measurement of complex samples with varying levels of complexity, i.e., Escherichia coli cells, HeLa cells, and human plasma. CZE-MS-based native proteomics enabled the detection of hundreds of complexoforms up to 800 kDa from the complex systems via consuming only nanograms of protein material. The results indicate that CZE-MS is sensitive and reproducible enough for broad adoption for multi-level proteomics-based biomedical research.

IntroductionRetinoids are bioactive vitamin A derivatives that regulate cellular differentiation and gene expression, yet their reliable quantification remains challenging due to low abundance, structural isomerism, and sensitivity to ionization conditions while handling. ObjectivesIn this study, we performed a systematic optimization of liquid chromatography-mass spectrometry (LC-MS)-based detection of retinoids across tissues and biofluids. MethodsChromatographic separation, adduct formation, ionization parameters, fragmentation behavior, and extraction procedures were evaluated in an integrated workflow. ResultsChromatographic conditions influenced not only retention time but also the ionic species detected, affecting precursor selection for MS{superscript 2} analysis. Retinoids exhibited compound-dependent responses to electrospray ionization and collision energy, requiring tailored acquisition parameters. Extraction experiments demonstrated differential recovery among retinoid classes and revealed matrix-dependent behavior, indicating that protocols used for tissues cannot be directly transferred to low-abundance biofluids. Using optimized conditions, retinoids were detected in mouse cerebrospinal fluid (CSF) at concentrations approaching the analytical detection limit, where MS{superscript 2} confirmation was necessary for reliable identification. ConclusionTogether, our results provide a framework for reproducible retinoid profiling across biological matrices and enables comparative studies of retinoid biology in low-volume and low-abundance biofluids.

BackgroundThe clinical translation of molecularly targeted therapeutics and imaging agents represents a cornerstone of precision oncology, with the global theranostics market projected to exceed $25 billion by 2030. However, the development of theragnostic agents or diagnostic companions remains constrained by analytical bottlenecks in quality control, such as target-binding specificity, which are increasingly required by regulatory agencies as product release criteria during the translation process. Current methods, including enzyme-linked immunosorbent assay (ELISA), which require specialized resources or external CROs, or bead-based assays for radiolabeled compounds, which involve complex multi-step protocols; these limitations and others hamper their practical implementation in clinical manufacturing environments. Assay delays can postpone clinical trial initiation, increase development costs, and delay patient access to these agents. ResultsWe have developed and validated a rapid, size-exclusion high-performance liquid chromatography (SE-HPLC) method for the determination of target-binding fractions of labeled biologics. The method separates the unbound biologic from the larger antigen-bound complex, allowing for rapid quantification. We validated the method using a panel of fluorescently labeled antibodies (panitumumab-IRDye800CW, nivolumab-IRDye800CW) and radiolabeled biologics ([18F]GEH200521, [18F]NOTA-ABY-030), assessing linearity, specificity, and concentration independence. The SE-HPLC method achieved excellent separation of bound and unbound species with a resolution (Rs) of 3.2. A strong linear relationship (R2 = 0.999) was observed between the antigen-to-antibody ratio and the measured binding fraction. The method demonstrated high specificity, with no binding detected with non-target antigens. The total assay and analysis time was less than 35 minutes, a significant improvement over traditional methods. ConclusionsSE-HPLC provides a rapid, specific, and cost-effective alternative to traditional binding fraction assessment methods, reducing quality control timelines from weeks/hours to minutes. The methods compatibility with both fluorescent and radiolabeled biologics and integration with existing HPLC infrastructure represents a significant advancement in development workflows.

Plasma contains diverse bioactive peptides that play crucial roles in maintaining homeostasis and regulating disease responses. However, the presence of peptides derived from high-abundance proteins such as albumin makes comprehensive analysis of native peptides secreted by organs challenging. This study aimed to establish a highly sensitive plasma peptidomic approach by combining data-independent acquisition (DIA) with spectral libraries of plasma and organs. First, peptides were extracted from plasma and eleven organ types using a high-yield peptide extraction method, the differential solubilization method. These peptides were then measured via data-dependent acquisition (DDA) analysis using a timsTOF HT for constructing empirical spectral library. Subsequently, DIA-MS data from plasma samples were measured and analyzed using this spectral library. This strategy achieved identification of, on average, over 5,500 peptides per run, with over 2,000 organ-derived peptides including 19 known bioactive peptides. The novel strategy proposed here enables highly sensitive quantitative analysis of organ-derived peptides in plasma, linking them to their secreting organs. It is expected to substantially contribute not only to the discovery of biomarkers and novel bioactive peptides but also to elucidating the pathophysiology of systemic diseases.

Subtyping of ketoacidosis, a metabolic state characterized by blood acidification due to various causes, remains challenging in forensic casework. Postmortem omics samples paired with machine learning offers an independent tool to address this challenge. However, such data, especially related to real forensic cases, are rare. In Sweden, high-resolution mass spectrometry data routinely collected in forensic toxicology, can be leveraged for metabolomic analysis. Here, we integrate postmortem metabolomics and machine learning models to detect and subtype ketoacidosis-related deaths using real forensic cases in Sweden. From femoral blood samples of 109 alcoholic ketoacidosis cases, 220 diabetic ketoacidosis cases, 140 hypothermia cases, and 1,229 controls (hanging cases), we developed and tested three machine learning models, which achieved over 90% accuracy in ketoacidosis detection and over 80% in subtyping. Validation with independent cohorts (21 starvation cases, 29 alcoholic controls, and 40 diabetic controls) confirmed robustness with over 80% of starvation cases classified as ketoacidosis-related. Feature clustering highlighted metabolites such as cortisol to be important for subtyping. In summary, our findings demonstrate that combining machine learning with postmortem metabolomics enables accurate detection and subtyping of ketoacidosis-related deaths, which is useful for forensic casework.

Untargeted proteomics enables quantitative determination of host cell proteins (HCPs) in biotherapeutics, yet no workflow has been validated under ICH Q2(R2) for regulated quality control. We report a prospective validation of label-free untargeted proteomics for HCP quantification using a total-error (TE) approach. A stable isotope-labeled whole-proteome standard was spiked into NISTmAb at seven levels (20-80 ng). Four independent assays (198 injections) supported hierarchical replication and one-way random-effects ANOVA variance decomposition with Welch-Satterthwaite adjustment. Dual entrapment analysis demonstrated empirical peptide-level false discovery proportions below 1% at q = 0.01. Deterministic parsimony inference ensured invariant protein-group definition. Weighted least-squares regression (R{superscript 2} = 0.993) identified stable proportional compression with recoveries of 81-85%. Repeatability dominated the variance structure (median CV 2.7%); intermediate precision total SD ranged from 0.69% to 3.81% over the validated range. Accuracy profiles integrating empirical bias with a log- log variance model showed 95% {beta}-expectation and 95/95 content tolerance intervals fully contained within {+/-}30%, with a lower limit of quantification (LLOQ) of 20 ng. Abundance-stratified TE analysis revealed concentration-dependent calibration heterogeneity masked by aggregate-level estimation; stratum-specific {beta}-expectation intervals within {+/-}35% defined an abundance-aware LLOQ of 3.6 ppm (P95 = 3.87 ppm). Robustness under independent search software (FragPipe, CCC = 0.998, LoA {+/-}9%) and cross-platform acquisition (Astral, CCC = 0.980, LoA {+/-}18%) remained within predefined {+/-}30% agreement limits. System suitability criteria were derived empirically from validation performance. This is the first prospective ICH Q2(R2)-aligned validation of untargeted proteomics for HCP quantification, with a statistical framework applicable to other high-dimensional analytical methods requiring regulatory qualification. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/710150v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1f5331aorg.highwire.dtl.DTLVardef@ee2234org.highwire.dtl.DTLVardef@798eaorg.highwire.dtl.DTLVardef@c84034_HPS_FORMAT_FIGEXP M_FIG C_FIG

Gel electrophoresis has been a cornerstone laboratory technique for decades, yet it is often viewed as cumbersome, costly, and has remained confined to laboratory settings. Recent advances in DNA nanotechnology have repurposed electrophoresis as a primary readout for some biosensing applications such as DNA nanoswitches, where a conformational change in a DNA structure indicates the presence of a target molecule. Conventional gel electrophoresis setups not ideal for such targeted applications, with moderate equipment cost, excessive reagent use, and time-consuming processes. Here, we adopt a reductionist, application-driven approach to redesign gel electrophoresis specifically for DNA nanoswitch-based detection. We present a fully 3D-printable mini gel electrophoresis system that incorporates conductive plastic electrodes, demonstrating performance comparable to conventional systems using platinum electrodes. By optimizing the inter-electrode distance and running parameters, our system resolves the on/off states of DNA nanoswitches in as little as one minute. We further show that the device operates reliably at low voltages, including when powered by a USB power bank, and even enables instrument-free nanoswitch readout using an LED with a cell-phone camera. Our design substantially reduces the cost, voltage requirements, material usage, operational complexity, and experiment time. These improvements make gel-based biosensing more practical outside traditional laboratory environments, paving the way for broader adoption of gel electrophoresis in point-of-care and resource-limited settings.

Ambient metabolomics techniques such as laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) enable fast, preparation-free fingerprinting of biological samples but are inherently limited by spectral congestion in the absence of chromatographic separation. While ion mobility spectrometry provides additional gas-phase separation, maintaining ion transmission under the transient signals characteristic of laser desorption, remains analytically challenging. Here, we define operating conditions for cyclic traveling-wave ion mobility spectrometry (cIMS) that preserve transmission under LA-REIMS duty-cycle constraints and systematically evaluate how cIMS integration reshapes biofluid fingerprints and enhances chemical specificity in chromatography-free metabolomics analysis. Under optimized single-pass conditions, cIMS separation reorganized LA-REIMS spectra into structured mass/mobility feature domains, enabling selective mobility-based filtering of matrix-derived salt cluster ions. This reduced non-biological background contributions by up to 35% of total spectral intensity while preserving over 90% of detected untargeted features. Although cIMS operation introduced a sensitivity penalty relative to time-of-flight-only acquisition, approximately 80% of the total ion current was recovered under optimized conditions. Mobility-resolved data revealed coherent homologous series and class-specific structural trends, particularly for lipids, supporting class-level annotation. Analysis of 101 metabolite and lipid standards covering a broad physicochemical range (logP -5.30 to 19.40) demonstrated comprehensive molecular coverage, high mass accuracy (mean 2.4 ppm), and good agreement with reference CCS values (mean deviation 4.0%), with isomer separation observed for biologically important secondary bile acids in extended separation cycles. Collectively, these results establish LA-REIMS-cIMS as a practical analytical strategy for enhancing chemical specificity and spectral interpretability in support of high-throughput large-scale metabolic fingerprinting. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/709786v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@18a2dfdorg.highwire.dtl.DTLVardef@d165d6org.highwire.dtl.DTLVardef@1750291org.highwire.dtl.DTLVardef@fbbce9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Ion mobility spectrometry adds an orthogonal gas-phase separation to LA-REIMS, reorganizing complex biofluid spectra into distinct mass-mobility feature bands and improving molecular resolution in rapid ambient ionization metabolomics. C_FIG

Dogma suggests protein quantification is a pre-requisite to LC-MS/MS based proteomics studies. Such quantification allows a standardized ratio of sample to digestion enzyme and enables physical normalization of protein digest loaded onto the mass spectrometer for analysis. Most proteomics studies include these steps. However, there are significant costs in time, money and experimental complexity, associated with performing protein quantification and physical normalization for every sample, especially for larger studies. Proteomics data analysis pipelines typically include computational normalization strategies to compensate for unavoidable systematic biases. These strategies also have the potential to compensate for avoidable variation such as omitting sample amount normalization. Here we investigate the effects of either physically normalizing the amount of protein for each individual sample or leaving it unnormalized. Our results show the relationship between increased protein amount variation in sample input, and the variance of quantified relative abundances of peptides and proteins output after data analysis. The experiments presented here suggest that protein quantification and physical normalization steps can be omitted from some quantitative proteomic experiments without incurring an unacceptable increase in measurement variability after computational normalization has been applied. This work will enable important time and cost saving optimizations to be made to many proteomics workflows.

Here we present a novel data independent acquisition (DIA) mass spectrometry (MS) operating mode termed parallel accumulation-mobility aligned fragmentation (PAMAF) that offers enhanced speed and sensitivity of ion fragmentation analysis for nontargeted discovery workflows such as bottom-up proteomics. This mode of operation leverages high-resolution ion mobility (HRIM) separation capabilities of the structures for lossless ion manipulation (SLIM) technology to achieve HRIM-based precursor isolation in place of traditional quadrupole filtering approaches. This PAMAF mode of operation increases the number of features that can be identified per MS1/MS2 acquisition cycle by employing mobility-based time alignment to associate fragment ions with their corresponding precursor ions. By using a high-speed, lossless separation technique for precursor isolation instead of the comparatively slow and wasteful quadrupole filtering method, we can avoid ion losses up to 99% while simultaneously increasing the rate at which precursor ions are sequentially fragmented and detected. Additionally, by storing ions in a trapping region while the previous packet of ions is being analyzed, the PAMAF mode achieves [~]100% ion utilization efficiency. Benchmarking results of LC-PAMAF-MS analysis of a whole cell protein digest showed approximately 6x more protein group identifications compared to a standard data-dependent acquisition (DDA) analysis without HRIM on the same QTOF instrument, and to over 100x improvement for low-load workflows. Quantitative evaluations demonstrated that PAMAF mode could quantify low abundance peptides, including those undetectable by DDA. Additionally, since precursor isolation in PAMAF mode is size-based rather than m/z-based, many coeluting isobars and isomers can be resolved prior to fragmentation to eliminate chimeric spectra that compromise identification accuracy. In this work we also explored the benefits of combining HRIM and quadrupole isolation to achieve improved specificity. This approach, known as DIA-PAMAF mode, further reduces the frequency of chimeric fragmentation spectra, and enabled the detection of over 8,000 protein groups from a HeLa digest analysis. PAMAF mode brings a powerful new technique to the field of proteomics that has the potential to improve the sensitivity and selectivity of mass spectrometry-based proteomics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/704896v1_ufig1.gif" ALT="Figure 1"> View larger version (114K): org.highwire.dtl.DTLVardef@7984c3org.highwire.dtl.DTLVardef@1fb2fe3org.highwire.dtl.DTLVardef@50d35org.highwire.dtl.DTLVardef@1a62926_HPS_FORMAT_FIGEXP M_FIG C_FIG

Flexible and textile-integrated electrochemical systems offer a convenient, user-friendly and non-invasive platform for continuous biochemical monitoring. In this study, a fully flexible and low-profile electrochemical system was developed by fabricating both the glucose biosensor and a compact potentiostat implemented on a polyimide (PI) filament circuit. The glucose biosensor was realized via direct laser writing (DLW), enabling precise electrode patterning and seamless integration with the potentiostat filament circuit. The integrated system exhibited a linear chronoamperometric response to glucose concentrations ranging from 0 to 0.25 mM in artificial sweat (AS). Further evaluation on cotton textiles soaked in AS and under mechanical bending confirmed stable performance, flexibility, and robustness. These findings highlight the potential of the PI-based potentiostat-sensor system for wearable, textile-integrated glucose monitoring and broader healthcare applications.

Extracellular vesicles (EVs) are promising biomarkers, yet their proteomic analysis from plasma is hampered by low abundance and co-purification of contaminants (e.g., lipoproteins, platelets) and technical variability, particularly in small-volume animal models. We developed and validated a modular protocol integrating Size Exclusion Chromatography (SEC) with Strong Anion Exchange (SEC-SAX) specifically tailored for quantitative LC-MS proteomics from small starting volumes (150 l of plasma). SEC alone successfully removed 99% of Albumin, and the SAX step significantly enriched EVs over contaminating lipoproteins. Downstream single pot solid phase enhanced (SP3) sample prep and STAGE tip solid phase extraction ensured maximum proteome depth. Critical confounding factors were objectively assessed: Platelet Factor 4 (PF4) was confirmed as a highly sensitive platelet marker, confirming the necessity of meticulous plasma preparation. Sample hemolysis impacted the plasma EV proteome data. As such, an objective measure (nanodrop spectrophotometer) of hemolysis and exclusion of hemolysed samples (heme >0.3 mg/ml) is recommended. The protocol is applicable to both human and mouse plasma as demonstrated by EV enrichment and quantification of biomarker proteins associated with neurodegenerative diseases from eight individual mouse plasma samples. Manuscript HighlightsO_LIDevelopmental workflow for a quantitative SEC-SAX protocol for EV proteomics from small plasma volumes (150 l). C_LIO_LIA range of variables tested including SAX beads amount, digestion buffer, digestion time, STAGE tip solid phase extraction, SAX elution buffer and sample filtration. C_LIO_LIThe SAX step significantly enhances EV proteome depth by increasing EV purity in relation to ApoB lipoproteins. C_LIO_LIShows the impact of the major confounding factors of sample hemolysis and platelet contamination on the EV proteome. C_LIO_LIPlatelet contamination increases the number and abundance of proteins detected including known disease biomarkers and sample hemolysis is associated with proteins derived from platelet and red blood cell derived EVs. C_LIO_LIPlatelet Factor 4 (PF4) is identified and confirmed as a sensitive marker for platelet contamination. C_LIO_LIApplicable to both human and mouse plasma. C_LI

ObjectiveGeneration and testing of IGF1 reference materials (RM), suitable for the harmonization of immunoassay (IA) and LC-MS/MS methods for the IGF1 determination in blood. In addition, establishment of age related reference intervals for men and women. MethodsIn a split sample study of 42 patients, and 30 healthy volunteers we tested the commutability of four RMs for IGF1, using four commercial IAs and an LC-MS/MS method. A new set of age dependent reference intervals was established using Lifelines biobank samples, based on the IGF1 LC-MS/MS method. ResultsThe four RMs were found to be commutable, except the RM with the lowest concentration measured with the Siemens Immulite method. The value assignment of the RMs was based on the IGF1 LC-MS/MS method, which was calibrated against WHO international standard 02/254. LC-MS/MS results were on average about 0 to 60% lower than those of the immunoassays. Combining the recalculated IGF1 results in patient samples from a former study with the data from healthy volunteers in this study, showed a reduction in the variation of the data points (standard error of estimate) of 42% and 62% respectively. ConclusionCommutable RMs for IGF1 can be made from serum of healthy blood donors. However, it remains necessary to test the commutability of these RMs in IAs that were not included in this study. By harmonizing methods using the four RMs, the same age-related reference intervals can be used.

We report a 0.18 {micro}m CMOS lab-on-a-chip system that monolithically integrates a passive radio frequency identification (RFID) interface and an 8 x 8 array of capacitance sensors configured for measuring the capacitance change resulting from an overlying biological specimen. This lab-on-CMOS platform is designed to operate wirelessly, first in a harvesting mode in which on-chip power is generated via the inductive coupling of an on-chip antenna to an external antenna, and second, in a sense-and-transmit mode where the capacitance sensor array is scanned and the measured data are transmitted to the external antenna using the same on-chip antenna. This paper presents characterization results of the passive RFID interface and of the sensor core, the latter utilizing several test analytes. The proposed system will facilitate the integration and packaging of a large number of chips in wet environments, paving the way for the inclusion of lab-on-CMOS technology in standard bio-analytical lab practice.

Peptide-level analyses are becoming increasingly popular in mass spectrometry-based proteomics and are being applied, for example, in immunopeptidomics, structural proteomics, and analyses of post-translational modifications. In such analyses, peptides that are not biologically meaningful but instead arise as artifacts prior to mass spectrometry analysis pose the risk of data misinterpretation. Here, we describe an approach based on retention time analysis and precise chromatographic peak matching to identify peptides generated by in-source fragmentation (ISF), which occurs between chromatographic separation of peptide mixtures and the first mass filter of a tandem mass spectrometer (MS). To understand the prevalence and properties of ISF, we generated 13 proteomics datasets and analyzed them along with additional 25 previously published datasets spanning a broad range of sample types, MS, and proteomics approaches including classical bottom-up proteomics, immunopeptidomics, structural proteomics, and phosphoproteomics. We found that, in typical trypsin-digested samples on average 1 % of fully-tryptic peptides and 22 % of semi-tryptic peptides originated from ISF. However, we observed large variations between datasets, and in-source fragments exceeded, in some cases, a third of the total peptide identifications. The extent of ISF was dependent on the peptide sequence, the instrument, method parameters, and sample complexity. Although ISF did not impair relative quantification across samples, it generated peptides that could be misinterpreted qualitatively, inflated peptide identifications, and comprised up to 37 percent of peptides shorter than 9 amino acids in immunopeptidomics datasets. We propose that, for peptide-centric applications, our open-source ISF detection approach be used to re-annotate peptides generated by ISF and remove them to avoid misinterpretation of data. ISF is an increasing concern with improving mass spectrometers, as they enable detection of an ever-increasing number of m/z features, including low abundance features like ISF products. Our work thus addresses a growing issue in proteomics and presents solutions to mitigate the impact of in-source fragment peptides. In the future, improved feature detection algorithms may enable elucidation of new ISF patterns affecting side chains that have been missed so far, which could contribute to explaining the vast space of as-yet unannotated proteomics data.