The Analyst

Raman spectroscopy is a popular tool for characterizing complex biological materials and their geological remains1-10. Ordination methods, such as Principal Component Analysis (PCA), rely on spectral variance to create a compositional space1, the ChemoSpace, grouping samples based on spectroscopic manifestations that reflect different biological properties or geological processes1-7. PCA allows to reduce the dimensionality of complex spectroscopic data and facilitates the extraction of relevant informative features into data formats suitable for downstream statistical analyses, thus representing an essential first step in the development of diagnostic biosignatures. However, there is presently no systematic survey of the impact of sample, instrument, and spectral processing on the occupation of the ChemoSpace. Here the influence of sample count, signal-to-noise ratios, spectrometer decalibration, baseline subtraction routines, and spectral normalization on ChemoSpace grouping is investigated using synthetic spectra. Increase in sample size improves the dissociation of sample groups in the ChemoSpace, however, a stable pattern in occupation can be achieved with less than 10 samples per group. Systemic noise of different amplitude and frequency, features that can be introduced by instrument or sample11,12, are eliminated by PCA even when spectra of differing signal-to-noise ratios are compared. Routine offsets ({+/-} 1 cm-1) in spectrometer calibration contribute to less than 0.1% of the total spectral variance captured in the ChemoSpace, and do not obscure biological information. Standard adaptive baselining, together with normalization, increase spectral comparability and facilitate the extraction of informative features. The ChemoSpace approach to biosignatures represents a powerful tool for exploring, denoising, and integrating molecular biological information from modern and ancient organismal samples.

The estimation of the biological sex of archeological remains is crucial information in bioarchaeology and forensic anthropology. In recent years, proteomics based on molecular sexual dimorphism have emerged as a preferred method, particularly because of its minimally-invasive approach to extracting amelogenin X and Y proteins from tooth enamel. However, there is an increasing demand to accelerate this process while facilitating the analysis of large archaeological assemblages. This study presents a novel high-throughput targeted paleoproteomics method for biological sex estimation using MALDI-CASI-FTICR mass spectrometry. This approach combines the strengths of existing methods, including ultra-high resolution, significantly reduced processing times, targeted analysis, and scalability to large archaeological sample sets. The method was initially validated on modern individuals with known sex and subsequently applied to 130 adult and juvenile individuals from medieval Great Moravia (present-day Czech Republic). Biological sex was successfully estimated for all but one of the individuals. The results not only provide a more efficient biological sex estimation but also help to resolve a few errors in sex assessment previously encountered with osteomorphological and tooth morphometric techniques. The implementation of this method significantly improves the accuracy and efficiency of biological sex estimation, offering a powerful tool for anthropological research. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/706309v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1ede7e6org.highwire.dtl.DTLVardef@13d2f5org.highwire.dtl.DTLVardef@17ee44dorg.highwire.dtl.DTLVardef@1be9dd9_HPS_FORMAT_FIGEXP M_FIG C_FIG

Tumour biomarkers such as CA125, CA15-3, CA19-9, AFP and CEA are routinely used in the oncology clinic to diagnose cancer, monitor response to therapy, and detect relapse. However, their quantification depends on immunoassay-based methods that are time-consuming, reagent-dependent, and poorly suited to resource-limited settings. Here, we present a machine learning-assisted ATR-FTIR spectroscopy approach for label-free tumour biomarker analysis to enable simple and rapid quantification at the bedside. Using principal component analysis (PCA), we first demonstrate that these five clinically relevant biomarkers are spectrally separable, with the protein-associated region (1200-1700 cm-1) providing the greatest discriminative information. We then develop partial least squares regression (PLSR) models to quantify CA125 in phosphate-buffered saline (R2 = 0.95) and in human serum across a clinically relevant concentration range, achieving reliable predictions at and above the clinical decision threshold of 35 U/mL. A semi-quantitative classification model further demonstrated robust identification of elevated CA125, with a macro-average sensitivity of 0.86 and specificity of 0.92. These results support ATR-FTIR spectroscopy as a rapid, reagent-free platform for cancer biomarker monitoring, with potential utility in resource-limited settings. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/714840v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1be9c03org.highwire.dtl.DTLVardef@f49e5eorg.highwire.dtl.DTLVardef@1c93e39org.highwire.dtl.DTLVardef@1141e6f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Metal homeostasis is a complex process wherein essential metals serving structural, catalytic and regulatory roles are acquired, trafficked, and exported once they are present in excess. Understanding changes in metal content and localization in heterogenous tissue types is critical to understanding fundamental physiology as well as a growing number of disease states. Laser ablation inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOF-MS) imaging is a powerful technique for untargeted quantitation and mapping of metals in biological systems. While the nematode Caenorhabditis elegans (C. elegans) is a well-established model organism for fundamental biological research and metal-based diseases, there have been few reports of mass spectrometry-based imaging of C. elegans, mostly due to challenges preparing samples that maintain the native distribution of the elements. In this study, we developed an embedding, quantitation and imaging workflow that preserves C. elegans using 3D-printed uniform layer media application tools (ULMATs). Multiple embedding media were evaluated, and petrolatum, commercially known as Vaseline, stood out for its performance in preserving C. elegans for imaging applications. Worms were subjected to microscopy and LA-ICP-TOF-MS imaging where we achieved a 2-m spatial resolution by over-sampling laser shots during ablation. Quantitative elemental maps were obtained using a series of gelatin standards that were sectioned at a 40-m thickness to closely mimic the average tissue ablation depth of a Day 1 gravid adult C. elegans. Our results establish a new workflow for comprehensive elemental profiling of C. elegans using LA-ICP-TOF-MS, which holds high potential for future spatial metal biology research with C. elegans. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/698490v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@7c66c5org.highwire.dtl.DTLVardef@13f4934org.highwire.dtl.DTLVardef@1df3215org.highwire.dtl.DTLVardef@512fd2_HPS_FORMAT_FIGEXP M_FIG C_FIG

We thank ODonnell et al, for their comments on our contribution and are grateful to be afforded this opportunity to formally respond to their critique24. We are surprised by the authors assertion relating to the biological relevance of SPM because a simple literature search for related terms such as resolvin in PubMed yields an abundance (>1,420 publications) of evidence supporting the potent biological activities and the diagnostic potential of some of these mediators. Several co-authors of the ODonnells et al manuscript, have published on the resolvins and SPMs, including some publications within recent weeks. Importantly, ODonnell et al, misreport as well as mis-apply criteria for peak identification reported in the Gomez et al, publication which lead to the flawed analysis they performed. In this response therefore, we provide a step-by-step clarification of the methodologies used in Gomez et al, and a side-by-side comparison of the underlying data to clarify any confusion. We also demonstrate that using the orthogonal criteria discussed by ODonnell et al, we obtain essentially identical results thus providing additional validation of our techniques and support the conclusions.

Metabolomics is an important approach for studying complex biological systems. Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming a mainstream strategy but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process which is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (Standard Curve Application for determining Linear Ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signal data into absolute quantitative data (https://www.lewisresearchgroup.org/software). The algorithm used in SCALiR automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from their LC-MS signal. Using a standard mix containing 77 metabolites, we found excellent correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99) and that SCALiR reproducibly calculated concentrations of mid-range standards across ten analytical batches (average coefficient of variation 0.091). SCALiR offers users several advantages, including that it (1) is open-source and vendor agnostic; (2) requires only 10 seconds of analysis time to compute concentrations of >75 compounds; (3) facilitates automation of quantitative workflows; and (4) performs deterministic evaluation of compound quantification limits. SCALiR provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

Same-tissue section multimodal imaging is a powerful strategy that spatially profiles tissue histology, cell populations and molecular composition while maximizing tissue economy, preserving spatial molecular relationships, and increasing co-registration capacity. However, performing multiple modalities on the same tissue section can destroy or chemically alter the tissue, compromising downstream data. Here, we systematically assess integration of picrosirius red staining, hematoxylin and eosin staining, and multiplexed immunofluorescence into N-glycan and extracellular matrix peptide matrix-assisted laser/desorption ionization imaging mass spectrometry (IMS) workflows. We evaluate alterations in tissue morphology, stain efficiency, IMS feature intensity as well as IMS feature localization after upstream modality integration. We propose an optimized multimodal sequence that maximizes data quality and follows a very specific order of: autofluorescence microscopy, multiplexed immunofluorescence, picrosirius red staining, N-glycan IMS, hematoxylin and eosin staining, and extracellular matrix peptide IMS. Overall, this work develops an optimized multimodal workflow that comprehensively images tissue morphology, collagen fibers, and cell populations at single-cell resolution as well as multiplexed N-glycan composition and multiplexed extracellular matrix peptides with post-translational modification status from a single tissue section.

D-amino acids are unique and essential signaling molecules in neural, hormonal, and immune systems. However, the presence of D-amino acids and their recruitment in early animals is mostly unknown due to limited information about prebilaterian metazoans. Here, we performed the comparative survey of L-/D-aspartate and L-/D-glutamate in representatives of four phyla of basal Metazoa: cnidarians (Aglantha); placozoans (Trichoplax), sponges (Sycon) and ctenophores (Pleurobrachia, Mnemiopsis, Bolinopsis, and Beroe), which are descendants of ancestral animal lineages distinct from Bilateria. Specifically, we used high-performance capillary electrophoresis for microchemical assays and quantification of the enantiomers. L-glutamate and L-aspartate were abundant analytes in all species studied. However, we showed that the placozoans, cnidarians, and sponges had high micromolar concentrations of D-aspartate, whereas D-glutamate was not detectable. In contrast, we found that in ctenophores, D-glutamate was the dominant enantiomer with no or trace amounts of D-aspartate. This situation illuminates prominent lineage-specific diversifications in the recruitment of D-amino acids and suggests distinct signaling functions of these molecules early in the animal evolution. We also hypothesize that a deep ancestry of such recruitment events might provide some constraints underlying the evolution of neural and other signaling systems in Metazoa. HighlightsO_LID-amino acids are essential for intercellular signaling and evolution C_LIO_LIEnantiomers have been quantified in early-branching animals C_LIO_LILineage-specific recruitment of D-glutamate could occur in ctenophores C_LIO_LID-aspartate is one of the primary enantiomers in other metazoans C_LIO_LIDeep ancestry of such events could provide constraints in the evolution of signaling C_LI Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY D-amino acids are essential for intercellular signaling. Direct microchemical quantification of enantiomers in representatives of early-branching animals suggests lineage-specific recruitments of D-glutamate and D-aspartate. Deep ancestry of such events might provide some constraints underlying the evolution of neural and other signaling systems in Metazoa. C_FIG_DISPLAY

Accurate biological sex estimation is a key objective in archaeological and bioanthropological research but remains challenging when skeletal remains are fragmented, juvenile, or poorly preserved. Paleoproteomics approaches based on the detection of sex-specific amelogenin peptides (AMELX/AMELY) have emerged as a powerful alternative to osteological and genetic methods. However, current workflows often lack standardized criteria for peptide-level confidence assessment, potentially affecting the reproducibility and reliability of sex assignments. In this study, I evaluated the impact of peptide-level confidence filtering on paleoproteomics-based sex estimation through the reanalysis of 164 Homo sapiens individuals from 10 published datasets and 26 Bos taurus individuals from 3 datasets, spanning contexts from the Pleistocene to the present. To address methodological inconsistencies, I developed SexPeptID, an R/Shiny-based framework that integrates Posterior Error Probability (PEP) filtering, standardized peptide selection, and explicit uncertainty assessment. Application of SexPeptID revealed that peptide-level filtering substantially affects sex assignment outcomes: 17 previously classified males (10.4%) were reclassified as non-conclusive, while 5 individuals (3.1%) were identified as potentially female. Despite this sensitivity, AMELX/AMELY-based sex estimation remained robust overall, with stable signal ratios observed across archaeological periods. Variability in peptide intensities was primarily associated with dataset-specific factors rather than temporal differences, highlighting the influence of analytical workflows and preservation conditions. By incorporating confidence-based filtering and a non-conclusive classification category, SexPeptID improves the transparency, reproducibility, and reliability of palaeoproteomics sex estimation, providing a standardized framework for future archaeological and bioanthropological studies. HighlightsO_LISexPeptID provides a reproducible framework for amelogenin-based sex estimation. C_LIO_LIPeptide-level confidence filtering significantly affects paleoproteomics sex estimates. C_LIO_LI13.4% of published male assignments were revised after confidence filtering. C_LIO_LIAMELX/AMELY ratios show temporal stability from modern to Pleistocene samples. C_LIO_LIStandardized uncertainty assessment strengthens palaeoproteomics inference. C_LI

AbstractGaining control of existing biomanufacturing chassis organisms, such as Escherichia coli K12, and novel isolates, such as the salt tolerant Halomonas bluephagenesis sp TD01 studied here may be facilitated by the investigation and monitoring of their metabolic and regulatry processes, particularly through proteomics. Here we consider the performance of a range of typically available proteomics platforms across a range of price points to map chasis organisms metabolic pathways. A set of model bacterial samples was prepared from E. coli and H. bluephagenesis sp. TD01 in 1:2 and 2:1 ratios and analysed using five LC-MS systems. Data from the timsTOF HT, Exploris 480, ZenoTOF 7600 and Select Series MRT were processed through DIANN and MSStats. Data from the Vion was processed through Skyline then MSstats. Of the 8,222 proteins identified across all samples analysed (4,401 proteins from E. coli; 3,821 from Halomonas sp. TD01), the TimsTOF and Exploris were able to achieve extensive proteome coverage quantifying 5.5k and 5k proteins respectively, with the ZenoTOF, Waters MRT and the legacy Waters Vion respectively quantifying 3.5k, 1.3k, and [~]850 proteins at 1% FDR. Proteins comprising the pathways of chassis organisms core metabolism critical to biomanufacturing were quantified with all instruments, demonstrating suitability of these platforms to explore their manipulation in the context of biomanufacturing.

Volatile organic compounds (VOCs) are a biologically important subset of an organisms metabolome, yet in vitro techniques for the analysis of these small molecules vary substantially in practice, restricting the interpretation and reproducibility of study findings. Here, we present an engineered culture tool, termed the "Biodome", designed to enhance analyte sensitivity by integrating dynamic headspace sampling methodology for the recovery of VOCs from viable biological cultures. We validate the functionality of the device for in vitro volatile metabolomics utilizing computational modeling and fluorescent imaging of mammalian cell culture. We then leverage comprehensive two-dimensional gas chromatography coupled with a time-of-flight mass spectrometer and the enhanced sampling capabilities afforded by our tool to identify seven VOCs not found in the media or exogenously derived from the sampling method (typical pitfalls with in vitro volatilome analysis). We further work to validate the endogenous production of these VOCs using two independent approaches: (i) glycolysis-mediated stable isotopic labeling techniques using 13C6-D-glucose and (ii) RNA interference (RNAi) to selectively knockdown {beta}-oxidation via silencing of CPT2. Isotope labeling reveals 2-Decen-1-ol as endogenously derived with glucose as a carbon source and, through RNAi, we find evidence supporting endogenous production of 2-ethyl-1-hexene, dodecyl acrylate, tridecanoic acid methyl ester and a low abundance alkene (C17) with molecular backbones likely derived from fatty acid degradation. To demonstrate applicability beyond mammalian cell culture, we assess the production of VOCs throughout the log and stationary phases of growth in ampicillin-resistant DH5 Escherichia coli. We identified nine compounds with results supporting endogenous production, six of which were not previously associated with E. coli. Our findings emphasize the improved capabilities of the Biodome for in vitro volatile metabolomics and provide a platform for the standardization of methodology.

Drug discovery continues to face a staggering 90% failure rate, with many setbacks occurring during late-stage clinical trials. To address this challenge, there is an increasing focus on developing and evaluating new technologies to enhance the "design" and "test" phases of antibody-based drugs (e.g., monoclonal antibodies, bispecifics, CAR-T therapies, ADCs) and biologics during early preclinical development, with the goal of identifying lead molecules with a higher likelihood of clinical success. Artificial intelligence (AI) is becoming an indispensable tool in this domain, both for improving molecules identified through traditional approaches and for the de novo design of novel therapeutics. However, critical bottlenecks persist in the "build" and "test" phases of AI-designed antibodies and protein binders, impeding early preclinical evaluation. While AI models can rapidly generate thousands to millions of putative drug designs, technological and cost limitations mean that only a few dozen candidates are typically produced and tested. Drug developers often face a tradeoff between ultra-high-throughput wet lab methods that provide binary yes/no binding data and biophysical methods that offer detailed characterization of a limited number of drug-target pairs. To address these bottlenecks, we previously reported the development of the Sensor-integrated Proteome On Chip (SPOC(R)) platform, which enables the production and capture-purification of 1,000 - 2,400 folded proteins directly onto a surface plasmon resonance (SPR) biosensor chip for measuring kinetic binding rates with picomolar affinity resolution. In this study, we extend the SPOC technology to the expression of single-chain antibodies (sc-antibodies), specifically scFv and VHH, and dual-chain Fab constructs. We demonstrate that these proteins are capture-purified at high levels on SPR biosensors and retain functionality as shown by the binding specificity to their respective target antigens, with affinities comparable to those reported in the literature. SPOC outputs comprehensive kinetic data including quantitative binding (Rmax), on-rate (ka), off-rate (kd), affinity (KD), and half-life (t1/2), for each of thousands of on-chip sc-antibodies. Additionally, we present a case study showcasing single amino acid mutational scan of the complementarity-determining regions (CDRs) of a HER2 VHH (nanobody) paratope. Using 92 unique mutated variants from four different amino acid substitutions, we pinpoint critical residues within the paratope that could further enhance binding affinity. This study serves as a demonstration of a novel high-throughput approach for biophysical screening of hundreds to thousands of single chain antibody sequences in a single assay, generating high affinity resolution kinetic data to support antibody discovery and AI-enabled pipelines.

Single-cell metabolomics remains analytically challenging due to the low abundance and chemical diversity of metabolites in individual cells. We have developed complementary microflow HILIC and ion pair LC-MS methods to expand metabolite coverage in single macrophages. Ion pair LC-MS was applied to single cells for the first time, enabling retention of highly polar and ionic metabolites that elute early under conventional reversed-phase conditions. Across Mycobacterium bovis BCG infected, uninfected bystander, and control unexposed THP-1 macrophages, both microflow methods detected significantly more features than a previously reported analytical-flow HILIC method. The two microflow methods provided complementary chemical space, together yielding 633 unique named metabolites with MS2 spectra. This depth enabled pathway-level interpretation at single-cell resolution, revealing infection-associated changes in purine-, arginine-, glutathione-, and one-carbon folate-associated metabolism. Metabolite-level interrogation indicated shared purine and amino acid changes in both infected and neighbouring macrophages, while revealing a distinct bystander phenotype characterised by elevated glycine and heterogeneous ATP levels. Finally, we demonstrate sequential IP and HILIC analysis of the same single cell, establishing a route toward maximal coverage from individual cells. These results position microflow HILIC and IP LC-MS as powerful, orthogonal strategies for advancing single-cell metabolomics and unveiling heterogeneity within complex biological microenvironments. Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/733771v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@d0d02dorg.highwire.dtl.DTLVardef@11364d5org.highwire.dtl.DTLVardef@40e1a1org.highwire.dtl.DTLVardef@19d24a5_HPS_FORMAT_FIGEXP M_FIG Figure made in BioRender. C_FIG

The identification and subsequent characterization of unknown analytes using mass spectrometry presents a long-standing challenge across many research fields, particularly when analyte levels are low and the compound class is underrepresented in mass spectral databases. We have developed a data analysis workflow for investigating classes of small molecules and demonstrated its application through the reanalysis of data collected to probe for modified nucleoside MR1-presented antigens. We reanalyzed the datasets to screen for additional classes of compounds within the MR1 ligandome using Compound Discoverer, a commercial software package designed for metabolomic analysis, featuring fragmentation filtering nodes, molecular networking, and spectral database searching. Our study identified two compound classes that bind to MR1. One class includes compounds characterized by the presence of a ribityl substructure and molecular formulas consistent with structural similarity to riboflavin, where the most abundant compound differs from riboflavin by two additional oxygen atoms and one fewer carbon atom. A second class comprises an adenosine monophosphate isomer and larger analytes that are putatively identified as consisting of di- and tri-covalently bound nucleotides. The application of our analytical approach to characterize the MR1 ligandome demonstrates the power of combining compound-class fragmentation, molecular networking, and mass spectral database searching in exploring receptor ligandomes and, more generally, identifying novel classes of compounds.

Polar metabolites, including amino acids, nucleotides, phosphorylated metabolites, and central carbon intermediates, drive essential physiological processes but remain difficult to measure by high-throughput, reversed-phase LC-MS due to poor retention on conventional stationary phases. We developed a 3-minute reversed-phase LC-MS method and benchmarked T3-type C18 and pentafluorophenyl (PFP) chemistries for profiling 123 polar metabolites across acidic and mildly acidic conditions. The T3 phase operated under mildly acidic conditions provided the best overall performance, achieving the highest coverage, robust retention-time stability, and improved detection of phosphorylated metabolites. To strengthen compound annotation under ultra-short gradients, we combined the method with iterative data-dependent MS/MS, acquiring spectra for 86 of 123 metabolite mixture compounds without extending runtime. Retention times and peak shapes remained stable over 480 consecutive injections (mean CV of 1.7%) in Escherichia coli extract. Together, these results define a rapid, scalable workflow for profiling of polar and phosphorylated metabolites on standard instrumentation for high-throughput biological studies.

Cancer surgery is a fundamental component of oncology treatment, its quality significantly impacts patient outcomes, influencing both relapse rates and survival. However, achieving this customization is contingent upon early collection of robust molecular data during surgery, providing accurate information for diagnosis, prognosis, and delineating surgical margins. The introduction of digital twin (DT) technology has recently opened a new era of precision and effectiveness in cancer surgery. Expanding from its successful implementations in the industrial sector, DT concept has evolved into a highly promising breakthrough in healthcare. Therefore, our study goal is on creating DT by using accurate and high-throughput molecular data obtained through mass spectrometry imaging. We developed a machine-learning-based pipeline that allow to depict infiltration of cancer cells into normal tissue that offer precise delineation of tumor margins thanks to SpiderMass. This process also enables the prediction of relative presence of bacterial strains in tumoral and healthy mammary glands.

Dissolution dynamic nuclear polarization NMR Spectroscopy (dDNP-NMR) has become a transformative tool for metabolic studies by significantly enhancing signal sensitivity more than three orders of magnitude compared to traditional NMR. However, NMR detection probes are optimized for round narrow glass tubes typically 5 mm in diameter, which impose constraints on their utility for metabolic studies of adhernt cells. Here, we present a novel NMR probe head integrated with a custom microfluidic chip that facilitates real-time monitoring of hyperpolarized substrate conversion from adhernt cells. This system enables metabolic flux analysis in a controlled, in vitro environment, as demonstrated by tracking the conversion of [1-13C] pyruvate to [1-13C] lactate in HeLa cells over 48 hours. To the best of our knowledge, this is the first demonstration of cell metabolism from an adhering monolayer of mammalian cells in combination with hyperpolarized NMR. The custom microfluidic chip design is modular and adaptable allowing expansion to dual-chamber chips, demonstrating its potential in applications for more complex cellular environments, such as Organ-on-a-Chip systems.

Lung tissue is composed of various functional units, each essential for maintaining the intricate functions of the lung. Disruptions in the molecular and cellular mechanisms in the lung can cause tissue fibrosis, inflammation, and severe breathing difficulties, which are common in conditions such as bronchopulmonary dysplasia (BPD). BPDs molecular changes are not well understood, which hinders effective diagnosis and treatment. Here, we present a new multimodal imaging workflow for detailed molecular and metabolic characterization of tissues at multiple spatial scales. We applied a combined imaging approach using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and ultrafast focused light-based imaging & photonics platform (U-FLIP) that included two-photon fluorescence (TPF), second harmonic generation (SHG), and stimulated Raman scattering (SRS). We also developed a hierarchical multimodal registration network (HiMReg) for the precise co-registration of each modality. This approach revealed previously unknown metabolic changes in distinct functional tissue units affected by BPD, including altered lipid distributions, reduced optical redox states, and specific collagen remodeling in bronchioles. Our findings evidenced alterations in lipid composition and metabolism of BPD-affected alveoli compared to healthy tissue, providing novel insights into disease pathophysiology. Our findings elucidate the intricate spatial and molecular complexity of BPD, building on prior research that did not provide the spatial resolution necessary to capture the nuances of metabolic alterations. This multimodal approach offers exceptional insights into disease exploration and could transform the way we study spatially heterogeneous conditions. By providing detailed maps of the metabolic shifts occurring in distinct tissue microanatomical features, the methods developed here could enable the discovery of new therapeutic avenues, making it highly attractive for the field of biomedical research.

Multi-modal imaging analyses of dosed tissue samples can provide more comprehensive insight into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multi-modal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry, but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity co-registration with higher resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high spatial resolution microscopy image. As a proof of concept, our multi-modal workflow was applied to brain tissue extracted from a Sprague Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models including linear regression, partial least squares regression (PLS), random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 m spatial resolutions, a significant improvement compared to the original images acquired at 25 m spatial resolution. The predicted mass spectrometry images were then co-registered with an H&E image and IHC fluorescence image of the - opioid receptor to assess co-localization of corynantheidine with brain cells. Our study also provides insight into the different evaluation parameters to consider when utilizing image fusion for biological applications.

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