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

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.

Luminescence is a powerful method for detecting trace analytes and monitoring biological processes. However, most bioluminescence reagents, including luciferase and its substrates, are sensitive to temperature, limiting their useable shelf lives, and resulting in inconsistent performance. Enhancing the stability of these reagents could improve data quality, simplify workflows, and address cold chain storage issues. In this study, we demonstrate the application of the platform stabilization technology, capillary-assisted vitrification (CAV), as a tool to stabilize different luciferases and their substrates, and the application of the stabilized reagents in both in vitro and in vivo bioluminescent assays. We demonstrate that CAV-stabilized reagents can be stored and shipped ambiently, maintain consistent performance over time, and are suitable for use in cell viability quantification, tumor monitoring, in vivo imaging, microbial detection, and immunoassays. Additionally, different reagents can be co-formulated to make ready-to-use assay kits that can also be shipped and stored ambiently. Our results demonstrate that CAV stabilization is a viable alternative to traditional storage methods, with broad potential to improve bioluminescence workflows.

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

Efficient and reproducible protein extraction is a critical step in mass spectrometry-based proteomics workflows, particularly for complex host-pathogen systems where low-abundance immune-associated proteins are difficult to detect. Probe sonication methods used for cell lysis requiring mitigation of excessive heat generation, to prevent degradation of biologically important proteins, while also limiting throughput and potentially introducing sample-to-sample variability. In this study, we evaluated adaptive focused acoustics (AFA) technology as an alternative approach for macrophage lysis and protein extraction and digestion within a standard proteomics workflow coupled with mass spectrometry. We observed that AFA technology reduced hands-on processing times and overall workflow timelines and single-sample AFA technology improves proteome coverage, dynamic range, and reproducibility. We also evaluated multiplexed AFA technology for lysis, and we observed an exclusive macrophage proteome and influence on replicate reproducibility and dynamic range detection for low abundant proteins. Moreover, multiplexed AFA technology for macrophage lysis and digestion further increased protein identifications, replicate reproducibility, and dynamic range. Considering the AFA-exclusive proteome, 86 proteins were detected across all AFA-based lysis and digestion methods, including low-abundance proteins associated with macrophage homeostasis, inflammatory response, and transport. Together, these findings demonstrate that AFA technology enhances reproducibility, throughput, and proteome depth for macrophage protein extraction while enabling the detection of biologically relevant low-abundance immune-associated proteins. These improvements provide a strong foundation for future investigation of host-pathogen infection models, where pathogen-derived proteins remain challenging to detect within complex host proteomes.

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.

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.

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.

Microfluidic devices enable high-throughput sample processing with remarkable parallelization and miniaturization. While fluorescence microscopy provides a convenient method for reading out signal from microfluidic assays, commercially-available microscopes impose a fundamental tradeoff between temporal resolution, spatial resolution, and numerical aperture (NA). Spatially tiled imaging enables high-resolution and high-NA imaging over a large area but reduces temporal resolution. Conversely, low magnification, low NA imaging captures large areas in one shot, but typically sacrifices spatial resolution and fluorescence sensitivity. To address this, we introduce an automated transfluorescence tandem-macro-lens optomechanical system (macroscope) capable of sensitive, multi-channel fluorescence imaging over a very large field of view (34 mm diameter, 740 mm2), with resolution determined by the sensor pixel size. We demonstrate bright-field resolution of low-micron features and detection of low-to mid-nanomolar concentrations of common fluorophores within microfluidic device channels. To demonstrate the utility of this macroscope, we image enzyme turnover within valved microfluidic devices (the HT-MEK system, for High-Throughput Microfluidic Enzyme Kinetics) and achieve >50-fold increased temporal resolution over common commercial instruments while maintaining high sensitivity. This macroscope imaging solution costs substantially less than commercially available alternatives, providing a powerful new imaging approach for microfluidic applications requiring sensitive and rapid wide-field fluorescence imaging.

SignificanceIndocyanine green (ICG) is the most widely used fluorophore in fluorescence-guided surgery (FGS), yet its spectral response depends on microenvironment, with implications for system design, inter-system comparisons, and phantom development. AimTo characterize ICG with excitation-emission matrices (EEMs) in the microenvironments of dimethyl sulfoxide (DMSO), bovine serum albumin (BSA) solutions, and 3D-printed (3DP) resin, and assess excitation-dependent emission, including red-edge excitation shifts (REES) and departures from Kashas rule of excitation-independent emission. ApproachEEMs and absorbance spectra were acquired with extracted excitation spectra, emission spectra, emission peaks, centroids, and integrated emission areas under the curve (AUCs). Concentration-dependent behavior was examined in DMSO, and albumin concentration dependence was assessed from 5-100 mg/mL. Data processing employed robust local regression to mitigate excitation scattering artifacts. ResultsICG in DMSO exhibited excitation-independent emission consistent with Kasha-Vavilov behavior. In contrast, ICG in BSA solution and 3DP resin displayed excitation-dependent emission with pronounced REES and additional non-linear departures from Kashas rule. To our knowledge, this represents the first documentation of REES and broader anti-Kasha effects for ICG or any FGS fluorophore. Within the excitation range most relevant to ICG-FGS ([~]760-805 nm), emission spectra of the BSA solution and 3DP resin overlapped closely, with similar AUC-based comparisons, suggesting that ICG in 3DP resin can serve as a suitable surrogate reference for albumin-bound ICG. ConclusionsThe EEM characterization shows that excitation-dependent behavior is a defining feature of ICG in biologically relevant environments, demonstrating that emission cannot be assumed to follow classical Kasha-Vavilov behavior. Reliable comparisons and imaging system design therefore require spectra acquired at defined excitation wavelengths with AUC integration within the emission detection band. Excitation-specific spectra from EEMs establish a consistent framework for inter-system comparisons and phantom standards, while the resulting datasets provide a practical reference for addressing excitation-dependent behavior in ICG sensing applications.

With the transition toward continuous bioprocessing, process analytical technology (PAT) is becoming necessary for rapid and reliable in-process monitoring during biotherapeutics manufacturing. Bioprocess 4.0 is looking to build an end-to-end bioprocesses that includes PAT-enabled real-time process control. This is especially important for drug product quality attributes that can change during bioprocessing, such as protein N-glycosylation, a critical quality attribute for most monoclonal antibody (mAb) therapeutics. Glycosylation of mAbs is known to influence their efficacy as therapeutics and is regulated for a majority of mAb products on the market today. Currently, there is no method to truly measure N-glycosylation using on-line PAT, hence making it impractical to design upstream process control strategies. We recently described the N-GLYcanyzer: an integrated PAT unit that measures mAb N-glycosylation within 3 hours of automated sampling from a bioreactor. Here, we integrated Agilents Instant PC (IPC) based chemistry workflow into the N-GLYcanzyer PAT unit to allow for nearly 10x faster mAb glycoforms analysis. Our methodology is explained in detail to allow for replication of the PAT workflow as well as present a case study demonstrating use of this PAT to autonomously monitor a mammalian cell perfusion process at the bench-scale to gain increased knowledge of mAb glycosylation dynamics during continuous biomanufacturing of biologics using Chinese Hamster Ovary (CHO) cells. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=169 SRC="FIGDIR/small/521623v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@16cf139org.highwire.dtl.DTLVardef@150db0borg.highwire.dtl.DTLVardef@15cbe36org.highwire.dtl.DTLVardef@1cbdf6a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Analytes during their journey from their natural sources to their identification and quantification are prone to adsorption to surfaces before they enter an analytical instrument, causing false quantities. This problem is especially severe in diverse omics. Here, thousands of analytes with a broad range of chemical properties and thus different affinities to surfaces are quantified within a single analytical run. For quantifying adsorption effects caused by surfaces of sample handling tools, an assay was developed, applying LC-MS/MS-based differential bottom-up proteomics and as probe a reference mixture of thousands of tryptic peptides, covering a broad range of chemical properties. The assay was tested by investigating the adsorption properties of several vials composed of polypropylene, including low-protein-binding polypropylene vials, borosilicate glass vials and low-retention glass vials. In total 3531 different peptides were identified and quantified across all samples and therefore used as probes. A significant number of hydrophobic peptides adsorbed on polypropylene vials. In contrast, only very few peptides adsorbed to low-protein-binding polypropylene vials. The highest number of peptides adsorbed to glass vials, driven by electrostatic as well as hydrophobic interactions. Calculation of the impact of the adsorption of peptides on differential quantitative proteomics showed significant false results. In summary, the new assay is suitable to characterize adsorption properties of surfaces getting into contact with analytes during sample preparation, thereby giving the opportunity to find parameters for minimizing false quantities. Insert Table of Contents artwork here O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/551632v3_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@bafb12org.highwire.dtl.DTLVardef@1b98219org.highwire.dtl.DTLVardef@c3b45org.highwire.dtl.DTLVardef@1074180_HPS_FORMAT_FIGEXP M_FIG C_FIG