ACS Measurement Science Au最新文献

{"title":"Dynamic Additive Scanning for Precise Control in Electrospray Ionization Mass Spectrometry.","authors":"I-Ting Wu, Decibel P Elpa, Hsien-Ning Chien, Pawel L Urban","doi":"10.1021/acsmeasuresciau.5c00183","DOIUrl":"10.1021/acsmeasuresciau.5c00183","url":null,"abstract":"<p><p>In order to achieve optimum conditions of electrospray ionization (ESI) mass spectrometry (MS) methods, samples and mobile phases are often supplemented with acids, bases, supercharging reagents, and electrospray-friendly solvents. Typically, one pH or concentration of these additives is used in a given method, which is selected based on iterative optimization or literature. However, different pH values and concentrations of additives can be suitable for the analysis of different species, and subtle changes can bring different analytical information. Therefore, here we demonstrate a precise MS optimization system enabling dynamic scans of acid-base and additive concentrations in ESI-MS. In the case of low-molecular-weight analytes, MS signals can be enhanced by selecting the optimum conditions in single scans. The online acid-base scan showed enhancement factors of ∼5.4-7.9 for amino acids and related compounds, ∼44.7 for glutathione, and ∼4.5-10.3 for some tested phospholipids at 25%, 75%, and 90% base (stock solution volume ratio), respectively. For proteins, charge state distributions (CSDs) can be manipulated, bringing information on vulnerability of the protein tertiary structures to the changing environment. Multiple charging of cytochrome <i>c</i> and myoglobin was enhanced to varying degrees upon increasing concentrations of sulfolane and dimethyl sulfoxide, while increasing concentrations of organic solvents shifted CSDs to lower charge states. The setup for such measurements was constructed by using off-the-shelf components and by taking advantage of a simple Python code. Coupling online additive scans with ESI-MS streamlines optimization by eliminating the need for multiple sequential analyses of additives used to enhance signal intensity or induce supercharging.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"6 2","pages":"445-453"},"PeriodicalIF":4.6,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13087944/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Direct Visualization and Quantitative Analysis of Silica Nanoparticle Dispersion States in Saline Solution via Cryo-TEM.","authors":"Masato Iwasawa, Katsuya Miura, Masaya Kashihara, Hirotake Kitagawa, Masashi Ohno, Daisuke Unabara, Tasuku Hamaguchi, Koji Yonekura","doi":"10.1021/acsmeasuresciau.5c00158","DOIUrl":"10.1021/acsmeasuresciau.5c00158","url":null,"abstract":"<p><p>Quantitative assessment of nanoparticle dispersion in dilute, realistic solvent environments remains a critical challenge for materials intended for use in complex ionic systems, such as carbon dioxide underground and storage (CCS) and enhanced oil recovery (EOR) technologies. While conventional techniques, including dynamic light scattering (DLS) and small-angle X-ray scattering (SAXS), provide valuable ensemble-averaged information, they are particularly limited in dilute systems and lack the spatial resolution required to characterize local dispersion heterogeneities and microscopic agglomeration behavior. Here, we present a quantitative analytical framework that combines cryogenic transmission electron microscopy (cryo-TEM) with AI-integrated automated imaging and Voronoi tessellation analysis to directly visualize and quantify silica nanoparticle dispersion states in saline solutions. Silica nanoparticles functionalized with various organic acidsincluding malonic, succinic, maleic, DL-malic, and citric acids, as well as L-arabinosewere prepared and examined using cryo-TEM. For each frozen-hydrated sample, several hundred images were acquired under standardized conditions. Automated particle identification and subsequent Voronoi tessellation yielded quantitative dispersion parameters. The coefficient of variation (CV) of Voronoi region areas was introduced as a dimensionless metric to enable intersample comparison. The results revealed distinct dispersion behaviors: samples modified with maleic, DL-malic, and citric acids demonstrated high dispersion stability (CV ≈ 0.4), while unmodified and L-arabinose-modified samples exhibited pronounced agglomeration tendencies (CV ≈ 0.8). Notably, the CV values correlated strongly with DLS-measured particle diameters, further validating the reliability of the proposed methodology. This approach advances the field from qualitative cryo-TEM observation toward quantitative materials characterization, providing mechanistic insights into the effects of surface modification on dispersion stability at the microscopic scale. Furthermore, it offers a robust platform for evaluating nanoparticle behavior under practically relevant solvent conditions.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"6 2","pages":"340-348"},"PeriodicalIF":4.6,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13087927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Delineation of Tumor Spheroids in Microscopic Images Using Deep-Learning.","authors":"Jens Maus, Janina Nitschke, Pavel Nikulin, Frank Hofheinz, Mareike Barth, Sandy Lemm, Lena Richter, Jens Pietzsch, Anja Braune, Martin Ullrich","doi":"10.1021/acsmeasuresciau.5c00172","DOIUrl":"10.1021/acsmeasuresciau.5c00172","url":null,"abstract":"<p><p>Tumor spheroid growth assays are used to evaluate the potential of cancer therapies in vitro. During such experiments, extensive microscopic image series are generated, which are commonly analyzed using threshold-based delineations. However, due to treatment-induced morphological changes of the spheroids, very time-consuming manual corrections are often required. The goal of our work was the development of an AI-based method for accurate and automated delineation of spheroid growth assays, ultimately reducing the reliance on manual delineation and corrections. Spheroids were grown from mouse pheochromocytoma (MPC) cells and subjected to irradiation with particle-emitting radioligands. Spheroid growth was monitored over 35 days. N = 38090 images, acquired within seven experiments and two studies, were included. Spheroids were delineated with a threshold-based method followed by manual corrections and the resulting delineations served as ground truth for network training and testing. The data were divided into two independent data sets: one for training and internal validation using a 5-fold cross-validation (N = 21567; main data set) and another for final independent testing (N = 16523). The network was developed using the nnU-Net v2 deep-learning (DL) framework. DL-based and manual delineations were compared using the <i>Dice similarity coefficient</i> (DSC). Additionally, treatment effects in a spheroid experiment were compared by quantifying half-maximum spheroid control doses (SCD₅₀). The median DSC values in the main and test data sets were 0.979 and 0.974, respectively. In the main data set, only 7% (N = 1571) of the DL-generated delineations and 8% (N = 1304) in the test data set showed DSC < 0.9, indicating high performance. The SCD₅₀ values were comparable between manual (day 13: 0.086 ± 0.001, day 35: 0.150 ± 0.001) and DL-based delineations (day 13: 0.083 ± 0.002, day 35: 0.149 ± 0.007). The network enables fast and accurate delineation of tumor spheroids in treatment response assays, reducing the time needed to delineate all spheroid images of a single experiment from several days with the previously applied method to a few hours only.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"6 2","pages":"411-420"},"PeriodicalIF":4.6,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13087938/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contemporary Techniques and Prospects in Pharmaceutical Tablet Surface Analysis.","authors":"Matjaž Finšgar","doi":"10.1021/acsmeasuresciau.5c00178","DOIUrl":"https://doi.org/10.1021/acsmeasuresciau.5c00178","url":null,"abstract":"<p><p>This work demonstrates how surface analysis can be applied for the chemical characterization of solid pharmaceutical tablets using time-of-flight secondary ion mass spectrometry (ToF-SIMS), X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and 3D profilometry as complementary tools. Two formulation extremes were examined, i.e., high-dose single active pharmaceutical ingredient (API) tablets and low-dose three-API combination tablets, with five formulations assessed in each set. AFM and 3D profilometry were employed to characterize the micro- to nanoscale topography, assess roughness, and measure the crater depths formed after gas cluster ion beam (GCIB) sputtering. To define ToF-SIMS markers, API reference standards were analyzed using tandem (MS/MS) ToF-SIMS. Multivariate curve resolution was used to identify ions unique to each API. After marker definition, ToF-SIMS images were acquired in 2D and, by using GCIB, in 3D. Large-area maps were produced by image stitching. Delayed extraction and fast imaging modes enabled submicrometric imaging at high mass resolving power. XPS survey and high-resolution spectra, combined with GCIB sputtering, quantified the elemental composition and chemical states within the outer few nanometers and into the subsurface region. It was demonstrated that ToF-SIMS can provide molecularly specific maps and depth profiles that localize APIs and excipients, revealing surface segregation and interfacial layering. In contrast, XPS supplies quantitative elemental and chemical-state information on the surface and in the subsurface. Overall, the study demonstrates that these surface analytical techniques offer spatially resolved insights not accessible with conventional methods for solid dosage forms and that they complement practices in formulation development, troubleshooting, and quality control. These techniques can confirm API and excipient localization, assess surface segregation and interfacial layers, detect contaminants, and compare batches. Despite this utility, they have seen limited adoption, most likely because they require specialized instrumentation, method development, and data interpretation expertise not yet widespread in pharmaceutical laboratories.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"6 2","pages":"430-444"},"PeriodicalIF":4.6,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13087966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breaking the ¹³C-¹³C Polarization Transfer Barrier for High-Dimensional Protein Solid-State NMR with Ultra-Fast MAS.","authors":"Tatsuya Matsunaga, Tsukito So, Ryo Takahashi, Yoshiki Shigemitsu, Yoshitaka Ishii","doi":"10.1021/acsmeasuresciau.5c00103","DOIUrl":"https://doi.org/10.1021/acsmeasuresciau.5c00103","url":null,"abstract":"<p><p>Recent advances in ¹H-detected solid-state NMR spectroscopy (SSNMR) using ultra-fast magic angle spinning (UFMAS) at frequencies above 60 kHz potentially facilitate high-dimensional SSNMR (HD-SSNMR) for protein analysis. A major limitation of HD-SSNMR is the exponential signal loss that occurs during successive polarization transfers. To overcome this bottleneck in HD-SSNMR for the most problematic ¹³C-¹³C transfers, we introduce a simple yet exceptionally efficient homonuclear cross-polarization (HCP) scheme called SeMi-selective Adiabatic Recoupling Transfer with HCP (SMART-HCP). We demonstrate that SMART-HCP with UFMAS at 90 kHz achieved nearly complete transfer with an efficiency of 76% from ¹³CO to ¹³C_α and 70% from ¹³C_α to ¹³CO for uniformly ¹³C,¹⁵N-labeled l-alanine. Semiselective HCP was achieved by optimizing radio frequency (RF)-offset frequency and amplitude modulations via a simple graphical method. For uniformly ¹³C,¹⁵N-labeled immunoglobulin-binding protein G (GB1) proteins, compared with the conventional dipolar recoupling enhancement through amplitude modulation (DREAM) scheme, SMART-HCP enhanced ¹³CO-¹³C_α transfers up to ∼3-fold (average 1.7-fold) for nonglycine residues, thereby accelerating various protein SSNMR experiments, including HD-SSNMR, by up to ∼9-fold. Our 3D ¹H-detected (H)-CACO-(N)H SSNMR spectra of the GB1 sample suggest that with the SMART-HCP method a usually time-consuming 3D protein SSNMR experiment can be achieved within 3.5 h for a trace amount of the protein sample (∼200 μg). Besides biological applications, this method is likely applicable to ¹³C SSNMR analysis of a wide range of samples, such as polymers, peptide-based pharmacological agents, and other solid organic materials.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"6 2","pages":"303-310"},"PeriodicalIF":4.6,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13087929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Interpretation of Electrochemical Impedance Spectra Using Numerical Complex Analysis.","authors":"Jithin D George, Willa Brenneis, Vinod K Sangwan, Dilara Meli, Heather Kurtz, Jeffrey Richards, Lincoln J Lauhon, Jonathan Rivnay, Mark C Hersam, Jeffrey Lopez, Maria K Y Chan, Valerie Taylor","doi":"10.1021/acsmeasuresciau.5c00151","DOIUrl":"10.1021/acsmeasuresciau.5c00151","url":null,"abstract":"<p><p>Electrochemical Impedance Spectroscopy (EIS) is a noninvasive technique widely used for understanding charge transfer and charge transport processes in electrochemical systems and devices. Standard approaches for the interpretation of EIS data involve starting with a hypothetical circuit model for the physical processes in the device based on experience/intuition and then fitting the EIS data to this circuit model. This work explores a mathematical approach for extracting key characteristic features from EIS data by relying on fundamental principles of complex analysis. These characteristic features can suggest the presence of inductors and constant phase elements (nonideal capacitors) from impedance data and enable us to answer questions about the identifiability and nonuniqueness of equivalent circuit models. In certain scenarios such as models with only resistors and capacitors, we are able to enumerate all possible families of circuit models. Finally, we apply the mathematical framework presented here to real-world electrochemical systems and highlight results using impedance measurements from a lithium-ion battery coin cell.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"6 2","pages":"324-339"},"PeriodicalIF":4.6,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13087945/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dark Reactions in Microdroplets Explain Widespread Artifacts in Metabolomic Profiling.","authors":"Xiaowei Song, Jinheng Xu, Chenglong Sun, Lecheng Lyu, Hongqian Kui, Ruiping Zhang, Zeper Abliz, Richard N Zare","doi":"10.1021/acsmeasuresciau.5c00146","DOIUrl":"10.1021/acsmeasuresciau.5c00146","url":null,"abstract":"<p><p>Reliable ion annotation and identification remain persistent challenges in mass-spectrometry-based untargeted metabolomics. Here, we elucidate the identities, sources, and formation mechanisms of numerous previously unexplained ions by showing that microdroplets formed during electrospray ionization can promote a wide array of chemical transformations. These include redox, addition, condensation, reductive amination, decarboxylative coupling, and radical reactions, many of which are facilitated at the gas-aqueous interface by reactive oxygen and nitrogen species. Activation of metabolite chemical bonds generates cations, anions, and radical intermediates through the loss of protons, electrons, or functional groups, ultimately leading to bond formation and the generation of artifactual ions or false positive ions. These artifacts are frequently misassigned as endogenous metabolites and account for hundreds of thousands of previously unidentified features. As an example, we show that in a recently published untargeted metabolomics analysis of 1969 ions, the annotation rate was substantially improved to over 50% from the previous value of 9%, showing the importance of this new form of identification. Finally, we describe practical strategies to minimize artifactual ion formation, thereby improving the reliability of metabolomic analyses.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"6 2","pages":"311-323"},"PeriodicalIF":4.6,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13087934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of Layer Thickness on the Detection of Plastic Films Using Optical Photothermal Infrared Spectroscopy (O-PTIR).","authors":"Jan Fridtjof Häusler, Florian Bittner, Madina Shamsuyeva","doi":"10.1021/acsmeasuresciau.5c00149","DOIUrl":"https://doi.org/10.1021/acsmeasuresciau.5c00149","url":null,"abstract":"<p><p>This study investigates how varying film thickness affects the qualitative identifiability of plastics using optical photothermal infrared spectroscopy (O-PTIR) on examples of common plastics such as polyamide 6 (PA6) and polyethylene terephthalate (PET). The main methodology consists of applying a thin layer of PA6 and PET separately to each substrate, namely, PET to polyethylene (PE) and PA6 to polypropylene (PP), and reducing the thickness of the coatings until the O-PTIR signal of the film is no longer detectable. As expected, the characteristic O-PTIR signal for PA and PET decreased with a decreasing film thickness. However, the results show that the O-PTIR detection limit for the plastics could not be reached, as the characteristic peaks of the substrate plastics are still clearly visible at a layer thickness of approximately 0.18 μm for PET and approximately 0.29 μm for PA6. These are the minimum stable film thicknesses that could be achieved since the selected film production process (drop deposition process) does not allow for thinner layers. As this work is a feasibility study, further factors influencing the O-PTIR measurement of plastic films should be investigated in a subsequent work.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"6 1","pages":"150-157"},"PeriodicalIF":4.6,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12921621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating Spectral Biomarker Candidates for Migratory Potential in Cancer Cells Using Micro-FTIR and O‑PTIR Spectroscopy.","authors":"Elisabeth Holub, Nikolaus Hondl, Kai-Lan Lin, Marjaana Parikainen, Cecilia Sahlgren, Bernhard Lendl, Georg Ramer","doi":"10.1021/acsmeasuresciau.5c00132","DOIUrl":"https://doi.org/10.1021/acsmeasuresciau.5c00132","url":null,"abstract":"<p><p>Routine diagnostic practice for cancer and metastasis relies on a time-consuming staining process and the use of antibodies to detect selected molecular markers and is hence limited by a lack of real-time data and the availability of molecular information. Against this background, techniques based on rapid chemical analysis to identify migratory properties are highly desirable. Fourier-Transform Infrared (FTIR) microspectroscopy has a long history in the label-free identification of infrared marker bands for cancer detection. However, it requires extensive postprocessing of the acquired spectra, is of limited suitability for analysis in aqueous environments, and has poor spatial resolution. To overcome these challenges, we are using a new method termed Optical Photothermal Infrared (O-PTIR) spectroscopy to detect local absorption to establish potential IR tumor markers and classification models. We report on experimental outcomes using machine learning and FTIR microspectroscopy for the classification of cells and the analysis of spectral features reflecting cancer and migratory properties, comparing a commercial FTIR microspectrometer to a custom-built O-PTIR instrument dedicated to spectroscopic measurement and imaging in microfluidic channels.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"6 1","pages":"96-106"},"PeriodicalIF":4.6,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12921600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical Framework for Quantifying Delocalization in MALDI-MSI via a Composite Scoring Approach.","authors":"Amin Jarrahi, Allison Jones, Weisheng Tang, Hairong Qi, Anna Colleen Crouch","doi":"10.1021/acsmeasuresciau.5c00148","DOIUrl":"https://doi.org/10.1021/acsmeasuresciau.5c00148","url":null,"abstract":"<p><p>Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has become a widely used tool for demonstrating the spatial distribution of biomolecules in tissue. One common issue in this technique that can have an impact on sensitivity and spatial resolution is analyte delocalization, in which the analyte spreads across the tissue or beyond the tissue boundaries, often due to sample handling or matrix application. In this study, we tested several metrics to evaluate delocalization using MALDI-MSI data from mouse brain sections. These metrics included the distance between the centers of mass of the nonzero-intensity pixels of tissue and global, the maximum and mean distances of off-tissue signal from the border, and the total area of background signal. After comparison of these metrics to the data set, a linear combination of area with mean distance was defined as the delocalization score. This score can be tuned depending on the application, and we found that a higher amount of weight on the area worked well in practice. Studying the Pearson correlation coefficients of the delocalization scores across the four groups of mice revealed a strong correlation among the analytes, suggesting that their delocalization patterns behave similarly across all groups.</p>","PeriodicalId":29800,"journal":{"name":"ACS Measurement Science Au","volume":"6 1","pages":"134-149"},"PeriodicalIF":4.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12921599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}