<p><strong>This study establishes a validated workflow for quantitative LA-ICP-MS imaging of elemental distributions in 3D tumor spheroids with spatial correlation to protein expression. Optimized cryo-embedding and freeze-drying minimized analyte redistribution, while matrix-matched calibration with <sup>31</sup>P normalization enabled reproducible pixel-level quantification consistent with bulk ICP-MS.</strong></p><p>Applied to boron, the method achieved 10 μm spatial resolution and detection limits of approximately 7.4 ng g–1. Consecutive-section immunohistochemistry showed strong spatial agreement between elemental and protein distributions, providing a robust platform for cross-modal analysis in heterogeneous 3D biological models.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/ancham/article/98/31/23164/5237316/Quantitative-LA-ICP-MS-Imaging-of-Elemental"><strong>Quantitative LA-ICP-MS Imaging of Elemental Distributions with Protein Correlation in 3D Tumor Models&nbsp;</strong></a></h4><p>Fatimah Zachariah Ali*; Alexander P. Morrell; Piotr Robert Golda; Norfazlina Mohd Nawi; Premkamon Chaipanichkul; John M. McArthur; Pascal F. Durrenberger; Huda Alnufaei; Gary Royle; Kate Ricketts*</p><p>Anal. Chem. (2026) 98 (31): 23164–23178</p><div><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.6c03012">https://doi.org/10.1021/acs.analchem.6c03012</a></div><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/"><strong>CC-BY 4.0</strong></a></h6></blockquote><p><strong>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.</strong></p><div><div><div><p>Quantitative spatial mapping of elements in biological systems remains a methodological challenge, particularly for achieving accurate and reproducible measurements at micrometre resolution in heterogeneous 3D samples. While many imaging approaches can visualize elemental distributions, quantitative determination is limited by preparation artifacts, matrix dependent signal variability, and the lack of validated analytical workflows. (<a href="javascript:;">1−7</a>) These limitations restrict mechanistic interpretation of elemental behavior in biological models and hinder comparison across studies.</p></div><div><p>Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) offers a powerful approach for spatially resolved elemental analysis, combining high sensitivity, broad elemental coverage, and micrometre-scale resolution. It has been widely applied to map metals, nanoparticles, and metal-based therapeutics in biological systems, including tumor spheroids, tissues, and brain models. (<a href="javascript:;">8−12</a>) Despite its sensitivity and multiplexing capability, applications in complex cellular models have largely remained qualitative or relatively quantitative. (<a href="javascript:;">13−15</a>) Quantitative implementation is hindered by methodological factors: sample preparation can redistribute elements, and matrix effects complicate signal interpretation, limiting reproducibility.</p><p>Here, we present a validated workflow for quantitative LA-ICP-MS imaging in heterogeneous 3D tumor spheroid models. We systematically evaluate fixation, embedding, sectioning, drying, calibration, and endogenous normalization to identify conditions that preserve spatial fidelity and analytical sensitivity. We show that endogenous phosphorus provides a robust internal normalizer, consistent with previous studies, (<a href="javascript:;">34−36</a>) and demonstrate that quantitative elemental maps can be integrated with biological context through correlation with immunohistochemistry on consecutive sections. Together, this work establishes an integrated and transferable framework providing a practical platform for quantitative elemental imaging and cross-modal spatial analysis in complex 3D biological systems, with applications spanning drug delivery, nanomedicine, toxicology, and metallomics.</p><h4><strong>Experimental Section</strong></h4><h5><strong>LA-ICP-MS Instrumentation and Acquisition</strong></h5><div><p>Elemental imaging was performed using a 193 nm ArF* excimer laser ablation system (Iridia, Teledyne Photon Machines) equipped with a cobalt long-pulse ablation cell and coupled via an Aerosol Rapid Introduction System (ARIS) to a triple-quadrupole ICP-MS (<a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1488"><strong>iCAP MTX</strong></a>, Thermo Fisher Scientific). Instrument control and data acquisition were conducted using <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1489"><strong>Qtegra software</strong></a> (Thermo Fisher Scientific).</p></div><div><p>Instrument performance was optimized using NIST SRM 612 glass to minimize laser-induced elemental fractionation (238U<sup>+</sup>/232Th<sup>+</sup>), maintain oxide formation below 1% (232Th16O<sup>+</sup>/232Th<sup>+</sup>), and maximize sensitivity of 59Co<sup>+</sup>, 115In<sup>+</sup>, and 238U<sup>+</sup>.</p></div><div><p>Spheroid sections mounted on glass slides were analyzed in fixed dosage raster mode with a 10 μm square laser spot, defining the spatial resolution. Imaging was performed with a laser energy density of 0.6 J cm<sup>–2</sup>, a repetition rate of 500 Hz (10 shots per pixel), and a helium carrier gas flow of 0.4 L min<sup>–1</sup>. Elemental detection was performed using Dynamic Reaction Cell (DRC) mode with oxygen as the reaction gas (0.16 mL min<sup>–1</sup>) to reduce polyatomic interferences on <sup>31</sup>P and <sup>32</sup>S. Two isotopes were monitored per acquisition (<sup>11</sup>B and <sup>31</sup>P, or <sup>11</sup>B and <sup>32</sup>S), with dwell times of 14.3 ms (<sup>11</sup>B) and 1 ms (<sup>31</sup>P or <sup>32</sup>S), a quadrupole total switching time of 4.7 ms, and a total acquisition time of 20 ms per pixel. A section thickness of 30 μm ensured that the imaging depth matched the physical section thickness. Slides were arranged within a four-position sample holder such that a boron-spiked fish gelatin calibration standard was analyzed within the same run to enable signal calibration.</p><h5><strong>ICP-MS Sample Preparation</strong></h5><p>For bulk quantification, spheroids were processed in parallel to LA-ICP-MS samples. Three spheroids per condition were collected into low-binding microcentrifuge tubes following incubation with BPA (4h, 37 <span>°</span>C). Samples were washed once with ice-cold PBS and pelleted by centrifugation (1500 rpm, 5 min). The supernatant was removed and spheroids were digested in 100 μL of 70% HNO<sub>3</sub> (trace-metal grade, Sigma-Aldrich) at 60 <span>°</span>C overnight. Following complete digestion, samples were diluted in 2% HNO<sub>3</sub> spiked with beryllium as an internal standard and analyzed using <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library?mainauthorItems=10&amp;search=7900"><strong>7900 ICP-MS</strong></a> (Agilent). Boron concentrations were quantified against external calibration standards and expressed as total boron mass per spheroid.</p><h4><strong>Results</strong></h4><h5><strong>Overview of the LA-ICP-MS Workflow</strong></h5><p>The overall experimental pipeline is summarized in <a href="javascript:;">Figure 1</a>, outlining the sequential steps from spheroid preparation to quantitative image generation. The workflow integrates cryo-embedding, sectioning, freeze-drying, laser ablation, matrix-matched calibration, signal normalization, and correlative immunohistochemistry, providing a unified framework for spatially resolved elemental quantification in 3D tumor models.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_31_23164_23178_Figure_1_Integrated_workflow_for_quantitative_LA_ICP_MS_imaging_in_tumor_spheroids_85357997aa.jpg" alt="Anal. Chem. (2026) 98 (31): 23164–23178: Figure 1. Integrated workflow for quantitative LA-ICP-MS imaging in tumor spheroids. (1) Tumour spheroids are generated from cultured cells and incubated with analyte. (2) Samples are cryo-embedded in 2% carboxymethyl cellulose, frozen, sectioned, and freeze-dried prior to imaging. (3) Sections and matrix-matched calibration standards are analyzed by LA-ICP-MS to generate quantitative elemental maps. (4) Elemental images are normalized using the endogenous 31P signal to correct for variations in ablation yield and tissue density. (5) Regions corresponding to nonviable cells are excluded using a propidium iodide (PI – in red) mask derived from confocal imaging, enabling downstream quantitative analysis." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_31_23164_23178_Figure_1_Integrated_workflow_for_quantitative_LA_ICP_MS_imaging_in_tumor_spheroids_85357997aa.jpg 116w," sizes="100vw" width="1600" height="2166"></p><h5><strong>Integration of Biological Context into LA-ICP-MS Imaging</strong></h5><p>To enable spatial correlation between elemental imaging and protein expression, LA-ICP-MS maps were paired with immunohistochemical (IHC) staining acquired from immediately adjacent spheroid sections (±30 μm). Representative data sets illustrate the multimodal workflow, combining normalized boron distributions with brightfield imaging, propidium iodide (PI) staining, and IHC for GLUT1 and LAT1 (<a href="javascript:;">Figure 5</a>a). PI staining was performed prior to freezing and cryosectioning to identify nonviable regions; all quantitative analyses were restricted to PI-negative regions to ensure correlation within viable cell populations.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_31_23164_23178_Figure_5_Correlative_analysis_of_elemental_distribution_and_protein_expression_in_tumor_spheroids_2b5e550a83.jpg" alt="Anal. Chem. (2026) 98 (31): 23164–23178: Figure 5. Correlative analysis of elemental distribution and protein expression in tumor spheroids. (a) Representative data sets used for correlation, including a normalized boron LA-ICP-MS map, brightfield image, propidium iodide (PI) staining, and immunohistochemistry (IHC) for GLUT1 and LAT1 from an immediately adjacent section. (b) Percentage variation in protein intensity between the LA-ICP-MS section and the immediately adjacent section (±30 μm) across GLUT1 and LAT1 in HT29 and HCT116 spheroids. (c) Pearson correlation of adjacent sections (±30 μm) across GLUT1 and LAT1 in HT29 and HCT116 spheroids (n = 3). (d) Validation of finding from analysis two analysis methods (n = 3, 3 spheroids per replicate) and flow cytometric validation of BPA uptake in LAT1-positive and LAT1-negative cell populations (n = 3, no. of events = 25, 000 each replicate). Statistical significance was determined using an unpaired two-tailed t test; exact p-values are as indicated." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_31_23164_23178_Figure_5_Correlative_analysis_of_elemental_distribution_and_protein_expression_in_tumor_spheroids_2b5e550a83.jpg 142w," sizes="100vw" width="1500" height="1646"></p><div><p>To assess the validity of consecutive-section correlation, protein expression was first quantified across matched sections separated by ± 30 μm. Across HT29 and HCT116 spheroids, mean variation in staining intensity between adjacent sections was 10.5 ± 6.7% and 9.5 ± 4.7% for LAT1, and 6.6 ± 0.9% and 11.4 ± 2.1% for GLUT1, respectively (<a href="javascript:;">Figure 5</a>b). Overall variability across all spheroids ranged from 0.9–23.7% (<i>n</i> = 3 spheroids per cell line).</p></div><div><p>Coefficients of variation were 7.4 ± 2.3% and 6.8 ± 2.7% for LAT1, and 4.9 ± 1.3% and 8.3 ± 1.9% for GLUT1 in HT29 and HCT116 spheroids’ consecutive sections, respectively, indicating that spatial protein distributions were largely preserved between consecutive sections at this scale.</p></div><div><p>Consistent with this observation, Pearson correlation analysis demonstrated strong spatial agreement between adjacent sections for most proteins analyzed. GLUT1 expression showed high correlation in both HT29, (<i>r</i> = 0.9678–0.9924) and HCT116 spheroids (<i>r</i> = 0.8274–0.9836), while LAT1 expression was highly correlated in HCT116 (<i>r</i> = 0.9796–09963) but more variable in HT29 (<i>r</i> = 0.5582–0.8190) (<a href="javascript:;">Figure 5</a>c). All correlations were statistically significant (<i>p</i> &lt; 0.05). Section-to-section variability must be quantified before assuming that a consecutive section faithfully represents the biology of the elementally mapped section; where this condition is met, consecutive-section correlation provides a reliable means of relating elemental and protein distributions.</p></div><div><p>Having established this framework, the known relationship between LAT1 expression and boronophenylalanine (BPA) uptake was used as a biological test case. BPA is transported into cells via the large neutral amino acid transporter LAT1, providing a mechanistic link between protein expression and intracellular boron accumulation.</p></div><div><p>Region-of-interest (ROI) analysis demonstrated that LAT-1 enriched regions exhibited higher boron signal compared to LAT1-low regions in HCT116 spheroids (<i>p</i> = 0.0331, paired two-tailed <i>t</i> test; <i>n</i> = 3, 3 spheroids per replicate). Radial analysis further revealed a positive correlation between LAT1 expression and boron distribution (<i>r</i> = 0.5214, <i>p</i> = 0.0221, <a href="javascript:;">Figure 5</a>d), supporting spatial coupling between transporter expression and uptake.</p></div><div><p>Orthogonal validation by flow cytometry confirmed that LAT1-positive cells exhibited significantly higher BPA levels than LAT1-negative cells in both cell lines (36% and 39% increase in HT29 and HCT116, respectively; <i>p</i> &lt; 0.05, <i>n</i> = 3, 25,000 events per replicate).</p></div><div><p>Together, these results demonstrate that combined LA-ICP-MS and consecutive section IHC workflow enables biologically meaningful spatial correlation between elemental distributions and tested proteins expression in 3D tumor models.</p></div><h4><strong>Conclusion</strong></h4><div><p>This study establishes a validated workflow for quantitative LA-ICP-MS imaging in heterogeneous three-dimensional spheroid models by systematically addressing key methodological determinants of accuracy, including sample preparation, calibration, endogenous normalization, and cross-modal validation. Optimized preparation (2% CMC cryo-embedding with freeze-drying) reduced analyte loss by ∼84% relative to OCT, while matrix-matched gelatin standards enabled linear calibration (<i>r</i><sup>2</sup> &gt; 0.99) and conversion of signal intensity to absolute concentration. Under these conditions, the workflow achieved 10 μm spatial resolution with ng g<sup>–1</sup>-level sensitivity (LOD 7.39 ± 2.24 ng g<sup>–1</sup>) and reproducible quantification across independent runs (CV &lt; 20% across most of the spheroid radius).</p></div><div><p>Quantitative accuracy was confirmed by strong agreement with bulk ICP-MS (<i>r</i><sup>2</sup> = 0.9965; &lt; 15% deviation above ∼3 ng), demonstrating that reconstructed LA-ICP-MS measurements provide reliable estimates of total uptake. Integration with protein expression using consecutive-section analysis was feasible when section-to-section variability was characterized (typically ∼5–12% variation; CV &lt; 10%), enabling biologically interpretable correlations between elemental distributions and molecular markers in multicellular systems.</p></div><div><p>While volumetric reconstruction requires simplifying assumptions (e.g., radial symmetry), these do not affect the spatial correlation analyses used to interpret elemental heterogeneity. By integrating preparation optimization, validated normalization, quantitative calibration and cross-modal benchmarking within a single framework, this work moves LA-ICP-MS from relative signal mapping toward reproducible, quantitatively interpretable imaging in complex 3D biological models, providing a practical platform for mechanistic studies of drug distribution and microenvironmental heterogeneity.</p></div></div></div></div></div>

Quantitative LA-ICP-MS Imaging of Elemental Distributions with Protein Correlation in 3D Tumor Models

<p><strong>In the week from August 17, the following webinars await you in the field of atomic and molecular spectroscopy and spectrometry</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinars"><strong>👉 DETAILS AND REGISTRATION IN THE WEBINARS SECTION</strong></a></h4><p><strong>👉 Search and filter in the largest global database</strong> with over <strong>5 500 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Unchained Labs: Make your LNP formulation workflow shine with Sunscreen</strong></h5><ul><li>Tu, 18.8.2026 17:00 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/6444">Registration</a></li></ul><p>Learn how to accelerate LNP formulation to 3–4 minutes per sample using just 400 µL. Discover how hands-free automation and reusable mixers improve workflow efficiency.</p><h5><strong>2. Agilent Technologies: Elemental Evaluation of MXene Degradation and Application in Ion Separation</strong></h5><ul><li>Tu, 18.8.2026 21:30 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/6402">Registration</a></li></ul><p>Learn how the Agilent 5900 ICP-OES evaluates MXene degradation and tracks metal leaching to support advanced ion-separation research.</p><h5><strong>3. SPECTRO Analytical Instruments: Introducing the SPECTROSCOUT kt: Accurate Precious Metals Assays — Anywhere</strong></h5><ul><li>Th, 20.8.2026 10:30 CEST</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/6446">Registration</a></li></ul><p>Discover how the SPECTROSCOUT kt uses ED-XRF for fast, nondestructive precious metals testing, including purity verification, alloy identification, hallmarking, recycling, and refining.</p>

Webinars LabRulezICPMS Week 34/2026

<p><strong>We are delighted to invite food scientists from academia and industry, representatives of (inter)national agencies, control authorities, governmental and commercial laboratories to attend </strong><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/events/928"><strong>RAFA 2026</strong></a><strong> (12th Symposium on Recent Advances in Food Analysis), that takes place 3–6 November 2026 in Prague..</strong></p><blockquote><p class="cke-user_style-important"><strong>👉</strong><a target="_blank" rel="noopener noreferrer" href="https://rafa.easyevent.eu/tickets/"><strong>SUBMIT ABSTRACT</strong></a> <strong>FOR POSTER PRESENTATION by August 31, 2026</strong></p><p>Do not miss the opportunity to present your latest results at <strong>RAFA 2026</strong>.</p></blockquote><h5><strong>Next deadlines:</strong></h5><ul><li><strong>August 31, 2026</strong>: 2nd window – &nbsp;<a target="_blank" rel="noopener noreferrer" href="https://www.rafa2026.eu/sponsors_exhibitors_media_partners_info.html"><strong>Application for participation as a Sponsor or Exhibitor</strong></a></li><li><strong>September 30, 2026:&nbsp;</strong><ul><li><a target="_blank" rel="noopener noreferrer" href="https://www.rafa2026.eu/registration.html"><strong>Registration at the regular rate</strong></a></li><li><strong>Abstract submission</strong> – <strong>Last minute posters </strong>(<i>May not appear in the RAFA 2026 program</i>)</li></ul></li></ul><p>Since its first edition in 2003, RAFA has grown into a <strong>prestigious international event</strong>, typically welcoming <strong>up to 850 participants</strong> from more than <strong>60 countries</strong> worldwide, with the support of more than 50 sponsors, exhibitors, media and supporting partners.</p><p>RAFA 2026 will provide an overview of contemporary trends in the <strong>ANALYTICAL &amp; BIOANALYTICAL STRATEGIES IN FOOD QUALITY AND SAFETY CONTROL</strong> and discuss the <strong>CHALLENGES AND NOVEL APPROACHES IN FOOD AND NATURAL PRODUCTS ANALYSIS</strong>.</p><p>View the <a target="_blank" rel="noopener noreferrer" href="https://www.rafa2026.eu/invited_speakers.html"><strong>list of distinguished speakers</strong></a> who have accepted our invitation to RAFA 2026 and will present current hot topics and future perspectives in food analysis!</p><blockquote><p>👉More details on the <a target="_blank" rel="noopener noreferrer" href="https://www.rafa2026.eu/scientific_programme.html"><strong>RAFA 2026 preliminary program</strong></a> can be found HERE.</p><p>👉OUR <a target="_blank" rel="noopener noreferrer" href="https://www.rafa2026.eu/about_sponsors_exhibitors_partners.html"><strong>SPONSORS, EXHIBITORS, MEDIA and SUPPORTING PARTNERS</strong></a></p></blockquote><p>You are kindly invited to join and contribute to our exciting scientific program and unforgettable social events!</p><p><strong>We look forward to meeting you again in Prague!</strong></p><ul><li>Prof. Jana Pulkrabova &amp; Dr. Stefan van Leeuwen, Symposium Chairs</li><li>Prof. Michele Suman, Symposium Co-chair</li><li>Prof. Jana Hajslova &amp; Prof. Michel Nielen, Symposium Honorary Chairs</li></ul>

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RAFA provides an overview of contemporary trends in ANALYTICAL & BIOANALYTICAL STRATEGIES IN FOOD QUALITY AND SAFETY CONTROL and discusses CHALLENGES / NOVEL APPROACHES IN FOOD AND NATURAL PRODUCTS ANALYSIS.

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Mestrelab Mnova ElViS software

By integrating Mnova ElViS into your analytical workflows, you can enhance the efficiency and accuracy of your spectroscopic data analysis across a wide range of techniques.

<h4><strong>Visualize, process, analyze and report various electronic and vibrational spectroscopic techniques</strong></h4><p>Carry out the analysis of various optical spectroscopy data including ultraviolet and visible (UV/Vis), near and mid infrared (NIR/MIR), Raman, fluorescence, and other spectroscopic methods operating in the whole region of wavelength from 100 nm to 100 µm.</p><h4><strong data-start="641" data-end="654">Benefits:</strong></h4><ul><li>Spectroscopic processing and analysis of ultraviolet and visible (UV/Vis), near and mid infrared (NIR/MIR), Raman, fluorescence methods.</li><li>Vendor and Operating System agnostic, supports most data file formats.</li><li>Wide range of processing functions, including a dynamic processing template.</li><li>Manual and automatic peak picking analysis.</li><li>Data Analysis of stacked/arrayed spectra.&nbsp;</li></ul><h4><strong>Features:</strong></h4><h5><strong data-start="1039" data-end="1108">PLS-Based Baseline Correction Tool (IarPLS)</strong></h5><p>To improve the processing of electronic and vibrational spectroscopy data, Mnova 15 introduces an enhanced PLS-based baseline correction tool, IarPLS (penalized-least-squares based). It now features an automatic button to compute the near-optimal asymmetry parameter.&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_PLS_Based_Baseline_Correction_Tool_Iar_PLS_f3180f6927.webp" alt="Mestrelab Research: Mnova EIVis - PLS-Based Baseline Correction Tool (IarPLS)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_PLS_Based_Baseline_Correction_Tool_Iar_PLS_f3180f6927.webp 245w," sizes="100vw" width="1500" height="845"></p><h5><strong>Normalization Processing Modes</strong></h5><p>Three Normalization Processing modes, namely Largest Peak, Integral and Vector Length are available in Mnova 15. These three features aim to simplify the baseline correction process, streamline your workflow, and provide a rapid method for normalizing data to address quantitative issues.&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Normalization_Processing_Modes_675cb7c407.webp" alt="Mestrelab Research: Mnova EIVis - Normalization Processing Modes" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Normalization_Processing_Modes_675cb7c407.webp 245w," sizes="100vw" width="1500" height="844"></p><h5><strong>Electronic and Vibrational Spectroscopies supported file formats</strong></h5><p>Mnova ElViS supports the most frequently used data file formats for these analytical techniques, including import and export of single and multi-column matrix-like spectra (i.e., ASCII XY and XYY, as .txt or .csv files). Supported formats include SPC (Galactic), JCAMP-DX, OPUS (Bruker), SPA and SPG (Omnic), Perkin Elmer's .sp files, and ASCII (XY and XYY).</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Electronic_and_Vibrational_Spectroscopies_supported_file_formats_bb6afc3eab.webp" alt="Mestrelab Research: Mnova EIVis - Electronic and Vibrational Spectroscopies supported file formats" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Electronic_and_Vibrational_Spectroscopies_supported_file_formats_bb6afc3eab.webp 245w," sizes="100vw" width="1500" height="910"></p><h5><strong>Manual &amp; automatic spectral analysis</strong></h5><p>Mnova ElViS provides both manual and automatic peak picking, offering flexibility and convenience in your spectral analysis. It also features advanced tools for the analysis of stacked or arrayed spectra, ensuring thorough data evaluation and interpretation for all your spectral needs.&nbsp;</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Manual_and_automatic_spectral_analysis_e24a19a151.webp" alt="Mestrelab Research: Mnova EIVis - Manual &amp; automatic spectral analysis" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Manual_and_automatic_spectral_analysis_e24a19a151.webp 240w," sizes="100vw" width="940" height="611"></p><h5><strong>Main processing features</strong></h5><p>Mnova ElViS comes with a range of useful processing features, including Multiplicative Scatter Correction, Probabilistic Quotient Normalization, Baseline correction (both automatic IarPLS and tailored Multipoint Baseline Correction), several smoothing techniques (such as Savitzky-Golay, Nonlocal Means, Exponential), normalization and Standard Normal Variate (SNV), derivatives, and units conversion between absorbance and transmittance.<br><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Mestrelab_Research_Mnova_EI_Vis_Main_processing_features_bc6eb64b99.webp" alt="Mestrelab Research: Mnova EIVis - Main processing features" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Mestrelab_Research_Mnova_EI_Vis_Main_processing_features_bc6eb64b99.webp 245w," sizes="100vw" width="1074" height="581"></p>

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RAFA 2026 - Deadline for Abstract Submission for poster presentation is coming

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Quantitative LA-ICP-MS Imaging of Elemental Distributions with Protein Correlation in 3D Tumor Models