<p><strong>Scientists at IOCB Prague are uncovering new details of gene transcription. They have identified a previously unknown molecular mechanism by which the transcription of genetic information from deoxyribonucleic acid (DNA) into ribonucleic acid (RNA) can be initiated. The researchers focused on a specific class of molecules known as alarmones, which are found in cells across a wide range of organisms and whose levels often increase under conditions of cellular stress. The results were published in the prestigious scientific journal </strong><i><strong>Nature Chemical Biology</strong></i><strong>.</strong></p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/UOCHB_Vyzkum_z_UOCHB_odhaluje_dosud_nepoznany_zpusob_prepisu_geneticke_informace_bc2bfffa6c.jpg" alt="IOCB Prague/Tomáš Belloň: Research from IOCB Prague reveals a previously unknown mechanism of genetic transcription: Dr. Hana Cahová, head of the Chemical Biology of Nucleic Acids research group." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_UOCHB_Vyzkum_z_UOCHB_odhaluje_dosud_nepoznany_zpusob_prepisu_geneticke_informace_bc2bfffa6c.jpg 234w," sizes="100vw" width="2000" height="1335"></p><div><p>Molecules of ribonucleic acid can carry a variety of chemical modifications at one end, known as caps. In eukaryotic organisms, including human cells, the best-known cap plays an important role in RNA stability and in regulating its subsequent fate. In recent years, however, it has become clear that alternative, non-canonical RNA caps also exist, although their formation and the mechanisms by which they are attached to RNA remain only partially understood. These include alarmone caps, formed by dinucleoside polyphosphate molecules, that protect cellular RNA at moments when the cell is under threat.</p><p>In their study,<strong> </strong><a target="_blank" rel="noopener noreferrer" href="https://cahova.group.uochb.cz/en"><strong>Dr. Hana Cahová</strong></a> and colleagues examined bacterial RNA polymerase and investigated how this enzyme can initiate transcription using dinucleoside polyphosphates (NpNs) instead of the standard RNA building blocks. For the first time, the scientists described, at the atomic level, how RNA bearing an alarmone cap can be generated directly at the initiation of gene transcription. They also observed that NpNs bind through a different type of base pairing than is typically observed.</p><p>The work was significantly shaped by <strong>Valentina Serianni from Hana Cahová’s team</strong>, who demonstrated the ability of dinucleoside polyphosphates to initiate gene transcription, and by <strong>Jana Škerlová</strong>, who focused on the structural analysis of RNA polymerase. Using cryogenic electron microscopy data, she showed how dinucleoside polyphosphate molecules bind within the active site of RNA polymerase – that is, the core of the enzyme where genetic information is transcribed. Together, their findings help to elucidate the processes that accompany the transcription of genetic information from DNA to RNA.</p><p>Hana Cahová adds: <i>“We’re describing something that truly occurs in cells and that we’re now able to observe directly at the level of individual molecules. This allows us to answer fundamental questions about cellular processes, such as how cells adapt to stress. RNA plays a central role in this, as it carries the cascade of information underlying any cellular response – for example, to threatening conditions caused by nutrient deprivation or temperature shock.”</i></p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/UOCHB_Vyzkum_z_UOCHB_odhaluje_dosud_nepoznany_zpusob_prepisu_geneticke_informace_2_0fe88ff9e9.jpg" alt="IOCB Prague/Tomáš Belloň: Research from IOCB Prague reveals a previously unknown mechanism of genetic transcription: Dr. Tomáš Kouba, head of the Cryogenic Electron Microscopy core facility." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_UOCHB_Vyzkum_z_UOCHB_odhaluje_dosud_nepoznany_zpusob_prepisu_geneticke_informace_2_0fe88ff9e9.jpg 234w," sizes="100vw" width="2000" height="1335"></p><div><p>Cryogenic electron microscopy (cryo-EM) was central to the project. This part of the work was led by<strong> Dr. Tomáš Kouba</strong>, one of the authors of the study. <i>“Cryogenic electron microscopy allows us to freeze biological molecules in a state very close to their natural form and then determine their three-dimensional structure. This makes it possible to look directly into the active centers of enzymes and observe their function down to the atomic level,”</i> he explains.</p><p>Last year, IOCB Prague opened a new <a target="_blank" rel="noopener noreferrer" href="https://cryoem.group.uochb.cz/en"><strong>cryo-EM center</strong></a><strong> </strong>that is unique within the Czech research environment. State-of-the-art cryogenic electron microscopes are housed in a specially designed building and enable the study of biological processes with exceptional structural precision.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/UOCHB_Vyzkum_z_UOCHB_odhaluje_dosud_nepoznany_zpusob_prepisu_geneticke_informace_3_4d881e7a58.jpg" alt="IOCB Prague: Research from IOCB Prague reveals a previously unknown mechanism of genetic transcription: This work shows how NpnNs initiate bacterial transcription. Cryo-EM reveals how nucleobases pair with the template: one in canonical and one in reverse Watson–Crick mode, providing a structural basis for their function as non-canonical RNA caps." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_UOCHB_Vyzkum_z_UOCHB_odhaluje_dosud_nepoznany_zpusob_prepisu_geneticke_informace_3_4d881e7a58.jpg 245w," sizes="100vw" width="2000" height="1077"></p><blockquote><h4><strong>The Original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.nature.com/articles/s41589-025-02134-5"><strong>Molecular insight into 5′ RNA capping with Np<sub>n</sub>Ns by bacterial RNA polymerase</strong></a></h4><p>Valentina M. Serianni, Jana Škerlová, Anna Knopp Dubánková, Anton Škríba, Hana Šváchová, Tereza Vučková, Anatolij Filimoněnko, Milan Fábry, Pavlína Řezáčová, Tomáš Kouba &amp; Hana Cahova&nbsp;</p><p>Nat. Chem. Biol. 21 (2026)</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1038/s41589-025-02134-5">https://doi.org/10.1038/s41589-025-02134-5</a></p><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><h4><strong>Abstract</strong></h4><p>RNA capped with dinucleoside polyphosphates has been discovered in bacteria and eukaryotes only recently. The likely mechanism of this specific capping involves direct incorporation of dinucleoside polyphosphates by RNA polymerase as noncanonical initiating nucleotides. However, how these compounds bind into the active site of RNA polymerase during transcription initiation is unknown. Here, we explored transcription initiation in vitro, using a series of DNA templates in combination with dinucleoside polyphosphates and model RNA polymerase from <i>Thermus</i> <i>thermophilus</i>. We observed that the transcription start site can vary on the basis of the compatibility of the specific template and dinucleoside polyphosphate. Cryo-electron microscopy structures of transcription initiation complexes with dinucleoside polyphosphates revealed that both nucleobase moieties can pair with the DNA template. The first encoded nucleotide pairs in a canonical Watson–Crick manner, whereas the second nucleobase pairs noncanonically in a reverse Watson–Crick manner. Our work provides a structural explanation of how dinucleoside polyphosphates initiate RNA transcription.</p></div></div>

Research from IOCB Prague reveals a previously unknown mechanism of genetic transcription

<p><strong>In the week from February 9, 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>5 000 GC, LC, MS and spectroscopy webinars</strong>.</p></blockquote><h5><strong>1. Thermo Fisher Scientific: Process Raman – A Reliable and Versatile Tool for Downstream Process Monitoring and Control for mAb Production</strong></h5><ul><li>Tu, 10.2.2026 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5808">Registration</a></li></ul><p>The presentation highlights how process Raman spectroscopy offers several advantages over traditional in-line UV-Vis analysis for process monitoring and metabolite production control.</p><h5><strong>2. Bruker: Meet Compact-Q DEER Spectrometer – Your Gateway to Quantum Innovation</strong></h5><ul><li>Tu, 10.2.2026 17:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5757">Registration</a></li></ul><p>Discover how the Compact-Q DEER spectrometer enables high-fidelity qubit characterization and control, combining advanced microwave architecture, AWGs, and cryogen-free operation.</p><h5><strong>3. Agilent Technologies: Novel Advancements in Trace Level Determination and Long-Term Stability of Metals in TMAH and UPW Matrices</strong></h5><ul><li>Tu, 10.2.2026 21:30 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/3733">Registration</a></li></ul><p>Explore novel approaches for trace-level metal determination and long-term stability assessment in TMAH and ultrapure water matrices.</p><h5><strong>4. AZoM: From First to Best – Introducing the Litesizer DIF Series</strong></h5><ul><li>We, 11.2.2026 15:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5765">Registration</a></li></ul><p>Join this live webinar introducing the new Litesizer DIF series of laser diffraction particle size analyzers.</p><h5><strong>5. Anton Paar: Polymer Rheology – Rotational and Transient Measurements on a Polymer Melt</strong></h5><ul><li>We, 11.2.2026 18:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5786">Registration</a></li></ul><p>Learn how rotational and transient viscosity measurements on rotational rheometers reveal polymer melt flow behavior, from method setup and accessories to data interpretation.</p><h5><strong>6. Milestone: Practical Guidance to Improve the Journey from Sample to Heavy Metals Analysis for Environmental Labs</strong></h5><ul><li>Th, 12.2.2026 16:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/5803">Registration</a></li></ul><p>Improve heavy metals analysis in environmental laboratories with practical tips on reagents, filtration, cleaning, and workflow optimization to enhance data quality and efficiency.</p><h5><strong>7. Agilent Technologies: Moving from R&amp;D and Limited QC on a UV-Vis-NIR Instrument to Large-Scale QC Using an Autosampler</strong></h5><ul><li>Th, 12.2.2026 20:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/webinar/2104">Registration</a></li></ul><p>This presentation covers key aspects of adding and using an autosampler to maximize UV-Vis-NIR QC workflows when scaling from R&amp;D to routine quality control.</p>

Webinars LabRulezICPMS Week 07/2026

<p><strong>LA-ICP-MS imaging, particularly in TOF mode, provides rapid, high-resolution elemental maps of complex geological materials, but quantitative accuracy is often limited by heterogeneous mineralogy. This study introduces a workflow using UMAP for unsupervised dimensionality reduction combined with k-means clustering to segment mineral phases directly from per-pixel elemental data.</strong></p><p>Cluster compositions are matched to known minerals based on stoichiometry, allowing phase-specific normalization and reducing quantification errors by up to 60%. Validated on dawsonite-bearing sandstones, the approach enables consistent phase-resolved geochemical comparisons across depth profiles and establishes UMAP as a practical tool for accurate, interpretable multielement imaging.</p><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05398"><strong>Mineral Phase-Resolved Quantification in LA-ICP-MS Imaging</strong></a></h4><p>Barbara Umfahrer, Jakub Buday, Pavel Pořízka, Jozef Kaiser, Paolo S. Garofalo*, and Detlef Günther*</p><p>Anal. Chem. 2026, 98, 1, 581–589</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.5c05398">https://doi.org/10.1021/acs.analchem.5c05398</a></p><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><p>Since its introduction in 1985, <a href="javascript:void(0);">(1)</a> Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) has matured into a routine technique for both qualitative and quantitative elemental analysis, finding use in diverse fields such as geology, <a href="javascript:void(0);">(2−4)</a> biology, <a href="javascript:void(0);">(5,6)</a> materials science, <a href="javascript:void(0);">(7)</a> and cultural heritage. <a href="javascript:void(0);">(8,9)</a> Especially in comparison to liquid analysis techniques, the method excels through minimal sample preparation, negligible sample destruction, and low limits of detection, which enable trace element analysis. However, besides the availability of standard reference materials, a longstanding challenge of quantitative LA-ICP-MS is the need for a suitable internal standard to account for variations in ablation rate and transport efficiency, as well as instrumental drift. <a href="javascript:void(0);">(10)</a> Developed for applications in geological matrices, this requirement was elegantly circumvented by Liu et al. <a href="javascript:void(0);">(11)</a> through 100 wt % normalization, which assumes that all detected elements form oxides summing up to 100 wt %. This approach was conceptually inspired by the summed-spectrum normalization calibration procedure introduced by Leach and Hieftje in 2000, which emphasized the use of total signal intensities for robust internal standardization. <a href="javascript:void(0);">(12)</a> The 100 wt % normalization strategy was subsequently extended to the bulk analysis of carbonates. <a href="javascript:void(0);">(13)</a></p><p>Concurrently, the advent of time-of-flight (TOF) mass analyzers and the development of fast-washout laser ablation cells <a href="javascript:void(0);">(14−16)</a> have transformed LA-ICP-MS into a powerful imaging tool, capable of generating spatial maps across a variety of sample matrices at high data acquisition rates. <a href="javascript:void(0);">(17−19)</a> Furthermore, in comparison to, e.g., X-ray fluorescence spectroscopy (XRF) or Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS), LA-ICP-MS offers a high depth resolution of approximately 0.2 μm on glass, enabling precise measurement of fine-scale structures not only laterally but also in depth. <a href="javascript:void(0);">(18)</a> However, as the volume and complexity of these elemental images have grown according to the progressive advancement of the method itself, now consisting of gigabytes of data produced within minutes, manual data interpretation has become increasingly impractical. To uncover meaningful details and patterns, researchers have applied multivariate methods such as Principal Component Analysis (PCA) and k-means clustering to reduce dimensionality and delineate distinct regions within the images. <a href="javascript:void(0);">(20−23)</a> The nonlinear dimensionality reduction technique Uniform Manifold Approximation and Projection (UMAP), <a href="javascript:void(0);">(24)</a> which supports data interpretation with its capability to capture and separate subtle local structures, has first been employed to LA-ICP-MS bulk analysis data in 2024, where it was used to classify pyrite samples and discriminate between Pb and Zn deposit types. <a href="javascript:void(0);">(25,26)</a> In combination with elemental imaging, UMAP has recently been applied to biological tissue data sets as an exploratory analysis tool, <a href="javascript:void(0);">(27)</a> further enhancing the ability to visualize and interpret complex elemental distributions.</p><p>Despite these advances, no existing approach combines the unsupervised segmentation of mineral phases by UMAP with phase-specific normalization. In this study, we introduce a UMAP-driven workflow that (1) automatically segments distinct mineralogical phases within fine-grained geological matrices and (2) applies phase-specific 100 wt % normalization (e.g., oxide <a href="javascript:void(0);">(11)</a> or carbonate <a href="javascript:void(0);">(13)</a>), thereby improving quantification accuracy and enabling phase-specific quantitative interpretation of mineral distributions in complex matrices.</p><h4><strong>Materials and Methods</strong></h4><h5><strong>LA-ICP-TOFMS Imaging</strong></h5><p>LA-ICP-TOFMS analyses were performed using a 193 nm ArF excimer laser (GeoLas C, Lambda Physik, Göttingen, Germany) coupled to an ICP-TOFMS instrument (icpTOF 2R, TOFWERK AG, Thun, Switzerland). Ablation was conducted in the modified parallel flow ablation cell (MPFAC) <a href="javascript:void(0);">(16,34)</a> under a He atmosphere (99.999%, Linde Gas Schweiz AG, Dagmersellen, Switzerland) with Ar (99.996%, Linde Gas Schweiz AG) used as the carrier gas. A piezoelectrically driven xyz translational stage (1 nm resolution, SmarAct GmbH, Oldenburg, Germany) enabled precise sample positioning. Laser, stage movement, and ICP-TOFMS data acquisition were synchronized using a custom-built imaging control system. <a href="javascript:void(0);">(19)</a> The system was optimized each day based on maximum sensitivity while ensuring a <sup>238</sup>U<sup>+</sup>/<sup>232</sup>Th<sup>+</sup> intensity ratio of 1.0 to 1.1 on NIST SRM 610 and a <sup>232</sup>Th<sup>16</sup>O<sup>+</sup>/<sup>232</sup>Th<sup>+</sup> rate of below 0.5%.</p><h5><strong>LIBS Imaging</strong></h5><p>The LIBS analyses were performed on a Firefly system (Lightigo, Czech Republic). The laser (1064 nm, 5 mJ, pulse duration ∼10 ns, diameter 6 mm, operating at 50 Hz) was focused on the sample with a singlet lens (focal length 50 mm) to a 30 μm spot. The spatial resolution of the acquired elemental maps, with a size of about 16 mm × 26 mm, was 20 μm. Plasma radiation is collected by a multifiber configuration with a collimator and an optical fiber (400 μm core diameter), which delivered the light to several Czerny–Turner spectrometers (resolution 0.25–0.4 nm). The spectral range of the spectrometers is 180–850 nm. The radiation is collected 1.5 μs after ablation, with a gate width of 15 μs.</p><p>Data processing was done with Python 3.11.8 in the Spyder environment and included background correction and per-pixel z-score normalization.</p><h4><strong>Results and Discussion</strong></h4><h5><strong>LIBS and XRD Measurements</strong></h5><p>To assess whether carbonate-based normalization <a href="javascript:void(0);">(13)</a> is warranted, first, large-scale LIBS elemental intensity maps of the samples across about a third of the thin sections were acquired. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05398#fig1">Figure 1</a> reveals the results for “Layer 2” of the Poggio del Gatto sandstone for selected elements. A complete mapping of all four thin sections can be found in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05398/suppl_file/ac5c05398_si_001.pdf">Figures S3 and S6</a> in the Supporting Information. The maps reveal that C is strongly enriched in fracture veins as well as in finer-grained domains within the matrix and correlates with Ca, indicating carbonate minerals rather than oxides. Bulk XRD analysis identifies calcite alongside quartz, plagioclase, white mica, and kaolinite. These complementary observations confirm calcite as a major phase and thus justify the need for performing the 100 wt % normalization on a carbonate basis for pixels representing calcite.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_1_581_589_Figure_1_LIBS_elemental_intensity_maps_of_the_data_set_from_Layer_2_of_the_Poggio_del_Gatto_sandstone_mounted_on_a_microscope_slide_73ada1b7b8.jpg" alt="Anal. Chem. 2026, 98, 1, 581–589: Figure 1. LIBS elemental intensity maps of the data set from “Layer 2” of the Poggio del Gatto sandstone mounted on a microscope slide, depicting calcite veins and fine-grained structures within the matrix, underlining the need for carbonate-based normalization for specific pixels. Emission lines for evaluation are shown in the corresponding subfigure title. The epoxy used to fix the thin section on the slide is visible along the margin." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_1_581_589_Figure_1_LIBS_elemental_intensity_maps_of_the_data_set_from_Layer_2_of_the_Poggio_del_Gatto_sandstone_mounted_on_a_microscope_slide_73ada1b7b8.jpg 245w," sizes="100vw" width="2088" height="414"></p><h5><strong>Renormalization Based on Carbonates</strong></h5><p>Applying an oxide-based normalization to carbonate samples will introduce composition-dependent biases that cannot immediately be generalized as a singular numerical value. The difference between the molecular masses of carbonate CO<sub>3</sub><sup>2–</sup> and its oxide equivalent O<sup>2–</sup> yields an intrinsic bias on the order of a factor of about two, depending on the stoichiometry. Once this data is renormalized to a total of 100 wt %, the magnitude of the error becomes highly dependent on the specific elemental composition of the sample. An exemplary calculation illustrating this effect for a representative calcite pixel is provided in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c05398/suppl_file/ac5c05398_si_001.pdf">Table S2</a>, yielding a relative error of 79%. In practice, this error varies across the image according to the local phase composition. <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c05398#fig6">Figure 6</a> illustrates the impact of normalization approaches for layer 2 of the samples as well as the spatial distribution of the resulting relative error. The figure presents Ca and Si elemental maps in two rows, while the first three columns compare different normalization strategies: normalization based on all oxides, normalization based on all carbonates, and a pixel-wise normalization guided by mineral phase identification, as outlined in the workflow proposed in this study. The final column displays the relative error between the bulk oxide- or carbonate-based normalization and the pixel-wise, phase-informed normalization.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_1_581_589_Figure_6_Comparison_of_normalization_strategies_for_Ca_and_Si_LA_ICP_TOFMS_elemental_maps_in_Layer_2_ce3a0c1a5c.jpg" alt="Anal. Chem. 2026, 98, 1, 581–589: Figure 6. Comparison of normalization strategies for Ca and Si LA-ICP-TOFMS elemental maps in “Layer 2” of the Poggio del Gatto sandstone. Rows show Ca (top) and Si (bottom); columns compare (1) oxide-based normalization, (2) carbonate-based normalization, (3) pixel-wise, phase-informed normalization (this study), and (4) relative error between the first two and the phase-informed approach." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_1_581_589_Figure_6_Comparison_of_normalization_strategies_for_Ca_and_Si_LA_ICP_TOFMS_elemental_maps_in_Layer_2_ce3a0c1a5c.jpg 202w," sizes="100vw" width="2088" height="1622"></p><p>For Ca (in this matrix primarily associated with calcite, a dominant carbonate phase in our samples), the relative error depicted quantifies the effect of applying oxide-based normalization across the entire image. This results in a systematic overestimation of the Ca concentration by approximately 60%. Conversely, for Si─in this matrix found mainly in quartz and various plagioclase phases─the error is shown for applying carbonate-based normalization across an oxidic matrix. In such cases, the Si concentrations are underestimated by roughly 10% to 40%, depending on the mineral phase.</p><p>These findings highlight that using a single normalization scheme across heterogeneous samples introduces substantial quantification errors. To mitigate this, the pixel-wise phase segmentation enabled by the proposed UMAP-based workflow offers a significant advantage, especially in fine-grained matrices. In short, phase-aware, pixel-wise normalization combines the strengths of both normalization approaches and proves to be highly beneficial.</p><h4><strong>Conclusion</strong></h4><p>This study demonstrates that phase-specific normalization, guided by unsupervised phase segmentation of LA-ICP-MS images, can substantially improve the accuracy of elemental quantification in fine-grained geological matrices. Rather than relying on a single global assumption (e.g., all oxides or all carbonates), the workflow adapts normalization to the actual mineralogy, minimizing systematic errors and enabling phase-resolved geochemical interpretation.</p><p>The segmentation step, based on UMAP embedding and <i>k</i>-means clustering, is fully unsupervised. However, the subsequent matching of clusters to specific minerals still relies on a limited set of preidentified stoichiometries and is therefore not yet fully unsupervised. Expansion to larger stoichiometric databases could help overcome this limitation and move toward automated mineral classification. A key challenge remains the similarity of cation ratios in certain mineral groups, which may still require complementary data to ensure robust assignments.</p><p>Overall, beyond the specific case of the Poggio del Gatto sandstone presented in this study, this work provides a framework for quantitative elemental imaging of complex geological samples and is readily transferable to other contexts that require spatially resolved composition-aware normalization. This contribution adds a practical implementation to a growing literature that already uses multivariate statistics to unlock the immense information depth of LA-ICP-TOFMS.</p>

Mineral Phase-Resolved Quantification in LA-ICP-MS Imaging

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The Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences (IOCB Prague) is a leading scientific institution in the Czech Republic, recognized internationally. Its primary mission is basic research in the fields of chemical biology and medicinal chemistry, organic and material oriented chemistry, chemistry of natural compounds, biochemistry and molecular biology, physical chemistry, theoretical chemistry, and analytical chemistry. 

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<blockquote><p>Unlock a broader spectrum of possibilities with Wiley SmartSpectra Databases, enhancing your chemical compound coverage to ensure more accurate and comprehensive unknown identification.</p><p><strong>More information on the Wiley Science Solutions website: </strong><a target="_blank" rel="noopener noreferrer" href="https://sciencesolutions.wiley.com/solutions/technique/raman/wiley-smartspectra-raman-database-collection/"><strong>Wiley SmartSpectra Raman Database Collection</strong></a></p></blockquote><p data-start="0" data-end="254">The <strong data-start="4" data-end="52">Wiley SmartSpectra Raman Database Collection</strong> offers access to <strong data-start="70" data-end="101">33,163 high-quality spectra</strong> for identifying unknown compounds and enhancing analytical workflows. Built on <strong data-start="181" data-end="210">Wiley’s trusted databases</strong>, it ensures scientifically sound results.</p><h5><strong>Key Benefits:</strong></h5><ul data-start="276" data-end="738" data-is-only-node="" data-is-last-node=""><li data-start="276" data-end="352">Aids in identifying rare compounds when empirical databases lack matches</li><li data-start="353" data-end="438">Supports <strong data-start="364" data-end="399">functional group classification</strong>, excelling with <strong data-start="416" data-end="436">rigid structures</strong></li><li data-start="439" data-end="530">Derived from <strong data-start="454" data-end="497">Wiley’s high-quality empirical datasets</strong>, including <strong data-start="509" data-end="528">Sadtler spectra</strong></li><li data-start="531" data-end="599">Covers a wide range of compound classes for diverse applications</li><li data-start="600" data-end="689">Includes <strong data-start="611" data-end="623">metadata</strong> (physical properties, chemical structures) for precise searches</li><li data-start="690" data-end="738" data-is-last-node=""><strong data-start="692" data-end="738" data-is-last-node="">Validated by Subject Matter Experts (SMEs)</strong></li></ul><h4><strong>So, just how much can the SmartSpectra database expand your compound coverage?</strong></h4><p>The <strong data-start="4" data-end="20">SmartSpectra</strong> database accelerates <strong data-start="42" data-end="69">spectral identification</strong> by expanding <strong data-start="83" data-end="113">chemical compound coverage</strong>. As shown, <strong data-start="125" data-end="152">SmartSpectra data (red)</strong> extends the <strong data-start="165" data-end="183">chemical space</strong> of the <strong data-start="191" data-end="219">empirical dataset (blue)</strong>, significantly increasing compound coverage.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Wiley_Wiley_Smart_Spectra_Raman_Database_Collection_Wiley_Raman_empirical_data_only_blue_Smart_Spectra_Raman_data_included_red_da42d540b6.jpg" alt="Wiley: Wiley SmartSpectra Raman Database Collection - Wiley Raman empirical data only (blue), SmartSpectra Raman data included (red)."></p><h4><strong>Applications</strong></h4><p data-start="19" data-end="229">Use the <strong data-start="27" data-end="43">SmartSpectra</strong> collection with <strong data-start="60" data-end="83">empirical databases</strong> for <strong data-start="88" data-end="110">Raman spectroscopy</strong>.</p><h4><strong>Trusted Data</strong></h4><p data-start="250" data-end="471" data-is-last-node="" data-is-only-node=""><strong data-start="250" data-end="259">Wiley</strong> is a leading source for spectral data. Its <strong data-start="303" data-end="324">Sadtler databases</strong> undergo <strong data-start="333" data-end="363">rigorous quality protocols</strong>, from <strong data-start="370" data-end="414" data-is-only-node="">data acquisition to database development</strong>. All partner data is thoroughly vetted before inclusion.</p><blockquote><p><i>KnowItAll is a trademark of John Wiley &amp; Sons, Inc. in certain jurisdictions.</i></p><p><i>Subscription content is subject to change as data may be added or deleted from collections periodically.</i></p></blockquote><h3><strong>Databases Included</strong></h3><p><strong>Subscription includes access to 11 databases including:</strong></p><ul><li>Wiley SmartSpectra Raman Database of General Compounds (24,585 spectra)</li><li>Wiley SmartSpectra Raman Database of Biochemicals (59 spectra)</li><li>Wiley SmartSpectra Raman Database of Chemical Standards (6,458 spectra)</li><li>Wiley SmartSpectra Raman Database of Drugs (130 spectra)</li><li>Wiley SmartSpectra Raman Database of Dyes (36 spectra)</li><li>Wiley SmartSpectra Raman Database of Explosives and Hazmat Compounds (51 spectra)</li><li>Wiley SmartSpectra Raman Database of Food Related Compounds (277 spectra)</li><li>Wiley SmartSpectra Raman Database of Hydrocarbons (527 spectra)</li><li>Wiley SmartSpectra Raman Database of Industrial Compounds (439 spectra)</li><li>Wiley SmartSpectra Raman Database of Pollutants (449 spectra)</li><li>Wiley SmartSpectra Raman Database of Monomers (152 spectra)</li></ul><h3><strong>Additional information</strong></h3><p>This SmartSpectra library contains the following valuable metadata to help you narrow and refine your search results:</p><ul><li>Chemical Structure</li><li>Chemical Name</li><li>Exact Mass</li><li>Formula</li><li>IUPAC InChI/InChIKey</li><li>Molecular Weight</li><li>Nominal mass</li><li>Spectrum/Structure Validation Score</li></ul><h3><strong>Compatibility</strong></h3><ul><li>Recommended as a supplement to the <i>KnowItAll IR Spectral Library</i> collection of curated empirical data.</li><li>For use with <i>KnowItAll 2024</i> software.</li><li>KnowItAll software is compatible with most major instrument formats. For full <i>KnowItAll</i> file format compatibility information, please visit <a target="_blank" rel="noopener noreferrer" href="https://sciencesolutions.wiley.com/compatibility/">sciencesolutions.wiley.com/compatibility</a>.</li></ul>

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