<div><div><section><div><div><div><div><div><div><p><strong>This study introduces a highly sensitive compound-specific isotope analysis method for identifying protein binders in aged paintings. Using direct inlet pyrolysis-GC-combustion-isotope ratio mass spectrometry (DIP-GC-C-IRMS), the method targets δ13C values of pyrolytic diketopiperazines and reduces the required sample size from milligrams to micrograms.</strong></p><p>Reference materials showed that diketopiperazine isotope signatures preserve the distinction between terrestrial and marine collagen sources. Analysis of two 16th-century Italian paintings yielded values consistent with terrestrial animal glue, demonstrating that these isotopic markers can survive aging and degradation and support minimally destructive analysis of cultural heritage objects.</p></div></div></div></div></div><div><blockquote><h4><strong>The original article</strong></h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/ancham/article/98/29/21183/5211080/Pyrolysis-Compound-Specific-Isotope-Analysis-of?searchresult=1"><strong>Pyrolysis Compound-Specific Isotope Analysis of Proteins in Aged Paintings: Diketopiperazine δ13C Values as Collagen Source Indicators&nbsp;</strong></a></h4><p>Eugenia Geddes da Filicaia* ; David A. Peggie; Iain P. Kendall; Ian D. Bull; Richard P. Evershed</p><p>Anal. Chem. (2026) 98 (29): 21183–21196.</p><p>&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.5c07901">https://doi.org/10.1021/acs.analchem.5c07901</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><div><div><div><p>The complexities and challenges presented by the analysis of protein paint binders illustrate the need for novel methods of identification for these materials, in particular to distinguish between terrestrial and fish-animal glues. An alternative approach with great potential may be found in the neighboring field of archeology, where collagen in bones and teeth has been used as a palaeodietary indicator since the 1970s. Determination of bulk δ<sup>15</sup>N values may suggest the position of an organism in the respective food web, while δ<sup>13</sup>C values offer insight into their diet. (<a href="javascript:;">27−29</a>) Bulk δ<sup>13</sup>C and δ<sup>15</sup>N values of collagen are weighted averages of the δ<sup>13</sup>C and δ<sup>15</sup>N values of their constituent AAs, which reflect both dietary sources and metabolic processes. Serine, glycine, and alanine (Ser, Gly, and Ala) are part of the glucogenic AA group, which is biosynthesized from intermediates of glycolysis. As pyruvate is the end product of glycolysis, considered the catabolic pathway of carbohydrates, the isotopic signature of these AAs should reflect that of dietary carbohydrates. Aspartic acid and glutamic acid (Asp and Glu), asparagine (Asn), and Pro are part of the ketogenic AA group, originating from intermediates of the tricarboxylic acid (TCA) cycle. Due to their TCA cycle precursors, the isotopic signature of these AAs should reflect that of the whole diet, if biosynthesized <i>de novo</i>. Finally, Hyp and hydroxylysine cannot be incorporated into proteins from the diet and are formed after the polypeptides are synthesized, during posttranslational processing of collagen. Hydroxylysine is formed from lysine (Lys), whereas Hyp results from hydroxylation of Pro residues. (<a href="javascript:;">30</a>) Moreover, AAs in the “trophic group“ tend to display a large enrichment in <sup>15</sup>N with increasing trophic level, unlike those belonging to the “source group.“</p></div><div><p>Significant limitations therefore surround the interpretation of bulk stable isotope ratios, which, however, may be overcome by compound-specific isotope analysis (CSIA). Individual AA δ<sup>13</sup>C values have proven invaluable for distinguishing terrestrial from marine diets of our ancestors, (<a href="javascript:;">30−35</a>) while AA δ<sup>15</sup>N values are being increasingly used in archeological trophic position studies. (<a href="javascript:;">36−39</a>) Despite the similarity in target material, CSIA has never been extended to the analysis of animal glue, or any other protein, in paintings. Reasons for this are likely linked to the sample sizes required for standard protocols for GC-combustion-isotope ratio MS (GC-C-IRMS), which, however, as illustrated by recent studies, (<a href="javascript:;">40−42</a>) may be significantly reduced by undertaking direct inlet pyrolysis (DIP)-GC-C-IRMS. Here, this technique is applied to the analysis of proteins of paintings, in particular by targeting, for the first time, the stable carbon isotope composition of DKPs, protein pyrolysates. Having confirmed that DKPs reflect the isotopic signature of their constituent AAs, DKP δ<sup>13</sup>C values may be used to distinguish terrestrial from aquatic animal glue in aged paintings.</p><h4><strong>Materials and Methods</strong></h4><h5><strong>Instrumental Analyses</strong></h5><div><p>For elemental analysis (EA)-IRMS, GC-FID, and GC-C-IRMS methods, please see SI1. All chromatograms were replotted in RStudio. DIP-GC-MS was carried out on an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=7890A"><strong>Agilent Technologies 7890A GC</strong></a> coupled to an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=5975C"><strong>Agilent Technologies 5975C MSD</strong></a> (Agilent Technologies, Santa Clara, CA, USA), a single quadrupole MS. The GC was equipped with a nonpolar fused-silica capillary column (30 m × 0.25 mm i.d. × 0.25 μm film thickness) with a BPX5 stationary phase (5% phenyl polysilphenylene-siloxane, SGE) and a TSP in the multimode inlet (MMI), operated in splitless mode. The initial TSP temperature was 50 <span>°</span>C, set to rise to 450 <span>°</span>C (900 <span>°</span>C min<sup>–1</sup>, 3 min isothermal hold), before cooling to 250 <span>°</span>C (25 <span>°</span>C min<sup>–1</sup>, 40 min isothermal hold). The oven temperature began at 40 <span>°</span>C, was increased to 200 <span>°</span>C (10 <span>°</span>C min<sup>–1</sup>), and then to 310 <span>°</span>C (6 <span>°</span>C min<sup>–1</sup>) with a final hold time of 20 min. He was used as a carrier gas at a constant flow (1.2 mL min<sup>–1</sup>).</p></div><div><p>The MS was set to acquire in full scan mode (<i>m</i>/<i>z</i> 50–650) at 0.2 scan s<sup>–1</sup>, and was operated at an EI potential of 70 eV with a solvent delay of 6.5 min. The MS transfer line temperature was set to 280 <span>°</span>C, the ion source temperature to 230 <span>°</span>C, and the quadrupole to 150 <span>°</span>C. Data acquisition was undertaken using Agilent Technologies Chemstation, and data processing with Agilent Technologies MassHunter software (version 10.0) and the AMDIS deconvolution software (NIST, version 2.66). Peaks were identified using a combination of mass spectral information, GC retention times (RTs), and comparison to the NIST mass spectral library (Mass Spectral Search Program version 2.0 and NIST 08 MS Library), published data sets, and NG databases. An <i>n</i>-alkane “ladder“ (with a characteristic bell-shaped peak profile) standard, containing C<sub>7</sub>–C<sub>40</sub> saturated alkanes (0.01 mg mL<sup>–1</sup> in <i>n</i>-hexane), was used to monitor instrument performance.</p></div><div><p>DIP-GC-QTOF-MS analysis was carried out on an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=7890B"><strong>Agilent Technologies 7890B GC</strong></a> coupled to an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=7200"><strong>Agilent Technologies 7200 Accurate Mass QTOF MS</strong></a> (Agilent Technologies, Santa Clara, CA, USA). The same method was used, except that the MMI was held at 450 <span>°</span>C for 1 min only. The MS was set with an acquisition rate of 5 spectra s<sup>–1</sup>, an acquisition time of 200 ms spectrum<sup>–1</sup>, and an emission current of 2.1 μA. DIP-GC-C-IRMS analysis was carried out on an <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library?mainauthorItems=10&amp;search=8890"><strong>Agilent Technologies 8890 GC</strong></a> coupled to an <strong>Elementar isoprime precisION IRMS</strong> via an Elementar GC5 combustion interface, using a combustion reactor containing CuO pellets and Ag wool (Elementar Analysensysteme GmbH, Hanau, Germany), maintained at 850 <span>°</span>C. The GC parameters were identical to those for DIP-GC-MS, except that the MMI, after being held at 450 <span>°</span>C for 3 min, was cooled to 300 <span>°</span>C (900 <span>°</span>C min<sup>–1</sup>, isothermal hold for 50 min). Additionally, the oven program was typically shortened by setting a lower maximum temperature (280 <span>°</span>C instead of 310 <span>°</span>C), where it was held for 10 min. He was used as a carrier gas at a constant flow (2 mL min<sup>–1</sup>).</p></div></div></div></div></div></div></section></div></div><h4><strong>Results and Discussion</strong></h4><h5><strong>Pyrolytic Marker Identification from DIP-GC-MS of Protein References</strong></h5><div><p>The thermal degradation of intact proteins has been shown to be a multistep reaction, (<a href="javascript:;">45</a>) where the formation of DKPs, occurring in the first degradation step (<i>T</i><sub>max</sub> = 325 <span>°</span>C), does not require high Py temperatures. (<a href="javascript:;">21</a>) Protein markers may therefore be detected through DIP-GC-MS, as testified by the analysis of a soapstone fragment from Qarmaaluit (18th–19th century CE). (<a href="javascript:;">46</a>) Nonetheless, given that this technique is not widely reported in the literature, modern protein references were analyzed by DIP-GC-MS, and pyrolytic profiles obtained were compared to published Py-GC-MS data. Studies have been undertaken with (<a href="javascript:;">22, 47, 48</a>) and without (<a href="javascript:;">22, 26</a>) TMAH, hence, both methods were carried out here. Results were scrutinized, in particular, to identify pyrolytic markers suitable for DIP-GC-C-IRMS analysis. Egg yolk, which presents a similar AA composition as egg white, was not examined, as its high fat content was deemed to interfere with the analysis.</p></div><div><p>In general, DIP-GC-MS analyses involving derivatization with TMAH, in particular for animal glues, featured many more compounds compared to the total ion chromatograms (TICs) obtained without derivatization (see <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/ancham/article-supplement/5211080/pdf/ac5c07901_si_001/">Figure S1</a> for G1, aged gelatin spread). In this case, GC-MS TICs exhibited better baseline separation with the most abundant peaks corresponding to the targeted DKPs. Further analysis of glue samples was therefore carried out without derivatization, as good baseline resolution is central to reliable compound-specific stable isotope determinations. (<a href="javascript:;">43</a>) Analysis of rabbit skin glues (GRS1 and GRS2) and isinglass glues (GI1 and GI2) yielded GC-MS TICs identical to each other. In accordance with the literature, (<a href="javascript:;">21, 22, 49</a>) as illustrated in <a href="javascript:;">Figure 1</a>a, they are dominated by peaks corresponding to diketodipyrrole (peak 8) and cyclo(Pro-Gly) (peak 10). Because of its relatively high polarity, or overloading, the latter produces a broad (fronting) peak. (<a href="javascript:;">50</a>)</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_29_21183_21196_Figure_1_Partial_DIP_GC_QTOF_MS_total_ion_chromatograms_TI_Cs_for_a_isinglass_GI_2_b_casein_C1_and_c_egg_white_EW_1_f4442d8498.jpg" alt="Anal. Chem. (2026) 98 (29): 21183–21196: Figure 1. Partial DIP-GC-QTOF-MS total ion chromatograms (TICs) for (a) isinglass, GI2, (b) casein, C1, and (c) egg white, EW1. Information on relevant DKPs (in purple) and other peaks (in pink) is listed in Table S1." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_29_21183_21196_Figure_1_Partial_DIP_GC_QTOF_MS_total_ion_chromatograms_TI_Cs_for_a_isinglass_GI_2_b_casein_C1_and_c_egg_white_EW_1_f4442d8498.jpg 127w," sizes="100vw" width="1500" height="1840"></p></div><h5><strong>DIP-GC-C-IRMS for Source Determination of Proteins in Paintings from The National Gallery</strong></h5><div><p>Ground layer samples from two Italian Renaissance paintings in the National Gallery collection, NG599 (Madonna of the Meadows) and NG6368 (Tanaquil), were analyzed by GC by White in the 1980s. (<a href="javascript:;">15</a>) The author reported AA profiles consistent with the presence of animal glues, which, for this purpose, would be expected to be of terrestrial origin. However, given the low proportion of Pro and Hyp in NG6368, White discussed a possible aquatic origin for this glue. Analysis was repeated with the aforementioned DIP techniques, revealing very similar profiles to those of the reference animal glues (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/ancham/article-supplement/5211080/pdf/ac5c07901_si_001/">Figure S6</a>). The protein binder in the ground layers of both paintings exhibited similar DKP stable carbon isotope signatures. In particular, δ<sup>13</sup>C<sub>Pro-Gly</sub> values were determined to be −18.4‰ (±0.1) and −17.6‰ (±0.1) for NG599 and NG6368, respectively. The δ<sup>13</sup>C<sub>Pro-Hyp</sub> values were found to equal −19.8‰ (±0.4) and −18.6‰ (±0.001), respectively.</p></div><div><p>It should be noted that the SD observed for these determinations is lower than what was reported for some of the reference binders discussed above. These errors are in line with those observed for standard GC-C-IRMS methods. As these paint fragments were in the form of very fine powder, typical of paint samples, these results support that sample inhomogeneities might be primarily responsible for wide SDs, rather than the DIP-GC-C-IRMS method itself. In the case of marine animal glues, more enriched δ<sup>13</sup>C<sub>DKP</sub> values would be expected than those observed here, especially for cyclo(Pro-Gly). Instead, the determined δ<sup>13</sup>C<sub>DKP</sub> values suggest a terrestrial origin for the glues in both paintings. Indeed, by plotting the values as described above, as shown in <a href="javascript:;">Figure 7</a>, both paintings fall extremely close to the reference rabbit skin glues. Hence, for NG599, these results are in agreement with the previous analysis by White (1984). However, regarding the origin of the binder from NG6368, these findings support a similar terrestrial origin, suggesting that a lower-than-average concentration of Pro and Hyp is not characteristic of fish glues.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2026_98_29_21183_21196_Figure_7_a_d13_C_Pro_Gly_values_for_the_ground_layers_from_paintings_NG_599_purple_star_and_NG_6368_pink_star_bb585e6957.jpg" alt="Anal. Chem. (2026) 98 (29): 21183–21196: Figure 7. (a) δ13CPro-Gly values for the ground layers from paintings NG599 (purple star) and NG6368 (pink star), against their δ13CPro-Hyp values. Modern references for marine (GI1 and GI2) and terrestrial (GRS1 and GRS2) animal glues are in gray. (b) Corresponding Δ13C(Pro-Gly)–(Pro-Hyp) values against their δ13CPro-Gly values. The error bars represent the SD on either side (±) of the value. The δ13C values have been corrected to take into account post-Industrial Revolution effects of burning fossil fuels by the addition of 1.2‰. (68) Instrumental precision was 0.3‰." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2026_98_29_21183_21196_Figure_7_a_d13_C_Pro_Gly_values_for_the_ground_layers_from_paintings_NG_599_purple_star_and_NG_6368_pink_star_bb585e6957.jpg 72w," sizes="100vw" width="625" height="1349"></p><p>Although a wider range of reference material needs to be analyzed before this method may be reliably used, these results indicate that DIP-GC-C-IRMS is able to differentiate between terrestrial and marine animal glues in painting samples. Given the challenges currently faced by this field to differentiate fish glues from other animal glues, this innovative technique might enable new information to be gathered on artist techniques and the history of use of these materials. It is important to note that the method can be applied to many other fields of research. In addition to painting analysis, this minimally invasive protocol is pertinent to any discipline that involves the analysis of degraded proteins, heterogeneous and complex matrices, or where access to samples is restricted due to ethical implications or lack of material. This includes environmental and forensic sciences, paleo- and zooarcheology, paleoecology, and anthropology.</p></div><h4><strong>Conclusions</strong></h4><div><p>Aged proteins encountered in painted cultural heritage have usually undergone extreme structural changes from the freshly applied material, often rendering unambiguous protein source determination unattainable. In particular, the distinction between terrestrial and aquatic animal glues is typically not possible without proteomics approaches. Nonetheless, this paper demonstrates that, with minimal sample sizes, DIP-GC-C-IRMS permits determination of δ<sup>13</sup>C values of DKPs arising from pyrolysis of aged proteinaceous material, providing the first record of the isotopic signature of these compounds. In this study, δ<sup>13</sup>C values of DKPs were shown to reflect the δ<sup>13</sup>C values of the parent AAs and, therefore, to permit unambiguous differentiation between terrestrial and marine animal glues, analogous to the use of CSIA of archeological collagen for palaeodietary reconstruction.</p></div><div><p>Remarkably, DIP-GC-C-IRMS, requiring a considerably smaller sample (in the microgram range) than typical GC-C-IRMS methods (in the milligram range), renders CSIA accessible for the investigation of proteinaceous material in painted cultural heritage for the first time. This widens the application of CSIA to precious, unique, and fragile objects in cultural heritage and beyond. Moreover, the rapidity of the analysis places DIP-GC-C-IRMS as a useful screening method to identify materials of interest for further proteomic analyses.</p></div><div><p>First, the isotopic imprint of modern references for rabbit skin glues, isinglass glues, casein, chicken, and duck eggs was confirmed through established EA-IRMS and GC-C-IRMS methods. As expected, bulk and individual AA δ<sup>13</sup>C and δ<sup>15</sup>N values for terrestrial animal glues exhibited a higher depletion in <sup>13</sup>C and <sup>15</sup>N than the marine glues. The δ<sup>13</sup>C values of DKPs arising from DIP-GC-C-IRMS of the same materials were found to retain this relationship. Furthermore, the ground layers of two 16th-century Italian paintings from the National Gallery collection, known to contain animal glue, yielded δ<sup>13</sup>C<sub>DKP</sub> values consistent with the terrestrial variety, confirming that the isotopic signature of these compounds is retained through aging and degradation.</p></div>

Pyrolysis Compound-Specific Isotope Analysis of Proteins in Aged Paintings: Diketopiperazine δ13C Values as Collagen Source Indicators

<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

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Thermo Scientific ARL SMS-2300 Robotics-Based Automation for OES or XRF

Thermo Scientific™ ARL™ SMS-2300 enables fast, reproducible, and unattended OES/XRF analysis to optimize steel production and meet the strictest quality standards.

<h4><strong>Meet stringent steel specifications while increasing efficiency and lowering production costs.</strong></h4><p><strong>The Thermo Scientific™ ARL™ SMS-2300 Robotics-Based Automation for OES or XRF is an automated quality control solution to optimize your steel production operation. With the high-performance ARL SMS-2300 system, even complex samples are prepared and analyzed under reproducible conditions without operator intervention, eliminating human variables and subjective factors.</strong><br>👉 <strong>Result</strong>: <strong>Increased output</strong>, <strong>reliable results</strong>, and <strong>reduced costs</strong>.</p><h3>🔧 <strong>ARL SMS-2300 Automation for OES or XRF</strong></h3><p>Provides <strong>ultimate power and flexibility</strong> for <strong>automatic, unattended 24/7 operation</strong> of:</p><ul><li><strong>ARL™ 4460</strong></li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1213"><strong>ARL™ iSpark</strong></a><strong> / </strong><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1977"><strong>iSpark Plus OES</strong></a></li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products?instrumentationItems=100&amp;manufacturerItems=3&amp;search=9900"><strong>ARL™ 9900 XRF</strong></a></li></ul><h3>✅ <strong>Main Features</strong></h3><ul><li><strong>Easy integration</strong> into <strong>fully automatic laboratories</strong></li><li><strong>Installed in ARL QuantoShelter</strong>, close to production process</li><li><strong>Parallel tasking</strong>:<ul><li>Sample preparation</li><li>Sample introduction</li><li>Analysis</li><li><strong>Instrument stand cleaning</strong> performed in hidden time</li></ul></li><li><strong>High throughput</strong> with:<ul><li><strong>Automatic sample preparation</strong></li><li><strong>Haas Mini Mill</strong> + <strong>ARL™ PrepControl™</strong> + <strong>EasyPrep™ software</strong></li></ul></li><li><strong>Direct access</strong> to <strong>Haas Mini Mill</strong> for <strong>faster ferrous sample processing</strong></li><li><strong>Factory-configured and customized</strong></li><li><strong>Compact chassis</strong>, <strong>simple installation</strong>, and <strong>integrated electronics</strong></li><li><strong>High reliability</strong>, <strong>rapid ROI</strong>, and <strong>fast result availability</strong></li><li><strong>Frees lab personnel</strong> from <strong>routine tasks</strong></li><li><strong>ISO 9001 compliant</strong> manufacturing and maintenance</li></ul><h3>🚀 <strong>Advanced Technology</strong></h3><ul><li><strong>Fanuc industrial robot</strong></li><li><strong>OXSAS analytical software</strong></li></ul>

Thermo Scientific ARL SMS-3300 Automation for Single or Twin OES or XRF

Thermo Scientific™ ARL SMS-3300 enables fully automated operation of one or two OES or XRF spectrometers, enhancing lab efficiency in industrial environments.

<p><strong>Drive process control laboratory efficiencies in the metals, mining and cement industries</strong> with the <strong>Thermo Scientific™ ARL™ SMS-3300 Automation for Single or Twin OES or XRF</strong>, a <strong>complete laboratory workflow automation solution</strong>.<br>The automation of <strong>single or multiple spectrometers and preparation machines</strong> with the ARL SMS-3300 system offers:</p><p>👉 <strong>Reduced sample analysis cost</strong>,<br>👉 <strong>Increased sample throughput</strong>,<br>👉 <strong>Better reproducibility</strong>, and<br>👉 <strong>Higher quality results</strong></p><p>for users requiring <strong>more comprehensive and integrated automation solutions</strong> in the <strong>metals, mining and cement industries</strong>.</p><h3>🔧 <strong>ARL SMS-3300 – Automation for Single or Twin OES or XRF</strong></h3><p>Provides <strong>ultimate power and flexibility</strong> for <strong>automatic, unattended, 24/7 operation</strong> of:</p><ul><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1213"><strong>Thermo Scientific™ ARL™ iSpark </strong></a><strong>/</strong><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products/1977"><strong> iSpark Plus</strong> </a>(Optical Emission Spectrometers)</li><li><a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/products?instrumentationItems=100&amp;manufacturerItems=3&amp;search=9900"><strong>Thermo Scientific™ ARL™ 9900</strong></a> (X-ray Fluorescence Spectrometer)</li></ul><h3>✅ <strong>Main Features</strong></h3><ul><li>Designed for <strong>easy integration</strong> into <strong>fully automatic laboratories</strong></li><li>Can be <strong>mounted in Thermo Scientific ARL QuantoShelter</strong>, close to the production process</li><li><strong>Parallel task execution</strong>:<ul><li>Sample preparation</li><li>Sample introduction</li><li>Analysis</li><li><strong>Instrument stand cleaning</strong> performed <strong>in hidden time</strong> by the spectrometer, independently of the robot</li></ul></li><li><strong>Direct access to milling machine</strong> for reduced processing times of <strong>ferrous samples</strong></li><li><strong>Higher throughput</strong> with:<ul><li><strong>Automatic sample preparation</strong></li><li><strong>ARL™ PrepControl™ system</strong> + <strong>ARL™ EasyPrep™ software</strong></li></ul></li><li><strong>Factory configured and customized</strong></li><li><strong>Compact chassis</strong> and <strong>simple installation</strong></li><li><strong>Integrated electronics</strong></li><li><strong>Improved analysis reliability and quality</strong></li><li><strong>Very rapid payback</strong></li><li><strong>Faster result availability</strong></li><li><strong>Qualified lab personnel freed from routine tasks</strong></li><li><strong>Manufactured and maintained</strong> according to <strong>ISO 9001-2000 procedures</strong></li></ul><h3>🚀 <strong>Advanced Technology</strong></h3><ul><li><strong>State-of-the-art Fanuc industrial robot</strong></li><li><strong>OXSAS analytical software</strong></li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Scientific_ARL_SMS_3300_Automation_for_Single_or_Twin_OES_or_XRF_fadcbb621a.jpg" alt="Thermo Scientific™ ARL SMS-3300 Automation for Single or Twin OES or XRF" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Scientific_ARL_SMS_3300_Automation_for_Single_or_Twin_OES_or_XRF_fadcbb621a.jpg 245w," sizes="100vw" width="650" height="366"></p>

Thermo Scientific ARL SMS-3500 Automation for Twin OES or XRF

The ARL SMS-3500 automates two OES or XRF spectrometers with smart sample flow management for fast, efficient, and continuous production control.

<p><strong>Drive process control laboratory efficiencies in the metals and mining industries</strong> with the <strong>Thermo Scientific™ ARL™ SMS-3500 Automation for Twin OES or XRF</strong>, a <strong>complete laboratory workflow automation solution</strong>.<br><strong>Primary metals producers' laboratories</strong> typically run <strong>two fully duplicate and separate spectrometry lines</strong> to assure <strong>fast response times</strong> when several urgent samples must be processed simultaneously.<br>👉 The <strong>ARL SMS-3500 system</strong> is an <strong>affordable alternative</strong>.</p><p><strong>The automation of multiple spectrometers and preparation machines with the ARL SMS-3500 system offers:</strong></p><ul><li>Reduced sample analysis cost</li><li>Increased sample throughput</li><li>Better reproducibility</li><li>Higher quality results</li></ul><p>for users requiring <strong>more comprehensive and integrated automation solutions</strong> in the <strong>metals and mining industries</strong>.</p><p>The <strong>ARL SMS-3500 Automation for Twin OES or XRF</strong> simplifies sample handling by providing:</p><ul><li>a <strong>single introduction location</strong> for all samples</li><li>a <strong>magazine storage function</strong> to free the introduction<br>👉 This reduces response times, increases sample processing cadence, and improves the <strong>availability of automatic sample preparation</strong> in <strong>highly critical production control environments</strong>.</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Thermo_Scientific_ARL_SMS_3500_Automation_for_Twin_OES_or_XRF_2_0553f2880b.jpg" alt="Thermo Scientific™ ARL™ SMS-3500 Automation for Twin OES or XRF - 2.jpg" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Thermo_Scientific_ARL_SMS_3500_Automation_for_Twin_OES_or_XRF_2_0553f2880b.jpg 217w," sizes="100vw" width="650" height="469"></p><h3>✅ <strong>Main Features</strong></h3><ul><li><strong>No need for time-consuming electro-mechanical transfer systems</strong> – the <strong>SMS-3500 robot</strong> moves samples between components <strong>within seconds</strong></li><li><strong>Simplified operation</strong> and <strong>reduced system complexity</strong> for easier <strong>maintenance and servicing</strong></li><li><strong>Single interface screen</strong> for all sample tracking and automation functions</li><li><strong>Unified registration and introduction point</strong> for all production samples, with <strong>automated routing</strong> to the appropriate system components</li><li><strong>Support for two sample preparation machines</strong>:<ul><li><strong>Reduces response times</strong></li><li><strong>Increases processing cadence</strong></li><li><strong>Improves automatic sample preparation availability</strong></li></ul></li><li><strong>Operator workload is reduced</strong> – the system manages samples <strong>by priority and availability of resources</strong><ul><li>Includes <strong>internal sample storage</strong> to <strong>free the introduction point immediately</strong></li></ul></li><li><strong>Each instrument can be used offline</strong> for <strong>manual tasks</strong> without interrupting automated operation</li><li>Uses the <strong>same analytical software for XRF and OES</strong></li><li>Automation is based on <strong>proven ARL SMS software</strong></li></ul><h3>🔧 <strong>Recommended for:</strong></h3><ul><li><strong>Iron and steel applications</strong></li><li><strong>High alloy, stainless steel, copper, brass, and bronze applications</strong></li><li><strong>Aluminum or fire assay applications</strong></li></ul>

Products from category Sample preparation automation focused on Optical Emission Spectroscopy (OES) - page 1

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

Pyrolysis Compound-Specific Isotope Analysis of Proteins in Aged Paintings: Diketopiperazine δ13C Values as Collagen Source Indicators