<p><strong>In the week of February 16, the following webinars await you in the field of liquid phase and especially HPLC and LC/MS.</strong></p><blockquote><h4><a target="_blank" rel="noopener noreferrer" href="https://lcms.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. Agilent Technologies: From Complex to Simplicity – Get More Peace of Mind on Your HPLC Workflows with InfinityLab Assist</strong></h5><ul><li>Mo, 16.2.2026 11:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5908">Registration</a></li></ul><p>Discover how Agilent InfinityLab Assist simplifies LC system control with intuitive multitouch operation, guided troubleshooting, and smart monitoring to boost efficiency and reduce downtime.</p><h5><strong>2. 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Waters Corporation: Ask Me Anything About Managing Computerized System Risks to Data Integrity</strong></h5><ul><li>Th, 19.2.2026 17:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5809">Registration</a></li></ul><p>Learn practical strategies to manage computerized system risks in regulated laboratories and protect data integrity while ensuring compliance.</p><h5><strong>11. Shimadzu Corporation: Automating a Comprehensive Amino Acid Panel for Inborn Errors of Metabolism</strong></h5><ul><li>Th, 19.2.2026 20:00 CET</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/5936">Registration</a></li></ul><p>Discover how Shimadzu’s CLAM-2040 automates LC-MS/MS sample preparation to standardize workflows, reduce manual handling, and improve consistency in clinical labs.</p>

Webinars LabRulezLCMS Week 08/2026

Imaging of Protein Assemblies up to 231 kDa in Tissues with Nano-DESI Mass Spectrometry

<p><strong>Metalloproteins play essential roles in marine phytoplankton, yet their metalloproteomes remain poorly characterized. This study combines protein modeling, quantitative proteomics, and ICP-MS measurements to assess metal requirements across several phytoplankton species, including chlorophytes, a haptophyte, and a cyanobacterium.</strong></p><p>Results show strong agreement between proteome-inferred metal demands and measured cellular metal quotas, while providing deeper insight into biological functions. Mg-containing proteins were most abundant overall, with Zn, Fe, and Mn as dominant trace cofactors. Distinct differences between prokaryotic and eukaryotic phytoplankton were observed, and strain-level analysis demonstrated how metalloproteomes reflect environmental adaptation and metal limitation strategies.</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.est.5c11233"><strong>Unlocking Phytoplankton Metallomes with Comparative Analysis of Metal Quotas, Quantitative Proteomics, and Inferred Metalloproteomes</strong></a></h4><p>Qiong Zhang*, Jiayou Ge, Fengjie Liu, Shabaz Mohammed, Kedong Yin, and Rosalind E. M. Rickaby</p><p>Environ. Sci. Technol. 2025, 59, 50, 27367–27377</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.est.5c11233">https://doi.org/10.1021/acs.est.5c11233</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>Many important biological processes of phytoplankton, including photosynthesis, carbon fixation, and nitrogen reduction, rely on enzymes that have metals as cofactors. <a href="javascript:void(0);">(1)</a> It has been estimated that half of all enzymes must be associated with a particular metal to function. <a href="javascript:void(0);">(2)</a> The availability of metals in the environment has changed over geological time and, given imperfections in the selectivity of metal transport systems, proteins have evolved to make the best use of metals that are more available. <a href="javascript:void(0);">(3−5)</a> Depending on their evolutionary histories, different groups of marine phytoplankton may have differences in their metalloprotein compositions and metal transport strategies. The evolving trace metal availability in the environment, thus, may provide a selective pressure to promote the proliferation of different phytoplankton groups in the oceanic environment, affecting the phytoplankton community structure with knock-on effects on the marine food web and carbon sequestration. <a href="javascript:void(0);">(5)</a> It is crucial to understand the utilization of trace metals by different groups of marine phytoplankton that inhabit contrasting niches in the modern ocean, not only by assessing the accumulation of trace metals in cells but also examining the functions of trace metals in different biological pathways.</p><p>In previous studies, the utilization or requirements of trace metals by marine phytoplankton were mostly determined by measuring the elemental stoichiometry in cells (metallome or quota of trace metals). <a href="javascript:void(0);">(6−8)</a> In the field, attempts have also been made to investigate the elements in phytoplankton <a href="javascript:void(0);">(9,10)</a> which have then been compared with profiles in the abiotic environment to understand their relationships. <a href="javascript:void(0);">(11)</a> The discovery of various metal storage proteins and the plasticity in elemental quotas under different environmental conditions raise the question of whether intracellular trace metal quotas reflect true differences in trace metal usage by phytoplankton. <a href="javascript:void(0);">(12,13)</a> In the past few decades, more and more studies have focused on metal–protein partnerships, using approaches such as genome-wide analysis to identify homologues of known metalloproteins and other deduced metal-binding motifs encoded within sequenced genomes. <a href="javascript:void(0);">(5,14)</a> However, this approach has limitations in estimating metal abundance in cells because not all genes predicted in the genome would be translated and expressed as proteins under all environments, and a single gene for a metalloprotein could be highly expressed, such that a translation from genetic presence to a required concentration is not linear and could be environment-dependent. To obtain <i>in situ</i> metal abundance in metalloproteins in cells, native separation of proteins by HPLC or PAGE followed by the detection of metals using ICP-MS <a href="javascript:void(0);">(15,16)</a> has been used. This approach also has limitations in that, without bioinformatics, it cannot identify the function of proteins within which the metals are found, and it may also depend on the composition of the media to which the organism has been exposed and in which it has been growing. An ideal way to solve the problem would be to separate and purify each of the proteins and then identify both the associated metal (<i>e.g.,</i> by ICP-MS) and the protein sequence (<i>e.g.,</i> by mass spectrometry). However, this approach is constrained by the resolution of chromatographic or electrophoretic techniques, making it challenging to establish the metal–protein associations unambiguously; many proteins often co-occur in metal-containing fractions. Additionally, this method requires huge volumes of biomass and is both time-consuming and complex, complicating the reconstruction of the expressed metalloproteome in cells. The techniques and challenges for metalloproteomics have been discussed in detail in previously published review articles (and the references therein). <a href="javascript:void(0);">(17)</a></p><p>In this study, we determined the trace metal utilization (the metallome) of different phytoplankton from their expressed proteome via a protein modeling and homologue identification approach, and then compared the resulting metallome with the metal quota data measured by the traditional inductively coupled plasma mass spectrometry (ICP-MS). This approach allows the building of a link between the truly used trace elements and their biological functions in phytoplankton. We aim to fill the gap between an estimation of metals encoded in the genome and metal analysis by ICP-MS in phytoplankton, which may be more broadly applicable to infer cellular metallomes.</p><h3><strong>Materials and Methods</strong></h3><h4><strong>ICP-MS Analysis</strong></h4><p>All preparation of samples for ICP-MS measurement was undertaken using trace metal clean methods. All plasticware used in this study, including culture flasks, pipette tips, and centrifuge tubes, was precleaned in 10% quartz-distilled HCl and thoroughly rinsed with 18 MΩ cm water in a clean laboratory. Trace elements in the filtered lysates of <i>G. huxleyi</i> (OA1, OA4, OA8, OA15, OA16, OA23, and RCC 1242) and <i>Synechocystis</i> (FACHB898) strains were measured using an ICP-MS. The detailed ICP-MS method and instrumental settings are described in Zhang et al. (2018). <a href="javascript:void(0);">(31)</a></p><h4><strong>Protein Mass Spectrometry</strong></h4><p>The peptides were resuspended in 5% formic acid and 5% DMSO prior to mass spectrometry. They were separated on an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=3&amp;search=Ultimate+3000+UHPLC"><strong>Ultimate 3000 UHPLC </strong></a>system (Thermo Fisher Scientific) and electrosprayed directly into a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/559"><strong>Q-Exactive Mass Spectrometer</strong></a> (Thermo Fisher Scientific) through an EASY-Spray nanoelectrospray ion source (Thermo Fisher Scientific). The peptides were trapped on a C18 PepMap100 precolumn (300 μm i.d. × 5 mm, 100 Å, Thermo Fisher Scientific) using solvent A (0.1% formic acid in water) at a pressure of 500 bar. The peptides were separated on an in-house packed analytical column (75 μm i.d. × 50 cm packed with ReproSil-Pur 120 C18-AQ, 1.9 μm, 120 Å, Dr. Maisch GmbH) using a linear gradient (length: 65 min, 15% to 35% solvent B [0.1% formic acid in acetonitrile], flow rate: 200 nL/min). The raw data were acquired on the mass spectrometer in a data-dependent mode (DDA). Full scan MS spectra were acquired in the Orbitrap (scan range 350–1500 <i>m</i>/<i>z</i>, resolution 70000, AGC target 3e6, maximum injection time 50 ms). After the MS scans, the 10 most intense peaks were selected for HCD fragmentation at 30% of normalized collision energy. HCD spectra were also acquired in the Orbitrap (resolution 17500, AGC target 5e4, maximum injection time 120 ms, isolation window 1.5 <i>m</i>/<i>z</i>) with the first fixed mass at 180 <i>m</i>/<i>z</i>. Charge exclusion was selected for unassigned and 1+ ions. The dynamic exclusion was set to 20 s.</p><h3><strong>Results and Discussion</strong></h3><h4><strong>Trace Elements Determined from the Expressed Proteomes of Different Phytoplankton</strong></h4><p>Based on the expressed proteome sequences, we predicted metal-binding proteins and compared their distribution among various phytoplankton species (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig2">Figure 2</a>). The inference revealed 28 different metals in <i>G. huxleyi</i>, 31 different metals in <i>C. reinhardtii</i>, and 30 different metals in <i>Synechocystis</i>. Although only 6 different metals were identified in <i>O. tauri</i> proteins, it has to be noted that the analysis for <i>O. tauri</i> was only based on the 40 most abundant proteins reported in this organism <a href="javascript:void(0);">(21)</a> and, hence, the listed elements, including Mg, Fe, Mn, Ca, Zn, and Cu, may represent the most essential metals for the species.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Environ_Sci_Technol_2025_59_50_27367_27377_Figure_2_The_number_of_metal_binding_proteins_identified_in_the_expressed_proteome_of_the_haptophyte_G_9b8ac0f004.jpeg" alt="Environ. Sci. Technol. 2025, 59, 50, 27367–27377: Figure 2. The number of metal-binding proteins identified in the expressed proteome of the haptophyte G. huxleyi (a), the chlorophyte C. reinhardtii (b) and O. tauri(c), and the cyanobacteria Synechocystis (d). Pie charts show the percentages of metalloproteins containing specific metals. Original proteome data for G. huxleyi are from Mckew et al. (2013) (24) for C. reinhardtii are from Hsieh et al. (2013) (77) for O.tauriare from Martin et al. (2012) (21) for Synechocystisare from Chen et al. (2018). (22) *Noted that for O. tauri, only the 40 most abundant proteins were included in this analysis. ND: no metal binding was found in the proteins." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Environ_Sci_Technol_2025_59_50_27367_27377_Figure_2_The_number_of_metal_binding_proteins_identified_in_the_expressed_proteome_of_the_haptophyte_G_9b8ac0f004.jpeg 123w," sizes="100vw" width="934" height="1181"></p><p>The number of proteins that bind with different metals varies dramatically in phytoplankton. For metalloproteins, there are variations in the binding affinities of different metals to organic molecules. For example, the affinities of divalent metals to proteins have a tendency to follow a universal order of preference, namely the Irving-Williams series (Mg<sup>2+</sup> and Ca<sup>2+</sup> [weakest binding] &lt; Mn<sup>2+</sup> &lt; Fe<sup>2+</sup> &lt; Co<sup>2+</sup> &lt; Ni<sup>2+</sup> &lt; Cu<sup>2+</sup> &gt; Zn<sup>2+</sup>). <a href="javascript:void(0);">(43)</a> Cells can simultaneously contain proteins having high-affinity-binding metals, such as Cu and Zn, and others that require uncompetitive metals, such as Mg and Ca. One of the least competitive metals for binding with biological chelators, Mg, is even found to be the most widely distributed metal in the metalloproteomes of various phytoplankton species in this study, supporting the observations of high concentrations in the protein fraction of cells (at millimolar concentrations <a href="javascript:void(0);">(31)</a>).</p><p>In all phytoplankton species in our analysis, the Mg-containing proteins are found to have the highest proportion in all metalloproteins identified. About 30% of the metalloproteins in different strains of phytoplankton are found to contain Mg or depend on it for function (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig2">Figure 2</a>), suggesting the important role of Mg in phytoplankton cells. It is generally coordinated in protein complexes by ATP or ADP and has also been found to be largely distributed in the chloroplasts or thylakoids in <i>G. huxleyi</i> and <i>Synechocystis</i>. <a href="javascript:void(0);">(22,24)</a> The essential roles of Mg in plants have been reported in various studies. Mg<sup>2+</sup> is known to be the central atom of the chlorophyll molecule, and its levels in the chloroplast regulate the activity of key photosynthetic enzymes. <a href="javascript:void(0);">(44)</a> It was reported that approximately 15–35% of total Mg is in the chloroplasts of a plant, <a href="javascript:void(0);">(45)</a> and it also serves as an osmotically active ion in the regulation of cells <a href="javascript:void(0);">(46)</a></p><h4><strong>Comparison Between Metallome Measured Directly and Reconstructed from Proteins</strong></h4><p>A large proportion of metals, such as Fe, Mn, and Cu, are found in membrane proteins,and may contribute significantly to total cellular quotas. <a href="javascript:void(0);">(54,55)</a> However, for method development purpose, we endeavored to remove the cell membranes from our samples to focus mostly on the analysis of intracellular metal quotas and metalloproteins in the cytosol fraction in this study. We note that our analysis includes some membrane-bound proteins likely derived from organelles or surface membranes disrupted during sonication.</p><p>The derivation of cellular metal composition, as determined by two different methods, is compared in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig3">Figure 3</a>. The abundance of metal in the proteins is determined as the relative abundance of the protein multiplied by the number of metal ions needed for the specific protein. The abundance of the proteins was normalized to ensure that the total intensities from each replicate measurement for the same species were similar. Since the proteomic data used here were generated from different studies using different instruments, we cannot quantify or compare the absolute biomass of proteins used for each species. Therefore, we cannot compare the absolute trace metal abundance between species solely from metal inferences of expressed proteomes. Nevertheless, we can estimate the relative abundance of different metals in the same species. For the elements that are both measured by the ICP-MS and identified in the proteome, there are generally good correlations (<i>r</i><sup>2</sup> = 0.81 in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig3">Figure 3</a>a and <i>r</i><sup>2</sup> = 0.74 in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig3">Figure 3</a>b) between the two approaches. This indicates that the metal compositions determined from the protein modeling may be reliable and, therefore, can reflect the metal usage in different phytoplankton. This would provide a level of assurance to further investigate the metal binding associated with various proteins, thereby deepening our understanding of the roles that different metals play in phytoplankton.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Environ_Sci_Technol_2025_59_50_27367_27377_Figure_3_Relationship_between_the_abundance_of_metals_determined_by_ICP_MS_analysis_e7b259e376.jpeg" alt="Environ. Sci. Technol. 2025, 59, 50, 27367–27377: Figure 3. Relationship between the abundance of metals determined by ICP-MS analysis (normalized to C, mmol/mol C) and metal abundance determined from the metalloproteome analysis (the unit is arbitrary). To account for strain variations, the ICP-MS data for the cellular metal content of G. huxleyi are average values from 7 G. huxleyi strains isolated from different areas of the ocean, and the proteome-derived metal composition data are average values calculated from 2 of those G. huxleyi strains (OA1 and OA16) isolated previously, and data from Mckew et al., 2013. (24) The ICP-MS data for Synechocystis are from Synechocystis sp. FACHB898 measured in this study, and the proteome data are calculated from Chen et al., 2018. (22) Noted that the abundance of metals from each proteome was calculated by using the metal stoichiometry multiplied by the relative abundance of proteins, which provides only a relative relationship of different trace metals, not absolute abundance. The symbols represent the mean values, and the error bars indicate standard deviations." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Environ_Sci_Technol_2025_59_50_27367_27377_Figure_3_Relationship_between_the_abundance_of_metals_determined_by_ICP_MS_analysis_e7b259e376.jpeg 245w," sizes="100vw" width="1384" height="641"></p><p>Compared to the other metals investigated in this study, Ca and Mg appear to have a much higher relative abundance in the ICP-MS analysis than in the proteomic analysis of <i>G. huxleyi</i> and <i>Synechocystis</i> (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig3">Figure 3</a>). These two elements are essential macronutrients for phytoplankton and plants. Ca is the most prominent secondary messenger that plays a crucial role in response to extracellular stimuli in all eukaryotes. Ca<sup>2+</sup> fluxes are transported via Ca channels and transporters, such as Ca-ATPase, regulating a series of important biological functions, including mineral uptake and utilization, K<sup>+</sup> homeostasis adjustment, and regulatory network activity in response to P or N deficiency. <a href="javascript:void(0);">(56)</a> Ca is not always incorporated into protein structures. The Ca abundance determined from the proteins in this study, therefore, is much lower than the actual Ca needed intracellularly. Similarly, the intracellular Mg<sup>2+</sup> and K<sup>+</sup> abundances should also be higher than those determined from the protein structures. Free Mg<sup>2+</sup> and K<sup>+</sup> are known to regulate cation–anion balance and serve as osmotically active ions in the regulation of cells <a href="javascript:void(0);">(46)</a> but free metal ion concentrations cannot be determined through the approach used in this study. Thus, it is not surprising to see much higher relative abundances of Ca and Mg in ICP-MS analysis than in proteomic analysis (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.est.5c11233#fig3">Figure 3</a>).</p>

Unlocking Phytoplankton Metallomes with Comparative Analysis of Metal Quotas, Quantitative Proteomics, and Inferred Metalloproteomes

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<h4><strong>Manual Options</strong></h4><p><strong>AccQ•Tag Ultra </strong>and <strong>AccQ•Tag chemistries</strong> are the first step in enabling your laboratory to obtain accurate amino acid composition from protein/peptide hydrolysates, physiologic fluids, feeds, foods, pharmaceutical preparations, and many other sample matrices. Combined with traditional HPLC, UHPLC, or the Waters ACQUITY UPLC, you can now have a system that is customized to separate amino acids from protein hydrolysates, cell culture media, and food/feed samples to get enhanced resolution. The end result is reliability with precise qualitative and quantitative results.</p><p><strong>AccQ•Tag Ultra and AccQ•Tag chemistry kits include:</strong></p><ul><li>Pre-column derivatization reagents</li><li>Dedicated reversed-phase columns QC tested for amino acid analysis</li><li>Eluents and standards</li><li>Documentation on methods for the analysis of these important biocompounds</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_Acc_Q_Tag_Ultra_Chemistry_kit_1_879ccc4140/Waters-AccQTag-Ultra-Chemistry-kit-1.jpg" alt="Waters: AccQ·Tag Ultra Chemistry kit" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_Acc_Q_Tag_Ultra_Chemistry_kit_1_879ccc4140/thumbnail_Waters-AccQTag-Ultra-Chemistry-kit-1.jpg 234w," sizes="100vw" width="660" height="440"></p><h4><strong>Automation Options</strong></h4><p><strong>AccQ•Tag Ultra Derivatization Automation Kit </strong>provides simplified tools to enhance high throughput amino acid automation via an easy-to-use, automation friendly format that maximizes workflow and accommodates residual volume. Compatible with Tecan, Hamilton, and Andrew Alliance systems, the AccQ•Tag Ultra Derivatization Automation Kit provides fast, accurate, and precise amino acid analyses, translating into cost and time savings for your lab.</p><ul><li>Parallel high-throughput sample prep (process up to 96 samples at a time)</li><li>Minimal user intervention</li><li>Larger reagent volume to accommodate automation needs</li><li>Starter kits include reagents, column, eluents, application standards and labware</li><li>Scripts ready for multiple automated platforms<ul><li>Tecan, Hamilton, and Andrew Alliance</li></ul></li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_Acc_Q_Tag_Ultra_Chemistry_kit_2_36d9723e92/Waters-AccQTag-Ultra-Chemistry-kit-2.jpg" alt="Waters: AccQ•Tag Ultra Derivatization Automation Kit " srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_Acc_Q_Tag_Ultra_Chemistry_kit_2_36d9723e92/thumbnail_Waters-AccQTag-Ultra-Chemistry-kit-2.jpg 240w," sizes="100vw" width="1200" height="780"></p><h4><strong>Let Andrew+ Pipette for You</strong></h4><p>One of the largest pain points in sample preparation is repetitive pipetting. Minimizing sources of contamination is also critical. The Andrew+ robotic pipetting platform helps alleviate those pain points and provides confidence and reliability when doing derivatization.</p><p><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/208"><strong>Andrew+ Pipetting Robot</strong></a><strong> provides:</strong></p><ul><li>Flexibility in the number of samples run using AccQ•Tag Ultra Automation</li><li>Sample Preparation Kit (32, 64, 96 sample)</li><li>Verified protocols available in <strong>OneLab</strong></li><li>Compatible with additional <strong>connected devices</strong></li><li>Easy-to-use, no in-depth training required</li><li>Self-guided installation, online troubleshooting, and help</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_Andrew_Pipetting_Robot_ab82a4584b.jpg" alt="Waters: Andrew+ Pipetting Robot" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_Andrew_Pipetting_Robot_ab82a4584b.jpg 245w," sizes="100vw" width="1200" height="726"></p><h4><strong>Amino Acid Reference Standards</strong></h4><p>Waters offers reference standards for Amino Acid Analysis (AAA) required in many applications in pharmaceutical and food and feed industries. A variety of standards containing free amino acids are offered for qualitative and quantitative determination of amino acids, method development, and troubleshooting of the AccQ•Tag Ultra and AccQ•Tag methods.</p><p><strong>Available standards include:</strong></p><ul><li>Cell Culture Standard Kit</li><li>Food &amp; Feed Standard Kit</li><li>Amino Acid Hydrolysates Standard</li><li>Internal Standard (Nva)</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_Amino_Acid_Reference_Standards_fd1db2e697.jpg" alt="Waters: Amino Acid Reference Standards" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_Amino_Acid_Reference_Standards_fd1db2e697.jpg 234w," sizes="100vw" width="838" height="559"></p><h4><strong>AccQ•Tag Ultra and AccQ•Tag Derivitization Chemistry</strong></h4><p>This line of products is a comprehensive, fully tested set of reagents, columns, eluents, and methods. &nbsp;</p><p>AccQ•Tag is optimized for use with HPLC/UHPLC.</p><p>AccQ•Tag Ultra is optimized for use with the UPLC Amino Acid Analysis Application Solution.</p><ul><li>Both methods are based on the AccQ•Tag pre-column derivatization method</li><li>Primary and secondary amino acids are reacted with a single reagent (AccQ•Fluor) in a high-throughput batch process resulting in exceptionally stable derivatives</li><li>AccQ•Tag Ultra provides high-resolution separations achieved using the AccQ•Tag Ultra UPLC column (1.7 µm particles) and mobile phases</li><li>Derivatized amino acids resolved and quantified to sub-pmol levels with single wavelength UV detection</li></ul><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_kits_9c7f40f8c4.jpg" alt="Waters: AccQ•Tag Ultra and AccQ•Tag Derivitization Chemistry" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_kits_9c7f40f8c4.jpg 133w," sizes="100vw" width="352" height="413"></p>

Products from category HPLC and LCMS kits from Waters - page 1

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