Impact of mobile and stationary phases on siRNA duplex stability in liquid chromatography

Evaluating the potential of hydrophilic interaction liquid chromatography for collagen peptide mapping analysis

NDMA in macrolides: GC-MS/MS method for its detection and study of its formation

<p><strong>The rapid spread of new psychoactive substances (NPS) presents global challenges due to their unpredictable effects and ability to evade routine drug tests. This study applied UPLC-HRMS combined with multivariate analysis to classify and identify unknown NPS.</strong></p><p>A dataset of 159 standards was processed for PCA and SIMCA modeling, showing clear clustering for several NPS classes. The validated SIMCA models successfully classified test analytes and seized drug samples, particularly at lower collision energies. Despite some misclassifications caused by limited standards, the models proved highly effective, highlighting their value for forensic screening of NPS.</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.5c02450"><strong>Soft Independent Modeling of Class Analogies for the Screening of New Psychoactive Substances through UPLC-HRMS/MS</strong></a></h4><p>Ilenia Bracaglia, Sara Gamberoni, Camilla Montesano*, Francesco Bartolini, Sabino Napoletano, Claudio D’Alfonso, Chiara Nieri, Federico Marini, Manuel Sergi</p><p>Anal. Chem. 2025, 97, 28, 15420–15429</p><p><a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.5c02450">https://doi.org/10.1021/acs.analchem.5c02450</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>The detection and analytical recognition of New Psychoactive Substances (NPS) represents a current challenge in forensic drug analysis. The spread of these substances has become a global phenomenon, involving more than 800 different compounds, as reported by the United Nations Office on Drugs and Crime (UNODC) through its Early Warning Advisory (EWA). <a href="javascript:void(0);">(1)</a> NPS are defined “substances of abuse, either in a pure form or a preparation, that are not controlled by the 1961 Single Convention on Narcotic Drugs or the 1971 Convention on Psychotropic Substances, but which may pose a public health threat”. <a href="javascript:void(0);">(2)</a> The development and production of NPS began decades ago with the aim of creating pharmacologically active compounds that could replicate the effects of the main categories of internationally controlled psychotropic drugs. <a href="javascript:void(0);">(3)</a> Many of these drugs share common chemical structural features with controlled substances, often differing by only a slight modification of the original molecule. <a href="javascript:void(0);">(4)</a> As a result, the detection and analytical identification of unknown NPS is difficult, since their rapid spread often exceeds existing analytical protocols, and is further complicated by the lack of reference standards. <a href="javascript:void(0);">(5)</a></p><p>When dealing with drug seizures, typically an initial qualitative analysis using low cost, portable instruments is carried out; these may identify the characteristic chemical groups of the substances but have low selectivity and specificity. <a href="javascript:void(0);">(6)</a> More sophisticated techniques including nuclear magnetic resonance (NMR), gas chromatography–mass spectrometry (GC-MS), liquid chromatography tandem mass spectrometry (LC-MS/MS) <a href="javascript:void(0);">(7,8)</a> and especially high-resolution mass spectrometry (HRMS) are then crucial for the identification of unexpected drugs.</p><p>At present HRMS is the gold standard for NPS detection, given its ability to record accurate mass measurements that provide information about molecular structures; <a href="javascript:void(0);">(3)</a> but the complexity of data obtained limits its routinary application. For this reason, it is necessary to combine top-down and bottom-up approaches <a href="javascript:void(0);">(5,9)</a> to interpret and simplify data with additional support from databases (such as HighResNPS, <a href="javascript:void(0);">(10)</a> SWGDRUG, <a href="javascript:void(0);">(11)</a> mzCloud) and data mining tools for retrospective analysis. <a href="javascript:void(0);">(7,8,12,13)</a> An analytical approach for identifying both known and unpredicted NPS could involve the analysis of diagnostic fragment ions characteristic of each NPS core. This methodology can be particularly useful for preliminary MS screening, especially when comparative confirmation with analytical reference standards is unavailable. <a href="javascript:void(0);">(14−16)</a> Vincenti et al. <a href="javascript:void(0);">(17)</a> employed a molecular networking strategy to group similar compounds or those sharing the same <i>m</i>/<i>z</i> into clusters based on the similarity of their MS/MS fragments. This approach facilitated the identification of similarities among MS/MS spectra within a data set and enabled the correlation of unknown but related molecules. Ventura et al. <a href="javascript:void(0);">(18)</a> exploited DART-HRMS technology to observe neutral losses typical of the tryptamine class and employed Hierarchical Cluster Analysis (HCA) <a href="javascript:void(0);">(19)</a> for data analysis; the observed clusters were then used to create a supervised classification model (partial least-squares discriminant analysis, or PLS-DA) to support the identification of unknown tryptamine structures. Other classification models have been used to identify the structural characteristics of illicit drugs from MS, <a href="javascript:void(0);">(20,21)</a> chromatographic <a href="javascript:void(0);">(22)</a> or IR data, <a href="javascript:void(0);">(23−25)</a> however, they have never been applied to putatively identify new drugs.</p><p>The present study combined analytical and chemometric tools to detect unknown NPS in seizures through UPLC-QTOF-MS analysis. The preliminary step consisted in analyzing 159 NPS standards to obtain chromatographic and MS/MS information. A data matrix, including <i>m</i>/<i>z</i> of precursor and fragments, retention time, and neutral losses, was then built and was split into training and test sets. Principal component analysis (PCA) <a href="javascript:void(0);">(16,26)</a> was then applied to explore the data, while a class modeling strategy based on Soft Independent Modeling of Class Analogies (SIMCA) <a href="javascript:void(0);">(20,27,28)</a> was employed to create robust models aimed at predicting the class of unknown NPS. The models were validated on the test set and with real seized samples obtained from the Scientific Police Service.</p><h4><strong>Experimental Section</strong></h4><h5><strong>UPLC-MS/MS Analysis</strong></h5><p>Data acquisition was performed using an UPLC-QTOF system consisting of an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/199"><strong>ACQUITY I-Class UPLC System </strong></a>coupled to a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=5&amp;search=SYNAPT+G2-Si+HDMS"><strong>SYNAPT G2-Si HDMS</strong></a> (Waters Corporation, Milford, MA). Instrument control, data acquisition, and initial data processing were performed with <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/635"><strong>MassLynxTM version 4.2</strong></a>. Chromatographic separation was achieved using a C18 column (<a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1952"><strong>ACQUITY UPLC HSS C18 Column</strong></a>, 100 Å, 1.8 μm, 2.1 150 mm) maintained at 50 °C with a flow rate of 0.4 mL min<sup>–1</sup>; injection volume was 10 μL. The mobile phases were 5 mM ammonium formate buffer, pH 3.0 (A), and acetonitrile 0.1% HCOOH (B). The chromatographic separation was performed with the following gradient: phase B was held at 13% in the first 0.5 min, then increased linearly to 50% in 10 min. Then, phase B was rapidly changed to 95% in 0.75 min and held for 1.5 min. After 0.25 min, the column was returned to the original ratio within 4 min. The total run time was 16.5 min.</p><p>The TOF-MS was operated in positive electrospray ionization mode (Z-SprayTM, Waters) with the following settings: 800 L/h of nitrogen as the nebulization gas at 500 °C, 40 L/h cone gas flow at 120 °C, a capillary voltage of 3 kV and argon as the collision gas. Data were recorded in profile mode using Full Scan and then MS/MS mode. These scans were carried out in Resolution mode (fwhm 20,000) with a mass range from 50 to 700 Da. MS/MS spectra were acquired with CE ramped from 10 to 30 V, with a scan time of 1 s for each time window centered on the retention time of each analyte ± 30 s (see <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c02450/suppl_file/ac5c02450_si_001.pdf">Table S1</a>). Mass calibration was performed daily with the Major Mix.</p><h4><strong>Result and Discussion</strong></h4><h5><strong>Data Matrix Building</strong></h5><p>The analytical standards were selected based on the substances most frequently reported by the UNODC Early Warning Advisory (EWA), the first global monitoring system for NPS. <a href="javascript:void(0);">(32)</a> In this context, the classes with the largest number of substances chosen are synthetic cathinones, synthetic cannabinoids, and synthetic opioids, which mainly include fentanyl analogues. <a href="javascript:void(0);">(33,34)</a></p><p>To achieve optimal chromatographic separation and maximize MS spectral response, the chromatographic column, mobile phases, and gradient were carefully selected and tailored, considering the nature of the analytes under examination. The choice of the column and mobile phases was supported by extensive literature studies <a href="javascript:void(0);">(35−37)</a> and experimental testing.</p><p>Full Scan and MS/MS acquisition modes were chosen to obtain fragmentation spectra for the analytes under study. The Full Scan analysis of the 159 NPS enabled the determination of characteristic retention times for these substances, allowing for simultaneous gradient adjustments to separate isomeric compounds, such as Ethcathinone, Dimethylcathinone, and 3-Methylmethcathinone, eluting at 1.86, 1.93, and 3.05 min, respectively, as well as α-methyl fentanyl and <i>cis</i>-3-methyl fentanyl eluting at 5.41 and 6.64 min, respectively.</p><p>Subsequently, MS/MS analysis was performed to obtain specific fragmentation patterns for each analyte, testing two collision energy ramps: 10–30 and 30–50 V. To this aim, the chromatographic run was divided into distinct acquisition time windows of approximately ± 0.7 min. This approach allowed efficient and targeted fragmentation data acquisition across all analytes within the optimized timeframes.</p><p>By analyzing the obtained spectra (some examples are shown in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c02450#fig2">Figure 2</a>), it was possible to identify and consider the most intense fragments characteristic of each analyte and compare them with the Waters Forensic Toxicology Library and HighResNPS. <a href="javascript:void(0);">(11)</a> Moreover, the neutral losses were calculated by examining the differences between the signal corresponding to the fragments, ranked in descending order of intensity and above a relative intensity threshold of 10%, and the base peak. This approach provided valuable information about the structure of the compounds, facilitating a deeper understanding of their chemical properties.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2025_97_28_15420_15429_Figure_2_MSMS_spectra_58ead7f820.jpg" alt="Anal. Chem. 2025, 97, 28, 15420–15429: Figure 2. MS/MS spectra (CE ramp of 10–30 V) for: (a) ethylenoxynitazene; (b) ADB-5Br-INACA; (c) 2-Methyl-α-PHP; (d) ADB-4en-PINACA(+H+)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2025_97_28_15420_15429_Figure_2_MSMS_spectra_58ead7f820.jpg 207w," sizes="100vw" width="2088" height="1581"></p><h5><strong>Data Treatment and Multivariate Data Analysis Strategies</strong></h5><p>All MS/MS spectral data acquired from the 159 NPS standards, including precursor <i>m</i>/<i>z</i>, the <i>m</i>/<i>z</i> values of the three most intense fragments, and the three corresponding neutral losses, were organized within two matrices, the first containing MS/MS spectra acquired in the CE range of 10–30 V (159 × 8), and the second containing MS/MS spectra acquired in the CE range of 30–50 V (142 × 8). These matrices also include chromatographic information such as retention time, which is a characteristic parameter influenced by the physicochemical properties, although it also depends on the chromatographic conditions used.</p><p>The preliminary PCA conducted on the training set (122 substances) of the data acquired in the CE range of 10–30 V (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c02450#fig3">Figure 3</a>a,b) already highlighted some well-defined clusters corresponding to specific classes of substances. Only the results of PCA on this matrix are presented, as the exploratory analysis performed on the matrix derived from data acquired in the CE range of 30–50 V did not show any clear groupings.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2025_97_28_15420_15429_Figure_3_a_b_Scores_and_loadings_plot_of_the_PCA_calculated_on_the_training_set_including_all_compound_classes_a9aebb010b.jpg" alt="Anal. Chem. 2025, 97, 28, 15420–15429: Figure 3. (a, b) Scores and loadings plot of the PCA calculated on the training set including all compound classes; (c, d) Scores and loadings plot of the PCA calculated on the Training set including only the nine most represented classes." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2025_97_28_15420_15429_Figure_3_a_b_Scores_and_loadings_plot_of_the_PCA_calculated_on_the_training_set_including_all_compound_classes_a9aebb010b.jpg 245w," sizes="100vw" width="2088" height="1178"></p><p>Following the graphical exploration of the data, we further subdivided the synthetic cannabinoid and synthetic opioid classes into the following respective subclasses: JWH, PINACA, WIN, SC1, SC2, and SC3 (synthetic cannabinoids), fentanyls and nitazenes (synthetic opioids). A graphical representation of the defined subclasses is shown in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c02450#fig4">Figure 4</a>, highlighting the common chemical groups.</p><p><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2025_97_28_15420_15429_Figure_4_Defined_subclasses_1702675fce.jpg" alt="Anal. Chem. 2025, 97, 28, 15420–15429: Figure 4. Defined subclasses within (a) synthetic cannabinoids (JWH-green, PINACA-violet, WIN-gray, SC1-purple, SC2-red, SC3-orange) and (b) synthetic opioids (brorphine analogues-blue, nitazenes-yellow, and fentanyl analogues-dark orange and blue)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2025_97_28_15420_15429_Figure_4_Defined_subclasses_1702675fce.jpg 245w," sizes="100vw" width="2088" height="1308"></p><h4><strong>Conclusions</strong></h4><p>The proposed approach integrates advanced analytical strategies based on UPLC-HRMS technologies with multivariate analysis to monitor the rapid emergence of NPS. The supervised SIMCA approach proved effective in correctly identifying the class of various compounds, even in real contexts, as highlighted by the analysis of real samples. External validation conducted on three real samples confirmed the models’ ability to generalize and adapt to data not included in the training set, reducing the risk of overfitting and demonstrating strong predictive performance, as evidenced by the positive validation parameters obtained for each model.</p><p>Despite the overall positive results, some cases of misclassification emerged. An example concerns the tryptamine model, which showed relatively low specificity, leading to false positives attributable to the scarcity of available standards in this class, thereby limiting the model’s robustness.</p><p>To further improve performance and minimize classification errors, continuous model updates are essential, including a greater number of analytes for each class. In parallel, increasing the number of descriptive variables in the data set could enhance the model’s ability to more precisely differentiate analytes based on their physicochemical similarities.</p><p>A key advantage of the SIMCA approach lies in its flexibility. Unlike other techniques, such as PLS-DA, which attempt to model all classes within a single model and require known samples, SIMCA constructs separate models for each class. This feature eliminates the need for samples from nontarget classes, allowing the possibility of not classifying samples into any of the modeled categories. This multivariate approach proves particularly useful for classifying unknown NPS structurally related to those already known identifying potential new classes and recognizing metabolites, paving the way for future applications in analyzing biological matrices. This capability represents an added value in the context of NPS monitoring, increasing the knowledge of this constantly evolving phenomenon and providing strong support for forensic investigations. In addition, the approach described can be valuable in other fields, such as doping analysis, metabolomics, and environmental analysis.</p>

Soft Independent Modeling of Class Analogies for the Screening of New Psychoactive Substances through UPLC-HRMS/MS

<p><strong>Our Library never stops expanding. What are the most recent contributions to LabRulezLCMS Library in the week of 8th September 2025? Check out new documents from the field of liquid phase, especially HPLC and LC/MS techniques!</strong></p><blockquote><p><span style="font-size:20px;">👉 </span><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library"><span style="font-size:20px;"><strong>SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT LCMS AND RELATED TECHNIQUES</strong></span></a></p></blockquote><blockquote><p><strong>👉 Need info about different analytical techniques?</strong> Peek into <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library"><strong>LabRulezGCMS</strong></a> or <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library"><strong>LabRulezICPMS</strong></a> libraries.</p></blockquote><p>This week we bring you application notes by <strong>Agilent Technologies</strong> and<strong> Shimadzu </strong>and<strong> </strong>posters by <strong>Thermo Fisher Scientific / HPLC Symposium </strong>and <strong>Waters Corporation!</strong></p><h3><strong>1. Agilent Technologies: Automated Solid Phase Extraction of PFAS from Aqueous Samples</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_1633_automated_spe_wax_carbon_s_5994_8289en_agilent_1fc425207e.pdf"><strong>Full PDF for download</strong></a></li></ul><p>In this study, three types of dual-phase SPE cartridges were evaluated based on the quality control requirements for aqueous matrices as specified in US EPA Method 1633 using automated SPE. The cartridges contained PFAS WAX and Carbon S for PFAS. In the first configuration, a 200 mg bed of PFAS WAX was layered on top of a 50&nbsp;mg bed of Carbon S with the sorbents separated by a frit. In the second configuration, 200 mg of PFAS WAX was mixed with 50 mg of Carbon S before cartridge packing. In the third configuration, 200 mg of PFAS WAX and 10&nbsp;mg of Carbon S were mixed before cartridge packing. All three configurations were evaluated under identical protocols using automated SPE and were compared to results obtained using the manual addition of carbon and separate WAX extraction. The blended sorbent containing 200 mg of PFAS WAX and 10 mg of Carbon S was selected for validation and further evaluation of several environmental matrices including groundwater from residential private wells, effluent from a wastewater storage lagoon, and a landfill monitoring&nbsp;well.</p><h4><strong>Experimental</strong></h4><h5><strong>Equipment and materials</strong></h5><p>The three Agilent dual-phase cartridges used in the study are listed in Table 1. As a performance benchmark, a single‑phase <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?search=Bond+Elut+PFAS+WAX"><strong>Bond Elut PFAS WAX sorbent</strong></a> cartridge (Agilent, part number 5610‑2151) was used with either Carbon S bulk (Agilent, part number 5610-2095) or Superclean ENVI-Carb (Millipore Sigma, part number 57210-U).</p><p>Samples were extracted using the PromoChrom eight‑channel automated SPE extractors with upside‑down shaker racks (part number SPE-03 with MOD004), anticlogging frits (part&nbsp;number CF-06), high-capacity inline filters (part&nbsp;number&nbsp;F-HC-30), and anticlogging tips (part&nbsp;number F-T-4). Sample analysis was performed using an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?search=1290+Infinity+II+LC"><strong>Agilent 1290 Infinity II LC</strong></a> system consisting of an Agilent 1290 Infinity&nbsp;II high-speed pump (G7120A), an Agilent 1290 Infinity II multisampler (G7167B), and an Agilent 1290 Infinity II multicolumn thermostat (G7116B). The LC system was modified for PFAS analysis using the Agilent <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?search=InfinityLab+PFC%E2%80%91free+HPLC+conversion+kit"><strong>InfinityLab PFC‑free HPLC conversion kit </strong></a>(part number 5004-0006). The LC system was coupled to an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?search=6495C"><strong>Agilent 6495C triple quadrupole LC/MS</strong></a> equipped with an Agilent Jet Stream technology ion source. Agilent <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?search=MassHunter+Workstation"><strong>MassHunter Workstation software</strong></a> was used for data acquisition (version 10.1) and <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?search=Quantitative+Analysis+"><strong>Quantitative Analysis </strong></a>for QQQ (version 12.1) was used for data analysis. Instrumental conditions were determined based on detailed multiparametric optimization experiments developed in‑house.&nbsp;</p><h4><strong>Conclusion&nbsp;</strong></h4><p>The adoption of automated solid phase extraction (SPE) for PFAS analysis in environmental matrices significantly enhances laboratory efficiency and reduces the potential for human error. Our evaluation of five extraction protocols revealed that the blended sorbent containing 200 mg of PFAS WAX and 10 mg of Carbon S provided the best performance in terms of recovery, matrix interference reduction, and fewer outliers. This configuration was successfully applied to various environmental samples, demonstrating its versatility and reliability. The findings support the use of automated SPE with optimized sorbent cartridges as a robust method for PFAS analysis, aligning with the quality control metrics of US EPA Method 1633. Future work should focus on further refining automated SPE techniques and exploring their application to other environmental matrices.</p><h3><strong>2. Shimadzu: Automated Preparation of Calibration Curves Using the Autosampler's Dilution Function</strong></h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_00914_en_089a8c34fa.pdf"><strong>Full PDF for download</strong></a></li></ul><h5><strong>User Benefits</strong></h5><ul><li>The auto-dilution function equipped in the autosampler eliminates the need for manual dilution during calibration curve creation and sample preparation.</li><li>Samples can be automatically diluted to create a calibration curve by specifying the desired dilution factor in the batch table.</li><li>Because the same method file can be used across different dilution factors, only minor adjustments are needed when changing dilution conditions.&nbsp;</li></ul><p>In LC quantification, the dilution ofstandard and sample solutions is typically performed manually using pipettes, which is a laborintensive and time-consuming process. Recent advancements have called for automation to reduce labor and improve efficiency. By automating the dilution process, workflows can be streamlined, leading to increased productivity. The Nexera series autosamplers are equipped with an automatic pretreatment function that enables automated dilution. Using the auto-dilution function, samples can be automatically prepared at any desired dilution factor and directly introduced into the analytical column. This article presents a method to automate the creation of calibration curves and subsequent quantification workflows using the autosampler’s auto-dilution function.</p><h4><strong>Automated Dilution Analysis of Organic Acid Standard Solution</strong></h4><p>Automated dilution analysis was conducted using a 1000 mg/L mixed standard solution of ten organic acids. The analytical conditions are shown in Table 2 (pretreatment program : Table 1). 1.0 mL polypropylene vials were used as mixing vials, and septum-sealed vials were used for the undiluted standard solution. Rinse solution (ultrapure water) was used as the diluent. Fig. 3 shows the chromatogram of the 100-fold diluted organic acid mixed standard solution (concentration after auto-dilution : 10 mg/L for each organic acid). The <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=1&amp;search=Nexera+Organic+Acid+Analysis+System"><strong>Nexera Organic Acid Analysis System</strong></a> (pH-buffered electric conductivity detection) was employed for this analysis.</p><h4><strong>Conclusion</strong></h4><p>By utilizing the auto-dilution function in the autosampler, the standard undiluted solution for calibration curve creation can be automatically diluted at any desired dilution ratio. This eliminates the need for manual sample dilution and streamlines the calibration curve creation workflow. Additionally, since the autodilution function can also be applied to sample solutions, only the preparation of undiluted standard and sample solutions is required, allowing for a fully automated process from calibration curve creation to quantitation. As a result, the reduction in pretreatment work contributes to improved work efficiency and productivity.</p><h3><strong>3. Thermo Fisher Scientific / HPLC Symposium: Wide-ranging polynucleotide separation capabilities using Reversed Phase particles with variable pore geometry</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/po_wide_ranging_polynucleotide_separation_capabilities_po_en_1fdaba9e08.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Polynucleotides such as DNA and RNA serve many uses in the areas of basic research, industrial manufacturing, therapeutics to up/down regulate cellular functions, and as therapeutic and vaccine intermediates (i.e., as an instructional template to produce proteins that 1) supplement/replace ineffective proteins or 2) function as antigens). CRISPR technology using guide RNA (gRNA, ~100mer) to directly modify cellular chromosomes was recently leveraged to create a customized treatment within 6-months for an infant with CPS1 deficiency1. This approach has provided long-term, life-saving enzyme production negating the need for daily treatments. Associated DNA and RNA such as gRNA and mRNA (1000+mer) require detailed characterization to ensure safety and efficacy (e.g., of sufficient quantity and fidelity in delivery vehicles such as lipid nanoparticles). Here, we present a RP particle with complex pore geometry and the effects of mobile phase and temperature for separating DNA/RNA with therapeutic benefits (siRNA-gRNA-mRNA:~ 20-100- 1000+mer). Semi-prep column separations, common for sample recovery for subsequent analyses or sample enrichment for in vitro or in vivo use, is also shown.</p><h4><strong>Materials and methods</strong></h4><h5><strong>Test method(s)&nbsp;</strong></h5><p><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=3&amp;search=DNAPac+RP+column"><strong>DNAPac RP columns</strong></a> packed with 4 µm DNAPac RP media include 2.1 × 50 mm (P/N 088924), 2.1 × 50 mm (P/N 088924), and 21.2 × 150 mm (P/N 080922- 1521232). For instruments, mobile phases, gradient conditions, and other chromatographic operating parameters (flow rate, temperature, injection volume, etc.), please reference the figures and associated text. For all analyses, samples were detected by UV absorbance at 260nm. For 21.2x150mm semi-prep separations, a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products?category=40&amp;manufacturerItems=3"><strong>conventional (U)HPLC chromatography system</strong></a> with a binary pump, autosampler and detector without column compartment (ambient temperature separations) was used for all analyses. Ion Pairing Agents (Cation Anion): TEAA – Triethylamine Acetate, HAA – Hexylamine Acetate, HFIP – Hexafluoroisopropanol.&nbsp;</p><h5><strong>Data analysis&nbsp;</strong></h5><p>The <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/76"><strong>Thermo Scientific Chromeleon </strong></a>7.2.10 or 7.3 Chromatography Data Systems (CDS) software was used for data acquisition and analysis.</p><h4><strong>Results&nbsp;</strong></h4><h5><strong>DNAPac RP particle characterization – chemistry and pore structure&nbsp;</strong></h5><p>Figure 1 summarizes the chemical and physical properties of the resin, which is based on polymeric divinylbenzene (top left). Unlike conventional silica particles, the polymeric resin is not susceptible to hydrolytic degradation at neither extreme pH (1-14) nor high temperature (90+oC). The aromatic groups also provide π- π bonding with aromatic nucleotides giving different selectivity from conventional alkyl phases incapable of such interactions. The SEM image visually shows the highly variated porous structure of the particles. Barrett-Joyner-Halenda (BJH) N2 adsorption analysis details the surface area contributions of the different pore sizes. From this analysis it is clear that a continuum of pores from 50Å to 2500Å (suitable for small molecules up to macromolecule adsorption, respectively) contributes to the overall surface area of the particle. Based on the complex geometry and resulting pore structure, we commonly refer to the media as a supermacroporous (SMP) particle.</p><h4><strong>Conclusions&nbsp;</strong></h4><ul><li>High resolution separation of nucleic acids varying in length from 10 to 10,000 nucleotides is achieved using an SMP particle with a variable pore structure.</li><li>Polymeric SMP particles are compatible with different mobile phase IP compositions, high pH and high separation temperature, which are critical parameters for optimizing the separation of polynucleotides&nbsp;</li><li>SMP particles provide high resolution separations in both analytical and semi-prep formats</li></ul><h3><strong>4. Waters Corporation / ASMS: Detection and Characterization of PFAS Molecules Using Inclusion and Exclusion Features of Data Directed Analysis (DDA)</strong></h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720008869_fb6d27e0a0.pdf"><strong>Full PDF for download</strong></a></li></ul><p>Polyfluorinated alkyl substances (PFAS) are bioaccumulative and suspected to contribute to increased cancer risk in humans. Monitoring PFAS in biofluids is possible, but their very low concentrations present analytical challenges.</p><p>Data-Directed Acquisition (DDA) isolates ions that meet defined criteria, fragments them at elevated collision energies, and generates product ion spectra. Because PFAS ions show a characteristic <strong>mass defect</strong> due to fluorine atoms, inclusion lists can enhance their detection. Conversely, exclusion lists derived from background matrix analyses help remove interferences, simplifying ion selection for fragmentation.</p><p>Preliminary tests with mixed small-molecule samples demonstrated the utility of these lists before applying them to PFAS, where inclusion lists contained hundreds of entries and exclusion lists thousands.</p><h4><strong>Methods</strong></h4><ul><li><strong>Instrument:</strong> <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/202"><strong>SELECT SERIES™ Cyclic™ Ion Mobility Mass Spectrometer</strong></a></li><li><strong>Acquisition:</strong> ToF-ESI negative DDA mode, 100 ms survey/MSMS scan times, collision energy ramps optimized by PFAS class</li><li><strong>LC System:</strong> <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/191"><strong>ACQUITY Premier </strong></a>with PFAS Kit and Atlantis Premier BEH C18 AX Isolator Column (2.1 × 50 mm, 5 µm)</li><li><strong>Analytical Column:</strong> <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1952"><strong>ACQUITY UPLC HSS T3 C18</strong></a> (100 × 2.1 mm, 1.8 µm) at 35 °C</li><li><strong>Flow Rate:</strong> 0.3 mL/min; <strong>Injection Volume:</strong> 5 µL; <strong>Autosampler Temp:</strong> 6 °C</li><li><strong>Mobile Phases:</strong><ul><li>A: H₂O:MeOH (95:5) with 2 mM ammonium acetate</li><li>B: MeOH with 2 mM ammonium acetate</li></ul></li><li><strong>Samples:</strong> LCMS QC mixture, native PFAS mixture (PFAC30PAR, Wellington Labs), and human serum</li><li><strong>Software:</strong> <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/635"><strong>MassLynx™ 4.2 for acquisition</strong></a>, <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1544"><strong>waters_connect™ with PFAS library</strong></a> (130 entries) for processing, Tibco Spotfire® for visualization</li></ul><h4><strong>Conclusion</strong></h4><ul><li>ToF-DDA mode on a cyclic ion mobility mass spectrometer is effective for PFAS analysis.</li><li>Inclusion lists based on PFAS mass defect improve real-time detection and intelligent switching.</li><li>Background-derived exclusion lists (up to 9000 entries) further reduce interferences and enhance specificity.</li></ul>

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