<h3>How are PFAS regulations changing, and what role will ion chromatography play in the future of environmental testing?</h3>In this episode, &nbsp;David interviews Jay Gandhi, PhD, Vertical Markets Manager at Metrohm USA, for an in-depth discussion on PFAS analysis, the rise of AOF (Adsorbable Organic Fluorine) testing, and the development of EPA 1621 and ISO 18127 standards.With more than 30 years of experience in ion chromatography, Jay shares valuable perspective on how the field has evolved—from early applications and NASA water recovery projects to today’s challenges involving PFAS, TFA, and emerging contaminants.<h4>Topics covered in this episode include:</h4><ul><li>What PFAS are and why they matter</li><li>The difference between targeted PFAS methods and AOF analysis</li><li>How combustion ion chromatography works</li><li>Behind the scenes of EPA 1621 and ISO 18127 method development</li><li>Regulatory trends in water, textiles, and food packaging</li><li>TFA analysis using IC-MS and IC-MS/MS</li><li>The future of ion chromatography in environmental labs</li></ul><h3>Video Transcription</h3>For more than three decades, Jay Gandhi has worked with ion chromatography (IC), watching the technique expand from a specialized tool for water analysis into an analytical platform used across environmental testing, food and beverage analysis, semiconductor manufacturing, battery applications, and, increasingly, PFAS analysis.In an interview for Concentrating on Chromatography, Gandhi discusses his introduction to IC during his work on a NASA Mars exploration project, the emergence of PFAS as a major analytical challenge, and why techniques such as adsorbable organic fluorine (AOF) determination and combustion ion chromatography (CIC) can provide information that targeted LC-MS analysis alone cannot deliver. He also examines the development of standardized methods, sample preparation challenges, and the future role of IC-MS and total fluorine approaches in PFAS monitoring.<h4>From NASA to ion chromatography</h4>Gandhi's introduction to ion chromatography came through his work as a principal scientist on a NASA Mars exploration project focused on water recovery under zero-gravity conditions. IC played an important role because it enabled researchers to establish the ionic balance of anions and cations in water.The technology was used both to investigate micronutrients relevant to agricultural applications and to evaluate water purity for potential astronaut consumption. During approximately three years of this work, Gandhi gained extensive experience with ion chromatography. This ultimately led him deeper into IC instrumentation and, 24 years ago, to joining Metrohm.At the beginning of his career, IC was strongly associated with relatively straightforward anion and cation analysis in water. The technique itself had been used for water analysis since the 1970s, but Gandhi has seen its application range expand dramatically.Food and beverage testing and environmental analysis became important fields, supported by both direct and indirect detection strategies. More recently, IC has also found applications in areas such as the semiconductor industry and battery recycling.In Gandhi's words, ion chromatography has effectively "bloomed" into numerous application areas.<h4>PFAS changes the analytical challenge</h4>PFAS represent a particularly important chapter in this development. These chemicals have been used for decades in numerous consumer and industrial products, including non-stick materials, firefighting foams, water-resistant textiles and other applications.Gandhi recalls work on PFOA and PFOS using ion chromatography with conductivity detection as early as 2008. However, the achievable detection limits were insufficient for the extremely low concentrations that later became important in PFAS analysis.A major turning point for him came in 2015, when he encountered research from Sweden using combustion ion chromatography to investigate total fluorine. Subsequent collaboration with researchers at Clarkson University and Harvard University, particularly on aqueous film-forming foam (AFFF), further demonstrated the importance of looking beyond individual targeted PFAS compounds.<h4>Why targeted LC-MS does not tell the whole story</h4>Targeted LC-MS is highly sensitive and selective and can provide detailed information about individual PFAS compounds. However, according to Gandhi, the number of possible PFAS structures in the environment is vastly greater than the number routinely included in targeted methods.A targeted method may monitor dozens or perhaps around one hundred compounds, while the overall PFAS universe can include thousands of chemical structures. Consequently, targeted analysis may characterize only a small fraction of the total PFAS burden.There are other practical considerations. LC-MS measurements can be affected by matrix-dependent ion suppression, while accurate quantification frequently relies on isotope-labelled internal standards. Such standards are available for only a limited number of compounds and can be expensive.This is where measurements of total or adsorbable organic fluorine can provide complementary information. Rather than asking which specific PFAS compounds are present?, these approaches can help answer a broader question: how much fluorine associated with this chemical space is present in the sample?<h4>AOF as a complementary approach</h4>Adsorbable organic fluorine analysis provides a different perspective from targeted LC-MS. In the workflow discussed by Gandhi, organic fluorine compounds are captured during sample preparation and subsequently measured using combustion ion chromatography.The principle of CIC builds upon older total organic halogen analysis. The sample is combusted at temperatures above approximately 1,000 °C, breaking carbon-halogen bonds. In PFAS-related analysis, carbon-fluorine bonds are broken and the resulting fluoride is collected in an absorption solution.Ion chromatography is then used to separate and quantify the fluoride.Replacing older detection approaches with IC provides both sensitivity and selectivity for the halogens being measured. Effective combustion is critical: oxygen supports the combustion process, while a carrier gas transports the products into the absorption solution for subsequent IC analysis.<h4>The challenge of inorganic fluoride</h4>The broad response of CIC is simultaneously one of its strengths and one of its analytical challenges. Combustion does not distinguish fluorine originating from PFAS from fluorine present in inorganic compounds such as fluoride salts.This becomes particularly important when analyzing water containing relatively high concentrations of inorganic fluoride while attempting to quantify organic fluorine at very low levels.Sample preparation therefore has a critical role. Inorganic fluoride must be effectively separated from the fraction of interest, while materials used during sample preparation must themselves have sufficiently low fluoride backgrounds.Contamination can come from unexpected sources. Gandhi points out that background fluoride may originate not only from reagents and sample preparation materials but potentially from the laboratory environment. Careful blank control, appropriate materials and rigorous evaluation of interferences are therefore essential when moving toward increasingly low detection limits.<h4>From research method to international standardization</h4>The development of standardized AOF methods illustrates the importance of collaboration between academia, industry, regulatory organizations and analytical laboratories.Gandhi describes work that began in Europe around 2015 and subsequently progressed through German standardization activities, ISO development, ASTM-related collaboration and the U.S. EPA. The process eventually contributed to methods such as EPA Method 1621.He emphasizes that this was not the work of a single scientist or organization. Developing a robust method required years of experiments, discussions and sometimes difficult technical debates among numerous stakeholders.For Gandhi, the essential principle was that experimental evidence ultimately had to determine the outcome. Collaboration becomes possible when scientists focus on the data rather than the organizations they represent.<h4>PFAS monitoring is moving beyond drinking water</h4>Regulatory attention to PFAS is also expanding beyond drinking water. Gandhi describes a broader strategy that begins with reducing the manufacture and use of PFAS-containing materials and continues with removing existing contamination from the environment.Wastewater represents an important part of this picture because PFAS can enter it from numerous sources. Historical contamination from landfills and contaminated sites can also migrate into groundwater and ultimately affect drinking-water supplies.Consumer products are another area of increasing attention. Food packaging, disposable containers, automotive fabrics, outdoor clothing and water-repellent textiles have historically used fluorinated substances to provide properties such as grease, water or stain resistance.Reducing PFAS use in such products could therefore gradually decrease the amount entering waste streams and the wider environment.<h4>Screening first, regulation later?</h4>Gandhi expects AOF to be particularly valuable as a screening tool, providing an estimate of broader fluorine contamination before laboratories proceed to more specific analyses.Its importance becomes clear when AOF results are compared with targeted PFAS measurements. Gandhi reports that laboratories working with EPA Method 1621 have observed differences between targeted PFAS results and broader AOF measurements ranging from approximately 300- to 4,000-fold in the total fluorine mass balance.Such discrepancies suggest that targeted methods can leave a substantial portion of fluorine unexplained.Rather than competing with LC-MS, AOF and CIC therefore provide complementary information. A broad screening method can reveal the magnitude of total contamination, while targeted mass spectrometry identifies and quantifies specific compounds of interest.<h4>Where IC-MS fits into the PFAS toolbox</h4>No single technique can cover the entire PFAS problem. Gandhi highlights very short-chain, highly polar fluorinated compounds as another analytical challenge.Compounds such as trifluoroacetic acid (TFA) are highly water-soluble and may not be quantitatively retained by the carbon materials used for AOF sample preparation. Direct ion chromatography can provide an alternative route for separating such compounds from common inorganic anions.Coupling IC with mass spectrometry adds another level of sensitivity and selectivity. Gandhi therefore sees IC-MS and IC-MS/MS as increasingly important tools for highly polar fluorinated compounds. Similar approaches can also be useful beyond PFAS, including the determination of polar pesticides such as glyphosate and glufosinate.<h4>Toward lower detection limits and more complete PFAS analysis</h4>For Gandhi, current standardized methods represent a beginning rather than an endpoint. Improvements in instrumentation and particularly sample preparation could push total fluorine analysis to substantially lower concentrations.He notes that current combustion IC configurations can reach approximately 0.5 ppb, or 500 parts per trillion, and believes that improvements in sample volume, preparation procedures and mitigation of inorganic fluoride backgrounds could eventually move detection below 100 ppt and potentially toward 50 ppt.Extractable organic fluorine workflows could provide another bridge between broad fluorine screening and targeted LC-MS methods. Combining improved extraction with CIC could allow samples to be screened for organic fluorine before more detailed compound-specific characterization.The broader message from Gandhi's career is that PFAS analysis is unlikely to be solved by a single analytical technique. Targeted LC-MS provides compound-specific sensitivity and selectivity; AOF and combustion IC reveal a broader fluorine picture; and IC-MS can address highly polar compounds that are difficult to capture using conventional workflows. Used together, these techniques can provide laboratories with a much more complete understanding of PFAS contamination than any one approach alone.<blockquote>This text has been automatically transcribed from a video presentation using AI technology. It may contain inaccuracies and is not guaranteed to be 100% correct.</blockquote><blockquote><h4>Concentrating on Chromatography Podcast</h4>Dive into the frontiers of chromatography, mass spectrometry, and sample preparation with host David Oliva. Each episode features candid conversations with leading researchers, industry innovators, and passionate scientists who are shaping the future of analytical chemistry. From decoding PFAS detection challenges to exploring the latest in AI-assisted liquid chromatography, this show uncovers practical workflows, sustainability breakthroughs, and the real-world impact of separation science. Whether you’re a chromatographer, lab professional, or researcher you'll discover inspiring content!You can find Concentrating on Chromatography Podcast in podcast apps:<ul><li><a target="_blank" rel="noopener noreferrer" href="https://open.spotify.com/show/1wZg67rCjoGju4yhqxI3tL">Spotify</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podcasts.apple.com/tz/podcast/concentrating-on-chromatography/id1788429185">Apple podcast</a></li><li><a target="_blank" rel="noopener noreferrer" href="https://podscan.fm/podcasts/concentrating-on-chromatography">podscan</a></li></ul>and on <a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/@ChromatographyTalk/videos">YouTube channel</a></blockquote>

The Future of Ion Chromatography | PFAS Testing, EPA 1621 & AOF Explained

In late February 2025, a train carrying benzene derailed in Hustopeče nad Bečvou, and within hours, a race was on to save the groundwater and surface water. One of the most toxic industrial substances began seeping into the gravel-sand subsoil. While fieldwork was underway to prevent the spread of contamination, decisions were already being made in the CXI TUL laboratories regarding how to subsequently clean up the contaminated rock environment.The accident ranks among the most serious environmental incidents in the Czech Republic in recent years. After a tanker train derailed, hundreds of metric tons of benzene leaked; due to the highly permeable geological subsoil, it quickly seeped into deeper soil layers and into the groundwater. The combination of the substance’s high toxicity and local conditions complicated both the emergency response and the subsequent remediation. The key was to stop further spread, determine the extent of the contamination, and establish a remediation strategy for the entire site.It was at this stage that CXI TUL became involved in the solution. At the request of the renowned remediation company DEKONTA, a.s., a team led by RNDr. Jan Němeček, Ph.D., began testing the feasibility of the in situ chemical oxidation (ISCO) method. This technology is designed to break down benzene directly within the rock matrix, without the need for extensive excavation of contaminated soil or the pumping out of large volumes of groundwater. The goal was simple: to find a method that would work quickly, safely, and under the actual conditions of the affected site.<h4>Benzene Remediation: Technically Simple, Complicated in Practice</h4>Benzene is a highly toxic and carcinogenic substance that rapidly penetrates into groundwater in permeable subsoil, where it dissolves and spreads further as the water flows toward surface water bodies. Benzene also binds to rock particles and migrates in the form of vapors into overlying unconsolidated layers.Scientists in Liberec therefore tested in situ chemical oxidation (ISCO) technology under laboratory conditions. The goal was to find an oxidant capable of breaking down benzene directly underground into less hazardous substances.The tests evaluated two main options: activated hydrogen peroxide and sodium peroxodisulfate. Both substances removed up to 99.99% of the benzene, but there was a difference in their kinetics and impacts on the natural environment. Activated hydrogen peroxide achieved nearly complete degradation of the contaminant within a single day. Activated sodium peroxodisulfate was slower and also significantly altered the chemistry of the water; it acidified the environment and increased the sulfate content in the water, which poses an additional environmental burden.For field applications, the researchers therefore unequivocally recommended activated hydrogen peroxide.<h4>Dosage Matters</h4>It was not just the oxidant’s effectiveness that was key. It was equally important to determine how the chemical reaction would behave in a real geological environment. The team of scientists therefore also analyzed what is known as soil oxidant consumption (SOD). It turned out that a significant portion of the oxidizing agent does not react solely with benzene but is also consumed to a significant extent by the rock environment itself, particularly by natural organic substances present in the fine-grained fraction of the soil.An oxidant consumption value was determined specifically for the local composition of the aquifers, which is essential for the correct dosing of chemicals in the field so that the remediation is not only effective but also economically viable and safe for surrounding ecosystems.<h4>One Year After the Accident: Research Turns into Real-World Assistance</h4>While the public often perceives university science as a distant world of laboratories and grant projects, it is precisely situations like this that demonstrate its practical impact. The results of the work by researchers at CXI TUL did not remain confined to academic studies but directly influenced decisions regarding the technology used to clean up one of the largest environmental accidents in recent years.

Science on the Front Lines: Cleanup of a Benzene Spill

This study uses ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) lipidomics to characterize plasma lipid alterations associated with long-term aromatase inhibitor therapy in postmenopausal breast cancer patients. A total of 649 lipid species across 23 classes and subclasses were relatively quantified, revealing significant differences in phosphatidylcholines, sphingomyelins, ceramides, hexosylceramides, and other lipid groups.Multivariate analysis showed clear group separation, while a Naive Bayes model based on lipid-class profiles achieved an AUC of 0.79. The results indicate that aromatase inhibitor therapy is associated with systematic lipidomic changes linked to estrogen deprivation and sphingolipid metabolism, providing potential mechanistic insight into long-term metabolic effects of treatment.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926">Aromatase Inhibitor Therapy Is Associated with Distinct Plasma Lipidomic Profiles in Postmenopausal Breast Cancer Patients&nbsp;</a></h4><div>Aleksandra Arsic, Ales Kvasnicka<a target="_blank" rel="noopener noreferrer" href="https://orcid.org/0000-0001-5973-316X"><img></a>, David Friedecky<a target="_blank" rel="noopener noreferrer" href="https://orcid.org/0000-0002-3448-9073"><img></a>, Nebojsa Ivanovic, Maja Milosevic<a target="_blank" rel="noopener noreferrer" href="https://orcid.org/0000-0002-0985-0728"><img></a> and Vesna Vucic</div>Int. J. Mol. Sci. 2026, 27(4), 1926<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.3390/ijms27041926">https://doi.org/10.3390/ijms27041926</a><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.<div><div>Breast cancer (BRC) is the most common malignancy among women, with over 2.2 million new cases diagnosed globally in 2022 [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B1-ijms-27-01926">1</a>]. A substantial proportion of breast cancers are estrogen receptor-positive (ER+) and/or progesterone receptor-positive (PR+) [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B2-ijms-27-01926">2</a>], necessitating multimodal treatment approaches, including surgery, chemotherapy, radiotherapy, and endocrine therapy. Aromatase inhibitors (AIs) represent the gold-standard adjuvant endocrine treatment for postmenopausal women, as they improve disease control and reduce the risk of distant metastasis by inhibiting peripheral aromatase activity [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B3-ijms-27-01926">3</a>]. This inhibition prevents the conversion of androgenic precursors into estrogens in peripheral tissues, resulting in a marked reduction in systemic estrogen levels. For postmenopausal women with ER+ and PR+ breast tumors, a five-year course of AI therapy, often extended beyond this duration, is recommended to reduce the risk of disease recurrence [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B4-ijms-27-01926">4</a>].AIs decrease plasma and tissue concentrations of endogenous estrogens by inhibiting aromatase (cytochrome P450), the rate-limiting enzyme responsible for estrogen biosynthesis from testosterone and adrenal androgens [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B5-ijms-27-01926">5</a>]. Because estrogen plays a protective role in lipid metabolism, AI therapy is frequently associated with dyslipidemia. Increased levels of triglycerides (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL) have been reported in patients receiving AIs [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B6-ijms-27-01926">6</a>], thereby increasing the risk of cardiovascular disease (CVD) in postmenopausal women with BRC [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B7-ijms-27-01926">7</a>].Disturbances in lipid metabolism are recognized as a hallmark of cancer, leading to increasing interest in lipidomic profiling as a tool for characterizing tumor-associated metabolic alterations [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B8-ijms-27-01926">8</a>]. In recent years, clinical lipidomics studies have been conducted across multiple malignancies, including lung [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B9-ijms-27-01926">9</a>], pancreatic [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B10-ijms-27-01926">10</a>], renal cell carcinoma [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B11-ijms-27-01926">11</a>], colorectal [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B12-ijms-27-01926">12</a>], and BRC [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B13-ijms-27-01926">13</a>], highlighting cancer-specific lipidomic signatures and the growing relevance of lipid metabolism in oncology. Lipidomic reprogramming is now recognized as a defining feature of BRC. To date, elevated levels of phosphatidylcholine (PC 32:1), stearic acid, and ceramide (Cer 43:1), along with reduced levels of diacylglycerol (DG 34:2), have been reported in BRC patients compared with healthy women [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B14-ijms-27-01926">14</a>]. These lipid species, together with phosphatidylinositol (PI 16:0/16:1) and PI (18:0/20:4), have been proposed as potential biomarkers for BRC [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B14-ijms-27-01926">14</a>].Despite these advances, there remains a paucity of studies investigating the lipidomic profiles in BRC patients in relation to endocrine therapy. The present cross-sectional study aims to address this gap by conducting mass spectrometry-based profiling of lipid classes and comparing profiles between postmenopausal BRC patients receiving AI therapy and another cohort assessed before this treatment. This approach aims to characterize associations between AI therapy status and plasma lipidomic patterns.<h4>2. Results</h4><h5>2.3. Lipidomic Analysis</h5>Plasma lipid profiling identified 649 lipids across 23 lipid classes and subclasses, covering the major structural families of the circulating lipidome, including neutral lipids (triacylglycerols, diacylglycerols, cholesteryl esters, and cholesterol), glycerophospholipids (phosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, phosphatidylglycerols, phosphatidylserines, and their lysophospholipid counterparts), sphingolipids (sphingomyelins, ceramides, monohexosylceramides, and dihexosylceramides), and free fatty acids. In addition to diacyl phospholipids, the dataset included ether-linked and plasmalogen species (denoted as O- and P-), most prominently within PC, PE, and LPC and LPE classes. An overview of the plasma lipidome is shown as a global lipid network (<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#fig_body_display_ijms-27-01926-f001">Figure 1</a>). To assess whether the observed lipid changes reflect coordinated remodeling rather than isolated signals, we performed Fisher’s combined probability test at the lipid-class level. This analysis revealed significant cumulative differences in several lipid classes, including Cer, LPC, ether/plasmalogen LPC, LPE, and SM (<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#fig_body_display_ijms-27-01926-f001">Figure 1</a>), supporting systematic class-level alterations despite limited power at the single-species level.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Int_J_Mol_Sci_2026_27_4_1926_Figure_1_Global_lipid_network_analysis_of_plasma_lipidomic_differences_between_breast_cancer_patients_BRC_prior_to_initiation_of_the_aromatase_inhibitor_6f82827773.jpg" alt="Int. J. Mol. Sci. 2026, 27(4), 1926: Figure 1. Global lipid network analysis of plasma lipidomic differences between breast cancer patients (BRC) prior to initiation of the aromatase inhibitor (AI) therapy and patients receiving AI. Cytoscape-based network visualization illustrates lipid species grouped according to structural similarity and lipid class. Each node represents an individual lipid species, while edges indicate class-based relationships. Node size corresponds to the −log10 p-value from the Mann–Whitney U test, reflecting the statistical significance (−log p-Value &gt; 1.3) of differences between groups. Node color indicates the direction and magnitude of change, based on log2 fold change, with red representing higher abundance in AI-treated patients and blue representing higher abundance in patients before AI therapy. Major lipid classes, including glycerophospholipids (GPL), sphingolipids (SP), glycerolipids (GL), fatty acids (FA), and cholesteryl esters (CE), are shown to highlight coordinated class- and species-level lipidomic alterations. Fisher’s combined p-value for individual lipid classes is shown at the bottom (significant p-Value &lt; 0.05). " srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Int_J_Mol_Sci_2026_27_4_1926_Figure_1_Global_lipid_network_analysis_of_plasma_lipidomic_differences_between_breast_cancer_patients_BRC_prior_to_initiation_of_the_aromatase_inhibitor_6f82827773.jpg 245w," sizes="100vw" width="1600" height="847"><h5>2.4. Multivariate Analysis of Plasma Lipidomic Profiles</h5>Principal component analysis (PCA) and partial least squares discriminant analysis (OPLS-DA) were applied to the lipidomic dataset to assess global variation and group separation between the BRC and AI groups (<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#fig_body_display_ijms-27-01926-f002">Figure 2</a>). PCA was performed as an unsupervised analysis, including all samples and quality control (QC) samples. The first two principal components explained 37.9% (PC1) and 15.6% (PC2) of the total variance, accounting for 53.5% of the cumulative variance. QC samples clustered tightly near the center of the PCA score plot, indicating analytical stability and reproducibility of the LC–MS measurements. In the PCA score plot, samples from the BRC and AI groups showed partial overlap, with no complete separation observed along the first two principal components. To further evaluate group discrimination, a supervised OPLS-DA model was constructed using the same dataset. The OPLS-DA score plot demonstrated improved separation between the BRC and AI groups along the first latent variable, with group-specific clustering highlighted by confidence ellipses. Permutation testing confirmed that the supervised OPLS-DA model was not overfitted, with a negative Q2 intercept (Q2 = −0.55), indicating that permuted models lacked predictive ability compared with the original model (<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#app1-ijms-27-01926">Supplementary Figure S1</a>). Together, these multivariate analyses indicate differences in lipidomic profiles between the BRC and AI groups, while also demonstrating acceptable analytical performance as evidenced by the clustering of QC samples.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Int_J_Mol_Sci_2026_27_4_1926_Figure_2_Multivariate_analysis_of_plasma_lipidomic_profiles_in_breast_cancer_patients_BRC_56f65f265b.jpg" alt="Int. J. Mol. Sci. 2026, 27(4), 1926: Figure 2. Multivariate analysis of plasma lipidomic profiles in breast cancer patients (BRC) prior to initiation of the aromatase inhibitor (AI) therapy and patients receiving AI. Principal component analysis (PCA) score plot (a) shows the distribution of plasma lipidomic profiles from patients before AI therapy (BRC, blue), patients receiving AI therapy (AI, red), and quality control samples (QC, gray). Orthogonal partial least squares–discriminant analysis (OPLS-DA) score plot (b) demonstrates improved separation between BRC and AI groups. The ellipse represents the 95% Hotelling’s T2 confidence interval. " srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Int_J_Mol_Sci_2026_27_4_1926_Figure_2_Multivariate_analysis_of_plasma_lipidomic_profiles_in_breast_cancer_patients_BRC_56f65f265b.jpg 245w," sizes="100vw" width="1600" height="652"><h4>4. Materials and Methods</h4><h5>4.5. Lipidomic Analysis</h5>Chromatographic separation was performed using an ultra-high-performance liquid chromatography (UHPLC) <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=55&amp;search=ExionLC">ExionLC system</a> (SCIEX, Framingham, MA, USA) coupled to a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=55&amp;search=6500">QTRAP® 6500+ mass spectrometer </a>(SCIEX, Framingham, MA, USA). Lipids were separated on an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1953">ACQUITY Premier BEH C8 column</a> (100 × 2.1 mm, 1.7 μm; Waters Corporation, Wilmslow, UK). The column temperature was kept at 55 °C, and the autosampler was set to 10 °C. The mobile phases consisted of solvent A (acetonitrile/water, 6:4, v/v, containing 10 mM ammonium acetate) and solvent B (isopropanol/acetonitrile, 9:1, v/v, containing 10 mM ammonium acetate), delivered at a flow rate of 0.4 mL/min. The total analytical run time was 20 min per sample. Data acquisition was carried out using Analyst software (version 1.6.2), and data processing and semiautomatic peak integration were performed with MultiQuant software (version 3.0; SCIEX, Framingham, MA, USA). Samples were analyzed in a randomized sequence with continuous monitoring of quality control samples. Raw peak areas were QC-normalized by LOESS [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B48-ijms-27-01926">48</a>] and IS-normalized by the corresponding IS class for each lipid.<h5>4.6. Statistical Analysis</h5>The data were processed using the Metabol package [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B48-ijms-27-01926">48</a>] in R program (v 3.6.3). Preprocessing of the data included locally estimated scatterplot smoothing (LOESS), Pareto scaling, filtering of analytes (coefficients of variation in the QC above 30%), mean centering, and the lnPQN normalization (<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#app1-ijms-27-01926">Supplementary Table S1</a>) [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B49-ijms-27-01926">49</a>]. Univariate statistical analysis (based on the non-parametric non-paired Mann–Whitney U test (p-values shown as −log) together with the log2 fold-change in medians) was performed and visualized using GraphPad Prism (version 9.0, GraphPad Software, LLC, Boston, MA, USA). Data were also evaluated using multivariate statistical analysis, namely the principal component analysis (PCA) and orthogonal discriminant analysis by partial least squares (OPLS-DA) and validated by permutation using SIMCA software (version 18.0, Sartorius Umetrics, Umeå, Sweden). Benjamini–Hochberg correction was used for false discovery rate correction. Detected lipid species were mapped to standardized lipid classes and subclasses according to accepted lipidomics nomenclature, enabling consistent grouping of lipid species for downstream statistical, class-level, and network analyses. LipidOne 2.4 was further used to support lipid-centric data integration and visualization prior to multivariate and pathway-based analyses (such as heatmaps, ROC analyses using a Naive-Bayesian model and Logistic Regression model, and protein–protein interaction network based on lipidomics data) [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B50-ijms-27-01926">50</a>]. Lipid network visualization was performed using Cytoscape software (v 3.8.2) [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B51-ijms-27-01926">51</a>]. Differentially abundant lipid species were organized according to lipid class and structural similarity. Node size reflected the −log10 p-value from univariate analysis, while node color represented log2 fold change between groups. Calculation of Fisher’s combined probability test for assessing systematic lipid class-level differences was performed (<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#app1-ijms-27-01926">Supplementary Table S2</a>) and calculated following procedures described previously [<a target="_blank" rel="noopener noreferrer" href="https://www.mdpi.com/1422-0067/27/4/1926#B52-ijms-27-01926">52</a>].<h4>5. Conclusions</h4>In conclusion, our findings demonstrate significant alterations in lipid metabolism in BRC patients undergoing AI therapy, with pronounced changes in Cer, SM, DG, TG, PC, PI, and PE lipids and lipid classes. However, the precise mechanisms underlying the differences in lipid metabolism between patients before and during AI treatment remain to be fully elucidated. Future longitudinal studies with paired pre- and post-treatment samples from the same individuals leveraging comprehensive lipidomic profiling are needed to deepen our understanding of AI-induced metabolic alterations and to inform the development of targeted interventions aimed at mitigating adverse effects, ultimately improving long-term outcomes and quality of life for these patients.</div></div>

Aromatase Inhibitor Therapy Is Associated with Distinct Plasma Lipidomic Profiles in Postmenopausal Breast Cancer Patients

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SIELC Alltesta Mini HPLC Autosampler

The Alltesta™ Mini Autosampler is an affordable and dependable solution for automated sample handling in various analytical and liquid handling applications, including HPLC.

The Alltesta™ Mini Autosampler is an affordable and dependable solution for automated sample handling in various analytical and liquid handling applications, including HPLC. With its proprietary alpha/beta mechanical design, this compact autosampler offers direct and comprehensive control of its features, making integration quick and easy. Users can tailor the mini autosampler to their needs with customizable valve configurations, tray designs, and syringe volumes and pressures. Additionally, a built-in shaking feature enables sample mixing before injection and/or simple sample extraction within the vial.
This compact autosampler comes designed with original equipment manufacturer (OEM) customization in mind. Custom shape, color, and communication interfaces can be adapted to integrate with any system. Comprehensive software integration support is provided. Other options include metal-free sampling and reagent addition and mixing capabilities.
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<li>FRACTION COLLECTION CAPABILITY: A simple software modification can quickly convert this small autosampler into a fraction collector.
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<h4>The price of the Alltesta™ Mini Autosampler varies depending on the specific configuration requested.</h4>
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<h4>Specifications</h4>
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<li>Dimensions (WHL): 160 x 145 x 195 mm / 6.3 x 5.71 x 7.68 in</li>
<li>Weight: 3 kg / 6.6 lb</li>
<li>Power: 24 V</li>
<li>Capacity: 48 vials or 96-well plate</li>
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<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/SIELC_Alltesta_Mini_Autosampler_A_very_simple_schematic_operation_of_the_Alltesta_Autosampler_b79b0ad187.jpg" alt="SIELC: Alltesta™ Mini Autosampler - A very simple schematic operation of the Alltesta™ Autosampler."/>
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The Alltesta™ Autosampler can be controlled:
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