Analysis Of Small Molecules Research Articles

Small Molecules Targeting Mitochondria: A Mechanistic Approach to Combating Doxorubicin-Induced Cardiotoxicity.

Doxorubicin (Dox) is a commonly used chemotherapy drug effective against a range of cancers, but its clinical application is greatly limited by dose-dependent and cumulative cardiotoxicity. Mitochondrial dysfunction is recognized as a key factor in Dox-induced cardiotoxicity, leading to oxidative stress, disrupted calcium balance, and activation of apoptotic pathways. Recent research has emphasized the potential of small molecules that specifically target mitochondria to alleviate these harmful effects. This review provides a comprehensive analysis of small molecules that offer cardioprotection by preserving mitochondrial function in the context of doxorubicin-induced cardiotoxicity (DIC). The mechanisms of action include the reduction of reactive oxygen species (ROS) production, stabilization of mitochondrial membrane potential, enhancement of mitochondrial biogenesis, and modulation of key signaling pathways involved in cell survival and apoptosis. By targeting mitochondria, these small molecules present a promising therapeutic strategy to prevent or reduce the cardiotoxic effects associated with Dox treatment. This review not only discusses the mechanistic actions of these agents but also emphasizes their potential in improving cardiovascular outcomes for cancer patients. Gaining insight into these mechanisms can help in creating more effective strategies to safeguard the heart during chemotherapy, allowing for the ongoing use of Dox with a lower risk to the patient's cardiovascular health. This review highlights the critical role of mitochondria-targeted therapies as a promising approach in addressing DIC.

Clustering Molecules at a Large Scale: Integrating Spectral Geometry with Deep Learning.

This study conducts an in-depth analysis of clustering small molecules using spectral geometry and deep learning techniques. We applied a spectral geometric approach to convert molecular structures into triangulated meshes and used the Laplace-Beltrami operator to derive significant geometric features. By examining the eigenvectors of these operators, we captured the intrinsic geometric properties of the molecules, aiding their classification and clustering. The research utilized four deep learning methods: Deep Belief Network, Convolutional Autoencoder, Variational Autoencoder, and Adversarial Autoencoder, each paired with k-means clustering at different cluster sizes. Clustering quality was evaluated using the Calinski-Harabasz and Davies-Bouldin indices, Silhouette Score, and standard deviation. Nonparametric tests were used to assess the impact of topological descriptors on clustering outcomes. Our results show that the DBN + k-means combination is the most effective, particularly at lower cluster counts, demonstrating significant sensitivity to structural variations. This study highlights the potential of integrating spectral geometry with deep learning for precise and efficient molecular clustering.

Skin sensing and wearable technology as tools to measure atopic dermatitis severity.

Wearable medical technology encompasses a range of electronic devices that act as biosensors. Atopic dermatitis (AD) is the commonest inflammatory skin disease and represents an important area of need in which to leverage the power of wearable biosensor technology, especially as the impact of COVID-19 increases the likelihood of virtual consultations becoming an integrated part of clinical practice. The aim of this review is to systematically define the published evidence for the utility of wearable biosensors in assessment and management of atopic dermatitis (AD). A systematic literature search was conducted for publications from 1995 onwards for 'sensor' OR 'sensing' OR 'biosensor' OR 'biomarker'. Results were combined ('AND') with a search for 'wearable' OR 'actigraphy' OR 'Internet of things' OR 'microneedle' OR 'patch' OR 'e-textile' OR 'smart textile' and atopic dermatitis (MESH terms). Fifty seven abstracts were identified from the database search of which 39 were selected for detailed review. Broadly, wearable sensing systems in atopic dermatitis were split into three categories: wearable biosensor modules (actigraphy and smartwatches), clothing and integrated fabrics placed onto the epidermis and intradermal or subcutaneous sensors. The best evidence for correlation with AD disease severity was with actigraphy measurements of itch. However, newer approaches including sensing skin barrier function, inflammation and small molecule analysis as well as employing artificial intelligence offer more potential for advanced disease monitoring. Skin diseases, specifically AD, stand to benefit greatly from wearable technology, because of the ease of direct contact to the skin, the high prevalence of the disease and the large unmet need for better disease control in this group. However, important emphasis must be placed on validating the correlation of data from such technology with patient-reported outcomes. Wearable biosensors offer a huge potential to deliver better diagnostics, monitoring and treatment outcomes for patients.

Hybrid organic monolithic column containing MIL-68(Al) for the separation of small molecules by capillary HPLC

A hybrid organic monolithic column made of poly(lauryl methacrylate-co-1,6-hexanediol dimethacrylate) and the metal-organic framework MIL-68(Al) was prepared for the first time. The column was used in capillary liquid chromatography, both in isocratic and gradient elution modes. Separation performance towards small molecules of different chemical nature (polycyclic aromatic hydrocarbons, alkylbenzenes, phenols, etc.) was studied. Monte Carlo simulations were made to both select the proper precursors to obtain empty metal-organic framework micropores in the monolithic polymer and also, to analyze the potential free access of the studied analytes into the micropores (necessary to improve mass transfer and column efficiency). The hereby synthesized metal-organic framework microcrystals allowed obtaining homogeneous hybrid monolithic columns. Adding of MIL-68(Al) (1030 m2 g-1 BET specific surface area) increased the surface area from 3.9 m2 g-1 for the parent monolith to 18.2 m2 g-1 for the hybrid column containing 8 mg mL-1 of the microcrystals. Chromatographic performance of this new column was evaluated by studying retention factors, resolution, and plate counts at room temperature. Different compounds, not completely resolved in the parent monolith, were partially or completely separated after metal-organic framework addition. Using the monolithic column with only 2 mg mL-1 of MIL-68(Al), five alkylbenzenes were completely separated with very symmetrical peak shapes, resolution factors up to 3.60 and plate counts of 4300 plates m-1 for n-hexylbenzene. This value is higher than those obtained by other authors who used organic monolithic columns with embedded metal-organic frameworks to perform separations at room temperature. Additionally, nine polycyclic aromatic hydrocarbons were partially or completely resolved in gradient elution mode. The hybrid monolithic columns exhibited very good intra-day (%RSD=1.9), inter-day (%RSD=2.6), and column-to-column (%RSD=4.3) reproducibility values. Easy and fast column preparation, and versatility to efficiently separate several compounds of different chemical nature in isocratic and gradient mode, makes this new hybrid column a very good option for the analysis of small molecules in capillary (or nano) HPLC.

Effect of protein adsorption on hyaluronic acid/curcumin/multi-walled carbon nanotube based electrochemical sensor for detection of dopamine

Resisting protein adsorption on electrode surfaces is one of pivotal challenges in the electrochemical analysis of small bioactive molecules. In this work, we constructed a novel electrochemical sensing electrode modified with hydrophilic hyaluronic acid (HA) and curcumin/multi-walled carbon nanotube (CM/MWCNTs). The modified electrodes were comprehensively characterized for dopamine detection including the sensing sensitivity, selectivity and stability. The constructed electrochemical sensor for dopamine detection exhibited remarkable resistance to protein adsorption because of the inherent hydrophilicity of HA, and synergistically, the superior electrochemical behavior of the CM/MWCNTs composite (quinone/hydroquinone redox couples enabled superior electrochemical behavior). The HA/CM/MWCNTs/GCE performed about 5.0 folds, 3.5 folds and 2.4 folds higher electrocatalytic current for dopamine than that revealed at bare GCE, MWCNTs/GCE, and CM/MWCNTs/GCE, respectively. In addition, the HA/CM/MWCNTs-based sensor demonstrated excellent sensing performance with a low detection limit of 0.009 μM (S/N = 3) within the concentration range from 50 to 200 μM. The constructed HA/CM/MWCNTs-modified glassy carbon electrodes offered a reliable platform for the real-time monitoring of dopamine with no protein absorption.

Design of metabolomic studies: pre-analytical process

Metabolomics is the comprehensive analysis of small molecules involved in metabolic pathways that control biochemical processes and functions of cells in the body. Metabolomic studies consist of three major steps: pre-analytical, analytical and post-analytical. The review emphasizes the importance of the pre-analytical stage, which is the journey of a biosample from the patient to the biobank and then to the analytical laboratory. Based on the literature analysis, the review presents the factors that influence the quality of the sample and therefore the quality of the final research result: clinical information collection, biosample selection, biosample collection and processing, and subsequent storage. Proper design of metabolomic studies, quality control of samples from collection to analysis by physicochemical methods provides data that can improve the quality of disease diagnosis, provide a transition to personalized medicine.

Large-scale analysis of small molecule-RNA interactions using multiplexed RNA structure libraries

The large-scale analysis of small-molecule binding to diverse RNA structures is key to understanding the required interaction properties and selectivity for developing RNA-binding molecules toward RNA-targeted therapies. Here, we report a new system for performing the large-scale analysis of small molecule–RNA interactions using a multiplexed pull-down assay with RNA structure libraries. The system profiled the RNA-binding landscapes of G-clamp and thiazole orange derivatives, which recognizes an unpaired guanine base and are good probes for fluorescent indicator displacement (FID) assays, respectively. We discuss the binding preferences of these molecules based on their large-scale affinity profiles. In addition, we selected combinations of fluorescent indicators and different ranks of RNA based on the information and screened for RNA-binding molecules using FID. RNAs with high- and intermediate-rank RNA provided reliable results. Our system provides fundamental information about small molecule–RNA interactions and facilitates the discovery of novel RNA-binding molecules.

Highly Efficient Fabrication of Fluorescent "Turn-On" Lateral Flow Strips for Highly Sensitive Detection of Small Molecules Based on Self-Assembly of AuAg Nanoclusters.

Currently, fluorescent "turn-on" lateral flow assay (FONLFA) has shown enhanced "naked eye" detection sensitivity for small molecules, while it is urgent to adopt biocompatible fluorescent nanomaterials and needs new strategies to simplify the preparation process. In this study, a highly effective method was proposed to produce FONLFA strips for the detection of small molecules. The gold-silver nanoclusters (AuAgNCs) were immobilized onto the nitrocellulose membrane of the strips by the self-assembly of poly(sodium 4-styrenesulfonate), antigen, and AuAgNCs. The immobilization process entails a straightforward mixing of the three components, taking merely 1 min, thereby bypassing the necessity for chemical modification of fluorescent nanomaterials. The strategy offers a significantly simplified process, which substantially enhances the efficiency of the strip fabrication. Utilizing this method, a FONLFA was developed for carbendazim with a visual limit of detection (vLOD) reduced by 40-fold compared with the conventional colorimetric lateral flow assay (LFA). Furthermore, the approach demonstrates versatility by enabling the immobilization of AuAgNCs and streptavidin, which facilitates the development of aptamer-based FONLFAs. The designed aptamer-based FONLFA for kanamycin exhibited a 50-fold reduction in the vLOD compared with conventional colorimetric LFAs. Therefore, FONLFA holds promising potential for widespread applications in the analysis of small molecules.

NFKB2 mediates colorectal cancer cell immune escape and metastasis in a STAT2/PD‐L1‐dependent manner

This study systematically analyzed the molecular mechanism and function of nuclear factor kappa B subunit 2 (NFKB2) in colorectal cancer (CRC) to investigate the potential of NFKB2 as a therapeutic target for CRC. Various experimental techniques, including RNA sequencing, proteome chip assays, and small molecule analysis, were used to obtain a deeper understanding of the regulation of NFKB2 in CRC. The results revealed that NFKB2 was upregulated in a significant proportion of patients with advanced hepatic metastasis of CRC. NFKB2 played an important role in promoting tumor growth through CD8+ T-cell exhaustion. Moreover, NFKB2 directly interacted with signal transducer and activator of transcription 2 (STAT2), leading to increased phosphorylation of STAT2 and the upregulation of programmed death ligand 1 (PD-L1). Applying a small molecule inhibitor of NFKB2 (Rg5) led to a reduction in PD-L1 expression and improved response to programmed death-1 blockade-based immunotherapy. In conclusion, the facilitated NFKB2-STAT2/PD-L1 axis may suppress immune surveillance in CRC and targeting NFKB2 may enhance the efficacy of immunotherapeutic strategies. Our results provide novel insights into the molecular mechanisms underlying the contribution of NFKB2 in CRC immune escape.

Matrix- and surface-assisted laser desorption/ionization-mass spectrometry analysis of fingermark components for forensic studies: current trends and future prospects.

The chemical analysis of fingermarks (FMs) has attracted considerable attention in the realm of forensic investigations. Techniques based on direct ionization of a sample by laser irradiation, specifically matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS), have provided excellent figures of merit for analyzing high molecular-weight compounds. However, it can be challenging to analyze low molecular-weight compounds using MALDI-MS owing to potential interference produced by the organic matrices in the low molecular-weight region, which can impede the detection of small molecules (m/z < 700Da). Alternately, surface-assisted laser desorption/ionization-mass spectrometry (SALDI-MS) has shown great promise for small molecules analysis owing to the unique properties of the nanostructures used, particularly, minimal chemical background in low m/z region improved theproduction of ions involved in this method. The advancement of MALDI-MS and SALDI-MS has propelled their application in the analysis of FM components, focused on gaining deep insights into individual traits. This review aims to outline the current role of MALDI-MS and SALDI-MS in the chemical analysis of FMs. It also describes the latest achievements in forensic intelligence derived from fingermark analysis using these powerful methods. The accomplishments include the understanding of certain characteristics and lifestyles of donors. The review offers a comprehensive overview of the challenges and demands in this field. It suggests potential enhancements in this rapidly expanding domain to bridge the gap between research and practical police casework.

Performance and robustness of small molecule retention time prediction with molecular graph neural networks in industrial drug discovery campaigns

This study explores how machine-learning can be used to predict chromatographic retention times (RT) for the analysis of small molecules, with the objective of identifying a machine-learning framework with the robustness required to support a chemical synthesis production platform. We used internally generated data from high-throughput parallel synthesis in context of pharmaceutical drug discovery projects. We tested machine-learning models from the following frameworks: XGBoost, ChemProp, and DeepChem, using a dataset of 7552 small molecules. Our findings show that two specific models, AttentiveFP and ChemProp, performed better than XGBoost and a regular neural network in predicting RT accurately. We also assessed how well these models performed over time and found that molecular graph neural networks consistently gave accurate predictions for new chemical series. In addition, when we applied ChemProp on the publicly available METLIN SMRT dataset, it performed impressively with an average error of 38.70 s. These results highlight the efficacy of molecular graph neural networks, especially ChemProp, in diverse RT prediction scenarios, thereby enhancing the efficiency of chromatographic analysis.

Systematic evaluation of benzoylation for liquid chromatography-mass spectrometry analysis of different analyte classes

LC-MS is an indispensable tool for small molecule analysis in many fields; however, many small molecules require chemical derivatization to improve retention on commonly used reversed-phase columns and increase ionization. Benzoyl chloride (BzCl) derivatization is commonly used for derivatization of primary and secondary amines and phenolic alcohols, though evidence exists that with proper reaction conditions (i.e., specific bases), other hydroxyl groups may be derivatized too. Previous studies have examined BzCl concentration, reaction times, and reaction temperatures for derivatization of amines and phenols for LC-MS analysis; however, use of different bases, base concentration, and extending to conditions to hydroxyl groups for LC-MS analysis has not been well-studied. To address this understudied area and identify reaction conditions for both amino and hydroxyl groups, we performed a systematic study of reaction conditions on multiple classes of potential targets. For selected derivatization methods, detection limits and performance in a variety of biological matrices were assessed. Results highlight the importance of tailoring derivatization methods for a given application as they varied by molecule and/or molecule class. Compared to the standard BzCl method commonly used, alternative methods were identified to better derivatize challenging analytes (glucosamine, choline, cortisol, uridine, cytidine) with detection limits reaching 1100, 9, 38, 170, and 67 nM compared to undetectable, 170, 86, 1000, and 86 nM respectively. Sub-nanomolar detection limits were achieved for norepinephrine with alternative derivatization approaches. Improved derivatization methods for several classes and molecules including nucleosides, steroids, and molecules containing hydroxyl groups were also identified.

Molecularly Imprinted Heterostructure-Assisted Laser Desorption Ionization Mass Spectrometry Analysis and Imaging of Quinolones.

Quinolone residues resulting from body metabolism and waste discharge pose a significant threat to the ecological environment and to human health. Therefore, it is essential to monitor quinolone residues in the environment. Herein, an efficient and sensitive matrix-assisted laser desorption/ionization mass spectrometry (MALDI/MS) method was devised by using a novel molecularly imprinted heterojunction (MIP-TNs@GCNs) as the matrix. Molecularly imprinted titanium dioxide nanosheets (MIP-TNs) and graphene-like carbon nitrides (GCNs) were associated at the heterojunction interface, allowing for the specific, rapid, and high-throughput ionization of quinolones. The mechanism of MIP-TNs@GCNs was clarified using their adsorption properties and laser desorption/ionization capability. The prepared oxygen-vacancy-rich MIP-TNs@GCNs heterojunction exhibited higher light absorption and ionization efficiencies than TNs and GCNs. The good linearity (in the quinolone concentration range of 0.5-50 pg/μL, R2 > 0.99), low limit of detection (0.1 pg/μL), good reproducibility (n = 8, relative standard deviation [RSD] < 15%), and high salt and protein resistance for quinolones in groundwater samples were achieved using the established MIP-TNs@GCNs-MALDI/MS method. Moreover, the spatial distributions of endogenous compounds (e.g., amino acids, organic acids, and flavonoids) and xenobiotic quinolones from Rhizoma Phragmitis and Rhizoma Nelumbinis were visualized using the MIP-TNs@GCNs film as the MALDI/MS imaging matrix. Because of its superior advantages, the MIP-TNs@GCNs-MALDI/MS method is promising for the analysis and imaging of quinolones and small molecules.

Direct and Continuous Monitoring of Multicomponent Antibiotic Gentamicin in Blood at Single-Molecule Resolution.

Point-of-care monitoring of small molecules in biofluids is crucial for clinical diagnosis and treatment. However, the inherent low degree of recognition of small molecules and the complex composition of biofluids present significant obstacles for current detection technologies. Although nanopore sensing excels in the analysis of small molecules, the direct detection of small molecules in complex biofluids remains a challenge. In this study, we present a method for sensing the small molecule drug gentamicin in whole blood based on the mechanosensitive channel of small conductance in Pseudomonas aeruginosa (PaMscS) nanopore. PaMscS can directly detect gentamicin and distinguish its main components with only a monomethyl difference. The 'molecular sieve' structure of PaMscS enables the direct measurement of gentamicin in human whole blood within 10 min. Furthermore, a continuous monitoring device constructed based on PaMscS achieved continuous monitoring of gentamicin in live rats for approximately 2.5 h without blood consumption, while the drug components can be analyzed in situ. This approach enables rapid and convenient drug monitoring with single-molecule level resolution, which can significantly lower the threshold for drug concentration monitoring and promote more efficient drug use. Moreover, this work also lays the foundation for the future development of continuous monitoring technology with single-molecule level resolution in the living body.

Rhabdoviral Endogenous Sequences Identified in the Leishmaniasis Vector Lutzomyia longipalpis Are Widespread in Sandflies from South America.

Sandflies are known vectors of leishmaniasis. In the Old World, sandflies are also vectors of viruses while little is known about the capacity of New World insects to transmit viruses to humans. Here, we relate the identification of RNA sequences with homology to rhabdovirus nucleocapsids (NcPs) genes, initially in the Lutzomyia longipalpis LL5 cell lineage, named NcP1.1 and NcP2. The Rhabdoviridae family never retrotranscribes its RNA genome to DNA. The sequences here described were identified in cDNA and DNA from LL-5 cells and in adult insects indicating that they are transcribed endogenous viral elements (EVEs). The presence of NcP1.1 and NcP2 in the L. longipalpis genome was confirmed in silico. In addition to showing the genomic location of NcP1.1 and NcP2, we identified another rhabdoviral insertion named NcP1.2. Analysis of small RNA molecules derived from these sequences showed that NcP1.1 and NcP1.2 present a profile consistent with elements targeted by primary piRNAs, while NcP2 was restricted to the degradation profile. The presence of NcP1.1 and NcP2 was investigated in sandfly populations from South America and the Old World. These EVEs are shared by different sandfly populations in South America while none of the Old World species studied presented the insertions.

Can Small Molecules Provide Clues on Disease Progression in Cerebrospinal Fluid from Mild Cognitive Impairment and Alzheimer's Disease Patients?

Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disease, which is currently diagnosed via clinical symptoms and nonspecific biomarkers (such as Aβ1-42, t-Tau, and p-Tau) measured in cerebrospinal fluid (CSF), which alone do not provide sufficient insights into disease progression. In this pilot study, these biomarkers were complemented with small-molecule analysis using non-target high-resolution mass spectrometry coupled with liquid chromatography (LC) on the CSF of three groups: AD, mild cognitive impairment (MCI) due to AD, and a non-demented (ND) control group. An open-source cheminformatics pipeline based on MS-DIAL and patRoon was enhanced using CSF- and AD-specific suspect lists to assist in data interpretation. Chemical Similarity Enrichment Analysis revealed a significant increase of hydroxybutyrates in AD, including 3-hydroxybutanoic acid, which was found at higher levels in AD compared to MCI and ND. Furthermore, a highly sensitive target LC-MS method was used to quantify 35 bile acids (BAs) in the CSF, revealing several statistically significant differences including higher dehydrolithocholic acid levels and decreased conjugated BA levels in AD. This work provides several promising small-molecule hypotheses that could be used to help track the progression of AD in CSF samples.

A Versatile Broadband Attached Proton Test Experiment for Routine 13C Nuclear Magnetic Resonance Spectroscopy.

The proposed broadband attached proton test sequence allows the user to easily record 13C nuclear magnetic resonance multiplicity-edited and quaternary-carbon-only spectra. Compared to earlier attached proton test experiments, it preserves both a tolerance for wide ranges of one-bond-coupling constant values and the effective suppression of residual CHn signals in the quaternary-carbon-only spectra. The recording of edited spectra or quaternary-carbon-only spectra is made easy by a single, user-controllable constant. These attributes make the broadband attached proton test experiment attractive for the 13C analysis of small molecules, including spectral editing, particularly in high-throughput analysis laboratories.

Triboelectric Spectroscopy for In Situ Chemical Analysis of Liquids.

Chemical analysis of ions and small organic molecules in liquid samples is crucial for applications in chemistry, biology, environmental sciences, and health monitoring. Mainstream electrochemical and chromatographic techniques often suffer from complex and lengthy sample preparation and testing procedures and require either bulky or expensive instrumentation. Here, we combine triboelectrification and charge transfer on the surface of electrical insulators to demonstrate the concept of triboelectric spectroscopy (TES) for chemical analysis. As a drop of the liquid sample slides along an insulating reclined plane, the local triboelectrification of the surface is recorded, and the charge pattern along the sample trajectory is used to build a fingerprinting of the charge transfer spectroscopy. Chemical information extracted from the charge transfer pattern enables a new nondestructive and ultrafast (<1 s) tool for chemical analysis. TES profiles are unique, and through an automated identification, it is possible to match against standard and hence detect over 30 types of common salts, acids, bases and organic molecules. The qualitative and quantitative accuracies of the TES methodology is close to 93%, and the detection limit is as low as ppb levels. Instruments for TES chemical analysis are portable and can be further miniaturized, opening a path to in situ and rapid chemical detection relying on inexpensive, portable low-tech instrumentation.

Recent Advances in Real-Time Label-Free Detection of Small Molecules.

The detection and analysis of small molecules, typically defined as molecules under 1000 Da, is of growing interest ranging from the development of small-molecule drugs and inhibitors to the sensing of toxins and biomarkers. However, due to challenges such as their small size and low mass, many biosensing technologies struggle to have the sensitivity and selectivity for the detection of small molecules. Notably, their small size limits the usage of labeled techniques that can change the properties of small-molecule analytes. Furthermore, the capability of real-time detection is highly desired for small-molecule biosensors' application in diagnostics or screening. This review highlights recent advances in label-free real-time biosensing technologies utilizing different types of transducers to meet the growing demand for small-molecule detection.

Ultrahigh-Performance Supercritical Fluid Chromatography-Multimodal Ionization-Tandem Mass Spectrometry as a Universal Tool for the Analysis of Small Molecules in Complex Plant Extracts.

Complex analysis of plant extracts usually requires a combination of several analytical approaches. Therefore, in this study, we developed a holistic two-injection approach for plant extract analysis, which is carried out within one instrument without the need for any manual intervention during the analysis. Ultrahigh-performance supercritical fluid chromatography (UHPSFC) was employed for the analysis of 17 volatile terpenes on a porous graphitic carbon column within 7.5 min, followed by analysis on short diol column where flavonoids, phenolic acids, and terpenoic acids were analyzed within 15.5 min. A multimodal ionization source combining electrospray and atmospheric pressure chemical ionization (ESCi) was selected for mass spectrometry detection as a simultaneous ionization of both lipophilic and polar compounds was required. The quantitative aspects of the final UHPSFC-ESI/ESCi-MS/MS two-injection approach were determined, and it was applied to the analysis of Eucalyptus sp. extracts prepared by supercritical fluid extraction. Current methods reported in the literature typically require a labor-intensive combination of liquid and gas chromatography for the complex analysis of plant extracts. We present for the first time a new UHPSFC approach requiring only a single instrument that provides an alternative approach to the analysis of complex plant extracts.