Conference Agenda

MicroRNAs (miRNAs) are small, non-coding RNA molecules recognized as promising biomarkers for various diseases. However, detecting them is challenging because they are typically present at very low concentration levels in biological samples, similar in sequence among family members, and susceptible to degradation.

Here, we will provide an overview of a comprehensive, hybrid analytical strategy that significantly enhances the detection and quantification of miRNAs. This approach integrates gold nanoparticle (AuNP)-based signal amplification with advanced separation and spectrometric techniques—specifically asymmetrical flow field-flow fractionation (AF4), multi-angle light scattering (MALS), and inductively coupled plasma tandem mass spectrometry (ICP-MS/MS). The method involves designing gold nanoparticles (AuNPs) functionalized with thiolated oligonucleotide probes that specifically hybridize with target miRNAs. This binding enhances optical readouts via plasmonic resonance shifts, offering unparalleled sensitivity and specificity, even in complex biological matrices. AF4-MALS-ICP-MS/MS is employed for precise fractionation and characterization of nanoparticle–miRNA conjugates, providing key information on their hydrodynamic radius, molar mass distribution, and aggregate formation.

Significant improvements in detection limits, down to the low femtomolar range, have been achieved with excellent reproducibility and multiplexing capability. This work paves the way for more sensitive, accurate, and non-invasive miRNA-based diagnostics and offers a blueprint for integrating nanomaterial-assisted detection into high-throughput bioanalytical pipelines.

Chronic exposure to inorganic arsenic is a major concern for millions of people around the world. Severity of health effects induced by arsenic exposure varies with individuals. However, a reliable biomarker of susceptibility has not been established. Detailed characterization of arsenic metabolites could improve understanding of the metabolism of arsenic compounds and its relationships with severity of health outcomes. We report here the separation and detection of twelve arsenic species in human urine using high performance liquid chromatography (HPLC) and inductively coupled plasma mass spectrometry (ICPMS). Separation of arsenite, arsenate, monomethylarsonic acid, dimethylarsinic acid, arsenobetaine, and up to seven unidentified arsenic species in human urine was achieved within 10 minutes using an anion exchange column and gradient elution. The HPLC-ICPMS method enabled arsenic speciation analysis of more than 1800 urine samples collected from from Araihazar, Bangladesh. Dimethylarsinic acid was the main arsenic species in most of the urine samples although arsenic speciation patterns vary among individuals. Majority of the urine samples had quantifiable inorganic arsenic (99.9% detection rate), monomethylarsonic acid (99.9%), dimethylarsinic acid (100%) and arsenobetaine (98%). Chromatographic peaks of unknown arsenic species were detected in more than 40% of the urine samples. The sum of inorganic and methylated arsenic species was strongly correlated with total urinary arsenic concentration. We also report on the identification of new arsenic metabolites, using HPLC separation with both ICPMS and electrospray ionization mass spectrometry (ESIMS). These results are useful for a better understanding of the relationships between arsenic metabolism and health effects and to for establishing a biomarker of susceptibility to arsenic toxicity.

Mercury (Hg) is recognised as a global pollutant, notable for its presence across various trophic levels. It is considered a bioaccumulative trace element, with the dietary pathway being the primary route of human exposure. Applying silica nanoparticles (SiO₂ NPs) as mitigating agents for heavy metal contamination in crop plants—thereby reducing abiotic stress induced by these contaminants—has shown promising results.

Several mechanisms have been proposed for the mitigation of metal toxicity by silica nanoparticles, including immobilisation in the soil, activation of enzymatic and non-enzymatic antioxidants, chelation and co-precipitation in the soil, and retention or compartmentalisation of toxic elements within the root cell wall.

In this study, we investigate the potential adsorption of Hg by SiO₂ NPs in soybean plant roots, which could lead to the immobilisation of the metal and a reduction in its translocation. To explore the interactions between mercury and silica nanoparticles, we employed analytical techniques such as asymmetric flow field-flow fractionation coupled with multi-angle laser light scattering (AF4-MALS) and single-particle ICP-ToF-MS, which provide complementary and enhanced insights into particle size and composition.

The AF4-MALS-ICP-MS technique enabled the characterisation of the SiO₂ NPs used in this study, identifying particles ranging from 20 to 30 nm in size. This characterisation provided valuable information on the potential mechanisms of action of silica NPs as mitigating agents against heavy metal contamination in soybean plants.

The fractogram correlating Hg and Si signals obtained from ICP-ToF-MS and AF4-MALS showed overlapping signals at the same retention time, suggesting the adsorption of Hg ions by silica nanoparticles. The simultaneous detection capability of ICP-ToF-MS allowed for the monitoring of Hg and Si in root extracts, enabling inferences regarding the elemental composition of the peaks in the fractogram.

The recovery of Hg associated with SiO₂ NPs, as measured by the online AF4/ICP-ToF-MS system, was calculated by integrating the fractogram area. A recovery rate of 94.3% was observed for Hg, demonstrating that the method effectively identified Hg bound to SiO₂ NPs. Area integration also indicated negligible analyte loss through the separation membrane, confirming the method's suitability for characterising the nanoparticles used in this study.

Additionally, metal-targeted metabolomics analyses using ICP-MS/MS and LC-ToF-MS revealed that mercury exposure induces significant alterations in the metabolite profiles of soybean plants. Principal component analysis (PCA) of root samples revealed a clear distinction between the control and Hg-treated groups, with a clustering trend observed between the NP + Hg samples and the control group.

These findings highlight the importance of investigating mercury toxicity and the role of silicon-based compounds (SiO₂ NPs and Na₂SiO₃) as remediation agents in soybean cultivation. This approach can help identify the compounds generated during these processes when applied to nanoparticle-organism interactions.

The Hyphenated multimodal techniques are employed to maximise data acquisition, aiming to extract comprehensive information about the elements of interest. This strategy enables a more detailed understanding of the system under study. The integration of ICP-MS and ESI-MS workflows has shown great potential for detecting and identifying unknown compounds and improving the quantification of suspect or novel analytes in the absence of reference standards. This provides relevant insights in environmental sciences by identifying contaminant species and metabolomics.

Changes in metabolite concentrations reflect the inhibition or activation of specific metabolic pathways, and pathway analysis can offer a broader view of the plant's stress response to contamination. Similarly, supplementing the growth medium with silica may help mitigate the harmful effects of toxic metals on plants. Thus, ICP-MS/MS and LC-ToF-MS-based metal metabolomics demonstrated that mercury exposure in soybean plants leads to significant changes in metabolite profiles. Principal component analysis of root samples clearly distinguished between control and Hg-treated groups, with clustering of the NP + Hg samples closer to the control group.

Coupling digital microfluidics (DMF) with both mass spectrometry (MS) and surface-enhanced Raman spectroscopy (SERS) represents a major advancement in the field of analytical science that combines sample handling and fluidic processing with multimodal chemical detection. DMF enables discrete droplet actuation through electrowetting-on-dielectric, allowing automated and unrestricted processing of nano- to microlitre-sized droplets on a closed chip format. Here, the droplets are moved between two plates to reduce evaporation and contamination. However, this makes chemical analysis of the droplets, which are trapped inside the chip, more challenging. Thus, subsequent analytics are usually carried out offline or with simple optical methods. This work addresses the challenges of coupling DMF with MS and SERS to enable real-time analysis for monitoring chemical and biochemical processes.

We developed an approach that allows on-the-fly mass spectrometric monitoring of chemical reactions in a DMF device, enabled by a chip-integrated microspray hole (μSH) [1]. This technique uses an electrostatic spray ionisation method to spray a portion of a sample droplet through a microhole, allowing its chemical content to be analysed by mass spectrometry. The broad applicability of the developed seamless coupling of DMF with MS was successfully applied to the study of various on-chip organic syntheses, enzymatic reactions as well as protein and peptide analysis. While we could successfully combine on-chip MS-detection and DMF, we also extended the scope for label-free Raman detection [2] for sample transfer from inside the chip to an external insulated SERS substrate. For this purpose, a new electrostatic spray-compatible stationary SERS substrate was developed and characterised for sensitive and reproducible SERS-based measurements and was successfully applied to study an organic reaction occurring in the DMF device, providing vibrational spectroscopic data.

Since the droplet does not get used up in these experiments, making it an inline detection technique, one can carry out further DMF processes with the same reaction mixture droplet. This could be, for example, another chemical reaction step and/or the implementation of a different analytical method at a different location on the same DMF chip, respectively. Therefore, in our recent work, we have enabled dual-mode detection by integrating both of our developed techniques (as mentioned above) within a single digital microfluidic chip. This is achieved by integrating the microspray hole (diameter: 10 μm) and the stationary SERS substrate (diameter: ~1.5 mm) on the top and bottom plate of the DMF chip, respectively. Therein, the performance of the developed system was evaluated to screen and analyse the starting material and product formation of a reaction by MS and subsequently by SERS and vice versa. This also marks the first integration of mass spectrometry and surface-enhanced Raman spectroscopy in a single digital microfluidics device.