Year: 2025
Type: article
Abstract: analysis remains a particular frontier challenge. While we can detect single molecules in some contexts, doing so reliably in complex biological systems such as blood or tissue with high specificity is extremely difficult. Nonetheless, recognizing the heterogeneity of individual cells has proven to be crucial for understanding disease [5]. Equally important, there remains the challenge of obtaining information from the thousands of different molecules within a single cell that affect cell function.Characterizing individual molecules is as important as detecting them and much more difficult [6]. It is important to note that properties of individual molecules may differ greatly from average properties of samples observed by ensemble methods. Yet physiological effects of molecules representing only a small fraction of the total population may be different from, and possibly more significant than, those attributable to the population as a whole [7]. This is particularly critical in early stages of disease diagnosis and treatment where small but important changes in the repertoire of cell molecules or individual molecules in body fluids may signal the onset of a disease process.Achieving high specificity and selectivity in bioanalytical chemistry remains a formidable challenge due to the complexity of biological matrices. Endogenous compounds, and overlapping signals can confound detection, while the wide dynamic range of analytes and structural heterogeneity-such as post-translational modifications or isomeric variants-further complicates accurate identification [8]. Affinity-based assays, mass spectrometry, and spectroscopic techniques all face distinct sources of interference and, at the single-cell or single-molecule level, nonspecific adsorption and environmental sensitivity exacerbate these limitations [9]. Integrated, state-of-the-art strategies may address these challenges. High-resolution separations, multidimensional chromatography, and high-resolution mass spectrometry enhance discrimination of closely related species, while orthogonal detection methods, engineered antibodies and aptamers, CRISPR-based sensors, and machine learning-assisted signal deconvolution improve both selectivity and specificity [10]. Looking forward, emerging technologies-including single-molecule and super-resolution imaging, nanopore sensing, highthroughput microfluidics, and AI-driven multi-omics-promise to deliver adaptive, multiplexed, and highly precise measurements [11]. Together, these innovations are poised to extend the limits of bioanalytical performance, enabling the reliable characterization of biomolecular complexity in situ and in real time.Real time monitoring of biological processes as they occur is crucial for understanding dynamic systems like metabolism, molecular signaling cascades in cells, or immune system responses involving a complex network of cells and soluble molecules [12]. While, for example, in situ monitoring of single-cell neurotransmitters was achieved over 50 years ago [14], current methods can, at best, provide snapshots of these processes without the advantage of discerning critical temporal information. The challenge of real-time monitoring is the detection of events in complex systems and maintaining the integrity of the biological system while making its components available for analysis [13]. The challenge is best appreciated when recognizing that bioanalytical methods often require the destruction of a sample as a prelude to extraction or purification of molecules of interest. This makes it impossible to maintain the sample's native environment where spatial and temporal relationships as well as chemical context may be key components of molecular function.The spatial "omics" challenge involves mapping of molecules in cells or tissues with molecular resolution to produce maps of molecule location and interactions within their native environments. A July 2025 review in Cell [15] examines barriers hindering clinical deployment of single-cell omics and outlines requirements for deriving actionable clinical readouts. Spatial analysis can then be combined with multi-omics integration [16] where genomics, proteomics, metabolomics, and other "omics", now evaluated separately, are integrated within large datasets for analysis of interactions and evaluation of biological phenotypes, an enormously complex challenge. Spatial omics technologies overlay molecular data onto biological spatial dimensions to provide high-resolution insights into cellular heterogeneity and tissue microenvironments, but analysis remains challenging due to high-dimensional, sparse data often contaminated by noise and uncertainty [17].Bioanalytical chemistry is increasingly applied beyond traditional clinical and pharmacokinetic studies [18], extending into environmental monitoring, food safety, and synthetic biology, where detection of trace analytes in complex matrices is essential [19]. These emerging applications pose unique challenges, including high sample heterogeneity, interfering compounds, and the need for minimally invasive measurements in living systems. To address these demands, hybrid approaches combining chromatography, mass spectrometry, microfluidics, single-cell analysis, and biosensor technologies [21]-augmented by machine learning for data interpretation-are being deployed to enhance sensitivity, specificity, and throughput [20]. Looking forward, continued innovation in miniaturization, automation, and integrative analytical strategies will enable real-time, precise monitoring across diverse contexts, ensuring that bioanalytical chemistry remains a versatile tool for addressing complex scientific and societal challenges.While laboratory results are essential elements in patient care, the use of sophisticated diagnostics at the patient bedside remains a persistent challenge [23]. The demand for sensitive, rapid, and affordable diagnostic techniques has surged, particularly following the COVID-19 pandemic, driving development of CRISPR-based diagnostic tools that provide precise, rapid, and portable diagnostics for on-site testing without access to extensive infrastructure. Machine learning is being embedded into various point-of-care testing modalities to enhance accuracy, sensitivity, and overall efficiency [22].Devices that are portable, affordable, require minimal training, yet capable of delivering rapid, accurate results for clinical decision-making, will become increasingly necessary for patient care, particularly in resource-limited settings [25]. Developing such instrumentation is clearly challenging. This is particularly obvious when considering that standardization and reproducibility across laboratories and platforms remains difficult. This is especially true for complex biological measurements that take place in an environment that demands broad analytical capabilities that meet strict regulatory standards [24] There has been recent progress in existing areas such as analytical instrumentation that have advanced bioanalytical chemistry in the context of chemical biology. These contributions are important and representative of topics welcome in this journal.Liquid Chromatography-Mass Spectrometry (LC-MS/MS) remains the cornerstone of quantitative proteomics, metabolomics, and drug-target engagement studies [26], while Capillary Electrophoresis-MS (CE-MS) [27] offers ultra-high resolution for charged biomolecules and singlecell metabolomics applications, and Ion Mobility Spectrometry (IMS) adds structural information and enhances resolution for complex molecular mixtures [41] and is now integrated with MS for 4D analyses.Similarly, the Optical and Spectroscopic Techniques of Fluorescence and Fluorescence Resonance Energy Transfer (FRET) methods remain central to probing biomolecular interactions and conformational dynamics in live cells [28] [29]. These approaches have been extended to Single-Molecule Fluorescence Microscopy [42] to provide direct visualization of reaction kinetics and heterogeneity at the single-molecule level. Related methods such as Raman and Surface-Enhanced Raman Scattering (SERS) now provide label-free vibrational spectroscopy for molecular fingerprinting and live-cell metabolite tracking [30]. Enhanced sensitivity via plasmonic substrates (and Infrared and Terahertz Spectroscopy provide complementary label-free detection of molecular structure and hydration dynamics in biological samples [43].Electrochemical Biosensors, including voltametric, impedimetric and amperometric devices, [31] [32] have a long involvement with chemical biology, including in situ measurements on living cells. These allow real-time monitoring of metabolites, nucleic acids, or enzyme activity at submicromolar to attomolar levels. More sophisticated electrochemical approaches including Field-Effect Transistors (FET) [33] and Plasmonic Nanosensors combine nanomaterials with transduction amplification for high-sensitivity, multiplexed detection [43] while Lab-on-a-Chip and Microfluidic Devices Enable integration of sample prep, reaction, and detection steps for highthroughput and point-of-care bioanalysis [34].Imaging and Spatially Resolved Analysis [40], in addition to optical imaging methods, especially fluorescence imaging, are providing new types of imaging information. Mass Spectrometry Imaging (MSI) offers spatial metabolomic and proteomic maps in tissues [34], bridging analytical chemistry and systems biology. Super-Resolution [37] and Correlative Microscopy [38] [39] integrates fluorescence, electron, and Atomic Force Microscopy (AFM) imaging to link molecular identity with cellular architecture. Cryo-Electron Microscopy (Cryo-EM) [36] offers sub-optical resolution structural elucidation of biomacromolecular assemblies.In conclusion, meeting the goal of these bioanalytical challenges will permit us to understand the role of molecules in complex environments as, for example, the relationship between the microbiome and its host, where new analytical approaches will be required. These challenges drive innovation in instrumentation, materials science, computational methods, and fundamental measurement science and interdisciplinary collaboration between chemists, biologists, engineers, and clinicians.
Source: Frontiers in Chemical Biology
Authors: George Barisas, Deborah A. Roess
Institution: Colorado State University
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W4417485146