Adductomics, an emerging field within the 'omics sciences, focuses on the formation and prevalence of DNA, RNA, and protein adducts induced by endogenous and exogenous agents in biological systems. These modifications often result from exposure to environmental pollutants, dietary components, and xenobiotics, impacting cellular functions and potentially leading to diseases such as cancer. This review highlights advances in mass spectrometry (MS) that enhance the detection of these critical modifications and discusses current and emerging trends in adductomics, including developments in MS instrument use, screening techniques, and the study of various biomolecular modifications from mono-adducts to complex hybrid crosslinks between different types of biomolecules. The review also considers challenges, including the need for specialized MS spectra databases and multi-omics integration, while emphasizing techniques to distinguish between exogenous and endogenous modifications. The future of adductomics possesses significant potential for enhancing our understanding of health in relation to environmental exposures and precision medicine.

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

Studying cell heterogeneity can provide a deeper understanding of biological activities, but appropriate studies cannot be performed using traditional bulk analysis methods. The development of diverse single cell bioanalysis methods is in urgent need and of great significance. Mass spectrometry (MS) has been recognized as a powerful technique for bioanalysis for its high sensitivity, wide applicability, label-free detection, and capability for quantitative analysis. In this review, the general development of single cell mass spectrometry (SCMS) field is covered. First, multiple existing SCMS techniques are described and compared. Next, the development of SCMS field is discussed in a chronological order. Last, the latest quantification studies on small molecules using SCMS have been described in detail.

Structured illumination microscopy (SIM) is a super-resolution technology for imaging living cells and has been used for studying the dynamics of lysosomes and mitochondria. Recently, new probes and analyzing methods have been developed for SIM imaging, enabling the quantitative analysis of these subcellular structures and their interactions. This review provides an overview of the working principle and advances of SIM, as well as the organelle-targeting principles and types of fluorescence probes, including small molecules, metal complexes, nanoparticles, and fluorescent proteins. Additionally, quantitative methods based on organelle morphology and distribution are outlined. Finally, the review provides an outlook on the current challenges and future directions for improving the combination of SIM imaging and image analysis to further advance the study of organelles. We hope that this review will be useful for researchers working in the field of organelle research and help to facilitate the development of SIM imaging and analysis techniques.

Imaging mass spectrometry (IMS) allows for the untargeted mapping of biomolecules directly from tissue sections. This technology is increasingly integrated into biomedical and clinical research environments to supplement traditional microscopy and provide molecular context for tissue imaging. IMS has widespread clinical applicability in the fields of oncology, dermatology, microbiology, and others. This review summarizes the two most widely employed IMS technologies, matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI), and covers technological advancements, including efforts to increase spatial resolution, specificity, and throughput. We also highlight recent biomedical applications of IMS, primarily focusing on disease diagnosis, classification, and subtyping.

Brain-on-a-chip (BoC) devices show typical characteristics of brain complexity, including the presence of different cell types, separation in different compartments, tissue-like three-dimensionality, and inclusion of the extracellular matrix components. Moreover, the incorporation of a vascular system mimicking the blood-brain barrier (BBB) makes BoC particularly attractive, since they can be exploited to test the brain delivery of different drugs and nanoformulations. In this review, we introduce the main innovations in BoC and BBB-on-a-chip models, especially focusing sensorization: electrical, electrochemical, and optical biosensors permit the real-time monitoring of different biological phenomena and markers, such as the release of growth factors, the expression of specific receptors/biomarkers, the activation of immune cells, cell viability, cell-cell interactions, and BBB crossing of drugs and nanoparticles. The recent improvements in signal amplification, miniaturization, and multiplication of the sensors are discussed in an effort to highlight their benefits limitations and delineate future challenges in this field.

Infectious diseases (such as sepsis, influenza, and malaria), caused by various pathogenic bacteria and viruses, are widespread across the world. Early and rapid detection of disease-related pathogens is necessary to reduce their spread in the world and prevent their potential global pandemics. The clustered regularly interspaced short palindromic repeats (CRISPR) technology, as the next-generation molecular diagnosis technique, holds immense promise in the detection of infectious diseases because of its remarkable advantages, including supreme flexibility, sensitivity, and specificity. While numerous CRISPR-based biosensors have been developed for application in environmental monitoring, food safety, and point-of-care diagnosis, there remains a critical need to summarize and explore their potential in human health. This review aims to address this gap by focusing on the latest advancements in CRISPR-based biosensors for infectious disease detection. We provide an overview of the current status, pre-amplification methods, the unique feature of each CRISPR system, and the design of CRISPR-based biosensing strategies to detect disease-associated nucleic acids. Last but not least, the review analyzes the current challenges and provides future perspectives, which will contribute to developing more effective CRISPR-based biosensors for human health.

Metabolic assays serve as pivotal tools in biomedical research, offering keen insights into cellular physiological and pathological states. While mass spectrometry (MS)-based metabolomics remains the gold standard for comprehensive, multiplexed analyses of cellular metabolites, innovative technologies are now emerging for the targeted, quantitative scrutiny of metabolites and metabolic pathways at the single-cell level. In this review, we elucidate an array of these advanced methodologies, spanning synthetic and surface chemistry techniques, imaging-based methods, and electrochemical approaches. We summarize the rationale, design principles, and practical applications for each method, and underscore the synergistic benefits of integrating single-cell metabolomics (scMet) with other single-cell omics technologies. Concluding, we identify prevailing challenges in the targeted scMet arena and offer a forward-looking commentary on future avenues and opportunities in this rapidly evolving field.

Understanding nanoparticle-cell interactions at single-nanoparticle and single-cell resolutions is crucial to improving the design of next-generation nanoparticles for safer, more effective, and more efficient applications in nanomedicine. This review focuses on recent advances in the continuous high-throughput analysis of nanoparticle-cell interactions at the single-cell level. We highlight and discuss the current trends in continual flow high-throughput methods for analyzing single cells, such as advanced flow cytometry techniques and inductively coupled plasma mass spectrometry methods, as well as their intersection in the form of mass cytometry. This review further discusses the challenges and opportunities with current single-cell analysis approaches and provides proposed directions for innovation in the high-throughput analysis of nanoparticle-cell interactions.

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

Molecular detection of SARS-CoV-2 in gargle and saliva complements the standard analysis of nasopharyngeal swabs (NPS) specimens. Although gargle and saliva specimens can be readily obtained non-invasively, appropriate collection and processing of gargle and saliva specimens are critical to the accuracy and sensitivity of the overall analytical method. This review highlights challenges and recent advances in the treatment of gargle and saliva samples for subsequent analysis using reverse transcription polymerase chain reaction (RT-PCR) and isothermal amplification techniques. Important considerations include appropriate collection of gargle and saliva samples, on-site inactivation of viruses in the sample, preservation of viral RNA, extraction and concentration of viral RNA, removal of substances that inhibit nucleic acid amplification reactions, and the compatibility of sample treatment protocols with the subsequent nucleic acid amplification and detection techniques. The principles and approaches discussed in this review are applicable to molecular detection of other microbial pathogens.

Tissues and other cell populations are highly heterogeneous at the cellular level, owing to differences in expression and modifications of proteins, polynucleotides, metabolites, and lipids. The ability to assess this heterogeneity is crucial in understanding numerous biological phenomena, including various pathologies. Traditional analyses apply bulk-cell sampling, which masks the potentially subtle differences between cells that can be important in understanding of biological processes. These limitations due to cell heterogeneity inspired significant efforts and interest toward the analysis of smaller sample sizes, down to the level of individual cells. Among the emerging techniques, the unique capabilities of capillary electrophoresis coupled with mass spectrometry (CE-MS) made it a prominent technique for proteomics and metabolomics analysis at the single-cell level. In this review, we focus on the application of CE-MS in the proteomic and metabolomic profiling of single cells and highlight the recent advances in sample preparation, separation, MS acquisition, and data analysis.

In the past two decades, the power of mass spectrometry imaging (MSI) for the label free spatial mapping of molecules in biological systems has been substantially enhanced by the development of approaches for imaging with high spatial resolution. With the increase in the spatial resolution, the experimental throughput has become a limiting factor for imaging of large samples with high spatial resolution and 3D imaging of tissues. Several experimental and computational approaches have been recently developed to enhance the throughput of MSI. In this critical review, we provide a succinct summary of the current approaches used to improve the throughput of MSI experiments. These approaches are focused on speeding up sampling, reducing the mass spectrometer acquisition time, and reducing the number of sampling locations. We discuss the rate-determining steps for different MSI methods and future directions in the development of high-throughput MSI techniques.

Protein phosphorylation is an essential post-translational modification that regulates many aspects of cellular physiology, and dysregulation of pivotal phosphorylation events is often responsible for disease onset and progression. Clinical analysis on disease-relevant phosphoproteins, while quite challenging, provides unique information for precision medicine and targeted therapy. Among various approaches, mass spectrometry (MS)-centered characterization features discovery-driven, high-throughput and in-depth identification of phosphorylation events. This review highlights advances in sample preparation and instrument in MS-based phosphoproteomics and recent clinical applications. We emphasize the preeminent data-independent acquisition method in MS as one of the most promising future directions and biofluid-derived extracellular vesicles as an intriguing source of the phosphoproteome for liquid biopsy.

3D printing, an additive manufacturing technology, has made significant inroads into improving systems for bioanalysis in recent years. This approach is particularly powerful due to the ease and flexibility in rapidly creating novel and complex designs for analytical applications. As such, 3D printing offers an emerging technology for creating systems for electrophoretic analysis. Here, we review 3D printing work on improving and miniaturizing capillary electrophoresis (CE), emphasizing publications from 2019‒2022. We describe enabling uses of 3D printing in interfacing upstream sample preparation or downstream detection with CE. Recent developments in miniaturized CE enabled by 3D printing are also elaborated, including key areas where 3D printing could further improve over the current state-of-the-art. Lastly, we highlight promising future trends for using 3D printing in miniaturizing CE and the significant potential for innovative advancements. 3D printing is poised to play a key role in moving forward miniaturized CE in the coming years.

Biomarker detection has attracted increasing interest in recent years due to the minimally or non-invasive sampling process. Single entity analysis of biomarkers is expected to provide real-time and accurate biological information for early disease diagnosis and prognosis, which is critical to the effective disease treatment and is also important in personalized medicine. As an innovative single entity analysis method, nanopore sensing is a pioneering single-molecule detection technique that is widely used in analytical bioanalytical fields. In this review, we overview the recent progress of nanopore biomarker detection as new approaches to disease diagnosis. In highlighted studies, nanopore was focusing on detecting biomarkers of different categories of communicable and noncommunicable diseases, such as pandemic Covid-19, AIDS, cancers, neurologic diseases, etc. Various sensitive and selective nanopore detecting strategies for different types of biomarkers are summarized. In addition, the challenges, opportunities, and direction for future development of nanopore-based biomarker sensors are also discussed.

The spread of COVID-19 has caused huge economic losses and irreversible social impact. Therefore, to successfully prevent the spread of the virus and solve public health problems, it is urgent to develop detection methods with high sensitivity and accuracy. However, existing detection methods are time-consuming, rely on instruments, and require skilled operators, making rapid detection challenging to implement. Biosensors based on fluorescent nanoparticles have attracted interest in the field of detection because of their advantages, such as high sensitivity, low detection limit, and simple result readout. In this review, we systematically describe the synthesis, intrinsic advantages, and applications of organic dye-doped fluorescent nanoparticles, metal nanoclusters, up-conversion particles, quantum dots, carbon dots, and others for virus detection. Furthermore, future research initiatives are highlighted, including green production of fluorescent nanoparticles with high quantum yield, speedy signal reading by integrating with intelligent information, and error reduction by coupling with numerous fluorescent nanoparticles.

The continuing evolution of the SARS-CoV-2 virus has led to the emergence of many variants, including variants of concern (VOCs). CRISPR-Cas systems have been used to develop techniques for the detection of variants. These techniques have focused on the detection of variant-specific mutations in the spike protein gene of SARS-CoV-2. These sequences mostly carry single-nucleotide mutations and are difficult to differentiate using a single CRISPR-based assay. Here we discuss the specificity of the Cas9, Cas12, and Cas13 systems, important considerations of mutation sites, design of guide RNA, and recent progress in CRISPR-based assays for SARS-CoV-2 variants. Strategies for discriminating single-nucleotide mutations include optimizing the position of mismatches, modifying nucleotides in the guide RNA, and using two guide RNAs to recognize the specific mutation sequence and a conservative sequence. Further research is needed to confront challenges in the detection and differentiation of variants and sublineages of SARS-CoV-2 in clinical diagnostic and point-of-care applications.

Pathogenic infection remains the primary threat to human health, such as the global COVID-19 pandemic. It is important to develop rapid, sensitive and multiplexed tools for detecting pathogens and their mutated variants, particularly the tailor-made strategies for point-of-care diagnosis allowing for use in resource-constrained settings. The rapidly evolving CRISPR/Cas systems have provided a powerful toolbox for pathogenic diagnostics via nucleic acid tests. In this review, we firstly describe the resultant promising class 2 (single, multidomain effector) and recently explored class 1 (multisubunit effector complexes) CRISPR tools. We present diverse engineering nucleic acid diagnostics based on CRISPR/Cas systems for pathogenic viruses, bacteria and fungi, and highlight the application for detecting viral variants and drug-resistant bacteria enabled by CRISPR-based mutation profiling. Finally, we discuss the challenges involved in on-site diagnostic assays and present emerging CRISPR systems and CRISPR cascade that potentially enable multiplexed and preamplification-free pathogenic diagnostics.

Real-time polymerase chain reaction (qPCR) enables accurate detection and quantification of nucleic acids and has become a fundamental tool in biological sciences, bioengineering and medicine. By combining multiple primer sets in one reaction, it is possible to detect several DNA or RNA targets simultaneously, a process called multiplex PCR (mPCR) which is key to attaining optimal throughput, cost-effectiveness and efficiency in molecular diagnostics, particularly in infectious diseases. Multiple solutions have been devised to increase multiplexing in qPCR, including techniques, using target-specific fluorescent oligonucleotide probes, and where segregation of the sample enables parallel amplification of multiple targets. However, these solutions are mostly limited to three or four targets, or highly sophisticated and expensive instrumentation. There is a need for innovations that will push forward the multiplexing field in qPCR, enabling for a next generation of diagnostic tools which could accommodate high throughput in an affordable manner. To this end, the use of machine learning (ML) algorithms (data-driven solutions) has recently emerged to leverage information contained in amplification and melting curves (AC and MC, respectively) - two of the most standard bio-signals emitted during qPCR - for accurate classification of multiple nucleic acid targets in a single reaction. Therefore, this review aims to demonstrate and illustrate that data-driven solutions can be successfully coupled with state-of-the-art and common qPCR platforms using a variety of amplification chemistries to enhance multiplexing in qPCR. Further, because both ACs and MCs can be predicted from sequence data using thermodynamic databases, it has also become possible to use computer simulation to rationalize and optimize the design of mPCR assays where target detection is supported by data-driven technologies. Thus, this review also discusses recent work converging towards the development of an end-to-end framework where knowledge-based and data-driven software solutions are integrated to streamline assay design, and increase the accuracy of target detection and quantification in the multiplex setting. We envision that concerted efforts by academic and industry scientists will help advance these technologies, to a point where they become mature and robust enough to bring about major improvements in the detection of nucleic acids across many fields.

Microfluidic technology provides a portable, cost-effective, and versatile tool for point-of-care (POC) bioanalysis because of its associated advantages such as fast analysis, low volumes of reagent consumption, and high portability. Along with microfluidics, the application of nanomaterials in biosensing has attracted lots of attention due to their unique physical and chemical properties for enhanced signal modulation such as signal amplification and signal transduction for POC bioanalysis. Hence, an enormous number of microfluidic devices integrated with nano-sensors have been developed for POC bioanalysis targeting low-resource settings. Herein, we review recent advances in POC bioanalysis on nano-sensor-based microfluidic platforms. We first briefly summarized the different types of cost-effective microfluidic platforms, followed by a concise introduction to nanomaterial-based biosensors. Then, we highlighted the application of microfluidic platforms integrated with nano-sensors for POC bioanalysis. Finally, we discussed the current limitations and perspective trends of the nano-sensor-based microfluidic platforms for POC bioanalysis.