Recent advances in microfluidic single-cell analysis and its applications in drug development

Advances in Microfluidic Single-Cell RNA Sequencing and Spatial Transcriptomics

The development of micro- and nano-fabrication technologies has greatly advanced single-cell and spatial omics technologies. With the advantages of integration and compartmentalization, microfluidic chips are capable of generating high-throughput parallel reaction systems for single-cell screening and analysis. As omics technologies improve, microfluidic chips can now integrate promising transcriptomics technologies, providing new insights from molecular characterization for tissue gene expression profiles and further revealing the static and even dynamic processes of tissues in homeostasis and disease. Here, we survey the current landscape of microfluidic methods in the field of single-cell and spatial multi-omics, as well as assessing their relative advantages and limitations. We highlight how microfluidics has been adapted and improved to provide new insights into multi-omics over the past decade. Last, we emphasize the contributions of microfluidic-based omics methods in development, neuroscience, and disease mechanisms, as well as further revealing some perspectives for technological advances in translational and clinical medicine.

Eosinophilic dialogues: a molecular exploration of sickle cell anemia severity

Sickle cell anemia (SCA) is a genetically inherited hemoglobinopathy characterized by the abnormal morphology of red blood cells, resulting in vaso-occlusive events and diverse clinical complications. Recent investigations have unveiled a novel dimension in understanding SCA severity through the lens of eosinophilic dialogues. This review article synthesizes current knowledge on the molecular intricacies of eosinophils in the context of SCA, exploring their biology, molecular markers, and interactions with other cellular components. Eosinophil-mediated inflammation and oxidative stress are dissected to elucidate their impact on the disease course. Furthermore, the review evaluates potential therapeutic interventions and outlines future directions in this burgeoning field. The term "Eosinophilic Dialogues" encapsulates the multifaceted molecular exchanges that influence SCA severity, presenting a promising avenue for targeted interventions and improved clinical outcomes. This review serves as a comprehensive resource for researchers, clinicians, and healthcare practitioners engaged in unraveling the complex pathophysiology of SCA and exploring novel therapeutic avenues.

A microfluidic approach to study variations in Chlamydomonas reinhardtii alkaline phosphatase activity in response to phosphate availability

Algal growth depends strongly on phosphorus (P) as a key nutrient, underscoring the significance of monitoring P levels. Algal species display a sensitive response to fluctuations in P availability, notably through the expression of alkaline phosphatase (AP) when challenged with P-depletion. As such, alkaline phosphatase activity (APA) serves as a valuable metric for P availability, offering insights into how algae utilize and fix available P resources. However, current APA quantification methods lack single cell resolution, while also being time- and reagent consuming. Microfluidics offers a promising cost-effective solution to these limitations, providing a platform for precise single-cell analysis. In this study, a trap-based microfluidic device was integrated with a commercially available AP live stain to study the single cell APA response of a model algae strain, Chlamydomonas reinhardtii, when exposed to different exogenous P levels. A three-step culture-starve-spike process was used to induce APA in cells cultured under two different basal P levels (1 and 21 mM). When challenged with different spiked P levels (ranging from 0.1-41 mM), C. reinhardtii cells demonstrated a highly heterogeneous APA response. Two-way ANOVA confirmed that this response is influenced by both spiked and basal P levels. Utilizing an unsupervised machine learning approach (HDBSCAN), distinct subpopulations of C. reinhardtii cells were identified exhibiting varying levels of APA at the single-cell level. These subpopulations encompass significant groups of individual cells with either notably high or low APA, contributing to the overall behavior of the cohorts. Considerable intrapopulation differences in APA were observed across cohorts with similar average behavior. For instance, while some cohorts exhibited a concentrated distribution around the overall average APA, others displayed subpopulations dispersed across a wider range of APA levels. This underscores the potential bias introduced by analyzing a small number of cells in bulk, which may skew results by overrepresenting extreme behavioral subpopulations. The findings if this study highlight the need for analytical approaches that account for single cell heterogeneity in APA and demonstrate the utility of microfluidics as a well-suited means for such investigations. This study illuminates the complexities of APA regulation at the single cell level, providing crucial insights that advance our understanding of algal phosphorus metabolism and environmental responses.

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

A Microfluidic Chip for Single-Cell Capture Based on Stagnation Point Flow and Boundary Effects

The capture of individual cells using microfluidic chips represents a widely adopted and efficient approach for investigating the biochemical microenvironment of singular cells. While conventional methods reliant on boundary effects pose challenges in precisely manipulating individual cells, single-cell capture grounded in the principle of stagnation point flow offers a solution to this limitation. Nevertheless, such capture mechanisms encounter inconsistency due to the instability of the flow field and stagnation point. In this study, a microfluidic device for the stable capture of single cells was designed, integrating the principle of fluid mechanics by amalgamating stagnation point flow and boundary effects. This innovative microfluidic chip transcended the limitations associated with single methodologies, leveraging the strengths of both stagnation point flow and boundary effects to achieve reliable single-cell capture. Notably, the incorporation of capture ports at the stagnation point not only harnessed boundary effects but also enhanced capture efficiency significantly, elevating it from 31.9% to 83.3%, thereby augmenting capture stability. Furthermore, computational simulations demonstrated the efficacy of the capture ports in entrapping particles of varying diameters, including 9 μm, 14 μm, and 18 μm. Experiment validation underscored the capability of this microfluidic system to capture single cells within the chip, maintaining stability even under flow rate perturbations spanning from 60 μL/min to 120 μL/min. Consequently, cells with dimensions between 8 μm and 12 μm can be reliably captured. The designed microfluidic system not only furnishes a straightforward and efficient experimental platform but also holds promise for facilitating deeper investigations into the intricate interplay between individual cells and their surrounding microenvironment.

Recent advancements in single-cell metabolic analysis for pharmacological research

Cellular heterogeneity is crucial for understanding tissue biology and disease pathophysiology. Pharmacological research is being advanced by single-cell metabolic analysis, which offers a technique to identify variations in RNA, proteins, metabolites, and drug molecules in cells. In this review, the recent advancement of single-cell metabolic analysis techniques and their applications in drug metabolism and drug response are summarized. High-precision and controlled single-cell isolation and manipulation are provided by microfluidics-based methods, such as droplet microfluidics, microchamber, open microfluidic probe, and digital microfluidics. They are used in tandem with variety of detection techniques, including optical imaging, Raman spectroscopy, electrochemical detection, RNA sequencing, and mass spectrometry, to evaluate single-cell metabolic changes in response to drug administration. The advantages and disadvantages of different techniques are discussed along with the challenges and future directions for single-cell analysis. These techniques are employed in pharmaceutical analysis for studying drug response and resistance pathway, therapeutic targets discovery, and in vitro disease model evaluation.

High-Throughput Single-Cell Analysis of Nanoparticle-Cell Interactions

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.

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.