Single-cell analysis tools for drug discovery and development

SERSomes for metabolic phenotyping and prostate cancer diagnosis

Molecular phenotypic variations in metabolites offer the promise of rapid profiling of physiological and pathological states for diagnosis, monitoring, and prognosis. Since present methods are expensive, time-consuming, and still not sensitive enough, there is an urgent need for approaches that can interrogate complex biological fluids at a system-wide level. Here, we introduce hyperspectral surface-enhanced Raman spectroscopy (SERS) to profile microliters of biofluidic metabolite extraction in 15 min with a spectral set, SERSome, that can be used to describe the structures and functions of various molecules produced in the biofluid at a specific time via SERS characteristics. The metabolite differences of various biofluids, including cell culture medium and human serum, are successfully profiled, showing a diagnosis accuracy of 80.8% on the internal test set and 73% on the external validation set for prostate cancer, discovering potential biomarkers, and predicting the tissue-level pathological aggressiveness. SERSomes offer a promising methodology for metabolic phenotyping.

Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors

Single-cell RNA sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening data sets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy-resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve the treatment of heterogenous tumors.

SIGNIFICANCE

A versatile method that infers cell-level drug response in scRNA-seq data facilitates the development of therapeutic strategies to target heterogeneous subpopulations within a tumor and address issues such as treatment failure and resistance.

A facile single-cell patterning strategy based on harbor-like microwell microfluidics

Single-cell analysis is an effective method for conducting comprehensive heterogeneity studies ranging from cell phenotype to gene expression. The ability to arrange different cells in a predetermined pattern at single-cell resolution has a wide range of applications in cell-based analysis and plays an important role in facilitating interdisciplinary research by researchers in various fields. Most existing microfluidic microwell chips is a simple and straightforward method, which typically use small-sized microwells to accommodate single cells. However, this method imposes certain limitations on cells of various sizes, and the single-cell capture efficiency is relatively low without the assistance of external forces. Moreover, the microwells limit the spatiotemporal resolution of reagent replacement, as well as cell-to-cell communication. In this study, we propose a new strategy to prepare a single-cell array on a planar microchannel based on microfluidic flip microwells chip platform with large apertures (50 μm), shallow channels (50 μm), and deep microwells (50 μm). The combination of three configuration characteristics contributes to multi-cell trapping and a single-cell array within microwells, while the subsequent chip flipping accomplishes the transfer of the single-cell array to the opposite planar microchannel for cells adherence and growth. Further assisted by protein coating of bovine serum albumin and fibronectin on different layers, the single-cell capture efficiency in microwells is achieved at 92.1% ± 1%, while ultimately 85% ± 3.4% on planar microchannel. To verify the microfluidic flip microwells chip platform, the real-time and heterogeneous study of calcium release and apoptosis behaviours of single cells is carried out. To our knowledge, this is the first time that high-efficiency single-cell acquisition has been accomplished using a circular-well chip design that combines shallow channel, large aperture and deep microwell together. The chip is effective in avoiding the shearing force of high flow rates on cells, and the large apertures better allows cells to sedimentation. Therefore, this strategy owns the advantages of easy preparation and user-friendliness, which is especially valuable for researchers from different fields.

Cell Electrokinetic Fingerprint: A Novel Approach Based on Optically Induced Dielectrophoresis (ODEP) for In-Flow Identification of Single Cells

A novel optically induced dielectrophoresis (ODEP) system that can operate under flow conditions is designed for automatic trapping of cells and subsequent induction of 2D multi-frequency cell trajectories. Like in a "ping-pong" match, two virtual electrode barriers operate in an alternate mode with varying frequencies of the input voltage. The so-derived cell motions are characterized via time-lapse microscopy, cell tracking, and state-of-the-art machine learning algorithms, like the wavelet scattering transform (WST). As a cell-electrokinetic fingerprint, the dynamic of variation of the cell displacements happening, over time, is quantified in response to different frequency values of the induced electric field. When tested on two biological scenarios in the cancer domain, the proposed approach discriminates cellular dielectric phenotypes obtained, respectively, at different early phases of drug-induced apoptosis in prostate cancer (PC3) cells and for differential expression of the lectine-like oxidized low-density lipoprotein receptor-1 (LOX-1) transcript levels in human colorectal adenocarcinoma (DLD-1) cells. The results demonstrate increased discrimination of the proposed system and pose an additional basis for making ODEP-based assays addressing cancer heterogeneity for precision medicine and pharmacological research.

An Extracellular Matrix Overlay Model for Bioluminescence Microscopy to Measure Single-Cell Heterogeneous Responses to Antiandrogens in Prostate Cancer Cells

Prostate cancer (PCa) displays diverse intra-tumoral traits, impacting its progression and treatment outcomes. This study aimed to refine PCa cell culture conditions for dynamic monitoring of androgen receptor (AR) activity at the single-cell level. We introduced an extracellular matrix-Matrigel (ECM-M) culture model, enhancing cellular tracking during bioluminescence single-cell imaging while improving cell viability. ECM-M notably tripled the traceability of poorly adherent PCa cells, facilitating robust single-cell tracking, without impeding substrate permeability or AR response. This model effectively monitored AR modulation by antiandrogens across various PCa cell lines. Single-cell imaging unveiled heterogeneous antiandrogen responses within populations, correlating non-responsive cell proportions with drug IC50 values. Integrating ECM-M culture with the PSEBC-TSTA biosensor enabled precise characterization of ARi responsiveness within diverse cell populations. Our ECM-M model stands as a promising tool to assess heterogeneous single-cell treatment responses in cancer, offering insights to link drug responses to intracellular signaling dynamics. This approach enhances our comprehension of the nuanced and dynamic nature of PCa treatment responses.

Siamese deep learning video flow cytometry for automatic and label-free clinical cervical cancer cell analysis

Automatic and label-free screening methods may help to reduce cervical cancer mortality rates, especially in developing regions. The latest advances of deep learning in the biomedical optics field provide a more automatic approach to solving clinical dilemmas. However, existing deep learning methods face challenges, such as the requirement of manually annotated training sets for clinical sample analysis. Here, we develop Siamese deep learning video flow cytometry for the analysis of clinical cervical cancer cell samples in a smear-free manner. High-content light scattering images of label-free single cells are obtained via the video flow cytometer. Siamese deep learning, a self-supervised method, is built to introduce cell lineage cells into an analysis of clinical cells, which utilizes generated similarity metrics as label annotations for clinical cells. Compared with other deep learning methods, Siamese deep learning achieves a higher accuracy of up to 87.11%, with about 5.62% improvement for label-free clinical cervical cancer cell classification. The Siamese deep learning video flow cytometry demonstrated here is promising for automatic, label-free analysis of many types of cells from clinical samples without cell smears.

VIBRANT: spectral profiling for single-cell drug responses

High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

Discrete Femtolitre Pipetting with 3D Printed Axisymmetrical Phaseguides

The capacity to precisely pipette femtoliter volumes of liquid enables many applications, for example, to functionalize a nanoscale surface and manipulate fluids inside a single-cell. A pressure-controlled pipetting method is the most preferred, since it enables the widest range of working liquids. However, precisely controlling femtoliter volumes by pressure is challenging. In this work, a new concept is proposed that makes use of axisymmetrical phaseguides inside a microfluidic channel to pipette liquid in discrete steps of known volume. An analytical model for the design of the femtopipettes is developed and verified experimentally. Femtopipettes are fabricated using a multi-scale 3D printing strategy integrating a digital light processing printed part and a two-photon-polymerization printed part. Three different variants are designed and fabricated with pipetting resolutions of 10 picoliters, 180 femtoliters and 50 femtoliters. As a demonstration, controlled amounts of a water-glycerol mixture were first aspirated and then dispensed into a mineral oil droplet.

13C-SpaceM: Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer

Metabolism has emerged as a key factor in homeostasis and disease including cancer. Yet, little is known about the heterogeneity of metabolic activity of cancer cells due to the lack of tools to directly probe it. Here, we present a novel method, 13C-SpaceM for spatial single-cell isotope tracing of glucose-dependent de novo lipogenesis. The method combines imaging mass spectrometry for spatially-resolved detection of 13C6-glucose-derived 13C label incorporated into esterified fatty acids with microscopy and computational methods for data integration and analysis. We validated 13C-SpaceM on a spatially-heterogeneous normoxia-hypoxia model of liver cancer cells. Investigating cultured cells, we revealed single-cell heterogeneity of lipogenic acetyl-CoA pool labelling degree upon ACLY knockdown that is hidden in the bulk analysis and its effect on synthesis of individual fatty acids. Next, we adapted 13C-SpaceM to analyze tissue sections of mice harboring isocitrate dehydrogenase (IDH)-mutant gliomas. We found a strong induction of de novo fatty acid synthesis in the tumor tissue compared to the surrounding brain. Comparison of fatty acid isotopologue patterns revealed elevated uptake of mono-unsaturated and essential fatty acids in the tumor. Furthermore, our analysis uncovered substantial spatial heterogeneity in the labelling of the lipogenic acetyl-CoA pool indicative of metabolic reprogramming during microenvironmental adaptation. Overall, 13C-SpaceM enables novel ways for spatial probing of metabolic activity at the single cell level. Additionally, this methodology provides unprecedented insight into fatty acid uptake, synthesis and modification in normal and cancerous tissues.

Spectral tweezers: Single sample spectroscopy using optoelectronic tweezers

A scheme that combines optoelectronic tweezers (OET) with spectroscopic analysis is presented. Referred to as spectral tweezers, the approach uses a single focused light beam that acts both as the trapping beam for OET and the probe beam for spectroscopy. Having simultaneous manipulation and spectral characterization ability, the method is used to isolate single micro-samples from clusters and perform spectral measurements. Experimental results show that a characteristic spectral signature can be obtained for a given sample. The proposed approach can be easily integrated into the optical setups used for conventional OETs with only a few additional optical components, making it a convenient tool for bio-analytical applications.

Analysis of cellular response to drugs with a microfluidic single-cell platform based on hyperspectral imaging

BACKGROUND

Cellular response to pharmacological action of drugs is significant for drug development. Traditional detection method for cellular response to drugs normally rely on cell proliferation assay and metabolomics examination. In principle, these analytical methods often required cell labeling, invasion analysis, and hours of co-culture with drugs, which are relatively complex and time-consuming. Moreover, these methods can only indicate the drug effectiveness on cell colony rather than single cells. Thus, to meet the requirements of personal precision medicine, the development of drug response analysis on the high resolution of single cell is demanded.

RESULTS

To provide precise result for drug response on single-cell level, a microfluidic platform coupled with the label-free hyperspectral imaging was developed. With the help of horizontal single-cell trapping sieves, hundreds of single cells were trapped independently in microfluidic channels for the purposes of real-time drug delivery and single-cell hyperspectral image recording. To significantly identify the cellular hyperspectral change after drug stimulation, the differenced single-cell spectrum was proposed. Compared with the deep learning classification method based on hyperspectral images, an optimal performance can be achieved by the classification strategy based on differenced spectra. And the cellular response to different reagents, for example, K+, Epidermal Growth Factor (EGF), and Gefitinib at different concentrations can be accurately characterized by the differenced single-cell spectra analysis.

SIGNIFICANCE AND NOVELTY

The high-throughput, rapid analysis of cellular response to drugs at the single-cell level can be accurately performed by our platform. After systematically analyzing the materials and the structures of the single-cell microfluidic chip, the optimal single-cell trapping method was proposed to contribute to the further application of hyperspectral imaging on microfluidic single-cell analysis. And the hyperspectral characterization of single-cell with cancer drug stimulation proved the application potential of our method in personal cancer medication.

Optical diffraction tomography for assessing single cell models in angular light scattering

Angularly resolved light scattering (ALS) has become a useful tool for assessing the size and refractive index of biological scatterers at cellular and organelle length scales. Sizing organelle populations with ALS relies on Mie scattering theory models, which require significant assumptions about the object, including spherical scatterers and a homogeneous medium. These assumptions may incur greater error at the single cell level, where there are fewer scatterers to be averaged over. We investigate the validity of these assumptions using 3D refractive index (RI) tomograms measured via optical diffraction tomography (ODT). We compute the angular scattering on digitally manipulated tomograms with increasingly strong model assumptions, including RI-matched immersion media, homogeneous cytosol, and spherical organelles. We also compare the tomogram-computed angular scattering to experimental measurements of angular scattering from the same cells to ensure that the ODT-based approach accurately models angular scattering. We show that enforced RI-matching with the immersion medium and a homogeneous cytosol significantly affects the angular scattering intensity shape, suggesting that these assumptions can reduce the accuracy of size distribution estimates.

Harnessing 3D in vitro systems to model immune responses to solid tumours: a step towards improving and creating personalized immunotherapies

In vitro 3D models are advanced biological tools that have been established to overcome the shortcomings of oversimplified 2D cultures and mouse models. Various in vitro 3D immuno-oncology models have been developed to mimic and recapitulate the cancer-immunity cycle, evaluate immunotherapy regimens, and explore options for optimizing current immunotherapies, including for individual patient tumours. Here, we review recent developments in this field. We focus, first, on the limitations of existing immunotherapies for solid tumours, secondly, on how in vitro 3D immuno-oncology models are established using various technologies - including scaffolds, organoids, microfluidics and 3D bioprinting - and thirdly, on the applications of these 3D models for comprehending the cancer-immunity cycle as well as for assessing and improving immunotherapies for solid tumours.

Fully Active Delivery of Nanodrugs In Vivo via Remote Optical Manipulation

The active delivery of nanodrugs has been a bottleneck problem in nanomedicine. While modification of nanodrugs with targeting agents can enhance their retention at the lesion location, the transportation of nanodrugs in the circulation system is still a passive process. The navigation of nanodrugs with external forces such as magnetic field has been shown to be effective for active delivery, but the existing techniques are limited to specific materials like magnetic nanoparticles. In this study, an alternative actuation method is proposed based on optical manipulation for remote navigation of nanodrugs in vivo, which is compatible with most of the common drug carriers and exhibits significantly higher manipulation precision. By the programmable scanning of the laser beam, the motion trajectory and velocity of the nanodrugs can be precisely controlled in real time, making it possible for intelligent drug delivery, such as inverse-flow transportation, selective entry into specific vascular branch, and dynamic circumvention across obstacles. In addition, the controlled mass delivery of nanodrugs can be realized through indirect actuation by the microflow field. The developed optical manipulation method provides a new solution for the active delivery of nanodrugs, with promising potential for the treatment of blood diseases such as leukemia and thrombosis.

Microarray-Based Electrochemical Biosensing

Microarrays are widely utilized in bioanalysis. Electrochemical biosensing techniques are often applied in microarray-based assays because of their simplicity, low cost, and high sensitivity. In such systems, the electrodes and sensing elements are arranged in arrays, and the target analytes are detected electrochemically. These sensors can be utilized for high-throughput bioanalysis and the electrochemical imaging of biosamples, including proteins, oligonucleotides, and cells. In this chapter, we summarize recent progress on these topics. We categorize electrochemical biosensing techniques for array detection into four groups: scanning electrochemical microscopy, electrode arrays, electrochemiluminescence, and bipolar electrodes. For each technique, we summarize the key principles and discuss the advantages, disadvantages, and bioanalysis applications. Finally, we present conclusions and perspectives about future directions in this field.

Exploring the mechanism underlying the therapeutic effects of butein in colorectal cancer using network pharmacology and single-cell RNA sequencing data

BACKGROUND

Butein has shown substantial potential as a cancer treatment, but its precise mechanism of action in colorectal cancer (CRC) remains unclear. This study aimed to uncover the underlying mechanisms through which butein operates in CRC and to identify potential biomarkers through a comprehensive investigation.

METHODS

Target genes associated with butein were sourced from SwissTargetPrediction, CTD, BindingDB and TargetNet. Gene expression data from the GSE38026 dataset and the single-cell dataset (GSE222300) were retrieved from the Gene Expression Omnibus database. The activation of disease-related pathways was assessed using Kyoto Encyclopedia of Genes and Genomes, Gene Ontology and differential gene analysis. Disease-associated genes were identified through differential analysis and weighted gene co-expression network analysis (WGCNA). The protein-protein interaction network was utilized to pinpoint potential drug targets. Molecular complex detection (MCODE) analysis was employed to uncover relevant genes influenced by butein within key subgroup networks. Machine learning techniques were applied for the screening of potential biomarkers, with receiver operating characteristic curves used to evaluate their clinical significance. Single-cell analysis was conducted to assess the pharmacological targets of butein in CRC, with validation performed using the external dataset GSE40967.

RESULTS

A total of 232 target genes for butein were identified. Functional enrichment analysis revealed significant enrichment of signaling pathways, including mitogen-activated protein kinase, JAK-STAT and NF-κB, among these genes. Differential analysis, in conjunction with WGCNA, yielded 520 disease-related genes. Subsequently, a disease-drug-gene network consisting of 727 targets was established, and a subnetwork containing 56 crucial genes was extracted. Important pathways such as the FoxO signaling pathway exhibited significant enrichment within these key genes. Machine learning applied to the 56 important genes led to the identification of a potential biomarker, UBE2C. Receiver operating characteristic analysis demonstrated the excellent clinical predictive utility of UBE2C. Single-cell analysis suggested that butein's therapeutic effects might be linked to its influence on epithelial and T cells, with UBE2C expression associated with these cell types. Validation using the external dataset GSE40967 further confirmed the exceptional clinical predictive capability of UBE2C.

CONCLUSION

This study combines network pharmacology with single-cell analysis to unravel the mechanisms underlying butein's effects in CRC. Notably, UBE2C emerged as a promising biomarker with superior clinical efficacy. These research findings contribute significantly to our understanding of specific molecular mechanisms, potentially shaping future clinical practices.

Generative artificial intelligence empowers digital twins in drug discovery and clinical trials

INTRODUCTION

The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing processes associated with high economic, ethical, or social burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel, realistic and complex data with desired properties.

AREAS COVERED

The authors provide a brief introduction to generative AI and describe how it facilitates the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be addressed before DTs can transform drug discovery and clinical trials.

EXPERT OPINION

The current state of DTs in drug discovery and clinical trials does not exploit the entire power of generative AI yet and is limited to simulation of a small number of characteristics. Nonetheless, generative AI has the potential to transform the field by leveraging recent developments in deep learning and customizing models for the needs of scientists, physicians and patients.

Microfluidics Coupled Mass Spectrometry for Single Cell Multi-Omics

Population-level analysis masks significant heterogeneity between individual cells, making it difficult to accurately reflect the true intricacies of life activities. Microfluidics is a technique that can manipulate individual cells effectively and is commonly coupled with a variety of analytical methods for single-cell analysis. Single-cell omics provides abundant molecular information at the single-cell level, fundamentally revealing differences in cell types and biological states among cell individuals, leading to a deeper understanding of cellular phenotypes and life activities. Herein, this work summarizes the microfluidic chips designed for single-cell isolation, manipulation, trapping, screening, and sorting, including droplet microfluidic chips, microwell arrays, hydrodynamic microfluidic chips, and microchips with microvalves. This work further reviews the studies on single-cell proteomics, metabolomics, lipidomics, and multi-omics based on microfluidics and mass spectrometry. Finally, the challenges and future application of single-cell multi-omics are discussed.

Nanosensor Chemical Cytometry: Advances and Opportunities in Cellular Therapy and Precision Medicine

With the definition of therapeutics now encompassing transplanted or engineered cells and their molecular products, there is a growing scientific necessity for analytics to understand this new category of drugs. This Perspective highlights the recent development of new measurement science on label-free single cell analysis, nanosensor chemical cytometry (NCC), and their potential for cellular therapeutics and precision medicine. NCC is based on microfluidics integrated with fluorescent nanosensor arrays utilizing the optical lensing effect of a single cell to real-time extract molecular properties and correlate them with physical attributes of single cells. This new class of cytometry can quantify the heterogeneity of the multivariate physicochemical attributes of the cell populations in a completely label-free and nondestructive way and, thus, suggest the vein-to-vein conditions for the safe therapeutic applications. After the introduction of the NCC technology, we suggest the technological development roadmap for the maturation of the new field: from the sensor/chip design perspective to the system/software development level based on hardware automation and deep learning data analytics. The advancement of this new single cell sensing technology is anticipated to aid rich and multivariate single cell data setting and support safe and reliable cellular therapeutics. This new measurement science can lead to data-driven personalized precision medicine.

Systems immunology spanning tumors, lymph nodes, and periphery

The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.

Enhancing Single-Cell Western Blotting Sensitivity Using Diffusive Analyte Blotting and Antibody Conjugate Amplification

While there are many techniques to achieve highly sensitive, multiplex detection of RNA and DNA from single cells, detecting protein content often suffers from low limits of detection and throughput. Miniaturized, high-sensitivity Western blots on single cells (scWesterns) are attractive because they do not require advanced instrumentation. By physically separating analytes, scWesterns also uniquely mitigate limitations to target protein multiplexing posed by the affinity reagent performance. However, a fundamental limitation of scWesterns is their limited sensitivity for detecting low-abundance proteins, which arises from transport barriers posed by the separation gel against detection species. Here we address the sensitivity by decoupling the electrophoretic separation medium from the detection medium. We transfer scWestern separations to a nitrocellulose blotting medium with distinct mass transfer advantages over traditional in-gel probing, yielding a 5.9-fold improvement in the limit of detection. We next amplify probing of blotted proteins with enzyme-antibody conjugates, which are incompatible with traditional in-gel probing to achieve further improvement in the limit of detection to 1000 molecules, a 120-fold improvement. This enables us to detect 100% of cells in an EGFP-expressing population using fluorescently tagged and enzyme-conjugated antibodies compared to 84.5% of cells using in-gel detection. These results suggest the compatibility of nitrocellulose-immobilized scWesterns with a variety of affinity reagents─not previously accessible for in-gel use─for further signal amplification and detection of low-abundance targets.

Single-Cell Proliferation Microfluidic Device for High Throughput Investigation of Replicative Potential and Drug Resistance of Cancer Cells

Introduction

Cell proliferation represents a major hallmark of cancer biology, and manifests itself in the assessment of tumor growth, drug resistance and metastasis. Tracking cell proliferation or cell fate at the single-cell level can reveal phenotypic heterogeneity. However, characterization of cell proliferation is typically done in bulk assays which does not inform on cells that can proliferate under given environmental perturbations. Thus, there is a need for single-cell approaches that allow longitudinal tracking of the fate of a large number of individual cells to reveal diverse phenotypes.

Methods

We fabricated a new microfluidic architecture for high efficiency capture of single tumor cells, with the capacity to monitor cell divisions across multiple daughter cells. This single-cell proliferation (SCP) device enabled the quantification of the fate of more than 1000 individual cancer cells longitudinally, allowing comprehensive profiling of the phenotypic heterogeneity that would be otherwise masked in standard cell proliferation assays. We characterized the efficiency of single cell capture and demonstrated the utility of the SCP device by exposing MCF-7 breast tumor cells to different doses of the chemotherapeutic agent doxorubicin.

Results

The single cell trapping efficiency of the SCP device was found to be ~ 85%. At the low doses of doxorubicin (0.01 µM, 0.001 µM, 0.0001 µM), we observed that 50-80% of the drug-treated cells had undergone proliferation, and less than 10% of the cells do not proliferate. Additionally, we demonstrated the potential of the SCP device in circulating tumor cell applications where minimizing target cell loss is critical. We showed selective capture of breast tumor cells from a binary mixture of cells (tumor cells and white blood cells) that was isolated from blood processing. We successfully characterized the proliferation statistics of these captured cells despite their extremely low counts in the original binary suspension.

Conclusions

The SCP device has significant potential for cancer research with the ability to quantify proliferation statistics of individual tumor cells, opening new avenues of investigation ranging from evaluating drug resistance of anti-cancer compounds to monitoring the replicative potential of patient-derived cells.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-023-00773-z.

Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles

Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0). More importantly, we examine our method's utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.

Microfluidics-free single-cell genomics with templated emulsification

Current single-cell RNA-sequencing approaches have limitations that stem from the microfluidic devices or fluid handling steps required for sample processing. We develop a method that does not require specialized microfluidic devices, expertise or hardware. Our approach is based on particle-templated emulsification, which allows single-cell encapsulation and barcoding of cDNA in uniform droplet emulsions with only a vortexer. Particle-templated instant partition sequencing (PIP-seq) accommodates a wide range of emulsification formats, including microwell plates and large-volume conical tubes, enabling thousands of samples or millions of cells to be processed in minutes. We demonstrate that PIP-seq produces high-purity transcriptomes in mouse-human mixing studies, is compatible with multiomics measurements and can accurately characterize cell types in human breast tissue compared to a commercial microfluidic platform. Single-cell transcriptional profiling of mixed phenotype acute leukemia using PIP-seq reveals the emergence of heterogeneity within chemotherapy-resistant cell subsets that were hidden by standard immunophenotyping. PIP-seq is a simple, flexible and scalable next-generation workflow that extends single-cell sequencing to new applications.

Non-Mass Spectrometric Targeted Single-Cell Metabolomics

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.

Selective Endocytic Uptake of Targeted Liposomes Occurs within a Narrow Range of Liposome Diameters

Cell surface receptors facilitate signaling and nutrient uptake. These processes are dynamic, requiring receptors to be actively recycled by endocytosis. Due to their differential expression in disease states, receptors are often the target of drug-carrier particles, which are adorned with ligands that bind specifically to receptors. These targeted particles are taken into the cell by multiple routes of internalization, where the best-characterized pathway is clathrin-mediated endocytosis. Most studies of particle uptake have utilized bulk assays rather than observing individual endocytic events. As a result, the detailed mechanisms of particle uptake remain obscure. To address this gap, we employed a live-cell imaging approach to study the uptake of individual liposomes as they interact with clathrin-coated structures. By tracking individual internalization events, we find that the size of liposomes rather than the density of the ligands on their surfaces primarily determines their probability of uptake. Interestingly, targeting has the greatest impact on endocytosis of liposomes of intermediate diameters, with the smallest and largest liposomes being internalized or excluded, respectively, regardless of whether they are targeted. These findings, which highlight a previously unexplored limitation of targeted delivery, can be used to design more effective drug carriers.

Pharmacogenomic Analysis of Combined Therapies against Glioblastoma Based on Cell Markers from Single-Cell Sequencing

Glioblastoma is the most common and aggressive form of primary brain cancer and the lack of viable treatment options has created an urgency to develop novel treatments. Personalized or predictive medicine is still in its infancy stage at present. This research aimed to discover biomarkers to inform disease progression and to develop personalized prophylactic and therapeutic strategies by combining state-of-the-art technologies such as single-cell RNA sequencing, systems pharmacology, and a polypharmacological approach. As predicted in the pyroptosis-related gene (PRG) transcription factor (TF) microRNA (miRNA) regulatory network, TP53 was the hub gene in the pyroptosis process in glioblastoma (GBM). A LASSO Cox regression model of pyroptosis-related genes was built to accurately and conveniently predict the one-, two-, and three-year overall survival rates of GBM patients. The top-scoring five natural compounds were parthenolide, rutin, baeomycesic acid, luteolin, and kaempferol, which have NFKB inhibition, antioxidant, lipoxygenase inhibition, glucosidase inhibition, and estrogen receptor agonism properties, respectively. In contrast, the analysis of the cell-type-specific differential expression-related targets of natural compounds showed that the top five subtype cells targeted by natural compounds were endothelial cells, microglia/macrophages, oligodendrocytes, dendritic cells, and neutrophil cells. The current approach-using the pharmacogenomic analysis of combined therapies-serves as a model for novel personalized therapeutic strategies for GBM treatment.

On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data

Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains technical variations that could affect the interpretation of the cell types. Therefore, gene selection, also known as feature selection in data science, plays an important role in selecting informative genes for scRNA-seq cell type identification. Generally speaking, feature selection methods are categorized into filter-, wrapper-, and embedded-based approaches. From the existing literature, methods from filter- and embedded-based approaches are widely applied in scRNA-seq gene selection tasks. The wrapper-based method that gives promising results in other fields has yet been extensively utilized for selecting gene features from scRNA-seq data; in addition, most of the existing wrapper methods used in this field are clustering instead of classification-based. With a large number of annotated data available today, this study applied a classification-based approach as an alternative to the clustering-based wrapper method. In our work, a quantum-inspired differential evolution (QDE) wrapped with a classification method was introduced to select a subset of genes from twelve well-known scRNA-seq transcriptomic datasets to identify cell types. In particular, the QDE was combined with different machine-learning (ML) classifiers namely logistic regression, decision tree, support vector machine (SVM) with linear and radial basis function kernels, as well as extreme learning machine. The linear SVM wrapped with QDE, namely QDE-SVM, was chosen by referring to the feature selection results from the experiment. QDE-SVM showed a superior cell type classification performance among QDE wrapping with other ML classifiers as well as the recent wrapper methods (i.e., FSCAM, SSD-LAHC, MA-HS, and BSF). QDE-SVM achieved an average accuracy of 0.9559, while the other wrapper methods achieved average accuracies in the range of 0.8292 to 0.8872.

Rapid, low-cost fabrication of electronic microfluidics via inkjet-printing and xurography (MINX)

Microfluidics has shown great promise for point-of-care assays due to unique chemical and physical advantages that occur at the micron scale. Furthermore, integration of electrodes into microfluidic systems provides additional capabilities for assay operation and electronic readout. However, while these systems are abundant in biological and biomedical research settings, translation of microfluidic devices with embedded electrodes are limited. In part, this is due to the reliance on expensive, inaccessible, and laborious microfabrication techniques. Although innovative prior work has simplified microfluidic fabrication or inexpensively patterned electrodes, low-cost, accessible, and robust methods to incorporate all these elements are lacking. Here, we present MINX, a low-cost <1 USD and rapid (∼minutes) fabrication technique to manufacture microfluidic device with embedded electrodes. We characterize the structures created using MINX, and then demonstrate the utility of the approach by using MINX to implement an electrochemical bead-based biomarker detection assay. We show that the MINX technique enables the scalable, inexpensive fabrication of microfluidic devices with electronic sensors using widely accessible desktop machines and low-cost materials.

DUPLETS: Deformability-Assisted Dual-Particle Encapsulation Via Electrically Activated Sorting

Co-encapsulation of bead carriers and biological cells in microfluidics has become a powerful technique for various biological assays in single-cell genomics and drug screening because of its distinct capability of single-cell confinement. However, current co-encapsulation approaches exist a trade-off between cell/bead pairing rate and probability of multiple cells in individual droplets, significantly limiting the effective throughput of single-paired cell-bead droplets production. Deformability-assisted dUal-Particle encapsuLation via Electrically acTivated Sorting (DUPLETS) system is reported to overcome this problem. The DUPLETS can differentiate the encapsulated content in individual droplets and sort out targeted droplets via a combined screening of mechanical and electrical characteristics of single droplets in label-free manners and with the highest effective throughput in comparison to current commercial platforms. The DUPLETS has been demonstrated to enrich single-paired cell-bead droplets to over 80% (above eightfold higher than current co-encapsulation techniques). It eliminates multicell droplets to 0.1% whereas up to ≈24% in 10× Chromium. It is believed that merging DUPLETS into the current co-encapsulation platforms can meaningfully elevate sample quality in terms of high purity of single-paired cell-bead droplets, low fraction of multicell droplets, and high cell viability, which can benefit a multitude of biological assay applications.

Single-Cell Spatial MIST for Versatile, Scalable Detection of Protein Markers

High-multiplex detection of protein biomarkers across tissue regions has been an attractive spatial biology approach due to significant advantages over traditional immunohistochemistry (IHC) methods. Different from most methods, spatial multiplex in situ tagging (MIST) transfers the spatial protein expression information to an ultrahigh-density, large-scale MIST array. This technique has been optimized to reach single-cell resolution by adoption of smaller array units and 30% 8-arm PEG polymer as transfer medium. Tissue cell nuclei stained with lamin B have been clearly visualized on the MIST arrays and are colocalized with detection of nine mouse brain markers. Pseudocells defined at 10 μm in size have been used to fully profile tissue regions including cells and the intercellular space. We showcased the versatility of our technology by successfully detecting 20 marker proteins in kidney samples with the addition of five minutes atop the duration of standard immunohistochemistry protocols. Spatial MIST is amenable to iterative staining and detection on the same tissue samples. When 25 proteins were co-detected on 1 mouse brain section for each round and 5 rounds were executed, an ultrahigh multiplexity of 125 proteins was obtained for each pseudocell. With its unique abilities, this single-cell spatial MIST technology has the potential to become an important method in advanced diagnosis of complex diseases.

Single-cell lipidomics enabled by dual-polarity ionization and ion mobility-mass spectrometry imaging

Single-cell (SC) analysis provides unique insight into individual cell dynamics and cell-to-cell heterogeneity. Here, we utilize trapped ion mobility separation coupled with dual-polarity ionization mass spectrometry imaging (MSI) to enable high-throughput in situ profiling of the SC lipidome. Multimodal SC imaging, in which dual-polarity-mode MSI is used to perform serial data acquisition runs on individual cells, significantly enhanced SC lipidome coverage. High-spatial resolution SC-MSI identifies both inter- and intracellular lipid heterogeneity; this heterogeneity is further explicated by Uniform Manifold Approximation and Projection and machine learning-driven classifications. We characterize SC lipidome alteration in response to stearoyl-CoA desaturase 1 inhibition and, additionally, identify cell-layer specific lipid distribution patterns in mouse cerebellar cortex. This integrated multimodal SC-MSI technology enables high-resolution spatial mapping of intercellular and cell-to-cell lipidome heterogeneity, SC lipidome remodeling induced by pharmacological intervention, and region-specific lipid diversity within tissue.

Rapid, One-Step Sample Processing for Label-Free Single-Cell Proteomics

Sample preparation for single-cell proteomics is generally performed in a one-pot workflow with multiple dispensing and incubation steps. These hours-long processes can be labor intensive and lead to long sample-to-answer times. Here we report a sample preparation method that achieves cell lysis, protein denaturation, and digestion in 1 h using commercially available high-temperature-stabilized proteases with a single reagent dispensing step. Four different one-step reagent compositions were evaluated, and the mixture providing the highest proteome coverage was compared to the previously employed multistep workflow. The one-step preparation increases proteome coverage relative to the previous multistep workflow while minimizing labor input and the possibility of human error. We also compared sample recovery between previously used microfabricated glass nanowell chips and injection-molded polypropylene chips and found the polypropylene provided improved proteome coverage. Combined, the one-step sample preparation and the polypropylene substrates enabled the identification of an average of nearly 2400 proteins per cell using a standard data-dependent workflow with Orbitrap mass spectrometers. These advances greatly simplify sample preparation for single-cell proteomics and broaden accessibility with no compromise in terms of proteome coverage.

Three-dimensional angular scattering simulations inform analysis of scattering from single cells

Significance

Organelle sizes, which are indicative of cellular status, have implications for drug development and immunology research. At the single cell level, such information could be used to study the heterogeneity of cell response to drugs or pathogens.

Aim

Angularly resolved elastic light scattering is known to be sensitive to changes in organelle size distribution. We developed a Mie theory-based simulation of angular scattering from single cells to quantify the effects of noise on scattering and size estimates.

Approach

We simulated randomly sampled organelle sizes (drawn from a log normal distribution), interference between different organelles' scattering, and detector noise. We quantified each noise source's effect upon the estimated mean and standard deviation of organelle size distributions.

Results

The results demonstrate that signal-to-noise ratio in the angular scattering increased with the number of scatterers, cell area, and exposure time and decreased with the size distribution width. The error in estimating the mean of the size distributions remained below 5% for nearly all experimental parameters tested, but the widest size distribution tested (standard deviation of 600 nm) reached 20%.

Conclusions

The simulator revealed that sparse sampling of a broad size distribution can dominate the mismatch between actual and predicted size parameters. Alternative estimation strategies could reduce the discrepancy.

Selective endocytic uptake of targeted liposomes occurs within a narrow range of liposome diameter

Cell surface receptors facilitate signaling and nutrient uptake. These processes are dynamic, requiring receptors to be actively recycled by endocytosis. Due to their differential expression in disease states, receptors are often the target of drug-carrier particles, which are adorned with ligands that bind specifically to receptors. These targeted particles are taken into the cell by multiple routes of internalization, where the best-characterized pathway is clathrin-mediated endocytosis. Most studies of particle uptake have utilized bulk assays, rather than observing individual endocytic events. As a result, the detailed mechanisms of particle uptake remain obscure. To address this gap, we have employed a live-cell imaging approach to study the uptake of individual liposomes as they interact with clathrin-coated structures. By tracking individual internalization events, we find that the size of liposomes, rather than the density of the ligands on their surfaces, primarily determines their probability of uptake. Interestingly, targeting has the greatest impact on endocytosis of liposomes of intermediate diameters, with the smallest and largest liposomes being internalized or excluded, respectively, regardless of whether they are targeted. These findings, which highlight a previously unexplored limitation of targeted delivery, can be used to design more effective drug carriers.

Enhancing single-cell western blotting sensitivity using diffusive analyte blotting and antibody conjugate amplification

While there are many techniques to achieve highly sensitive, multiplex detection of RNA and DNA from single cells, detecting protein contents often suffers from low limits of detection and throughput. Miniaturized, high-sensitivity western blots on single cells (scWesterns) are attractive since they do not require advanced instrumentation. By physically separating analytes, scWesterns also uniquely mitigate limitations to target protein multiplexing posed by affinity reagent performance. However, a fundamental limitation of scWesterns is their limited sensitivity for detecting low-abundance proteins, which arises from transport barriers posed by the separation gel against detection species. Here we address sensitivity by decoupling the electrophoretic separation medium from the detection medium. We transfer scWestern separations to a nitrocellulose blotting medium with distinct mass transfer advantages over traditional in-gel probing, yielding a 5.9-fold improvement in limit of detection. We next amplify probing of blotted proteins with enzyme-antibody conjugates which are incompatible with traditional in-gel probing to achieve further improvement in the limit of detection to 103 molecules, a 520-fold improvement. This enables us to detect 85% and 100% of cells in an EGFP-expressing population using fluorescently tagged and enzyme-conjugated antibodies respectively, compared to 47% of cells using in-gel detection. These results suggest compatibility of nitrocellulose-immobilized scWesterns with a variety of affinity reagents - not previously accessible for in-gel use - for further signal amplification and detection of low abundance targets.

Recent Advances in Metaphotonic Biosensors

Metaphotonic devices, which enable light manipulation at a subwavelength scale and enhance light-matter interactions, have been emerging as a critical pillar in biosensing. Researchers have been attracted to metaphotonic biosensors, as they solve the limitations of the existing bioanalytical techniques, including the sensitivity, selectivity, and detection limit. Here, we briefly introduce types of metasurfaces utilized in various metaphotonic biomolecular sensing domains such as refractometry, surface-enhanced fluorescence, vibrational spectroscopy, and chiral sensing. Further, we list the prevalent working mechanisms of those metaphotonic bio-detection schemes. Furthermore, we summarize the recent progress in chip integration for metaphotonic biosensing to enable innovative point-of-care devices in healthcare. Finally, we discuss the impediments in metaphotonic biosensing, such as its cost effectiveness and treatment for intricate biospecimens, and present a prospect for potential directions for materializing these device strategies, significantly influencing clinical diagnostics in health and safety.

Rapid, High-Throughput Single-Cell Multiplex In Situ Tagging (MIST) Analysis of Immunological Disease with Machine Learning

The cascade of immune responses involves activation of diverse immune cells and release of a large amount of cytokines, which leads to either normal, balanced inflammation or hyperinflammatory responses and even organ damage by sepsis. Conventional diagnosis of immunological disorders based on multiple cytokines in the blood serum has varied accuracy, and it is difficult to distinguish normal inflammation from sepsis. Herein, we present an approach to detect immunological disorders through rapid, ultrahigh-multiplex analysis of T cells using single-cell multiplex in situ tagging (scMIST) technology. scMIST permits simultaneous detection of 46 markers and cytokines from single cells without the assistance of special instruments. A cecal ligation and puncture sepsis model was built to supply T cells from two groups of mice that survived the surgery or died after 1 day. The scMIST assays have captured the T cell features and the dynamics over the course of recovery. Compared with cytokines in the peripheral blood, T cell markers show different dynamics and cytokine levels. We have applied a random forest machine learning model to single T cells from two groups of mice. Through training, the model has been able to predict the group of mice through T cell classification and majority rule with 94% accuracy. Our approach pioneers the direction of single-cell omics and could be widely applicable to human diseases.

scRAA: the development of a robust and automatic annotation procedure for single-cell RNA sequencing data

A critical task in single-cell RNA sequencing (scRNA-Seq) data analysis is to identify cell types from heterogeneous tissues. While the majority of classification methods demonstrated high performance in scRNA-Seq annotation problems, a robust and accurate solution is desired to generate reliable outcomes for downstream analyses, for instance, marker genes identification, differentially expressed genes, and pathway analysis. It is hard to establish a universally good metric. Thus, a universally good classification method for all kinds of scenarios does not exist. In addition, reference and query data in cell classification are usually from different experimental batches, and failure to consider batch effects may result in misleading conclusions. To overcome this bottleneck, we propose a robust ensemble approach to classify cells and utilize a batch correction method between reference and query data. We simulated four scenarios that comprise simple to complex batch effect and account for varying cell-type proportions. We further tested our approach on both lung and pancreas data. We found improved prediction accuracy and robust performance across simulation scenarios and real data. The incorporation of batch effect correction between reference and query, and the ensemble approach improve cell-type prediction accuracy while maintaining robustness. We demonstrated these through simulated and real scRNA-Seq data.

Recent advances in microfluidics for single-cell functional proteomics

Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.

Single-cell infrared vibrational analysis by optical trapping mid-infrared photothermal microscopy

Single-cell analysis by means of vibrational spectroscopy combined with optical trapping is a reliable platform for unveiling cell-to-cell heterogeneities in vast populations. Although infrared (IR) vibrational spectroscopy provides rich molecular fingerprint information on biological samples in a label-free manner, its application with optical trapping has never been achieved due to weak gradient forces generated by the diffraction-limited focused IR beam and strong background of water absorption. Herein, we present single-cell IR vibrational analysis that incorporates mid-infrared photothermal (MIP) microscopy with optical trapping. Optically trapped single polymer particles and red blood cells (RBCs) in blood could be chemically identified owing to their IR vibrational fingerprints. This single-cell IR vibrational analysis further allowed us to probe the chemical heterogeneities of RBCs originating from the variation in the intracellular characteristics. Our demonstration paves the way for the IR vibrational analysis of single cells and chemical characterization in various fields.

Dupuytren's disease: a localised and accessible human fibrotic disorder

We review the biology of Dupuytren's disease (DD), a common localised fibrotic disorder of the hand. The disease develops through a complex interplay of genetic and environmental factors, and epigenetic signalling. The early-stage disease nodules comprise a complex milieu of stromal and immune cells which interact to promote disease development. Recently, inhibition of tumour necrosis factor (TNF) locally resulted in softening and a decrease in nodule size, potentially controlling disease progression. Unlike fibrotic disorders of the visceral organs, the easy access to tissue in DD patients enables dissection of the cellular landscape and molecular signalling pathways. In addition, the study of DD may have wider benefits in enhancing our understanding of less-accessible fibrotic tissues.

CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity

MOTIVATION

Integration of growing single-cell RNA sequencing datasets helps better understand cellular identity and function. The major challenge for integration is removing batch effects while preserving biological heterogeneities. Advances in contrastive learning have inspired several contrastive learning-based batch correction methods. However, existing contrastive-learning-based methods exhibit noticeable ad hoc trade-off between batch mixing and preservation of cellular heterogeneities (mix-heterogeneity trade-off). Therefore, a deliberate mix-heterogeneity trade-off is expected to yield considerable improvements in scRNA-seq dataset integration.

RESULTS

We develop a novel contrastive learning-based batch correction framework, CIAIRE, which achieves superior mix-heterogeneity trade-off. The key contributions of CLAIRE are proposal of two complementary strategies: construction strategy and refinement strategy, to improve the appropriateness of positive pairs. Construction strategy dynamically generates positive pairs by augmenting inter-batch mutual nearest neighbors (MNN) with intra-batch k-nearest neighbors (KNN), which improves the coverage of positive pairs for the whole distribution of shared cell types between batches. Refinement strategy aims to automatically reduce the potential false positive pairs from the construction strategy, which resorts to the memory effect of deep neural networks. We demonstrate that CLAIRE possesses superior mix-heterogeneity trade-off over existing contrastive learning-based methods. Benchmark results on six real datasets also show that CLAIRE achieves the best integration performance against eight state-of-the-art methods. Finally, comprehensive experiments are conducted to validate the effectiveness of CLAIRE.

AVAILABILITY AND IMPLEMENTATION

The source code and data used in this study can be found in https://github.com/CSUBioGroup/CLAIRE-release.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs

Single-cell RNA sequencing technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD) that defines a drug score to recommend drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. ASGARD shows significantly better average accuracy on single-drug therapy compared to two bulk-cell-based drug repurposing methods. We also demonstrated that it performs considerably better than other cell cluster-level predicting methods. In addition, we validate ASGARD using the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. We find that many top-ranked drugs are either approved by the Food and Drug Administration or in clinical trials treating corresponding diseases. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD .

High-Resolution Bacterial Cytological Profiling Reveals Intrapopulation Morphological Variations upon Antibiotic Exposure

Phenotypic heterogeneity is crucial to bacterial survival and could provide insights into the mechanism of action (MOA) of antibiotics, especially those with polypharmacological actions. Although phenotypic changes among individual cells could be detected by existing profiling methods, due to the data complexity, only population average data were commonly used, thereby overlooking the heterogeneity. In this study, we developed a high-resolution bacterial cytological profiling method that can capture morphological variations of bacteria upon antibiotic treatment. With an unprecedented single-cell resolution, this method classifies morphological changes of individual cells into known MOAs with an overall accuracy above 90%. We next showed that combinations of two antibiotics induce altered cell morphologies that are either unique or similar to that of an antibiotic in the combinations. With these combinatorial profiles, this method successfully revealed multiple cytological changes caused by a natural product-derived compound that, by itself, is inactive against Acinetobacter baumannii but synergistically exerts its multiple antibacterial activities in the presence of colistin. The findings have paved the way for future single-cell profiling in bacteria and have highlighted previously underappreciated intrapopulation variations caused by antibiotic perturbation.

Multi-Targeting Nano-Systems Targeting Heterogeneous Cancer Cells for Therapeutics and Biomarker Detection

Cancer heterogeneity plays a vital part in cancer resistance and metastasis. To provide a reliable approach to exert a therapy action and evaluate its efficiency in heterogeneous cancer cells, a multiple targeting delivery vector composed of histone encapsulating the therapeutic or diagnostic agent, hyaluronic acid targeting CD44 overexpressed in stem tumor cells, SYL3C aptamer targeting epithelial cell adhesion molecule (EpCAM) overexpressed in epithelial cancer cells, and CL4 aptamer targeting epidermal growth factor receptor (EGFR) overexpressed in mesenchymal cancer cells, is developed. The vector can efficiently target different cancer cells and circulating tumor cells (CTCs) in the peripheral blood of patients for mucin 1 (MUC1) knockout. Furthermore, the multiple targeting vector can be used to co-encapsulate three types of molecular beacons for probing various mRNA biomarkers at single-cell resolution after genome editing. This study provides an efficient approach for exerting therapeutic actions in heterogeneous cancer cells and assessing the therapeutic efficacy by detection of cancer biomarkers via liquid biopsy.

Application of Single-Cell and Spatial Omics in Musculoskeletal Disorder Research

Musculoskeletal disorders, including fractures, scoliosis, heterotopic ossification, osteoporosis, osteoarthritis, disc degeneration, and muscular injury, etc., can occur at any stage of human life. Understanding the occurrence and development mechanism of musculoskeletal disorders, as well as the changes in tissues and cells during therapy, might help us find targeted treatment methods. Single-cell techniques provide excellent tools for studying alterations at the cellular level of disorders. However, the application of these techniques in research on musculoskeletal disorders is still limited. This review summarizes the current single-cell and spatial omics used in musculoskeletal disorders. Cell isolation, experimental methods, and feasible experimental designs for single-cell studies of musculoskeletal system diseases have been reviewed based on tissue characteristics. Then, the paper summarizes the latest findings of single-cell studies in musculoskeletal disorders from three aspects: bone and ossification, joint, and muscle and tendon disorders. Recent discoveries about the cell populations involved in these diseases are highlighted. Furthermore, the therapeutic responses of musculoskeletal disorders, especially single-cell changes after the treatments of implants, stem cell therapies, and drugs are described. Finally, the application potential and future development directions of single-cell and spatial omics in research on musculoskeletal diseases are discussed.

A Simple Micromilled Microfluidic Impedance Cytometer with Vertical Parallel Electrodes for Cell Viability Analysis

Microfluidic impedance cytometry has been demonstrated as an effective platform for single cell analysis, taking advantage of microfabricated features and dielectric cell sensing methods. In this study, we present a simple microfluidic device to improve the sensitivity, accuracy, and throughput of single suspension cell viability analysis using vertical sidewall electrodes fabricated by a widely accessible negative manufacturing method. A microchannel milled through a 75 µm platinum wire, which was embedded into poly-methyl-methacrylate (PMMA), created a pair of parallel vertical sidewall platinum electrodes. Jurkat cells were interrogated in a custom low-conductivity buffer (1.2 ± 0.04 mS/cm) to reduce current leakage and increase device sensitivity. Confirmed by live/dead staining and electron microscopy, a single optimum excitation frequency of 2 MHz was identified at which live and dead cells were discriminated based on the disruption in the cell membrane associated with cell death. At this frequency, live cells were found to exhibit changes in the impedance phase with no appreciable change in magnitude, while dead cells displayed the opposite behavior. Correlated with video microscopy, a computational algorithm was created that could identify cell detection events and determine cell viability status by application of a mathematical correlation method.

Mid-Infrared Photothermal-Fluorescence In Situ Hybridization for Functional Analysis and Genetic Identification of Single Cells

Simultaneous identification and metabolic analysis of microbes with single-cell resolution and high throughput are necessary to answer the question of "who eats what, when, and where" in complex microbial communities. Here, we present a mid-infrared photothermal-fluorescence in situ hybridization (MIP-FISH) platform that enables direct bridging of genotype and phenotype. Through multiple improvements of MIP imaging, the sensitive detection of isotopically labeled compounds incorporated into proteins of individual bacterial cells became possible, while simultaneous detection of FISH labeling with rRNA-targeted probes enabled the identification of the analyzed cells. In proof-of-concept experiments, we showed that the clear spectral red shift in the protein amide I region due to incorporation of ¹³C atoms originating from ¹³C-labeled glucose can be exploited by MIP-FISH to discriminate and identify ¹³C-labeled bacterial cells within a complex human gut microbiome sample. The presented methods open new opportunities for single-cell structure-function analyses for microbiology.