Single-cell analysis tools for drug discovery and development

Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line

The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAFV600E mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.

EndoDB: a database of endothelial cell transcriptomics data

Endothelial cells (ECs) line blood vessels, regulate homeostatic processes (blood flow, immune cell trafficking), but are also involved in many prevalent diseases. The increasing use of high-throughput technologies such as gene expression microarrays and (single cell) RNA sequencing generated a wealth of data on the molecular basis of EC (dys-)function. Extracting biological insight from these datasets is challenging for scientists who are not proficient in bioinformatics. To facilitate the re-use of publicly available EC transcriptomics data, we developed the endothelial database EndoDB, a web-accessible collection of expert curated, quality assured and pre-analyzed data collected from 360 datasets comprising a total of 4741 bulk and 5847 single cell endothelial transcriptomes from six different organisms. Unlike other added-value databases, EndoDB allows to easily retrieve and explore data of specific studies, determine under which conditions genes and pathways of interest are deregulated and assess reprogramming of metabolism via principal component analysis, differential gene expression analysis, gene set enrichment analysis, heatmaps and metabolic and transcription factor analysis, while single cell data are visualized as gene expression color-coded t-SNE plots. Plots and tables in EndoDB are customizable, downloadable and interactive. EndoDB is freely available at https://vibcancer.be/software-tools/endodb, and will be updated to include new studies.

Correlating single-molecule characteristics of the yeast aquaglyceroporin Fps1 with environmental perturbations directly in living cells

Membrane proteins play key roles at the interface between the cell and its environment by mediating selective import and export of molecules via plasma membrane channels. Despite a multitude of studies on transmembrane channels, understanding of their dynamics directly within living systems is limited. To address this, we correlated molecular scale information from living cells with real time changes to their microenvironment. We employed super-resolved millisecond fluorescence microscopy with a single-molecule sensitivity, to track labelled molecules of interest in real time. We use as example the aquaglyceroporin Fps1 in the yeast Saccharomyces cerevisiae to dissect and correlate its stoichiometry and molecular turnover kinetics with various extracellular conditions. We show that Fps1 resides in multi tetrameric clusters while hyperosmotic and oxidative stress conditions cause Fps1 reorganization. Moreover, we demonstrate that rapid exposure to hydrogen peroxide causes Fps1 degradation. In this way we shed new light on aspects of architecture and dynamics of glycerol-permeable plasma membrane channels.

An Improved Boosting to Amplify Signal with Isobaric Labeling (iBASIL) Strategy for Precise Quantitative Single-cell Proteomics

Mass spectrometry (MS)-based proteomics has great potential for overcoming the limitations of antibody-based immunoassays for antibody-independent, comprehensive, and quantitative proteomic analysis of single cells. Indeed, recent advances in nanoscale sample preparation have enabled effective processing of single cells. In particular, the concept of using boosting/carrier channels in isobaric labeling to increase the sensitivity in MS detection has also been increasingly used for quantitative proteomic analysis of small-sized samples including single cells. However, the full potential of such boosting/carrier approaches has not been significantly explored, nor has the resulting quantitation quality been carefully evaluated. Herein, we have further evaluated and optimized our recent boosting to amplify signal with isobaric labeling (BASIL) approach, originally developed for quantifying phosphorylation in small number of cells, for highly effective analysis of proteins in single cells. This improved BASIL (iBASIL) approach enables reliable quantitative single-cell proteomics analysis with greater proteome coverage by carefully controlling the boosting-to-sample ratio (e.g. in general <100×) and optimizing MS automatic gain control (AGC) and ion injection time settings in MS/MS analysis (e.g. 5E5 and 300 ms, respectively, which is significantly higher than that used in typical bulk analysis). By coupling with a nanodroplet-based single cell preparation (nanoPOTS) platform, iBASIL enabled identification of ∼2500 proteins and precise quantification of ∼1500 proteins in the analysis of 104 FACS-isolated single cells, with the resulting protein profiles robustly clustering the cells from three different acute myeloid leukemia cell lines. This study highlights the importance of carefully evaluating and optimizing the boosting ratios and MS data acquisition conditions for achieving robust, comprehensive proteomic analysis of single cells.

Aqueous Processed Biopolymer Interfaces for Single-Cell Microarrays

Single-cell microarrays are emerging tools to unravel intrinsic diversity within complex cell populations, opening up new approaches for the in-depth understanding of highly relevant diseases. However, most of the current methods for their fabrication are based on cumbersome patterning approaches, employing organic solvents and/or expensive materials. Here, we demonstrate an unprecedented green-chemistry strategy to produce single-cell capture biochips onto glass surfaces by all-aqueous inkjet printing. At first, a chitosan film is easily inkjet printed and immobilized onto hydroxyl-rich glass surfaces by electrostatic immobilization. In turn, poly(ethylene glycol) diglycidyl ether is grafted on the chitosan film to expose reactive epoxy groups and induce antifouling properties. Subsequently, microscale collagen spots are printed onto the above surface to define the attachment area for single adherent human cancer cells harvesting with high yield. The reported inkjet printing approach enables one to modulate the collagen area available for cell attachment in order to control the number of captured cells per spot, from single-cells up to double- and multiple-cell arrays. Proof-of-principle of the approach includes pharmacological treatment of single-cells by the model drug doxorubicin. The herein presented strategy for single-cell array fabrication can constitute a first step toward an innovative and environmentally friendly generation of aqueous-based inkjet-printed cellular devices.

Decomposition of a set of distributions in extended exponential family form for distinguishing multiple oligo-dimensional marker expression profiles of single-cell populations and visualizing their dynamics

Single-cell expression analysis is an effective tool for studying the dynamics of cell population profiles. However, the majority of statistical methods are applied to individual profiles and the methods for comparing multiple profiles simultaneously are limited. In this study, we propose a nonparametric statistical method, called Decomposition into Extended Exponential Family (DEEF), that embeds a set of single-cell expression profiles of several markers into a low-dimensional space and identifies the principal distributions that describe their heterogeneity. We demonstrate that DEEF can appropriately decompose and embed sets of theoretical probability distributions. We then apply DEEF to a cytometry dataset to examine the effects of epidermal growth factor stimulation on an adult human mammary gland. It is shown that DEEF can describe the complex dynamics of cell population profiles using two parameters and visualize them as a trajectory. The two parameters identified the principal patterns of the cell population profile without prior biological assumptions. As a further application, we perform a dimensionality reduction and a time series reconstruction. DEEF can reconstruct the distributions based on the top coordinates, which enables the creation of an artificial dataset based on an actual single-cell expression dataset. Using the coordinate system assigned by DEEF, it is possible to analyze the relationship between the attributes of the distribution sample and the features or shape of the distribution using conventional data mining methods.

Single cell imaging with near-field terahertz scanning microscopy

OBJECTIVES

Terahertz (THz)-based imaging techniques hold great potential for biological and biomedical applications, which nevertheless are hampered by the low spatial resolution of conventional THz imaging systems. In this work, we report a high-performance photoconductive antenna microprobe-based near-field THz time-domain spectroscopy scanning microscope.

MATERIALS AND METHODS

A single watermelon pulp cell was prepared on a clean quartz slide and covered by a thin polyethylene film. The high performance near-field THz microscope was developed based on a coherent THz time-domain spectroscopy system coupled with a photoconductive antenna microprobe. The sample was imaged in transmission mode.

RESULTS

We demonstrate the direct imaging of the morphology of single watermelon pulp cells in the natural dehydration process with our near-field THz microscope.

CONCLUSIONS

Given the label-free and non-destructive nature of THz detection techniques, our near-field microscopy-based single-cell imaging approach sheds new light on studying biological samples with THz.

Ultrasensitive detection of nonlabelled bovine serum albumin using photothermal optical phase shift detection with UV excitation

Ultrasensitive detection of nonlabelled bovine serum albumin is performed in micro/nanofluidic chips using a photothermal optical phase shift (POPS) detection system. Currently, micro- and nanofluidics allow the analysis of various single cells, and their targets of interest are shifting from nucleic acids to proteins. Previously, our group developed photothermal detection techniques for the sensitive detection of nonfluorescent molecules. For example, we developed a thermal lens microscope (TLM) with ultrahigh sensitivity at the single-molecule level and a POPS detector that is applicable to nanochannels smaller than the wavelength of light. The POPS detector also realized the detection of nonlabelled proteins in nanochannels, although its detection sensitivity is less than that of the TLM in microchannels due to insufficient background light reduction. To overcome this problem, we developed a new POPS detector using relay optics for further reduction of the background light. In addition, heat transfer from the sample solution to the nanochannel wall was thoroughly investigated to achieve ultrahigh sensitivity. The limit of detection (LOD) obtained with the new POPS detector is 30 molecules in 1.0 fL. Considering this LOD, the performance of the new POPS detector is comparable with that of the TLM. Owing to the applicability of the POPS detector for sensitive detection even in nanochannels or single-μm channels, which cannot be realized with the TLM, combinations of the POPS detector and separation techniques employing unique nanochannel properties will contribute to advances in single-cell proteomics in the future.

The expanding landscape of inflammatory cells affecting cancer therapy

Tumour-infiltrating myeloid cells (TIMCs) are critical regulators of cancer growth. The different phenotypes, functions and therapeutic effects of these phagocytes have, however, been difficult to study. With the advent of single-cell-based technologies, a new 'worldview' is emerging: the classification of TIMCs into subtypes that are conserved across patients and across species. As the landscape of TIMCs is beginning to be understood, it opens up questions about the function of each TIMC subtype and its drugability. In this Perspective, we outline the current map of TIMC populations in cancer and their known and presumed functions, and discuss their therapeutic implications and the biological research questions that they give rise to. The answers should be particularly relevant for bioengineers, materials scientists and the chemical and pharmaceutical communities developing the next generation of cancer therapies.

Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches

Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient-specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient-specific combined therapy should be designed. Large-scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.

Single-cell analysis on stromal fibroblasts in the microenvironment of solid tumours

Besides malignant cells, the tumour microenvironment consists of various stromal cells such as cancer-associated fibroblasts (CAFs) and myofibroblasts. Accumulation of heterogeneous populations of stromal cells in solid tumours is associated with lower survival rates and cancer recurrence in patients. Certain limitations presented by conventional experimental designs and techniques in cancer research have led to poor understanding of the fundamental basis of cancer niche. Recent developments in single-cell techniques allow more in-depth studies of the tumour microenvironment. Analyses at the single-cell level enables the detection of rare cell types, characterization of intra-tumour cellular heterogeneity and analysis of the lineage output of malignant cells. This subsequently, provides valuable insights on better diagnostic methods and treatment avenues for cancer. This review explores the recent advancements and applications of single-cell technologies in cancer research pertaining to the study of stromal fibroblasts in the microenvironment of solid tumours.

Single-Cell Clustering Based on Shared Nearest Neighbor and Graph Partitioning

Clustering of single-cell RNA sequencing (scRNA-seq) data enables discovering cell subtypes, which is helpful for understanding and analyzing the processes of diseases. Determining the weight of edges is an essential component in graph-based clustering methods. While several graph-based clustering algorithms for scRNA-seq data have been proposed, they are generally based on k-nearest neighbor (KNN) and shared nearest neighbor (SNN) without considering the structure information of graph. Here, to improve the clustering accuracy, we present a novel method for single-cell clustering, called structural shared nearest neighbor-Louvain (SSNN-Louvain), which integrates the structure information of graph and module detection. In SSNN-Louvain, based on the distance between a node and its shared nearest neighbors, the weight of edge is defined by introducing the ratio of the number of the shared nearest neighbors to that of nearest neighbors, thus integrating structure information of the graph. Then, a modified Louvain community detection algorithm is proposed and applied to identify modules in the graph. Essentially, each community represents a subtype of cells. It is worth mentioning that our proposed method integrates the advantages of both SNN graph and community detection without the need for tuning any additional parameter other than the number of neighbors. To test the performance of SSNN-Louvain, we compare it to five existing methods on 16 real datasets, including nonnegative matrix factorization, single-cell interpretation via multi-kernel learning, SNN-Cliq, Seurat and PhenoGraph. The experimental results show that our approach achieves the best average performance in these datasets.

Examining the co-expression, transcriptome clustering and variation using fuzzy cluster network of testicular stem cells and pluripotent stem cells compared with other cell types

Stem cells are crucial in the field of tissue regeneration and developmental biology. Embryonic stem cells (ESCs) which are pluripotent in nature are derived from the inner cell mass of blastocyst. The gene expression profiles of ESCs and Induced pluripotent stem cells (iPSCs) were compared to identify the differences. Spermatogonial stem cells (SSCs) are also known as Germ-line stem cells (GSCs) present in testis is having the capability of producing the sperm in their whole lifetime. Therefore can be reprogrammed into pluripotent cells called male germline pluripotent cells (gPSCs). It is very difficult to interpret the larger genomic data sets which are available in public databases without high computational facilities. In order to identify the similar groups We studied the co-expression, clustering of the transcriptome and variation of the transcriptome of the GSCs, gPSCs, ESCs and other cell types using fuzzy clustering using AutoSOME. The series matrix file with GSE ID GSE11274 was retrieved and subjected to the various normalization methods, corresponding rows and columns were clustered using p values, ensemble runs, and different running modes. Transcriptome analysis using the proposed approach intuitively and consistently characterized the variation in cell-cell significantly. Collectively, our results suggest that the GSCs and the ESCs displayed differential gene expression profiles, and the GSCs possessed the potential to acquire pluripotency based on the high expression of epigenetic factors and transcription factors. These data may provide novel insights into the reprogramming mechanism of GSCs.

High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery

The development of new drugs is multidisciplinary and systematic work. High-throughput techniques based on “-omics” have driven the discovery of biomarkers in diseases and therapeutic targets of drugs. A transcriptome is the complete set of all RNAs transcribed by certain tissues or cells at a specific stage of development or physiological condition. Transcriptome research can demonstrate gene functions and structures from the whole level and reveal the molecular mechanism of specific biological processes in diseases. Currently, gene expression microarray and high-throughput RNA-sequencing have been widely used in biological, medical, clinical, and drug research. The former has been applied in drug screening and biomarker detection of drugs due to its high throughput, fast detection speed, simple analysis, and relatively low price. With the further development of detection technology and the improvement of analytical methods, the detection flux of RNA-seq is much higher but the price is lower, hence it has powerful advantages in detecting biomarkers and drug discovery. Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery. Therefore, (sc)RNA-seq has broader application prospects, especially in the field of drug discovery. In this overview, we will review the application of these technologies in drug, especially in natural drug and biomarker discovery and development. Emerging applications of scRNA-seq and the third generation RNA-sequencing tools are also discussed.

Single-cell adhesion force kinetics of cell populations from combined label-free optical biosensor and robotic fluidic force microscopy

Single-cell adhesion force plays a crucial role in biological sciences, however its in-depth investigation is hindered by the extremely low throughput and the lack of temporal resolution of present techniques. While atomic force microcopy (AFM) based methods are capable of directly measuring the detachment force values between individual cells and a substrate, their throughput is limited to few cells per day, and cannot provide the kinetic evaluation of the adhesion force over the timescale of several hours. In this study a high spatial and temporal resolution resonant waveguide grating based label-free optical biosensor was combined with robotic fluidic force microscopy to monitor the adhesion of living cancer cells. In contrast to traditional fluidic force microscopy methods with a manipulation range in the order of 300-400 micrometers, the robotic device employed here can address single cells over mm-cm scale areas. This feature significantly increased measurement throughput, and opened the way to combine the technology with the employed microplate-based, large area biosensor. After calibrating the biosensor signals with the direct force measuring technology on 30 individual cells, the kinetic evaluation of the adhesion force and energy of large cell populations was performed for the first time. We concluded that the distribution of the single-cell adhesion force and energy can be fitted by log-normal functions as cells are spreading on the surface and revealed the dynamic changes in these distributions. The present methodology opens the way for the quantitative assessment of the kinetics of single-cell adhesion force and energy with an unprecedented throughput and time resolution, in a completely non-invasive manner.

Metallic Nanoparticle-Based Optical Cell Chip for Nondestructive Monitoring of Intra/Extracellular Signals

The biosensing platform is noteworthy for high sensitivity and precise detection of target analytes, which are related to the status of cells or specific diseases. The modification of the transducers with metallic nanoparticles (MNPs) has attracted attention owing to excellent features such as improved sensitivity and selectivity. Moreover, the incorporation of MNPs into biosensing systems may increase the speed and the capability of the biosensors. In this review, we introduce the current progress of the developed cell-based biosensors, cell chip, based on the unique physiochemical features of MNPs. Mainly, we focus on optical intra/extracellular biosensing methods, including fluorescence, localized surface plasmon resonance (LSPR), and surface-enhanced Raman spectroscopy (SERS) based on the coupling of MNPs. We believe that the topics discussed here are useful and able to provide a guideline in the development of new MNP-based cell chip platforms for pharmaceutical applications such as drug screening and toxicological tests in the near future.

High throughput profiling drug response and apoptosis of single polar cells

The drug response of single polar cells was evaluated via single cell trapping on anisotropic microwells for tumor heterogeneity.

Selective detection of phospholipids in human blood plasma and single cells for cancer differentiation using dispersed solid-phase microextraction combined with extractive electrospray ionization mass spectrometry

Rapid and selective determination of phospholipids in microvolume biofluid samples for cancer differentiation was achieved by d-SPME–iEESI-MS.

Single-Cell Multiplexed Proteomics on the IsoLight Resolves Cellular Functional Heterogeneity to Reveal Clinical Responses of Cancer Patients to Immunotherapies

Cancer immunotherapies, in particular adoptive T cell therapy and immune-checkpoint blockade therapy have demonstrated a remarkable success in the treatment of cancer. However, due to heterogeneous functionality and complex immune response of immune cells, it remains challenging to identify predictive biomarkers which have the potential to correlate with efficacy and adverse effects of immunotherapies and help selecting patients who might benefit from the therapy, developing more personalized therapeutics as well as reducing clinical trial cost. The single-cell IsoCode chip in conjunction with fluorescent ELISA-based assay enables a simultaneous detection up to 40+ proteins secreted from live single immune cells, providing a large portion of the assayable functions for each immune cell type, and thus precise assessment of multifunctional, or polyfunctional, heterogeneity of each immune cell type.This unique functional detection capability provides a powerful solution to unmet needs in immunotherapy patient profiling today. Recently, the single-cell metric termed polyfunctional strength index (PSI™) by IsoCode chip computed from preinfusion anti-CD19 chimeric antigen receptor (CAR)-T cell products has demonstrated a significant association with clinical response and cytokine release syndrome (CRS) of cancer patient to the therapy after cell product infusion. This chapter elucidates IsoPlexis single-cell highly multiplexed proteomic platform and provides technical details for characterizing cell products and various cell subsets from peripheral blood, bone marrow, or tumor tissues using this assay.

Single-Cell Metabolomics by Mass Spectrometry

Single-cell level metabolomics gives a snapshot of small molecules, intermediates, and products of cellular metabolism within a biological system. These small molecules, typically less than 1 kDa in molecular weight, often provide the basis of biochemical heterogeneity within cells. The molecular differences between cells with a cell type are often attributed to random stochastic biochemical processes, cell cycle stages, environmental stress, and diseased states. In this chapter, current limitations and challenges in single-cell analysis by mass spectrometry will be discussed alongside the prospects of single-cell metabolomics in systems biology. A few selected example of the recent development in mass spectrometry tools to unravel single-cell metabolomics will be described as well.

Single Cell Analysis of Neutrophils NETs by Microscopic LSPR Imaging System

A simple microengraving cell monitoring method for neutrophil extracellular traps (NETs) released from single neutrophils has been realized using a polydimethylsiloxane (PDMS) microwell array (MWA) sheet on a plasmon chip platform. An imbalance between NETs formation and the succeeding degradation (NETosis) are considered associated with autoimmune disease and its pathogenesis. Thus, an alternative platform that can conduct monitoring of this activity on single cell level at minimum cost but with great sensitivity is greatly desired. The developed MWA plasmon chips allow single cell isolation of neutrophils from 150 µL suspension (6.0 × 10⁵ cells/mL) with an efficiency of 36.3%; 105 microwells with single cell condition. To demonstrate the utility of the chip, trapped cells were incubated between 2 to 4 h after introducing with 100 nM phorbol 12-myristate 13-acetate (PMA) before measurement. Under observation using a hyperspectral imaging system that allows high-throughput screening, the neutrophils stimulated by PMA solution show a significant release of fibrils and NETs after 4 h, with observed maximum areas between 314–758 µm². An average absorption peak wavelength shows a redshift of Δλ = 1.5 nm as neutrophils release NETs.

Single-molecule measurements in microwells for clinical applications

The ability to detect and analyze proteins, nucleic acids, and other biomolecules is critical for clinical diagnostics and for understanding the underlying mechanisms of disease. Current detection methods in clinical and research laboratories rely upon bulk measurement techniques such as immunoassays, polymerase chain reaction, and mass spectrometry to detect these biomarkers. However, many potentially useful protein or nucleic acid biomarkers in blood, saliva, or other biofluids exist at concentrations well below the detection limits of current methods, necessitating the development of more sensitive technologies. Single-molecule measurements are poised to address this challenge, vastly improving sensitivity for detecting low abundance biomarkers and rare events within a population. Microwell arrays have emerged as a powerful tool for single-molecule measurements, enabling ultrasensitive detection of disease-relevant biomolecules in easily accessible biofluids. This review discusses the development, fundamentals, and clinical applications of microwell-based single-molecule methods, as well as challenges and future directions for translating these methods to the clinic.

Single-Cell Analysis Using Droplet Microfluidics

Droplet microfluidics has revolutionized the study of single cells. The ability to compartmentalize cells within picoliter droplets in microfluidic devices has opened up a wide range of strategies to extract information at the genomic, transcriptomic, proteomic, or metabolomic level from large numbers of individual cells. Studying the different molecular landscapes at single-cell resolution has provided the authors with a detailed picture of intracellular heterogeneity and the resulting changes in cellular phenotypes. In addition, these technologies have aided in the discovery of rare cells in tumors or in the immune system, and left the authors with a deeper understanding of the fundamental biological processes that determine cell fate. This review aims to provide a detailed overview of the various droplet microfluidic strategies reported in the literature, taking into account the sometimes subtle differences in workflow or reagents that enable or improve certain protocols. Specifically, approaches to targeted- and whole-genome analysis, as well as whole-transcriptome profiling techniques, are reviewed. In addition, an up-to-date overview of new methods to characterize and quantify single-cell protein levels, and of developments to screen secreted molecules such as antibodies, cytokines, or metabolites at the single-cell level, is provided.

Immune cell type, cell activation, and single cell heterogeneity revealed by label-free optical methods

Measurement techniques that allow the global analysis of cellular responses while retaining single-cell sensitivity are increasingly needed in order to understand complex and dynamic biological processes. In this context, compromises between sensitivity, degree of multiplexing, throughput, and invasiveness are often unavoidable. We present here a noninvasive optical approach that can retrieve quantitative biomarkers of both morphological and molecular phenotypes of individual cells, based on a combination of quantitative phase imaging and Raman spectroscopy measurements. We then develop generalized statistical tools to assess the influence of both controlled (cell sub-populations, immune stimulation) and uncontrolled (culturing conditions, animal variations, etc.) experimental parameters on the label-free biomarkers. These indicators can detect different macrophage cell sub-populations originating from different progenitors as well as their activation state, and how these changes are related to specific differences in morphology and molecular content. The molecular indicators also display further sensitivity that allow identification of other experimental conditions, such as differences between cells originating from different animals, allowing the detection of outlier behaviour from given cell sub-populations.

Omics studies for comprehensive understanding of immunoglobulin A nephropathy: state-of-the-art and future directions

Immunoglobulin A nephropathy (IgAN) is the most common worldwide primary glomerulonephritis with a strong autoimmune component. The disease shows variability in both clinical phenotypes and endpoints and can be potentially subdivided into more homogeneous subtypes through the identification of specific molecular biomarkers. This review focuses on the role of omics in driving the identification of potential molecular subtypes of the disease through the integration of multilevel data from genomics, transcriptomics, epigenomics, proteomics and metabolomics. First, the identification of molecular biomarkers, including mapping of the full spectrum of common and rare IgAN risk alleles, could permit a more precise stratification of IgAN patients. Second, the analysis of transcriptomic patterns and their modulation by epigenetic factors like microRNAs has the potential to increase our understanding in the pathogenic mechanisms of the disease. Third, the specificity of urinary proteomic and metabolomic signatures and the understanding of their functional relevance may contribute to the development of new non-invasive biomarkers for a better molecular characterization of the renal damage and its follow-up. All these approaches can give information for targeted therapeutic decisions and will support novel clinical decision making. In conclusion, we offer a framework of omic studies and outline barriers and potential solutions that should be used for improving the diagnosis and treatment of the disease. The ongoing decade is exploiting novel high-throughput molecular technologies and computational analyses for improving the diagnosis (precision nephrology) and treatment (personalized therapy) of the IgAN subtypes.

Label-Free Assay of Protein Tyrosine Phosphatase Activity in Single Cells

Populations of cells exhibit variations in biochemical activity, resulting from many factors including random stochastic variability in protein production, metabolic and cell-cycle states, regulatory mechanisms, and external signaling. The development of methods for the analysis of single cells has allowed for the measurement and understanding of this inherent heterogeneity, yet methods for measuring protein activities on the single-cell scale lag behind their genetic analysis counterparts and typically report on expression rather than activity. This paper presents an approach to measure protein tyrosine phosphatase (PTP) activity in individual cells using self-assembled monolayers for matrix-assisted laser desorption/ionization mass spectrometry. Using flow cytometry, individual cells are first sorted into a well plate containing lysis buffer and a phosphopeptide substrate. After lysis and incubation-during which the PTP enzymes act on the peptide substrate-the reaction substrate and product are immobilized onto arrays of self-assembled monolayers, which are then analyzed using mass spectrometry. PTP activities from thousands of individual cells were measured and their distributions analyzed. This work demonstrates a general method for measuring enzyme activities in lysates derived from individual cells and will contribute to the understanding of cellular heterogeneity in a variety of contexts.

Ultrasimple Single-Cell Detection of Multiple Cytokines by a Nanowell Chip Integrated with Encoded Microarrays

Cytokine production is often regarded as the marker of immune cells' activation status. The spectrum and temporal secretion of cytokines are dramatically varied between cell phenotypes and even within the same phenotype. Multiparameter analysis of individual immune cell's cytokine secretion has always been a challenging and complicated process that needs special facilities in a laboratory setting. Herein, we present an ultrasimple method with high sensitivity and high robustness to quantify cytokine expression at the single-cell resolution. A microchip is developed based on poly(dimethylsiloxane) nanowells on sticky tape, while each nanowell is integrated with a DNA-antibody convertible microarray. Only pipetting is needed for the whole single-cell analysis process. The sensitivity of the assay is evaluated by measuring various concentrations of six recombinant cytokine proteins, which was found comparable to conventional methods. Once single cells are loaded to nanowells and incubated there, a Fluorinert FC-40 is used to isolate nanowells; so, cytokines from those cells are captured by separate microarrays. The rest of the sandwich enzyme-linked immunosorbent assay detection process is also executed simply by pipetting of various reagents. This method is validated by measuring cytokine production from hundreds of single cells. It has simplified a typically sophisticated multiplex single-cell assay into an instrument-free, point-of-detection technology, and thus it may find a broad utility in clinical diagnostics.

High-Throughput Single Cell Proteomics Enabled by Multiplex Isobaric Labeling in a Nanodroplet Sample Preparation Platform

Effective extension of mass spectrometry-based proteomics to single cells remains challenging. Herein we combined microfluidic nanodroplet technology with tandem mass tag (TMT) isobaric labeling to significantly improve analysis throughput and proteome coverage for single mammalian cells. Isobaric labeling facilitated multiplex analysis of single cell-sized protein quantities to a depth of ∼1 600 proteins with a median CV of 10.9% and correlation coefficient of 0.98. To demonstrate in-depth high throughput single cell analysis, the platform was applied to measure protein expression in 72 single cells from three murine cell populations (epithelial, immune, and endothelial cells) in <2 days instrument time with over 2 300 proteins identified. Principal component analysis grouped the single cells into three distinct populations based on protein expression with each population characterized by well-known cell-type specific markers. Our platform enables high throughput and unbiased characterization of single cell heterogeneity at the proteome level.

Single-cell RNA-Seq: a next generation sequencing tool for a high-resolution view of the individual cell

Communicated by Ramaswamy H. Sarma.

Taking Systems Medicine to Heart

Systems medicine is a holistic approach to deciphering the complexity of human physiology in health and disease. In essence, a living body is constituted of networks of dynamically interacting units (molecules, cells, organs, etc) that underlie its collective functions. Declining resilience because of aging and other chronic environmental exposures drives the system to transition from a health state to a disease state; these transitions, triggered by acute perturbations or chronic disturbance, manifest as qualitative shifts in the interactions and dynamics of the disease-perturbed networks. Understanding health-to-disease transitions poses a high-dimensional nonlinear reconstruction problem that requires deep understanding of biology and innovation in study design, technology, and data analysis. With a focus on the principles of systems medicine, this Review discusses approaches for deciphering this biological complexity from a novel perspective, namely, understanding how disease-perturbed networks function; their study provides insights into fundamental disease mechanisms. The immediate goals for systems medicine are to identify early transitions to cardiovascular (and other chronic) diseases and to accelerate the translation of new preventive, diagnostic, or therapeutic targets into clinical practice, a critical step in the development of personalized, predictive, preventive, and participatory (P4) medicine.

Integrating a generalized data analysis workflow with the Single-probe mass spectrometry experiment for single cell metabolomics

We conducted single cell metabolomics studies of live cancer cells through online single cell mass spectrometry (SCMS) experiments combined with a generalized comprehensive data analysis workflow. The SCMS experiments were carried out using the Single-probe device coupled with a mass spectrometer to measure molecular profiles of cells in response to two mitotic inhibitors, taxol and vinblastine, under a series of treatment conditions. SCMS metabolomic data were analyzed using a comprehensive approach, including data pre-treatment, visualization, statistical analysis, machine learning, and pathway enrichment analysis. For comparative studies, traditional liquid chromatography-MS (LC-MS) experiments were conducted using lysates prepared from bulk cell samples. Metabolomic profiles of single cells were visualized through Partial Least Square-Discriminant Analysis (PLS-DA), and the phenotypic biomarkers associated with emerging phenotypes induced by drug treatment were discovered and compared through a series of rigorous statistical analysis. Species of interest were further identified at both the single cell and population levels. In addition, four biological pathways potentially involved in the drug treatment were determined through pathway enrichment analysis. Our work demonstrated the capability of comprehensive pipeline studies of single cell metabolomics. This method can be potentially applied to broader types of SCMS datasets for future pharmaceutical and chemotherapeutic research.

Recent advances in microfluidic platforms for single-cell analysis in cancer biology, diagnosis and therapy

Understanding molecular, cellular, genetic and functional heterogeneity of tumors at the single-cell level has become a major challenge for cancer research. The microfluidic technique has emerged as an important tool that offers advantages in analyzing single-cells with the capability to integrate time-consuming and labour-intensive experimental procedures such as single-cell capture into a single microdevice at ease and in a high-throughput fashion. Single-cell manipulation and analysis can be implemented within a multi-functional microfluidic device for various applications in cancer research. Here, we present recent advances of microfluidic devices for single-cell analysis pertaining to cancer biology, diagnostics, and therapeutics. We first concisely introduce various microfluidic platforms used for single-cell analysis, followed with different microfluidic techniques for single-cell manipulation. Then, we highlight their various applications in cancer research, with an emphasis on cancer biology, diagnosis, and therapy. Current limitations and prospective trends of microfluidic single-cell analysis are discussed at the end.

Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering

Imaging flow cytometry is a powerful tool for analyzing every single cell in a large heterogeneous population but relies on fluorescent labeling, which comes with cytotoxicity, nonspecific binding, and interference with natural cellular functions. This paper presents label-free multicolor chemical imaging flow cytometry based on stimulated Raman scattering (SRS), a highly sensitive method of molecular vibrational spectroscopy. With the help of deep learning, it demonstrates high-precision characterization and classification of microalgal cells and cancer cells without the need for fluorescent labeling.

Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.

Single Cell Proteomics by Data-Independent Acquisition To Study Embryonic Asymmetry in Xenopus laevis

Techniques that allow single cell analysis are gaining widespread attention, and most of these studies utilize genomics-based approaches. While nanofluidic technologies have enabled mass spectrometric analysis of single cells, these measurements have been limited to metabolomics and lipidomic studies. Single cell proteomics has the potential to improve our understanding of intercellular heterogeneity. However, this approach has faced challenges including limited sample availability, as well as a requirement of highly sensitive methods for sample collection, cleanup, and detection. We present a technique to overcome these limitations by combining a micropipette (pulled glass capillary) based sample collection strategy with offline sample preparation and nanoLC-MS/MS to analyze proteins through a bottom-up proteomic strategy. This study explores two types of proteomics data acquisition strategies namely data-dependent (DDA) and data-independent acquisition (DIA). Results from the study indicate DIA to be more sensitive enabling analysis of >1600 proteins from ∼130 μm Xenopus laevis embryonic cells containing <6 nL of cytoplasm. The method was found to be robust in obtaining reproducible protein quantifications from single cells spanning the 1-128-cell stages of development. Furthermore, we used micropipette sampling to study intercellular heterogeneity within cells in a single embryo and investigated embryonic asymmetry along both animal-vegetal and dorsal-ventral axes during early stages of development. Investigation of the animal-vegetal axis led to discovery of various asymmetrically distributed proteins along the animal-vegetal axis. We have further compared the hits found from our proteomic data sets with other studies and validated a few hits using an orthogonal imaging technique. This study forms the first report of vegetal enrichment of the germ plasm associated protein DDX4/VASA in Xenopus embyos. Overall, the method and data presented here holds promise to enable important leads in developmental biology.

Towards quantitative and multiplexed in vivo functional cancer genomics

Large-scale sequencing of human tumours has uncovered a vast array of genomic alterations. Genetically engineered mouse models recapitulate many features of human cancer and have been instrumental in assigning biological meaning to specific cancer-associated alterations. However, their time, cost and labour-intensive nature limits their broad utility; thus, the functional importance of the majority of genomic aberrations in cancer remains unknown. Recent advances have accelerated the functional interrogation of cancer-associated alterations within in vivo models. Specifically, the past few years have seen the emergence of CRISPR-Cas9-based strategies to rapidly generate increasingly complex somatic alterations and the development of multiplexed and quantitative approaches to ascertain gene function in vivo.

Single-cell RT-LAMP mRNA detection by integrated droplet sorting and merging

Recent advances in transcriptomic analysis at single-cell resolution reveal cell-to-cell heterogeneity in a biological sample with unprecedented resolution. Partitioning single cells in individual micro-droplets and harvesting each cell's mRNA molecules for next-generation sequencing has proven to be an effective method for profiling transcriptomes from a large number of cells at high throughput. However, the assays to recover the full transcriptomes are time-consuming in sample preparation and require expensive reagents and sequencing cost. Many biomedical applications, such as pathogen detection, prefer highly sensitive, reliable and low-cost detection of selected genes. Here, we present a droplet-based microfluidic platform that permits seamless on-chip droplet sorting and merging, which enables completing multi-step reaction assays within a short time. By sequentially adding lysis buffers and reactant mixtures to micro-droplet reactors, we developed a novel workflow of single-cell reverse transcription loop-mediated-isothermal amplification (scRT-LAMP) to quantify specific mRNA expression levels in different cell types within one hour. Including single cell encapsulation, sorting, lysing, reactant addition, and quantitative mRNA detection, the fully on-chip workflow provides a rapid, robust, and high-throughput experimental approach for a wide variety of biomedical studies.

Combined Scaffold Evaluation and Systems-Level Transcriptome-Based Analysis for Accelerated Lead Optimization Reveals Ribosomal Targeting Spirooxindole Cyclopropanes

With evolutionary drug resistance impacting efforts to treat disease, the need for small molecules that exhibit novel molecular mechanisms of action is paramount. In this study, we combined scaffold-directed synthesis with a hybrid experimental and transcriptome analysis to identify bis-spirooxindole cyclopropanes that inhibit cancer cell proliferation through disruption of ribosomal function. These findings demonstrate the value of an integrated, biologically inspired synthesis and assay strategy for the accelerated identification of first-in-class cancer therapeutic candidates.