Enabling drug discovery and development through single-cell imaging

Recent Advances in Mass Spectrometry-Based Spatially Resolved Molecular Imaging of Drug Disposition and Metabolomics

Mass spectrometric imaging is a nontargeted, tag-free, high-throughput, and highly responsive analytical approach. The highly accurate molecular visualization detection technology enables qualitative and quantitative analyses of biologic tissues or cells scanned by mass spectrometry in situ, extracting known and unknown multiple compounds, and simultaneously assessing relative contents of targeting molecules by monitoring their molecular ions and pinpointing the spatial locations of those molecules distributed. Five mass spectrometric imaging techniques and their characteristics are introduced in the review, including matrix-assisted laser desorption ionization mass spectrometry, secondary ion mass spectrometry, desorption electrospray ionization mass spectrometry, laser ablation electrospray ionization mass spectrometry, and laser ablation inductively coupled plasma mass spectrometry. The mass spectrometry-based techniques provide the possibility for spatial metabolomics with the capability of high throughput and precision detection. The approaches have been widely employed to spatially image not only metabolome of endogenous amino acids, peptides, proteins, neurotransmitters, and lipids but also the disposition of exogenous chemicals, such as pharmaceutical agents, environmental pollutants, toxicants, natural products, and heavy metals. The techniques also provide us with spatial distribution imaging of analytes in single cells, tissue microregions, organs, and whole animals. SIGNIFICANCE STATEMENT: The review article includes an overview of five commonly used mass spectrometers for spatial imaging and describes the advantages and disadvantages of each. Examples of the technology applications cover drug disposition, diseases, and omics. Technical aspects of relative and absolute quantification by mass spectrometric imaging and challenges for future new applications are discussed as well. The reviewed knowledge may benefit the development of new drugs and provide a better understanding of biochemical processes related to physiology and diseases.

Microfluidics-Enabled Isolation and Single-Cell Analysis of Circulating Tumor Cells

Microfluidic platforms enable the enrichment and analysis of circulating tumor cells (CTCs), a potential biomarker for cancer diagnosis, prognosis, and theragnosis. Combined with immunocytochemistry/immunofluorescence (ICC/IF) assays for CTCs, microfluidics-enabled detection presents a unique opportunity to study tumor heterogeneity and predict treatment response, both of which can help cancer drug development. In this chapter, we detail the protocols and methods employed to fabricate and use a microfluidic device for the enrichment, detection, and analysis of single CTCs from the blood samples of sarcoma patients.

From imaging a single cell to implementing precision medicine: an exciting new era

In the age of high-throughput, single-cell biology, single-cell imaging has evolved not only in terms of technological advancements but also in its translational applications. The synchronous advancements of imaging and computational biology have produced opportunities of merging the two, providing the scientific community with tools towards observing, understanding, and predicting cellular and tissue phenotypes and behaviors. Furthermore, multiplexed single-cell imaging and machine learning algorithms now enable patient stratification and predictive diagnostics of clinical specimens. Here, we provide an overall summary of the advances in single-cell imaging, with a focus on high-throughput microscopy phenomics and multiplexed proteomic spatial imaging platforms. We also review various computational tools that have been developed in recent years for image processing and downstream applications used in biomedical sciences. Finally, we discuss how harnessing systems biology approaches and data integration across disciplines can further strengthen the exciting applications and future implementation of single-cell imaging on precision medicine.

The prospects of tumor chemosensitivity testing at the single-cell level

Tumor chemosensitivity testing plays a pivotal role in the optimal selection of chemotherapeutic regimens for cancer patients in a personalized manner. High-throughput drug screening approaches have been developed but they failed to take into account intratumor heterogeneity and therefore only provided limited predictive power of therapeutic response to individual cancer patients. Single cancer cell drug sensitivity testing (SCC-DST) has been recently developed to evaluate the variable sensitivity of single cells to different anti-tumor drugs. In this review, we discuss how SCC-DST overcomes the obstacles of traditional drug screening methodologies. We outline critical procedures of SCC-DST responsible for single-cell generation and sorting, cell-drug encapsulation on a microfluidic chip and detection of cell-drug interactions. In SCC-DST, droplet-based microfluidics is emerging as an important platform that integrated various assays and analyses for drug susceptibility tests for individual patients. With the advancement of technology, both fluorescence imaging and label-free analysis have been used for detecting single cell-drug interactions. We also discuss the feasibility of integrating SCC-DST with single-cell RNA sequencing to unravel the mechanisms leading to drug resistance, and utilizing artificial intelligence to facilitate the analysis of various omics data in the evaluation of drug susceptibility. SCC-DST is setting the stage for better drug selection for individual cancer patients in the era of precision medicine.

Analysis of Live Single Cells by Confocal Microscopy and High-Resolution Mass Spectrometry to Study Drug Uptake, Metabolism, and Drug-Induced Phospholipidosis

The analysis of large numbers of cells from a population results in information that does not reflect differences in cell phenotypes. Individual variations in cellular drug uptake, metabolism, and response to drug treatment may have profound effects on cellular survival and lead to the development of certain disease states, drug persistence, and resistance. Herein, we present a method that combines live cell confocal microscopy imaging with high-resolution mass spectrometry to achieve absolute cell quantification of the drug amiodarone (AMIO) and its major metabolite, N-desethylamiodarone (NDEA), in single liver cells (HepG2 and HepaRG cells). The method uses a prototype system that integrates a confocal microscope with an XYZ stage robot to image and automatically sample selected cells from a sample compartment, which is kept under growth conditions, with nanospray tips. Besides obtaining the distributions of AMIO and NDEA cell concentrations across a population of individual cells, as well as variabilities in drug metabolism, the effect of these on phospholipidosis and cell morphology was studied. The method was suited to identify subpopulations of cells that metabolized less drug and to correlate cell drug concentrations with cell phospholipid content, cell volume, sphericity, and other cell phenotypic features. Using principal component analysis (PCA), the treated cells could be clearly distinguished from vehicle control cells (0 μM AMIO) and HepaRG cells from HepG2 cells. The potential of using multidimensional and multimodal information collected from single cells to build predictive models for cell classification is demonstrated.

Multidimensional profiling of drug-treated cells by Imaging Mass Cytometry

In pharmaceutical research, high‐content screening is an integral part of lead candidate development. Measuring drug response in vitro by examining over 40 parameters, including biomarkers, signaling molecules, cell morphological changes, proliferation indices, and toxicity in a single sample, could significantly enhance discovery of new therapeutics. As a proof of concept, we present here a workflow for multidimensional Imaging Mass Cytometry™ (IMC™) and data processing with open source computational tools. CellProfiler was used to identify single cells through establishing cellular boundaries, followed by histoCAT™ (histology topography cytometry analysis toolbox) for extracting single‐cell quantitative information visualized as t‐SNE plots and heatmaps. Human breast cancer‐derived cell lines SKBR3, HCC1143, and MCF‐7 were screened for expression of cellular markers to generate digital images with a resolution comparable to conventional fluorescence microscopy. Predicted pharmacodynamic effects were measured in MCF‐7 cells dosed with three target‐specific compounds: growth stimulatory EGF, microtubule depolymerization agent nocodazole, and genotoxic chemotherapeutic drug etoposide. We show strong pairwise correlation between nuclear markers pHistone3^S28, Ki‐67, and p4E‐BP1^T37/T46 in classified mitotic cells and anticorrelation with cell surface markers. Our study demonstrates that IMC data expand the number of measured parameters in single cells and brings higher‐dimension analysis to the field of cell‐based screening in early lead compound discovery.