Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations

Isolation Techniques of Micro/Nano-Scaled Species for Biomedical Applications

Isolation of micro/nano-scaled bioparticles, such as circulating tumor cells (CTCs), exosomes, bacteria, white blood cells (WBCs), platelets, and viruses, from the sample is essential for cancer diagnosis and treatment, preventing bacterial infections, and monitoring human health. Numerous separation techniques, including magnetophoresis, dielectrophoresis, acoustophoresis, optophoresis, and fluorescence-activated sorting (FAS) have been developed to isolate the target bioparticles from complex samples accurately. However, these active methods usually rely on sophisticated instruments which are expensive and bulky. Passive platforms with high throughput, low cost, and small volume have gradually become alternative methods. Alongside this context, this review paper is no longer confined to one specific category of isolation techniques, advanced systems that have been developed in recent years are comprehensively introduced. Characteristics and limitations of each technology are discussed according to the critical performance parameters including purity, recovery rate, throughput, resolution, size, and convenience. Specific biomedical applications of separation techniques are summarized to provide practical implications for disease diagnosis, treatment, and mechanism research. This review also addresses the current challenges, potential solutions, and prospects in this field, laying the foundation for further optimization, innovation, and cross-integration of isolation techniques in the future.

Lab-on-a-chip device for microfluidic trapping and TIRF imaging of single cells

Total internal reflection fluorescence (TIRF) microscopy is a powerful imaging technique that visualizes the outer surface of specimens in close proximity to a substrate, yielding crucial insights in cell membrane compositions. TIRF plays a key role in single-cell studies but typically requires chemical fixation to ensure direct contact between the cell membrane and substrate, which can compromise cell viability and promote clustering. In this study, we present a microfluidic device with structures designed to trap single yeast cells and fix them in direct contact with the substrate surface to enable TIRF measurements on the cell membrane. The traps are fabricated using two-photon polymerization, allowing high-resolution printing of intricate structures that encapsulate cells in all three dimensions while maintaining exposure to the flow within the device. Our adaptable trap design allows us to reduce residual movement of trapped cells to a minimum while maintaining high trapping efficiencies. We identify the optimal structure configuration to trap single yeast cells and demonstrate that trapping efficiency can be tuned by modifying cell concentration and injection methods. Additionally, by replicating the cell trap design with soft hydrogel materials, we demonstrate the potential of our approach for further single-cell studies. The authors have no relevant financial or non-financial interests to disclose and no competing interests to declare.

scCamAge: A context-aware prediction engine for cellular age, aging-associated bioactivities, and morphometrics

Current deep-learning-based image-analysis solutions exhibit limitations in holistically capturing spatiotemporal cellular changes, particularly during aging. We present scCamAge, an advanced context-aware multimodal prediction engine that co-leverages image-based cellular spatiotemporal features at single-cell resolution alongside cellular morphometrics and aging-associated bioactivities such as genomic instability, mitochondrial dysfunction, vacuolar dynamics, reactive oxygen species levels, and epigenetic and proteasomal dysfunctions. scCamAge employed heterogeneous datasets comprising ∼1 million single yeast cells and was validated using pro-longevity drugs, genetic mutants, and stress-induced models. scCamAge also predicted a pro-longevity response in yeast cells under iterative thermal stress, confirmed using integrative omics analyses. Interestingly, scCamAge, trained solely on yeast images, without additional learning, surpasses generic models in predicting chemical and replication-induced senescence in human fibroblasts, indicating evolutionary conservation of aging-related morphometrics. Finally, we enhanced the generalizability of scCamAge by retraining it on human fibroblast senescence datasets, which improved its ability to predict senescent cells.

Unifying fragmented perspectives with additive deep learning for high-dimensional models from partial faceted datasets

Biological systems are complex networks where measurable functions emerge from interactions among thousands of components. Many studies aim to link biological function with molecular elements, yet quantifying their contributions simultaneously remains challenging, especially at the single-cell level. We propose a machine-learning approach that integrates faceted data subsets to reconstruct a complete view of the system using conditional distributions. We develop both polynomial regression and neural network models, validated with two examples: a mechanical spring network under external forces and an 8-dimensional biological network involving the senescence marker P53, using single-cell data. Our results demonstrate successful system reconstruction from partial datasets, with predictive accuracy improving as more variables are measured. This approach offers a systematic method to integrate fragmented experimental data, enabling unbiased and holistic modeling of complex biological functions.

Swift induction of human spinal lower motor neurons and robust ALS cell screening via single-cell imaging

This study introduces a novel method for rapidly and efficiently inducing human spinal lower motor neurons (LMNs) from induced pluripotent stem cells (iPSCs) to eventually elucidate the pathomechanisms of amyotrophic lateral sclerosis (ALS) and facilitate drug screening. Previous methods were limited by low induction efficiency, poor LMN purity, or labor-intensive induction and evaluation processes. Our protocol overcomes these challenges, achieving around 80% induction efficiency within just two weeks by combining a small molecule-based approach with transcription factor transduction. Moreover, to exclude non-LMN cells from the analysis, we utilized time-lapse microscopy and machine learning to analyze the morphology and viability of iPSC-derived LMNs on a single-cell basis, establishing an effective pathophysiological evaluation system. This rapid, efficient, and streamlined protocol, along with our single-cell-based evaluation method, enables large-scale analysis and drug screening using iPSC-derived motor neurons.

Deciphering cellular complexity: advances and future directions in single-cell protein analysis

Single-cell protein analysis has emerged as a powerful tool for understanding cellular heterogeneity and deciphering the complex mechanisms governing cellular function and fate. This review provides a comprehensive examination of the latest methodologies, including sophisticated cell isolation techniques (Fluorescence-Activated Cell Sorting (FACS), Magnetic-Activated Cell Sorting (MACS), Laser Capture Microdissection (LCM), manual cell picking, and microfluidics) and advanced approaches for protein profiling and protein-protein interaction analysis. The unique strengths, limitations, and opportunities of each method are discussed, along with their contributions to unraveling gene regulatory networks, cellular states, and disease mechanisms. The importance of data analysis and computational methods in extracting meaningful biological insights from the complex data generated by these technologies is also highlighted. By discussing recent progress, technological innovations, and potential future directions, this review emphasizes the critical role of single-cell protein analysis in advancing life science research and its promising applications in precision medicine, biomarker discovery, and targeted therapeutics. Deciphering cellular complexity at the single-cell level holds immense potential for transforming our understanding of biological processes and ultimately improving human health.

Phenomics Demonstrates Cytokines Additive Induction of Epithelial to Mesenchymal Transition

Epithelial to mesenchymal transition (EMT) is highly plastic with a programme where cells lose adhesion and become more motile. EMT heterogeneity is one of the factors for disease progression and chemoresistance in cancer. Omics characterisations are costly and challenging to use. We developed single cell phenomics with easy to use wide-field fluorescence microscopy. We analyse over 70,000 cells and combined 53 features. Our simplistic pipeline allows efficient tracking of EMT plasticity, with a single statistical metric. We discriminate four high EMT plasticity cancer cell lines along the EMT spectrum. We test two cytokines, inducing EMT in all cell lines, alone or in combination. The single cell EMT metrics demonstrate the additive effect of cytokines combination on EMT independently of cell line EMT spectrum. The effects of cytokines are also observed at the front of migration during wound healing assay. Single cell phenomics is uniquely suited to characterise the cellular heterogeneity in response to complex microenvironment and show potential for drug testing assays.

A highly efficient, scalable pipeline for fixed feature extraction from large-scale high-content imaging screens

Applying artificial intelligence (AI) to image-based morphological profiling cells offers significant potential for identifying disease states and drug responses in high-content imaging (HCI) screens. When differences between populations (e.g., healthy vs. diseased) are unknown or imperceptible to the human eye, large-scale HCI screens are essential, providing numerous replicates to build reliable models and accounting for confounding factors like donor and intra-experimental variations. As screen sizes grow, so does the challenge of analyzing high-dimensional datasets in an efficient way while preserving interpretable features and predictive power. Here, we introduce ScaleFEx℠, a memory-efficient, open-source Python pipeline that extracts biologically meaningful features from HCI datasets using minimal computational resources or scalable cloud infrastructure. ScaleFEx can be used together with AI models to successfully identify phenotypic shifts in drug-treated cells and rank interpretable features, and is applicable to public datasets, highlighting its potential to accelerate the discovery of disease-associated phenotypes and new therapeutics.

Stable Isotope Probing-nanoFTIR for Quantitation of Cellular Metabolism and Observation of Growth-Dependent Spectral Features

This study utilizes nanoscale Fourier transform infrared spectroscopy (nanoFTIR) to perform stable isotope probing (SIP) on individual bacteria cells cultured in the presence of 13C-labelled glucose. SIP-nanoFTIR simultaneously quantifies single-cell metabolism through infrared spectroscopy and acquires cellular morphological information via atomic force microscopy. The redshift of the amide I peak corresponds to the isotopic enrichment of newly synthesized proteins. These observations of single-cell translational activity are comparable to those of conventional methods, examining bulk cell numbers. Observing cells cultured under conditions of limited carbon, SIP- nanoFTIR is used to identify environmentally-induced changes in metabolic heterogeneity and cellular morphology. Individuals outcompeting their neighboring cells will likely play a disproportionately large role in shaping population dynamics during adverse conditions or environmental fluctuations. Additionally, SIP-nanoFTIR enables the spectroscopic differentiation of specific cellular growth phases. During cellular replication, subcellular isotope distribution becomes more homogenous, which is reflected in the spectroscopic features dependent on the extent of 13C-13C mode coupling or to specific isotopic symmetries within protein secondary structures. As SIP-nanoFTIR captures single-cell metabolism, environmentally-induced cellular processes, and subcellular isotope localization, this technique offers widespread applications across a variety of disciplines including microbial ecology, biophysics, biopharmaceuticals, medicinal science, and cancer research.

A statistical simulation model to guide the choices of analytical methods in arrayed CRISPR screen experiments

An arrayed CRISPR screen is a high-throughput functional genomic screening method, which typically uses 384 well plates and has different gene knockouts in different wells. Despite various computational workflows, there is currently no systematic way to find what is a good workflow for arrayed CRISPR screening data analysis. To guide this choice, we developed a statistical simulation model that mimics the data generating process of arrayed CRISPR screening experiments. Our model is flexible and can simulate effects on phenotypic readouts of various experimental factors, such as the effect size of gene editing, as well as biological and technical variations. With two examples, we showed that the simulation model can assist making principled choice of normalization and hit calling method for the arrayed CRISPR data analysis. This simulation model is implemented in an R package and can be downloaded from Github.

AMPK regulates phagophore-to-autophagosome maturation

Autophagy is an important metabolic pathway that can non-selectively recycle cellular material or lead to targeted degradation of protein aggregates or damaged organelles. Autophagosome formation starts with autophagy factors accumulating on lipid vesicles containing ATG9. These phagophores attach to donor membranes, expand via ATG2-mediated lipid transfer, capture cargo, and mature into autophagosomes, ultimately fusing with lysosomes for their degradation. Autophagy can be activated by nutrient stress, for example, by a reduction in the cellular levels of amino acids. In contrast, how autophagy is regulated by low cellular ATP levels via the AMP-activated protein kinase (AMPK), an important therapeutic target, is less clear. Using live-cell imaging and an automated image analysis pipeline, we systematically dissect how nutrient starvation regulates autophagosome biogenesis. We demonstrate that glucose starvation downregulates autophagosome maturation by AMPK-mediated inhibition of phagophore tethering to donor membrane. Our results clarify AMPKs regulatory role in autophagy and highlight its potential as a therapeutic target to reduce autophagy.

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.

Microfluidic Biochips for Single-Cell Isolation and Single-Cell Analysis of Multiomics and Exosomes

Single-cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in-depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single-cell isolation, which is crucial for single-cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high-throughput single-cell isolation and real-time single-cell analysis on- or off-chip. Therefore, most current single-cell isolation and analysis technologies are based on the single-cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single-cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single-cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single-cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single-cell isolation and multiomic and exosome analyses.

Pleomorphism in Biological Units of Life: Morphological Heterogeneity in Cells Does Not Translate Uniformly to Subcellular Components

The interplay of the three-dimensional (3D) distribution of various subcellular components and their interactions are expected to control overall cellular morphology in biology. In this study, we aimed to determine whether the pleomorphy observed at the whole-cell level is being reflected by the components constituting the cells by focusing on the 3D distribution of pixel intensities at the single-cell level of the whole (cell) and its parts (the seven subcellular components of the cells-self-assemblies of smaller units). We rigorously acquired and analyzed the image data of RAW264.7 cells at the single-cell level. We report asymmetries in the spatial distribution of pixel intensities at the whole-cell and subcellular component levels along with the occurrence of alterations when pleomorphism is reduced by synchronization of the cell cycle. From our repertoire of seven subcellular components, we report ER, mitochondria, and tubulin to be independent of whole-cell apico-basal heterogeneity of optical density while nuclear, plasma membrane, lysosomal, and actin fluorescence distributions are found to contribute to the apico-basal polarity of the whole cell. While doing so, we have also developed an image analysis algorithm utilizing 2D segmentation to analyze the single cells in 3D using confocal microscopy, a technique that allows us to analyze cellular states in their native hydrated state.

Liebig's law of the minimum in the TGF-β/SMAD pathway

Cells use signaling pathways to sense and respond to their environments. The transforming growth factor-β (TGF-β) pathway produces context-specific responses. Here, we combined modeling and experimental analysis to study the dependence of the output of the TGF-β pathway on the abundance of signaling molecules in the pathway. We showed that the TGF-β pathway processes the variation of TGF-β receptor abundance using Liebig's law of the minimum, meaning that the output-modifying factor is the signaling protein that is most limited, to determine signaling responses across cell types and in single cells. We found that the abundance of either the type I (TGFBR1) or type II (TGFBR2) TGF-β receptor determined the responses of cancer cell lines, such that the receptor with relatively low abundance dictates the response. Furthermore, nuclear SMAD2 signaling correlated with the abundance of TGF-β receptor in single cells depending on the relative expression levels of TGFBR1 and TGFBR2. A similar control principle could govern the heterogeneity of signaling responses in other signaling pathways.

Cellular heterogeneity in TNF/TNFR1 signalling: live cell imaging of cell fate decisions in single cells

Cellular responses to TNF are inherently heterogeneous within an isogenic cell population and across different cell types. TNF promotes cell survival by activating pro-inflammatory NF-κB and MAPK signalling pathways but may also trigger apoptosis and necroptosis. Following TNF stimulation, the fate of individual cells is governed by the balance of pro-survival and pro-apoptotic signalling pathways. To elucidate the molecular mechanisms driving heterogenous responses to TNF, quantifying TNF/TNFR1 signalling at the single-cell level is crucial. Fluorescence live-cell imaging techniques offer real-time, dynamic insights into molecular processes in single cells, allowing for detection of rapid and transient changes, as well as identification of subpopulations, that are likely to be missed with traditional endpoint assays. Whilst fluorescence live-cell imaging has been employed extensively to investigate TNF-induced inflammation and TNF-induced cell death, it has been underutilised in studying the role of TNF/TNFR1 signalling pathway crosstalk in guiding cell-fate decisions in single cells. Here, we outline the various opportunities for pathway crosstalk during TNF/TNFR1 signalling and how these interactions may govern heterogenous responses to TNF. We also advocate for the use of live-cell imaging techniques to elucidate the molecular processes driving cell-to-cell variability in single cells. Understanding and overcoming cellular heterogeneity in response to TNF and modulators of the TNF/TNFR1 signalling pathway could lead to the development of targeted therapies for various diseases associated with aberrant TNF/TNFR1 signalling, such as rheumatoid arthritis, metabolic syndrome, and cancer.

High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery

Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.

AMPK Regulates Phagophore-to-Autophagosome Maturation

Autophagy is an important metabolic pathway that can non-selectively recycle cellular material or lead to targeted degradation of protein aggregates or damaged organelles. Autophagosome formation starts with autophagy factors accumulating on lipid vesicles containing ATG9. These phagophores attach to donor membranes, expand via ATG2-mediated lipid transfer, capture cargo, and mature into autophagosomes, ultimately fusing with lysosomes for their degradation. Autophagy can be activated by nutrient stress, for example by a reduction in the cellular levels of amino acids. In contrast, how autophagy is regulated by low cellular ATP levels via the AMP-activated protein kinase (AMPK), an important therapeutic target, is less clear. Using live-cell imaging and an automated image analysis pipeline, we systematically dissect how nutrient starvation regulates autophagosome biogenesis. We demonstrate that glucose starvation downregulates autophagosome maturation by AMPK mediated inhibition of phagophores tethering to donor membranes. Our results clarify AMPK's regulatory role in autophagy and highlight its potential as a therapeutic target to reduce autophagy.

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.

Cell types as species: Exploring a metaphor

The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, "species." In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the "cell types as species" metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.

Live single cell imaging assays in glass microwells produced by laser-induced deep etching

Miniaturization of cell culture substrates enables controlled analysis of living cells in confined micro-scale environments. This is particularly suitable for imaging individual cells over time, as they can be monitored without escaping the imaging field-of-view (FoV). Glass materials are ideal for most microscopy applications. However, with current methods used in life sciences, glass microfabrication is limited in terms of either freedom of design, quality, or throughput. In this work, we introduce laser-induced deep etching (LIDE) as a method for producing glass microwell arrays for live single cell imaging assays. We demonstrate novel microwell arrays with deep, high-aspect ratio wells that have rounded, dimpled or flat bottom profiles in either single-layer or double-layer glass chips. The microwells are evaluated for microscopy-based analysis of long-term cell culture, clonal expansion, laterally organized cell seeding, subcellular mechanics during migration and immune cell cytotoxicity assays of both adherent and suspension cells. It is shown that all types of microwells can support viable cell cultures and imaging with single cell resolution, and we highlight specific benefits of each microwell design for different applications. We believe that high-quality glass microwell arrays enabled by LIDE provide a great option for high-content and high-resolution imaging-based live cell assays with a broad range of potential applications within life sciences.

Identifying cell-to-cell variability in internalization using flow cytometry

Biological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as the internalization of material by cells. Given that internalization is a critical process by which many therapeutics and viruses reach their intracellular site of action, quantifying cell-to-cell variability in internalization is of high biological interest. Yet, it is common for studies of internalization to neglect cell-to-cell variability. We develop a simple mathematical model of internalization that captures the dynamical behaviour, cell-to-cell variation, and extrinsic noise introduced by flow cytometry. We calibrate our model through a novel distribution-matching approximate Bayesian computation algorithm to flow cytometry data of internalization of anti-transferrin receptor antibody in a human B-cell lymphoblastoid cell line. This approach provides information relating to the region of the parameter space, and consequentially the nature of cell-to-cell variability, that produces model realizations consistent with the experimental data. Given that our approach is agnostic to sample size and signal-to-noise ratio, our modelling framework is broadly applicable to identify biological variability in single-cell data from internalization assays and similar experiments that probe cellular dynamical processes.

Phenomics approaches to understand genetic networks and gene function in yeast

Over the past decade, major efforts have been made to systematically survey the characteristics or phenotypes associated with genetic variation in a variety of model systems. These so-called phenomics projects involve the measurement of 'phenomes', or the set of phenotypic information that describes an organism or cell, in various genetic contexts or states, and in response to external factors, such as environmental signals. Our understanding of the phenome of an organism depends on the availability of reagents that enable systematic evaluation of the spectrum of possible phenotypic variation and the types of measurements that can be taken. Here, we highlight phenomics studies that use the budding yeast, a pioneer model organism for functional genomics research. We focus on genetic perturbation screens designed to explore genetic interactions, using a variety of phenotypic read-outs, from cell growth to subcellular morphology.

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.