High-content imaging (HCI) is an automated technique that combines advanced microscopy, fluorescent labeling, and image analysis to study cellular samples. Automation and image analysis software in HCI is critical for efficient and accurate analysis of cellular images.
HCI provides researchers with detailed information about cellular processes and molecular interactions, enabling them to visualize and quantify therapeutic interactions within cell populations, providing valuable insights.
By leveraging multiplexing and innovative techniques such as cell painting, disease-relevant cell-based assays can provide precise measurement and monitor phenotypic changes, facilitating optimization of early discovery efforts.
In HCI, cells are labeled with fluorescent probes and imaged using a microscope and a sensitive camera. The system's components, including robotic plate handlers and liquid handling systems, enable high-throughput analysis of multiple samples.
Sophisticated image analysis software automatically analyzes the captured images, extracting quantitative data on cellular features and biomarkers. HCI finds applications in drug discovery, toxicology studies, phenotypic screening, and cell biology research.
HCI offers advantages such as simultaneous analysis of multiple parameters, comprehensive assessment of cellular phenotypes, and data-rich results. It has revolutionized research by enabling large-scale analysis, accelerating discoveries, and advancing scientific understanding.
In this article, you will gain a concise understanding of high-content imaging, its applications, the components of an HCI system, and how it differs from high-throughput screening and other types.
The imaging process in HCI uses several steps to capture and analyze cellular images. Here is a general overview of the imaging process in an HCI workflow:
To summarize, the key features and characteristics of HCI include:
Due to its ability to provide valuable insights into cellular processes, HCI is quite powerful. Its most effective application may be in drug discovery and development, providing critical data for several other areas of focus. Let’s review:
HCI plays a crucial role in developing and optimizing cellular assays. By providing detailed visualization and quantitative cellular feature analysis, this technique helps researchers design robust assays that can accurately measure specific biological processes or endpoints. This allows researchers to assess target engagement and understand downstream signaling pathways (i.e., target validation).
HCI is widely employed in transfection assays, assessing the efficiency and effects of gene delivery into cells. By visualizing and quantifying gene expression, HCI helps researchers optimize transfection protocols, evaluate gene function, and investigate molecular interactions.
HCI was originally created to complement high-throughput screening, enabling the rapid testing of large compound libraries to identify potential drug candidates. HCI can evaluate compound effects on cell morphology, protein expression, subcellular localization, and more by assessing multiple cellular parameters simultaneously. This aids in the identification of specific compounds with desired properties.
HCI has proven quite valuable for phenotypic screening. It is used to identify compounds that induce specific phenotypic changes in cells. By capturing and analyzing cellular morphology, protein localization, or other phenotypic features, HCI aids in discovering compounds with desired therapeutic effects or novel targets for drug development.
HCI is also highly useful in toxicity studies through the assessment of safety profiles of potential drug compounds or environmental factors. It enables monitoring cellular viability, apoptosis, oxidative stress, and other toxicological endpoints, helping identify compounds or conditions that may pose risks to cells or tissues.
HCI aids in understanding how drugs interact with cellular components and modulate specific biological functions. By observing the effects of compounds on cellular pathways and processes, HCI provides insights into the underlying mechanisms of drug action.
Because HCI’s applicability has grown to the point where it is now a mainstay in the life science toolkit, it isn’t easy to offer a complete overview of its real-world applications. However, there are some areas we want to highlight, including cytometry, cancer research, and more.
HCI is used in cytometry to analyze and quantify cellular characteristics. It allows for assessing cell cycle dynamics, DNA content, nuclear shape, and chromatin organization. By providing detailed measurements of cellular parameters, HCI helps identify aberrant cell populations and study cell heterogeneity in various biological contexts.
HCI plays a crucial role in cancer research by enabling the study of cellular processes, tumor progression, and drug responses. It allows for the analysis of cancer cell morphology, proliferation, migration, invasion, and apoptosis. HCI aids in identifying potential therapeutic targets, evaluating drug efficacy, and studying the mechanisms underlying cancer development and metastasis.
HCI is well-suited for live cell analysis, enabling real-time monitoring of dynamic cellular processes. It facilitates the study of cellular behaviors, such as cell migration, cell-cell interactions, and intracellular signaling events. HCI allows researchers to capture time-lapse images and track changes in cellular phenotypes over extended periods, providing valuable insights into cellular dynamics and responses to stimuli.
HCI has significant applications in virology research. It enables the investigation of virus-host interactions, viral replication, and the effects of antiviral compounds on infected cells. HCI allows for the visualization and quantification of viral infection dynamics, viral protein expression, and the impact of viral infections on cellular phenotypes.
In neuroscience, HCI is used to study neuronal morphology, synapse formation, and neuronal activity. It aids in analyzing neuronal networks, neurite outgrowth, and dendritic spine dynamics. HCI provides valuable information for understanding neurodevelopmental disorders, neurodegenerative diseases, and the effects of drugs on neuronal function.
HCI plays a vital role in stem cell research by facilitating the characterization of stem cells, differentiation processes, and tissue engineering. It allows for the analysis of pluripotency markers, cell fate determination, and the evaluation of differentiation protocols. HCI helps researchers optimize culture conditions and monitor stem cell behavior for regenerative medicine applications.
HCI supports various cell biology studies, such as investigating cellular processes, studying protein localization and interactions, exploring signaling pathways, and understanding disease mechanisms. It provides detailed information on cellular structures and functions, facilitating discoveries and advancing understanding in cell biology research.
HCI also has potential applications in personalized medicine, allowing researchers and physicians to assess patient-specific cellular responses to drugs or treatments. By analyzing cellular features and responses, HCI may help guide treatment decisions and optimize personalized therapeutic interventions.
High-content screening (HCS) and high-content analysis (HCA) are distinct from HCI despite being interconnected concepts used in cellular imaging and analysis.
While HCI is used to capture and analyze detailed images of cellular samples using advanced microscopy and imaging systems, HCS and HCA play different roles.
HCS is a broader concept incorporating HCI as a screening method in drug discovery and optimization. HCS aims to screen large compound libraries, typically hundreds to millions of compounds, to identify potential drug targets, hits, or lead compounds. It leverages HCI by capturing phenotypic data on cellular responses to compounds, offering insights into both on-target and off-target effects within complex cellular systems.
HCS is considered more predictive than traditional plate reader-based high throughput screening (HTS) as it provides phenotypic data in the same experiment, offering a holistic view of compound effects.
High-content analysis (HCA) builds upon the data generated from HCS experiments. It applies advanced multiparameter algorithms to develop detailed cellular physiology profiles, particularly in complex cellular systems like spheroids, organoids, co-cultures, and microenvironments. The goal of HCA is typically to optimize hits identified in HCS.
By utilizing sophisticated algorithms, HCA enables complex analysis of cell populations and delivers highly predictive and translatable preclinical data. This comprehensive analysis aids in predicting in vivo effects based on the detailed cellular analysis conducted during HCS.
What’s the difference between high-content imaging and high-throughput screening (HTS)? While the names are similar, HCI and HTS are distinct approaches in biomedical research and drug discovery.
HTS is used in drug discovery to rapidly test large compound libraries for activity against specific targets or biological processes. It involves automated screening systems that can evaluate hundreds of thousands to millions of compounds, enabling the identification of potential drug candidates and the exploration of diverse chemical space in a time-efficient manner.
The main differences between the two approaches include:
Despite these differences, there is a time and place for each technique. One may be a better choice depending on your specific situation and research objectives.
Considering factors such as your goals, the depth, and complexity of your data, the sample types being used, the flexibility your experimentation needs, and your throughput requirements will make it easier to understand which technique will suit your situation best.
In some cases, a combination of both approaches may be beneficial, leveraging the strengths of each to obtain a comprehensive understanding of the research you’re performing.
High-content imaging, or HCI, is an automated technique combining advanced microscopy, fluorescent labeling, and image analysis to study cellular samples.
It works by labeling cells with fluorescent probes, capturing images using a microscope and sensitive camera, and analyzing them using sophisticated image analysis software. HCI provides detailed information about cellular processes, molecular interactions, and cellular phenotypes.
Its uses and real-world applications encompass diverse fields such as drug discovery, toxicology studies, cancer research, neuroscience, stem cell research, virology, and personalized medicine.
By harnessing the power of HCI, researchers can gain deeper insights into cellular mechanisms, accelerate breakthroughs, and ultimately contribute to advancements in healthcare and beyond.
If you’re a researcher or organization looking to leverage the power of high-content imaging, consider leasing state-of-the-art HCI systems with Excedr. We offer high quality equipment, including microscopes and high-content imaging systems, to meet your imaging and screening needs. Leasing provides a cost-effective and flexible solution, allowing you to access cutting-edge technology without the burden of large upfront costs.
Contact our team or request a lease estimate to learn more about our leasing program and unlock the potential of high-content imaging for your research and development.
