HCA — Introduction to High-Content Imaging and High-Content Analysis
By the end of this module, you are expected to be able to:
- define, in general terms, what High-Content Imaging (HCI) and High-Content Analysis (HCA) are;
- recognize the didactic difference between image acquisition and quantitative analysis;
- identify, in broad terms, what is meant by image-based profiling and phenotypic profiling;
- understand why image-based assays require care in planning, execution, and interpretation.
Module deliverable
- a short description, in your own words, of what HCI and HCA mean;
- a simple distinction between image acquisition, image analysis, and phenotypic profiling.
Core idea
If HCI produces images in a structured way, HCA transforms those images into analyzable data.
1) Introduction
Biological image analysis has existed for many decades and brings together different approaches, objectives, and tools. In recent years, image-based approaches have gained even more prominence, especially those in which images are no longer just illustration or a “representative image” and instead take on a central role in answering the scientific question.
In this context, terms such as High-Throughput Screening (HTS), High-Content Screening (HCS), High-Content Imaging (HCI), High-Content Analysis (HCA), and image-based profiling emerge. These terms are not always used consistently in the literature, which can be confusing for those who are just starting out. Here, the goal is not to exhaust the terminological debate, but to establish a conceptual foundation that is useful for the rest of our journey.
Core idea
In image-based high-content approaches, it is not enough to acquire images; it is necessary to transform images into quantitative data capable of supporting a biological inference.
Throughout the course, many of these concepts will become more concrete in practice. For now, let us align on a common language: what we are calling HCI and HCA, and why this changes the way we plan, execute, and interpret an experiment.
2) Defining important terms
Historically, the field of large-scale screening was strongly shaped by High-Throughput Screening (HTS), aimed at the automated evaluation of a large number of conditions, often with simpler and more direct readouts. With the advance of automated microscopy and quantitative image analysis, multiparametric approaches capable of extracting more biological information from each sample gained prominence.
In this context, the term High-Content Screening (HCS) became widely used, especially in drug discovery and microscopy-based phenotypic screening. More recently, terms such as High-Content Analysis (HCA), High-Content Imaging (HCI), and image-based profiling have also become common, although not always with clearly defined boundaries.
In this course, we will use a simple didactic distinction:
- HCI (High-Content Imaging): automated, standardized, and scalable acquisition of biological images;
- HCA (High-Content Analysis): quantitative analysis of those images to extract measurements and interpret phenotypes.
Core idea
If HCI produces images in a structured way, HCA transforms those images into analyzable data.
Thus, instead of manually observing a few samples under the microscope, the experiment begins to produce images systematically, across multiple wells, fields, channels, time points, or conditions. These images can then be processed to generate quantitative measurements and, in some cases, phenotypic profiles.
In recent years, one HCA approach has sought to extract a large number of characteristics from images without being limited to a single previously chosen measurement, generally referred to as image-based profiling. It is based on determining a phenotypic profile, which, in simple terms, is the set of quantitative characteristics extracted from images that describe the state of a biological sample, such as a cell, an organoid, or another experimental system. It aims to identify phenotypic signatures capable of distinguishing perturbations in biological models.
It is important to note that these definitions do not follow a universal agreement. In practice, different papers and different groups may use these terms in partially overlapping ways. Here, we adopt this convention because it helps organize the reasoning throughout the course.
Exercises
1) HCI or HCA?
Explain, in your own words, the difference between High-Content Imaging (HCI) and High-Content Analysis (HCA).
2) Creating parallels
Create an analogy to help a colleague understand the relationship between the different concepts covered in this lesson: HCI, HCA, HCS.
3) Phenotypic profile for a lay audience
Try to create a simple explanation, without jargon, to explain what a phenotypic profile is.
4) Where does this appear in your project?
Think about your research context and answer: at what stage of your project do image acquisition, quantitative analysis, and biological interpretation come in?