What is High Content Imaging?

What is High Content Imaging?

High content imaging (HCI), is a powerful image-based paradigm used across the full spectrum of biochemistry, cellular biology, microbiology, molecular biology, drug discovery, and more. The primary thrust of high content imaging platforms is the extraction of high content data from an image of a biological sample.  The ‘content’ in the data includes information such as size, shape, color, quantity, brightness and relative or absolute location. Many different types of imaging systems could be described as high content – but there are key details to bear in mind.

Delving into the specifics: high content imaging is generally described as a highly automated three-step process used to dramatically increase imaging throughput and quantification. These three steps are:

  1. Image acquisition
  2. Image processing
  3. Image analysis

Each step is associated with high degrees of computer-driven automation, allowing users to accelerate repetitive operations and eliminate human bias from the process. In this fashion, Tera-bites of imaged data can be acquired, processed and analysed within minutes, with minimal human operation. Such data often covers a variety of biological assays that, using traditional manual microscopy & analysis tools, would take between weeks and months to complete.

The image acquisition technology behind HCI seeks to rapidly place the focal plane of an objective lens within a biological sample to create a sharp image automatically and without human intervention. The requirements include ultra-fast autofocusing and sample detection, accurate and repeatable precision-motion modules, and enhanced acquisition speeds through multiple cameras, polychromatic illumination and/or spinning disc pinholes. When put together, these systems enable high content imaging platforms to extract detailed, multi-parametric data at the single-cell and subcellular level.

Once acquired, the images may need further processing that must be done in an automated fashion, and ideally in batch.  Below is a list of common image processing operations.

  • Image stitching: joining several, adjacent fields of view together into a single image that visualizes a large, continuous area of the sample.
  • Z-stack processing: reducing the 3D volume visualized to a single 2D image.
    • Selection of specific z-planes, either through user choice or analysis to identify those having the sharpest focus, is a simple operation to obtain a representative 2D image.
    • Intensity projections generate a new, representative 2D image by:
      • First, extracting the intensity information contained in the sample volume covered by each pixel area.
      • Second, performing a statistical operation, such as average or maximal intensity calculation.
      • Each pixel in the new image then records the output of this statistic through the volume.
    • Fluorescence deconvolution: improve contrast in images, most commonly when using widefield microscopes.
      • This mathematical operation estimates the contribution of out-of-focus fluorescence within each image and removes it.
      • The output image has higher contrast and is sometimes referred to as a digital confocal image.

Several of these operations can be performed together, such as deconvolution followed by intensity projection and stitching.  Batch processing to perform the operations over several datasets saves substantial time, as researchers do not need to load datasets one at a time for processing. 

Once images are processed, automated image analysis extracts the data of interest from the acquired images.  As described in our post , this approach is the heart of High Content Analysis (HCA).  It begins with segmentation to identify objects of interest, then extraction of metrics for each object, such as intensity and morphology information.  The power of HCA is that once parameters for segmentation are identified and defined, they can be applied to several data sets without bias. 

Analysis of processed images can sometimes be preferable over raw images, such as instances when studying interactions involving many cells, such as confluency, colonies of cells, intercellular signalling or infection studies (e.g., viral plaque formation), or tissue slices.  In these cases, stitched images allow for segmentation of large area objects that may extend beyond the edges of any single, raw image acquired.  Intensity projections can expedite identifying cells in Z-stacks and avoid potential double-counting of cells if 3D volumetric analysis is not performed.   Finally, deconvolution can assist with identification of small, intracellular structures such as foci, granules or organelles, where background fluorescence can impede reliable segmentation.

Covid19 infection studies at HCS
Screening of Zebrafish in 96 well plates
Mitochondria- Intra-cellular features studies
Ecoli studies in high content screening
Trans location
Cytoplasm to Nucleus translocation studies
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What Defines High Content Screening?

The terms high content analysis (HCA) and high content screening (HCS) are often used synonymously with high content imaging, but they can be differentiated. High content imaging typically refers to the imaging technology, for instance multiplexed fluorescence microscopy. HCA covers the use of automated image analysis software for data processing and study – often focusing on hit optimization. HCS, meanwhile, best describes the whole process of screening compounds in a high-throughput format, including experimental design and desired output. 

Is High Content Imaging the same as High Throughput Screening?

High content imaging was initially developed to complement high-throughput screening (HTS), but it has emerged as a powerhouse tool in its own right. It uses a live cell imaging platform, similar to high-throughput screening. Indeed, both HTS and HCI use similar looking multi-well plate samples but have different quality requirements. Importantly, the thickness of the bottom must be thin and flat for HCI, since it needs to transmit light for generation of an image.  Researchers should ensure their plate is imaging-quality before planning to use it for HCI.

Conventional HTS plate-reading outputs a single, averaged (ensemble) measurement to enable many hundreds of thousands of small molecules to be rapidly interrogated in a wide range of cell-based and in vitro assays.  High-content imaging extracts single-cell measurements to provide superior biological specificity and phenotypic complexity, providing insight into underlying biological distributions and heterogeneity. This leads to greater opportunities for data mining and classification of cellular populations within HCS, permitting researchers to test more detailed and difficult hypotheses.  Moreover, data localized specifically to the target site, plus off-target effects, can potentially be extracted in a single assay.

High content analysis can subsequently detail key phenotypic changes at the single cell level, helping users to profile compounds used in cell-based assays. Key parameters of interest include cellular localization/translocation, morphology, proliferation, and so on, and HCA allows changes in any of these to be both observed and quantified.

Applications of High Content Imaging

High content imaging is useful in assay development, compound screening, transfection assays, time-based kinetic studies, and general sample observation. Key real-world applications of these workflows include cancer research, cytometry, live cell analysis, virology studies, and more. Trying to offer a comprehensive overview of the practical uses of high content imaging would be reductive, as it has grown in applicability to the extent that it is now one of the mainstays in the life science toolkit. But if you would like to learn about some of the impressive achievements of high content imaging, refer to our citations page.

Looking for High Content Imaging Systems?

If you are looking for more information on how a high content imaging system works, or key features to bear in mind during your purchasing process, IDEA Bio can help. We are experts in high content imaging applications and development, specializing in making automation and image analysis solutions easy-to-use and accessible to all life science researchers.

Why not request a demo of one of our systems today to start your journey with us? 


Buchser, William, et al. “Assay development guidelines for image-based high content screening, high content analysis and high content imaging.” Assay guidance manual (2014).