Everything about high content imaging

High content imaging (HCI) is a strong image-based concept used in biochemistry, cellular biology, microbiology, molecular biology, drug discovery, and other disciplines. The main objective of high content imaging platforms is to retrieve high content information from a biological sample image.

Size, shape, color, quantity, brightness, and relative or absolute position are examples of “content” in information. Several different kinds of imaging systems could be classified as high content, but there are some important details to consider.

Everything about high content imaging

Image Credit: IDEA Bio-Medical Ltd.

Everything about high content imaging

Image Credit: IDEA Bio-Medical Ltd.

Everything about high content imaging

Image Credit: IDEA Bio-Medical Ltd.

Everything about high content imaging

Image Credit: IDEA Bio-Medical Ltd.

Everything about high content imaging

Image Credit: IDEA Bio-Medical Ltd.

To boost imaging throughput and measurement, high content imaging is usually considered a highly automated three-step procedure. These three steps are as follows:

  • Image acquisition
  • Image processing
  • Image analysis

Each step is associated with greater levels of computer-driven automation, letting users speed up repetitive tasks while eliminating human bias from the process. Terabytes of imaged data can be obtained, processed, and analyzed in minutes with minimal human interaction. This data frequently covers a wide range of biological assays that would take weeks or months to finish using conventional manual microscopy and analysis skills.

HCI’s image capture technology aims to automatically and without human intervention locate the focal plane of an objective lens within a biological sample to generate a sharp image. Ultra-fast autofocusing and sample detection are required, as well as accurate and consistent precision-motion modules and faster acquisition speeds via multiple cameras, polychromatic illumination, and/or spinning disc pinholes.

When these systems are combined, they allow high content imaging platforms to retrieve thorough, multi-parametric data at the single-cell and subcellular levels.

Once obtained, the images may require additional processing, which must be done in an automated and, ideally in batch. A set of prevalent image processing operations is provided below.

  • Image stitching: Joining numerous, adjacent fields of view together into a single image that pictures a large, uninterrupted area of the sample.
  • Z-stack processing: Decreasing the 3D volume visualized to a single 2D image
    • Choosing specific z-planes, either by user preference or analysis to identify those with the sharpest focus, is a simple operation for obtaining a representative 2D image.
    • Intensity projections produce a fresh, representative 2D image by
      • First, remove the intensity data confined in the sample volume covered by each pixel area.
      • Second, is the accomplishment of a statistical operation, like average or maximal intensity calculation.
      • Each pixel in the new image then records the output of this statistic through the volume.
    • Fluorescence deconvolution: Increase contrast in images, most commonly when using widefield microscopes.
      • This mathematical operation estimates the input of out-of-focus fluorescence within each image and eliminates it.
      • The output image has greater contrast and is sometimes mentioned as a digital confocal image.

Some of these operations, like deconvolution accompanied by intensity projection and stitching, can be done concurrently. Batch processing to perform tasks on multiple datasets saves time because scientists do not have to stack datasets one at a time for processing.

Once the images have been processed, automated image analysis extracts the data of interest from them. This method is at the heart of High Content Analysis (HCA).

It starts with segmentation to identify items of interest, followed by metric extraction for each object, like intensity and morphology data. The strength of HCA is that once segmentation parameters are defined and described, they can be implemented to multiple data sets without bias.

When analyzing interactions encompassing numerous cells, like confluency, colonies of cells, intercellular signaling or infection studies (e.g., viral plaque formation), or tissue slices, processed images may still be more desirable than raw images.

Stitched images, in these instances, authorize the segmentation of large area objects that may stretch beyond the edges of any raw images obtained. If the 3D volumetric analysis is not conducted, intensity projections can help to identify cells in Z-stacks faster and avoid possible double-counting of cells.

Eventually, deconvolution can help identify small intracellular structures like foci, granules, or organelles where background fluorescence can make reliable segmentation difficult.

What defines high content screening?

The terms high content analysis (HCA) and high content screening (HCS) are frequently used interchangeably with high content imaging, but they are different. In most cases, high content imaging pertains to imaging technology, such as multiplexed fluorescence microscopy.

HCA is concerned with the use of automated image analysis software for data processing and analysis, with a particular emphasis on hit optimization. In the meantime, high content screening best describes the overall process of screening compounds in a high-throughput format, such as research setup and expected outputs.

Is high content imaging the same as high throughput screening?

High content imaging was created to supplement high-throughput screening (HTS), but it has evolved into a powerful tool in its own right. It employs a live cell imaging platform, comparable to that used in high-throughput screening.

Undoubtedly, both HTS and HCI use multi-well plate samples that look similar but have distinct quality standards. Notably, the thickness of the bottom must be thin and flat for HCI, as it must transmit light for image generation. Before using a plate for HCI, researchers must ensure that it is imaging-quality.

Conventional HTS plate-reading generates a single, averaged (ensemble) measurement, allowing hundreds of thousands of small molecules to be interrogated in a variety of cell-based and in vitro assays in a short period.

High-content imaging retrieves single-cell measurements to provide superior biological precision and phenotypic complexity, allowing researchers to gain an understanding of the underlying biological distributions and heterogeneity.

This opens up many opportunities for data mining and cellular population classification within HCS, allowing researchers to test more thorough and complicated hypotheses. Furthermore, data specific to the target site, as well as off-target effects, may be retrieved in a single assay.

Following that, high content analysis can detail crucial phenotypic changes at the single cell level, allowing users to profile compounds used in cell-based assays. Cellular localization/translocation, morphology, proliferation, and other important parameters of interest can all be observed and measured using HCA.

Applications of high content imaging

Assay development, compound screening, transfection assays, time-based kinetic studies, and general sample analysis all benefit from high content imaging. Cancer research, cytometry, live cell analysis, virology studies, and other fields benefit from these workflows.

Attempting to provide a complete overview of the practical applications of high content imaging would be reductive, as its applicability has grown to the point where it is now a mainstay in the life science toolkit.

Looking for high content imaging systems?

For more information on how a high content imaging systems work, or the key features to bear in mind during a purchasing process, contact IDEA Bio today. IDEA Bio-Medical experts specialize in high-content imaging applications and advancement, making automation and image analysis alternatives simple to use and available to all life science scientists.

Reference

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

About IDEA Bio-Medical Ltd.

IDEA Bio-Medical is founded in 2007 through a partnership between YEDA (the Weizmann Institute’s commercialization arm) and IDEA Machine Development (an innovation hub).

We specialize in automated imaging systems and image analysis software, offering a broad range of biological applications based on the company’s unique algorithms library. The company is developing novel image-based screening platforms for the pharmaceutical industry and medical centers, dedicated to broadening the scope of personalized medicine.

Our WiScan Hermes system incorporates the most advanced technologies currently available in the machine vision field, integrated with engineering methodologies of high reliability and quality at the level of semi-conductors and digital printing industries, which are the specialty of our mother company, IDEA Machine Development Design and Production Ltd.


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Last updated: Jul 14, 2022 at 10:54 AM

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