With the many image analysis services that Visikol has to offer, cell counting is by far one of the most popular services we provide. Cell counting is a service that provides insights into the cell populations present within a multiplex imaged sample by analyzing the positivity of client selected markers and marker combinations in cells within client images. The analysis is conducted through the segmentation of cells using a nuclear marker and colocalization of the other segmented markers to the segmented cells to determine positivity for the markers within each cell as seen in Figure 1. The analysis can also be extended to determining the positivity of markers within specific compartments of the cell such as the cytoplasm or the membrane with the addition of markers to outline those compartments.

This type of analysis is useful because it can help to elucidate answers into how a treatment is affecting cell populations within a treated tissue sample. Specifically, cell counting can be useful because it can provide quantitative answers about the treatment’s effect. Some use cases involve the analysis of immune cell populations, cells expressing certain genes/proteins, or cells undergoing certain biological processes. Additionally, with multiple treatment groups, the images can be compared so that statistical metrics can be calculated to quantify the difference between the groups. This type of data is useful because it can provide insights into the biological change seen between control and treated groups to help a researcher gain a better understanding of how their treatment affects the cells within a tissue with quantitative metrics.

In order to augment the analysis, Visikol offers a spatial analysis service where distance metrics can be calculated between cells or cells to specific regions. This utilizes the information gained from cell counting to inform researchers about interactions of identified cells to other cells and specific regions within the tissue. Spatial analysis can help to answer questions such as cell infiltration into regions of a tissue (ex. immune cell infiltration into a tumor) and relations between different cell subtypes (ex. immune cell interactions). Furthermore, Visikol offers a wide variety of analyses that can take the analysis to the next level such as morphological analyses and bioinformatics to take a deeper dive into the data and extract more metrics to compare. Please contact us if you are interested in any of these services and wish to find out more information about them.

When it comes to biomedical research, quantitative data is always preferred to qualitative data since it is measurable and not left up to interpretation. When it comes to drug discovery, gathering quantitative data is needed to objectively say whether a drug works or not, and this data can be gathered through image analysis. Image analysis is the extraction of data from images and is accomplished through a multitude of image analysis techniques. At Visikol, image analysis is a routine service offered to clients to analyze a variety of aspects about an image to answer the research question at hand. Some common biomedical image analysis techniques include cell counting, spatial analysis, and morphological analysis.

The most common type of image analysis is cell counting, which involves segmentation of cells through a nuclear stain and quantifying positivity of specific labels within the segmented cells to obtain a cell count of specific cell subtypes, or cells with the specific target of interest as seen in Figure 1. This works through the selection of positivity criteria for each label within a cell, such as a certain level of intensity or amount of label within the cell. Additionally, cell subtypes can be outlined using colocalization, which is the spatial overlap between labels. For example, segmented cells positive for CD68 and MHCII label could outline M1 Macrophages.  This type of quantification is useful when comparing different treatment groups because it helps to quantify how a drug affects specific targets or cell types of interest and allows for statistical comparisons between the groups.

An expansion upon cell counting is spatial analysis, which can utilize the cell counting data to determine the spatial relationship between different cell subtypes or between a specific cell type and a region of interest as seen in Figure 2. One use case for this type of analysis is in immuno-oncology, where spatial analysis can help by determining the average distances between cancer cells and immune cells within an image or help determine the average distance of immune cell infiltration into a tumor region.

Another image analysis technique is morphological analysis, which analyses the structure of segmented objects within an image. This type of analysis will measure aspects like circularity, eccentricity, area, texture, and many other types of features. An application of this is in drug discovery, where one can analyze the toxicity and safety of a drug by seeing how it changes the structure of cells when comparing a control versus a treated group.

Additionally, the above-mentioned analyses can be done in 3D as well, which offers an extra dimension both literally and figuratively to the data generated. Not only that, but the data generated from analyzed images can be used to train machine learning or deep learning algorithms to create a more robust tool for areas like drug discovery or clinical diagnostics.  Being able to generate quantitative data is critical for the future of biomedical research, and image analysis is a critical tool in helping to accomplish this goal. At Visikol, we are experts in traditional 2D and 3D image analysis techniques as well as more advanced techniques involving machine learning and deep learning algorithms. Please feel free to contact us for your image analysis needs to add an extra level to your research.