Characterizing the Complex Microenvironment of Individual Immune Cells
In addition to identifying and characterizing the morphology of individual cells, a more complex phenotypic profile includes the cell’s activation state and function within the tissue microenvironment.
Recent developments in quantitative imaging hardware and software have allowed researchers to study immune cells in situ but, as only four biomarkers can be studied at one time on standard microscopy systems, this does not capture a complete picture of the cell’s activity.
Meanwhile, flow cytometry is a well-established method that allows multiple biomarkers to be studied at one time, but it has the disadvantage of dissociating the cells from important information about the tissue microenvironment.
The development of a system that could provide flow cytometry-like data on cell biomarkers while retaining information on the tissue microenvironment, as in conventional microscopy, would therefore be extremely valuable for meeting the demands of modern clinical practice.
A recent study by researchers at Yale University School of Medicine (Blenman K & Bosenberg M, 2019) demonstrates an approach that can overcome the limitations discussed above. They show that applying new analytic technology to a conventional microscopy setup makes possible a complex characterization of immune cells in situ.
Adapting a familiar setup
Dr Kim Blenman studied tissue samples from the spleen in a mouse model of melanoma developed by Dr Marcus Bosenberg. Dr Blenman employed a conventional microscopy setup to perform multiplexed fluorochrome-based histology on formalin-fixed paraffin-embedded tissue sections, with biomarkers conjugated to Cy3 or Cy5.
Dr Blenman was interested in the CD4 transmembrane protein – a biomarker of multiple T-cell subtypes – and six biomarkers of cell activity, such as proliferation and transcription, associated with these cells.
She used multiple staining rounds, inactivating the dyes between each round, to study the different biomarkers of cell activity including Foxp3, RORγ(t), Ki-67, T-bet, Granzyme B, and IL-6.
Dr Blenman used an all-in-one system from TissueGnostics. High-magnification images were acquired with the TissueFAXS Quantitative Imaging System for downstream analysis integrated with advanced image processing software (TissueGnostics StrataQuest) that reconstructs whole images in silico and performs cell phenotyping and tissue cytometry.
The software first uses and algorithm to stitch the tiled images together to recreate the whole image. It then creates a composite image that includes all the biomarkers from each staining round. Following this, cell isolation is performed using two algorithms: one to identify the cell nucleus and a second which identifies the biomarker for the cell phenotype. Quantification was then achieved by assessing in turn which cells were positive for each biomarker, using a backgating algorithm to determine cutoffs at which cells or cell clusters should or should not be included.
Using this method on the spleen tissue samples, Dr Blenman was able to show that it can obtain multiple biomarkers at one time in situ.
With this information, they were able to phenotypically characterize cells within the tissue sample and assign them to categories based on the combination of biomarkers expressed by individual and clusters of cells. Through this, they showed that levels of CD4 expression within cell clusters correlated with distinct patterns of biomarker expression and cell proliferation.
Visualizing the future
The immune system is incredibly complex and being restricted to profiling only four biomarkers per cell type has been a limiting factor. One advantage of the system used by the authors is that it harnesses a microscopy set up that is already present in most laboratories, making it highly accessible. And, while the authors chose to study six biomarkers, there is theoretically no limit to the number of biomarkers that can be studied at one time.
Dr Blenman says that this method has an advantage of being able to objectively quantify cell biomarkers. Reporting in Cytometry Part A, she says it has potential to be very useful in cancer treatment where therapies are becoming increasingly targeted to the individual’s disease and its underlying mechanism. For example, the technique could be used to detect response to immunotherapy through the analysis of biopsies before, during, and after treatment.
The method helps to retain spatial information about the cells while characterizing them phenotypically, facilitated by flow cytometry-like capabilities including gating, backgating and histogram/dot scatterplot outputs.
The process described by Dr Blenman in the Cytometry A paper was made possible through the TissueFAXS Quantitative Imaging System and TissueGnostics StrataQuest analysis software. These technologies combined lead to a streamlined, highly automated process which frees the user from hands-on time. The authors highlight the potential of TissueFAXs to incorporate MATLAB scripts into the StrataQuest analysis software. There are also over 50 compatible ready-to-load apps which can facilitate specialized analyses. Each app provides a specific analysis from start-to-finish and deliver the data ready to export to the program of your choice.
The TissueFAXS system is a versatile upright system which can scan and analyze slides, cytospins, smears and tissue microarrays. It comes equipped with both fluorescence and brightfield modes but, with supplemental components, can also be customized for contrast microscopy methods.
The system can be equipped with one of three software choices, the most advanced and comprehensive being StrataQuest. StrataQuest can automatically detect structures within a digital slide, such as blood vessels and can identify and quantify millions of cells within a cell sample. The software is also available as a standalone products for use with existing scanning systems and is compatible with imported images and slides in a wide range of formats.
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Blenman KRM & Bosenberg MW. Immune Cell and Cell Cluster Phenotyping, Quantitation, and Visualization Using In Silico Multiplexed Images and Tissue Cytometry. Cytometry A. 2019; 95:399-410. doi: 10.1002/cyto.a.23668.