The tumor microenvironment (TME) is inherently complex. Due to its highly variable cell composition and the high number of proteins and structures involved in tumor formation, an in-depth analysis of tumor tissues requires the examination of a plethora of parameters.
Novel imaging technologies like ultra-high content imaging or 3D visualization by light sheet microscopy allow researchers to dive deep into limited sample material, laying the foundation for understanding TME complexity.
Ultra high content imaging using MICS (MACSima Imaging Cyclic Staining) technology on the MACSima Imaging Platform allows for automated fluorescence microscopic analysis of hundreds of markers on a single sample. MICS technology can be applied to decipher complex questions in TME research, like target or biomarker identification, protein characterization, TIL phenotyping, or cancer classification as shown in the exemplary data below.
Expression of a multitude of CAR target candidates within the primary pancreatic cancer tissues from patients was assessed using the MACSima Imaging Platform. The full story is published in Nature Communications (Schäfer, D. et al. 2021). Licensed under a Creative Commons Attribution 4.0 International License.
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MICS technology allows for cyclic staining and analysis of hundreds of potential marker candidates on one tissue section. The data below show exemplary immunofluorescence images for different markers of interest analyzed in an automated 97-marker imaging run.
The automated MACSima Imaging Platform enables assessment of multiple samples at a time. Here, the expression of MUC1 was analyzed in patient-derived ovarian carcinoma tissues. Multiparameter expression analysis of MUC1 and markers indicative of cell lineages and proliferation status, i.e., CD326 (epithelial), Ki-67 (proliferation), CD31 (endothelial), CD90 (stromal), and CD45 (differentiated hematopoietic cells) are shown.
Classification of different patient-derived primary glioblastoma tumors based on the expression of PDGFRα, p53, synaptophysin, CD44, nestin, podoplanin, GFAP, and EGFR based on a classification scheme published by Motomura, K. et al. 2012.
In this experiment, 75 different markers were detected on a colon carcinoma section. Images were acquired with the MACSima Imaging System and data were analyzed using the powerful and intuitive Qi Tissue Image Analysis Software.
Based on the intensity profile of the cells, several distinct cell populations could be identified. Using this information, it was possible to identify the populations that could infiltrate the tumor region.
Learn how the MACSima Imaging System can stain hundreds of markers on a single sample, and stain multiple samples at a time, all in an entirely automated fashion. See more than ever before: Unlock new insights and advance your research further, faster!
Light sheet microscopy uses a laser light sheet that is perpendicular to the axis of observation to excite a single plane of a labeled sample. Illumination of only a thin layer of the z-axis results in high-quality 3D imaging of entire organs with only low and strictly localized photodamage and bleaching effects. Moving the sample along the z-axis through the light sheet produces a stack of images that can be combined to visualize large biological samples in 3D with high resolution (Mohr, H. et al. 2021 and Taranda, J. and Turcan, S. 2021).
The fully automated UltraMicroscope Blaze light sheet microscope in combination with tissue clearing enables 3D visualization of heterogeneous tumor physiology. It allows for quantification of multiple tumor parameters and CAR T cell infiltration at a cellular and even subcellular level for multiple samples and can be applied for:
The video shows a xenograft of a human pancreatic carcinoma cell line with infiltrating CAR T cells. Sample preparation involved whole-mount immunolabeling followed by ethyl cinnamate (ECi) clearing. The 3D data of the tumor were created with the UltraMicroscope II light sheet microscope. CAR T cells were labeled using the REA844 clone conjugated with Vio® 667 (violet), detecting human CD271 (LNGFR). Vasculature was stained by rhodamine-lectin (orange). Remaining GFP expression of carcinoma cells is shown in green.
3D visualization of a mouse thorax with the UltraMicroscope Blaze detects metastases targeted by a drug candidate (white) versus metastases missed by the drug candidate (magenta).
Source: Chenchen Pan et al. Deep learning reveals cancer metastasis and therapeutic antibody targeting in the entire body, P1661–1676.E19.
Copyright (2019), with permission from Elsevier.