Immune evasion is a hallmark of cancer and the presence and interaction of immune cells within a tumor and its microenvironment can have a great impact on disease progression and prognosis. Unravelling the complex interplay of neoplastic and immune cells requires both phenotypic and spatial analysis of each involved cell population in the tissue. The aim of this study is to compare 3 methods of biomarker analysis in tissues, flow cytometry, immunohistochemistry (IHC) and Chipcytometry, in their ability to detect immune cells in a tumor model. NOD-Scid tg HLA-A2.1 mice were implanted with human Ma-Mel-19 melanoma cells. After 21 days, the mice were injected with human PBMC at the tumor site. Local treatment with Tasquinimod was performed three times once a week. Tumors, including the neighbouring PBMC injection site were excised on day 48 and immune cell content was analysed by Chipcytometry, flow cytometry and IHC. All three methods were successful in detecting human immune cells in the samples consisting mostly of T-cells (CD45+CD3+). However, flow cytometry and IHC were unable to provide either accurate localization or phenotyping data, respectively. With Chipcytometry we were able to specifically quantify tumor-infiltrating immune cells. In treated samples immune cell count (110/mm^2) and composition (72% T-cells) was significantly higher than in the untreated samples (54/mm^2, 49% T-cells). We show that Chipcytometry is able to produce “flow-like” quantitative data, while also retaining spatial information. This makes Chipcytometry a powerful tool for the study of immunological processes in tumors.

Cancer therapies that rely on manipulating or engineering immune cells have shown much promise in recent years for effectively combating a broad array of cancer types. In order to determine the effectiveness of such therapies, it is necessary to accurately profile the complement of immune cell types present in the tumor microenvironment. Methods to reliably obtain immune cell signatures require the combination of powerful resolution to discriminate cell boundaries, broad dynamic range to capture markers of varying expression levels, and accurate cell segmentation across a wide range of cell sizes and morphologies. Many methods for quantitative immune cell profiling have demonstrated shortcomings when it comes to the aforementioned attributes. To remedy these shortcomings, we present here ChipCytometry, an imaging technology for immune profiling of cells and sectioned tissues. ChipCytometry is a fluorescence-based imaging system that utilizes multiplexed immuno-fluorescence staining in combination with high-dynamic-range (HDR) imaging to facilitate quantitative phenotyping of individual cells within tissue samples. Employing this technology to profile metastatic and primary tumors, multiple biomarkers were stained in iterative multiplex assays to profile single cells with an AI-powered cell segmentation algorithm. The HDR imaging allows for both strong and weak fluorescent signals to be simultaneously obtained without loss of sensitivity. This immune profiling was completed on 6 different cryosectioned human tumor tissues (head & neck, liver, lung, breast, colon, and pancreas).