Document Type


Journal Title

Scientific Reports

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Macrophages, apart from being the key effector cells of the innate immune system, also play critical roles during the development and progression of various complex diseases, including cancer. Tumor-associated macrophages, infiltrate tumors during different stages of cancer progression to regulate motility, invasion, and intravasation to metastatic sites. Macrophages can exist in different polarization states associated with unique function in tumors. Since tumor-associated macrophages constitute a very small proportion of tumor cells, analysis of gene expression pattern using normal extraction buffer-based methods remains a challenging task. Therefore, it is imperative to develop low-throughput strategies to investigate transcriptional regulations from a small number of immune cells. Here, we describe an efficient, sensitive, and cost-effective approach for gene expression analysis of a small number of fluorescence-activated sorted tumor-associated macrophages. Our analyses from the different number of stable, primary, and sorted macrophages suggest 5,000 cells is an optimal number for performing quantitative, real-time PCR analysis of multiple genes. Our studies could detect expression of macrophage-specific genes from cultured primary macrophages, and FACS-sorted macrophages from different biological tissues without introducing biases in comparative gene expression ratios. In conclusion, our kit-based method for quantitative gene expression analysis from a small number of cells found in biological tissues will provide an opportunity to study cell-specific, transcriptional changes.

MeSH Headings

Animals, Cell Count, Cell Separation, Female, Flow Cytometry, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Macrophages, Mice, Mice, Inbred C57BL, Neoplasm Proteins, Neoplasm Transplantation, Pancreatic Neoplasms, Real-Time Polymerase Chain Reaction, U937 Cells



Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.