Flow cytometry has been a source of abundant, detailed data about cells of the immune system for decades and is critical component of biomedical research as it transitions into a quantitative, data-driven discipline. Flow cytometry and cell sorting work well as upstream tools to phenotype and sort specific cell subsets for an array of downstream applications. These combined analyses are instrumental to defining accurate biomarker signatures of diseases or chronic conditions. Consider flow cytometry data analysis techniques to take your research into the realm of big data.

1. Single cell sequencing and genomics. Flow cytometry and cell sorting can be used to isolate individual cells for downstream sequencing of discrete genetic elements or whole genome sequencing. This technique is useful for basic immunology research related to adaptive B cell and T cell responses, as well as understanding the impact of experimental treatments on hematologic malignancies.
2. Single cell transcriptomics and RNASeq. The collection of RNA transcripts within an individual cell reflects how external or internal factors can influence gene expression, such as driving inflammatory responses or derailing anti-tumor responses. RNA-seq analysis of single cells allows you to identify, quantify and profile mRNAs at unprecedented levels of detail.
3. Single cell proteomics. Beyond gene expression, flow cytometry and cell sorting can be used to isolate single cells for proteomics analysis. This approach provides a detailed picture of proteins expressed within cells, including the abundance of immune cell proteins like cytokines, chemokines, and cell surface receptors.

Flow cytometry and cell sorting are powerful techniques that can be used in combination with other techniques to create a personalized medicine approach in clinical trial applications. Consider these combined approaches to take your immunology and biomedical research into the era of Big Data.