Getting More from Flow Cytometry

Robin Lynn White on October 2, 2017

Immunology studies rely on identifying unique cell populations, such as T cells and B cells, and then analyzing these cells to understand the biology of these cells. Studying different types of immune cells has provided insight into many fields, such as revolutionizing cancer treatments with immuno-oncology. To continue to advance the immunology field, scientists are asking what is going on inside increasingly rare and unique immune cells. How is their biology changing over time? What biomarkers change across samples or even between patients? Can we leverage these biological changes to advance our knowledge of the underlying immune response to therapeutics, disease, or environmental insult? Questions like these inspire us to push past the boundaries of what is known. But answering complex questions like these from increasingly rare cell types requires cutting edge technology.

Rare immune cell subtypes, such as regulatory T cells and antigen-specific T cells, can be difficult to analyze. Studying these diverse and rare cells in depth requires isolating these populations from large numbers of heterogenous cells, often using flow cytometry. While flow cytometry remains a powerful technique to identify cells, additional techniques are needed to maximize the data extracted from small numbers of rare immune cells. And as any scientist will tell you, there’s a lot more going on inside these cells than what a few extracellular markers show.

Flow cytometry excels at isolating and analyzing cells using multiple makers at single cell resolution and gives us information to better understand population based changes over time. However, only limited experiments can be run with only a few thousand cells, so maximizing the amount of data from each experiment is paramount. Flow cytometry remains the gold standard method for isolating rare populations of cells and enables novel technologies like phospho-flow, CyTOF and single cell RNAseq.

Combining these techniques creates profiles of biological change over DNA, RNA, protein and even posttranslational modification. While challenging, this multi-omic analysis lets us understand the interrelation of these biological systems and drives discovery. Equally important, reduced sample consumption is necessary to observe how these changes occur not just in a Petri dish of cultured cells, but directly from patient derived samples.

To address these challenges and facilitate multi-omic exploration of rare cell types, we are excited to introduce 3D Flow™ Analysis, integrating flow cytometry cell sorting with NanoString 3D Biology™ Technology, to obtain high-plex multi-omic RNA:Protein data from as few as 500 cells. The NanoString nCounter® platform quantitates up to 770 RNA and 30 proteins from a single sample, enabling researchers to glean more data directly from their flow sorted samples without molecular biology, such as RNA amplification or additional proteomic analysis.

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In 3D Flow, cells are co-stained with both fluorescently-labeled antibodies (for sorting) and oligo-tagged NanoString antibodies. These co-stained cells are then sorted with standard workflows, lysed, and directlyanalyzed to obtain high-plex RNA:Protein data. Accomplishing this using traditional lab methods would require separate RNA and protein workflows starting with large quantities of patient derived sample. The nCounter workflow optimizes usage of precious samples, streamlines time to data, and increases data quality, getting to biological insight faster.

Click here to explore more in the 3D Flow Application Note.

FOR RESEARCH USE ONLY. Not for use in diagnostic procedures.

Post by Robin Lynn White