Flow cytometry has moved into the era of “big data,” and data plots have also shifted from scatter plots and histograms toward visualization methods that can handle this complex data. This era of computational cytometry has ushered in the use of T-distributed Stochastic Neighbor Embedding (t-SNE) data analysis, a machine learning algorithm applied to complex flow cytometry data. Check out these three reasons why t-SNE data analysis is a valuable data visualization tool for flow cytometry.
Flow Cytometry Blog
Cell sorting is not limited to studying cells derived from blood or tissue samples, but it is also a valuable too for characterizing cell lines. Cell lines are critical to basic research studies as well as biopharmaceutical product development and production. Consider these cell sorting applications for your cell line needs.
Regulatory T cells (Tregs) are an important cell subpopulation because they can modulate immune responses that target self-antigens but are also associated with poor outcomes in cancer patients. Tregs are found in peripheral blood and are defined by both surface and intracellular markers, but there is still some debate as to the optimal staining panel for monitoring Tregs, particularly during immunotherapeutic interventions for cancer therapy and autoimmune diseases.
Flow cytometry analysis is a critical technique for guiding preclinical drug development. Flow cytometry data can provide mechanistic insights and define potential biomarkers that correspond to clinical outcomes. But getting the most out of your preclinical flow cytometry analysis may hinge on how your cell samples are handled, specifically whether you are working with fresh cells or fixed cells.