Cells can undergo different treatment regimens for different types of drugs or quantities of a drug to assess percent changes in cellular analyte expression.įigure 2. An example can be seen when the same target analyte is analyzed through multiple experimental samples across different treatment parameters. Data plotted in this format is used to evaluate cells that possess a physical expression of target markers at notable levels, indicated as positive datasets. This is generally represented with the relative fluorescence on the X-axis and the number of events on the Y-axis. When only one parameter is considered, univariate histograms are the most common means to represent data. This is followed by selective gating of target populations for further study, which will be explored later in this TechNote. For most traditional flow cytometry experiments, compensated values are plotted with single or multivariate parameters. Representing flow cytometry data can be done in many different ways. Doing this allows for reasonable comparison of data sets acquired over extended periods of time. For this reason, data normalization is conducted as a means to eliminate as much variation as possible. These interferences make biologically equivalent populations difficult to match across different samples. Further variation is also introduced when using different instruments, which could introduce different setting configurations and sensitivity. This may result from inconsistencies in reagents such as degradation over time, lot production variation, and sample handling as common examples. Types of transformations commonly used include logarithmic, linear-logarithmic hybrids such as Logicle, biexponential, and power transformations such as Box-Cox.Īnother universal problem is technical variation in sample acquisition. This is done by choosing a set of common transformation parameters amongst multiple samples to ensure proper representation on a common scale fit for comparison. Enzo Life Science’s spectra viewer depicts emission spectral overlap between Alexa Fluor 647 (Em: 655 nm) and Propidium Iodide (Em: 617 nm).Ĭonducting data transformation is also necessary to mitigate negative downstream influences of sample asymmetry and overlapping cell populations in future data analysis. These values alongside total fluorescence signal can then be used to produce a spillover matrix to generate compensated data.įigure 1. This establishes a baseline measurement that is representative for that fluorophore at that specific wavelength. Signal compensation is when flow cytometers account for this signal spillover by running a representative stains for only one fluorophore at a time.
While each channel is specifically designed to read at a certain wavelength, readings typically reflect peak emission intensity given off by all sample fluorophores in tandem with that wavelength. One of the first major obstacles is fluorescence spectra overlap. Prior to data plotting and analysis, flow cytometry datasets must undergo pre-processing to remove technical interference and poor quality data. Here, we discuss the processes for analyzing flow cytometry data and addressing these concerns. This introduces challenges for reproducibility, cohesive analysis, and subsequently the ability to generate meaningful discoveries. However, every increased parameter significantly increases the quantity of data points to consider and the complexity of analysis. Additional access to stains and targets allows for significant increases in one’s ability to refine cell populations and isolate target subgroups of interest. Significant advances have been made in fluorophore and instrument technologies such that operators can now quantify up to 18 markers at a time. Anti-CD63.ġ vial (100 µg) of HBM-exosome standards (lyophilized), from COLO1 cell culture supernatant (number of particles/ml 1x10 10 ).Īll the reagents are shipped at 4☌ and storage conditions as recommended in the product insert.Įxosome isolation and exosome marker characterization via FACS.Up until the early 21st century, flow cytometry operators were only capable of measuring a few fluorescent markers at a time. Primary antibody for exosome marker detection as positive control (40 ul).
Kit provides a proprietary antibody against a common exosome (CD9 or CD63) marker and a set of purified Exosome Standards as positive control.Ĥ µm Aldheyde-Sulfate latex beads (100 µl). Kit allows fast and easy exosome isolation and detection of exosome markers via FACS. Primary and secondary antibody must be appropriately diluted in sample buffer. Beads are ready to use for exosome capture. Exosome standards must be reconstituted in 100 µl of deionized water. Exo-FACS Ready to Use Kit for FACS analysis.Įxo-FACS contains reagents and antibodies for 20 reaction.
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