Designing a successful flow experiment – selecting compatible reagents and optimizing your protocol – can be challenging and time-consuming. And yet, as we all know, a well-designed experiment is critical to the collection of high-quality flow data.
What do we think about when designing flow experiments?
What buffers should I use when probing intracellular targets?
Which surface antibodies work well on my sample and with my buffers?
What is the best concentration for my antibody?
Are there alternative protocols that work better with my samples?
We are excited to announce the arrival of two resources that will help you answer those questions and streamline your reagent selection process. BD Biosciences has released the FACSelect™ series, consisting of a Multicolor Panel Designer and a Buffer Compatibility Resource.
Are you working with a collaborator who needs to see your raw data? Are you looking for help from a Cytobank administrator relating to experiment analysis?
Don’t bother opening your email client, searching for an email address, and digging through folders for your flow files. Instead, use the easy sharing features built into Cytobank. Once you have uploaded files to your account, they can be easily shared with others from within the Cytobank interface.
As always, your experiment is visible only to you until you actively choose to give permission to another user to see it. When you do choose to share an experiment, follow these easy steps:
Cytobank users have uploaded and analyzed data collected from more than 30 different flow cytometer models, so chances are that Cytobank can handle your data! In a recent post, we featured the ability of Cytobank to facilitate the mining of data from large datasets generated by the DVS Sciences CyTOF. This time, we will walk you through analysis of data collected on the Accuri cytometers using their CFlow software.
Accuri provided us with a set of sample files demonstrating the collection of data from cells stained with a PE-anti-CD4 antibody, and we’ll use this as an example. You can see from their CFlow software analysis that they achieve separation of and gate on the lymphocyte population (P1, first panel), and further separate CD4+ from CD4- cells (second two panels). We’ll show you how to do the same in Cytobank!
SPADE (Spanning-tree Progression Analysis of Density-normalized Events) is a way to automatically identify populations in multidimensional flow cytometry data files. SPADE clusters cells into populations and then projects them into a tree like the one shown below. SPADE works for data from both ‘classic’ fluorescence flow cytometry and mass cytometry.
Mass cytometry, a technique developed by DVS Sciences, now a Fluidigm Company represents a revolutionary spin on classic fluorescence-based flow cytometry. Instead of using antibodies tagged with fluorophores (in which spectral overlap quickly limits the number of parameters available for simultaneous detection), mass cytometry relies on antibodies tagged with transition element isotopes. Antibody-bound cells are vaporized, ionized, and analyzed on a mass spectrometer.
The ‘Cell Signaling’ course at LACI was taught by local instructors Nathalie Auphan-Anezin and Pierre Grenot, both of CIML, and Jonathan and Chris. The course led course participants through staining, collection, upload, and analysis of a phospho-flow experiment. We’ve briefly described the experiment here, made a version of the dataset public along with the original course protocol, and prepared a tutorial (part 1 and part 2) to lead you through Cytobank analysis of the course data.
Quantifying the percentage of cells expressing a protein of interest is a frequent goal in both basic research and clinical studies. Paired with per-cell comparisons of the level of protein expression, this approach provides a powerful way to track and immunophenotype populations of cells present in a particular sample.
One widely recognized application of flow cytometric immunophenotyping is determining the percentage of CD4+ cells in a gated lymphocyte population in order to determine prognosis for an HIV patient. Other applications include measuring a series of markers in order to distinguish between different forms of leukemia.
In Cytobank, you can use the “percent in gate” statistic to measure and display the percentage of cells in a selected gate as compared to each active population in your figure. To illustrate with a simple example, let’s examine a sample dataset looking at the percentage of CD25+ cells in a CD3+ T cell population.