Have you ever wanted to know how many tumor cells were in each microliter of a blood sample? The newest CellEngine release expands the ability to calculate absolute cell counts, which determine the number of a specific cell type in a given volume of sample.
Creating a figure to communicate a gating hierarchy is a common task for data review and for sharing gating strategies with other scientists. These consist of a series of flow plots showing the gate positions and parent-child relationships between gated populations. CellEngine can now generate these plots automatically, making analysis even faster.
As a large contract research organization (CRO), CellCarta analyzes thousands of flow cytometry samples every month. CellCarta is leveraging automation tools such as CellEngine and laboratory information management systems (LIMS) for a seamless process.
This week at CYTO, we presented our poster examining the potential to identify cell types in CITE-seq data using hierarchical gating. Compared to clustering, gated populations have clear definitions based on specific marker expression levels. We compared cell type assignment by hierarchical gating vs. unbiased clustering.
Making a good figure is an art, requiring careful consideration of the data and the reader. Color is one tool that can help communicate information, and should be a consideration whenever preparing to present data. Many readers will look at figures before (or instead of!) reading a paper. During talks, it is easy to miss context from the speaker. For this reason, the easier a figure is to understand by itself, the easier it will be for your audience. There are numerous factors to consider when choosing a palette, but the right choices can make it easier to understand the underlying data.
Correctly scaling flow cytometry data is critical to effective interpretation and the proper quantitative handling of data.
Gating is the backbone of cytometry analysis. Some data is consistent enough that gate positions can be set globally, but oftentimes, gates need to be adjusted for every sample. This process can be extremely time consuming, and maintaining consistency can be challenging, especially if multiple people are working on a project. To make analysis easier, we developed an algorithm that quickly tailors gates to the underlying data.
No one is surprised to read that individuals coordinate their behavior and act as groups. We see collective behavior everywhere: from family gatherings, to corporate workplaces, and on the playing field. We also know that other animals act collectively. Birds flock, fish school, and wolf packs hunt. What might surprise us, however, is how ubiquitous the phenomenon is. Remarkably, coordinated, collective behavior can be seen in organisms as simple as bacteria, single-celled creatures without even a nucleus, much less a nervous system. Because they are so simple, bacteria can serve as a model organism for studying collective behavior. And because each individual is a single cell, CellEngine can be an important tool for analyzing that behavior.