We are releasing a new point version of our data analysis library pixelator (0.18.3). This update will support Python programmers with improved memory management when aggregating multiple PXL files, as demonstrated in several of our tutorials.

Consider the following scenario: you have processed your samples using the Molecular pixelation (MPX) protocol and after analyzing the data you have made intriguing discoveries about changes in the abundance and spatial arrangement of a few proteins in a particular population of cells. Communicating your findings to your peers is one of the critical steps for better understanding of the data.

Creating the right visualization to communicate you findings is always a challenge, but when done effectively, it becomes a powerful tool to get your message across. In our tutorials section, we provide a variety of examples on how to create different types of visualizations after performing MPX data analysis so feel free to take a look for some inspiration. In this post, we will focus on how to visualize spatial patters on individual cells, which is a frequent request from users who are looking to present their findings. We have already covered how to visualize spatial patterns in a static way in this tutorial, but here we will show you how to create dynamic visualizations. At the end of this post you will have a dynamic 3D visualization of a cell with a protein of interest rotating in 3D such as the one below.

We are delighted to inform all our Pixelators that we are releasing new versions of our data processing pipeline (nf-core/pixelator, 1.3.0) and our pixelator software (0.18 series). These updates will support your experiments with our new Single Cell Spatial Proteomics Kit, Immunology Panel 2, Human v2 (product number PXGIMM002), or MPX v2.

We want to extend a warm welcome to all Pixelators to our Developers (and Data Scientists!) Blog.