CryoCOMPASS (Condition Optimization by Modelling and Parameter Analysis for Sample Screening ) reduces the amount of trial and error in the process of grid vitrification, minimizing the amount of time and effort needed to obtain high-quality grids for CryoEM data collection.

CryoCOMPASS allows you to approach your CryoEM sample optimization more strategically using principles of Design of Experiments. Using CryoCOMPASS, you can test a wide range of input conditions using relatively few test samples, build a linear regression model from the resulting quality markers of those test samples, and use that model to calculate optimal inputs which yield high-quality grids for data collection.

There are three tabs at the top of the screen upon opening CryoCOMPASS: Home, Experimental Design, and Desirability Analysis. You’ll begin with the Experimental Design tab, with which you will produce a list of condition sets to test. You will then freeze sample grids according to that list of condition sets, then screen them according to a qualitative rating scheme. Then, in the Desirability Analysis tab, you will upload your screening results back into CryoCOMPASS and run linear regression analysis, which will provide statistical data as well as a list of optimized input parameters that should yield high-quality samples, depending on the accuracy of your model dataset.