Oligopy, a phyton module to determine the oligomerization state of nuclear particles in images from live cells

OLSZANOWSKI, E; PRESMAN DM; PECCI A

Buenos Aires

Congreso; WBDS (Women in Bioinformatics & Data Sciences); 2020

WBDS

A major challenge in cell biology is to determine the quaternary structure of proteins within their natural environment. We present a Python-based module for analyzing images from live cells acquired with confocal microscopy to obtain the oligomerization state of a fluorescent-tagged protein. This routine is based on the Number & Brightness (N&B) technique first introduced by the Gratton lab in 2008, which use moment-analysis for the measurement of the average number of molecules and brightness in each pixel in fluorescence microscopy images. The average brightness of the particle is obtained from the ratio of the variance to the average intensity at each pixel. The representation of the brightness versus the average intensity at each pixel is used to distinguish and select the different compartments of the cell (cytoplasm, nucleus, nucleolus, etc.); and a brightness value for the desired section can be obtained. Comparing the measured brightness value to a known sample, one can determine the particle?s oligomerization state. Oligopy relies on numpy, scipy, matplotlib, pandas and seaborn, providing computations and graphic visualization of results. As a proof-of-concept, we compared data analysis from mammary adenocarcinoma 3617 cells transiently transfected with Glucocorticoid Receptor tagged with Green Fluorescent Protein, previously analyzed by the SimFCS software (Globals for images). We show our routine present comparable results to SimFCS, and since our implementation requires less steps from the end-user, it works as a suitable alternative for N&B analysis.