Measure fluorescence intensity at the nuclear envelope¶
This example reproduces a well-established workflow in bioimage data analysis for measuring the fluorescence intensity localized to the nuclear envelope, in a time sequence of cell images (each with two channels and two spatial dimensions) which shows a process of protein re-localization from the cytoplasmic area to the nuclear envelope. This biological application was first presented by Andrea Boni and collaborators in 1; it was used in a textbook by Kota Miura 2 as well as in other works (3, 4). In other words, we port this workflow from ImageJ Macro to Python with scikit-image.
Boni A, Politi AZ, Strnad P, Xiang W, Hossain MJ, Ellenberg J (2015) “Live imaging and modeling of inner nuclear membrane targeting reveals its molecular requirements in mammalian cells” J Cell Biol 209(5):705–720. ISSN: 0021-9525. DOI:10.1083/jcb.201409133
Miura K (2020) “Measurements of Intensity Dynamics at the Periphery of the Nucleus” in: Miura K, Sladoje N (eds) Bioimage Data Analysis Workflows. Learning Materials in Biosciences. Springer, Cham. DOI:10.1007/978-3-030-22386-1_2
Klemm A (2020) “ImageJ/Fiji Macro Language” NEUBIAS Academy Online Course: https://www.youtube.com/watch?v=o8tfkdcd3DA
Vorkel D and Haase R (2020) “GPU-accelerating ImageJ Macro image processing workflows using CLIJ” https://arxiv.org/abs/2008.11799
import matplotlib.pyplot as plt import numpy as np import plotly.io import plotly.express as px from scipy import ndimage as ndi from skimage import ( filters, measure, morphology, segmentation ) from skimage.data import protein_transport
We start with a single cell/nucleus to construct the workflow.
shape: (15, 2, 180, 183)
The dataset is a 2D image stack with 15 frames (time points) and 2 channels.