LiveCellMiner: A new tool to analyze mitotic progression

Abstract

Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.

End-to-End Classification of Cell-Cycle Stages With Center-Cell Focus Tracker Using Recurrent Neural Networks

“…The 3-class dataset: This dataset is provided by LiveCellMiner [26] and contains microscopic image sequences of the mitosis process. The LiveCellMiner tool allows the analysis of mitotic phases in 2D+t microscopy images.…”

“…The acquired images have many cells in each time frame. The software [26] tracks the position of each cell in the image and then extracts the tracked single-cell image. LiveCellMiner then identifies and eliminates cells that do not undergo cell division.…”

Weakly-supervised Temporal Segmentation of Cell-cycle Stages with Center-cell Focus using Recurrent Neural Networks

“…Here we regard post-mitosis as the period where very gradual changes bring the compact anaphase chromatin to a decondensed G2-like mass in early G1. In this work, we consider the classification of the cell-cycle stages into three different phases as in [2]: interphase, mitosis, and post-mitosis. Current state-of-the-art methods [2, 3] extract hand-engineered features, which are then classified using clustering methods.…”

“…In this work, we consider the classification of the cell-cycle stages into three different phases as in [2]: interphase, mitosis, and post-mitosis. Current state-of-the-art methods [2, 3] extract hand-engineered features, which are then classified using clustering methods. Zhong et al [3] proposed an unsupervised clustering algorithm based on a temporally constrained combinatorial clustering (TC3) method.…”

“…a) demonstrates a plot from LiveCellMiner[2], which shows interphase recovery for the LSM710 dataset happens around 20 frames after mitosis. Fig.5 (b) illustrates the label matrix of the proposed model when predicted into four classes.…”