Label-Free Detection of Neuronal Differentiation in Cell Populations Using High-Throughput Live-Cell Imaging of PC12 Cells

Abstract

Detection of neuronal cell differentiation is essential to study cell fate decisions under various stimuli and/or environmental conditions. Many tools exist that quantify differentiation by neurite length measurements of single cells. However, quantification of differentiation in whole cell populations remains elusive so far. Because such populations can consist of both proliferating and differentiating cells, the task to assess the overall differentiation status is not trivial and requires a high-throughput, fully automated approach to analyze sufficient data for a statistically significant discrimination to determine cell differentiation. We address the problem of detecting differentiation in a mixed population of proliferating and differentiating cells over time by supervised classification. Using nerve growth factor induced differentiation of PC12 cells, we monitor the changes in cell morphology over days by phase-contrast live-cell imaging. For general applicability, the classification procedure starts out with many features to identify those that maximize discrimination of differentiated and undifferentiated cells and to eliminate features sensitive to systematic measurement artifacts. The resulting image analysis determines the optimal post treatment day for training and achieves a near perfect classification of differentiation, which we confirmed in technically and biologically independent as well as differently designed experiments. Our approach allows to monitor neuronal cell populations repeatedly over days without any interference. It requires only an initial calibration and training step and is thereafter capable to discriminate further experiments. In conclusion, this enables long-term, large-scale studies of cell populations with minimized costs and efforts for detecting effects of external manipulation of neuronal cell differentiation.

Morphological Evaluation of Nonlabeled Cells to Detect Stimulation of Nerve Growth Factor Expression by Lyconadin B

“…As demonstrated by several image-processing software packages, our image-based method for recognition and quantitation of neurite outgrowth could also successfully profile the PC12 response, 24 even in the presence of lyconadin B. We used our image-based method to evaluate the effective drug concentration and speed of neurite growth.…”

Rapid Assessment and Visualization of Normality in High-Content and Other Cell-Level Data and Its Impact on the Interpretation of Experimental Results

“…Currently, methods exist to define many different classes for a cell line. 8,34 These result when multiple proteins that show broad independent distributions are sorted (two proteins that independently show high, medium, and low levels can be considered to stratify a cell into nine subpopulations). Comparing this work to that of Sisan 6 suggests that these states could persist throughout several generations.…”

Machine learning and computer vision approaches for phenotypic profiling

“…This approach assigns weights to each feature in the dataset, highlighting features that have high variance in the data and may be more useful for classification. This model was recently applied to the study of neuronal differentiation in PC12 cells (Weber et al, 2013) and has been used to classify a diverse range of image-based datasets (Wang et al, 2008; Horn et al, 2011; Pardo-Martin et al, 2013). Fisher’s method performs best for low-dimensional, small datasets, whereas the combination of PCA and naive Bayes can provide a performance similar to Fisher’s method when applied to large datasets.…”