Tracking stem cell differentiation without biomarkers using pattern recognition and phase contrast imaging

Abstract

AbstractBio-image informatics is the systematic application of image analysis algorithms to large image datasets to provide an objective method for accurately and consistently scoring image data. Within this field, pattern recognition (PR) is a form of supervised machine learning where the computer identifies relevant patterns in groups (classes) of images after being trained on examples. Rather than segmentation, image-specific algorithms or adjustable parameter sets, PR relies on extracting a common set of image descriptors (features) from the entire image to determine similarities and differences between image classes.Gross morphology can be the only available description of biological systems prior to their molecular characterization, but these descriptions can be subjective and qualitative. In principle, generalized PR can provide an objective and quantitative characterization of gross morphology, thus providing a means of computationally defining morphological biomarkers. In this study, we investigated the potential of a pattern recognition approach to a problem traditionally addressed using genetic or biochemical biomarkers. Often these molecular biomarkers are unavailable for investigating biological processes that are not well characterized, such as the initial steps of stem cell differentiation.Here we use a general contrast technique combined with generalized PR software to detect subtle differences in cellular morphology present in early differentiation events in murine embryonic stem cells (mESC) induced to differentiate by the overexpression of selected transcription factors. Without the use of reporters, or a priori knowledge of the relevant morphological characteristics, we identified the earliest differentiation event (3 days), reproducibly distinguished eight morphological trajectories, and correlated morphological trajectories of 40 mESC clones with previous micro-array data. Interestingly, the six transcription factors that caused the greatest morphological divergence from an ESC-like state were previously shown by expression profiling to have the greatest influence on the expression of downstream genes.