Multiresolution identification of germ layer components in teratomas derived from human and nonhuman primate embryonic stem cells

We propose a system for identification of germ layer components in teratomas derived from human and nonhuman primate embryonic stem cells. Tissue regeneration and repair, drug testing and discov-ery, the cure of genetic and developmental syndromes all may rest on the understanding of the biology and behavior of embryonic stem (ES) cells. Within the field of stem cell biology, an ES cell is not con-sidered an ES cell until it can produce a teratoma tumor (the ”gold” standard test); a seemingly disorganized mass of tissue derived from all three embryonic germ layers; ectoderm, mesoderm, and endo-derm. Identification and quantification of tissue types within ter-atomas derived from ES cells may expand our knowledge of abnor-mal and normal developmental programming and the response of ES cells to genetic manipulation and/or toxic exposures. In addition, because of the tissue complexity, identifying and quantifying the tis-sue is tedious and time consuming, but in turn the teratoma provides an excellent biological platform to test robust image analysis algo-rithms. We use a multiresolution (MR) classification system with texture features, as well as develop novel nuclear texture features to recognize germ layer components. With redundant MR transform, we achieve a classification accuracy of approximately 88%. Index Terms — Stem cell biology, multiresolution, classifica-tion, feature extractio

Nicotine exposure during differentiation causes inhibition of N-myc expression

Background: The ability of chemicals to disrupt neonatal development can be studied using embryonic stem cells (ESC). One such chemical is nicotine. Prenatal nicotine exposure is known to affect postnatal lung function, although the mechanisms by which it has this effect are not clear. Since fibroblasts are a critical component of the developing lung, providing structure and secreting paracrine factors that are essential to epithelialization, this study focuses on the differentiation of ESC into fibroblasts using a directed differentiation protocol.Methods: Fibroblasts obtained from non-human primate ESC (nhpESC) differentiation were analyzed by immunohistochemistry, immunostaining, Affymetrix gene expression array, qPCR, and immunoblotting.Results: Results of these analyses demonstrated that although nhpESCs differentiate into fibroblasts in the presence of nicotine and appear normal by some measures, including H&E and SMA staining, they have an altered gene expression profile. Network analysis of expression changes demonstrated an over-representation of cell-cycle related genes with downregulation of N-myc as a central regulator in the pathway. Further investigation demonstrated that cells differentiated in the presence of nicotine had decreased N-myc mRNA and protein expression and longer doubling times, a biological effect consistent with downregulation of N-myc.Conclusions: This study is the first to use primate ESC to demonstrate that nicotine can affect cellular differentiation from pluripotency into fibroblasts, and in particular, mediate N-myc expression in differentiating ESCs. Given the crucial role of fibroblasts throughout the body, this has important implications for the effect of cigarette smoke exposure on human development not only in the lung, but in organogenesis in general. © 2013 Ben-Yehudah et al.; licensee BioMed Central Ltd

Embryonic Stem Cells

Embryonic stem cells are one of the key building blocks of the emerging multidisciplinary field of regenerative medicine, and discoveries and new technology related to embryonic stem cells are being made at an ever increasing rate. This book provides a snapshot of some of the research occurring across a wide range of areas related to embryonic stem cells, including new methods, tools and technologies; new understandings about the molecular biology and pluripotency of these cells; as well as new uses for and sources of embryonic stem cells. The book will serve as a valuable resource for engineers, scientists, and clinicians as well as students in a wide range of disciplines

Local Histograms and Image Occlusion Models

The local histogram transform of an image is a data cube that consists of the histograms of the pixel values that lie within a fixed neighborhood of any given pixel location. Such transforms are useful in image processing applications such as classification and segmentation, especially when dealing with textures that can be distinguished by the distributions of their pixel intensities and colors. We, in particular, use them to identify and delineate biological tissues found in histology images obtained via digital microscopy. In this paper, we introduce a mathematical formalism that rigorously justifies the use of local histograms for such purposes. We begin by discussing how local histograms can be computed as systems of convolutions. We then introduce probabilistic image models that can emulate textures one routinely encounters in histology images. These models are rooted in the concept of image occlusion. A simple model may, for example, generate textures by randomly speckling opaque blobs of one color on top of blobs of another. Under certain conditions, we show that, on average, the local histograms of such model-generated-textures are convex combinations of more basic distributions. We further provide several methods for creating models that meet these conditions; the textures generated by some of these models resemble those found in histology images. Taken together, these results suggest that histology textures can be analyzed by decomposing their local histograms into more basic components. We conclude with a proof-of-concept segmentation-and-classification algorithm based on these ideas, supported by numerical experimentation

Semi-automatic identification of punching areas for tissue microarray building: the tubular breast cancer pilot study

Background: Tissue MicroArray technology aims to perform immunohistochemical staining on hundreds of different tissue samples simultaneously. It allows faster analysis, considerably reducing costs incurred in staining. A time consuming phase of the methodology is the selection of tissue areas within paraffin blocks: no utilities have been developed for the identification of areas to be punched from the donor block and assembled in the recipient block.Results: The presented work supports, in the specific case of a primary subtype of breast cancer (tubular breast cancer), the semi-automatic discrimination and localization between normal and pathological regions within the tissues. The diagnosis is performed by analysing specific morphological features of the sample such as the absence of a double layer of cells around the lumen and the decay of a regular glands-and-lobules structure. These features are analysed using an algorithm which performs the extraction of morphological parameters from images and compares them to experimentally validated threshold values. Results are satisfactory since in most of the cases the automatic diagnosis matches the response of the pathologists. In particular, on a total of 1296 sub-images showing normal and pathological areas of breast specimens, algorithm accuracy, sensitivity and specificity are respectively 89%, 84% and 94%.Conclusions: The proposed work is a first attempt to demonstrate that automation in the Tissue MicroArray field is feasible and it can represent an important tool for scientists to cope with this high-throughput technique

Local Histograms for Per-Pixel Classification

We introduce a rigorous mathematical theory for the analysis of local histograms, and study how they interact with textures that can be modeled as occlusions of simpler components. We first show how local histograms can be computed as a system of convolutions and discuss some basic local histogram properties. We then introduce a probabilistic, occlusion-based model for textures and formally demonstrate that local histogram transforms are natural tools for analyzing the textures produced by our model. Next, we characterize all nonlinear transforms which satisfy the three key properties of local histograms and consider the appropriateness of local histogram features in the automated classification of textures commonly encountered in histological images. We discuss how local histogram transforms can be used to produce numerical features that, when fed into mainstream classification schemes, mimic the baser aspects of a pathologist\u27s thought process