Live embryo imaging to follow cell cycle and chromosomes stability after nuclear transfer

White paper on guidelines concerning enteric nervous system stem cell therapy for enteric neuropathies

Over the last 20 years, there has been increasing focus on the development of novel stem cell based therapies for the treatment of disorders and diseases affecting the enteric nervous system (ENS) of the gastrointestinal tract (so-called enteric neuropathies). Here, the idea is that ENS progenitor/stem cells could be transplanted into the gut wall to replace the damaged or absent neurons and glia of the ENS. This White Paper sets out experts' views on the commonly used methods and approaches to identify, isolate, purify, expand and optimize ENS stem cells, transplant them into the bowel, and assess transplant success, including restoration of gut function. We also highlight obstacles that must be overcome in order to progress from successful preclinical studies in animal models to ENS stem cell therapies in the clinic.

Recent advances in quantitative high throughput and high content data analysis

INTRODUCTION

High throughput screening has become a basic technique with which to explore biological systems. Advances in technology, including increased screening capacity, as well as methods that generate multiparametric readouts, are driving the need for improvements in the analysis of data sets derived from such screens.

AREAS COVERED

This article covers the recent advances in the analysis of high throughput screening data sets from arrayed samples, as well as the recent advances in the analysis of cell-by-cell data sets derived from image or flow cytometry application. Screening multiple genomic reagents targeting any given gene creates additional challenges and so methods that prioritize individual gene targets have been developed. The article reviews many of the open source data analysis methods that are now available and which are helping to define a consensus on the best practices to use when analyzing screening data.

EXPERT OPINION

As data sets become larger, and more complex, the need for easily accessible data analysis tools will continue to grow. The presentation of such complex data sets, to facilitate quality control monitoring and interpretation of the results will require the development of novel visualizations. In addition, advanced statistical and machine learning algorithms that can help identify patterns, correlations and the best features in massive data sets will be required. The ease of use for these tools will be important, as they will need to be used iteratively by laboratory scientists to improve the outcomes of complex analyses.