First Studies from NHLBI Stem Cell Consortium Published
Stem cells (stained green) grew throughout the pores of the scaffold (stained red). Credit: Guilak Lab, Washington University
A group of NIH-funded scientists has published the first studies using the largest, most diverse stem cell collection of its kind ever made available to researchers. The results provide fresh insights into the genetic underpinnings of common conditions such as cardiovascular disease, high blood pressure, diabetes, and sickle cell disease, which take a heavy toll on American lives and resources.
In the future, discoveries from these studies of adult stem cells could lead to new ways to diagnose and treat disease, the researchers say. The first 11 studies resulting from this collaborative effort from multiple U.S. institutions appear in the journals Cell Stem Cell, Stem Cell Reports, and EBioMedicine, which are published by Cell Press.
In 2011, the National Heart, Lung, Blood Institute (NHLBI), part of NIH, convened its Next Generation Genetic Association Studies (NextGen) Consortium with the goal of using induced pluripotent stem cells (link is external) (iPS cells) to better understand how complex genetic changes affect heart, lung, and blood cells. More than 1,000 iPS cell lines were obtained from more than 1,000 volunteers of different genders and ethnic backgrounds, making it one of the one of the most diverse stem cell collections ever studied. That diversity, the researchers note, ultimately will prove useful in helping reduce health disparities based on gender and ethnicity.
Though still in their early stages, the NextGen studies are already beginning to produce results. For example, one research group created a library of iPS cells from a geographically- and ethnically-diverse group of people with sickle cell disease. This well-characterized stem cell library could provide the basis for improved pre-clinical drug development for sickle cell disease, the study’s researchers say.
In addition to sickle cell disease, the cell lines from NextGen will prove helpful for studying other complex diseases, particularly cardiovascular disease. In the future, researchers hope to make these stem cell lines available for other researchers to study worldwide.
Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). This finding could help researchers identify new cell subtypes and differentiate between healthy and diseased cells.