Current drug therapies for cardiovascular disease alleviate symptoms for only 50 to 70 percent of patients, often with unwanted side effects. As a result, there is a pressing need for better treatment options.
Induced pluripotent stem cells (iPSCs; cells generated from adult cells, such as human skin or blood, which can potentially differentiate into any kind of human cell) are a promising new field of medical research yielding novel insights into the molecular mechanisms of heart disease.
Researchers at Stanford University in California hope that their iPSC studies will produce better cardiovascular disease models and lead to new patient-specific therapies and screening approaches for drugs.
Dr. Elena Matsa is using iPSCs for studying cardiovascular disease within a wider research remit looking at biological mechanisms of adult stem cells, embryonic stem cells, and iPSCs. The lab uses a combination of next generation sequencing, tissue engineering, physiological testing, and molecular imaging technologies in its research.
For the last year, Dr. Matsa has been using Qlucore's Omics Explorer software to analyze data from experiments that use iPSCs to study Dilated Cardiomyopathy (DCM), a fatal heart disease that affects 5 in 100,000 adults.
DCM is the third leading cause of heart failure in the US. It has various causes, one of which is mutations in genes involved in sarcomeric proteins in the heart muscle, which make the heart muscle baggy and thin so it can no longer pump blood efficiently.
For the DCM studies, the lab works closely with cardiologists to find genetically affected patients at their heart clinics. Heart muscle cells (cardiomyocytes) are collected from these individuals if they have heart surgery. iPSCs are made from 'reprogrammed' skin or blood cells from the same patients and then turned into beating heart muscle cells for direct comparison. It takes 6 to 12 months and several thousands of dollars to generate the cells and sequencing data for these experiments.
Since the technique for making iPSCs is relatively new (John B. Gurdon and Shinya Yamanaka received the Nobel prize for the work in 2012), one aim of the DCM research is to assess whether 'lab-made' heart cells are a good representation of equivalent adult human cells.
A second goal is to see how both cell types respond to various drugs used to treat DCM in the clinic. "If the two types of heart cell respond similarly, it means we can potentially do pre-clinical drug tests on iPSC cardiomyocytes confident that the results will accurately predict how the real human heart will react to a new drug before it is released on the market," explains Dr Matsa who is a specialist on iPSCs and in charge of the ongoing DCM studies.
Today, new drugs are tested on transgenic (genetically modified) cells or in small and large animals before patients. As good as these models are, they are not human. "A human platform of functional cells such as iPSC-derived cardiomyocytes for testing drugs would increase confidence that there will be fewer or no side effects, and the efficacy of the drugs will be improved against the disease they're used for,” explains Dr Matsa.
Good Statistical Tools
To study the behavior of the lab-made heart cells and the adult human cells, the lab carries out RNA sequencing and compares the differences in their gene expression. This data is then uploaded into Omics Explorer.
"Right now we are performing principal component and hierarchical heat map analyses to see how the samples are clustering and we identify genes that can group together the heart tissue and cardiomyocytes from the same patient. We then look at the gene ontologies (molecular function) of the genes to compare similarities and differences between the two," she says.
Currently, Dr. Matsa is working with around 40 samples (20 heart samples and 20 iPSC cardiomyocyte samples) that came from DCM patients and testing different drug treatments for each. As she explains: "We are also looking at maturation of iPSC cardiomyocytes to see if they match the human heart better, so we test different agents and use a PCA plot to see which chemical we added brings those iPSC-heart cells closer to the human heart."
Fast, Flexible Analysis
Typically, several different experiments are underway at the same time in the lab. Another colleague, for example, is using Qlucore for genome editing. "We take skin and blood from a patient carrying a mutation associated with DCM. We use genetic tools to correct the mutation, remove it, and then use the Qlucore tool to see how the gene expression has changed so we can identify any pathways involved in the disease," explains Dr Matsa. A year ago the lab acquired a next generation DNA sequencing machine, which has increased the quantity of data being generated.
"We have a high volume of experiments and we want results promptly.
The Qlucore software is definitely helping," she says. "It means that cell biologists like myself can look at data, analyze and perform statistical analyses for a presentation or a paper without having to go through our bioinformatician. He is one out of 20 or so people in the lab and is overloaded with work.
"That's why I was so excited when I first discovered the software as it was really fulfilling a need we had," she adds.
Prior to acquiring the Qlucore software, some of the biologists worked on developing programming skills but it takes a lot of time to gain this expertise, says Dr. Matsa. Other tools in the lab can align sequences, generate a heat map and apply some statistical tests but there is little flexibility and they are slow. "With Qlucore, you can see how things are changing in real time when you set a p value cut-off for statistical analysis and it's more flexible, so you can also run custom R scripts if required," she says.
Dr Matsa and her team are hoping there will be a point where most analyses can be done on the Omics Explorer platform, including incorporation of different normalization strategies, genome browser viewing, and circular visualization plots. The lab is also thinking of using Qlucore more for other types of analyses such as methylome-sequencing and ChIP-sequencing looking at epigenetic modifications associated with heart disease and response to drug treatment.
"These analyses would deepen our understanding regarding the mechanisms involved in these processes, and could facilitate the discovery of novel therapies,” says Dr Matsa