NEWS
Tags: 

Qlucore software accelerates research into heart disease

Scientists at Stanford University are using Qlucore software to accelerate research into the study of Dilated Cardiomyopathy (DCM), a fatal heart disease that affects five in 100,000 adults.

The researchers at Stanford University in California are investigating the use of induced pluripotent stem cells (iPSCs) to produce better cardiovascular disease models that could lead to new patient-specific therapies and screening approaches for drugs to treat DCM.  iPSCs are cells generated from adult cells, such as human skin or blood, that can potentially differentiate into any kind of human cell.

To accelerate the study the Stanford researchers have been using Qlucore Omics explorer to collate and process data efficiently as Dr Elena Matsa, a specialist on iPSCs and the lead researcher in the ongoing DCM studies explains.

She said: ‘We have a high volume of experiments and we want results promptly and the Qlucore software is definitely helping. It means that cell biologists like myself can look at data, analyse 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’ Matsa added.

Matsa is using iPSCs to study cardiovascular disease within a wider research remit of 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.

One example of the work that is being carried out by Matsa and her colleagues is gene editing, using Qlucore software has made this task much easier with its built-in tools to assist with gene expression. Matsa stated: ‘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.’

DCM is the third biggest 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. Current drug therapies for cardiovascular disease alleviate symptoms for only 50 to 70 per cent of patients, often with unwanted side effects. As a result, there is a pressing need for better treatment options.

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.

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, said 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 relatively 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 said.

Matsa and her team are hoping there will be a point where most analyses can be done on the Omics Explorer platform, including the incorporation of different normalisation strategies, genome browser viewing, and circular visualization plots. The lab is also planning to use Qlucore for other types of analyses such as methylome-sequencing and ChIP-sequencing to investigate epigenetic modifications associated with heart disease and response to drug treatment.

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 do not as accurate as using a human model for obvious reasons but this research offers a way of improving the accuracy of models used for research into cardiovascular disease.

‘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’ concluded Matsa.

Twitter icon
Google icon
Del.icio.us icon
Digg icon
LinkedIn icon
Reddit icon
e-mail icon
Feature

Sophia Ktori highlights the role of the laboratory software in the use of medical diagnostics

Feature

Gemma Church explores the use of modelling and simulation to predict weather and climate patterns

Feature

Robert Roe reviews the latest in accelerator technology and finds that GPUs and coprocessors will be key fixtures in the future of deep learning

Feature

Robert Roe finds that commoditisation of flash and SSD technology and the uptake of machine learning and AI applications are driving new paradigms in storage technology.