Researchers at Rutgers University have developed a novel tool for detecting and omitting the extraneous DNA and RNA that often contaminate analyses of single-celled organisms. The method could also assist in avoiding mismatching sequenced gene fragments.
The software developed, called Single-cell Analysis of Host-Microbiome Interactions or SAHMI, could be used to increase medical research accuracy. In the study, published in Nature Computational Science, the researchers demonstrated the potential broad applications of the software to increase data quality by selectively omitting contaminants. "Sample contamination happens frequently because extraneous genetic material is everywhere: flecking off patient fingers, floating through the air, lurking inside the laboratory's reagents," said Bassel Ghaddar, lead author of the study.
After development, the researchers tested the software on various datasets finding that SAHMI successfully identified and quantified pathogens within the sample while excluding contaminants and false positives. The study also revealed SAHMI’s potential to ID microbe-associated cells as well as analyze the spatial distribution of microbes within a sample.
The researchers also believe that SAHMI could be used to identify microbes that are associated with specific diseases or track disease progression through changes in the microbiome. The team has already used SAHMI to examine the microbiome of pancreatic cancer. They successfully identified microorganisms that are associated with inflammation and poor survival. The team believes microorganisms present a promising early diagnosis or treatment target in pancreatic cancer patients.
"The results this technique produced in our study of pancreatic cancer provided unexpected and important new insight into tumor development while also suggesting new ways to attack tumors," said Subhajyoti De, a principal investigator at Rutgers Cancer Institute. "We think it could produce similar levels of insight in many other fields of study and ultimately in normal patient care, which is why we're making it freely available via Git Hub."