Rutgers researchers have created an analytical technique for detecting and removing stray DNA and RNA that contaminate single-celled organism genetic analysis.
Their findings, published in Nature Computational Science, could help scientists avoid mismatching sequenced gene snippets from various species in the same sample.
The free software, known as Single-cell Analysis of Host-Microbiome Interactions, or SAHMI, can increase the accuracy of medical research, particularly study into the microbiome’s influence on health, and potentially guide clinical treatment based on genetic studies of tissue samples.
Sample contamination happens frequently because extraneous genetic material is everywhere: flecking off patient fingers, floating through the air, lurking inside the laboratory’s reagents.”
Bassel Ghaddar, Study Lead Author and Ph.D. Student, Robert Wood Johnson Medical School, Rutgers University
Ghaddar added, “There is also a challenge arising from the algorithms we use to understand where sequenced gene segments come from. hey need to figure out whether a bit of DNA or RNA belongs to the patient or a bacterium in the microbiome or an invading virus or something else. And these algorithms can make a lot of mistakes.”
SAHMI's creators tested it on several datasets comprising samples of human tissues with known microbial diseases after designing it. SAHMI effectively detected and measured recognized pathogens in all samples while filtering out pollutants and false positives, they discovered.
The tests also revealed that SAHMI could help to detect microbe-associated cells and examine the geographic distribution of microorganisms in tissues.
The capacity of the software to enhance result accuracy could help in the research of diverse tissues and diseases. Ghaddar believes it will be especially useful in tissue samples, which often include a significant number of undiscovered microbes.
These tissue types naturally include those that interact with the microbiomes of the intestines, skin, nose, and lungs. They include many different forms of tissue that were long assumed to be microbe-free, such as those from organs like the pancreas and many malignancies.
With this in mind, the SAHMI developers stated that it could be used to identify bacteria linked with specific diseases or to follow changes in the microbiome throughout disease progression. It can also be utilized to investigate the impacts of drugs or other treatments on the microbiome, as well as the influence of initial microbiome composition on disease risk.
SAHMI has previously been employed by the Rutgers team to study the microbiome of pancreatic cancers and identify specific microbes related to inflammation and poor survival at single-cell resolution. According to the researchers, microorganisms could represent potential targets for earlier detection or therapy of pancreatic cancer, the fourth highest cause of cancer mortality in both men and women in the United States.
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. 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.”
Subhajyoti De, Study Senior Author and Principal Investigator, Rutgers Cancer Institute
Ghaddar, B., et al. (2023). Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues. Nature Computational Science. doi.org/10.1038/s43588-023-00507-1