TruDiagnostic has announced their newest research project: expanding and validating a new cellular deconvolution method to separate epithelial cells from immune cells in saliva samples.
Epigenetics is the interface through which our body, at a molecular level, adapts to the world we experience. Epigenetic markers like DNA methylation can react to outside stressors to activate or silence genes, an action that then impacts the rest of the body.
Longevity.Technology: Cellular deconvolution, also called “cell-type composition analysis” or “cell-type ratio analysis” is used to estimate the proportions of different cell types collected in a single sample. This is highly useful for epigenetic research because it allows a more accurate examination of the methylation happening in isolated cell types.
Changes to DNA methylation are specific to cell types. Cells specific to the immune system, for instance, have a different methylation pattern from epithelial cells. Cellular Deconvolution lets researchers isolate data from one cell type, instead of pulling combined data from all the different cell types at the same time.
Additionally, cellular deconvolution can help detect pathological conditions, or predict treatment options. A very low lymphocyte count, a type of cell from the immune system, can indicate a possible infection or other significant illness.
Saliva is a relatively easy tissue to access. DNA in saliva can be easily extracted, and saliva contains both epithelial cells and immune cells.
Validating this method would expand the number of epigenetic samples available to researchers because the public is more familiar and comfortable with offering saliva samples for genomic research. Blood as a sample still makes many people uncomfortable to extract or handle, and this can limit research participation.
TruDiagnostic and external research partners worked to create a DNA methylation matrix. A DNA methylation matrix is a way to identify the locations on the DNA where methylation can occur, methylation values, and cell types within a sample. This matrix model, when accurate, could be used as a reference guide for cellular deconvolution.
So far, this matrix has proven very accurate in predicting the true cell-type fractions of independent samples when examining 2 cell types. Importantly, the tested performance remained robust while using different technologies, like Stem-Cell-Matrix Compendium-2 (SCM2), 450k technology, BLUEPRINT, and EPIC technology.
“Almost all epigenetic algorithms have been created via blood cell datasets, which is why blood tissue is widely used for accurate methylation detection and quantification,” says Dr Dwaraka, Head of Bioinformatics at TruDiagnostic. “However, even in the blood,
measurements can be highly variable if the different types of immune cell subsets are not controlled for during the analysis. This is where cell deconvolution methods are so vital, as they allow us to control for these variables during analysis.”
Saliva epigenetic samples face the same problem, but face more confounding variables due to the greater number of epithelial cell types found from the buccal tissue. To alleviate this issue, TruDiagnostic have created this saliva deconvolution method to make sure it accurately controls for these cell quantities which can change with age and conditions such as smoking. This is a critical step in making sure that the innovations created from blood methylation can also be applied to saliva tissue samples.
Longevity.Technology reached out to Ryan Smith, Vice President of Business Development at TruDiagnostic, to find out why research is so key.
“Having an accurate saliva deconvolution method is extremely important to control for the types of cells we are testing in a sample,” he told us. “The reason is that the epigenetic signatures of each of our cell types are different. They also age epigenetically at different rates. For instance, if we tested brain tissue, we would have much slower rates of aging than we see in blood.
Conversely, we often see breast tissue age at higher rates. When we are testing saliva, we have a mixture of cell types and cell types which change with age. If we don’t take this into account, any predictive algorithm made in saliva can have high ranges of error.
“With a saliva deconvolution method, we can predict the relative concentration of epithelial and immune cells found in saliva to make our insights and algorithms in saliva much more accurate and informative. Since saliva is the least invasive tissue to collect, this is a huge step forward for the development of Biological age algorithms in saliva. It will open up a new population of individuals willing to do age diagnostics.”
TruDiagnostic is now working on developing this matrix further, to create and validate a saliva DMRM for 8 cell types. The 8 cell-type references would estimate fractions for all 7 immune cell subtypes, in addition to the epithelial cell fraction. This would create a way to closely examine methylation of immune cell types and give more useful clinical data from each saliva sample.