16S rRNA Sequencing - Determining Bacterial Presence in a Sample

In order to determine what bacteria are present in a sample, our team uses 16S rRNA sequencing technology.

Science Behind the Technology

The 16S rRNA gene encodes for the 16S rRNA molecule that is a key component of the microbial ribosome; it plays an integral role in peptide synthesis. Because this gene encodes for an RNA molecule necessary for life, the gene is found in all bacteria, and hence the gene can show divergences among bacteria. Thus, this gene can differentiate between different bacterial taxa. The variable regions (we use V1-V3) of the gene encoding for this ribosomal RNA are sequenced and used to determine what bacteria are present in a sample.

What this Technology Does for Us

In many of our studies, we aim to characterize the microbiome of a sample. For different studies, we have different biospecimen types, including tumor tissue, stool, and oral gargles. To determine the different microbes that are present within a sample, we use 16S rRNA sequencing. This technique allows us to identify bacteria (and only bacteria, since human DNA does not encode for 16S rRNA) based on their genomic sequence. The different sequences are compared to known 16S rRNA sequences (housed in public databases) to determine the taxonomic names of the microbes present in a specimen. Rather than relying on culture techniques that do not identify un-culturable organisms, genomic sequencing is a highly sensitive method enabling us to accurately detect bacterial presence in a sample without worry of serious underrepresentation.  With this technology, our team is able to glean data from tissue, stool, and oral gargles to characterize the microbiota. This data can then be examined for associations with TIL metrics, clinical features (such as response to therapy), and functional and metabolic profiles of the microbiota. 

For more information about 16S rRNA sequencing, refer to the Illumina® website.




ImmunoSeq® High Throughput Sequencing - Determining T cell Receptor Characteristics in a Sample

Our team has used the ImmunoSeq® platform from Adaptive Biotechnologies® to sequence variations in the CDR3 beta region of the T cell receptors that were present on tumor infiltrating lymphocytes (TILs) in head and neck tumor tissue.

Science Behind the Technology

A T cell receptor recognizes and binds an antigen, activating a signal cascade that helps to turn the T cell on. These receptors are composed of several parts, including the CDR3 beta chain. The CDR3 beta chain is an integral part of the antigen binding portion of the T cell receptor, and thus will vary in amino acid sequence based on the specific antigen it recognizes. Recombination at the variable, diversity and joining (VDJ) regions of the DNA encoding for this protein receptor allows for enhanced T cell diversity. In other words, it allows for the T cells of the body to have the ability to recognize a vast expanse of different antigens.

What this Technology Does for Us

Using this sequencing technology, the various types of TILs within head and neck tumor tissue samples were able to be determined and calculated into TIL fraction (measure of activated and functional TILs present per all nucleated cells) and clonality (measure of TIL diversity (i.e., as a ratio, are the T cells very similar in the antigen they recognize or very disparate?)). These TIL metrics, taken together, may be able to help predict outcomes of cancer therapeutics and prognosis. For example, a recent study from Nguyen and colleagues correlates higher TIL count to better survival [1]. Our team hopes to utilize TIL counts in relation to the presence of certain microbes in the same tumor to better predict patient prognosis.

For more information on how ImmunoSeq® works, click here to visit Adaptive Biotechnologies® website. 





  1. Nguyen, N., Bellile, E., Thomas, D., McHugh, J., Rozek, L., Virani, S., . . . Wolf, G. T. (2016). Tumor infiltrating lymphocytes and survival in patients with head and neck squamous cell carcinoma. Head Neck, 38(7), 1074-1084. doi:10.1002/hed.24406