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Nos, G. A.

Publications and source records attributed to Nos, G. A..

2 recordsLinked to original sources

ITRAP2, a flexible and robust strategy to assign antigen recognition of T-cells in a coupled single-cell TCR-pMHC assay

Determining T-cell specificity forms a crucial step toward understanding T-cell involvement in health and disease. Single-cell sequencing technologies allow for co-capture of TCR alpha and beta chains, and their antigen specificity can be determined through peptide-MHC (pMHC) multimer binding and capture of a co-attached barcode oligo. However, SC sequencing often includes a high level of dropouts and risk of cross-contamination. Similarly, barcoded pMHC readouts often suffer from significant background noise. These issues complicate the automatic assignment of pMHC recognition to TCR clonotypes. To overcome these challenges, we developed a method for data denoising - Improved T-cell Receptor Antigen Paring 2 (ITRAP2). This approach significantly reduces noise in single-cell pMHC readouts and allows for accurate identification of TCR specificity. ITRAP2 incorporates statistical tests and confidence metrics for each TCR-pMHC pairing, offering user flexibility in pairing rigor, and allows multiple pMHC assignments to the same T-cell clone in the event of cross-binding within the pMHC multimer library. We tested this method on an in-house generated dataset of 8141 single cells, screened for CD8 T-cell binding using a panel of 100 different barcode-labelled pMHC multimers holding virus-derived peptides, and on a larger public dataset from 10x Genomics with 208,589 T-cells evaluated for recognition using a panel of 50 different pMHCs. In both datasets, ITRAP2 was able to recover TCR-pMHC hits that were missed either when investigating the raw data or analyzing the data using alternative tools. Importantly, we demonstrate that the size of the pMHC multimer library is crucial for accurate pMHC-TCR pairing and that a minimum of 25 pMHC multimer should be included to optimally determine background characteristic, and assigning true positive events.

bioinformatics↗

Non-Human Peptides Revealed in Blood Reflect the Composition of Small Intestine Microbiota

The previously underestimated effects of commensal gut microbiota on the human body are increasingly being investigated using omics. The discovery of active molecules of interaction between the microbiota and the host may be an important step towards elucidating the mechanisms of symbiosis. Here, we show that in the bloodstream of healthy people, there are over 900 peptides that are fragments of proteins from microorganisms which naturally inhabit human biotopes, including the intestinal microbiota. Absolute quantitation by multiple reaction monitoring has confirmed the presence of bacterial peptides in the blood plasma and serum in the range of approximately 0.1 nM to 1 M. The abundance of microbiota peptides reaches its maximum about 5h after a meal. Most of the peptides correlate with the bacterial composition of the small intestine and are likely obtained by hydrolysis of membrane proteins with trypsin, chymotrypsin and pepsin -- the main proteases of the gastrointestinal tract. The peptides have physicochemical properties allowing them selectively pass the intestinal mucosal barrier and resist fibrinolysis. Proposed approach to the identification of microbiota peptides in the blood may be useful for determining the microbiota composition of hard-to-reach intestinal areas and for monitoring the permeability of the intestinal mucosal barrier.

biochemistry↗