Biology subjects

Perdigoto, A. L.

Publications and source records attributed to Perdigoto, A. L..

2 recordsLinked to original sources

bioRxiv2020

Tet2 Controls Beta cells Responses to Inflammation in Type 1 Diabetes

{beta} cells may participate and contribute to their own demise during Type 1 diabetes (T1D). We identified a novel role of Tet2 in regulating immune killing of {beta} cells. Tet2 is induced in murine and human {beta} cells with inflammation but its expression is reduced in surviving {beta} cells. Tet2-KO mice that receive WT bone marrow transplants develop insulitis but not diabetes and islet infiltrates do not eliminate {beta} cells even though immune cells from the mice can transfer diabetes to NOD/scid recipients. Tet2-KO {beta} cells show reduced expression of inflammatory genes, associated with closed transcription factor binding sites. Tet2-KO recipients are protected from transfer of disease by diabetogenic immune cells. We conclude that Tet2 regulates pathologic interactions between {beta} cells and immune cells and controls intrinsic protective pathways. Modulating TET2 may enable survival of {beta} cells or their replacements in the setting of pathologic immune cells.

immunology↗

bioRxiv2019

Enhancing experimental signals in single-cell RNA-sequencing data using graph signal processing

Current methods for comparing scRNA-seq datasets collected in multiple conditions focus on discrete regions of the transcriptional state space, such as clusters of cells. Here, we quantify the effects of perturbations at the single-cell level using a continuous measure of the effect of a perturbation across the transcriptomic space. We describe this space as a manifold and develop a relative likelihood estimate of observing each cell in each of the experimental conditions using graph signal processing. This likelihood estimate can be used to identify cell populations specifically affected by a perturbation. We also develop vertex frequency clustering to extract populations of affected cells at the level of granularity that matches the perturbation response. The accuracy of our algorithm to identify clusters of cells that are enriched or depleted in each condition is on average 57% higher than the next best-performing algorithm tested. Gene signatures derived from these clusters are more accurate compared to six alternative algorithms in ground-truth comparisons.

bioinformatics↗