Publications by authors named "Nigatu Adossa"

Article Synopsis
  • Human reproductive success hinges on the proper development of the uterine endometrium, which is essential for implantation and pregnancy, involving key cell types: decidual stromal cells (dS) and decidual natural killer (dNK) cells.
  • The study used single-cell transcriptomics from early pregnancy to uncover new transcription factors affecting these cell types, revealing factors like DDIT3 and BRF2 in dS cells that help manage oxidative stress and factors like IRX3 and RELB in dNK cells that promote immune tolerance.
  • The research also linked these findings to pregnancy disorders, showing that specific transcription factors are significantly associated with gene expression changes observed in conditions like recurrent pregnancy loss and preeclampsia.*
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In Brief: Preeclampsia is a common serious disorder that can occur during pregnancy. This study uses integrative analysis of preeclampsia transcriptomes and single-cell transcriptomes to predict cell type-specific contributions to preeclampsia.

Abstract: Preeclampsia is a devastating pregnancy disorder and a major cause of maternal and perinatal mortality.

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Clustering of cells based on gene expression is one of the major steps in single-cell RNA-sequencing (scRNA-seq) data analysis. One key challenge in cluster analysis is the unknown number of clusters and, for this issue, there is still no comprehensive solution. To enhance the process of defining meaningful cluster resolution, we compare Bayesian latent Dirichlet allocation (LDA) method to its non-parametric counterpart, hierarchical Dirichlet process (HDP) in the context of clustering scRNA-seq data.

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Single-cell omics technologies are currently solving biological and medical problems that earlier have remained elusive, such as discovery of new cell types, cellular differentiation trajectories and communication networks across cells and tissues. Current advances especially in single-cell multi-omics hold high potential for breakthroughs by integration of multiple different omics layers. To pair with the recent biotechnological developments, many computational approaches to process and analyze single-cell multi-omics data have been proposed.

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While antibiotics are intended to specifically target bacteria, most are known to affect host cell physiology. In addition, some antibiotic classes are reported as immunosuppressive for reasons that remain unclear. Here, we show that Linezolid, a ribosomal-targeting antibiotic (RAbo), effectively blocked the course of a T cell-mediated autoimmune disease.

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