Single cell RNA-seq denoising using a deep count autoencoder

“Single cell RNA-seq denoising using a deep count autoencode” by Gokcen Eraslan, Lukas M. Simon (both first-authors contributed equally), Maria Mircea, Nikola S. Mueller, Fabian J. Theis. https://doi.org/10.1101/300681 I selected this article for review for two reasons: I work with single-cell RNA-Sequencing (scRNA-seq) data quite a bit and I have a general interest in methods that remove technical sources of variation from it. I have a general interest in methods that use machine learning approaches (in this case an autoencoder network) with genomic data. »

Comprehensive catalog of dendritically localized mRNA isoforms from sub4 cellular sequencing of single mouse neurons

“Comprehensive catalog of dendritically localized mRNA isoforms from sub-cellular sequencing of single mouse neurons” Sarah A. Middleton, James Eberwine and Junhyong Kim https://doi.org/10.1101/278648 I selected this article for review for the following reasons: In neurons, RNA localization is a fundamental way to control protein expression. To my knowledge this is the first attempt to map dendritically localized RNAs at single cell resolution The findings reported are very intriguing, in particular the potential role of SINE elements in localization of RNA to dendrites. »

Active degradation of a regulator controls coordination of downstream genes

“Active degradation of a regulator controls coordination of downstream genes” by Nicholas A. Rossi, Thierry Mora, Aleksandra M. Walczak, and Mary J. Dunlop https://doi.org/10.1101/272120 In this article, Rossi and colleagues observed how noise develops in the expression of genes downstream of stress response activator gene MarA by manipulating MarA degradation and expression levels and then measuring variability in downstream gene expression in single cells. As the authors note, active degradation is rare in rapidly growing bacterial cells and imposes a significant fitness cost. »

Building a tumor atlas: integrating single-cell RNA-Seq data with spatial transcriptomics in pancreatic ductal adenocarcinoma

“Building a tumor atlas: integrating single-cell RNA-Seq data with spatial transcriptomics in pancreatic ductal adenocarcinoma” by Reuben Moncada, Marta Chiodin, Joseph C. Devlin, Maayan Baron, Cristina H. Hajdu, Diane Simeone, and Itai Yanai. https://doi.org/10.1101/254375 I selected this article for review for a few reasons: I thought the spatial aspects of the work were intriguing. I have an interest in methods that use single cell data to deconvolve bulk samples. I was curious about heterogeneity in this disease based on some of our previous work with FNA-derived PDX models. »