Biology subjects

Furuno, M.

Publications and source records attributed to Furuno, M..

3 recordsLinked to original sources

bioRxiv2022

xcore: an R package for inference of gene expression regulators

Elucidating the Transcription Factors (TFs) that drive the gene expression changes in a given experiment is one of the most common questions asked by researchers. The existing methods rely on the predicted Transcription Factor Binding Site (TFBS) to model the changes in the motif activity. Such methods only work for TFs that have a motif and assume the TF binding profile is the same in all cell types. Given the wealth of the ChIP-seq data available for a wide range of the TFs in various cell types, we propose that the modeling can be done using ChiP-seq "signatures" directly, effectively skipping the motif finding and TFBS prediction steps. We present xcore, an R package that allows Transcription Factor activity modeling based on their ChiP-seq signatures and users gene expression data. We also provide xcoredata a companion data package that provides a collection of preprocessed ChiP-seq signatures. We demonstrate that xcore leads to biologically relevant predictions using TGF-beta induced epithelial-mesenchymal transition and rinderpest infection time-course CAGE data as examples. Availability and Implementationxcore and xcoredata R packages are freely available at www.github.com/bkaczkowski/xcore and www.github.com/mcjmigdal/xcoredata. xcore user guide is available www.bkaczkowski.github.io/xcore/articles/xcore_vignette.html. Contactb.kaczkowski@gmail.com

bioinformatics↗

bioRxiv2020

Efficient Development of Platform Cell Lines Using CRISPR-Cas9 1 and Transcriptomics Analysis

Antibody-drug conjugates offers many advantages as a drug delivery platform that allows for highly specific targeting of cell types and genes. Ideally, testing the efficacy of these systems requires two cell types to be different only in the gene targeted by the drug, with the rest of the cellular machinery unchanged, in order to minimize other potential differences from obscuring the effects of the drug. In this study, we created multiple variants of U87MG cells with targeted mutation in the TP53 gene using the CRISPR-Cas9 system, and determined that their major transcriptional differences stem from the loss of p53 function. Using the transcriptome data, we predicted which mutant clones would have less divergent phenotypes from the wild type and thereby serve as the best candidates to be used as drug delivery testing platforms. Further in vitro and in vivo assays of cell morphology, proliferation rate and target antigen-mediated uptake supported our predictions. Based on the combined analysis results, we successfully selected the best qualifying mutant clone. This study serves as proof-of-principle of the approach and paves the way for extending to additional cell types and target genes.

bioengineering↗

bioRxiv2018

C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution

Single-cell transcriptomic profiling is a powerful tool to explore cellular heterogeneity. However, most of these methods focus on the 3-end of polyadenylated transcripts and provide only a partial view of the transcriptome. We introduce C1 CAGE, a method for the detection of transcript 5-ends with an original sample multiplexing strategy in the C1 microfluidic system. We first quantified the performance of C1 CAGE and found it as accurate and sensitive as other methods in C1 system. We then used it to profile promoter and enhancer activities in the cellular response to TGF-{beta} of lung cancer cells and discovered subpopulations of cells differing in their response. We also describe enhancer RNA dynamics revealing transcriptional bursts in subsets of cells with transcripts arising from either strand within a single-cell in a mutually exclusive manner, which was validated using single molecule fluorescence in-situ hybridization.

genomics↗