Biology subjects

Jensen, R. V.

Publications and source records attributed to Jensen, R. V..

4 recordsLinked to original sources

bioRxiv2024

Comprehensive multi-site profiling of the malignant pleural mesothelioma micro-environment identifies candidate molecular determinants of histopathologic type

Pleural mesothelioma (PM) comprises sarcomatoid, epithelioid and biphasic histologic subtypes. Bulk PM RNA-sequencing identifies a histology-associated molecular gradient with features of epithelial-mesenchymal (EM) transition but cannot parse malignant, stromal, and immune tumor components. The mechanisms driving PM malignant cell phenotype and associated histology is not well-characterized. Here, we use single-cell RNA-sequencing (scRNA-seq) paired with exome, bulk RNA-sequencing, and histologic analysis of adjacent samples to characterize malignant cell EM state, parse the tumor microenvironment (TME), and identify candidate drivers of PM cell fate. We observe EM variation in malignant cells analogous to bulk samples. We characterize epithelioid and sarcomatoid malignant cell programs and identify a new uncommitted malignant cell EM phenotype enriched in biphasic histology samples. Using inferred CNVs we observe that single individual PM clones consist of cells exhibiting all three EM cell states. We find that distinct non-malignant microenvironments associated with tumors consisting of mostly cells in each state, and identify WNT inhibition, GAS6-AXL, and HBEGF-EGFR signaling as pathways associated with distinct EM cell states. These findings provide deeper insight into the molecular drivers of PM malignant cells and identify non-malignant cell signals as potential EMT and growth drivers in PM.

cancer biology↗

bioRxiv2019

Production of interspecies somatic/pluripotent heterokaryons using polyethylene glycol (PEG) and selection by imaging flow cytometry for the study of nuclear reprogramming.

Fusion of somatic cells to pluripotent cells such as mouse embryonic stem (ES) cells induces reprogramming of the somatic nucleus, and can be used to study the effect of trans-acting factors from the pluripotent cell on the somatic nucleus. Moreover, fusion of cells from different species permits the identification of the transcriptome of each cell, so the gene expression changes can be monitored. However, fusion only happens in a small proportion of the cells exposed to fusogenic conditions, hence the need for a protocol that produces high fusion rate with minimal cell damage, coupled with a method capable of identifying and selecting fusion events from the bulk of the cells. Polyethylene glycol (PEG) is a polymer of repeated ethylene oxide units known to induce cell fusion within a certain range of molecular weight. Here, we describe a method to induce formation of bi-species heterokaryons from adherent mammalian cells, which can then be specifically labeled and selected using live cell immunostaining and a combination of imaging and traditional flow cytometry. First, we tested several PEG-based fusion conditions to optimize a protocol to consistently produce both mouse NIH/3T3 fibroblast and primary bovine fetal fibroblast (bFF) homokaryons. Initially, we obtained 7.28% of NIH/3T3 homokaryons when using 50% PEG 1500. Addition of 10% of DMSO to the PEG solution increased the percentage of NIH/3T3 homokaryons to 11.71%. In bFFs, treatment with 50% PEG 1500 plus 10% DMSO produced 11.05% of homokaryons. We then produced interspecies heterokaryons by fusing mouse embryonic stem (mES) cells to bFFs. To identify bi-species fusion products, heterokaryons were labeled using indirect immunostaining in live cells and selected using imaging (Amnis ImageStream Mark II) and traditional (BD FACSAria I) flow cytometry. Heterokaryons selected with this method produced ES cell-like colonies when placed back in culture. The method described here can also be combined with downstream applications such as nucleic acid isolation for RT-PCR and RNA-seq, and used as a tool to study cellular processes in which the effect of trans-acting factors is relevant, such as in nuclear reprogramming.

cell biology↗

bioRxiv2018

Expanded Analysis of the Pantoea stewartii subsp. stewartii DC283 Complete Genome Reveals Plasmid-borne Virulence Factors

Pantoea stewartii subsp. stewartii, a Gram-negative proteobacterium, causes Stewarts wilt disease in corn. Bacterial transmission to plants occurs primarily via the corn flea beetle insect vector, which is native to North America. P. stewartii DC283 is the wild-type reference strain most used to study pathogenesis. Previously the complete genome of P. stewartii was released. Here, the method whereby the genome was assembled is described in greater detail. Data from a matepair library preparation with 3.5 kilobase insert size and high-throughput sequencing from the MiSeq Illumina platform, together with the available incomplete genome sequence of AHIE00000000.1 (containing 65 contigs) was used. This work resulted in the complete assembly of one circular chromosome, ten circular plasmids and one linear phage from P. stewartii DC283. A high number of sequences encoding repetitive transposases (> 400) were found in the complete genome. The separation of plasmids from genomic DNA revealed that two Type III secretion systems in P. stewartii DC283 are located on two separate mega-plasmids. Interestingly, the assembly identified a previously unknown 66-kb region in a location interior to a contig in the previous reference genome. Overall, a novel approach was successfully utilized to fully assemble a prokaryotic genome that contains large numbers of repetitive sequences and multiple plasmids, which resulted in some interesting biological findings.

bioinformatics↗

bioRxiv2017

FastViromeExplorer: A Pipeline for Virus and Phage Identification and Abundance Profiling in Metagenomics Data

Identifying viruses and phages in a metagenomics sample has important implication in improving human health, preventing viral outbreaks, and developing personalized medicine. With the rapid increase in data files generated by next generation sequencing, existing tools for identifying and annotating viruses and phages in metagenomics samples suffer from expensive running time. In this paper, we developed a stand-alone pipeline, FastViromeExplorer, for rapid identification and abundance quantification of viruses and phages in big metagenomic data. Both real and simulated data validated FastViromeExplorer as a reliable tool to accurately identify viruses and their abundances in large data, as well as in a time efficient manner.

bioinformatics↗