Biology subjects

Kanter, I.

Publications and source records attributed to Kanter, I..

3 recordsLinked to original sources

bioRxiv2020

Molecular characterization of clonal human renal forming cells

The adult kidney replaces its parenchyma in vivo in steady state and during regeneration by segment-specific clonal cell proliferation. To understand human adult kidney clonal cell growth, we derived tissue from human nephrectomies and performed limiting dilution to establish genuine clonal cultures from one single cell. Clonal efficiency of the human kidney was x%. Remarkably, a single renal cell could give rise to up to 3.3*10(6) cells. Phenotypically, two types of clonal cultures were apparent; a stably proliferating cuboidal epithelial-like appearing (EL) and a rapidly proliferating fibroblast-like appearing (FL). RNA sequencing of all clonal cultures separated FL from EL cultures according to proximal-distal/collecting renal epithelial tubular identity, respectively. Moreover, distinct molecular features in respect to cell-cycle, epithelial-mesenchymal transition, oxidative phosphorylation, BMP signaling pathway and cell surface markers were observed for each clone type. Surprisingly, clonal expansion (>3 months) was sustained in EL clones harboring markers of mature kidney epithelia (high CD24, CDH1, EpCAM, EMA) in contrast to de-differentiated FL clones (high NCAM1, serpine1), which showed fast lineage amplification and exhausted in a few weeks. Thus, the human adult kidney harbors progenitor cell function in which segment identity and the level of epithelial differentiation dictate clonal characteristics.

cell biology↗

bioRxiv2019

Characterization of splice isoform switching during human kidney development

Nephrons are the functional units of the kidney. During kidney development, cells from the cap mesenchyme - a transient kidney-specific progenitor state - undergo a mesenchymal to epithelial transition (MET) and subsequently differentiate into the various epithelial cell types that create the tubular structures of the nephron. Faults in this transition can lead to a pediatric malignancy of the kidney called Wilms tumor that mimics normal kidney development. While kidney development has been characterized at the gene expression level, a comprehensive characterization of alternative splicing is lacking. We therefore performed RNA sequencing on cell populations representing early, intermediate, and late developmental stages of the human fetal kidney, as well as three blastemal-predominant Wilms tumor patient-derived xenografts. We identified a set of transcripts that are alternatively spliced between the different developmental stages. Moreover, we found that cells from the earliest developmental stage have a mesenchymal splice-isoform profile that is similar to that of blastemal-predominant Wilms tumors. RNA binding motif enrichment analysis suggests that the mRNA binding proteins ESRP1, ESRP2, RBFOX2, and QKI regulate mRNA splice isoform switching during human kidney development. These findings illuminate new molecular mechanisms involved in kidney development and pediatric kidney tumors.\n\nHIGHLIGHTSO_LIDuring fetal kidney development, kidney progenitor cells undergo a mesenchymal to epithelial transition (MET) and subsequently differentiate into the various epithelial cell types that create the tubular structures of the nephron.\nC_LIO_LIRNA sequencing identifies a set of transcripts that undergo splice isoform switching during the mesenchymal to epithelial transition (MET) that occurs in the course of human fetal kidney development.\nC_LIO_LICells in the early stages of kidney development have a mesenchymal splice-isoform profile that is similar to that observed in blastemal-predominant Wilms tumor patient-derived xenografts (WT-PDX) that represent an aggressive subtype of Wilms tumors.\nC_LIO_LIRNA binding motif enrichment analysis indicates that the mRNA binding proteins ESRP1, ESRP2, RBFOX2, and QKI regulate splice isoform switching during human kidney development.\nC_LI

genomics↗

bioRxiv2019

Applications of community detection algorithms to large biological datasets

Recent advances in data acquiring technologies in biology have led to major challenges in mining relevant information from large datasets. For example, single-cell RNA sequencing technologies are producing expression and sequence information from tens of thousands of cells in every single experiment. A common task in analyzing biological data is to cluster samples or features (e.g. genes) into groups sharing common characteristics. This is an NP-hard problem for which numerous heuristic algorithms have been developed. However, in many cases, the clusters created by these algorithms do not reflect biological reality. To overcome this, a Networks Based Clustering (NBC) approach was recently proposed, by which the samples or genes in the dataset are first mapped to a network and then community detection (CD) algorithms are used to identify clusters of nodes. Here, we created an open and flexible python-based toolkit for NBC that enables easy and accessible network construction and community detection. We then tested the applicability of NBC for identifying clusters of cells or genes from previously published large-scale single-cell and bulk RNA-seq datasets. We show that NBC can be used to accurately and efficiently analyze large-scale datasets of RNA sequencing experiments.

bioinformatics↗