Biology subjects

Wu, P.-H.

Publications and source records attributed to Wu, P.-H..

40 records · Page 3Linked to original sources

bioRxiv2019

The role of integrin αv and CD44 in GBM migration in human brain

Glioblastoma (GBM) is the most common primary adult malignant brain tumor. Recurrence is driven invading tumor cells that escape surgical resection and demonstrate resistance to standard-of-care chemotherapy and radiotherapy. A large body of research has been conducted on tumor cell motility. However, typical in vitro models make use of polystyrene culture dishes, which exhibit significantly different physical parameters than brain tissue. Here we report on the use of human organotypic brain slices as an ex vivo approach for the dynamic study of GBM cell motility. Temporal lobectomy tissue from epilepsy patients was obtained and cut into 350{micro}m thick slices. After the tissue slices had a weeks incubation for recovery, fluorescently labeled tumor cells were seeded. We then tracked individual tumor cells using time-lapse fluorescent confocal microscopy. Quantification of motility characteristics, including mean squared displacement, total path length, and consistency, allowed for comparison of different conditions, including knockdown of cell surface proteins integrin v (ITGAV) and CD44. Human organotypics demonstrated minimal variability across specimen in terms of motility parameters, including total path length, averaged instantaneous velocity, and consistency. Knockdown of the traditional motility protein ITGAV showed little effect on overall motility while knockdown of CD44 resulted in a significant reduction in both averaged instantaneous velocity and total path length. When the same parameters were examined using Matrigel, ITGAV and CD44 both showed decreased motility, highlighting the impact of the physical environment on cell behavior. Finally, cell motility in mouse organotypic slices was decreased when compared to human organotypic slices. Here we demonstrate the use of human organotypic brain slices in the study of GBM cell invasion. This model system offers a physiologically-relevant environment in which to examine the dynamic process of cell motility.

cancer biology↗

bioRxiv2018

An Evolutionarily Conserved piRNA-producing Locus Required for Male Mouse Fertility

Pachytene piRNAs, which comprise >80% of small RNAs in the adult mouse testis, have been proposed to bind and regulate target RNAs like miRNAs, cleave targets like siRNAs, or lack biological function altogether. Although piRNA pathway protein mutants are male sterile, no biological function has been identified for any mammalian piRNA-producing locus. Here, we report that males lacking piRNAs from a conserved mouse pachytene piRNA locus on chromosome 6 (pi6) produce sperm with defects in capacitation and egg fertilization. Moreover, heterozygous embryos sired by pi6-/- fathers show reduced viability in utero. Molecular analyses suggest that pi6 piRNAs repress gene expression by cleaving mRNAs encoding proteins required for sperm function. pi6 also participates in a network of piRNA-piRNA precursor interactions that initiate piRNA production from a second piRNA locus on chromosome 10 as well as pi6 itself. Our data establish a direct role for pachytene piRNAs in spermiogenesis and embryo viability.\n\nHighlightsO_LINormal male mouse fertility and spermiogenesis require piRNAs from the pi6 locus\nC_LIO_LISperm capacitation and binding to the zona pellucida of the egg require pi6 piRNAs\nC_LIO_LIHeterozygous embryos sired by pi6-/- fathers show reduced viability in utero\nC_LIO_LIDefects in pi6 mutant sperm reflect changes in the abundance of specific mRNAs.\nC_LI

genetics↗

bioRxiv2018

Elimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers

RNA-seq and small RNA-seq are powerful, quantitative tools to study gene regulation and function. Common high-throughput sequencing methods rely on polymerase chain reaction (PCR) to expand the starting material, but not every molecule amplifies equally, causing some to be overrepresented. Unique molecular identifiers (UMIs) can be used to distinguish undesirable PCR duplicates derived from a single molecule and identical but biologically meaningful reads from different molecules. We have incorporated UMIs into RNA-seq and small RNA-seq protocols and developed tools to analyze the resulting data. Our UMIs contain stretches of random nucleotides whose lengths sufficiently capture diverse molecule species in both RNA-seq and small RNA-seq libraries generated from mouse testis. Our approach yields high-quality data while allowing unique tagging of all molecules in high-depth libraries. Using simulated and real datasets, we demonstrate that our methods increase the reproducibility of RNA-seq and small RNA-seq data. Notably, we find that the amount of starting material and sequencing depth, but not the number of PCR cycles, determine PCR duplicate frequency. Finally, we show that computational removal of PCR duplicates based only on their mapping coordinates introduces substantial bias into data analysis.

genomics↗

bioRxiv2017

Computational discovery of tissue morphology biomarkers in very long-term survivors with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest forms of cancer, with an average 5-year survival rate of only 8%. Within PDAC patients, however, there is a small subset of patients who survive >10 years. Deciphering underlying reasons behind prolonged survival could potentially provide new opportunities to treat PDAC; however, no genomic, transcriptomic, proteomic, or clinical signatures have been found to robustly separate this subset of patients. Digital pathology, in combination with machine learning, provides an opportunity to computationally search for tissue morphology patterns associated with disease outcomes. Here, we developed a computational framework to analyze whole-slide images (WSI) of PDAC patient tissue and identify tissue-morphology signatures for very long term surviving patients. Our results indicate that less tissue morphology heterogeneity is significantly linked to better patient survival and that the extra-tumoral space encodes prognostic information for survival. Based on information from morphological heterogeneity in the tumor and its adjacent area, we established a machine learning model with an AUC of 0.94. Our analysis workflow highlighted a quantitative visual-based tissue phenotype analysis that also allows direct interaction with pathology. This study demonstrates a pathway to accelerate the discovery of undetermined tissue morphology associated with pathogenesis states and prognosis and diagnosis of patients by utilizing new computational approaches.

bioinformatics↗