scholarly journals Critical changes in hypothalamic gene networks in response to pancreatic cancer as found by single-cell RNA sequencing

2022 ◽  
pp. 101441
Author(s):  
Christian Huisman ◽  
Mason A. Norgard ◽  
Peter R. Levasseur ◽  
Stephanie M. Krasnow ◽  
Monique G.P. van der Wijst ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Networks ◽  
Single Cell Rna Sequencing

Abstract NG04: Diversity of circulating tumor cells in a mouse pancreatic cancer model identified by single cell RNA sequencing

2014 ◽  
Author(s):  
David T. Ting ◽  
Ben S. Wittner ◽  
Ajay M. Shah ◽  
David T. Miyamoto ◽  
Brian W. Brannigan ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Single Cell ◽  
Tumor Cells ◽  
Rna Sequencing ◽  
Circulating Tumor Cells ◽  
Cancer Model ◽  
Single Cell Rna Sequencing

scholarly journals Single-cell RNA sequencing to characterize the response of pancreatic cancer to anti-PD-1 immunotherapy

2022 ◽  
Vol 15 (1) ◽  
pp. 101262
Author(s):  
Jing Zhou ◽  
Yuexu Jiang ◽  
Yue Huang ◽  
Qiongling Wang ◽  
Jussuf T. Kaifi ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cell Rna Sequencing

scholarly journals MuDCoD: Multi-Subject Community Detection in Personalized Dynamic Gene Networks from Single Cell RNA Sequencing

2021 ◽  
Author(s):  
Ali Osman Berk Sapci ◽  
Shan Lu ◽  
Oznur Tastan ◽  
Sunduz Keles
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Community Detection ◽  
Gene Networks ◽  
Induced Pluripotent Stem Cell ◽  
Cell Types ◽  
Dynamic Community Detection ◽  
Subject Specific ◽  
Single Cell Rna Sequencing ◽  
Gene Modules

Developments in single-cell RNA sequencing (scRNA-seq) advanced our understanding of transcriptional programs of different cell types and cellular stages at the individual cell level. Single-cell RNA-seq datasets across multiple individuals and time points are now routinely generated for different conditions. Analysis of personalized dynamic gene networks constructed from these datasets could unravel subject-specific network-level variation critical for phenotypic differences. While there have been developments in the gene module discovery methods on networks estimated from scRNA-seq data, these have mostly focused on static gene networks. In this work, we develop MuDCoD to cluster genes in personalized dynamic gene networks and identify gene modules that vary not only across time but also among subjects. To this end, MuDCoD extends the global spectral clustering framework of the previously developed method, PisCES, to promote information sharing among the subject as well as the time domain. Our computational experiments across a wide variety of settings indicate that, when present, MuDCoD leverages shared signals among networks of the subjects, and performs robustly when subjects do not share any apparent information. An application to human-induced pluripotent stem cell scRNA-seq data during dopaminergic neuron differentiation indicates that MuDCoD enables robust inference for identifying time-varying personalized gene modules. Our results illustrate how personalized dynamic community detection can aid the exploration of subject-specific biological processes that vary across time.

scholarly journals Joint Gene Network Construction by Single-Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Meichen Dong ◽  
Yiping He ◽  
Yuchao Jiang ◽  
Fei Zou
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Graphical Models ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Single Gene ◽  
Matrix Completion ◽  
Low Rank ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

In contrast to differential gene expression analysis at single-gene level, gene regulatory networks (GRN) analysis depicts complex transcriptomic interactions among genes for better understandings of underlying genetic architectures of human diseases and traits. Recently, single-cell RNA sequencing (scRNA-seq) data has started to be used for constructing GRNs at a much finer resolution than bulk RNA-seq data and microarray data. However, scRNA-seq data are inherently sparse which hinders the direct application of the popular Gaussian graphical models (GGMs). Furthermore, most existing approaches for constructing GRNs with scRNA-seq data only consider gene networks under one condition. To better understand GRNs under different but related conditions with single-cell resolution, we propose to construct Joint Gene Networks with scRNA-seq data (JGNsc) using the GGMs framework. To facilitate the use of GGMs, JGNsc first proposes a hybrid imputation procedure that combines a Bayesian zero-inflated Poisson (ZIP) model with an iterative low-rank matrix completion step to efficiently impute zero-inflated counts resulted from technical artifacts. JGNsc then transforms the imputed data via a nonparanormal transformation, based on which joint GGMs are constructed. We demonstrate JGNsc and assess its performance using synthetic data. The application of JGNsc on two cancer clinical studies of medulloblastoma and glioblastoma identifies novel findings in addition to confirming well-known biological results.

72 - Single Cell RNA Sequencing Points to Altered IL-10 Signalling and Metabolism in Hyperinsulinemia-Driven Pancreatic Cancer Initiation

2020 ◽  
Vol 44 (7) ◽  
pp. S30
Author(s):  
Anni Zhang ◽  
Twan De Winter ◽  
Xioake Hu ◽  
Hong Li ◽  
Janel Kopp ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cancer Initiation ◽  
Single Cell Rna Sequencing

scholarly journals Elucidation of tumor-stromal heterogeneity and the ligand-receptor interactome by single cell transcriptomics in real-world pancreatic cancer biopsies

2020 ◽  
Author(s):  
Jaewon J. Lee ◽  
Vincent Bernard ◽  
Alexander Semaan ◽  
Maria E. Monberg ◽  
Jonathan Huang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Clinical Decision Making ◽  
De Novo ◽  
Ex Vivo ◽  
Ductal Adenocarcinoma ◽  
Precision Oncology ◽  
Single Cell Rna Sequencing

AbstractPrecision medicine approaches in pancreatic ductal adenocarcinoma (PDAC) are imperative for improving disease outcomes. However, the long-term fidelity of recently deployed ex vivo preclinical platforms, such as patient-derived organoids (PDOs) remains unknown. Through single-cell RNA sequencing (scRNA-seq), we identify substantial transcriptomic evolution of PDOs propagated from the parental tumor, which may alter predicted drug sensitivity. In contrast, scRNA-seq is readily applicable to limited biopsies from human primary and metastatic PDAC and identifies most cancers as being an admixture of previously described epithelial transcriptomic subtypes. Integrative analyses of our data provide an in-depth characterization of the heterogeneity within the tumor microenvironment, including cancer-associated fibroblast (CAF) subclasses, and predicts for a multitude of ligand-receptor interactions, revealing potential targets for immunotherapy approaches. While PDOs continue to enable prospective therapeutic prediction, our analysis also demonstrates the complementarity of using orthogonal de novo biopsies from PDAC patients paired with scRNA-seq to inform clinical decision-making.Statement of SignificanceThe application of single-cell RNA sequencing to diagnostic pancreatic cancer biopsies provides in-depth transcriptomic characterization of the tumor epithelium and microenvironment, while minimizing potential artifacts introduced by an intervening ex vivo passaging step. Thus, this approach can complement the use of patient-derived organoids in implementing precision oncology.

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