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Your search keyword '"Li, Ruoxin"' showing total 167 results
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167 results on '"Li, Ruoxin"'

1. Adipocytes Encapsulating Telratolimod Recruit and Polarize Tumor‐Associated Macrophages for Cancer Immunotherapy

Author

Wen, Di, Liang, Tingxizi, Chen, Guojun, Li, Hongjun, Wang, Zejun, Wang, Jinqiang, Fu, Ruxing, Han, Xiao, Ci, Tianyuan, Zhang, Yuqi, Abdou, Peter, Li, Ruoxin, Bu, Linlin, Dotti, Gianpietro, and Gu, Zhen

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Immunology, Cancer, Humans, Tumor-Associated Macrophages, Macrophages, Immunotherapy, Melanoma, Adipocytes, Tumor Microenvironment, adipocyte, cancer immunotherapy, drug delivery, macrophage
Abstract

Tumor-associated adipocytes (TAAs) recruit monocytes and promote their differentiation into tumor-associated macrophages (TAMs) that support tumor development. Here, TAAs are engineered to promote the polarization of TAMs to the tumor suppressive M1 phenotype. Telratolimod, a toll-like receptor 7/8 agonist, is loaded into the lipid droplets of adipocytes to be released at the tumor site upon tumor cell-triggered lipolysis. Locally administered drug-loaded adipocytes increased tumor suppressive M1 macrophages in both primary and distant tumors and suppressed tumor growth in a melanoma model. Furthermore, drug-loaded adipocytes improved CD8+ T cell-mediated immune responses within the tumor microenvironment and favored dendritic cell maturation in the tumor draining lymph nodes.

Published
2023

2. siVAE: interpretable deep generative models for single-cell transcriptomes

Author

Choi, Yongin, Li, Ruoxin, and Quon, Gerald

Subjects
Information and Computing Sciences, Biological Sciences, Machine Learning, Acquired Cognitive Impairment, Dementia, Brain Disorders, Genetics, Neurosciences, Human Genome, Neurological, Transcriptome, Neural Networks, Computer, Environmental Sciences, Bioinformatics
Abstract

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.

Published
2023

18. Representation learning methods developed for single cell genomics analysis

Author

Li, Ruoxin

Subjects
Biostatistics, Molecular biology, Bioinformatics
Abstract

Advances in high throughput omics technologies allow for assaying increasing compendium of molecular layers, from genome and epigenome profiling, transcriptomics to proteomics. Such data provide detailed snapshots which can characterize the molecular state for a given biology system from very fine resolution. Single cell genomics assays such as scRNA-seq and scATAC-seq specifically captures the landscape of genomic features across large collections of cells and have become one of the most popular molecular profiling techniques for investigating diverse problems related to gene regulation, such as identification of novel cell types and their regulatory signatures, trajectory inference for the analysis of continuous processes such as differentiation, high resolution analysis of transcriptional dynamics, and characterization of transcriptional heterogeneity within population of cells.Despite the rapidly evolving technologies which can scales up to millions of cells across multiple individuals , one of the most pressing challenges in single cell genomics analysis is to address the amount of technical noise that can drive approximately 50% of the cell-cell variation in expression measurements. And such technical noise often times associated with high-sparsity of the genomic feature measurements. In chapter 2, we are mainly focusing on alleviating the effect of such technical variation in feature measurements of single cell genomics data, such as gene expression and locus accessibility. We show that this technical variation in both scRNA-seq and scATAC-seq datasets can be mitigated by analyzing feature detection patterns alone and ignoring feature quantification measurements. This result holds when datasets have low detection noise relative to quantification noise. We demonstrate state-of-the-art performance of detection pattern models using our new framework, scBFA, for both cell type identification and trajectory inference.While single cell genomics assays are inherently high dimensional, the variations of individual cells are often summarized in a low dimensional space reflecting the change of gene’s mean expression. Gene co-expression networks, which often inferred from RNA sequencing data are another perspective to study cell type specific functional modules and complex regulatory interactions from transcriptomics profile. The increasing availability of large-scale scRNA-seq datasets is now making it possible to infer many gene networks from diverse cell populations. However, there are no mature tools currently available to visualize and compare large collections of networks across single cell populations, or for identifying correlations between variance in gene network structure with cell population-level phenotypes. In chapter 3, we present an unsupervised framework scMultiAE enabling comparison and visualization of multiple gene networks in a low-dimensional space with a focus on studying the heterogeneity of iPSCs during differentiation.

Published
2024

29. Controlling Circular RNA Encapsulation within Extracellular Vesicles for Gene Editing and Protein Replacement

Author

Fang, Liang, Gu, Wenchao, Li, Ruoxin, Chen, Chaoxin, Cai, Simian, Luozhong, Sijin, Chen, Michelle, Hsu, Annie, Tsai, Yi-Chih, Londhe, Ketaki, and Jiang, Shaoyi

Abstract

Extracellular vesicles (EVs) are a population of vesicular bodies originating from cells, and EVs have been proven to have the potential to deliver different cargos, such as RNAs. However, conventional methods are not able to encapsulate long RNAs into EVs efficiently or may compromise the integrity of EVs. In this study, we have devised a strategy to encapsulate long circRNAs (>1000 nt) into EVs by harnessing the sorting mechanisms of cells. This strategy utilizes the inherent richness of circular RNAs in EVs and a genetic engineering method to increase the cytoplasmic concentration of target circRNAs, facilitating highly efficient RNA back-splicing to drive the circularization of RNAs. This allows target circRNAs to load into EVs with high efficiency. Furthermore, we demonstrate the practical applications of this strategy, showing that these circRNAs can be delivered by EVs to recipient cells for protein expression and to mice for gene editing.

Published
2024
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49. Adipocytes Encapsulating Telratolimod Recruit and Polarize Tumor‐Associated Macrophages for Cancer Immunotherapy

Author

Wen, Di, primary, Liang, Tingxizi, additional, Chen, Guojun, additional, Li, Hongjun, additional, Wang, Zejun, additional, Wang, Jinqiang, additional, Fu, Ruxing, additional, Han, Xiao, additional, Ci, Tianyuan, additional, Zhang, Yuqi, additional, Abdou, Peter, additional, Li, Ruoxin, additional, Bu, Linlin, additional, Dotti, Gianpietro, additional, and Gu, Zhen, additional

Published
2022
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