Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. Electronic address: xuwh@pumch.cn.
Department of Radiology and Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk National University Hospital, 567 Baekje-daero, deokjin-gu, Jeonju-si, Jeollabuk-do, 561-756, Republic of Korea. kwak8140@jbnu.ac.kr.
Department of Radiology and Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk National University Hospital, 567 Baekje-daero, deokjin-gu, Jeonju-si, Jeollabuk-do, 561-756, Republic of Korea.
Department of Neurosurgery and Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, P.R. China.
Anatomy and Neuroscience (JC), School of Biomedical Sciences, University of Melbourne, Parkville; Department of Surgery (JC), Alfred Hospital, Melbourne, Victoria; Interventional Radiology Service (JM, MB, HA), Department of Radiology, Austin Hospital, Melbourne; School of Medicine (JM, MB, HA), Faculty of Health, Deakin University, Waurn Ponds; Stroke Division (JM, MB, HA), Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, Victoria; Interventional Neuroradiology Service (HA), Department of Radiology, St Vincent's Hospital; Interventional Neuroradiology Unit (RVC, L-AS, HA), Monash Imaging, Monash Health; and Faculty of Medicine (RVC, HA), Nursing and Health Sciences, Monash University, Melbourne, Australia.
Anatomy and Neuroscience (JC), School of Biomedical Sciences, University of Melbourne, Parkville; Department of Surgery (JC), Alfred Hospital, Melbourne, Victoria; Interventional Radiology Service (JM, MB, HA), Department of Radiology, Austin Hospital, Melbourne; School of Medicine (JM, MB, HA), Faculty of Health, Deakin University, Waurn Ponds; Stroke Division (JM, MB, HA), Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, Victoria; Interventional Neuroradiology Service (HA), Department of Radiology, St Vincent's Hospital; Interventional Neuroradiology Unit (RVC, L-AS, HA), Monash Imaging, Monash Health; and Faculty of Medicine (RVC, HA), Nursing and Health Sciences, Monash University, Melbourne, Australia.
Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatment...
scMuffin provides a series of functions to calculate qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories,...
The analyses offered by scMuffin and the results achieved in the case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumo...
A common feature of single-cell RNA-seq (scRNA-seq) data is that the number of cells in a cell cluster may vary widely, ranging from a few dozen to several thousand. It is not clear whether scRNA-seq ...
We addressed this question by performing scRNA-seq and poly(A)-dependent bulk RNA-seq in comparable aliquots of human induced pluripotent stem cells-derived, purified vascular endothelial and smooth m...
Findings of the current study provide a quantitative reference for designing studies that aim for identifying DEGs for specific cell clusters using scRNA-seq data and for interpreting results of such ...
There is a wealth of software that utilizes single-cell RNA-seq (scRNA-seq) data to deconvolve spatial transcriptomic spots, which currently are not yet at single-cell resolution. Here we provide prot...
Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are reduction of systematic biases primarily introduced through technica...
The widely adopted bulk RNA-seq measures the gene expression average of cells, masking cell type heterogeneity, which confounds downstream analyses. Therefore, identifying the cellular composition and...
We propose a new deconvolution algorithm, DSSC, which infers cell type-specific gene expression and cell type proportions of heterogeneous samples simultaneously by leveraging gene-gene and sample-sam...
DSSC provides a practical and promising alternative to the experimental techniques to characterize cellular composition and heterogeneity in the gene expression of heterogeneous samples....
Cell clustering is a prerequisite for identifying differentially expressed genes (DEGs) in single-cell RNA sequencing (scRNA-seq) data. Obtaining a perfect clustering result is of central importance f...
Here, we propose single-cell minimum enclosing ball (scMEB), a novel and fast method for detecting single-cell DEGs without prior cell clustering results. The proposed method utilizes a small part of ...
We compared scMEB to two different approaches that could be used to identify DEGs without cell clustering. The investigation of 11 real datasets revealed that scMEB outperformed rival methods in terms...
The aim of this study was to reveal the key genes associated with macrophage polarization in liver cancer....
Data were downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas databases (TCGA). R package Seurat 4.0 was used to preprocess the downloaded single-cell sequencing data, princi...
Two thousand highly variable genes were obtained after the normalization of single-cell profiles. In all, 16 principal components and 15 cell clusters were obtained. Monocytes and macrophages were the...
The key genes associated with macrophage polarization, namely CD53, TGFBI, S100A4, pyruvate kinase M, LSP1, and SPP1, may be potential therapeutic targets for liver cancer....
As an essential regulator of type I interferon (IFN) response, TMEM173 participates in immune regulation and cell death induction. In recent studies, activation of TMEM173 has been regarded as a promi...
Quantitative real-time PCR (qRT-PCR) and western blotting (WB) were applied to determine the mRNA and protein levels of TMEM173 in peripheral blood mononuclear cells (PBMCs). TMEM173 mutation status w...
The mRNA and protein levels of TMEM173 were increased in PBMCs from B-ALL patients. Besides, frameshift mutation was presented in TMEM173 sequences of 2 B-ALL patients. ScRNA-seq analysis identified t...
Our findings provide insights into the transcriptomic features of TMEM173 in the BM of high-risk B-ALL patients. Targeted activation of TMEM173 in specific cells might provide new therapeutic strategi...
The poor prognosis of sepsis warrants the investigation of biomarkers for predicting the outcome. Several studies have indicated that PANoptosis exerts a critical role in tumor initiation and developm...
We obtained Sepsis samples and scRNA-seq data from the GEO database. PANoptosis-related genes were subjected to consensus clustering and functional enrichment analysis, followed by identification of d...
Unsupervised clustering analysis using 16 PANoptosis-related genes identified three subtypes of sepsis. Kaplan-Meier analysis showed significant differences in patient survival among the subtypes, wit...
We developed a machine learning based Boruta algorithm for profiling PANoptosis related subgroups with in predicting survival and clinical features in the sepsis....
Macrophages play an important role in the occurrence and development of atherosclerosis. However, few existing studies have deliberately analyzed the changes in characteristic genes in the process of ...
Carotid atherosclerotic plaque single-cell RNA (scRNA) sequencing data were analyzed to define the cells involved and determine their transcriptomic characteristics. KEGG enrichment analysis, CIBERSOR...
Nine cell clusters were identified. M1 macrophages, M2 macrophages, and M2/M1 macrophages were identified as three clusters within the macrophages. According to pseudotime analysis, M2/M1 macrophages ...
IL1RN...
