Journal Article10.1038/S41586-020-2157-4
Construction of a human cell landscape at single-cell level.
Xiaoping Han,Ziming Zhou,Lijiang Fei,Huiyu Sun,Renying Wang,Yao Chen,Haide Chen,Jingjing Wang,Huanna Tang,Wenhao Ge,Yincong Zhou,Fang Ye,Mengmeng Jiang,Junqing Wu,Yanyu Xiao,Xiaoning Jia,Tingyue Zhang,Xiaojie Ma,Qi Zhang,Xueli Bai,Shujing Lai,Chengxuan Yu,Lijun Zhu,Rui Lin,Yuchi Gao,Min Wang,Yiqing Wu,Jianming Zhang,Renya Zhan,Saiyong Zhu,Hailan Hu,Changchun Wang,Ming Chen,He Huang,Tingbo Liang,Jianghua Chen,Weilin Wang,Dan Zhang,Guoji Guo +38 more
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TL;DR: The results provide a useful resource for the study of human biology and find that stem and progenitor cells exhibit strong transcriptomic stochasticity, whereas differentiated cells are more distinct.
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Abstract: Single-cell analysis is a valuable tool for dissecting cellular heterogeneity in complex systems1. However, a comprehensive single-cell atlas has not been achieved for humans. Here we use single-cell mRNA sequencing to determine the cell-type composition of all major human organs and construct a scheme for the human cell landscape (HCL). We have uncovered a single-cell hierarchy for many tissues that have not been well characterized. We established a 'single-cell HCL analysis' pipeline that helps to define human cell identity. Finally, we performed a single-cell comparative analysis of landscapes from human and mouse to identify conserved genetic networks. We found that stem and progenitor cells exhibit strong transcriptomic stochasticity, whereas differentiated cells are more distinct. Our results provide a useful resource for the study of human biology.
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Citations
Identification of a single cell-based signature for predicting prognosis risk and immunotherapy response in patients with glioblastoma.
Nan Zhang,Ran Zhou,Hao Zhang,Liyang Zhang,Zeyu Wang,Wen-Jing Zeng,Peng Luo,Jian Zhang,Zhixiong Liu,Quan Cheng +9 more
TL;DR: Wang et al. as mentioned in this paper constructed a novel gene pair signature based on bulk and single-cell sequencing samples in relative expression order within the samples, which possessed a solid ability to predict the prognosis of glioblastoma and pan-cancer.
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CelltypeR: A flow cytometry pipeline to annotate, characterize and isolate single cells from brain organoids
Rhalena A. Thomas,Julien Sirois,Shuming Li,Alex Gestin,Valerio Piscopo,Paula Lépine,Meghna Mathur,Carol X.-Q. Chen,Vincent Soubannier,Taylor M. Goldsmith,Lama Fawaz,Thomas M. Durcan,Edward A. Fon +12 more
TL;DR: In this article , a high-throughput, standardized approach for reproducibly characterizing and isolating cell types in complex neuronal tissues based on protein expression levels was developed, which combines a flow cytometry (FC) antibody panel targeting brain cells with a computational pipeline called CelltypeR, which has scripts for aligning and transforming datasets, optimizing unsupervised clustering, annotating, and quantifying cell types, and comparing cells across different conditions.
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BulkECexplorer: a bulk RNAseq compendium of five endothelial subtypes that predicts whether genes are active or leaky
James T. Brash,Guillermo Diez-Pinel,Alessandro Fantin,Christiana Ruhrberg +3 more
TL;DR: A compendium for vascular endothelial cells from several mouse and human organs is created, termed the BulkECexplorer, which combines many bulk RNAseq datasets into a compendium and applies established classification models to predict whether the detected genes are likely active or leaky in that cell type.
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Structure-Preserving Visualization for Single-cell RNA-Seq Profiles Using Deep Manifold Transformation with Batch-Correction
I Wayan Widana
- 11 Jul 2022
TL;DR: Deep visualization as discussed by the authors learns a structure graph to describe the relationships between data samples, transforms the data into visualization space while preserving the geometric structure of the data and correcting batch effects in an end-to-end manner.
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Identification of therapy-induced clonal evolution and resistance pathways in minimal residual clones in multiple myeloma through single-cell sequencing
Jian Cui,X.J. Li,Shuhui Deng,Chenxing Du,Huishou Fan,Wenqiang Yan,Jingyu Xu,Xiaoqing Li,T. F. Yu,S. Zhang,Rui Lyu,Weiguo Sui,Mu Hao,Xin Du,Yan Xu,Shuhua Yi,Dehui Zou,Tao Cheng,Lugui Qiu,Xin Gao,Gang An +20 more
TL;DR: ScRNA-seq identifies therapy-induced clonal evolution and resistance pathways in minimal residual clones in multiple myeloma. The data suggest that MM cells could rapidly adapt to induction treatment through transcriptional and metabolic adaptation, and targeting therapy-induced resistance mechanisms may help to avert refractory disease.
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