scholarly journals Analysis of cell diversity in human and mouse basal ganglia by single-cell RNA sequencing

IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S328
Author(s):  
Fengjiao Li ◽  
Mohammad Imam Hasan Bin Asad ◽  
Xiangshan Yuan ◽  
Weiwei Xian ◽  
Qiong Liu ◽  
...  
BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Shouguo Gao ◽  
Zhijie Wu ◽  
Xingmin Feng ◽  
Sachiko Kajigaya ◽  
Xujing Wang ◽  
...  

Abstract Background Presently, there is no comprehensive analysis of the transcription regulation network in hematopoiesis. Comparison of networks arising from gene co-expression across species can facilitate an understanding of the conservation of functional gene modules in hematopoiesis. Results We used single-cell RNA sequencing to profile bone marrow from human and mouse, and inferred transcription regulatory networks in each species in order to characterize transcriptional programs governing hematopoietic stem cell differentiation. We designed an algorithm for network reconstruction to conduct comparative transcriptomic analysis of hematopoietic gene co-expression and transcription regulation in human and mouse bone marrow cells. Co-expression network connectivity of hematopoiesis-related genes was found to be well conserved between mouse and human. The co-expression network showed “small-world” and “scale-free” architecture. The gene regulatory network formed a hierarchical structure, and hematopoiesis transcription factors localized to the hierarchy’s middle level. Conclusions Transcriptional regulatory networks are well conserved between human and mouse. The hierarchical organization of transcription factors may provide insights into hematopoietic cell lineage commitment, and to signal processing, cell survival and disease initiation.


Nature ◽  
2013 ◽  
Vol 500 (7464) ◽  
pp. 593-597 ◽  
Author(s):  
Zhigang Xue ◽  
Kevin Huang ◽  
Chaochao Cai ◽  
Lingbo Cai ◽  
Chun-yan Jiang ◽  
...  

2021 ◽  
Author(s):  
Periklis Paganos ◽  
Danila Voronov ◽  
Jacob Musser ◽  
Detlev Arendt ◽  
Maria I. Arnone

AbstractIdentifying the molecular fingerprint of organismal cell types is key for understanding their function and evolution. Here, we use single cell RNA sequencing (scRNA-seq) to survey the cell types of the sea urchin early pluteus larva, representing an important developmental transition from non-feeding to feeding larva. We identified 21 distinct cell clusters, representing cells of the digestive, skeletal, immune, and nervous systems. Further subclustering of these revealed a highly detailed portrait of cell diversity across the larva, including the identification of 12 distinct neuronal cell types. Moreover, we corroborated co-expression of key regulatory genes previously shown to drive sea urchin gene regulatory networks, and revealed additional domains in which these regulatory networks are likely to function within the larva. Lastly, we recovered a neuronal cell type co-expressingPdx-1andBrn1/2/4, which had previously been shown to share similar gene expression with vertebrate pancreas. Our results extend this finding, revealing twenty transcription factors shared by this population of neurons in sea urchin and vertebrate pancreatic cells. Using differential expression results from Pdx-1 knockdown experiments, we generate a draft of the Pdx-1 regulatory network in these cells, and hypothesize this network was present in an ancestral deuterostome neuron before being co-opted into the pancreas developmental lineage in vertebrates.


2018 ◽  
Author(s):  
Jesse D. Bloom

ABSTRACTIn single-cell RNA-sequencing, it is important to know the frequency at which the sequenced transcriptomes actually derive from multiple cells. A common method to estimate this multiplet frequency is to mix two different types of cells (e.g., human and mouse), and then determine how often the transcriptomes contain transcripts from both cell types. When the two cell types are mixed in equal proportion, the calculation of the multiplet frequency from the frequency of mixed transcriptomes is straightforward. But surprisingly, there are no published descriptions of how to calculate the multiplet frequency in the general case when the cell types are mixed unequally. Here I derive equations to analytically calculate the multiplet frequency from the numbers of observed pure and mixed transcriptomes when two cell types are mixed in arbitrary proportions, under the assumption that the loading of cells into droplets or wells is Poisson.


PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5578 ◽  
Author(s):  
Jesse D. Bloom

In single-cell RNA-sequencing, it is important to know the frequency at which the sequenced transcriptomes actually derive from multiple cells. A common method to estimate this multiplet frequency is to mix two different types of cells (e.g., human and mouse), and then determine how often the transcriptomes contain transcripts from both cell types. When the two cell types are mixed in equal proportion, the calculation of the multiplet frequency from the frequency of mixed transcriptomes is straightforward. But surprisingly, there are no published descriptions of how to calculate the multiplet frequency in the general case when the cell types are mixed unequally. Here, I derive equations to analytically calculate the multiplet frequency from the numbers of observed pure and mixed transcriptomes when two cell types are mixed in arbitrary proportions, under the assumption that the loading of cells into droplets or wells is Poisson.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Massimo Andreatta ◽  
Jesus Corria-Osorio ◽  
Sören Müller ◽  
Rafael Cubas ◽  
George Coukos ◽  
...  

AbstractSingle-cell RNA sequencing (scRNA-seq) has revealed an unprecedented degree of immune cell diversity. However, consistent definition of cell subtypes and cell states across studies and diseases remains a major challenge. Here we generate reference T cell atlases for cancer and viral infection by multi-study integration, and develop ProjecTILs, an algorithm for reference atlas projection. In contrast to other methods, ProjecTILs allows not only accurate embedding of new scRNA-seq data into a reference without altering its structure, but also characterizing previously unknown cell states that “deviate” from the reference. ProjecTILs accurately predicts the effects of cell perturbations and identifies gene programs that are altered in different conditions and tissues. A meta-analysis of tumor-infiltrating T cells from several cohorts reveals a strong conservation of T cell subtypes between human and mouse, providing a consistent basis to describe T cell heterogeneity across studies, diseases, and species.


2021 ◽  
Vol 8 (9) ◽  
pp. 208-222
Author(s):  
Wanqiu Huang ◽  
Danni Wang ◽  
Yu-Feng Yao

Infections are highly orchestrated and dynamic processes, which involve both pathogen and host. Transcriptional profiling at the single-cell level enables the analysis of cell diversity, heterogeneity of the immune response, and detailed molecular mechanisms underlying infectious diseases caused by bacteria, viruses, fungi, and parasites. Herein, we highlight recent remarkable advances in single-cell RNA sequencing (scRNA-seq) technologies and their applications in the investigation of host-pathogen interactions, current challenges and potential prospects for disease treatment are discussed as well. We propose that with the aid of scRNA-seq, the mechanism of infectious diseases will be further revealed thus inspiring the development of novel interventions and therapies.


Sign in / Sign up

Export Citation Format

Share Document