scholarly journals Aging-associated alterations in the mammary gland revealed by single-cell RNA sequencing

2019 ◽  
Author(s):  
Carman Man-Chung Li ◽  
Hana Shapiro ◽  
Christina Tsiobikas ◽  
Laura Selfors ◽  
Huidong Chen ◽  
...  
Keyword(s):  
Mammary Gland ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Cell Types ◽  
Single Cell Rna Sequencing ◽  
Numerous Cell ◽  
Luminal Cells ◽  
Increased Susceptibility ◽  
Mammary Tissues

AbstractAging of the mammary gland is closely associated with increased susceptibility to diseases such as cancer, but there have been limited systematic studies of aging-induced alterations within this organ. We performed high-throughput single-cell RNA-sequencing (scRNA-seq) profiling of mammary tissues from young and old nulliparous mice, including both epithelial and stromal cell types. Our analysis identified altered proportions and distinct gene expression patterns in numerous cell populations as a consequence of the aging process, independent of parity and lactation. In addition, we detected a subset of luminal cells that express both hormone-sensing and alveolar markers and decrease in relative abundance with age. These data provide a high-resolution landscape of aging mammary tissues, with potential implications for normal tissue functions and cancer predisposition.

scholarly journals Comprehensive analysis of a mouse model of spontaneous uveoretinitis using single-cell RNA sequencing

2019 ◽  
Vol 116 (52) ◽  
pp. 26734-26744 ◽  
Author(s):  
Jacob S. Heng ◽  
Sean F. Hackett ◽  
Genevieve L. Stein-O’Brien ◽  
Briana L. Winer ◽  
John Williams ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Cell Types ◽  
Retinal Cell ◽  
Central Tolerance ◽  
Single Cell Rna Sequencing ◽  
Unbiased Manner ◽  
Lymphoid Structures

Autoimmune uveoretinitis is a significant cause of visual loss, and mouse models offer unique opportunities to study its disease mechanisms.Aire−/−mice fail to express self-antigens in the thymus, exhibit reduced central tolerance, and develop a spontaneous, chronic, and progressive uveoretinitis. Using single-cell RNA sequencing (scRNA-seq), we characterized wild-type andAire−/−retinas to define, in a comprehensive and unbiased manner, the cell populations and gene expression patterns associated with disease. Based on scRNA-seq, immunostaining, and in situ hybridization, we infer that 1) the dominant effector response inAire−/−retinas is Th1-driven, 2) a subset of monocytes convert to either a macrophage/microglia state or a dendritic cell state, 3) the development of tertiary lymphoid structures constitutes part of theAire−/−retinal phenotype, 4) all major resident retinal cell types respond to interferon gamma (IFNG) by changing their patterns of gene expression, and 5) Muller glia up-regulate specific genes in response to IFN gamma and may act as antigen-presenting cells.

scholarly journals Single-Cell RNA Sequencing Revealed the Heterogeneity of Gonadal Primordial Germ Cells in Zebra Finch (Taeniopygia guttata)

2021 ◽  
Vol 9 ◽  
Author(s):  
Kyung Min Jung ◽  
Minseok Seo ◽  
Young Min Kim ◽  
Jin Lee Kim ◽  
Jae Yong Han
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Zebra Finch ◽  
Germ Cells ◽  
Expression Patterns ◽  
Primordial Germ Cells ◽  
Cell Types ◽  
Avian Species ◽  
Single Population ◽  
Single Cell Rna Sequencing

Primordial germ cells (PGCs) are undifferentiated gametes with heterogeneity, an evolutionarily conserved characteristic across various organisms. Although dynamic selection at the level of early germ cell populations is an important biological feature linked to fertility, the heterogeneity of PGCs in avian species has not been characterized. In this study, we sought to evaluate PGC heterogeneity in zebra finch using a single-cell RNA sequencing (scRNA-seq) approach. Using scRNA-seq of embryonic gonadal cells from male and female zebra finches at Hamburger and Hamilton (HH) stage 28, we annotated nine cell types from 20 cell clusters. We found that PGCs previously considered a single population can be separated into three subtypes showing differences in apoptosis, proliferation, and other biological processes. The three PGC subtypes were specifically enriched for genes showing expression patterns related to germness or pluripotency, suggesting functional differences in PGCs according to the three subtypes. Additionally, we discovered a novel biomarker, SMC1B, for gonadal PGCs in zebra finch. The results provide the first evidence of substantial heterogeneity in PGCs previously considered a single population in birds. This discovery expands our understanding of PGCs to avian species, and provides a basis for further research.

scholarly journals FIRM: Fast Integration of single-cell RNA-sequencing data across Multiple platforms

2020 ◽  
Author(s):  
Jingsi Ming ◽  
Zhixiang Lin ◽  
Xiang Wan ◽  
Can Yang ◽  
Angela Ruohao Wu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Biology ◽  
Developmental Trajectories ◽  
Expression Patterns ◽  
Cell Types ◽  
Original Structure ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA-sequencing (scRNA-seq) has now been used extensively to discover novel cell types and reconstruct developmental trajectories by measuring mRNA expression patterns of individual cells. However, datasets collected using different scRNA-seq technology platforms, including the popular SMART-Seq2 (SS2) and 10X platforms, are difficult to compare because of their heterogeneity. Each platform has unique advantages, and integration of these datasets would provide deeper insights into cell biology and gene regulation. Through comprehensive data exploration, we found that accurate integration is often hampered by differences in cell-type compositions. Herein we describe FIRM, an algorithm that addresses this problem and achieves efficient and accurate integration of heterogeneous scRNA-seq datasets across multiple platforms. We applied FIRM to numerous scRNA-seq datasets generated using SS2 and 10X from mouse, mouse lemur, and human, comparing its performance in dataset integration with other state-of-the-art methods. The integrated datasets generated using FIRM show accurate mixing of shared cell type identities and superior preservation of original structure for each dataset. FIRM not only generates robust integrated datasets for downstream analysis, but is also a facile way to transfer cell type labels and annotations from one dataset to another, making it a versatile and indispensable tool for scRNA-seq analysis.

scholarly journals Simultaneous Identification of Brain Cell Type and Lineage via Single Cell RNA Sequencing

2021 ◽  
Author(s):  
Donovan J. Anderson ◽  
Florian M. Pauler ◽  
Aaron McKenna ◽  
Jay Shendure ◽  
Simon Hippenmeyer ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Cortical Cell ◽  
Cell Types ◽  
F1 Hybrids ◽  
Mouse Strains ◽  
Monoallelic Expression ◽  
Cell Divisions ◽  
Single Cell Rna Sequencing

ABSTRACTAcquired mutations are sufficiently frequent such that the genome of a single cell offers a record of its history of cell divisions. Among more common somatic genomic alterations are loss of heterozygosity (LOH). Large LOH events are potentially detectable in single cell RNA sequencing (scRNA-seq) datasets as tracts of monoallelic expression for constitutionally heterozygous single nucleotide variants (SNVs) located among contiguous genes. We identified runs of monoallelic expression, consistent with LOH, uniquely distributed throughout the genome in single cell brain cortex transcriptomes of F1 hybrids involving different inbred mouse strains. We then phylogenetically reconstructed single cell lineages and simultaneously identified cell types by corresponding gene expression patterns. Our results are consistent with progenitor cells giving rise to multiple cortical cell types through stereotyped expansion and distinct waves of neurogenesis. Compared to engineered recording systems, LOH events accumulate throughout the genome and across the lifetime of an organism, affording tremendous capacity for encoding lineage information and increasing resolution for later cell divisions. This approach can conceivably be computationally incorporated into scRNA-seq analysis and may be useful for organisms where genetic engineering is prohibitive, such as humans.

scholarly journals Single-cell data clustering based on sparse optimization and low-rank matrix factorization

2021 ◽  
Author(s):  
Yinlei Hu ◽  
Bin Li ◽  
Falai Chen ◽  
Kun Qu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Matrix Factorization ◽  
Data Clustering ◽  
Cell Types ◽  
Low Rank ◽  
Sequencing Data ◽  
Rank Matrix ◽  
Single Cell Rna Sequencing ◽  
Low Rank Matrix

Abstract Unsupervised clustering is a fundamental step of single-cell RNA sequencing data analysis. This issue has inspired several clustering methods to classify cells in single-cell RNA sequencing data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for single-cell RNA sequencing data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single-cell RNA sequencing data.

scholarly journals Cryopreservation of microglia enables single-cell RNA sequencing with minimal effects on disease-related gene expression patterns

iScience ◽  
2021 ◽  
Vol 24 (4) ◽  
pp. 102357
Author(s):  
Brenda Morsey ◽  
Meng Niu ◽  
Shetty Ravi Dyavar ◽  
Courtney V. Fletcher ◽  
Benjamin G. Lamberty ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Related Gene ◽  
Single Cell Rna Sequencing ◽  
Disease Related Gene

scholarly journals Single cell RNA sequencing of the Strongylocentrotus purpuratus larva reveals the blueprint of major cell types and nervous system of a non-chordate deuterostome

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Periklis Paganos ◽  
Danila Voronov ◽  
Jacob M Musser ◽  
Detlev Arendt ◽  
Maria Ina Arnone
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Sea Urchin ◽  
Regulatory Networks ◽  
Sea Urchins ◽  
Neuronal Cell ◽  
Cell Types ◽  
Molecular Fingerprint ◽  
Larval Body ◽  
Single Cell Rna Sequencing

Identifying 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 identify 21 distinct cell clusters, representing cells of the digestive, skeletal, immune, and nervous systems. Further subclustering of these reveal a highly detailed portrait of cell diversity across the larva, including the identification of neuronal cell types. We then validate important gene regulatory networks driving sea urchin development and reveal new domains of activity within the larval body. Focusing on neurons that co-express Pdx-1 and Brn1/2/4, we identify an unprecedented number of genes shared by this population of neurons in sea urchin and vertebrate endocrine pancreatic cells. Using differential expression results from Pdx-1 knockdown experiments, we show that Pdx1 is necessary for the acquisition of the neuronal identity of these cells. We hypothesize that a network similar to the one orchestrated by Pdx1 in the sea urchin neurons was active in an ancestral cell type and then inherited by neuronal and pancreatic developmental lineages in sea urchins and vertebrates.

Localization of migraine susceptibility genes in human brain by single-cell RNA sequencing

Cephalalgia ◽  
2018 ◽  
Vol 38 (13) ◽  
pp. 1976-1983 ◽  
Author(s):  
William Renthal
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Cell Types ◽  
Susceptibility Genes ◽  
Brain Cell ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Brain Cell Types

Background Migraine is a debilitating disorder characterized by severe headaches and associated neurological symptoms. A key challenge to understanding migraine has been the cellular complexity of the human brain and the multiple cell types implicated in its pathophysiology. The present study leverages recent advances in single-cell transcriptomics to localize the specific human brain cell types in which putative migraine susceptibility genes are expressed. Methods The cell-type specific expression of both familial and common migraine-associated genes was determined bioinformatically using data from 2,039 individual human brain cells across two published single-cell RNA sequencing datasets. Enrichment of migraine-associated genes was determined for each brain cell type. Results Analysis of single-brain cell RNA sequencing data from five major subtypes of cells in the human cortex (neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells) indicates that over 40% of known migraine-associated genes are enriched in the expression profiles of a specific brain cell type. Further analysis of neuronal migraine-associated genes demonstrated that approximately 70% were significantly enriched in inhibitory neurons and 30% in excitatory neurons. Conclusions This study takes the next step in understanding the human brain cell types in which putative migraine susceptibility genes are expressed. Both familial and common migraine may arise from dysfunction of discrete cell types within the neurovascular unit, and localization of the affected cell type(s) in an individual patient may provide insight into to their susceptibility to migraine.

Identifying cell types to interpret scRNA-seq data: how, why and more possibilities

2020 ◽  
Vol 19 (4) ◽  
pp. 286-291 ◽  
Author(s):  
Ziwei Wang ◽  
Hui Ding ◽  
Quan Zou
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Biological Phenomena ◽  
Single Cell Rna Sequencing ◽  
Numerous Data ◽  
Near Future

Abstract Single-cell RNA sequencing (scRNA-seq) has generated numerous data and renewed our understanding of biological phenomena at the cellular scale. Identification of cell types has been one of the most prevalent means for interpreting scRNA-seq data, based upon which connections are made between the transcriptome and phenotype. Herein, we attempt to review the methods and tools that dedicate to the task regarding their feature and usage and look at the possibilities for scRNA-seq development in the near future.

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