Suppr超能文献

产后小鼠乳腺的综合单细胞转录组图谱有助于发现管腔区新的发育轨迹。

An Integrative Single-cell Transcriptomic Atlas of the Post-natal Mouse Mammary Gland Allows Discovery of New Developmental Trajectories in the Luminal Compartment.

作者信息

García Solá Martín E, Stedile Micaela, Beckerman Inés, Kordon Edith C

机构信息

Instituto de Fisiología, Biología Molecular y Neurociencias (IFIByNE), CONICET, Departamento de Fisiología y Biología Molecular y Celular. Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.

Facultad de Ciencias Exactas y Naturales, Departamento de Química Biológica, Universidad de Buenos Aires, Buenos Aires, Argentina.

出版信息

J Mammary Gland Biol Neoplasia. 2021 Mar;26(1):29-42. doi: 10.1007/s10911-021-09488-1. Epub 2021 Apr 28.

Abstract

The mammary gland is a highly dynamic organ which undergoes periods of expansion, differentiation and cell death in each reproductive cycle. Partly because of the dynamic nature of the gland, mammary epithelial cells (MECs) are extraordinarily heterogeneous. Single cell RNA-seq (scRNA-seq) analyses have contributed to understand the cellular and transcriptional heterogeneity of this complex tissue. Here, we integrate scRNA-seq data from three foundational reports that have explored the mammary gland cell populations throughout development at single-cell level using 10× Chromium Drop-Seq. We center our analysis on post-natal development of the mammary gland, from puberty to post-involution. The new integrated study corresponds to RNA sequences from 53,686 individual cells, which greatly outnumbers the three initial data sets. The large volume of information provides new insights, as a better resolution of the previously detected Procr stem-like cell subpopulation or the identification of a novel group of MECs expressing immune-like markers. Moreover, here we present new pseudo-temporal trajectories of MEC populations at two resolution levels, that is either considering all mammary cell subtypes or focusing specifically on the luminal lineages. Interestingly, the luminal-restricted analysis reveals distinct expression patterns of various genes that encode milk proteins, suggesting specific and non-redundant roles for each of them. In summary, our data show that the application of bioinformatic tools to integrate multiple scRNA-seq data-sets helps to describe and interpret the high level of plasticity involved in gene expression regulation throughout mammary gland post-natal development.

摘要

乳腺是一个高度动态的器官,在每个生殖周期中都会经历扩张、分化和细胞死亡阶段。部分由于乳腺的动态特性,乳腺上皮细胞(MECs)具有极高的异质性。单细胞RNA测序(scRNA-seq)分析有助于了解这种复杂组织的细胞和转录异质性。在这里,我们整合了来自三篇基础报告的scRNA-seq数据,这些报告使用10×铬滴液测序技术在单细胞水平上探索了乳腺在整个发育过程中的细胞群体。我们的分析集中在乳腺从青春期到 involution 后的产后发育阶段。这项新的整合研究对应于来自53,686个单个细胞的RNA序列,大大超过了最初的三个数据集。大量的信息提供了新的见解,例如对先前检测到的Procr干细胞样亚群有更好的分辨率,或者鉴定出一组表达免疫样标记的新型MECs。此外,我们在此展示了MEC群体在两个分辨率水平上的新的拟时间轨迹,即要么考虑所有乳腺细胞亚型,要么专门关注管腔谱系。有趣的是,管腔限制分析揭示了各种编码乳蛋白的基因的不同表达模式,表明它们各自具有特定且非冗余的作用。总之,我们的数据表明,应用生物信息学工具整合多个scRNA-seq数据集有助于描述和解释乳腺产后发育过程中基因表达调控所涉及的高度可塑性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验