Suppr超能文献

基于图像的转录组图谱揭示了小鼠肠道的区域以及微生物群依赖的分子、细胞和空间结构。

An image-based transcriptomics atlas reveals the regional and microbiota-dependent molecular, cellular, and spatial structure of the murine gut.

作者信息

Xu Rosalind J, Cadinu Paolo, Nicol Phillip B, Herrmann Uli S, Lee Tyrone, Geistlinger Ludwig, Irizarry Rafael A, Moffitt Jeffrey R

机构信息

Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA.

Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA.

出版信息

bioRxiv. 2025 Jul 24:2025.07.21.665958. doi: 10.1101/2025.07.21.665958.

Abstract

The gastrointestinal environment is home to a massive diversity of diet-, host-, and microbiota-derived small molecules, collectively sensed by a remarkable variety of cells. To explore the cellular and spatial organization of sensation, we used MERFISH to profile receptor expression across 2.1 million cells in multiple regions of the murine gut under specific-pathogen-free (SPF) and germ-free (GF) conditions. This atlas revealed expected and novel cell types-including a candidate murine homolog of human BEST4 enterocytes-demonstrated cell-type regional specialization, discovered extensive location-dependent spatial fine-tuning in mucosal cell expression, and suggested cell-type specific mediators of the effects of microbiota-derived small molecules. In addition, this atlas revealed that, aside from immune cell abundance, many aspects of the murine gut are host-intrinsic and modified only modestly in the absence of a microbiota. Collectively, this atlas provides a valuable resource for understanding the cellular and spatial organization underlying small molecule sensation in the gut.

摘要

胃肠道环境中存在着大量源自饮食、宿主和微生物群的小分子,由各种各样的细胞共同感知。为了探索感觉的细胞和空间组织,我们使用多重抗误差荧光原位杂交技术(MERFISH)在无特定病原体(SPF)和无菌(GF)条件下,对小鼠肠道多个区域的210万个细胞进行受体表达分析。该图谱揭示了预期的和新的细胞类型,包括人类BEST4肠细胞的候选小鼠同源物,证明了细胞类型的区域特化,发现了粘膜细胞表达中广泛的位置依赖性空间微调,并提出了微生物群衍生小分子作用的细胞类型特异性介质。此外,该图谱还显示,除了免疫细胞丰度外,小鼠肠道的许多方面是宿主内在的,在没有微生物群的情况下仅发生适度改变。总体而言,该图谱为理解肠道中小分子感觉的细胞和空间组织提供了宝贵资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/013f/12330561/656ba1ea1399/nihpp-2025.07.21.665958v1-f0001.jpg

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