01-08 人体内配体-受体介导的多细胞信号网络草图

A draft network of ligand–receptor-mediated multicellular signalling in human

题目:人体内配体-受体介导的多细胞信号网络草图

作者及单位:

Jordan A. Ramilowski, Tatyana Goldberg, Jayson Harshbarger, [...], Alistair R. R. Forrest

Jordan A. Ramilowski:

RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 Japan

Alistair R. R. Forrest:

RIKEN Center for Life Science Technologies, Division of Genomic Technologies, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 Japan

Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, the University of Western Australia, PO Box 7214, 6 Verdun Street, Nedlands, Perth, Western Australia 6008, Australia

发表期刊及时间:

Nature Communications volume 6, Article number: 7866 (2015)

Published: 22 July 2015

摘要:

Cell-to-cell communication across multiple cell types and tissues strictly governs proper functioning of metazoans and extensively relies on interactions between secreted ligands and cell-surface receptors. Herein, we present the first large-scale map of cell-to-cell communication between 144 human primary cell types. We reveal that most cells express tens to hundreds of ligands and receptors to create a highly connected signalling network through multiple ligand–receptor paths. We also observe extensive autocrine signalling with approximately two-thirds of partners possibly interacting on the same cell type. We find that plasma membrane and secreted proteins have the highest cell-type specificity, they are evolutionarily younger than intracellular proteins, and that most receptors had evolved before their ligands. We provide an online tool to interactively query and visualize our networks and demonstrate how this tool can reveal novel cell-to-cell interactions with the prediction that mast cells signal to monoblastic lineages via the CSF1–CSF1R interacting pair.

多种细胞类型和组织之间的细胞-细胞通讯严格控制着后生动物的正常功能,并广泛地依赖于分泌配体和细胞表面受体之间的相互作用。再次,我们率先提出了144中人类原始细胞类型当中的大规模细胞-细胞间通讯图谱。我们揭示了大部分表达数十至数百个配体和受体的细胞会通过多种配体-受体通路产生高度连接的信号传导网络。同时,我们也观察到广泛的自分泌信号传导,大约2/3的伴侣可能跟同种细胞类型互作。我们发现质膜和分泌蛋白有着最高的细胞类型特异性,他们在进化上比胞内蛋白更年轻,并且大多数受体比它们的配体先形成。我们提供了一个在线工具,用于交互式查询和可视化我们的网络,并演示该工具如何通过CSF1-CSF1R相互作用对来预测肥大细胞对单核细胞谱系信号传导,从而揭示新的细胞间相互作用。

图表选析

01-08 人体内配体-受体介导的多细胞信号网络草图_第1张图片
image

Figure 1. Relationship between protein subcellular localization, cell-type specificity and gene ages. 蛋白质亚细胞位点、细胞类型特异性和基因年龄之间的关系.

(a) Breakdown of known subcellular localization of protein-coding genes expressed >1 TPM in at least one primary state for which protein ages were available. (b) Interquartile range distributions (whisker boxes) and relative cell-type specificity for each protein subcellular compartment from FANTOM5 primary cell expression profiles. Both secreted and plasma membrane proteins are significantly more cell-type specific than nuclear and cytoplasmic proteins (each Mann–Whitney U-test-adjusted P value<000.1). (c) Relative fractions of proteins at each evolutionary stage for selected subcellular localization (secreted, plasma membrane, nucleus, cytoplasmic and other) using the methods of Wagner21. All fractions at a given age add to 100%. (d) As in c but scaled for visualization purposes to the number of nuclear proteins. Both secreted (average age: 412.2 mya) and plasma membrane (average age: 517.2 mya) proteins are significantly younger than nuclear (average age: 663.1 mya) and cytoplasmic proteins (average age: 855.1 mya), each Mann–Whitney U-test-adjusted Pvalue<000.1. Note: exact numbers of proteins for each subcellular localization class in each phylostrata are available in Supplementary Data 1.

a)蛋白质编码基因的已知亚细胞定位的分解在至少一种可获得蛋白质年龄的原始状态中表达> 1TPM。(b)FANTOM5原代细胞表达谱的每个蛋白质亚细胞区室的四分位范围分布(箱线图),以及相对细胞类型特异性。分泌蛋白和质膜蛋白的细胞类型特异性显著高于核蛋白和胞质蛋白(每个Mann-Whitney U-检验调节的P值<000.1)。(c)使用Wagner 21的方法,在选择的亚细胞定位(分泌蛋白,质膜蛋白,核蛋白,胞质蛋白和其他)的每个进化阶段的相对分数。给定年龄的所有部分均增加至100%。(d)如在c中,但为了可视化目的而缩放核蛋白的数量。分泌蛋白(平均年龄:412.2 mya)和质膜蛋白(平均年龄:517.2 mya)比核蛋白(平均年龄:663.1 mya)和胞质蛋白(平均年龄:855.1 mya)显着更年轻,每个Mann-Whitney U -检验调节的P值<000.1。注意:补充数据1中提供了每组柱中每个亚细胞定位类别的确切蛋白质数量。

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