To investigate how surrounding cells may shape signaling in the surface and glandular compartments, we developed CellPhoneDB v.3.0, an updated version of our cell–cell communication pipeline that takes into account spatial cellular colocalization when map-ping ligand–receptor pairs define three endometrial microenvironments centered on epithelial cells based on the cellular coordinates provided by cell2location: (1) lumenal—preciliated, ciliated and SOX9+LGR5+ epithelium (proliferative phase) and ciliated and lumenal (secretory phase); (2) functional—SOX9+ proliferative epithelium, immune and eS (proliferative phase) and immune, glandular and dS (secretory phase); and (3) basal—SOX9+LGR5? and fibroblasts C7 其中生态位的判定To account for the distinct temporal and spatial location of cells (that is, microenvironment), we further classified the epithelial interactions based on (1) the phase of the menstrual cycle where cell subsets coexist and (2) their location in the three main endometrial layers (luminal,glandular and basal) according to cell2location
To account for the distinct spatial location of cells, we further classified the cells according to their location in the developing ovaries (outer cortex, inner cortex, medulla) as observed by Visium and smFISH. We filtered cell–cell interactions to exclude cell pairs that do not share the same location.
1、CellSign模块,利用基于转录因子下游受体活性的相互作用。本模块还包括211个描述良好的受体-转录因子直接关系的集合。 2、一种查询CellphoneDB结果的新方法search_utils.search_analysis_results。 3、一个新的数据库(cellphonedb-data v5.0),其中有更多手动管理的交互,总共有3,000个交互。这个版本的CellphoneDB数据库有三个主要的变化: (1)整合新的人工审查的相互作用,证明了在细胞-细胞通信中的作用。 (2)包括作为配体的非蛋白分子。 (3)对于与已证明的信号方向性的相互作用,合作伙伴已按顺序排列(ligand is partner A, receptor partner B)。 4、相互作用在信号通路中被分类(这一点借鉴的cellchat)。
bubble <- netVisual_bubble(
IPF,
#sources.use = c(1:4),
#targets.use = c(16,17),
# signaling = c("MHC-I","MHC-II"),
pairLR.use = immune_net_ssg_pairs,
remove.isolate = FALSE,
thresh=0.05,
sources.use = c("B/Plasma","Bronchial Vessel","Dendritic","pDC","T cell"),
targets.use = c("B/Plasma","Bronchial Vessel","Dendritic","pDC","T cell"),
title.name = "Immune Niche - Secreted Signaling",)
ggsave(filename="./figures/CellChat_IPF_ImmuneNiche_SSG_pairs_heatmap.pdf", plot=bubble, dpi = 300)
Single-cell NicheNet’s ligand activity analysis
niches = list(
"KC_niche" = list(
"sender" = c("LSECs_portal","Hepatocytes_portal","Stellate cells_portal"),
"receiver" = c("KCs")),
"MoMac2_niche" = list(
"sender" = c("Cholangiocytes","Fibroblast 2"),
"receiver" = c("MoMac2")),
"MoMac1_niche" = list(
"sender" = c("Capsule fibroblasts","Mesothelial cells"),
"receiver" = c("MoMac1"))
)
原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。
原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。