doi:10.1016/j.cell.2021.06.024, Quinn, T. P., Richardson, M. F., Lovell, D., and Crowley, T. M. (2017). FIGURE 3. Single-Cell Analysis of the Cellular Heterogeneity and Interactions in the Injured Mouse Spinal Cord. We used the ADMM algorithm to efficiently solve this optimization problem. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). (B) Heatmap showing the differential number of interactions between E14.5, E16.5, and E18.5. We can compare the total information flow in the cellcell communication network of each signaling pathway across different datasets under different conditions, leading to the identification of changes in important signaling pathways. Complementary & Alternative Medicine (CAM), Talking to Others about Your Advanced Cancer, Coping with Your Feelings During Advanced Cancer, Emotional Support for Young People with Cancer, Young People Facing End-of-Life Care Decisions, Late Effects of Childhood Cancer Treatment, Tech Transfer & Small Business Partnerships, Frederick National Laboratory for Cancer Research, Milestones in Cancer Research and Discovery, Step 1: Application Development & Submission, National Cancer Act 50th Anniversary Commemoration, Supportive & Palliative Care Editorial Board, Levels of Evidence: Supportive & Palliative Care, Levels of Evidence: Screening & Prevention, Levels of Evidence: Integrative Therapies, U.S. Department of Health and Human Services. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. To find out the interaction between which cell groups was significantly changed, we computed the differential number of interactions for both outgoing and incoming signaling of pairwise cell groups between any pair of two time points. We selected TF-target gene interactions with high confidence levels A, B, and C from the OmniPath database. The downstream signaling network was constructed by integrating the receptor-TFs and TFs-target gene interactions (MATERIALS AND METHODS). Genes were considered as enriched genes with an adjusted p-value < 0.05. Methods. For the inferred multiscale signaling network of CTL-to- secretory, we observed many interferon, cytokines, and growth factorsrelated upstream ligands, such as IFNG, IFNB1, IFNE, IL6, IL10, IL2, IL17, OSM, IL4, IL7, VEGFA, EREG, TGFA, and EGF, as well as their corresponding receptors activated in secretory cells, such as IFNGR1, IFNGR2, IL6ST, IL2RG, KDR, and EGFR. (F) Circle plots displaying the inferred network of the OSM signaling pathway at uninjured, 1, 3, and 7dpi. We observed that the residual value exhibited a slight fluctuation (Supplementary Figure S5A), suggesting that our inference is relatively robust. To demonstrate the capability of our approaches in capturing predominant signaling changes across multiple time points, we first applied our generalized CellChat to our previously published mouse skin scRNA-seq datasets, which described epidermal development at three embryonic stages: E14.5, E16.5, and E18.5 (newborn) (Lin, et al., 2020). Rev. For the secretory-related signaling, CXCL, IFN-II, and IL1 increased either outgoing or incoming signaling; for the ciliated-related signaling, CCL, IFN-II, and IL2 increased either outgoing or incoming signaling. We then build a multiscale signaling network by integrating the cellcell communication between interacting cells (i.e., intercellular communication) with the downstream signaling inside target cells (i.e., intracellular signaling). With the increasing number of scNRA-seq datasets collected from multiple conditions, time points, and disease states, easy-to-use tools that can seamlessly identify signaling changes across any biological conditions from multiple scRNA-seq datasets are highly needed. The weighted average is an ensemble strategy that has been widely used in many other studies. Specifically, CellChat can identify the changes of the dominant sender and receiver in cell groups by comparing any two datasets using network centrality metrics such as out-degree and in-degree. All authors approved the final manuscript. By examining the list of inferred signaling pathways, interestingly, Connectome did not produce the IFN-II signaling while CellChat did. To address these limitations, we first generalized our previously developed R package CellChat to enable the comparison analysis of any number of datasets from multiple conditions, allowing ready identification of signaling changes across conditions. doi:10.1016/j.gpb.2020.11.006, Lin, Z., Jin, S., Chen, J., Li, Z., Lin, Z., Tang, L., et al. We focused on the cell typespecific signaling network and thus first identified enriched genes and TFs in each cell group. doi:10.1038/s41576-020-00292-x, Browaeys, R., Saelens, W., and Saeys, Y. (2020). Nat. Syst. Edge width is proportional to the number of interactions, which assess how many ligandreceptor pairs contributing to the communication between two interacting cell populations. Overview of identifying intercellular signaling changes across conditions and inferring the multiscale signaling network from multiple scRNA-seq datasets. Although recent studies have developed different computational methods to investigate cellcell communication, our study adds important understanding of the cellcell communication in several aspects. Acta Neuropathol. Commun. Integrating spatial location with scRNA-seq data will likely reduce the false positive inference of cellcell communication. (B) Multiscale signaling network is inferred to link intercellular communication to intracellular signaling, which integrates the ligandreceptor interactions, receptor-TFs interactions, and TFs-target gene interactions. This analysis classified all significant signaling pathways into four groups. In this way, the network we build will likely be more precise and more biologically explanatory. In addition to WNT signaling, we also observed other increased signaling changes for both outgoing and incoming signaling including BMP, MIF, GALECTIN, and IL1, and decreased signaling including MK and PTN (Figure 2C). (C) Scatter plots comparing the outgoing and incoming interaction strength in the 2D space among uninjured, 1dpi, and 7dpi. Rep. 7 (1), 16252. doi:10.1038/s41598-017-16520-0, Raredon, M., Yang, J., Garritano, J., Wang, M., and Niklason, L. E. (2021). (C) Identifying the specific signaling changes of IFE-B.1 and IFE-B.2 from E14.5 to E18.5. We found that, compared to control, about half of the signaling pathways were highly enriched in moderate and critical (green and blue colors in left and middle panels in Figure 4D). In the color bar, red (or blue) represents increased (or decreased) signaling in the second dataset compared to the first one. threshold = 0.25). Given the predominant signaling change of the immune cell CTL and epithelial cell secretory and ciliated, we investigate important ligandreceptor pairs sending from CTL cells to secretory and ciliated cells in moderate and critical. We then identified the enriched TFs in certain cell groups using the differential expression analysis based on the computed TF activity data. Age-Dependent Assessment of Genes Involved in Cellular Senescence, Telomere, and Mitochondrial Pathways in Human Lung Tissue of Smokers, COPD, and IPF: Associations With SARS-CoV-2 COVID-19 ACE2-TMPRSS2-Furin-DPP4 Axis. Interestingly, compared to control, all cell types exhibited increased signaling in either outgoing or incoming signaling. Furthermore, comparing the communication probabilities mediated by ligandreceptor pairs from macrophages to fibroblasts and astrocytes, we identified ligand-receptor pairs that were only enriched at 1, 3, and 7dpi, including SPP1 signaling such as Spp1 - (Itgav + Itgb5) and Spp1 - (Itga5+Itgb1) and OSM signaling such as Osm - (Osmr + Il6st) (Figure 3E and Supplementary Figure S2C). Different plots are provided to allow ready comparison analysis. The processed transcriptomic data of 135,600 cells from patients and control patients with no signs of disease with COVID-19 were downloaded from FigShare: https://doi.org/10.6084/m9.figshare.12436517. Step 5, the cell typespecific multiscale signaling network is finally constructed by only retaining the cell typeenriched TFs and target genes based on differential expression analysis (Figure 1B). The advantage is that the single-cell datasets used for comparative analysis can be any number, not just limited to the comparison between two datasets. Next, we examined the major source and target changes in different stages of COVID-19 by computing the differential outgoing and incoming differential interaction strength associated with each cell type (Figure 4C). The intercellular communication is given by CellChat while the intracellular signaling is inferred by constructing a genegene network linking receptors, TFs, and target genes. (A) The generalized CellChat identifies intercellular signaling changes across conditions from multiple scRNA-seq datasets. This dataset comprises 20 cell populations, including ciliated-diff cells (differentiating ciliated), secretory-diff cells (differentiating secretory), ciliated cells, FOXN4+ cells, squamous cells, secretory cells, cytotoxic T lymphocytes (CTL), natural killer T cells (NKT), B lymphocytes (BC), plasmacytoid dendritic cells (pDC), monocyte-derived macrophages (moMa), basal cells, proliferating NKT cells (NKT-p), IFNG-responsive cells (IFNRep), regulatory T cell (Treg), neutrophils (Neu), monocyte-derived dendritic cells (moDC), nonresident macrophages (nrMa), resident macrophages (rMa), and ionocytes. doi:10.1038/s41592-019-0667-5, Cheng, J., Zhang, J., Wu, Z., and Sun, X. iteration stops when both ||Rk||F and ||Sk||F values become smaller than pri and dual, respectively, After obtaining the solution X, we determined the weight of the network by considering another proportionality-based association measure propr (Quinn, et al., 2017), which was shown to perform very well in inferring gene networks across multiple scRNA-seq datasets and technologies (Skinnider, et al., 2019). doi:10.1101/2021.01.21.427529, Sha, Y., Wang, S., Bocci, F., Zhou, P., and Nie, Q. To better model the relationship between TFs and their target genes, we estimated TF activity based on the targets mRNA expression level from scRNA-seq data using DoRothEA (Garcia-Alonso, et al., 2019) since TF activity is difficult to measure directly and it may be possible to infer changes in the TF activity level from changes in the expression levels of the TFs target genes. CTL is the dominant signaling source and FOXN4, ciliated, and ionocyte cells emerge as new signaling targets in moderate and critical (Figure 5A). Moreover, identification of signaling changes across conditions is important for understanding how distinct cell states respond to evolution, perturbations, and diseases (Armingol, et al., 2021). Med. (B) Heatmaps of the differential number of interactions between uninjured and 1dpi as well as uninjured and 7dpi, showing the outgoing and incoming signaling change of each cell group in a greater detail (The top colored bar plot represents the sum of each column of values displayed in the heatmap (incoming signaling). (B) Expression of IFN-II signalingrelated genes such as IFNG, IFNGR1, and IFNGR2 in control, moderate, and critical COVID-19. (2020). Plasma ACE2 and Risk of Death or Cardiometabolic Diseases: a Case-Cohort Analysis. To visualize the inferred network, we only retained the top 25 edges based on the inferred edge weights.

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