(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). New Avenues for Systematically Inferring Cell-Cell Communication: Through Single-Cell Transcriptomics Data. 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). CellChat identifies biologically significant signaling pathways for each dataset separately and then performs comparative analysis across multiple datasets in a systematic and quantitative manner. 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. A., Cang, Z., Jin, S., and Nie, Q. 74, 114125. 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). In addition, we infer a multiscale signaling network which integrates the ligandreceptor interactions inferred from CellChat, the receptor-TF interactions from public databases, and the TF- gene regulations from a mathematical optimization model taking into account the prior network information from public databases. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. iTALK: An R Package to Characterize and Illustrate Intercellular Communication. Here, we first generalized our previously developed tool CellChat, enabling flexible comparison analysis of cellcell communication networks across any number of scRNA-seq datasets from interrelated biological conditions. Although a number of computational methods have been recently developed to infer cellcell communication by integrating scRNA-seq data with a prior ligandreceptor interaction database, most of these methods only focus on the intercellular communications in one biological condition (Almet, et al., 2021; Armingol, et al., 2021), lacking the capability of identifying signaling changes across conditions. (2021). Step 5: Inference of cell typespecific multiscale signaling network. Step 2: Construction of the receptor-TF subnetwork. (2021). Compared to the uninjured tissue, we found that both the outgoing and incoming interaction strength of several myeloid cell populations, including the macrophage, monocyte, neutrophil, microglia, and dendritic cells, were significantly increased at 1dpi, and the outgoing and incoming interaction strength of fibroblasts was increased at 3dpi and then, further enhanced at 7dpi (Figure 3C and Supplementary Figure S2B). (A) The generalized CellChat identifies intercellular signaling changes across conditions from multiple scRNA-seq datasets. doi:10.1016/j.cell.2021.06.024, Quinn, T. P., Richardson, M. F., Lovell, D., and Crowley, T. M. (2017). This comparative analysis of the interactions between cell types across different biological conditions is essential for a biologically meaningful understanding of the role of cellcell communication from scRNA-seq data. To understand the role of cellcell communication in activating ACE2 expression in the target cell, we constructed a multiscale signaling network by integrating the intercellular communications with the intracellular downstream signaling response (MATERIALS AND METHODS). These results agreed well with the previous findings: 1) At 1dpi, peripheral myeloid cells, mainly neutrophils and monocytes, migrate to the injury site and then enhance the innate immune response initiated by the microglia (Milich, et al., 2019); 2) Fibrosis is initiated at 3dpi and the number of fibroblasts reaches its peak at 7dpi (Zhu, et al., 2015). Although recent studies have developed different computational methods to investigate cellcell communication, our study adds important understanding of the cellcell communication in several aspects. (B) Expression of IFN-II signalingrelated genes such as IFNG, IFNGR1, and IFNGR2 in control, moderate, and critical COVID-19. Propr: An R-Package for Identifying Proportionally Abundant Features Using Compositional Data Analysis. Moreover, to identify the signaling pathways contributing to the dramatic signaling changes of IFE-B.1 and IFE-B.2, we calculated the differential outgoing and incoming interaction strength of each signaling pathway between E14.5 and E18.5. We observed that the residual value exhibited a slight fluctuation (Supplementary Figure S5A), suggesting that our inference is relatively robust. We demonstrated the effectiveness of our proposed approaches by studying the signaling changes across three mouse embryonic developmental stages, four time points after mouse spinal cord injury, and patients with different COVID-19 severities (i.e., control, moderate, and critical cases). From the public databases, we get the receptor-TF prior network from the OmniPath database (Trei, et al., 2016; Trei et al., 2021), kinaseextra and pathwayextra using OmnipathR package (https://github.com/saezlab/OmnipathR). FIGURE 4. A is the target gene expression matrix (rows are target genes and columns are cells. We then computed the residual value of the model using five-fold cross-validation under each parameter combination. (2019). In addition, CCL signaling was also clearly increased at 1dpi compared to the uninjured (Figure 3D), which agreed with the innate immune response initiated by myeloid cells at 1dpi (Milich, et al., 2019). Our current strategy that combines clustering analysis can help to identify signaling networks that show a relatively large difference in network architecture if they are located in different clusters and far away from each other in the low-dimensional space. (2019). 2020). Biotechnol. (D) The inferred multiscale signaling network of CTL-to- ciliated in critical COVID-19. Compared to control, certain chemokine and cytokine signaling pathways in moderate and critical were increased in their interaction strength (Supplementary Figure S3A). (2020). doi:10.1101/gr.240663.118, Hu, Y., Peng, T., Gao, L., and Tan, K. (2020). Once we constructed the intercellular communication network mediated by ligandreceptor interactions, the receptor-TF network, and TF-target gene network, we integrated them together to obtain a multiscale signaling network, linking the intercellular communication network with intracellular signaling network. Overall, the number of interactions was largely increased in moderate and critical samples compared to control, but exhibited dynamic changes when comparing moderate and critical (Figure 4B). This dataset includes eight moderate cases, eleven critical cases, and five control cases (According to the World Health Organization (WHO) guidelines, the severity of the disease is classified) (Chua, et al., 2020). To demonstrate this point, we conducted robustness analysis and varied the regularization parameter values within a certain range to explore the robustness of our model. CellChat quantifies the similarity of multiple cellular communication networks using structural similarity and functional similarity and performs joint manifold learning and classification of the inferred communication networks based on the computed similarity to identify signaling networks with a certain difference. doi:10.1016/s0140-6736(20)31964-4, Plikus, M. V., Wang, X., Sinha, S., Forte, E., Thompson, S. M., Herzog, E. L., et al. 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). 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. Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), China, Rutgers, The State University of New Jersey, United States. NicheNet: Modeling Intercellular Communication by Linking Ligands to Target Genes. 66,176 cells were classified into 15 distinct cell groups: microglia, astrocytes, monocytes, macrophages, neutrophils, div-myeloid cells, dendritic cells, lymphocytes, oligodendrocytes (OLs), OPCs, neurons, fibroblasts, pericytes, ependymal cells, and endothelial cells. Comparison analysis predicts WNT signaling as a predominant signaling change during mouse embryonic skin development. In this study, we generalized our previously developed tool CellChat to perform comparison analysis of cellcell communication across multiple conditions or time points and established an optimization-based framework to construct a multiscale signaling network linking intercellular communication with intracellular downstream signaling response. Mol. To better infer the TF-target gene regulatory network, we integrated TF-target gene interactions from public databases with scRNA-seq data. We rewrite the optimization problem as: The augmented Lagrangian with penalty parameter t > 0 is: We then solved this optimization problem by following the update rules and stop criterion.

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). Constructing a multiscale signaling network, which links data-driven intercellular communications with intracellular TF-target regulations, still remains challenging, preventing the better understanding of cell typespecific response to cellcell communication. Normalized data were used for all the analyses. We found that CellChat produced upregulated and downregulated signaling genes that were more differentially expressed compared to Connectome, as reflected by a higher avg [log2(FC)] and log10 (p_val_adj) of genes in the predicted ligand-receptor pairs (Supplementary Figure S4B).

Integrating spatial location with scRNA-seq data will likely reduce the false positive inference of cellcell communication. (C) Scatter plots comparing the outgoing and incoming interaction strength in the 2D space among uninjured, 1dpi, and 7dpi. 38 (8), 970979. Due to the ongoing pandemic caused by the new coronavirus (SARS-CoV-2), it is of great significance to investigate the level of cell-to-cell communication in patients with different severity of diseases related to COVID-19. The intercellular communication is given by CellChat while the intracellular signaling is inferred by constructing a genegene network linking receptors, TFs, and target genes. Nat. Therefore, two cell-cell communication networks showing less functional similarity suggest that they change their signaling sources and targets across different datasets, implying the difference in network architecture. CellChat displays the results of comparative analysis of multiple datasets in a variety of intuitive visualization methods, such as scatter plots, heatmaps, bar plots, and bubble plots (Figure 1A). CellChat can also identify the predominantly altered signaling pathways and ligandreceptor pairs by comparing the inferred communication probabilities and projecting the inferred cellcell communication networks onto a shared low-dimensional space for any number of datasets. Benchmark and Integration of Resources for the Estimation of Human Transcription Factor Activities. Of note, we build cell typespecific networks from the integrated network by identifying enriched TFs and target genes based on the differential expression analysis. Compared to moderate cases, the outgoing signaling of FOXN4, ciliated, and some immune cells such as nrMa, moMa, Neu, CTL, NKT, and NKT-P was predominantly increased in critical cases (Figure 4B). (D) The inferred WNT signaling pathway network and Wnt4 - (Fzd10 + Lrp5) signaling network at E18.5. In contrary, our work lies in the integration of mathematical optimization models and prior network information based on a data-driven approach. We found that the IFN-II signaling pathway has changed significantly in the patients of COVID-19 and can activate the master regulator STAT1 to regulate the downstream ACE2 expression in the secretory cells.

Nat. Indeed, we calculated the contribution of each ligandreceptor pair to the WNT signaling pathway and observed that Wnt4 - (Fzd10 + Lrp5) makes a relatively large contribution (Supplementary Figure S1B). Briefly, compared to these three tools, the updated CellChat is the only easy-to-use tool that can seamlessly identify signaling changes across any number of scRNA-seq datasets. Although previous studies showed that incorporating such prior information as a network constraint can improve the model performance (e.g., Zhang and Zhang, 2020), reconstruction of the TF-target network directly from single-cell data using a more advanced method such as scLink (Li and Li, 2021) will be likely helpful to build a better multiscale signaling network. This dataset includes five control cases, eight moderate cases, and eleven critical cases. 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. Integrated Intra- and Intercellular Signaling Knowledge for Multicellular Omics Analysis. *Correspondence: Suoqin Jin, sqjin@whu.edu.cn, Machine Learning and Mathematical Models for Single-Cell Data Analysis, View all However, CellChat focuses primarily on the comparison analysis between two datasets from two interrelated biological conditions. 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. Integrating Single-Cell and Spatial Transcriptomics to Elucidate Intercellular Tissue Dynamics. doi:10.1007/s00401-019-01992-3, Narula, S., Yusuf, S., Chong, M., Ramasundarahettige, C., Rangarajan, S., Bangdiwala, S. I., et al. Here, we choose the two regularization parameters 1 and 2 as 50 and 10, respectively. Of note, cell type compositions in different datasets do not need to be exactly the same. Comparison analysis of cellcell communication identifies major signaling changes in patients with COVID-19 across control, moderate, and critical cases. Interestingly, previous studies showed that HNF1A is a master regulator of ACE2, and overexpression of HNF1A and ACE2 indicates greater risk of death or cardiovascular disease events (Narula, et al., 2020). We generalized some functions and analysis in our previously developed R package CellChat, which can then be used for comparative analysis across multiple datasets. We focused on the cell typespecific signaling network and thus first identified enriched genes and TFs in each cell group. Methods. 29 (8), 13631375. By examining the inferred cellcell communication network of the IFN-II signaling pathway (Figure 5A), we found that, compared to control, IFN-II signaling is strongly activated in moderate with a stronger interaction strength and more signaling targets.

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. Genome Biol. Step 1: Construction of the ligandreceptor subnetwork. These included many inflammatory signaling pathways such as IFN- II, CCL, CXCL, IL1, and IL2, suggesting that moderate and critical COVID-19 strongly trigger a series of inflammatory responses. The downstream signaling network was constructed by integrating the receptor-TFs and TFs-target gene interactions (MATERIALS AND METHODS). Then, the inference of the TF-target gene regulatory network can be formulated as the following mathematical optimization problem. Syst. Different plots are provided to allow ready comparison analysis. Compared to control, the number of outgoing and incoming interactions of FOXN4, ciliated, and secretory cells in moderate and critical cases is higher. Genet. These results suggested that STAT1 was the major regulator to activate ACE2 in secretory cells, which is consistent with the previous finding (Chua, et al., 2020) and the known important role of the JAK-STAT signaling pathway during viral infection. Nat. (2020). doi:10.1007/s13238-020-00727-5, Skinnider, M. A., Squair, J. W., and Foster, L. J. (2020). 22 (2), 7188. 26, 1223. doi:10.1038/s41576-021-00370-8, Maremanda, K. P., Sundar, I. K., Li, D., and Rahman, I. The Origin, Fate, and Contribution of Macrophages to Spinal Cord Injury Pathology. Dis. However, there are only rudimentary efforts to link cellcell communication to downstream response via GRNs (Browaeys, et al., 2020; Cheng, et al., 2021; Hu, et al., 2020; Sha, et al., 2020), such as NicheNet, scMLnet, and CytoTalk. 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.