CellCallEXT: Analysis of Ligand-Receptor and Transcription Factor Activities in Cell-Cell Communication of Tumor Immune Microenvironment

被引:3
|
作者
Gao, Shouguo [1 ]
Feng, Xingmin [1 ]
Wu, Zhijie [1 ]
Kajigaya, Sachiko [1 ]
Young, Neal S. [1 ]
机构
[1] NHLBI, Hematopoiesis & Bone Marrow Failure Lab, Hematol Branch, NIH, Bldg 10, Bethesda, MD 20892 USA
关键词
single-cell RNA-seq; cell-cell interaction; ligand-receptor-transcription factor axis;
D O I
10.3390/cancers14194957
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Simple Summary CellCall is an R package tool that is used to analyze cell-cell communication based on transcription factor (TF) activities calculated by cell-type specificity of target genes and thus cannot directly handle two-condition comparisons. We developed CellCallEXT to complement CellCall. CellCallEXT can directly identify ligand-receptor (L-R) interactions that alter the expression profiles of downstream genes between two conditions, such as tumor and healthy tissue. Scoring in CellCallEXT quantitatively integrates expression of ligands, receptors, TFs, and target genes (TGs). The pathway enrichment analysis and visualization modules allow biologists to investigate how disease alters cell-cell communication. Furthermore, Reactome pathways were added into CellCallEXT to expand the L-R-TF database. (1) Background: Single-cell RNA sequencing (scRNA-seq) data are useful for decoding cell-cell communication. CellCall is a tool that is used to infer inter- and intracellular communication pathways by integrating paired ligand-receptor (L-R) and transcription factor (TF) activities from steady-state data and thus cannot directly handle two-condition comparisons. For tumor and healthy status, it can only individually analyze cells from tumor or healthy tissue and examine L-R pairs only identified in either tumor or healthy controls, but not both together. Furthermore, CellCall is highly affected by gene expression specificity in tissues. (2) Methods: CellCallEXT is an extension of CellCall that deconvolutes intercellular communication and related internal regulatory signals based on scRNA-seq. Information on Reactome was retrieved and integrated with prior knowledge of L-R-TF signaling and gene regulation datasets of CellCall. (3) Results: CellCallEXT was successfully applied to examine tumors and immune cell microenvironments and to identify the altered L-R pairs and downstream gene regulatory networks among immune cells. Application of CellCallEXT to scRNA-seq data from patients with deficiency of adenosine deaminase 2 demonstrated its ability to impute dysfunctional intercellular communication and related transcriptional factor activities. (4) Conclusions: CellCallEXT provides a practical tool to examine intercellular communication in disease based on scRNA-seq data.
引用
收藏
页数:14
相关论文
共 50 条
  • [21] ANALYSIS OF LIGAND-RECEPTOR INTERACTIONS WITH THE FLUORESCENCE ACTIVATED CELL SORTER
    SKLAR, LA
    FINNEY, DA
    CYTOMETRY, 1982, 3 (03): : 161 - 165
  • [22] Single-cell RNA sequencing and cell-cell communication analysis reveal tumor microenvironment associated with chemotherapy responsiveness in ovarian cancer
    Jiang, Xiaoyan
    Chen, Ningxuan
    Wei, Qinglv
    Luo, Xin
    Liu, Xiaoyi
    Xie, Lingcui
    Yi, Ping
    Xu, Jing
    CLINICAL & TRANSLATIONAL ONCOLOGY, 2025, 27 (03): : 1000 - 1012
  • [23] EXOSOMAL MIRNAS: KEY REGULATORS OF CELL-CELL COMMUNICATION BETWEEN BLADDER CANCER CELLS AND TUMOR MICROENVIRONMENT
    Baumgart, Sophie
    Heinzelmann, Joana
    Krause, Elmar
    Ostenfeld, Marie Stampe
    Hartmann, Arndt
    Stoeckle, Michael
    Junker, Kerstin
    JOURNAL OF UROLOGY, 2016, 195 (04): : E607 - E607
  • [24] Exosome-mediated cell-cell communication within pancreatic cancer tumor microenvironment: a narrative review
    Qin Cheng
    Zhao Bangbo
    Wang Yuanyang
    Li Tianhao
    Li Zeru
    Li Tianyu
    Zhao Yutong
    Wang Weibin
    胰腺病学杂志(英文), 2023, 06 (01)
  • [25] Exosome-mediated cell-cell communication within pancreatic cancer tumor microenvironment: a narrative review
    Qin, Cheng
    Zhao, Bangbo
    Wang, Yuanyang
    Li, Tianhao
    Li, Zeru
    Li, Tianyu
    Zhao, Yutong
    Wang, Weibin
    JOURNAL OF PANCREATOLOGY, 2023, 6 (01) : 1 - 7
  • [26] TimiGP: Inferring cell-cell interactions and prognostic associations in the tumor immune microenvironment through gene pairs
    Li, Chenyang
    Zhang, Baoyi
    Schaafsma, Evelien
    Reuben, Alexandre
    Wang, Linghua
    Turk, Mary Jo
    Zhang, Jianjun
    Cheng, Chao
    CELL REPORTS MEDICINE, 2023, 4 (07)
  • [27] Expression Analysis of Ligand-Receptor Pairs Identifies Cell-to-Cell Crosstalk between Macrophages and Tumor Cells in Lung Adenocarcinoma
    Yang, Xiaodong
    An, Zhao
    Hu, Zhengyang
    Xi, Junjie
    Dai, Chenyang
    Zhu, Yuming
    JOURNAL OF IMMUNOLOGY RESEARCH, 2022, 2022
  • [28] CellEnBoost: A Boosting-Based Ligand-Receptor Interaction Identification Model for Cell-to-Cell Communication Inference
    Peng, Lihong
    Yuan, Ruya
    Han, Chendi
    Han, Guosheng
    Tan, Jingwei
    Wang, Zhao
    Chen, Min
    Chen, Xing
    IEEE TRANSACTIONS ON NANOBIOSCIENCE, 2023, 22 (04) : 705 - 715
  • [29] Altering the T cell receptor repertoire in the sarcoma tumor immune microenvironment
    Pollack, Seth M.
    Seo, Y. Dave
    CLINICAL CANCER RESEARCH, 2022, 28 (18)
  • [30] Ligand-receptor signaling analysis and tumor-immune microenvironment deconvolution reveal differential adipose-tumor crosstalk in lean and obese colon cancer patients
    Lin, Tengda
    Bandera, Victoria M.
    Glenny, Elaine M.
    Himbert, Caroline
    Ose, Jennifer
    Warby, Christy
    Aksonova, Olena
    Carpanese, Alessandro
    Stubben, Chris
    Nix, David
    Boucher, Kenneth M.
    Schirmacher, Peter
    Strehli, Ildiko
    Jedrzkiewicz, Jolanta
    Scaife, Courtney L.
    Pickron, Bartley
    Brobeil, Alexander
    Schneider, Martin
    Kahlert, Christoph
    Siegel, Erin M.
    Toriola, Adetunji T.
    Shibata, David
    Li, Christopher I.
    Figueiredo, Jane C.
    Roper, Jatin
    Gigic, Biljana
    Hursting, Stephen D.
    Ulrich, Cornelia M.
    Tan, Aik Choon
    CANCER RESEARCH, 2024, 84 (06)