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. 2022 Jun 30:20:3556-3566.
doi: 10.1016/j.csbj.2022.06.056. eCollection 2022.

Single-cell entropy network detects the activity of immune cells based on ribosomal protein genes

Affiliations

Single-cell entropy network detects the activity of immune cells based on ribosomal protein genes

Qiqi Jin et al. Comput Struct Biotechnol J. .

Abstract

We developed a new computational method, Single-Cell Entropy Network (SCEN) to analyze single-cell RNA-seq data, which used the information of gene-gene associations to discover new heterogeneity of immune cells as well as identify existing cell types. Based on SCEN, we defined association-entropy (AE) for each cell and each gene through single-cell gene co-expression networks to measure the strength of association between each gene and all other genes at a single-cell resolution. Analyses of public datasets indicated that the AE of ribosomal protein genes (RP genes) varied greatly even in the same cell type of immune cells and the average AE of RP genes of immune cells in each person was significantly associated with the healthy/disease state of this person. Based on existing research and theory, we inferred that the AE of RP genes represented the heterogeneity of ribosomes and reflected the activity of immune cells. We believe SCEN can provide more biological insights into the heterogeneity and diversity of immune cells, especially the change of immune cells in the diseases.

Keywords: Association-entropy; Immune cell; Ribosomal protein gene; Single-cell RNA-seq; Single-cell entropy network.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
Schematic illustration of SCEN method. (a) First, make a scatter diagram for every two genes based on the gene expression profile comprised of m genes and n cells, where points = cells, and then m genes can produce m*(m −1)/2 scatter diagrams. (b) In the scatter diagram of genes × and y, make a light orange box and a dark orange box near the cell k (red plot) to represent the neighborhood of xk and yk respectively. The number of plots/cells in the two boxes is nx(k) and ny(k) respectively, and the number of plots/cells in the intersection of the two boxes is nxy(k). Then, calculate MI(x, y) and MI(k)(x, y). (c) Calculate w(k)(x, y) that represents the weight of edge x-y in cell-k network, and construct n single-cell networks. (d) Calculate the association entropy (AE) of each gene in each cell, and get an AE matrix. (e) Based on SCEN method, we can discover “dark” genes ignored by original gene expression matrix, characterize network heterogeneity at a single-cell level, and distinguish the active or resting state of immune cells. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2
Fig. 2
Performance of SCEN on the PBMC dataset with 2700 cells. (a) Clustering based on AE matrix can identify two new groups of cells, RP + Monocyte and RP + T cell. (b) AE and gene expression of some RP genes. Plots are colored by log(1 + AE) or log(1 + normalized counts). (c) Differential AE genes (DAEGs) and differential expression genes (DEGs) analyses identify 176 “dark” genes (DAEGs but not DEGs) between RP + Monocytes and other CD14 + Monocytes, and 228 “dark” genes between RP + T cells and other T cells. Some translation-related GOs are enriched from these “dark” genes. (d) 4 single-cell entropy networks from 4 single cells with different sources, where the nodes are some RP genes.
Fig. 3
Fig. 3
The wide existence of RP + cells in (a) Peripheral blood mononuclear cells, (b) Human umbilical cord blood cells, and (c) Immune cells from melanoma tumor tissue. Clustering performance, AE of all RP genes and expression of all RP genes are illustrated.
Fig. 4
Fig. 4
The characteristics of RP + cells in UCB dataset. (a, d) Clustering and pseudo-trajectory results of Monocytes or T cell based on AE matrix, as well as the dynamic change of RP genes along the pseudotime. (b) Clustering of all UCB cells based on AE matrix. (c) AE levels of RP genes. (e) The network among 5 KEGG pathways in 4 different T cell subtypes.
Fig. 5
Fig. 5
The associations between RP levels and diseases: (a) COVID-19, (b) Lung adenocarcinoma (LUAD) (nLN = normal lymph node; mLN = metastatic lymph node; nLung = distant normal lung tissue; tLung = primary tumor tissue; tL/B = tumor tissue from advanced stage LUAD; mBrain = metastatic brain tissue; PE = pleural fluid), (c) Immune compromised (TSCC = immune compromised organ transplant recipients; SCC = immune competent patients with cutaneous squamous cell carcinoma tumors), (d) multiple myeloma (NBM = healthy donors; MGUS = monoclonal gammopathy of undetermined significance; SMM = smoldering myeloma; MM = full-blown multiple myeloma). RP level of each sample was illustrated in box plots (center line = median; box limits = upper (3/4) and lower (1/4) quartiles; whiskers = 1.5 × interquartile range).

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