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. 2023 Jun 28;24(13):10765.
doi: 10.3390/ijms241310765.

Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells

Natalia Alonso-Moreda  1 , Alberto Berral-González  1 , Enrique De La Rosa  1 , Oscar González-Velasco  1   2 , José Manuel Sánchez-Santos  1   3 , Javier De Las Rivas  1
Affiliations

Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells

Natalia Alonso-Moreda et al. Int J Mol Sci. .

Abstract

In the last two decades, many detailed full transcriptomic studies on complex biological samples have been published and included in large gene expression repositories. These studies primarily provide a bulk expression signal for each sample, including multiple cell-types mixed within the global signal. The cellular heterogeneity in these mixtures does not allow the activity of specific genes in specific cell types to be identified. Therefore, inferring relative cellular composition is a very powerful tool to achieve a more accurate molecular profiling of complex biological samples. In recent decades, computational techniques have been developed to solve this problem by applying deconvolution methods, designed to decompose cell mixtures into their cellular components and calculate the relative proportions of these elements. Some of them only calculate the cell proportions (supervised methods), while other deconvolution algorithms can also identify the gene signatures specific for each cell type (unsupervised methods). In these work, five deconvolution methods (CIBERSORT, FARDEEP, DECONICA, LINSEED and ABIS) were implemented and used to analyze blood and immune cells, and also cancer cells, in complex mixture samples (using three bulk expression datasets). Our study provides three analytical tools (corrplots, cell-signature plots and bar-mixture plots) that allow a thorough comparative analysis of the cell mixture data. The work indicates that CIBERSORT is a robust method optimized for the identification of immune cell-types, but not as efficient in the identification of cancer cells. We also found that LINSEED is a very powerful unsupervised method that provides precise and specific gene signatures for each of the main immune cell types tested: neutrophils and monocytes (of the myeloid lineage), B-cells, NK cells and T-cells (of the lymphoid lineage), and also for cancer cells.

Keywords: bioinformatics; blood cells; cell mixture; deconvolution; gene signature; immune cells.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Corrplots comparing real versus estimated cell proportions. Pearson correlations were calculated with the 12 samples of the dataset (GSE64385), between the real proportions (rows) and the estimated proportions (columns) obtained with 4 methods: (a) CIBERSORT, (b) FARDEEP, (c) DECONICA and (d) LINSEED. The samples included 6 cell types mixed in known proportions: Cancer Cells (CC), Neutrophils, Monocytes, B cells, NK cells and T cells. GSE64385 includes the bulk gene expression data used in the deconvolution analyses.
Figure 2
Figure 2
Cell-signature plots obtained for 3 cell types: Cancer Cells (CC), B-cells and T-cells, Monocytes and Neutrophils; using GSE64385 dataset. The plots include in blue the real proportions (Real) of each cell type in each of the 12 samples (marked with squared dots) and in red the estimated proportions (Estimated). The cellular signatures obtained with 2 different methods are presented: for CIBERSORT (a–e); and for LINSEED (f–j). The RSME (Root Mean Square Error) calculated between the real data (blue) and the estimated data (red) is presented at the top of each graph.
Figure 3
Figure 3
Bar-mixture plots. Bar plots presenting the cell mixtures in each sample as proportional sections of each cell type, which are marked with the colors presented in the color panel at the bottom of the figure. The estimated proportions in each sample were calculated with (a) CIBERSORT and (b) LINSEED. The real proportions in each sample (determined experimentally by flow cytometry) are presented as bars on the right in pale colors. The first sample (S01) only includes Cancer Cells (100% HCT116 cells). The RMSEs (Root Mean Square Errors) calculated between the real data and the estimated data for each cell type, are presented at the top of each graph.
Figure 4
Figure 4
Correlations obtained with the gene expression profiles from 13 PBMC samples (taken from dataset GSE107011), calculated using 3 deconvolution methods. Pearson correlations were calculated between the real proportions (rows) and the estimated proportions (columns) for 17 cell types and subtypes (12 from the lymphoid lineage and 5 from the myeloid lineage). The correlations were calculated using: (a) CIBERSORT (corrplot) and (b) FARDEEP and ABIS (see table; in this case, only the diagonal values of real versus estimated for each cell type are included). (Labels of the cells: B.Naive = B Cells Naïve; B.Memory = B Cells Memory; Plasmablasts; T.CD4.Naive = CD4+ T Cells Naive; T.CD8.Naive = CD8+ T Cells Naive; T.CD4.Memory = CD4+ T Cells Memory; T.CD8.Naive = CD8+ T Cells Naive; T.gd.Vd2 = γδ2+ T Cells; T.gd.non.Vd2 = γδ2− T Cells; MAIT = Mucosal-Associated Invariant T Cells; NK = Natural Killer Cells; pDCs = Plasmacytoid Dendritic Cells; mDCs = Myeloid Dendritic Cells; Monocytes.C = Classical Monocytes; Monocytes.NC.I = Non-Classical Intermediate Monocytes; Neutrophils.LD = Low-Density Neutrophils; Basophils.LD = Los-Density Basophils.) The real mean proportions of the cells in the 13 PBMC samples, determined experimentally, are indicated (in %) in the second column of the table (b).
Figure 5
Figure 5
Heatmap of expression profiles corresponding to the genes selected in the signature matrices provided by CIBERSORT and LINSEED for 5 major cell types: Neutrophils, Monocytes, B-cells, NK cells and T-cells. The analysis was performed using dataset GSE64385. (a) Heatmap presenting the expression profiles of the LM22 data matrix, provided by CIBERSORT platform, which includes 547 genes used to identify the 5 cell types tested. (b) Heatmap presenting the expression profiles of the 117 genes selected by LINSEED (unsupervised method) from the gene list provided in the LM22 matrix (i.e., the same used by CIBERSORT). (c) Venn diagram presenting the genes that each method uses in the signatures to identify the 5 cell types in dataset GSE64385. All the genes selected by LINSEED are included in the ones used by CIBERSORT. The set of 117 genes, selected by LINSEED, provides more precise and specific gene signatures for each of the 5 cell types analyzed.
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