Literature DB >> 34671548

Analysis of Cellular Heterogeneity in Immune Microenvironment of Primary Central Nervous System Lymphoma by Single-Cell Sequencing.

Boyuan Wei1, Zhe Liu2, Yue Fan3, Shuwei Wang1, Chao Dong1, Wei Rao1, Fan Yang1, Gang Cheng1, Jianning Zhang1.   

Abstract

BACKGROUND: Primary central nervous system lymphoma (PCNSL) is characterized by a lack of specificity and poor prognosis. Further understanding of the tumor heterogeneity and molecular phenotype of PCNSL is of great significance for improving the diagnosis and treatment of this disease.
METHODS: To explore the distinct phenotypic states of PCNSL, transcriptome-wide single-cell RNA sequencing was performed on 34,851 PCNSL cells from patients. The cell types, heterogeneity, and gene subset enrichment of PCNSL were identified. A comparison of the PCNSL cells with 21,250 normal human fetal brain (nHFB) cells was further analyzed to reveal the differences between PCNSL and normal sample.
RESULTS: Six cell populations were mainly identified in the PCNSL tissue, including four types of immune cells-B cell, T cell, macrophage and dendritic cell-and two types of stromal cells: oligodendrocyte and meningeal cell. There are significant cellular interactions between B cells and several other cells. Three subpopulations of B cells indicating diffident functions were identified, as well as a small number of plasma cells. Different subtypes of T cells and dendritic cells also showed significant heterogeneity. It should be noted that, compared with normal, the gene expression and immune function of macrophages in PCNSL were significantly downregulated, which may be another important feature of PCNSL in addition to B cell lesions and may be a potential target for PCNSL therapy.
Copyright © 2021 Wei, Liu, Fan, Wang, Dong, Rao, Yang, Cheng and Zhang.

Entities:  

Keywords:  cell communication; cellular heterogeneity; immune microenvironment; pathogenic and therapeutic; primary central nervous system lymphoma (PCNSL); single cell sequencing

Year:  2021        PMID: 34671548      PMCID: PMC8523033          DOI: 10.3389/fonc.2021.683007

Source DB:  PubMed          Journal:  Front Oncol        ISSN: 2234-943X            Impact factor:   6.244


Introduction

Primary central nervous system (CNS) lymphoma (PCNSL), defined as diffuse large B cell lymphoma (DLBCL) confined to the CNS, can occur in the setting of immunosuppression (HIV/AIDS, post-transplant) or in immunocompetent individuals (1, 2). The incidence of PCNSL accounting for 2% to 3% of all CNS neoplasms (3) has reportedly increased in the immunocompetent population (4). Incidence of PCNSL is recently increasing in the elderly (5). It is not possible to morphologically distinguish PCNSL and peripheral DLBCL. This non-Hodgkin aggressive B-cell lymphoma is distinguished from extra-cerebral DLBCL by its poorer prognosis. The tumor cells are mainly large, with numerous apoptotic cells or widespread necrosis, which commonly hinders diagnosis in small biopsies (6, 7). The molecular subtype of PCNSL has been studied by different methodological approaches with conflicting conclusions. On the basis of several IHC studies, an ABC-like immunophenotype is typical (8, 9), but immunoglobulin heavy-chain gene mutational signatures also provide evidence for germinal center exposure, indicating that PCNSL develops from a B-cell that has been exposed to a germinal center influence outside the CNS (10–12). In addition, results of immunophenotyping studies suggest that tumor cells originate from a late germinal center to an early postgerminal center stage (13), while gene expression profiling studies indicate that PCNSLs are distributed among the spectrum of systemic DLBCL with roughly equal proportion of ABC and GC cases (14, 15). Standard chemotherapeutic regimens for systemic DLBCL show little efficacy in PCNSL, likely because of inefficient drug delivery across the blood–brain barrier (16). High-dose methotrexate-based chemotherapy is the standard therapy for PCNSL. Chemotherapy with whole-brain radiation therapy has produced response rates up to 80%–90% and median overall survival up to 5 years. While treatment response rates are high, relapses are frequent and prognosis after recurrence is poor with 5-year survival rates ranging from 22% to 40% (17, 18). Up to 50% of patients with PCNSL will relapse, and 10%–15% demonstrate primary refractory disease, indicating a significant unmet therapeutic need (19). However, PCNSL has a poor prognosis compared with that of DLBCL (20) and the reason for the difference in prognosis between PCNSL and DLBCL has not been elucidated. The rarity of the disease and the difficulty of obtaining intracranial specimens have hindered understanding of the pathophysiology of PCNSL. The observed overexpression of BCL6 and aberrant somatic hypermutation (aSHM) of many genes, together with expression of immunoglobulin M at the cell surface, have suggested that PCNSL cells may be arrested at the stage of terminal B-cell differentiation (21). The genomic alterations (GAs) underlying PCNSL have not been comprehensively studied. Single-nucleotide mutations in various genes, including MYD88, CD79b, PIM1, and BTG2, have been reported as the most prevalent genetic alterations in PCNSL (22–24). Genome-wide gene expression in PCNSL compared with non-CNS DLBCL suggests that PCNSL has specific signatures to be distinguished from non-CNS DLBCL and greater molecular heterogeneity (14, 25). Of the genetic changes that lead to PCNSL, very little is known and no characteristic genetic alterations have been defined thus far. However, few retrospective studies have examined the detailed molecular network and cell signaling based on diagnosis with gene mutations and CNVs or the prognosis of patients with PCNSL. Some vague evidences suggest that CNS lymphoma and peripheral lymphoma are heterogeneous, which may be related to different immune environments and different origins of lymphoma cells. At present, there is no clinical significance to reveal the molecular characteristics of CNS lymphoma. Here, we performed an analysis of scRNA-seq data of CNS tissue sequenced by 10x Genomics to identify the cell types of CNS cells; we next performed differential expression (DE) analysis on two PCNSL-associated cells (i.e., B cell, plasma cell) and gene set enrichment analysis (GSEA) of their DE genes on the cell type-specific marker genes to examine the cell-specific functionalities in PCNSL development.

Materials and Methods

Single-Cell RNA Sequencing

Human PCNSL tumor samples were obtained surgically from clinical patients confirmed by pathology from our hospital. After sample collection processing and suspensions, we performed scRNA-seq following the manufacturer’s protocol. Briefly, the cells were washed with PBS and resuspended in 500 μl PBS. scRNA-seq libraries were prepared using a Chromium Single cell 3′ Reagent kit, version 2. Amplified cDNA and final libraries were evaluated using a High Sensitivity DNA Kit (Agilent Technologies). Sequencing was performed on NovaSeq 6000 (Illumina) at a depth of approximately 400M reads.

Single-Cell Data Processing

The Cell Ranger software pipeline (version 4.0, http://support.10Xgenomics.com/single-cell-gene-expression/software/overview/welcome) provided by 10x Genomics was used to demultiplex cellular barcodes. Unique molecular identifier (UMI) counts were obtained by mapping reads to the human reference genome (GRCH38 3.1.0) genome and align transcriptomes using the STAR aligner and down-sample reads as required to generate normalized aggregate data across samples. In the end, a matrix of gene counts by cells was produced. We processed the UMI count matrix using the Seurat R package (version 4.0.2), resulting in 34,851 cells with 36,601 genes for PCNSL samples. We first removed the likely multiplet captures, which is a major concern in microdroplet-base experiments through DoubletFinder. We filtered cells at the cell and gene levels to obtain the reliability results of PCNSL scRNA-seq data, respectively. We removed the low-quality cells with the following criteria: (i) the number of expressed genes was <200 or >2,000; (ii) the number of total counts was > 20,000; and (iii) the percentage of mitochondrial counts > 10%. We only kept the genes detected in at least 20 cells. After applying these quality control criteria, we obtained 20,307 cells with 12,229 genes in total, which were used for downstream analysis. To analyze the differences between PCNSL and normal brain cells, we referred to a published scRNA-seq data of normal human fetal brain (nHFB). The raw data are from the European Genome-Phenome Archive: EGAS00001004422 (https://ega-archive.org/studies/EGAS00001004422). For accessibility reasons, we directly used the cellranger matrices from HFA567_total.filtered_gene_matrices, HFA570_total.filtered_gene_matrices, and HFA571_total.filtered_gene_matrices (https://datahub-262-c54.p.genap.ca/GBM_paper_data/GBM_cellranger_matrix.tar.gz) (26). The following criteria were used for nHFB scRNA data processing: (i) the number of expressed genes was <100 or >3,000; (ii) the number of total counts was > 20,000; and (iii) the percentage of mitochondrial counts > 10%. We only kept the genes detected in at least 20 cells. After applying these quality control criteria, we obtained 20,015 cells with 16,608 genes from 21,255 cells with 33,694 genes in total, which were used for downstream analysis.

Cell Subpopulation Detection

scRNA-seq data were normalized using log transforms and scaled so that the mean expression across cells is 0 and the variance across cells is 1. Then, we performed dimension reduction and visualization on the single-cell data through the Seurat R package (version 4.0.2) (1, 27). We used the VST method (28) to obtain the top 3,000 highly variable genes and used the PCA method to reduce dimensionality. The top 21 principal components (PCs) were selected for tSNE to visualize the cell clustering (29). We then clustered the cells using the Leiden algorithm (30) implemented in the Seurat package, with the resolution parameter set to be 0.7. There are two methods for annotation cell types: (i) the Wilcox test was used to identify significantly differentially expressed genes (DEGs) between clusters. To identify the marker genes of each cluster, we combined the rest of the cells and set them to control cells. For the process of identifying DEGs between PCNSL and normal samples, we set cells of one cluster in PCNSL samples as the treatment group and the cells of this cluster in normal samples as the control group. We considered the genes with adjusted p-value < 0.05 and |log2FC|> 0.58 for each scRNA-seq cell type as the DEGs. The top 30 genes are used for the annotation cell type manual. According to the detailed intracellular marker gene, we combined with the CellMarker (http://bio-bigdata.hrbmu.edu.cn/CellMarker/) database. (ii) By comparing scRNA-seq with HumanPrimaryCellAtlasData and BlueprintEncodeData data, we utilized both the SingleR R package (version 1.4.1) and manual work to assign cell types to cluster automatic annotation. Finally, we compare the annotation results and determine the cell types.

Gene Function Enrichment Analysis

We then performed gene ontology (GO) enrichment analysis using the database for annotation, visualization, and integrated discovery database (DAVID: version 6.8, with the significant DEGs https://david.ncifcrf.gov/summary.jsp). It is an essential tool for systematically extracting biological information from a set of genes. Adj. p-value < 0.05 was considered statistically significant.

Cell Communication Analysis

In order to concretely prove the direct interaction between cell subpopulations, we used CellPhoneDB (version 2.1.7) to conduct interactive analysis on cell subpopulations. CellPhoneDB is a publicly available repository of selected receptors, ligands, and their interactions (31). Here, we used it (version 2.1.7) to explore the cell–cell communication by immune-related proteins. Here, we focus on the four classes of immune cell subpopulations and immune-related genes. There are eight classes of gene lists collected in our research: chemokines, T helper type 1 genes (Th1), T helper type 2 genes (Th2), T helper 17 genes (Th17), T regulators (Treg), and costimulatory, coinhibitory, and immune niche genes. The circle edge width is proportional to the number of cells in each cell cluster and the communication score between interacting cell clusters, respectively.

Results

Identification of Cell Types in PCNSL and nHFB

After quality filtering, 40,322 cells with 20,452 genes were obtained from the PCNSL and nHFB samples (details in Materials and Methods). All cells were classified into 13 subpopulations by combining the automatic and manual annotation methods. The cell types were identified as B cell, T cell, macrophage, dendritic cell, oligodendrocyte, meningeal cell, excitatory neuron (EN), interneuron (IN), radial glia (RG), inhibitory neuronal progenitor cell (INP), excitatory neuronal progenitor (ENP), astrocyte, and oligodendrocyte progenitor cell (OPC) ( and ). The combination comparison ( ) indicated that PCNSL and nHFB had significant differences in cell heterogeneity, and there were certain similarities only in macrophages and meningeal cells. The B cell, T cell, macrophage, and dendritic cell population accounted for the highest proportion in PCNSL ( , ). By extracting the average value of top 30 gene expression in each group, and performing Pearson correlation coefficient (PCC) expression correlation analysis, the difference between groups is greater than the difference within the groups, which proves that our subpopulation annotation has high sensitivity and specificity ( ). The result of the bubble plot reveals that typical marker genes are particularly expressed in the cell subpopulations ( ). Overall, the annotation results show the complex cell composition in PCNSL tissue.
Figure 1

Single-cell analysis of CNS scRNA-seq data sets. (A) The visualization results of cell subpopulations in PCNSL and normal CNS tissue samples. (B) The tSNE displays PCNSL and normal CNS cells: red indicates PCNSL cells, and green indicates normal cells. (C) The barplot represents the proportion of various cell subgroups in PCNSL and normal CNS tissue samples individually. (D) The heatmap describes the PCC values of the average expression values of the top 30 genes in each cluster. (E) The bubble chart shows that three marker genes are selected for each cell subgroup, and their expression in all cell subpopulations is plotted.

Table 1

The top30 marker genes in scRNA-seq data.

series no.p_valavg_log2FCpct.1pct.2p_val_adjclustergene
100.6374470.180.6580B_cellPDLIM1
200.6310360.2330.6920B_cellCHCHD10
300.6121040.1280.6480B_cellSH3TC1
400.5753070.9960.9950B_cellRPS2
500.5748160.3040.7570B_cellLTB
600.5521740.9270.9460B_cellRPL5
700.5373860.2330.7250B_cellCD53
800.5248880.0890.5670B_cellS100A13
900.5215120.1810.6980B_cellRGS10
1000.5193350.1450.6560B_cellPOU2F2
1100.5165380.1030.6190B_cellMGST1
1200.5154770.1390.6440B_cellMTHFD2
1300.5147640.9950.9950B_cellRPS16
1400.5026410.9120.9480B_cellRPL7A
1500.5023930.9810.9880B_cellRPL12
166.43E-2670.5018240.8490.9131.29E-263B_cellRPS20
171.10E-2490.6342660.3540.432.19E-246B_cellNCL
182.27E-2430.8040430.30.7044.55E-240B_cellCD79A
191.99E-2060.7387470.2960.6893.97E-203B_cellGRHPR
203.12E-1460.5117180.6230.7336.24E-143B_cellHSP90AB1
211.37E-1080.514920.7170.8642.74E-105B_cellEEF1B2
221.09E-550.5695540.4820.662.18E-52B_cellTPI1
231.56E-440.5468260.5970.813.11E-41B_cellNAP1L1
242.28E-420.5181540.4370.84.55E-39B_cellARPC1B
259.31E-340.5094250.4290.6231.86E-30B_cellENO1
263.17E-240.5022530.3670.5716.34E-21B_cellPRDX1
271.71E-150.5826390.5580.8173.42E-12B_cellNME2
282.95E-140.5321310.4640.7015.90E-11B_cellHNRNPA2B1
294.09E-070.7781020.4510.7670.000818B_cellCD37
302.90E-050.7410660.4680.7860.057983B_cellLAPTM5
3101.2071990.7830.8490T_cellCCL5
3201.0326560.7790.8540T_cellS100A4
3301.0089330.4740.4490T_cellVIM
3400.9180820.2610.680T_cellITGB2
3500.9107730.2290.6670T_cellIL7R
365.79E-2851.192010.6580.7961.16E-281T_cellCRIP1
373.44E-2430.8679360.2920.6996.88E-240T_cellCD53
383.20E-2321.1509130.6450.7916.41E-229T_cellRGS1
391.63E-1991.0879390.6370.7993.27E-196T_cellSRGN
401.02E-1930.8677670.3230.7142.05E-190T_cellNEAT1
412.29E-1931.1419270.2940.6774.58E-190T_cellITM2A
424.65E-1551.018620.630.7949.29E-152T_cellCCL4
431.28E-1530.8918720.3320.7012.56E-150T_cellPRDM1
444.56E-1480.9196380.3320.7069.11E-145T_cellCD69
451.32E-1330.8751340.3360.7032.63E-130T_cellIFITM2
464.80E-1281.0794050.6260.7959.60E-125T_cellHMGB2
471.47E-1150.9170950.3450.7092.95E-112T_cellRAC2
482.11E-1001.0791140.3430.6944.21E-97T_cellTRBC1
492.47E-960.9994570.3530.7024.95E-93T_cellDUSP2
503.97E-891.0997230.3640.7157.93E-86T_cellCCL4L2
518.47E-870.9253250.3730.7291.69E-83T_cellLTB
529.13E-641.16520.5490.7611.83E-60T_cellTRAC
531.10E-581.1020350.3690.6992.20E-55T_cellTRBC2
541.25E-420.9485140.40.7292.50E-39T_cellPTPRC
555.60E-400.9564390.5510.7751.12E-36T_cellS100A6
564.81E-321.0133350.5330.7519.62E-29T_cellZFP36L2
575.08E-291.0119430.4020.7131.02E-25T_cellLGALS1
588.52E-160.9961550.5180.7641.70E-12T_cellHLA-E
597.12E-140.9740210.5150.7591.42E-10T_cellHCST
600.0015931.0606560.440.721T_cellFXYD2
6101.3565590.8940.2320ENENC1
6200.7505110.7430.2720ENEGR1
6300.6551060.8350.2840ENSLA
6400.5995880.8570.3830ENJUN
6500.5516190.2730.0430ENNRP1
6600.4830260.9050.3090ENBASP1
6700.4815320.8070.2710ENSDCBP
6800.4529780.7990.2920ENEIF1B
6900.4211840.8980.4230ENFOS
7000.4076310.7380.2160ENMIAT
7100.3604420.2850.0630ENCOL9A2
723.82E-1830.355390.6950.217.64E-180ENAPLP1
731.89E-1280.5089650.6510.1933.79E-125ENSTMN4
741.74E-1190.3902350.6120.1653.47E-116ENMDK
755.25E-1130.6316590.3870.0741.05E-109ENEPHB6
762.18E-1120.3827220.3990.084.37E-109ENEEF1A2
779.93E-1070.3678380.2740.0761.99E-103ENKLHL23
784.55E-730.3266340.3440.1149.09E-70ENMED19
791.36E-600.3183470.4410.0912.73E-57ENFRMD4B
802.36E-580.7073970.5880.1994.72E-55ENSRM
812.71E-450.3954020.380.1045.42E-42ENFOXO3
827.79E-430.542510.3960.0961.56E-39ENSERPINE2
833.03E-370.3243710.4350.1046.07E-34ENTTLL7
844.47E-250.3267710.5720.1698.95E-22ENRHOB
854.47E-230.4302620.5730.1338.94E-20ENPPP2R2B
863.75E-190.4393560.3440.0917.50E-16ENMCUR1
878.63E-120.3807930.3640.1191.73E-08ENDLGAP4
883.86E-070.4984750.4510.170.000772ENMSMO1
890.0008860.3170820.5310.1451ENZNF462
900.0013220.3798110.4910.1461ENRNF24
9101.1760930.8430.3010INMAP1B
9200.8636690.2670.1520INVCAN
9300.6853660.3060.1650INRNF24
9400.6063770.3050.2110INDDX6
9500.5879950.2750.2110INMDK
961.05E-2830.6073140.2630.1772.09E-280INFBXO21
976.00E-2730.6169470.2760.211.20E-269INRHOB
983.29E-2710.7261790.6770.2886.57E-268INTCF4
992.30E-1960.5588740.3780.2314.60E-193INBCL11A
1001.02E-1750.6415510.4070.2992.05E-172INENC1
1014.35E-1751.2964280.3260.0628.70E-172INPDE4DIP
1021.73E-1700.6020250.3720.2343.45E-167INGPM6B
1033.09E-1680.6045730.7570.4246.18E-165INFOS
1044.13E-1610.6540750.3940.1228.26E-158INRBP1
1051.42E-1460.9630710.3390.1382.85E-143INTMEM123
1062.34E-1220.8742890.3810.2174.68E-119INCITED2
1071.37E-910.7433140.3840.2192.73E-88INKLC1
1089.12E-751.6751680.3890.0231.82E-71INPLS3
1091.00E-740.840270.410.2772.01E-71INIGFBP2
1102.07E-710.772590.9990.7544.14E-68INCXCR4
1114.90E-640.7113770.4250.2389.79E-61INAPP
1125.25E-640.8078290.4240.2391.05E-60INAPLP1
1133.96E-491.0938980.5410.2097.92E-46INPFN2
1141.36E-270.5846140.4570.2422.73E-24INFSCN1
1155.58E-241.1186580.4720.1111.12E-20INMEG3
1165.81E-150.7810820.5080.2941.16E-11INEGR1
1179.38E-090.9342760.5530.2761.88E-05INPOLR2J3
1187.60E-070.5992530.4930.2520.001521INFEZ1
1190.003531.1327640.4610.2111INHBA2
1200.0048910.8439260.5370.2261INMIAT
12101.01460410.9620RGRPS26
12200.8868490.7890.2280RGFDFT1
12300.8253420.8020.1740RGTSPAN13
12400.7815910.7340.2030RGGADD45G
12500.6052440.8020.2540RGIGFBP2
12600.5898590.8690.3450RGIER2
12700.5677360.7930.2770RGC1orf61
12800.5675290.8140.2660RGNFIB
12900.5660180.9220.4440RGFOS
1301.25E-2850.8059040.7730.2912.50E-282RGEGR1
1315.47E-2620.743240.7280.2081.09E-258RGSTMN4
1323.04E-2510.5909310.3070.0616.08E-248RGMYC
1332.03E-2300.7745180.7210.2144.06E-227RGBCL11A
1341.05E-1861.1018510.6590.1632.11E-183RGMSMO1
1357.67E-1740.8373030.6690.1511.53E-170RGNELL2
1369.27E-1700.8330790.6560.1861.85E-166RGSQLE
1373.38E-1450.6676560.7130.2496.77E-142RGEZR
1385.61E-1100.6622480.2920.0471.12E-106RGEPB41L3
1395.28E-1050.6470770.3320.0811.06E-101RGFGFBP3
1401.25E-890.5677490.3820.0552.50E-86RGPRDM8
1413.72E-800.5879630.3860.0887.45E-77RGEPHB6
1427.85E-620.572080.6040.1631.57E-58RGNFIA
1433.31E-600.5892740.6120.156.63E-57RGPPP2R2B
1445.69E-560.7459580.3340.0581.14E-52RGCDK2AP1
1452.73E-420.8752570.5060.1375.47E-39RGSSBP2
1463.01E-310.849950.5050.1226.02E-28RGPLK2
1472.38E-220.6347890.5550.1514.75E-19RGACAT2
1481.99E-180.7535730.5560.1413.98E-15RGHMGCS1
1492.60E-090.6400160.4370.0755.21E-06RGLIMCH1
1502.78E-090.670920.520.115.55E-06RGSEZ6
15103.2221960.7670.6010MacrophageLYZ
15201.9188490.9710.860MacrophageFTL
15301.8832970.9510.8580MacrophageFTH1
15401.5636970.4950.2850MacrophageCST3
15501.5012450.7690.7660MacrophageHLA-DRB1
15601.2906520.8990.8570MacrophageHLA-DRA
1578.32E-2381.6367650.4590.3221.66E-234MacrophagePSAP
1581.52E-1321.0972580.1710.5043.03E-129MacrophageGPNMB
1592.80E-961.391120.6110.6795.60E-93MacrophageSAT1
1605.33E-651.0836370.1650.41.07E-61MacrophageCYBB
1611.47E-602.0621640.4750.5262.94E-57MacrophageNPC2
1621.39E-571.2417210.3080.3492.79E-54MacrophageCTSD
1631.76E-361.5779390.2750.5573.53E-33MacrophagePTGDS
1644.99E-291.6591690.2680.5259.99E-26MacrophageFCER1G
1655.06E-271.0903630.4790.5841.01E-23MacrophageNFKBIA
1661.27E-251.0404740.1850.442.54E-22MacrophageFCGRT
1672.38E-242.5449810.3910.474.77E-21MacrophageC1QB
1686.06E-241.7286850.2490.4861.21E-20MacrophageC1QC
1694.75E-211.2598640.30.5539.50E-18MacrophageTYMP
1706.84E-212.1551820.4320.5491.37E-17MacrophageTYROBP
1718.42E-201.3460760.3060.5541.68E-16MacrophageCTSS
1728.23E-192.6004180.4130.531.65E-15MacrophageC1QA
1731.49E-181.0696530.2030.4092.98E-15MacrophageTMEM176B
1748.04E-142.5968870.4370.5711.61E-10MacrophageAPOE
1757.43E-061.2031990.1770.3730.014856MacrophageIER3
1761.37E-051.2933480.3070.5160.027378MacrophageCTSB
1770.0002282.2244620.3470.5570.455699MacrophageAPOC1
1780.0004431.1936030.4650.6490.8867MacrophageNEAT1
1790.002271.9399780.3140.5151MacrophageS100A9
1800.0070661.6499950.3950.5671MacrophageAIF1
1812.14E-2801.2248180.9820.7064.29E-277INPTUBA1B
1828.01E-2720.5377670.8570.4161.60E-268INPPLK1
1831.07E-2481.2546340.9890.7552.15E-245INPHMGB2
1841.21E-2001.0680640.8320.4722.42E-197INPUBE2C
1856.38E-1810.6208440.8120.5011.28E-177INPNUSAP1
1861.21E-1650.6615360.8450.4152.42E-162INPSOX4
1871.93E-1640.646190.980.553.85E-161INPHMGN2
1881.20E-1090.722750.7220.3792.40E-106INPMAD2L1
1891.15E-970.9061190.7050.2362.30E-94INPGADD45G
1902.94E-690.7433150.6140.0725.88E-66INPZWINT
1913.94E-630.5690610.6140.1447.88E-60INPUBE2S
1929.37E-611.3653330.7070.371.87E-57INPHIST1H4C
1931.19E-550.6059580.6390.0892.37E-52INPRRM1
1941.07E-501.2547130.640.2852.14E-47INPTOP2A
1954.06E-400.6400390.6120.2318.12E-37INPH1F0
1962.21E-320.5516870.6390.4434.42E-29INPTYMS
1971.10E-310.7061080.660.2342.19E-28INPTUBB4B
1981.36E-280.5508290.4030.2952.72E-25INPMCM2
1995.40E-230.5684660.440.3481.08E-19INPCDCA8
2002.65E-210.6664280.6140.4795.30E-18INPCDK1
2016.59E-210.5682690.6080.3751.32E-17INPMKI67
2027.71E-210.6861430.5630.1381.54E-17INPNDC80
2033.70E-150.8700810.60.4827.39E-12INPCENPF
2042.45E-140.5946740.440.1434.90E-11INPKIF23
2052.05E-110.7896920.5440.2124.11E-08INPGTSE1
2062.02E-100.7545880.750.4754.05E-07INPCENPA
2075.51E-100.7321570.5490.2271.10E-06INPPRC1
2082.96E-090.5914120.6140.4455.91E-06INPAURKB
2094.96E-090.8051260.5480.2239.92E-06INPCCNB2
2101.61E-070.591840.3660.1050.000322INPCKAP2L
21103.702690.8220.2850Dendritic_cellCST3
21201.9730410.9610.8570Dendritic_cellHLA-DRA
21301.7539250.9940.9440Dendritic_cellCD74
2142.37E-2992.0981120.8820.7524.73E-296Dendritic_cellHLA-DPB1
2155.01E-2772.0150710.8780.7631.00E-273Dendritic_cellHLA-DRB1
2164.61E-2520.763202119.22E-249Dendritic_cellTMSB4X
2175.28E-2462.0488470.8280.7121.06E-242Dendritic_cellHLA-DPA1
2187.11E-1061.7178290.4120.2861.42E-102Dendritic_cellSNX3
2199.82E-982.2948160.650.611.96E-94Dendritic_cellLYZ
2202.67E-850.9154720.1020.4915.34E-82Dendritic_cellFGL2
2214.65E-761.0826960.5390.4529.30E-73Dendritic_cellVIM
2228.29E-760.9793160.1280.5131.66E-72Dendritic_cellFGD2
2231.08E-700.8389860.1550.5472.16E-67Dendritic_cellHLA-DMB
2247.55E-641.0271260.1240.4711.51E-60Dendritic_cellSPI1
2252.23E-580.8879470.1250.4924.47E-55Dendritic_cellPTPRE
2269.41E-580.9093670.1730.5461.88E-54Dendritic_cellTYMP
2273.79E-400.7622690.2210.5727.57E-37Dendritic_cellANXA2
2282.84E-321.4621340.1870.4775.67E-29Dendritic_cellC1orf162
2293.35E-250.7775210.2670.3336.70E-22Dendritic_cellPSAP
2307.26E-251.5045470.2060.5021.45E-21Dendritic_cellIRF8
2311.97E-230.9694840.2750.5943.94E-20Dendritic_cellRGS10
2321.22E-180.905930.2690.5722.44E-15Dendritic_cellTAGLN2
2333.75E-180.8314390.2590.5627.51E-15Dendritic_cellCKLF
2344.01E-140.8523430.3320.6398.02E-11Dendritic_cellS100A11
2351.52E-080.9934950.3450.6233.03E-05Dendritic_cellHLA-DMA
2361.71E-081.5758680.2770.5263.43E-05Dendritic_cellRGCC
2374.19E-072.3737850.3640.460.000838Dendritic_cellCPVL
2382.61E-051.6457030.4470.6060.052238Dendritic_cellS100A10
2390.0022371.1386360.4730.6561Dendritic_cellLGALS1
2400.0082811.4358780.3390.5621Dendritic_cellAIF1
2416.49E-3031.9044160.8680.0681.30E-299ENPEOMES
2423.29E-2250.425820.9790.4156.58E-222ENPSOX4
2433.75E-1540.4004630.2540.1967.51E-151ENPRASGRP1
2444.14E-1380.6258440.9830.5178.28E-135ENPPRDX1
2451.66E-1180.4407640.9890.5533.32E-115ENPSTK17A
2462.24E-1090.505076114.48E-106ENPTMSB4X
2471.66E-1060.5391250.730.193.33E-103ENPNFIA
2483.75E-1050.9852270.7430.137.51E-102ENPCORO1C
2494.56E-980.6682520.7980.299.11E-95ENPIGFBP2
2501.04E-800.7954550.7330.2672.08E-77ENPFDFT1
2512.70E-680.4669220.6550.2615.41E-65ENPPFDN4
2526.93E-680.5001780.3290.0771.39E-64ENPCDK2AP1
2531.14E-650.6644170.7870.3232.28E-62ENPEGR1
2543.54E-570.4002390.2510.0797.07E-54ENPMYC
2557.72E-560.6472430.2780.0331.54E-52ENPLYPD1
2561.33E-480.5995180.9990.9642.65E-45ENPRPS26
2573.05E-450.6818360.690.1586.10E-42ENPPHLDA1
2581.19E-440.4242290.3580.0542.37E-41ENPZEB1
2592.62E-410.6525340.6880.1965.24E-38ENPCCND2
2603.48E-340.4620490.6450.2326.95E-31ENPH1F0
2611.04E-320.8859230.6140.0912.08E-29ENPADGRG1
2622.88E-250.5303150.6310.1455.75E-22ENPIVNS1ABP
2631.94E-240.5110570.6370.1783.89E-21ENPFBLN1
2642.41E-190.4692110.4280.1134.83E-16ENPOLFM2
2659.40E-140.4777020.6240.2391.88E-10ENPCITED2
2663.62E-110.4760860.4530.1677.23E-08ENPVCAN
2672.04E-080.7348490.5420.1644.07E-05ENPRBP1
2683.21E-060.5077310.5780.1360.006419ENPSEZ6
2690.0001910.5063870.4040.1910.382023ENPZNF462
2700.002360.3975470.4070.2251ENPTSPAN13
27101.6145230.9280.2040AstrocytePTN
27200.8104430.2850.0080AstrocyteTFPI
2731.42E-2360.7555420.3330.0252.84E-233AstrocyteEEPD1
2745.24E-1671.0682490.780.0591.05E-163AstrocyteTTYH1
2757.75E-1410.8708030.9620.4441.55E-137AstrocyteVIM
2761.32E-1031.2917450.7230.0532.64E-100AstrocytePON2
2772.42E-941.116260.7090.0624.85E-91AstrocyteCLU
2787.41E-930.6430010.3080.1151.48E-89AstrocytePDGFD
2792.38E-821.1480980.7040.124.76E-79AstrocytePSAT1
2807.00E-730.7003650.7350.2251.40E-69AstrocyteCNN3
2812.07E-690.7188010.7150.1274.15E-66AstrocytePEA15
2825.00E-560.8849160.7020.1671.00E-52AstrocyteHMGCS1
2836.36E-490.9360890.6480.0551.27E-45AstrocyteSLC1A3
2843.06E-470.8004030.6610.1256.12E-44AstrocyteZFP36L1
2854.65E-451.4043850.7390.5179.31E-42AstrocyteHOPX
2862.66E-441.025010.6630.0925.32E-41AstrocyteFGFBP3
2876.01E-400.9159950.6370.1661.20E-36AstrocyteGATM
2881.20E-390.6479760.9090.672.41E-36AstrocyteSAT1
2892.72E-340.6889020.9560.6735.45E-31AstrocyteJUNB
2903.10E-210.6725020.6030.1786.21E-18AstrocyteACAT2
2914.41E-190.7604040.5970.0488.83E-16AstrocytePHGDH
2921.51E-180.988210.5860.0173.01E-15AstrocytePLPP3
2932.75E-100.6858590.4240.1575.50E-07AstrocyteABHD3
2947.99E-101.0869460.560.0251.60E-06AstrocyteMOXD1
2953.06E-090.6930820.4270.0726.12E-06AstrocyteCDCA7
2964.94E-060.6980060.3980.0470.009875AstrocyteB3GAT2
2970.0018651.0987590.4580.0151AstrocyteGFAP
2980.0021150.8743270.5120.0651AstrocyteITM2C
2990.0026790.8202950.4720.0631AstrocyteMFGE8
3000.0090710.6356720.5320.0511AstrocyteSPARC
3013.79E-1853.8056860.6670.1537.59E-182OligodendrocytePLP1
3022.58E-1392.5399780.3880.0525.16E-136OligodendrocytePPP1R14A
3032.21E-1362.2720360.3050.054.41E-133OligodendrocyteCLDN11
3048.42E-962.686640.40.1511.68E-92OligodendrocyteCRYAB
3051.08E-951.9278660.3260.1082.17E-92OligodendrocyteCNP
3061.04E-520.8248730.8770.8642.09E-49OligodendrocyteFTH1
3074.26E-430.5770260.1190.5248.52E-40OligodendrocyteRGCC
3087.87E-340.6126830.0870.4631.57E-30OligodendrocyteAFMID
3091.78E-320.9904120.3540.3093.55E-29OligodendrocyteCD63
3101.92E-302.7217240.4450.3733.83E-27OligodendrocyteMBP
3116.38E-300.8110370.1590.5251.28E-26OligodendrocyteAPOL2
3121.56E-290.6468760.1740.5463.12E-26OligodendrocyteCD82
3135.02E-250.9802430.1840.5391.00E-21OligodendrocyteISG15
3141.30E-220.9284240.1910.5212.60E-19OligodendrocyteIFI6
3152.36E-211.2165570.1570.4784.72E-18OligodendrocyteAPOD
3165.57E-211.4698770.250.261.11E-17OligodendrocyteGPM6B
3172.73E-182.4788410.5040.545.45E-15OligodendrocytePTGDS
3183.43E-180.5794920.0660.3696.86E-15OligodendrocyteUGT8
3193.00E-170.6313930.2610.6066.00E-14OligodendrocyteLCP1
3208.04E-140.6712660.1740.2541.61E-10OligodendrocyteHSPA1A
3212.68E-120.7173550.3050.6075.35E-09OligodendrocyteIFI16
3221.20E-111.1154760.1310.4132.41E-08OligodendrocyteCA2
3231.81E-110.5746220.3090.6373.63E-08OligodendrocyteDUSP2
3241.90E-081.3975350.1550.3473.80E-05OligodendrocyteTF
3252.35E-060.9136460.3450.6410.004702OligodendrocyteNEAT1
3263.63E-050.6284240.3330.6030.072666OligodendrocyteCTNNB1
3270.0001490.6091160.3790.6630.297713OligodendrocyteLTB
3280.0004930.5783260.3470.4750.986082OligodendrocyteDBI
3290.0052351.2595940.2370.4631OligodendrocyteTUBA1A
3300.0055770.6139520.1250.2751OligodendrocyteAPLP1
33102.8356610.7710.1170Miningeal_cellDCN
33202.5046640.7190.030Miningeal_cellLUM
33301.9041080.6570.0120Miningeal_cellC1S
33401.4936260.5920.0120Miningeal_cellCCL2
3351.58E-2204.1252750.8810.2273.17E-217Miningeal_cellIGFBP7
3362.97E-1901.3687580.610.0695.93E-187Miningeal_cellIFI27
3373.51E-1711.9470110.6680.0397.01E-168Miningeal_cellMGP
3387.74E-1701.067960.5610.0091.55E-166Miningeal_cellNNMT
3397.13E-1671.0043150.5920.0221.43E-163Miningeal_cellPCOLCE
3402.65E-1563.3954560.8260.4715.31E-153Miningeal_cellAPOD
3412.14E-1554.5647320.8650.4714.28E-152Miningeal_cellTIMP1
3425.67E-1551.640020.6360.0551.13E-151Miningeal_cellFN1
3431.80E-1401.1581990.5690.0173.59E-137Miningeal_cellCOL1A2
3441.28E-1031.5931080.5770.112.56E-100Miningeal_cellC1R
3456.72E-991.2512080.6290.2161.34E-95Miningeal_cellMT1X
3464.92E-922.3789980.7270.2949.83E-89Miningeal_cellCST3
3474.77E-792.2616520.730.5389.55E-76Miningeal_cellPTGDS
3483.54E-731.3583630.3950.0097.07E-70Miningeal_cellTFPI
3491.67E-722.3514170.6680.4073.34E-69Miningeal_cellIFITM3
3501.81E-631.3453630.7010.3063.62E-60Miningeal_cellCD63
3518.62E-611.0446530.4230.0621.72E-57Miningeal_cellPON2
3521.81E-571.2568140.4230.0713.62E-54Miningeal_cellCLU
3531.73E-491.7939790.6210.2993.46E-46Miningeal_cellIGFBP2
3543.77E-381.0420010.4680.1867.55E-35Miningeal_cellFBLN1
3556.80E-291.3682420.8340.7171.36E-25Miningeal_cellMT2A
3569.04E-230.9336530.3710.1611.81E-19Miningeal_cellLGALS3BP
3576.99E-191.7662210.3610.0181.40E-15Miningeal_cellTIMP3
3582.21E-140.9448420.5920.5224.42E-11Miningeal_cellNPC2
3596.44E-141.0874250.3320.131.29E-10Miningeal_cellCALD1
3603.21E-120.9992240.2420.3636.42E-09Miningeal_cellID3
3611.06E-1961.0264740.6920.0192.12E-193OPCALCAM
3628.54E-1610.9267130.8420.0531.71E-157OPCPPP1R14A
3633.67E-972.6605420.9520.0117.34E-94OPCOLIG1
3643.59E-763.1563760.9450.4737.19E-73OPCAPOD
3651.40E-460.7177110.9930.4892.79E-43OPCDBNDD2
3661.88E-411.54390.8080.043.76E-38OPCSCRG1
3672.35E-221.0039880.7670.1394.69E-19OPCSCD5
3682.78E-222.7419790.6990.0325.57E-19OPCS100B
3699.43E-200.7526230.7880.3211.89E-16OPCC1orf61
3703.17E-171.7286590.6640.0716.34E-14OPCITM2C
3711.86E-150.7284620.6030.1393.73E-12OPCATP1B1
3722.31E-151.2140950.6030.0054.61E-12OPCCMTM5
3734.46E-150.7228290.7120.2168.92E-12OPCPTN
3741.62E-110.8733440.3290.0443.23E-08OPCDUSP6
3752.19E-110.9621170.7190.34.38E-08OPCIGFBP2
3764.85E-111.2543040.6230.1019.69E-08OPCSIRT2
3775.32E-111.5294030.6370.1771.06E-07OPCMEG3
3787.61E-101.3057090.3630.1521.52E-06OPCBAMBI
3792.01E-090.7177180.5270.0424.01E-06OPCRAB40B
3801.04E-080.8401250.3630.192.07E-05OPCETV1
3812.32E-071.3648140.5480.0120.000465OPCPLLP
3822.13E-060.8914710.5820.2520.004265OPCSTMN4
3832.34E-061.2401830.630.1340.004677OPCSERPINE2
3847.07E-061.3955960.5750.0880.014136OPCPCDH9
3850.0001581.3296780.5750.0640.315588OPCPON2
3860.0001680.8319650.610.1690.336561OPCPHLDA1
3870.0006351.619120.5820.1041OPCDLL3
3880.0010360.7349480.50.51OPCCTHRC1
3890.0016120.7611990.4250.0611OPCNCALD
3900.001721.0825340.5750.1481OPCCLDND1
Table 2

The proportion of various subgroups in PCNSL and normal samples.

cancernormalcancer_pernormal_perTotal%Total
B_cell85971330.2132090.00329887300.216507
T_cell77821360.1929960.00337379180.196369
EN752790.0001740.13092152860.131095
IN875070.0001980.18617675150.186375
RG0353800.08774435380.087744
Macrophage22522620.055850.00649825140.062348
INP1611290.0003970.02811450.028396
Dendritic_cell99800.02475109980.024751
ENP98970.0002230.0222469060.022469
Astrocyte37667.44E-050.0189977690.019071
Oligodendrocyte454180.0112590.0004464720.011706
Miningeal_cell1792060.0044390.0051093850.009548
OPC21444.96E-050.0035711460.003621
203072001540322
Single-cell analysis of CNS scRNA-seq data sets. (A) The visualization results of cell subpopulations in PCNSL and normal CNS tissue samples. (B) The tSNE displays PCNSL and normal CNS cells: red indicates PCNSL cells, and green indicates normal cells. (C) The barplot represents the proportion of various cell subgroups in PCNSL and normal CNS tissue samples individually. (D) The heatmap describes the PCC values of the average expression values of the top 30 genes in each cluster. (E) The bubble chart shows that three marker genes are selected for each cell subgroup, and their expression in all cell subpopulations is plotted. The top30 marker genes in scRNA-seq data. The proportion of various subgroups in PCNSL and normal samples.

The Heterogeneity of B Cells in PCNSL and Their Communication Interaction With Other Immune Cells

Malignant proliferation and abnormal differentiation of B cells (toward plasma cells) are the main pathological characteristics of PCNSL. Molecular targeted therapy targeting B cells has also been considered as a promising way to treat PCNSL in recent years. Nevertheless, the heterogeneity of B cells in PCNSL remains unclear. Therefore, we first analyzed the heterogeneity of B-cell subtypes and genetic characteristics in PCNSL from single-cell resolution. CD79A served as a typical marker gene of the B cell population in total PCNSL cells ( ). Then, the identical methods with cell population detection were used to re-cluster B cells into four distinct subclusters: B cell-1, B cell-2, B cell-3, and plasma cell ( ). We identified the marker genes for each subcluster among the B cell population ( and ). The functional annotations of each subcluster were counted ( ) and the three significant pathways are shown in . The B cell-1 cluster highly expressed marker genes (e.g., CD79A, CD79B, TCL1A) of the classical B cells. The B cell-2 cluster was significantly marked by, e.g., NCL, LTB, NEAT1, and enriched functional pathways of antigen processing and presentation of endogenous peptide antigen via MHC class I and positive regulation of T cell-mediated cytotoxicity, suggesting that these B cells have strong antigen–antibody immunity and synergistic disturbance with T cells. The B cell-3 cluster (e.g., CST7, CXCL13, MT-ND3) indicated functions of cell–cell adhesion regulation, neuroinflammation response, and hydrogen peroxide biosynthetic process. Plasma cells (e.g., IGHG1, IGHG3, IGHG4) played a significant role in complement activation.
Figure 2

Single-cell analysis of heterogeneity for B cell and cell communication. (A) The expression distribution of the B cell maker gen -- CD79A in all cells. (B) tSNE visualization describes the re-cluster results of B cell subgroups. (C) The bubble chart shows that selected three marker genes are selected for each cell subgroup, and their expression in all cell subpopulations is plotted. (D) The top3 representative GO functional annotation categories of marker genes for each sub-population. (E) The interactions among immune-related cell subpopulations: B cell, T cell, macrophage, and dendritic cell subpopulation. (F) The interaction pairs of B cell with T cell, macrophage, and dendritic cell, respectively. (G) The ligand-receptor pairs are contributing to the signaling from B cells to the other three clusters.

Single-cell analysis of heterogeneity for B cell and cell communication. (A) The expression distribution of the B cell maker gen -- CD79A in all cells. (B) tSNE visualization describes the re-cluster results of B cell subgroups. (C) The bubble chart shows that selected three marker genes are selected for each cell subgroup, and their expression in all cell subpopulations is plotted. (D) The top3 representative GO functional annotation categories of marker genes for each sub-population. (E) The interactions among immune-related cell subpopulations: B cell, T cell, macrophage, and dendritic cell subpopulation. (F) The interaction pairs of B cell with T cell, macrophage, and dendritic cell, respectively. (G) The ligand-receptor pairs are contributing to the signaling from B cells to the other three clusters. Cell–cell communication is a fundamental process that shapes biological tissue. In particular, ligand–receptor pairs can infer intercellular communication from the coordinated expression of their homologous genes (32). Here, we focused on four classes of immune cell subpopulations and immune-related genes. shows the interactions among immune-related cell subpopulations: B cell, T cell, macrophage, and dendritic cell subpopulation. The circle edge width is proportional to the number of cells in each cell cluster and the communication score between interacting cell clusters, respectively. The circle plot ( ) showed the interactions of B cell with T cell, macrophage, and dendritic cell, respectively. The bubble plots ( ) show the ligand–receptor pairs contributing to the signaling from B cells to other three clusters. The circle plots show that the interaction between macrophage and T cell has the most count numbers among others. Interestingly, we identified that significant target CD74 with MIF, COPA, and APP might participate the process of B cell interacting with T cell, macrophage, and dendritic cell based on the CellPhoneDB results, and CD74–MIF interaction was the most significant among these.

The Heterogeneity of T Cells and Dendritic Cells in PCNSL

T cells and dendritic cells have a high proportion in PCNSL and are closely related to the immune microenvironment of PCNSL. The subpopulations of T cells and dendritic cells in PCNSL were analyzed. LTB serves as a marker gene of the T cell population ( ). Then, T cells were re-clustered into four distinct subclusters: naive T cells, natural killer T (NKT) cells, T helper cells, and MPCs ( ). Further clustering of the T cell subpopulation in PCNSL gave rise to four subpopulations with specific gene signatures, including T helper cell group (LTB, NEAT1, and IL7R), NKT cell group (DUSP1, CCL5, and JUNB), MPC cell group (UBE2C, STMN1, and BIRC5), and classical T cell group (NCL, HNRNPA3, and DUT). Bubble heatmap showing expression levels of selected signature genes in ESCA. Dot size indicates a fraction of expressing cells, colored based on normalized expression levels ( ). The proportions of these subtypes are shown in . The marker genes were identified for each subcluster among the T cell population ( ), and the three typical marker genes for annotation are shown in a bubble plot ( ). Three significant function pathways enriched by the marker genes of the four subclusters are shown in ( ). DEGs of MPC were highly enriched in the G2/M transition of the mitotic cell cycle pathway, which was closely related to the proliferation and differentiation of various lymphocytes in PCNSL and might be the main source of tumor germination.
Figure 3

Single-cell analysis of heterogeneity for T cell and Dendritic cell sub-population. (A) The expression distribution of the T cell maker gene -- LTB in all cells. (B) The tSNE visualization describes the re-cluster results of T cell subgroups. (C) The bubble chart shows that selected three marker genes are selected for each cell subgroup, and their expression in all cell subpopulations is plotted. (D) The proportion of each subpopulation for T cell. (E) The top three significant GO categories of marker genes for each subgroup are enriched. (F) The expression distribution of the Dendritic cell maker gene -- CPVL in all cells. (G) The tSNE visualization describes the re-cluster results of dendritic cell subgroups. (H) The bubble chart shows that selected three marker genes are selected for each cell subgroup, and their expression in all cell subpopulations is plotted. (I) The proportion of each subpopulation for dendritic cells. (J) The top three significant GO categories of marker genes for each subgroup are enriched.

Single-cell analysis of heterogeneity for T cell and Dendritic cell sub-population. (A) The expression distribution of the T cell maker gene -- LTB in all cells. (B) The tSNE visualization describes the re-cluster results of T cell subgroups. (C) The bubble chart shows that selected three marker genes are selected for each cell subgroup, and their expression in all cell subpopulations is plotted. (D) The proportion of each subpopulation for T cell. (E) The top three significant GO categories of marker genes for each subgroup are enriched. (F) The expression distribution of the Dendritic cell maker gene -- CPVL in all cells. (G) The tSNE visualization describes the re-cluster results of dendritic cell subgroups. (H) The bubble chart shows that selected three marker genes are selected for each cell subgroup, and their expression in all cell subpopulations is plotted. (I) The proportion of each subpopulation for dendritic cells. (J) The top three significant GO categories of marker genes for each subgroup are enriched. The dendritic cell population was labeled by a marker gene CPVL ( ). Then, the dendritic cells were re-clustered into three distinct clusters: conventional dendritic cell (cDC), myeloid dendritic cell (mDC), and plasmacytoid dendritic cell (pDC) ( ). We identified the marker genes for each subcluster among the dendritic cell population ( ). The typical marker genes for annotation are shown in a bubble plot (cDC: e.g., STMN1, HMGB2, HIST1H4C; mDC: e.g., CST3, HLA-DPA1, HLA-DQB1; pDC: IGLC2, GAS5, IL32) ( and ). The proportions of the subclusters are shown in . Evaluating known pathway expression in every three subpopulations of dendritic cell group using GO enrichment analysis revealed strong enrichment of regulation of viral entry into host cell, adaptive immune response, and neutrophil degranulation for mDC subpopulation; SRP-dependent cotranslational protein targeting to membrane , interferon-gamma-mediated signaling pathway, and antigen processing and presentation of exogenous peptide antigen via MHC class II for pDC; and positive regulation of NIK/NF-kappaB signaling, regulation of G2/M transition of mitotic cell cycle, and viral process in cDC. The function pathways of the marker genes enriched by GO analysis are shown in and .

Identification and Variation Analysis of Macrophage Between PCNSL and Normal Brain

It is noted that macrophage in PCNSL and HFB shows obvious difference in cell proportion and distribution. Then they may also have some differences in function. LYZ was used as a marker gene of the macrophage population ( ). NFKBIA (log2FC = -0.38; adj. p-value = 1.32E-8), BTG2 (log2FC = -0.59, adj. p-value = 1.44E-18), NFKBIZ (log2FC = -0.28, adj. p-value = 1.18E-20), TLR4 (log2FC = –0.53, adj. p-value = 7.35E-34), and TLR10 (log2FC = -0.54, adj. p-value = 3.69E-40) are selected for visualization of the differentially expressed between PCNSL and nHFB samples ( ). There are 143 DEGs between cancer and normal cells. Fifty genes are upregulated in cancer cells compared to the normal cells, while 93 genes are downregulated in cancer cells compared to normal cells ( and ). Moreover, the top 20 enriched functional pathways of these DEGs are shown in . Compared with PCNSL, macrophages in nHFB exhibit normal immune function, and the un-regulated DEGs are mainly enriched in pathways including immune response, neutrophil chemotaxis, and chemokine-mediated signaling pathway ( ). However, this classic immune function of macrophage in PCNSL was significantly downregulated, and no significant pathways were found ( ). These results suggest that the immune function of macrophages in PCNSL is significantly suppressed.
Figure 4

The differential expression analysis of macrophage between cancer and normal samples. (A) The expression distribution of the macrophage cell marker gene -- LYZ in all cells. (B) The scatter plot of genes selected five significant DEGs in the macrophage cell population between cancer and normal samples. (C) The heatmap of DEGs of macrophage. (D, E) The GO categories of down-regulated genes (green) and up-regulated genes (red) in PCNSL samples compared with normal samples.

Table 3

The DEGs between cancer and normal samples in macrophage cell subpopulation.

p_valavg_log2FCpct.1pct.2p_val_adj
KLRC200.3912390.0370.9690
VCAM100.3664720.04410
DBNDD200.2771820.0320.9240
CRIP20-0.2987900.1110
FGL200.4879560.0510.9690
P2RX500.4993760.05410
CNOT6L00.4106540.0480.9920
MRPL3800.3143330.03710
CPM1.04E-3030.3089210.0560.9962.08E-300
MT1E5.78E-3000.5567360.0570.9961.16E-296
APOL26.86E-3000.4447240.05911.37E-296
TRDC4.55E-2840.5630920.0460.8899.10E-281
IQGAP29.89E-2810.5585430.0550.9771.98E-277
RAB11FIP11.22E-2780.359210.0320.1072.44E-275
SDC43.20E-2770.3894710.0380.8746.40E-274
DZIP31.85E-2750.3118010.0270.1153.70E-272
FAR27.90E-2750.2645860.0090.1341.58E-271
CXCL86.84E-17-1.663170.0710.631.37E-13
MGST11.69E-2690.4574220.0630.9853.38E-266
TRIAP11.75E-2650.2791750.0410.1073.51E-262
CDCA7L1.96E-2640.5392320.0560.9663.92E-261
CDK62.95E-2640.2792970.0380.9275.91E-261
TRADD4.66E-2590.4105230.0470.1079.31E-256
SMC28.43E-2570.4042360.0410.1151.69E-253
BTG33.07E-2560.4165850.0410.1156.14E-253
SRSF85.57E-2540.3959060.0470.1111.11E-250
GMNN6.60E-2530.257860.0320.1261.32E-249
TRIP121.55E-2500.2526730.0270.1343.10E-247
POLD21.34E-2450.2818140.0460.1182.68E-242
TSPAN131.39E-244-0.364670.0050.162.78E-241
ISG151.23E-2420.4727440.08212.45E-239
RHOT15.52E-242-0.282440.0140.1531.10E-238
STMN42.41E-241-0.4253800.1684.82E-238
MRTO41.43E-2400.3742740.0390.132.87E-237
HSPA1A2.24E-21-1.546450.1270.6914.47E-18
UBE2S2.27E-2380.255980.040.134.55E-235
HRH27.11E-235-0.267390.0150.8441.42E-231
PROCR1.33E-232-0.265720.0010.1722.67E-229
FGFBP37.58E-232-0.263470.0030.1721.52E-228
SH2B21.37E-230-0.250180.0030.8212.74E-227
SMC44.80E-2290.4219290.0830.9899.60E-226
RGS166.35E-227-0.377950.0080.1681.27E-223
DNMT11.82E-2260.5124290.0650.1183.64E-223
CD822.17E-2250.5086790.09114.34E-222
C1QB7.92E-73-1.432030.3230.9731.58E-69
IRF91.33E-2230.3886970.060.9392.65E-220
IFI64.23E-2220.5365960.09218.47E-219
CCL32.11E-44-1.371710.3170.9394.23E-41
CXCL35.97E-2130.4152410.0510.841.19E-209
C1QC1.63E-98-1.362870.1690.9353.26E-95
AURKB2.36E-2120.2631250.030.134.72E-209
CCL22.28E-77-1.347480.0020.2944.57E-74
IL1B2.10E-48-1.273490.0620.714.20E-45
BLVRA1.38E-2090.2788680.0430.1532.77E-206
CORO1C2.63E-208-0.308890.0130.1795.25E-205
MT1X9.56E-2080.5337010.0980.9961.91E-204
METTL56.09E-1990.3043520.050.1561.22E-195
MCM71.11E-1950.3317870.0630.1492.22E-192
RARRES23.44E-1930.5180630.0330.1686.89E-190
TMEM161B-AS11.06E-192-0.330520.0150.1912.12E-189
FRMD4B2.23E-192-0.274040.0110.1954.45E-189
MRPS353.35E-1880.3120660.0530.1646.70E-185
CCL42.38E-14-1.250750.5360.8974.75E-11
C1QA1.24E-65-1.150280.34512.48E-62
PAQR83.28E-178-0.279280.0080.2066.56E-175
ASCC38.82E-1780.2537270.020.1911.76E-174
BIRC59.26E-1740.2649450.0470.8661.85E-170
HES61.53E-171-0.31130.0040.2183.05E-168
ADGRG13.75E-171-0.403280.0040.2187.49E-168
MDK3.90E-170-0.338220.0010.2217.80E-167
CHD71.21E-169-0.353560.0160.212.42E-166
CCL4L21.28E-24-0.980750.3060.8782.56E-21
LINC009962.36E-165-0.40.0040.2214.73E-162
SGK15.45E-64-0.974310.0520.7251.09E-60
RBM423.92E-1630.2982740.0520.1917.84E-160
UBALD22.08E-1620.2968310.0590.1874.17E-159
TIMP13.39E-1610.4934130.0890.9276.77E-158
SLC11A17.70E-160-0.415940.0040.2211.54E-156
CD745.47E-10-0.962120.96411.09E-06
GADD45G2.17E-159-0.359410.0080.2254.35E-156
RGS12.82E-14-0.939020.5130.9545.64E-11
ITGB3BP2.78E-1550.3707990.040.2065.55E-152
HIST1H2BG3.18E-89-0.900980.0040.2986.36E-86
TRAF59.07E-1520.2686770.0280.1831.81E-148
STK17A1.08E-1480.3043510.1412.16E-145
PRPF311.41E-1480.3421620.0590.2022.82E-145
FN13.78E-1480.2920250.0220.2257.55E-145
CD1809.33E-147-0.376120.0060.2021.87E-143
RNASEH2B1.55E-1460.4763780.0760.1953.09E-143
UQCC22.24E-1460.3455370.0630.2024.48E-143
FCGR1A2.72E-59-0.885090.0380.715.45E-56
VAMP54.93E-1420.5637770.14519.86E-139
AXL3.68E-141-0.268090.0150.2377.36E-138
MKKS1.38E-138-0.29570.0080.2442.76E-135
MT1F7.66E-1330.5255020.0430.1871.53E-129
CENPK4.14E-1320.3357740.030.2378.29E-129
HBB1.17E-130-0.520880.0020.2562.34E-127
SNCA3.41E-128-0.464970.0050.2566.83E-125
IRS27.91E-128-0.361950.0120.2521.58E-124
CX3CR18.48E-09-0.869550.0020.5571.70E-05
SSBP28.74E-126-0.577920.0030.261.75E-122
UCHL16.12E-124-0.5389200.2631.22E-120
ARHGAP251.02E-1210.3770420.0530.2372.04E-118
NDRG21.46E-120-0.42640.0050.2632.91E-117
CDKN1A1.91E-119-0.563420.0130.2443.82E-116
RASGRP18.18E-1190.4423360.0390.7941.64E-115
BBX5.74E-1180.3740910.0720.2331.15E-114
RPA33.40E-1170.496070.1320.9396.80E-114
HELLS1.21E-1160.2931250.0410.2482.42E-113
UBE2C2.20E-1120.5300150.090.2334.41E-109
NUSAP14.00E-1120.3000280.0570.8138.00E-109
SPN5.14E-1110.4780720.0580.2441.03E-107
TLR78.90E-111-0.291690.0040.2211.78E-107
MILR16.99E-108-0.334030.0160.2711.40E-104
ENC11.19E-107-0.313810.0090.2752.38E-104
MCM33.03E-1050.2536740.0550.8026.06E-102
P2RY132.75E-07-0.86740.0080.5570.00055
ANXA22.27E-1040.440990.18314.54E-101
OTUD11.24E-05-0.843460.0060.4580.024869
CXorf214.08E-102-0.315380.010.2638.16E-99
ADRB21.34E-20-0.823210.0080.3742.69E-17
TMIGD21.34E-100-0.474150.0020.2862.68E-97
PDCD101.41E-990.3418210.0770.2562.81E-96
FNIP24.81E-99-0.275420.0060.2759.63E-96
PTAFR6.60E-22-0.795430.0120.6031.32E-18
AP1S21.71E-980.3004210.0720.263.42E-95
NLRP38.56E-98-0.284720.0050.2521.71E-94
FSCN11.33E-11-0.785680.0110.5762.65E-08
CEBPA7.33E-96-0.457450.010.2751.47E-92
HSD17B149.87E-96-0.350790.0050.291.97E-92
LRRFIP12.14E-950.5720490.120.8854.28E-92
IL17RA3.33E-95-0.285770.0080.2866.66E-92
SLC9A3R14.69E-950.3525110.0430.2759.37E-92
A2M1.21E-16-0.785570.0190.5992.43E-13
PFDN45.43E-940.284170.0790.2631.09E-90
DDX63.96E-920.2861090.0660.2717.91E-89
NFIB4.30E-91-0.4908500.2988.60E-88
CST31.62E-26-0.781570.4390.9733.24E-23
OLFML36.86E-15-0.780250.0030.581.37E-11
EGR21.17E-07-0.778540.0050.4050.000233
QKI2.47E-88-0.266840.0160.2944.95E-85
TMEM106C1.41E-870.3008360.0510.7712.83E-84
TPM11.09E-85-0.363280.0150.2982.18E-82
SLC31A22.55E-85-0.27280.0260.2945.09E-82
GGCT1.39E-840.2624810.0620.2822.79E-81
MAFF1.77E-84-0.301330.0070.2523.53E-81
IFITM33.96E-820.4359270.1080.8447.92E-79
SERPINA12.42E-81-0.501810.0790.7944.84E-78
RNASE14.92E-46-0.741560.0260.3519.84E-43
EGFL71.42E-79-0.4058500.3022.83E-76
FCGR1B6.62E-09-0.716270.0120.5571.32E-05
ACY31.28E-24-0.711380.0040.3852.56E-21
SPP11.10E-12-0.709020.0160.4312.21E-09
ZNF3317.42E-760.2844180.0750.7861.48E-72
ADAM171.12E-750.3351390.0750.7862.25E-72
QPRT2.22E-75-0.548640.0090.3134.44E-72
TOP14.52E-750.4986030.0960.299.04E-72
CARHSP11.26E-740.4782580.1030.292.52E-71
CCR15.43E-74-0.258380.0140.2631.09E-70
ABCG28.91E-06-0.7078600.4470.017817
LMNA3.18E-720.262780.0260.3056.35E-69
CKS1B4.68E-720.2620690.0670.3029.35E-69
PTN5.08E-157-0.7059300.2331.02E-153
DUSP17.06E-33-0.690280.3580.9621.41E-29
RIN23.15E-28-0.687830.0230.3896.31E-25
TGIF13.30E-700.2947180.0480.3096.60E-67
RHOC1.39E-690.4433750.0680.3052.78E-66
ADA1.46E-690.3193960.0280.3172.91E-66
GRPEL11.15E-680.2980270.0420.3132.31E-65
HIST2H2BE2.85E-68-0.56420.0030.3245.70E-65
TLR12.93E-68-0.388470.0050.3095.87E-65
S100A93.90E-680.4343690.2350.9967.80E-65
ALOX56.12E-68-0.260590.0040.3241.22E-64
UBE2T3.87E-660.2778790.0420.3177.73E-63
JUNB5.95E-19-0.686740.4230.9351.19E-15
DAB28.49E-19-0.675830.0180.3741.70E-15
MS4A71.17E-45-0.672680.0240.3472.33E-42
MKI673.90E-650.313440.050.3177.79E-62
RNH11.47E-640.3801260.0790.3132.93E-61
DHRS39.05E-12-0.668090.0180.4351.81E-08
RAB321.96E-16-0.659580.0180.423.91E-13
TSC22D18.93E-63-0.33960.0160.3281.79E-59
SAT11.40E-17-0.654360.570.9582.80E-14
KIF20B1.54E-590.2712910.0390.3283.08E-56
CKB1.34E-25-0.643320.0140.6182.68E-22
NAGA1.01E-58-0.387280.0130.3282.02E-55
SFMBT21.08E-58-0.479380.0130.3172.16E-55
RHBDF21.26E-58-0.366980.0190.3322.52E-55
NUCB22.43E-580.4194030.0690.7484.86E-55
S100A14.11E-270-0.616250.0020.1458.22E-267
CTSB5.99E-56-0.300690.2320.951.20E-52
MEF2C3.03E-09-0.615580.0270.586.06E-06
SLF15.69E-530.3161370.0380.341.14E-49
CCND13.95E-52-0.57560.0040.3477.89E-49
SLA6.19E-520.4430160.1150.3361.24E-48
LSM46.55E-520.4744950.1110.3361.31E-48
POU2F27.51E-520.418510.0980.3361.50E-48
SH3TC15.86E-510.47390.0750.341.17E-47
IFI301.47E-500.5405190.26612.94E-47
MCL12.51E-50-0.284750.1230.7985.01E-47
DNAJC97.72E-500.2818690.0510.3441.54E-46
SPRY11.97E-104-0.596930.0050.2793.94E-101
CENPH7.97E-490.2611250.0430.6981.59E-45
BCL2A11.03E-480.4209060.0760.3442.05E-45
CSF1R1.13E-48-0.434180.0440.6982.26E-45
NASP1.64E-480.4488270.1280.3443.28E-45
DNAJB11.78E-20-0.593510.1090.6913.56E-17
CMTM31.94E-470.2580290.0630.3473.87E-44
CSF3R8.52E-11-0.593470.0060.4241.70E-07
ITGAM1.88E-46-0.327910.0070.3473.76E-43
ATP1B13.45E-41-0.591570.0210.3636.90E-38
RGS22.22E-22-0.589790.190.7754.43E-19
AFMID1.97E-450.2571820.0240.3553.93E-42
ATF31.40E-440.2688050.0390.3552.81E-41
RPS201.12E-130.5865950.8580.9392.25E-10
H2AFV1.71E-310.5886660.1840.3973.42E-28
SLC25A372.05E-42-0.374630.0390.3594.10E-39
CCAR12.11E-420.3485370.0470.3594.22E-39
ATAD26.76E-420.3283830.0320.6721.35E-38
SOCS67.35E-42-0.461650.0030.3471.47E-38
HHEX1.37E-41-0.308420.0180.3632.74E-38
DUSP41.47E-2390.589220.0820.9962.94E-236
HLA-DMA4.33E-410.2813010.28818.66E-38
DHRS96.30E-41-0.374540.0290.3631.26E-37
IGF11.61E-40-0.560320.0060.3633.21E-37
CTNND12.77E-40-0.32140.0060.3665.54E-37
TMEM2195.40E-400.300650.0860.3631.08E-36
CD832.86E-39-0.312310.0530.6915.71E-36
YBX34.98E-39-0.40240.0280.3669.95E-36
ATP2A31.11E-380.2816580.0230.1952.22E-35
MARCKS2.69E-38-0.439340.0690.7065.39E-35
WIPI11.07E-36-0.272340.0070.3632.13E-33
VSIG41.33E-36-0.454860.1070.7442.65E-33
TRIB19.16E-36-0.532350.0120.3591.83E-32
SNX52.86E-350.2634350.0640.3745.71E-32
IFNGR17.66E-35-0.503210.080.711.53E-31
TNF1.10E-34-0.370260.0110.3022.19E-31
CD558.11E-340.5915130.0710.3781.62E-30
IQGAP11.67E-2100.5978350.0910.9853.35E-207
ZFHX39.08E-34-0.444220.0060.3361.82E-30
TUBB4B6.80E-330.2662440.0990.3821.36E-29
ANP32B7.06E-150.598770.2210.4851.41E-11
IER22.68E-32-0.402160.1440.7715.35E-29
ICAM12.87E-32-0.336940.0210.3555.74E-29
LYL17.67E-32-0.336120.0150.3821.53E-28
TAGLN21.05E-1000.6002710.18712.10E-97
GIMAP42.09E-310.314960.1520.7824.18E-28
FERMT34.32E-310.5418150.0790.3858.65E-28
KLF46.25E-31-0.44730.0320.3631.25E-27
CTHRC13.51E-300.3645470.0280.1877.03E-27
CDK2AP22.02E-900.6069570.0880.2674.05E-87
CYBB8.45E-30-0.493430.1010.711.69E-26
FXYD55.69E-630.6076640.2350.9891.14E-59
HSPA61.20E-28-0.296130.0130.3282.39E-25
BTG21.78E-28-0.534560.0750.6873.57E-25
IRF11.10E-100.6094840.0990.4622.20E-07
BUB34.82E-280.5010250.1280.4019.63E-25
FOLR21.41E-27-0.297860.0470.6562.83E-24
PCSK73.68E-300.6106070.0890.3897.36E-27
RAD216.62E-270.4377490.0780.3971.32E-23
TAGAP6.96E-27-0.530860.0490.6531.39E-23
MAP1B1.12E-26-0.5370.0010.3932.23E-23
HOPX9.22E-2250.615980.0870.9921.84E-221
AIF13.98E-26-0.409660.330.957.97E-23
BATF1.50E-1830.6362630.11513.01E-180
RPL176.62E-260.4639580.97611.32E-22
RGCC5.00E-490.6369310.0720.3441.00E-45
NAAA1.18E-240.2982520.0310.3972.37E-21
ANXA17.98E-1650.6484310.1250.9961.60E-161
PTPN74.65E-2160.6590710.09519.30E-213
NRP21.84E-24-0.461820.0090.3893.68E-21
MAD2L12.18E-240.378010.0530.6534.37E-21
LITAF2.26E-240.5833090.1410.4164.51E-21
PTPRC7.69E-240.4871120.34711.54E-20
EPSTI11.34E-230.4933130.0610.4052.68E-20
GNB42.20E-23-0.256010.0120.4014.41E-20
FTH14.54E-23-0.559990.9470.9899.07E-20
PLIN27.14E-230.324260.0750.4081.43E-19
C3AR11.06E-22-0.479540.0450.6412.11E-19
TLR101.29E-22-0.524540.0090.4012.57E-19
RPLP11.49E-220.384943112.99E-19
FCER1G2.11E-22-0.517610.2070.7944.22E-19
DEK4.60E-140.6594720.180.4739.20E-11
JUN3.18E-22-0.447570.1870.7796.35E-19
ANP32E1.79E-190.6684330.110.4273.57E-16
TUBA1A6.62E-220.2998920.0620.6561.32E-18
C1orf611.19E-21-0.446430.0030.4052.37E-18
EIF1B1.31E-210.3100690.0970.4162.62E-18
ADAM282.16E-21-0.552650.0130.5534.33E-18
HIST1H4C2.89E-570.6742570.1830.3215.78E-54
ANXA62.22E-890.6769740.1070.2634.45E-86
EMP31.76E-940.6804610.19413.51E-91
TYROBP2.31E-20-0.523580.3720.954.63E-17
GADD45B7.92E-20-0.534120.1050.6871.58E-16
RPS27L1.20E-190.5794240.2660.872.41E-16
SERPINB91.25E-190.257490.0790.422.50E-16
RPL54.60E-190.6865090.8830.9549.20E-16
HLA-DPB19.81E-100.6874840.64611.96E-06
CTNNB12.71E-540.6918420.25715.42E-51
DDIT47.56E-800.6973450.2090.9921.51E-76
AHNAK1.02E-180.5270360.0430.2332.04E-15
C16orf741.11E-180.2781820.020.1342.21E-15
LILRB41.21E-18-0.373150.0410.6262.42E-15
PLD31.80E-18-0.283250.080.663.60E-15
ITGB22.29E-180.2724460.2490.8364.59E-15
MTHFD21.07E-090.6981480.1170.4732.13E-06
STMN12.49E-080.7005050.2670.7984.97E-05
TM6SF12.15E-17-0.365960.0060.4124.31E-14
RPS23.32E-170.3736970.9916.64E-14
IFITM21.03E-460.7251210.27612.06E-43
C28.03E-17-0.569340.0230.4121.61E-13
CD8A8.91E-2130.7260320.09711.78E-209
GMFG1.86E-160.4045560.38513.73E-13
KLRD18.05E-1600.7430470.13111.61E-156
GYG13.68E-160.4120840.070.6457.36E-13
GNG75.68E-16-0.447680.0020.4121.14E-12
RGS106.61E-160.4660950.1910.7631.32E-12
CEBPD6.88E-16-0.343490.0440.4241.38E-12
ZFP368.09E-16-0.336520.210.7711.62E-12
MFNG8.61E-160.3011420.050.4271.72E-12
IER31.34E-15-0.534460.1190.6832.68E-12
RPLP01.53E-150.5199620.9413.05E-12
FTL1.77E-15-0.515630.96713.55E-12
RPS61.91E-150.3610880.99713.82E-12
HCST1.92E-080.7454330.42613.85E-05
TRAF3IP37.83E-1800.7623330.1020.1411.57E-176
LGALS39.74E-150.3564590.1290.451.95E-11
DUSP69.84E-15-0.578150.020.421.97E-11
LCP11.63E-600.7793130.2270.9733.26E-57
ARID5B1.01E-760.7861260.0840.8022.03E-73
EEF1D4.73E-120.7910440.5610.7679.45E-09
RPL36A3.25E-140.326220.92616.50E-11
NUCKS19.00E-110.798460.2010.51.80E-07
RPS261.03E-130.2772330.91612.05E-10
TXNIP2.09E-290.8218980.270.9244.19E-26
PTTG11.07E-1270.8464450.15912.14E-124
SDCBP1.79E-13-0.293240.1510.7023.58E-10
PFN12.23E-130.3065360.8850.9774.47E-10
TRIM593.39E-130.3489030.0370.1726.78E-10
IGSF65.97E-13-0.397530.0730.631.19E-09
S100A81.76E-140.8652350.1220.4393.52E-11
AC058791.12.37E-2100.869790.09914.74E-207
CD534.10E-120.2810580.2480.8028.20E-09
PRDM15.12E-660.9001150.23611.02E-62
LGALS14.56E-340.9087170.3570.4279.12E-31
S100A101.28E-700.9156610.22612.56E-67
MT-CO22.04E-110.305260.99214.08E-08
GAS62.86E-11-0.319590.0090.4355.72E-08
RUNX13.52E-11-0.374410.0160.4357.04E-08
TMEM1074.10E-11-0.289940.0440.4438.19E-08
FOXP13.02E-1550.9171820.1320.9966.05E-152
IL7R4.46E-1450.9320140.14318.91E-142
PDLIM19.17E-1680.9409590.12511.83E-164
SRI1.52E-100.412810.1710.4893.05E-07
TSC22D32.31E-100.2640040.3620.9084.62E-07
IPCEF12.33E-10-0.404880.0160.424.65E-07
IRF83.07E-10-0.373160.0460.5996.14E-07
ANXA53.39E-100.3127220.1610.4896.78E-07
SPINT23.42E-10-0.527030.0260.4436.83E-07
SERPINB14.42E-100.3652710.1050.4668.85E-07
SLC25A52.66E-120.9824280.4960.6985.33E-09
RAC22.29E-651.0552260.23614.59E-62
FXYD21.66E-421.0691180.2813.33E-39
TREM21.29E-09-0.318720.0050.5652.58E-06
SHTN11.69E-09-0.498650.0140.4433.38E-06
MT-ND31.81E-090.4731590.8590.9813.63E-06
S100A41.16E-131.1143010.67412.32E-10
LPAR63.65E-09-0.578930.0220.5767.30E-06
GAPDH3.66E-090.3608370.92717.32E-06
HMGB18.28E-181.1150850.6270.8241.66E-14
RPL22L18.43E-090.5387120.1630.6951.69E-05
PTGDS8.57E-971.2399140.1911.71E-93
ADORA31.48E-08-0.414220.0090.5462.97E-05
ITM2A7.52E-1031.2529470.18311.50E-99
TRBC21.28E-701.2911330.22612.56E-67
SLC7A77.95E-08-0.460960.0370.450.000159
MT-ND18.79E-080.5222670.8030.9770.000176
CMC12.43E-271.2956330.32314.85E-24
TRAC1.44E-071.3614530.40910.000287
MT-ND21.76E-070.3287040.93710.000352
CHCHD102.50E-070.3354930.2020.5230.000499
CD79A1.88E-761.4527710.21813.77E-73
SLC16A32.83E-07-0.432180.0290.5650.000566
EGR13.64E-07-0.381360.0440.5840.000727
H1F04.30E-07-0.430140.0040.450.00086
RPL7A4.81E-070.3857610.8760.9890.000962
EVI2B7.48E-070.3631810.1330.4960.001497
C1orf1311.26E-060.3483450.0480.5840.002514
SKA21.53E-060.3198020.0780.6070.003054
VIM1.55E-060.5484180.4270.9690.003102
TRBC14.84E-721.4892430.22419.68E-69
LTB4.01E-261.8231510.3150.9858.02E-23
RGS191.31E-05-0.438090.0570.5840.026262
GPR1831.42E-05-0.403390.0990.4890.028442
LGMN2.37E-05-0.485990.0380.5690.047404
NPL2.52E-05-0.521440.0260.4660.050337
TMIGD32.77E-05-0.486110.0120.4620.055331
HSPA1B2.81E-05-1.250590.0550.5760.0563
S100A112.92E-05-0.576630.3960.8210.058485
NUPR12.95E-05-0.336960.030.4660.059012
CXCR43.23E-051.0935250.61310.064537
PDK43.35E-05-1.076950.0120.550.066911
BHLHE414.36E-05-0.849450.0130.550.087294
HLA-DRB15.41E-05-0.350410.74210.108272
HMGN25.57E-050.5422790.3420.8470.111311
DUT6.89E-050.5927440.1790.5340.137793
SLCO2B18.59E-05-0.314480.0140.450.171702
LPAR50.000128-0.484520.0060.4620.255606
NR4A10.000144-0.288510.0160.4620.287745
RHOB0.000148-0.848360.0170.550.295977
CSF2RA0.000244-0.558980.0250.550.487616
SPI10.000246-0.296570.0690.5840.49193
HSPB10.000269-0.283550.1390.5110.537404
CEBPB0.000327-0.324160.0640.580.654669
HNRNPH10.000330.7607910.46810.659242
INPP5D0.00035-0.433560.0320.5570.699692
GPR340.000359-0.448020.0150.5460.717113
HMGB20.0003591.3111710.4510.9960.71738
ITM2C0.000378-0.381230.0210.550.756766
PIK3IP10.0003810.2792220.0740.4920.762633
LDHA0.0004080.5594440.3030.6180.816764
FCGR3A0.000416-0.47450.0630.5760.83165
TBXAS10.000554-0.426830.0220.4661
OLR10.000763-0.621950.0080.4581
CCL50.0007970.4372490.63411
TUBA1B0.000840.7794230.5490.8821
APOC10.001233-0.629580.3030.7211
ZFP36L20.0015380.3371450.3890.8591
FOSB0.001875-0.550430.0580.5691
CSF10.002307-0.578720.0050.4661
C12orf450.0028070.3373510.0480.4891
LTC4S0.003435-0.721420.020.5421
SERPINF10.003714-0.303050.0520.5611
CYBA0.0038450.5256430.5430.9011
HTRA10.003884-0.656280.0030.5311
CD630.0041530.364580.3210.7751
ARHGDIB0.0044820.2507190.5310.8211
TPI10.0056050.7090650.4520.791
EZR0.0060220.3204870.1160.5231
NFKBID0.008314-0.313970.0280.5421
HNRNPA2B10.0089750.6376670.4320.7521
COTL10.0114320.8083020.4860.8281
KLF20.011787-0.354170.0390.551
FCGR2A0.011833-0.332680.0210.5341
NBL10.012004-0.624580.0060.4771
PPT10.0169390.3340680.0750.5731
TUBB0.0255571.0605030.4060.9051
NCL0.0296920.7604660.3060.7671
CLIC10.0297480.7379110.4110.7521
TLR40.030913-0.502830.0110.4811
CSRNP10.032545-0.36150.0150.4731
EEF1B20.0354990.4078610.6640.9241
VAMP80.0396280.3117650.2670.6261
LAT20.054515-0.484670.0280.4921
PHACTR10.057219-0.807590.0370.4961
IFI160.0652280.6081250.2190.6031
CYSLTR10.078568-0.62720.0190.5191
ELF10.0908470.3264170.1350.6031
HLA-E0.0960290.4423110.4390.8971
REST0.1274540.2934890.0420.5041
MAF0.169999-0.313420.0140.4921
CXCL160.173623-0.561870.0310.4961
MKNK10.204109-0.457560.0290.51
CH25H0.216392-1.136840.0030.4541
DBI0.2191910.5663690.3150.6791
HBA20.272516-1.852340.0010.5111
FPR10.279672-0.473130.0150.5191
HLA-DPA10.3587350.5337640.55611
ADAP20.392723-0.430150.0130.4731
PLTP0.41267-0.379780.0420.5311
HSP90AB10.4624180.4494220.5250.8931
ARL6IP10.4853810.3026490.1660.6071
RNASE60.514019-0.267250.0520.5341
LAPTM50.5185580.5914890.450.8591
GNG50.5201910.6042910.2210.6221
PKM0.5206070.5947870.3340.7441
MYL12A0.5656110.6570660.5611
CD370.5743150.4246050.3710.7821
CD860.630939-0.435940.0260.5081
HLA-DRA0.658756-0.326530.88711
CD990.6895830.337580.2370.6411
SNRPB0.6968120.3718370.2590.6531
PLA2G70.739163-0.683840.0240.5111
S100A60.7611511.5239160.50911
CORO1A0.7685980.383420.58711
CRIP10.8209231.6019830.49711
CITED20.841241-0.851630.0680.5311
HIST1H1C0.892343-0.442280.0260.5111
NME20.9838610.7036670.53211
The differential expression analysis of macrophage between cancer and normal samples. (A) The expression distribution of the macrophage cell marker gene -- LYZ in all cells. (B) The scatter plot of genes selected five significant DEGs in the macrophage cell population between cancer and normal samples. (C) The heatmap of DEGs of macrophage. (D, E) The GO categories of down-regulated genes (green) and up-regulated genes (red) in PCNSL samples compared with normal samples. The DEGs between cancer and normal samples in macrophage cell subpopulation.

Discussion

CNS tissue is the key site of PCNSL pathogenesis and progression which consists of diverse types of cells. Understanding the molecular phenotype of each cell type to the PCNSL is an important step to reveal the pathogenesis of PCNSL. The availability of scRNA-seq data allows us to perform molecular and functional heterogeneity analysis for different cells in the PCNSL tissue. In this study, we investigated CNS tissues at a single-cell resolution using gene expression profiling. We mapped the immune microenvironment of PCNSL and identified the cellular markers and functional signals of immune cells such as B cells, macrophages, and dendritic cells in PCNSL, which provided a partial basis for guiding the precise treatment of PCNSL. Abnormal proliferation and differentiation of B cells are directly related to the pathological basis of PCNSL, and scRNA-seq enables us to directly parse the heterogeneity of B cells from single-cell resolution. We identified that B cells can be divided into three subgroups with completely different gene expression and function, including a small number of terminally differentiated plasma cells (IGHG1, IGHG3, IGHG4). We identified that B cells can be divided into three subgroups with completely different gene expression and function, including a small number of terminally differentiated plasma cells. In addition to the subsets of normal B cells (B cell-1 overexpressed CD79A and CD79B), B cell-2 showed an important role in antigen processing and regulating the cytotoxicity of T cells, and its function was similar to that of dendritic cells, which may be related to the abnormal differentiation of B cells in PCNSL. This also indicated from the molecular phenotype that B cells in PCNSL were in the pathological overactivation state. Cell–cell communication between B cells and other immune cells is also an important mechanism for the progression of PCNSL disease (12, 15, 33). Surprisingly, we found that CD74 might be a key target to regulate the communication between B cells and the other three types of immune cells (T cell, macrophage, dendritic cell) and CD74–MIF was identified to be the most significant interaction. The macrophage migration inhibitory factor (MIF) receptor CD74 is overexpressed in various neoplasms, mainly in hematologic tumors, and currently investigated in clinical studies. Zeiner et al. summarized that the MIF–CD74A interaction is restricted to macrophages, associated with survival in glioma (34). CD74 is quickly internalized and recycles after antibody binding; therefore, it constitutes an attractive target for antibody-based treatment strategies. CD74 has been further described as one of the most upregulated molecules in human glioblastomas. They also pointed that CD74 expression in human gliomas is restricted to GAMs and positively associated with patient survival. Moreover, CD74 represents a positive prognostic marker most probably because of its association with an M1-polarized immune milieu in high-grade gliomas. Our study strongly suggests that CD74 of B cells in PCNSL may be an important cellular and molecular pathological basis for its coordination and disturbance with other immune cells to induce the disorder of the immune microenvironment. This may provide an important basis for targeted therapy of PCNSL. However, there are still limitations in the above conclusions, namely, that malignant and non-malignant B cells in PCNSL have not been identified and distinguished. These analyses are mainly based on the mixed population of B cells in PCNSL. In this regard, the separated malignant and non-malignant B cells from PCNSL tumors should be accurately analyzed for the tumor cell heterogeneity of PCNSL in the future. In addition, we also demonstrated that immune cells would be affected by the tumor microenvironment. Heterogeneities were also observed in these cell types. We identified T cell, T helper cell, NKT cell, and MPC subtypes within T cell populations that possessed different ontologies. Moreover, we also identified three subtypes within the dendritic cells, including conventional dendritic cell, myeloid dendritic cell, and plasmacytoid dendritic cell. They also enriched into NF-κB signaling, antigen processing and presentation, and neutrophil degranulation pathways, respectively. Further studies on the immune regulation mechanism of these subtypes of T cells and dendritic cells might provide a new strategy for the treatment of PCNSL. In conclusion, our single-cell analysis enabled us to study the cellular heterogeneity and gene expression characteristics of PCNSL at a single-cell resolution and whole-transcriptome scale. Our study is the first to analyze the cellular and molecular pathological map at single-cell resolution. Despite the small size of single-cell samples in our study, it reveals novel potential for targeted immunotherapy of PCNSL.

Data Availability Statement

The raw and processed data supporting the findings in this study are available from the corresponding author, and has been submitted to the Gene Expression Omnibus (GEO) database under the accession code GSE181304 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181304).

Ethics Statement

Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author Contributions

Conceptualization, B.Y.W. and Z.L.; Methodology, B.Y.W. and Z.L.; Validation, Y.F. and G.C.; Formal Analysis, B.Y.W. and Z.L.; Resources, B.Y.W., S.W.W., C.D., W.R. and F.Y.; Data Curation, Y.F.; Visualization, Y.F.; Writing-Original Draft, B.Y.W., Z. L. and Y.F.; Writing-Review & Editing, B.Y.W., Z.L, and J.N.Z.; Supervision, G.C., and J.N.Z. All authors contributed to the article and approved the submitted version.

Funding

This study was funded by the Innovation Cultivation Fund of the Sixth Medical Center of PLA General Hospital (CXPY201913) and the Youth Science Foundation Project of the National Natural Science Foundation of China (81801165).

Conflict of Interest

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.

Publisher’s Note

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. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
  33 in total

1.  SHP-1 expression in primary central nervous system B-cell lymphomas in immunocompetent patients reflects maturation stage of normal B cell counterparts.

Authors:  Yasuo Sugita; Osamu Tokunaga; Akihiko Nakashima; Minoru Shigemori
Journal:  Pathol Int       Date:  2004-09       Impact factor: 2.534

2.  Primary diffuse large B-cell lymphomas of central nervous system exhibit remarkably high prevalence of oncogenic MYD88 and CD79B mutations.

Authors:  So Yamada; Yasuo Ishida; Akira Matsuno; Kazuto Yamazaki
Journal:  Leuk Lymphoma       Date:  2015-01-14

3.  Frequency of MYD88 and CD79B mutations, and MGMT methylation in primary central nervous system diffuse large B-cell lymphoma.

Authors:  Mei Zheng; Anamarija M Perry; Philip Bierman; Fausto Loberiza; Michel R Nasr; David Szwajcer; Marc R Del Bigio; Lynette M Smith; Weiwei Zhang; Timothy C Greiner
Journal:  Neuropathology       Date:  2017-08-30       Impact factor: 1.906

4.  Recurrent mutations of CD79B and MYD88 are the hallmark of primary central nervous system lymphomas.

Authors:  T Nakamura; K Tateishi; T Niwa; Y Matsushita; K Tamura; M Kinoshita; K Tanaka; S Fukushima; H Takami; H Arita; A Kubo; T Shuto; M Ohno; Y Miyakita; S Kocialkowski; T Sasayama; N Hashimoto; T Maehara; S Shibui; T Ushijima; N Kawahara; Y Narita; K Ichimura
Journal:  Neuropathol Appl Neurobiol       Date:  2015-07-20       Impact factor: 8.090

5.  V(H) gene sequences from primary central nervous system lymphomas indicate derivation from highly mutated germinal center B cells with ongoing mutational activity.

Authors:  A R Thompsett; D W Ellison; F K Stevenson; D Zhu
Journal:  Blood       Date:  1999-09-01       Impact factor: 22.113

6.  A medical research council randomized trial in patients with primary cerebral non-Hodgkin lymphoma: cerebral radiotherapy with and without cyclophosphamide, doxorubicin, vincristine, and prednisone chemotherapy.

Authors:  G M Mead; N M Bleehen; A Gregor; J Bullimore; D Shirley; R P Rampling; J Trevor; M G Glaser; P Lantos; J W Ironside; T H Moss; M Brada; J B Whaley; S P Stenning
Journal:  Cancer       Date:  2000-09-15       Impact factor: 6.860

7.  Long-term follow-up of high-dose methotrexate-based therapy with and without whole brain irradiation for newly diagnosed primary CNS lymphoma.

Authors:  Igor T Gavrilovic; Adília Hormigo; Joachim Yahalom; Lisa M DeAngelis; Lauren E Abrey
Journal:  J Clin Oncol       Date:  2006-10-01       Impact factor: 44.544

8.  Immunohistochemical profile and prognostic significance in primary central nervous system lymphoma: Analysis of 89 cases.

Authors:  Jing Liu; Yaming Wang; Yuantao Liu; Zhe Liu; Qu Cui; Nan Ji; Shengjun Sun; Bingxu Wang; Yajie Wang; Xuefei Sun; Yuanbo Liu
Journal:  Oncol Lett       Date:  2017-09-06       Impact factor: 2.967

9.  Current and emerging pharmacotherapies for primary CNS lymphoma.

Authors:  Prathima Prodduturi; Philip J Bierman
Journal:  Clin Med Insights Oncol       Date:  2012-05-21

10.  Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression.

Authors:  Christoph Hafemeister; Rahul Satija
Journal:  Genome Biol       Date:  2019-12-23       Impact factor: 13.583

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.