A theoretical framework of immune cell phenotypic classification and discovery

Cell Population-resolved Multiomics Atlas of the Developing Lung

The lung is a vital organ that undergoes extensive morphological and functional changes during postnatal development. To disambiguate how different cell populations contribute to organ development, we performed proteomic and transcriptomic analyses of four sorted cell populations from the lung of human subjects 0-8 years of age with a focus on early life. The cell populations analyzed included epithelial, endothelial, mesenchymal, and immune cells. Our results revealed distinct molecular signatures for each of the sorted cell populations that enable the description of molecular shifts occurring in these populations during postnatal development. We confirmed that the proteome of the different cell populations was distinct regardless of age and identified functions specific to each population. We identified a series of cell population protein markers, including those located at the cell surface, that show differential expression and distribution on RNA in situ hybridization and immunofluorescence imaging. We validated the spatial distribution of alveolar type 1 and endothelial cell surface markers. Temporal analyses of the proteomes of the four populations revealed processes modulated during postnatal development and clarified the findings obtained from whole-tissue proteome studies. Finally, the proteome was compared with a transcriptomics survey performed on the same lung samples to evaluate processes under post-transcriptional control.

Analysis of mutually exclusive expression in cancer cells identifies a previously unknown intergenic regulatory paradigm

Mutual exclusion of gene expression has received limited attention. Gene (expression) plasticity analysis provides an efficient way to identify highly plastic genes (HPGs) based on changes in expression rank. In this study, we quantitatively measured the expression plasticity of 19 961 protein-coding genes in 24 human cancer cell lines and identified HPGs in these cells. By comparing methods, we showed that virtual sorting and cosine similarity, rather than Pearson and Spearman rank correlations, are suitable for mutual exclusion. Mutually exclusive gene pairs were identified in each cell type. Experimental validation showed that thiol methyltransferase 1B (TMT1B; also known as METTL7B) and CD274 molecule (CD274; also known as PD-L1) were mutually exclusively expressed at either the mRNA or protein level. METTL7B negatively regulated PD-L1 expression in several cell types, and the JAK/STAT3 pathway was involved. Knockdown of METTL7B in Huh7 cells inhibited interleukin 2 (IL-2) secretion by Jurkat cells in co-culture experiments, and the inhibition was blocked by anti-PD-L1 antibodies. Therefore, this study provides an efficient method of expressional mutual exclusion and implies a newly identified intergenic regulatory paradigm.

Vanin-2 is expressed in peripheral blood T cells and upregulated in patients with systemic lupus erythematosus

Members of the vanin gene family include VNN1, VNN2, and VNN3 in humans. Although the functions of vanins have been widely examined in myeloid cells, their expression and functions have not been clarified in T lymphocytes. This study aimed to elucidate the significance of Vanin-2 (VNN2) on human peripheral blood T lymphocytes and study its expression in systemic lupus erythematosus (SLE). The differential expression of Vanins was analyzed by bioinformatics. VNN2 expressions in peripheral blood T-cell subsets were analyzed by single-cell RNA sequencing data and flow cytometry. Changes of VNN2 expression before and after T-cell activation were further clarified by western blot. The function of VNN2+ cells was studied by granzyme B (GZMB) and perforin detection. Changes in VNN2+ proportions in T-cell subsets of patients with SLE were further analyzed. In the present study, only VNN2 among vanins showed distinguishable expression in T cells. VNN2+ percentages were higher in CD8+ T cells those in CD4+ T cells. VNN2+ T cells were with a higher memory T-cell composition. VNN2 expression was significantly increased after T-cell stimulation. VNN2+ T cells had higher levels of GZMB and perforin secretion than VNN2- T cells. Clinically, VNN2+ percentages in T cells of patients with SLE were upregulated. Together, these data suggested that VNN2 is expressed in peripheral blood T cells characterized more GZMB and perforin secretion, and increased VNN2+ T cells in patients with SLE could reflect altered T-cell functions in vivo.

KLRB1 defines an activated phenotype of CD4+ T cells and shows significant upregulation in patients with primary Sjögren's syndrome

OBJECTIVE

This study aimed to investigate the role of KLRB1 (CD161) in human CD4+ T cells and elucidate its significance in primary Sjögren's syndrome (pSS).

METHODS

Peripheral blood samples from 37 healthy controls and 44 pSS patients were collected. The publicly available single-cell RNA-Seq data from pSS patient PBMCs were utilized to analyse KLRB1 expression in T cells. KLRB1-expressing T lymphocyte subset proportions in pSS patients and healthy controls were determined by flow cytometry. CD25, Ki-67, cytokine secretion, and chemokine receptor expression in CD4+ KLRB1+ T cells were detected and compared with those in CD4+ KLRB1- T cells. Correlation analysis was conducted between KLRB1-related T-cell subsets and clinical indicators. ROC curves were generated to explore the diagnostic potential of KLRB1 for pSS.

RESULTS

KLRB1 was significantly upregulated following T-cell activation, and Ki-67 and CD25 expression was significantly greater in CD4+ KLRB1+ T cells than in CD4+ KLRB1- T cells. KLRB1+ CD4+ T cells exhibited greater IL-17A, IL-21, IL-22, and IFN-γ secretion upon stimulation, and there were significantly greater proportions of CCR5+, CCR2+, CX3CR1+, CCR6+, and CXCR3+ cells among CD4+ KLRB1+ T cells than among CD4+ KLRB1- T cells. Compared with that in HCs, KLRB1 expression in CD4+ T cells was markedly elevated in pSS patients and significantly correlated with clinical disease indicators.

CONCLUSION

KLRB1 is a characteristic molecule of the CD4+ T-cell activation phenotype. The increased expression of KLRB1 in the CD4+ T cells of pSS patients suggests its potential involvement in the pathogenesis of pSS and its utility as an auxiliary diagnostic marker for pSS.

FCRL3 expression is upregulated and closely correlates with TIGIT expression in regulatory T cells of patients with systemic lupus erythematosus

Using data from single-cell RNA sequencing and flow cytometry, we initially examined the expression of FCRL3, finding it to be elevated and positively associated with TIGIT expression in the regulatory T cells of patients with systemic lupus erythematosus. This also suggests that the co-expression of FCRL3 and TIGIT warrants further attention.

Upregulation of CX3CR1 expression in circulating T cells of systemic lupus erythematosus patients as a reflection of autoimmune status through characterization of cytotoxic capacity

OBJECTIVE

This study investigated CX3CR1 expression in human peripheral blood T lymphocytes and their subsets, exploring changes in SLE patients and its diagnostic potential.

METHODS

Peripheral blood samples from 31 healthy controls and 50 SLE patients were collected. RNA-Seq data from SLE patient PBMCs were used to analyze CX3CR1 expression in T cells. Flow cytometry determined CX3CR1-expressing T lymphocyte subset proportions in SLE patients and healthy controls. Subset composition and presence of GZMB, GPR56, and perforin in CX3CR1+ T lymphocytes were analyzed. T cell-clinical indicator correlations were assessed. ROC curves explored CX3CR1's diagnostic potential for SLE.

RESULTS

CX3CR1+CD8+ T cells exhibited higher GPR56, perforin, and GZMB expression than other T cell subsets. The proportion of CX3CR1+ was higher in TEMRA and lower in Tn and TCM. PMA activation reduced CX3CR1+ T cell proportions. Both RNA-Seq and flow cytometry revealed elevated CX3CR1+ T cell proportions in SLE patients. Significantly lower perforin+ and GPR56+ proportions were observed in CX3CR1+CD8+ T cells in SLE patients. CX3CR1+ T cells correlated with clinical indicators.

CONCLUSION

CX3CR1+ T cells display cytotoxic features, with heightened expression in CD8+ T cells, particularly in adult SLE patients. Increased CX3CR1 expression in SLE patient T cells suggests its potential as an adjunctive diagnostic marker for SLE.

Identification of rat Vstm1 with conservative anti-inflammatory effect between rat and human homologs

Human VSTM1 (also known as SIRL1) is an inhibitory immune checkpoint receptor involved in leukocyte activation. Identification of the homologous genes in other species, such as mice and rats, will undoubtedly contribute to functional studies and clinical applications. Here, we successfully cloned the Vstm1 gene in rats, as supported by high-throughput sequencing data. However, Vstm1 is degenerated to a pseudogene in the mouse genome. Rat Vstm1 mRNA contains a complete open reading frame (ORF) of 630 nucleotides encoding 209 amino acids. Rat Vstm1 is highly expressed in bone marrow, especially in granulocytes. The expression levels of Vstm1 gradually increase with the development of granulocytes in bone marrow but are downregulated in response to inflammatory stimuli. Rat VSTM1 does not have an immunoreceptor tyrosine-based inhibitory motif (ITIM), however, it shows a conservative function of inflammatory inhibition with human VSTM1, and both are anti-correlated with many inflammatory cytokines, such as IL-1α and TNF-α. In bone marrow-derived macrophages (BMDMs), either rat or human VSTM1 suppressed the secretion of inflammatory cytokines in response to LPS stimulation. Further analysis in lung cancer microenvironment revealed that VSTM1 is mainly expressed in myeloid cells, anti-correlated with inflammatory cytokines and associated with tumor development and metastasis.

Omic horizon expression: a database of gene expression based on RNA sequencing data

BACKGROUND

Gene expression profiles have important significance for gene expression characteristics and further functional studies. More attention has been given to the expression databases in humans and mice, but less attention has been given to rats, while rat models also play an irreplaceable role in biomedical experiments.

RESULTS

To depict the rat gene expression profiles in mRNA expression levels, we analyzed over 2,700 RNA sequencing (RNA-Seq) samples from 48 tissues, 40 primary cell types and 25 cell lines; and then mapped them to the latest version of the rat genome reference, mRatBN7.2. Based on these datasets and reanalysis, we constructed a new database, the Omic Horizon Expression Database ( http://immudb.bjmu.edu.cn/expression.html ), which allows expressional profile query of over 25,000 rat genes based on non-redundant gene symbols. The database supports requests using gene symbols (or alias), Ensemble and Entrez gene IDs. Gene expression profiles can be queried in three categories: tissues, primary cells and cell lines. Application examples including expression profiling and comparison, as well as identification of novel rat genes, were illustrated to show the utility of the database.

CONCLUSIONS

As an omic resource, the Omic Horizon Expression Database provides horizons of gene expression profiles across various tissues and cells, which greatly facilitates the identification of rat genes as well as functional clues.

G Protein-Coupled Receptor 56 Characterizes CTLs and Reflects the Progression of Lung Cancer Patients

CTLs play important roles in host immune responses to tumors. CD4 CTLs are characterized by their ability to secrete cytotoxic effector molecules, such as granzyme B and perforin, and kill target cells in a MHC class II-restricted manner. However, the cell surface markers of CD4 CTLs remain unknown, which hinders their separation and research on their function. In this study, we performed a bioinformatics analysis and experimental validation that revealed that G protein-coupled receptor 56 (GPR56) is a cell surface marker that can be used to characterize CD4 CTLs. We found that GPR56 and granzyme B were coexpressed in extremely high levels in human peripheral blood T cells, and that anti-GPR56 stimulation significantly upregulated the expression of granzyme B in both CD4+GPR56+ and CD8+GPR56+ T cells. These findings suggest that GPR56 expression and the GPR56 signaling pathway could contribute directly to the toxic function of either CD4+ or CD8+ T cells. We also used GPR56 as a biomarker to investigate the clinical significance of CD4 CTLs. GPR56+ T cell levels were increased in patients with lung cancer, and GPR56 expression was significantly correlated with lung cancer progression. A further analysis revealed an increase in exhausted cell states in lung cancer patients because of upregulation of programmed cell death protein 1 expression in GPR56+ T cells. The findings of this study suggest that GPR56 characterizes the cytotoxic states of either CD4+ or CD8+ T cells.

Evaluating imputation methods for single-cell RNA-seq data

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) enables the high-throughput profiling of gene expression at the single-cell level. However, overwhelming dropouts within data may obscure meaningful biological signals. Various imputation methods have recently been developed to address this problem. Therefore, it is important to perform a systematic evaluation of different imputation algorithms.

RESULTS

In this study, we evaluated 11 of the most recent imputation methods on 12 real biological datasets from immunological studies and 4 simulated datasets. The performance of these methods was compared, based on numerical recovery, cell clustering and marker gene analysis. Most of the methods brought some benefits on numerical recovery. To some extent, the performance of imputation methods varied among protocols. In the cell clustering analysis, no method performed consistently well across all datasets. Some methods performed poorly on real datasets but excellent on simulated datasets. Surprisingly and importantly, some methods had a negative effect on cell clustering. In marker gene analysis, some methods identified potentially novel cell subsets. However, not all of the marker genes were successfully imputed in gene expression, suggesting that imputation challenges remain.

CONCLUSIONS

In summary, different imputation methods showed different effects on different datasets, suggesting that imputation may have dataset specificity. Our study reveals the benefits and limitations of various imputation methods and provides a data-driven guidance for scRNA-seq data analysis.