PRMT5 Inhibition Promotes PD-L1 Expression and Immuno-Resistance in Lung Cancer

Protein arginine transferase 5 (PRMT5) has been implicated as an important modulator of tumorigenesis as it promotes tumor cell proliferation, invasion, and metastasis. Studies have largely focused on PRMT5 regulating intrinsic changes in tumors; however, the effects of PRMT5 on the tumor microenvironment and particularly immune cells are largely unknown. Here we found that targeting PRMT5 by genetic or pharmacological inhibition reduced lung tumor progression in immunocompromised mice; however, the effects were weakened in immunocompetent mice. PRMT5 inhibition not only decreased tumor cell survival but also increased the tumor cell expression of CD274 in vitro and in vivo, which activated the PD1/PD-L1 axis and eliminated CD8+T cell antitumor immunity. Mechanistically, PRMT5 regulated CD274 gene expression through symmetric dimethylation of histone H4R3, increased deposition of H3R4me2s on CD274 promoter loci, and inhibition of CD274 gene expression. Targeting PRMT5 reduced this inhibitory effect and promoted CD274 expression in lung cancer. However, PRMT5 inhibitors represent a double-edged sword as they may selectively kill cancer cells but may also disrupt the antitumor immune response. The combination of PRMT5 inhibition and ani-PD-L1 therapy resulted in an increase in the number and enhanced the function of tumor-infiltrating T cells. Our findings address an unmet clinical need in which combining PRMT5 inhibition with anti-PD-L1 therapy could be a promising strategy for lung cancer treatment.

CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

Innovations in the Insect Cell Expression System for Industrial Recombinant Vaccine Antigen Production

The insect cell expression system has previously been proposed as the preferred biosecurity strategy for production of any vaccine, particularly for future influenza pandemic vaccines. The development and regulatory risk for new vaccine candidates is shortened as the platform is already in use for the manufacturing of the FDA-licensed seasonal recombinant influenza vaccine Flublok®. Large-scale production capacity is in place and could be used to produce other antigens as well. However, as demonstrated by the 2019 SARS-CoV-2 pandemic the insect cell expression system has limitations that need to be addressed to ensure that recombinant antigens will indeed play a role in combating future pandemics. The greatest challenge may be the ability to produce an adequate quantity of purified antigen in an accelerated manner. This review summarizes recent innovations in technology areas important for enhancing recombinant-protein production levels and shortening development timelines. Opportunities for increasing product concentrations through vector development, cell line engineering, or bioprocessing and for shortening timelines through standardization of manufacturing processes will be presented.

R code and downstream analysis objects for the scRNA-seq atlas of human breast spanning normal, preneoplastic and tumorigenic states

Breast cancer is a common and highly heterogeneous disease. Understanding the cellular diversity in the mammary gland and its surrounding micro-environment across different states can provide insight into the cancer development in human breast. Recently, a large-scale single-cell RNA expression atlas was constructed of the human breast spanning normal, preneoplastic and tumorigenic states. Single-cell expression profiles of nearly 430,000 cells were obtained from 69 distinct surgical tissue specimens from 55 patients. This article extends the study by providing downstream processed R data objects, complete cell annotation and R code to reproduce all the analyses. Details of all the bioinformatic analyses that produced the results described in the study are provided.

Asthma reduces glioma formation by T cell decorin-mediated inhibition of microglia

To elucidate the mechanisms underlying the reduced incidence of brain tumors in children with Neurofibromatosis type 1 (NF1) and asthma, we leverage Nf1 optic pathway glioma (Nf1OPG) mice, human and mouse RNAseq data, and two different experimental asthma models. Following ovalbumin or house dust mite asthma induction at 4–6 weeks of age (WOA), Nf1OPG mouse optic nerve volumes and proliferation are decreased at 12 and 24 WOA, indicating no tumor development. This inhibition is accompanied by reduced expression of the microglia-produced optic glioma mitogen, Ccl5. Human and murine T cell transcriptome analyses reveal that inhibition of microglia Ccl5 production results from increased T cell expression of decorin, which blocks Ccl4-mediated microglia Ccl5 expression through reduced microglia NFκB signaling. Decorin or NFκB inhibitor treatment of Nf1OPG mice at 4–6 WOA inhibits tumor formation at 12 WOA, thus establishing a potential mechanistic etiology for the attenuated glioma incidence observed in children with asthma.

Stromal Cells Promote Matrix Deposition, Remodelling and an Immunosuppressive Tumour Microenvironment in a 3D Model of Colon Cancer

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths worldwide. CRC develops in a complex tumour microenvironment (TME) with both mesenchymal stromal cells (MSCs) and immune infiltrate, shown to alter disease progression and treatment response. We hypothesised that an accessible, affordable model of CRC that combines multiple cell types will improve research translation to the clinic and enable the identification of novel therapeutic targets. A viable gelatine-methacrloyl-based hydrogel culture system that incorporates CRC cells with MSCs and a monocyte cell line was developed. Gels were analysed on day 10 by PCR, cytokine array, microscopy and flow cytometry. The addition of stromal cells increased transcription of matrix remodelling proteins FN1 and MMP9, induced release of tumour-promoting immune molecules MIF, Serpin E1, CXCL1, IL-8 and CXCL12 and altered cancer cell expression of immunotherapeutic targets EGFR, CD47 and PD-L1. Treatment with PD153035, an EGFR inhibitor, revealed altered CRC expression of PD-L1 but only in gels lacking MSCs. We established a viable 3D model of CRC that combined cancer cells, MSCs and monocytic cells that can be used to research the role the stroma plays in the TME, identify novel therapeutic targets and improve the transitional efficacy of therapies.

ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion

Background With single-cell RNA sequencing (scRNA-seq) methods, gene expression patterns at the single-cell resolution can be revealed. But as impacted by current technical defects, dropout events in scRNA-seq lead to missing data and noise in the gene-cell expression matrix and adversely affect downstream analyses. Accordingly, the true gene expression level should be recovered before the downstream analysis is carried out. Results In this paper, a novel low-rank tensor completion-based method, termed as scLRTC, is proposed to impute the dropout entries of a given scRNA-seq expression. It initially exploits the similarity of single cells to build a third-order low-rank tensor and employs the tensor decomposition to denoise the data. Subsequently, it reconstructs the cell expression by adopting the low-rank tensor completion algorithm, which can restore the gene-to-gene and cell-to-cell correlations. ScLRTC is compared with other state-of-the-art methods on simulated datasets and real scRNA-seq datasets with different data sizes. Specific to simulated datasets, scLRTC outperforms other methods in imputing the dropouts closest to the original expression values, which is assessed by both the sum of squared error (SSE) and Pearson correlation coefficient (PCC). In terms of real datasets, scLRTC achieves the most accurate cell classification results in spite of the choice of different clustering methods (e.g., SC3 or t-SNE followed by K-means), which is evaluated by using adjusted rand index (ARI) and normalized mutual information (NMI). Lastly, scLRTC is demonstrated to be also effective in cell visualization and in inferring cell lineage trajectories. Conclusions a novel low-rank tensor completion-based method scLRTC gave imputation results better than the state-of-the-art tools. Source code of scLRTC can be accessed at https://github.com/jianghuaijie/scLRTC.

Sincast: a computational framework to predict cell identities in single cell transcriptomes using bulk atlases as references

Characterizing the molecular identity of a cell is an essential step in single cell RNA-sequencing (scRNA-seq) data analysis. Numerous tools exist for predicting cell identity using single cell reference atlases. However, many challenges remain, including correcting for inherent batch effects between reference and query data and insufficient phenotype data from the reference. One solution is to project single cell data onto established bulk reference atlases to leverage their rich phenotype information. Sincast is a computational framework to query scRNA-seq data based on bulk reference atlases. Prior to projection, single cell data are transformed to be directly comparable to bulk data, either with pseudo-bulk aggregation or graph-based imputation to address sparse single cell expression profiles. Sincast avoids batch effect correction, and cell identity is predicted along a continuum to highlight new cell states not found in the reference atlas. In several case study scenarios, we show that Sincast projects single cells into the correct biological niches in the expression space of the bulk reference atlas. We demonstrate the effectiveness of our imputation approach that was specifically developed for querying scRNA-seq data based on bulk reference atlases. We show that Sincast is an efficient and powerful tool for single cell profiling that will facilitate downstream analysis of scRNA-seq data.

Correlation between CD44+ cancer stem cell expression and histopathological types of nasopharyngeal carcinoma

Background: Nasopharyngeal carcinoma (NPC) recurrency rate is still high despite patients receiving complete treatment. The response to treatment may vary depending on the type of histopathology and Epstein-Barr virus, however the mechanism remains unclear. Recent studies have found that there is a relationship between response to treatment and the presence of cancer stem cells (CSCs). CD44+ cancer stem cells may cause cancer cells to be resistant to treatment. Therefore, this cross-sectional study aims to determine the correlation between CD44 + cancer stem cell expression and the histopathological types of NPC. Method: Samples were obtained from NPC biopsies of type I, II, III patients (based on WHO histopathology criteria), who had not received prior treatment. CD44+ expression was examined using immunohistochemistry methods by staining CD44+ monoclonal antibodies. The degree of CD44+ cell membrane expression was based on the immunoreactive score scale or the Remmele index scale. Results: Most histopathological types were WHO type III (21 patients, 50%), followed by type II (18 patients, 42.86%), and type I (3 patients, 7.14%). CD44 + expression on type I showed one patient had moderate positive and two patients had a high-positive expression. In type II, 10 were moderate positive and eight were high-positive. In type III, one patient was low-positive, 11 were moderate positive and nine patients were high-positive. Statistical analysis showed that the CD44+ expression difference between the three histopathology types were not statistically significant. Conclusion: There were no correlations between CD44 + expression and histopathological type of NPC.