An immune cell map of human lung adenocarcinoma development reveals an anti-tumoral role of the Tfh-dependent tertiary lymphoid structure.

The immune responses during the initiation and invasion stages of human lung adenocarcinoma (LUAD) development are largely unknown. Here, we generated a single-cell RNA sequencing map to decipher the immune dynamics during human LUAD development. We found that T follicular helper (Tfh)-like cells, germinal center B cells, and dysfunctional CD8+ T cells increase during tumor initiation/invasion and form a tertiary lymphoid structure (TLS) inside the tumor. This TLS starts with an aggregation of CD4+ T cells and the generation of CXCL13-expressing Tfh-like cells, followed by an accumulation of B cells, and then forms a CD4+ T and B cell aggregate. TLS and its associated cells are correlated with better patient survival. Inhibiting TLS formation by Tfh or B cell depletion promotes tumor growth in mouse models. The anti-tumoral effect of the Tfh-dependent TLS is mediated through interleukin-21 (IL-21)-IL-21 receptor signaling. Our study establishes an anti-tumoral role of the Tfh-dependent TLS in the development of LUAD.

Gene Module Analysis Reveals Cell-Type Specificity and Potential Target Genes in Autism's Pathogenesis.

Multiple genetic factors contribute to the pathogenesis of autism spectrum disorder (ASD), a kind of neurodevelopmental disorder. Genes were usually studied separately for their associations with ASD. However, genes associated with ASD do not act alone but interact with each other in a network module. The identification of these modules is the basis for the systematic understanding of the pathogenesis of ASD. Moreover, ASD is characterized by highly pathogenic heterogeneity, and gene modules associated with ASD are cell-type-specific. In this study, based on the single-nucleus RNA sequencing data of 41 post-mortem tissue samples from the prefrontal cortex and anterior cingulate cortex of 19 ASD patients and 16 control individuals, we applied sparse module activity factorization, a matrix decomposition method consistent with the multi-factor and heterogeneous characteristics of ASD pathogenesis, to identify cell-type-specific gene modules. Then, statistical procedures were performed to detect highly reproducible cell-type-specific ASD-associated gene modules. Through the enrichment analysis of cell markers, 31 cell-type-specific gene modules related to ASD were further screened out. These 31 gene modules are all enriched with curated ASD risk genes. Finally, we utilized the expression patterns of these cell-type-specific ASD-associated gene modules to build predictive models for ASD. The excellent predictive performance also proved the associations between these gene modules and ASD. Our study confirmed the multifactorial and cell-type-specific characteristics of ASD pathogeneses. The results showed that excitatory neurons such as L2/3, L4, and L5/6-CC play essential roles in ASD's pathogenic processes. We identified the potential ASD target genes that act together in cell-type-specific modules, such as NRG3, KCNIP4, BAI3, PTPRD, LRRTM4, and LINGO2 in the L2/3 gene modules. Our study offers new potential genomic targets for ASD and provides a novel method to study gene modules involved in the pathogenesis of ASD.

Gain-of-function variants in SYK cause immune dysregulation and systemic inflammation in humans and mice.

Spleen tyrosine kinase (SYK) is a critical immune signaling molecule and therapeutic target. We identified damaging monoallelic SYK variants in six patients with immune deficiency, multi-organ inflammatory disease such as colitis, arthritis and dermatitis, and diffuse large B cell lymphomas. The SYK variants increased phosphorylation and enhanced downstream signaling, indicating gain of function. A knock-in (SYK-Ser544Tyr) mouse model of a patient variant (p.Ser550Tyr) recapitulated aspects of the human disease that could be partially treated with a SYK inhibitor or transplantation of bone marrow from wild-type mice. Our studies demonstrate that SYK gain-of-function variants result in a potentially treatable form of inflammatory disease.

scHinter: imputing dropout events for single-cell RNA-seq data with limited sample size.

MOTIVATION: Single-cell RNA-sequencing (scRNA-seq) is fast and becoming a powerful technique for studying dynamic gene regulation at unprecedented resolution. However, scRNA-seq data suffer from problems of extremely high dropout rate and cell-to-cell variability, demanding new methods to recover gene expression loss. Despite the availability of various dropout imputation approaches for scRNA-seq, most studies focus on data with a medium or large number of cells, while few studies have explicitly investigated the differential performance across different sample sizes or the applicability of the approach on small or imbalanced data. It is imperative to develop new imputation approaches with higher generalizability for data with various sample sizes. RESULTS: We proposed a method called scHinter for imputing dropout events for scRNA-seq with special emphasis on data with limited sample size. scHinter incorporates a voting-based ensemble distance and leverages the synthetic minority oversampling technique for random interpolation. A hierarchical framework is also embedded in scHinter to increase the reliability of the imputation for small samples. We demonstrated the ability of scHinter to recover gene expression measurements across a wide spectrum of scRNA-seq datasets with varied sample sizes. We comprehensively examined the impact of sample size and cluster number on imputation. Comprehensive evaluation of scHinter across diverse scRNA-seq datasets with imbalanced or limited sample size showed that scHinter achieved higher and more robust performance than competing approaches, including MAGIC, scImpute, SAVER and netSmooth. AVAILABILITY AND IMPLEMENTATION: Freely available for download at https://github.com/BMILAB/scHinter. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.

BACKGROUND: Single-cell RNA-sequencing (scRNA-seq) is fast becoming a powerful tool for profiling genome-scale transcriptomes of individual cells and capturing transcriptome-wide cell-to-cell variability. However, scRNA-seq technologies suffer from high levels of technical noise and variability, hindering reliable quantification of lowly and moderately expressed genes. Since most downstream analyses on scRNA-seq, such as cell type clustering and differential expression analysis, rely on the gene-cell expression matrix, preprocessing of scRNA-seq data is a critical preliminary step in the analysis of scRNA-seq data. RESULTS: We presented scNPF, an integrative scRNA-seq preprocessing framework assisted by network propagation and network fusion, for recovering gene expression loss, correcting gene expression measurements, and learning similarities between cells. scNPF leverages the context-specific topology inherent in the given data and the priori knowledge derived from publicly available molecular gene-gene interaction networks to augment gene-gene relationships in a data driven manner. We have demonstrated the great potential of scNPF in scRNA-seq preprocessing for accurately recovering gene expression values and learning cell similarity networks. Comprehensive evaluation of scNPF across a wide spectrum of scRNA-seq data sets showed that scNPF achieved comparable or higher performance than the competing approaches according to various metrics of internal validation and clustering accuracy. We have made scNPF an easy-to-use R package, which can be used as a versatile preprocessing plug-in for most existing scRNA-seq analysis pipelines or tools. CONCLUSIONS: scNPF is a universal tool for preprocessing of scRNA-seq data, which jointly incorporates the global topology of priori interaction networks and the context-specific information encapsulated in the scRNA-seq data to capture both shared and complementary knowledge from diverse data sources. scNPF could be used to recover gene signatures and learn cell-to-cell similarities from emerging scRNA-seq data to facilitate downstream analyses such as dimension reduction, cell type clustering, and visualization.

Comparative Transcriptome Analysis of Genes Involved in GA-GID1-DELLA Regulatory Module in Symbiotic and Asymbiotic Seed Germination of Anoectochilus roxburghii (Wall.) Lindl. (Orchidaceae).

Anoectochilus roxburghii (Wall.) Lindl. (Orchidaceae) is an endangered medicinal plant in China, also called "King Medicine". Due to lacking of sufficient nutrients in dust-like seeds, orchid species depend on mycorrhizal fungi for seed germination in the wild. As part of a conservation plan for the species, research on seed germination is necessary. However, the molecular mechanism of seed germination and underlying orchid-fungus interactions during symbiotic germination are poorly understood. In this study, Illumina HiSeq 4000 transcriptome sequencing was performed to generate a substantial sequence dataset of germinating A. roxburghii seed. A mean of 44,214,845 clean reads were obtained from each sample. 173,781 unigenes with a mean length of 653 nt were obtained. A total of 51,514 (29.64%) sequences were annotated, among these, 49 unigenes encoding proteins involved in GA-GID1-DELLA regulatory module, including 31 unigenes involved in GA metabolism pathway, 5 unigenes encoding GID1, 11 unigenes for DELLA and 2 unigenes for GID2. A total of 11,881 genes showed significant differential expression in the symbiotic germinating seed sample compared with the asymbiotic germinating seed sample, of which six were involved in the GA-GID1-DELLA regulatory module, and suggested that they might be induced or suppressed by fungi. These results will help us understand better the molecular mechanism of orchid seed germination and orchid-fungus symbiosis.

[Study on discrimination of varieties of fire resistive coating for steel structure based on near-infrared spectroscopy].

In order to achieve the rapid identification of fire resistive coating for steel structure of different brands in circulating, a new method for the fast discrimination of varieties of fire resistive coating for steel structure by means of near infrared spectroscopy was proposed. The raster scanning near infrared spectroscopy instrument and near infrared diffuse reflectance spectroscopy were applied to collect the spectral curve of different brands of fire resistive coating for steel structure and the spectral data were preprocessed with standard normal variate transformation(standard normal variate transformation, SNV) and Norris second derivative. The principal component analysis (principal component analysis, PCA)was used to near infrared spectra for cluster analysis. The analysis results showed that the cumulate reliabilities of PC1 to PC5 were 99. 791%. The 3-dimentional plot was drawn with the scores of PC1, PC2 and PC3 X 10, which appeared to provide the best clustering of the varieties of fire resistive coating for steel structure. A total of 150 fire resistive coating samples were divided into calibration set and validation set randomly, the calibration set had 125 samples with 25 samples of each variety, and the validation set had 25 samples with 5 samples of each variety. According to the principal component scores of unknown samples, Mahalanobis distance values between each variety and unknown samples were calculated to realize the discrimination of different varieties. The qualitative analysis model for external verification of unknown samples is a 10% recognition ration. The results demonstrated that this identification method can be used as a rapid, accurate method to identify the classification of fire resistive coating for steel structure and provide technical reference for market regulation.