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In lung adenocarcinoma (LUAD), the appearance of morphologically diverse tumor regions, termed histological patterns, is closely associated with disease progression and lymph node metastasis. However, the molecular characteristics of the histological patterns in LUAD and the underlying molecular evolutionary mechanisms between the histological patterns in primary tumors and lymph node metastases are poorly understood. Here, we re-analyzed the large TCGA-LUAD dataset and depicted a comprehensive profiling of the genome and transcriptome across the histological patterns in LUAD. Tumor phylogenetic trajectory analysis suggested that the complex glands is more apt to metastasize to the lymph node. Further deconvolution of the tumor microenvironment demonstrated that the complex glands had a higher infiltration of cancer-associated fibroblasts (CAFs). Single-cell transcriptome profiling of complex glands pattern identified a novel CAF subtype co-expressing fibroblast activation protein-alpha (FAP) and stimulator of interferon genes (STING). Moreover, our data demonstrated that FAP is an important downstream effector of STING in CAFs. In summary, our results provide the basis for the development of innovative therapeutic guidelines and intervention strategies for LUAD patients.

THEMIS plays an indispensable role in T cells, but its mechanism of action is highly controversial. Using the systematic proximity labeling methodology PEPSI, we identified THEMIS as an uncharacterized substrate for the phosphatase SHP1. Saturated mutagenesis analysis revealed that THEMIS phosphorylation at the evolutionally conserved Tyr34 residue was oppositely regulated by SHP1 and the kinase LCK. Like THEMIS-/-mice, THEMISY34F/Y34Fknock-in mice showed a significant decrease in CD4 thymocytes and mature CD4 T cells, but a normal thymic development and peripheral homeostasis of CD8 T cells. Mechanistically, phosphorylated THEMIS induced by TCR activation acts as a “priming substrate” to bind SHP1 and convert its phosphatase activity from basal level to nearly fully activated level, ensuring an appropriate negative regulation of TCR signaling. However, cytokine signaling in CD8 T cells failed to elicit THEMIS Y34 phosphorylation, revealing both phosphorylation-dependent and -independent roles of THEMIS in controlling T cell maturation and expansion.

Analysing temporal patterns in plant communities is extremely important to quantify the extent and the consequences of ecological changes, especially considering the current biodiversity crisis. Long-term data collected through the regular sampling of permanent plots represent the most accurate resource to study ecological succession, analyse the stability of a community over time and understand the mechanisms driving vegetation change. We hereby present the LOng-Term Vegetation Sampling (LOTVS) initiative, a global collection of vegetation time-series derived from the regular monitoring of vascular plants in permanent plots. With 79 datasets from five continents and 7789 vegetation time-series monitored for at least six years and mostly on an annual basis, LOTVS possibly represents the largest collection of temporally fine-grained vegetation time-series derived from permanent plots and made accessible to the research community. As such, it has an outstanding potential to support innovative research in the fields of vegetation science, plant ecology and temporal ecology.

Genome alteration signatures reflect recurring patterns caused by distinct endogenous or exogenous mutational events during the evolution of cancer. Signatures of single base substitution (SBS) have been extensively studied in different types of cancer. Copy number alterations are important drivers for the progression of multiple cancer. However, practical tools for studying the signatures of copy number alterations are still lacking. Here, a user-friendly open source bioinformatics tool “sigminer” has been constructed for copy number signature extraction, analysis and visualization. This tool has been applied in prostate cancer (PC), which is particularly driven by complex genome alterations. Five copy number signatures are identified from human PC genome with this tool. The underlying mutational processes for each copy number signature have been illustrated. Sample clustering based on copy number signature exposure reveals considerable heterogeneity of PC, and copy number signatures show improved PC clinical outcome association when compared with SBS signatures. This copy number signature analysis in PC provides distinct insight into the etiology of PC, and potential biomarkers for PC stratification and prognosis., Author summary Genomic DNA alteration signatures are recurring genomic patterns that are the imprints of mutagenic processes accumulated over the lifetime of cancer cell. Copy number alteration is a key driver for the progression of multiple cancer, including prostate cancer, which is particularly driven by complex genome alterations. However, practical tools for studying the signatures of copy number alterations are still lacking. Here a novel bioinformatics tool for copy number signature analysis has been constructed. With this newly developed bioinformatics tool “sigminer”, we performed the first copy number signature analysis in prostate cancer, and an unprecedentedly clear map connecting genome alteration driving factors and prostate cancer clinical outcomes have been illustrated. These analyses provide novel insight into the mutational processes and clinical outcomes of prostate cancer.

Normalization with respect to sequencing depth is a crucial step in single-cell RNA sequencing preprocessing. Most methods normalize data using the whole transcriptome based on the assumption that the majority of transcriptome remains constant and are unable to detect drastic changes of the transcriptome. Here, we develop an algorithm based on a small fraction of constantly expressed genes as internal spike-ins to normalize single-cell RNA sequencing data. We demonstrate that the transcriptome of single cells may undergo drastic changes in several case study datasets and accounting for such heterogeneity by ISnorm (Internal Spike-in-like-genes normalization) improves the performance of downstream analyses.

Normalization with respect to sequencing depth is a crucial step in single-cell RNA sequencing preprocessing. Most methods normalize data using the whole transcriptome based on the assumption that the majority of transcriptome remains constant and are unable to detect drastic changes of the transcriptome. Here, we develop an algorithm based on a small fraction of constantly expressed genes as internal spike-ins to normalize single cell RNA sequencing data. We demonstrate that the transcriptome of single cells may undergo drastic changes in several case study datasets and accounting for such heterogeneity by ISnorm improves the performance of downstream analyzes.