ColorCells: a database of expression, classification and functions of lncRNAs in single cells

Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era

As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

Noncoding RNAs: Emerging regulators of behavioral complexity

The mammalian genome encodes thousands of non-coding RNAs (ncRNAs), ranging in size from about 20 nucleotides (microRNAs or miRNAs) to kilobases (long non-coding RNAs or lncRNAs). ncRNAs contribute to a layer of gene regulation that could explain the evolution of massive phenotypic complexity even as the number of protein-coding genes remains unaltered. We propose that low conservation, poor expression, and highly restricted spatiotemporal expression patterns-conventionally considered ncRNAs may affect behavior through direct, rapid, and often sustained regulation of gene expression at the transcriptional, post-transcriptional, or translational levels. Besides these direct roles, their effect during neurodevelopment may manifest as behavioral changes later in the organism's life, especially when exposed to environmental cues like stress and seasonal changes. The lncRNAs affect behavior through diverse mechanisms like sponging of miRNAs, recruitment of chromatin modifiers, and regulation of alternative splicing. We highlight the need for synthesis between rigorously designed behavioral paradigms in model organisms and the wide diversity of behaviors documented by ethologists through field studies on organisms exquisitely adapted to their environmental niche. Comparative genomics and the latest advancements in transcriptomics provide an unprecedented scope for merging field and lab studies on model and non-model organisms to shed light on the role of ncRNAs in driving the behavioral responses of individuals and groups. We touch upon the technical challenges and contentious issues that must be resolved to fully understand the role of ncRNAs in regulating complex behavioral traits. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

SCAN: Spatiotemporal Cloud Atlas for Neural cells

The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer's disease, Parkinson's disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.

A global database for modeling tumor-immune cell communication

Communications between tumor cells and surrounding immune cells help shape the tumor immunity continuum. Recent breakthroughs in high-throughput technologies as well as computational algorithms had reported many important tumor-immune cell (TIC) communications, which were scattered in thousands of published studies and impeded systematical characterization of the TIC communications across cancer. Here, a comprehensive database, TICCom, was developed to model TIC communications, containing 739 experimentally-validated or manually-curated interactions collected from more than 3,000 literatures as well as 4,537,709 predicted interactions inferred via six computational algorithms by reanalyzing 32 scRNA-seq datasets and bulk RNA-seq data across 25 cancer types. The communications between tumor cells and 14 types of immune cells were characterized, and the involved ligand-receptor interactions were further integrated. 14190 human and 3650 mouse integrated ligand-receptor interactions with supplemented corresponding function information were also stored in the TICCom database. Our database would serve as a valuable resource for investigating TIC communications.

Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function

Psychiatric and neurological disorders are influenced by an undetermined number of genes and molecular pathways that may differ among afflicted individuals. Functionally testing and characterizing biological systems is essential to discovering the interrelationship among candidate genes and understanding the neurobiology of behavior. Recent advancements in genetic, genomic, and behavioral approaches are revolutionizing modern neuroscience. Although these tools are often used separately for independent experiments, combining these areas of research will provide a viable avenue for multidimensional studies on the brain. Herein we will briefly review some of the available tools that have been developed for characterizing novel cellular and animal models of human disease. A major challenge will be openly sharing resources and datasets to effectively integrate seemingly disparate types of information and how these systems impact human disorders. However, as these emerging technologies continue to be developed and adopted by the scientific community, they will bring about unprecedented opportunities in our understanding of molecular neuroscience and behavior.

Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants

Plant transcriptomes encompass a large number of functional non-coding RNAs (ncRNAs), only some of which have protein-coding capacity. Since their initial discovery, ncRNAs have been classified into two broad categories based on their biogenesis and mechanisms of action, housekeeping ncRNAs and regulatory ncRNAs. With advances in RNA sequencing technology and computational methods, bioinformatics resources continue to emerge and update rapidly, including workflow for in silico ncRNA analysis, up-to-date platforms, databases, and tools dedicated to ncRNA identification and functional annotation. In this review, we aim to describe the biogenesis, biological functions, and interactions with DNA, RNA, protein, and microorganism of five major regulatory ncRNAs (miRNA, siRNA, tsRNA, circRNA, lncRNA) in plants. Then, we systematically summarize tools for analysis and prediction of plant ncRNAs, as well as databases. Furthermore, we discuss the silico analysis process of these ncRNAs and present a protocol for step-by-step computational analysis of ncRNAs. In general, this review will help researchers better understand the world of ncRNAs at multiple levels.

Long non-coding RNAs: Biogenesis, functions, and clinical significance in gastric cancer

Gastric cancer (GC) is one of the most prevalent malignant tumor types and the third leading cause of cancer-related death worldwide. Its morbidity and mortality are very high due to a lack of understanding about its pathogenesis and the slow development of novel therapeutic strategies. Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs with a length of more than 200 nt. They play crucial roles in a wide spectrum of physiological and pathological processes by regulating the expression of genes involved in proliferation, differentiation, apoptosis, cell cycle, invasion, metastasis, DNA damage, and carcinogenesis. The aberrant expression of lncRNAs has been found in various cancer types. A growing amount of evidence demonstrates that lncRNAs are involved in many aspects of GC pathogenesis, including its occurrence, metastasis, and recurrence, indicating their potential role as novel biomarkers in the diagnosis, prognosis, and therapeutic targets of GC. This review systematically summarizes the biogenesis, biological properties, and functions of lncRNAs and highlights their critical role and clinical significance in GC. This information may contribute to the development of better diagnostics and treatments for GC.