STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization.

Recent technological developments in spatial transcriptomics allow researchers to measure gene expression of cells and their spatial locations at the single-cell level, generating detailed biological insight into biological processes. A comprehensive database could facilitate the sharing of spatial transcriptomic data and streamline the data acquisition process for researchers. Here, we present the Spatial TranscriptOmics DataBase (STOmicsDB), a database that serves as a one-stop hub for spatial transcriptomics.

Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex.

Elucidating the cellular organization of the cerebral cortex is critical for understanding brain structure and function. Using large-scale single-nucleus RNA sequencing and spatial transcriptomic analysis of 143 macaque cortical regions, we obtained a comprehensive atlas of 264 transcriptome-defined cortical cell types and mapped their spatial distribution across the entire cortex. We characterized the cortical layer and region preferences of glutamatergic, GABAergic, and non-neuronal cell types, as well as regional differences in cell-type composition and neighborhood complexity.

Phytochemistry and biological activities of corynanthe alkaloids.

Medicinal plants constitute a source for designing clinically useful drugs targeting diseases through various mechanisms. Plant secondary metabolites can be used as lead compounds of drugs. Corynanthe alkaloids are highly abundant natural bioactive substances of various core structures possessing important properties such as nerve excitation and antimalarial and analgesic effects.

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq).

Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis.

A major challenge in understanding vertebrate embryogenesis is the lack of topographical transcriptomic information that can help correlate microenvironmental cues within the hierarchy of cell-fate decisions. Here, we employed Stereo-seq to profile 91 zebrafish embryo sections covering six critical time points during the first 24 h of development, obtaining a total of 152,977 spots at a resolution of 10 × 10 × 15 μm (close to cellular size) with spatial coordinates. Meanwhile, we identified spatial modules and co-varying genes for specific tissue organizations.

Full-Length Transcript-Based Proteogenomics of Rice Improves Its Genome and Proteome Annotation.

Rice () molecular breeding has gained considerable attention in recent years, but inaccurate genome annotation hampers its progress and functional studies of the rice genome. In this study, we applied single-molecule long-read RNA sequencing (lrRNA_seq)-based proteogenomics to reveal the complexity of the rice transcriptome and its coding abilities. Surprisingly, approximately 60% of loci identified by lrRNA_seq are associated with natural antisense transcripts (NATs).

Identification of 12 cancer types through genome deep learning.

Cancer is a major cause of death worldwide, and an early diagnosis is required for a favorable prognosis. Histological examination is the gold standard for cancer identification; however, large amount of inter-observer variability exists in histological diagnosis. Numerous studies have shown cancer genesis is accompanied by an accumulation of harmful mutations, potentiating the identification of cancer based on genomic information.

Chromosome-level reference genome of the Siamese fighting fish Betta splendens, a model species for the study of aggression.

Background: Siamese fighting fish Betta splendens are notorious for their aggressiveness and accordingly have been widely used to study aggression. However, the lack of a reference genome has, to date, limited the understanding of the genetic basis of aggression in this species. Here, we present the first reference genome assembly of the Siamese fighting fish.

Proteogenomic analysis reveals alternative splicing and translation as part of the abscisic acid response in Arabidopsis seedlings.

In eukaryotes, mechanisms such as alternative splicing (AS) and alternative translation initiation (ATI) contribute to organismal protein diversity. Specifically, splicing factors play crucial roles in responses to environment and development cues; however, the underlying mechanisms are not well investigated in plants. Here, we report the parallel employment of short-read RNA sequencing, single molecule long-read sequencing and proteomic identification to unravel AS isoforms and previously unannotated proteins in response to abscisic acid (ABA) treatment.

Draft genome of the living fossil Ginkgo biloba.

Background: Ginkgo biloba L. (Ginkgoaceae) is one of the most distinctive plants. It possesses a suite of fascinating characteristics including a large genome, outstanding resistance/tolerance to abiotic and biotic stresses, and dioecious reproduction, making it an ideal model species for biological studies.