Spatial omics technologies have become powerful methods to provide valuable insights into cells and tissues within a complex context, significantly enhancing our understanding of the intricate and multifaceted biological system. With an increasing focus on spatial heterogeneity, there is a growing need for unbiased, spatially resolved omics technologies. Laser capture microdissection (LCM) is a cutting-edge method for acquiring spatial information that can quickly collect regions of interest (ROIs) from heterogeneous tissues, with resolutions ranging from single cells to cell populations. Thus, LCM has been widely used for studying the cellular and molecular mechanisms of diseases. This review focuses on the differences among four types of commonly used LCM technologies and their applications in omics and disease research. Key attributes of application cases are also highlighted, such as throughput and spatial resolution. In addition, we comprehensively discuss the existing challenges and the great potential of LCM in biomedical research, disease diagnosis, and targeted therapy from the perspective of high-throughput, multi-omics, and single-cell resolution.

Gut microbiota and circulating metabolite dysbiosis predate important pathological changes in glucose metabolic disorders; however, comprehensive studies on impaired glucose tolerance (IGT), a diabetes mellitus (DM) precursor, are lacking. Here, we perform metagenomic sequencing and metabolomics of 47 pairs of individuals with IGT and newly diagnosed DM, and 46 controls with normal glucose tolerance (NGT); patients with IGT are followed-up after 4 years for progression to DM. Analysis of baseline data reveal significant differences in gut microbiota and serum metabolites among the IGT, DM, and NGT groups. In addition, 13 types of gut microbiota and 17 types of circulating metabolites show significant differences at baseline before IGT progressed to DM, including higher levels of Eggerthella unclassified, Coprobacillus unclassified, Clostridium ramosum, L-valine, L-norleucine, and L-isoleucine, and lower levels of Eubacterium eligens, Bacteroides faecis, Lachnospiraceae bacterium 3_1_46FAA, Alistipes senegalensis, Megaspaera elsdenii, Clostridium perfringens, α-linolenic acid, 10E,12Z octadecadienoic acid, and dodecanoic acid. A random forest model based on differential intestinal microbiota and circulating metabolites can predict the progression from IGT to DM (AUC = 0.87). These results suggest that microbiome and metabolome dysbiosis occur in individuals with IGT and have important predictive values and potential for intervention in preventing IGT from progressing to DM.

Soil salinization is an essential environmental stressor, threating agricultural yield and ecological security worldwide. Saline soils accumulate excessive soluble salts which are detrimental to most plants by limiting plant growth and productivity. It is of great necessity for plants to efficiently deal with the adverse effects caused by salt stress for survival and successful reproduction. Multiple determinants of salt tolerance have been identified in plants, and the cellular and physiological mechanisms of plant salt response and adaption have been intensely characterized. Plants respond to salt stress signals and rapidly initiate signaling pathways to re-establish cellular homeostasis with adjusted growth and cellular metabolism. This review summarizes the advances in salt stress perception, signaling and response in plants. A better understanding of plant salt resistance will contribute to improving crop performance under saline conditions using multiple engineering approaches. The rhizosphere microbiome-mediated plant salt tolerance as well as chemical priming for enhanced plant salt resistance are also discussed in this review.

Creating a multi-gene alignment matrix for phylogenetic analysis using organelle genomes involves aligning single-gene datasets manually, a process that can be time-consuming and prone to errors. The HomBlocks pipeline has been created to eliminate the inaccuracies arising from manual operations. The processing of a large number of sequences, however, remains a time-consuming task. To conquer this challenge, we develop a speedy and efficient method called Organelle Genomes for Phylogenetic Analysis (ORPA). ORPA can quickly generate multiple sequence alignments for whole-genome comparisons by parsing the result files of NCBI BLAST, completing the task in just one minute. With increasing data volume, the efficiency of ORPA is even more pronounced, over 300 times faster than HomBlocks in aligning 60 high-plant chloroplast genomes. The phylogenetic tree outputs from ORPA are equivalent to HomBlocks, indicating its outstanding efficiency. Due to its speed and accuracy, ORPA can identify species-level evolutionary conflicts, providing valuable insights into evolutionary cognition.

Coordinated plant adaptation involves the interplay of multiple traits driven by habitat-specific selection pressures. Pleiotropic effects, wherein genetic variants of a single gene control multiple traits, can expedite such adaptations. Until present, only a limited number of genes have been reported to exhibit pleiotropy. Here, we create a recombinant inbred line (RIL) population derived from two Arabidopsis thaliana (A. thaliana) ecotypes originating from divergent habitats. Using this RIL population, we identify an allelic variation in a MADS-box transcription factor, SHORT VEGETATIVE PHASE (SVP), which exerts a pleiotropic effect on leaf size and drought-versus-humidity tolerance. Further investigation reveals that a natural null variant of the SVP protein disrupts its normal regulatory interactions with target genes, including GRF3, CYP707A1/3, and AtBG1, leading to increased leaf size, enhanced tolerance to humid conditions, and changed in flowering time in humid conditions in A. thaliana. Remarkably, polymorphic variations in this gene have been traced back to early A. thaliana populations, providing a genetic foundation and plasticity for subsequent colonization of diverse habitats by influencing multiple traits. These findings advance our understanding of how plants rapidly adapt to changing environments by virtue of the pleiotropic effects of individual genes on multiple trait alterations.

Bread wheat provides an essential fraction of the daily calorific intake for humanity. Due to its huge and complex genome, progresses in studying on the wheat genome are substantially trailed behind those of other two major crops, rice and maize, for at least a decade. With rapid advances in genome assembling and reduced cost of high-throughput sequencing, emerging de novo genome assemblies of wheat and whole-genome sequencing data are leading a paradigm shift in wheat research. Here, we review recent progress in dissecting the complex genome and germplasm evolution of wheat since the release of the first high-quality wheat genome. New insights have been gained in the evolution of wheat germplasm during domestication and modern breeding progress, genomic variations at multiple scales contributing to the diversity of wheat germplasm, and complex transcriptional and epigenetic regulations of functional genes in polyploid wheat. Genomics databases and bioinformatics tools meeting the urgent needs of wheat genomics research are also summarized. The ever-increasing omics data, along with advanced tools and well-structured databases, are expected to accelerate deciphering the germplasm and gene resources in wheat for future breeding advances.

Monogenic disorders refer to a group of human diseases caused by mutations in single genes. While disease-modifying therapies have offered some relief to symptoms and delayed progression for some monogenic diseases, most of these diseases still lack effective treatments. In recent decades, gene therapy has emerged as a promising therapeutic strategy for genetic disorders. Researchers have developed various gene manipulation tools and gene delivery systems to treat monogenic diseases. Despite this progress, concerns about inefficient delivery, persistent expression, immunogenicity, toxicity, capacity limitation, genomic integration, and limited tissue specificity still need to be addressed. This review gives an overview of commonly used gene therapy and delivery tools, along with the challenges they face and the potential strategies to counter them.

Messenger RNA (mRNA) translation consists of initiation, elongation, termination, and ribosome recycling, carried out by the translation machinery, primarily including tRNAs, ribosomes, and translation factors (TrFs). Translational regulators transduce signals of growth and development, as well as biotic and abiotic stresses, to the translation machinery, where global or selective translational control occurs to modulate mRNA translation efficiency (TrE). As the basis of translational control, the translation machinery directly determines the quality and quantity of newly synthesized peptides and, ultimately, the cellular adaption. Thus, regulating the availability of diverse machinery components is reviewed as the central strategy of translational control. We provide classical signaling pathways (e.g., integrated stress responses) and cellular behaviors (e.g., liquid-liquid phase separation) to exemplify this strategy within different physiological contexts, particularly during host-microbe interactions. With new technologies developed, further understanding this strategy will speed up translational medicine and translational agriculture.