Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells. Integrating such additional data from other modalities can effectively enhance spatial transcriptomics data, and, conversely, spatial transcriptomics data can supplement scRNA-seq with spatial information. Moreover, the rapid development of spatial multi-omics technology has spurred the demand for the integration of spatial multi-omics data to present a more detailed molecular landscape within tissues. Numerous deep learning (DL) methods have been developed for integrating spatial transcriptomics with other modalities. However, a comprehensive review of DL approaches for integrating spatial transcriptomics data with other modalities remains absent. In this study, we systematically review the applications of DL in integrating spatial transcriptomics data with other modalities. We first delineate the DL techniques applied in this integration and the key tasks involved. Next, we detail these methods and categorize them based on integrated modality and key task. Furthermore, we summarize the integration strategies of these integration methods. Finally, we discuss the challenges and future directions in integrating spatial transcriptomics with other modalities, aiming to facilitate the development of robust computational methods that more comprehensively exploit multimodal information.

Microfluidic system, as a kind of miniature integrated operating platform, has been applied to solid-state nanopore sensors after many years of experimental study. In the process of introducing microfluidic into solid-state nanopore sensors, many novel device structures are designed due to the abundance of analytes and the diversity of detection methods. Here we review the fundamental setup of nanopore-based microfluidic systems and the developments and advancements that have been taking place in the field. The microfluidic systems with a multichannel strategy to elevate the throughput and efficiency of nanopore sensors are then presented. Multifunctional detection represented by optical-electrical detection, which is realized by microfluidic integration, is also described. A high integration microfluidic system with nanopore is further discussed, which shows the prototype of commercialization.

G-quadruplexes (G4s) are guanine-rich DNA secondary structures with known regulatory roles in eukaryotes, but their functions in bacteria remain largely unexplored. Here, we systematically analyzed potential G-quadruplexes (PG4s) across 1566 bacterial genomes. PG4s showed non-random distributions, with strong enrichment in intergenic regions, particularly near gene start sites, suggesting potential roles in transcriptional regulation. Structural analysis revealed a higher proportion of short-loop G4s in these regions, indicating enhanced stability. Comparisons with randomized genomes confirmed that the observed patterns cannot be explained by GC content or genome size alone. Ecological analysis further revealed that bacteria associated with homothermic hosts exhibit significantly lower G4 densities than free-living species, suggesting reduced reliance on G4-mediated regulation in stable environments. Together, these findings highlight G4s as conserved yet context-dependent elements of bacterial genome organization and provide new insights into their potential regulatory roles in prokaryotes.