Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics.

Uncovering the tissue molecular architecture at single-cell resolution could help better understand organisms’ biological and pathological processes. However, bulk RNA-seq can only measure gene expression in cell mixtures, without revealing the transcriptional heterogeneity and spatial patterns of single cells. Herein, we introduce Bulk2Space (https://github.com/ZJUFanLab/bulk2space), a deep learning framework-based spatial deconvolution algorithm that can simultaneously disclose the spatial and cellular heterogeneity of bulk RNA-seq data using existing single-cell and spatial transcriptomics references. The use of bulk transcriptomics to validate Bulk2Space unveils, in particular, the spatial variance of immune cells in different tumor regions, the molecular and spatial heterogeneity of tissues during inflammation-induced tumorigenesis, and spatial patterns of novel genes in different cell types. Moreover, Bulk2Space is utilized to perform spatial deconvolution analysis on bulk transcriptome data from two different mouse brain regions derived from our in-house developed sequencing approach termed Spatial-seq. We have not only reconstructed the hierarchical structure of the mouse isocortex but also further annotated cell types that were not identified by original methods in the mouse hypothalamus.

The clustered regularly interspaced short palindromic repeats (CRISPR)‐based genetic screening has been demonstrated as a powerful approach for unbiased functional genomics research. Single‐cell CRISPR screening (scCRISPR) techniques, which result from the combination of single‐cell toolkits and CRISPR screening, allow dissecting regulatory networks in complex biological systems at unprecedented resolution. These methods allow cells to be perturbed en masse using a pooled CRISPR library, followed by high‐content phenotyping. This is technically accomplished by annotating cells with sgRNA‐specific barcodes or directly detectable sgRNAs. According to the integration of distinct single‐cell technologies, these methods principally fall into four categories: scCRISPR with RNA‐seq, scCRISPR with ATAC‐seq, scCRISPR with proteome probing, and imaging‐based scCRISPR. scCRISPR has deciphered genotype–phenotype relationships, genetic regulations, tumor biological issues, and neuropathological mechanisms. This review provides insight into the technical breakthrough of scCRISPR by systematically summarizing the advancements of various scCRISPR methodologies and analyzing their merits and limitations. In addition, an application‐oriented approach guide is offered to meet researchers’ individualized demands.

Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.

Traditional Medicine (TM), especially Traditional Chinese Medicine (TCM), is renowned for its distinctive "multi-component-multi-target-multi-pathway" mode of action, which exhibits a unique overall regulatory therapeutic effect. However, the intricate nature of TCM poses significant challenges in identifying active components, elucidating mechanisms of action, and standardizing clinical practices. The advancement of modern science and technology has led to the gradual modernization of TCM research. Network pharmacology (NP) has emerged as a pivotal framework for comprehending the holistic mechanisms of TCM, offering a crucial avenue for unveiling intricate biological networks by integrating chemical information, omics data, and clinical efficacy evidence. Nevertheless, conventional NP approaches exhibit notable limitations, including substantial noise, high dimensionality, challenges in capturing dynamics and time series, and inadequate cross-scale integration, thereby constraining their utility in precise mechanism analysis and clinical translation. In recent years, artificial intelligence (AI), particularly machine learning (ML), deep learning (DL), and graph neural networks (GNN), have empowered NP in an unprecedented way, enabling it to systematically and accurately analyze the cross-scale mechanisms of TCM from molecular interactions to patient efficacy. This review will systematically examine the latest developments in artificial intelligence-network pharmacology (AI-NP) methodology, with a focus on typical research cases of multi-scale mechanism analysis at the molecular, cellular, tissue, and patient levels. It will systematically summarize the challenges currently faced and explore future development directions to fully unlock the systemic therapeutic wisdom of TCM.