Gene expression regulation underpins cellular function and disease progression, yet its complexity and the limitations of conventional detection methods hinder clinical translation. In this review, we define “predict” as the AI-driven inference of gene expression levels and regulatory mechanisms from non-invasive multimodal data (e.g., histopathology images, genomic sequences, and electronic health records) instead of direct molecular assays. We systematically examine and analyze the current approaches for predicting gene expression and diagnosing diseases, highlighting their respective advantages and limitations. Machine learning algorithms and deep learning models excel in extracting meaningful features from diverse biomedical modalities, enabling tools like PathChat and Prov-GigaPath to improve cancer subtyping, therapy response prediction, and biomarker discovery. Despite significant progress, persistent challenges—such as data heterogeneity, noise, and ethical issues including privacy and algorithmic bias—still limit broad clinical adoption. Emerging solutions like cross-modal pretraining frameworks, federated learning, and fairness-aware model design aim to overcome these barriers. Case studies in precision oncology illustrate AI’s ability to decode tumor ecosystems and predict treatment outcomes. By harmonizing multimodal data and advancing ethical AI practices, this field holds immense potential to propel personalized medicine forward, although further innovation is needed to address the issues of scalability, interpretability, and equitable deployment.

Embryonic development is a complex and intricately regulated process that encompasses precise control over cell differentiation, morphogenesis, and the underlying gene expression changes. Recent years have witnessed a remarkable acceleration in the development of single-cell and spatial omic technologies, enabling high-throughput profiling of transcriptomic and other multi-omic information at the individual cell level. These innovations offer fresh and multifaceted perspectives for investigating the intricate cellular and molecular mechanisms that govern embryonic development. In this review, we provide an in-depth exploration of the latest technical advancements in single-cell and spatial multi-omic methodologies and compile a systematic catalog of their applications in the field of embryonic development. We deconstruct the research strategies employed by recent studies that leverage single-cell sequencing techniques and underscore the unique advantages of spatial transcriptomics. Furthermore, we delve into both the current applications, data analysis algorithms and the untapped potential of these technologies in advancing our understanding of embryonic development. With the continuous evolution of multi-omic technologies, we anticipate their widespread adoption and profound contributions to unraveling the intricate molecular foundations underpinning embryo development in the foreseeable future.

Intracerebral hemorrhage (ICH) is a severe stroke subtype associated with high mortality and disability. Understanding the molecular and cellular mechanisms behind ICH is vital for developing effective therapeutic interventions. We utilized a single-nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq) to profile changes in chromatin accessibility over the first month following ICH in murine models. We examined 262,506 cells, identifying 77,978 upregulated and 35,023 downregulated candidate cis-regulatory elements (cCREs) across days 1, 3, 7, 14, and 28. Our results revealed disruptions in multiple super-enhancers in microglia and macrophages, along with a transition of reactive astrocytes from a protective to a neurotoxic state. We also observed a significant upregulation of genes involved in hypoxia response and lipid metabolism within the remyelination pathways post-ICH. This study provides crucial insights into the epigenetic changes that occur following ICH, laying a solid foundation for future research and potential therapeutic interventions.

Background: Intracerebral haemorrhage (ICH) progresses rapidly with complex pathology and limited treatment options, making it a severe subtype of stroke. The extravasation of blood into the brain parenchyma triggers a cascade of inflammatory responses, contributing to secondary injury. Single‐nucleus RNA sequencing (snRNA‐seq) data have enabled more profound insights into the cellular heterogeneity and dynamic interactions within the haemorrhagic brain. Immune cells play a crucial role in shaping neuroinflammation. However, the lack of comprehensive longitudinal studies limits our understanding of the temporal evolution of these inflammatory processes, posing a challenge to the development of targeted therapeutic strategies. Methods: We used snRNA‐seq in collagenase‐induced ICH mouse models at Days 1, 3, 7, 14 and 28 post‐injury, alongside naive controls, to profile the dynamics of gene expression over time. Results: We obtained 281 577 high‐quality transcriptional profiles representing 21 distinct cell types. Co‐expression network analysis revealed a prominent ‘inflammation module’ that remained active throughout ICH. Integrative single‐cell transcriptomic and immunofluorescence staining suggested that the various Mif ‐expressing cells may contribute to local inflammation, potentially engaging macrophages via receptor–ligand pairs such as Cd44 and Cd74 . Over time, microglia appeared to serve as key recipients of pro‐inflammatory signals increasingly. During the resolution phase, oligodendrocytes exhibited transcriptional signatures consistent with enhanced maturation and remyelination, which T cell‐mediated interactions may have facilitated. Conclusions: These findings offer a systems‐level perspective on cell‐type–specific responses and immune‐mediated interactions during ICH progression and resolution. Key points: Establish intracerebral haemorrhage (ICH) mouse models at various time points (Days 1, 3, 7, 14, 28) and construct a high‐quality single‐nucleus RNA sequencing (snRNA‐seq) atlas. Computational analyses suggest that macrophage recruitment in the early stage of ICH potentially involves migration inhibitory factor (MIF) signalling pathways. T cells may interact with myelin‐forming oligodendrocytes during the resolution phase, potentially contributing to remyelination after ICH.

The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) is a fundamental epigenomics approach and has been widely used in profiling the chromatin accessibility dynamics in multiple species. A comprehensive reference of ATAC-seq datasets for mammalian tissues is important for the understanding of regulatory specificity and developmental abnormality caused by genetic or environmental alterations. Here, we report an adult mouse ATAC-seq atlas by producing a total of 66 ATAC-seq profiles from 20 primary tissues of both male and female mice. The ATAC-seq read enrichment, fragment size distribution, and reproducibility between replicates demonstrated the high quality of the full dataset. We identified a total of 296,574 accessible elements, of which 26,916 showed tissue-specific accessibility. Further, we identified key transcription factors specific to distinct tissues and found that the enrichment of each motif reflects the developmental similarities across tissues. In summary, our study provides an important resource on the mouse epigenome and will be of great importance to various scientific disciplines such as development, cell reprogramming, and genetic disease.