Search Results (6)

The dynamic and meticulously regulated networks established the foundation for embryonic development, where the intercellular interactions and signal transduction assumed a pivotal role. In recent years, high-throughput technologies such as single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have advanced dramatically, empowering the systematic dissection of cell-to-cell regulatory networks. The emergence of comprehensive databases and analytical frameworks has further provided unprecedented insights into embryonic development and cell–cell interactions (CCIs). This paper reviewed the exponential increased CCIs works related to developmental biology from 2008 to 2023, comprehensively collected and categorized 93 analytical tools and 39 databases, and demonstrated its practical utility through illustrative case studies. In parallel, the article critically scrutinized the persistent challenges within this field, such as the intricacies of spatial localization and transmembrane state validation at single-cell resolution, and underscored the interpretative limitations inherent in current analytical frameworks. The development of CCIs’ analysis tools with harmonizing multi-omics data and the construction of cross-species dynamically updated CCIs databases will be the main direction of future research. Future investigations into CCIs are poised to expeditiously drive the application and clinical translation within developmental biology, unlocking novel dimensions for exploration and progress. Full article

Understanding the dynamic nature of biological systems is fundamental to deciphering cellular behavior, developmental processes, and disease progression. Single-cell RNA sequencing (scRNA-seq) has provided static snapshots of gene expression, offering valuable insights into cellular states at a single time point. Recent advancements in temporally resolved scRNA-seq, spatial transcriptomics (ST), and time-series spatial transcriptomics (temporal-ST) have further revolutionized our ability to study the spatiotemporal dynamics of individual cells. These technologies, when combined with computational frameworks such as Markov chains, stochastic differential equations (SDEs), and generative models like optimal transport and Schrödinger bridges, enable the reconstruction of dynamic cellular trajectories and cell fate decisions. This review discusses how these dynamical system approaches offer new opportunities to model and infer cellular dynamics from a systematic perspective. Full article

Juvenile localized and systemic scleroderma are rare autoimmune diseases which cause significant disability and morbidity in children. The mechanisms driving juvenile scleroderma remain unclear, necessitating further cellular and molecular level studies. The Visium CytAssist spatial transcriptomics (ST) platform, which preserves the spatial location of cells and simultaneously sequences the whole transcriptome, was employed to profile the histopathological slides from skin lesions of juvenile scleroderma patients. (1) Spatial domains were identified from ST data and exhibited strong concordance with the pathologist’s annotations of anatomical structures. (2) The integration of paired ST data and single-cell RNA sequencing (scRNA-seq) from the same patients validated the comparable accuracy of the two platforms and facilitated the estimation of cell type composition in ST data. (3) The pathologist-annotated immune infiltrates, such as perivascular immune infiltrates, were clearly delineated by the ST analysis, underscoring the biological relevance of the findings. This is the first study utilizing spatial transcriptomics to investigate skin lesions in juvenile scleroderma patients. The validity of the ST data was corroborated by gene expression analyses and the pathologist’s assessments. Integration with scRNA-seq data facilitated the cell type-level analysis and validation. Analyses of immune infiltrates through combined ST data and pathological review enhances our understanding of the pathogenesis of juvenile scleroderma. Full article

The heterogeneity of tumors poses a challenge for understanding cell interactions and constructing complex ecosystems within cancer tissues. Current research strategies integrate spatial transcriptomics (ST) and single-cell sequencing (scRNA-seq) data to thoroughly analyze this intricate system. However, traditional deep learning methods using scRNA-seq data tend to filter differentially expressed genes through statistical methods. In the context of cancer tissues, where cancer cells exhibit significant differences in gene expression compared to normal cells, this heterogeneity renders traditional analysis methods incapable of accurately capturing differences between cell types. Therefore, we propose a graph-based deep learning method, GTADC, which utilizes Silhouette scores to precisely capture genes with significant expression differences within each cell type, enhancing the accuracy of gene selection. Compared to traditional methods, GTADC not only considers the expression similarity of genes within their respective clusters but also comprehensively leverages information from the overall clustering structure. The introduction of graph structure effectively captures spatial relationships and topological structures between the two types of data, enabling GTADC to more accurately and comprehensively resolve the spatial composition of different cell types within tissues. This refinement allows GTADC to intricately reconstruct the cellular spatial composition, offering a precise solution for inferring cell spatial composition. This method allows for early detection of potential cancer cell regions within tissues, assessing their quantity and spatial information in cell populations. We aim to achieve a preliminary estimation of cancer occurrence and development, contributing to a deeper understanding of early-stage cancer and providing potential support for early cancer diagnosis. Full article

The heterogeneity and intricate cellular architecture of complex cellular ecosystems play a crucial role in the progression and therapeutic response of cancer. Understanding the regulatory relationships of malignant cells at the invasive front of the tumor microenvironment (TME) is important to explore the heterogeneity of the TME and its role in disease progression. In this study, we inferred malignant cells at the invasion front by analyzing single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data of ER-positive (ER+) breast cancer patients. In addition, we developed a software pipeline for constructing intercellular gene regulatory networks (IGRNs), which help to reduce errors generated by single-cell communication analysis and increase the confidence of selected cell communication signals. Based on the constructed IGRN between malignant cells at the invasive front of the TME and the immune cells of ER+ breast cancer patients, we found that a high expression of the transcription factors FOXA1 and EZH2 played a key role in driving tumor progression. Meanwhile, elevated levels of their downstream target genes (ESR1 and CDKN1A) were associated with poor prognosis of breast cancer patients. This study demonstrates a bioinformatics workflow of combining scRNA-seq and ST data; in addition, the study provides the software pipelines for constructing IGRNs automatically (cIGRN). This strategy will help decipher cancer progression by revealing bidirectional signaling between invasive frontline malignant tumor cells and immune cells, and the selected signaling molecules in the regulatory network may serve as biomarkers for mechanism studies or therapeutic targets. Full article

Perineural invasion (PNI) as the fourth way for solid tumors metastasis and invasion has attracted a lot of attention, recent research reported a new point that PNI starts to include axon growth and possible nerve “invasion” to tumors as the component. More and more tumor–nerve crosstalk has been explored to explain the internal mechanism for tumor microenvironment (TME) of some types of tumors tends to observe nerve infiltration. As is well known, the interaction of tumor cells, peripheral blood vessels, extracellular matrix, other non-malignant cells, and signal molecules in TME plays a key role in the occurrence, development, and metastasis of cancer, as to the occurrence and development of PNI. We aim to summarize the current theories on the molecular mediators and pathogenesis of PNI, add the latest scientific research progress, and explore the use of single-cell spatial transcriptomics in this invasion way. A better understanding of PNI may help to understand tumor metastasis and recurrence and will be beneficial for improving staging strategies, new treatment methods, and even paradigm shifts in our treatment of patients. Full article