InterCellDB: A User-Defined Database for Inferring Intercellular Networks

The diversification of methods for studying cell-cell interactions and communication

No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.

SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

CellEnBoost: A Boosting-Based Ligand-Receptor Interaction Identification Model for Cell-to-Cell Communication Inference

Cell-to-cell communication (CCC) plays important roles in multicellular organisms. The identification of communication between cancer cells themselves and one between cancer cells and normal cells in tumor microenvironment helps understand cancer genesis, development and metastasis. CCC is usually mediated by Ligand-Receptor Interactions (LRIs). In this manuscript, we developed a Boosting-based LRI identification model (CellEnBoost) for CCC inference. First, potential LRIs are predicted by data collection, feature extraction, dimensional reduction, and classification based on an ensemble of Light gradient boosting machine and AdaBoost combining convolutional neural network. Next, the predicted LRIs and known LRIs are filtered. Third, the filtered LRIs are applied to CCC elucidation by combining CCC strength measurement and single-cell RNA sequencing data. Finally, CCC inference results are visualized using heatmap view, Circos plot view, and network view. The experimental results show that CellEnBoost obtained the best AUCs and AUPRs on the collected four LRI datasets. Case study in human head and neck squamous cell carcinoma (HNSCC) tissues demonstrates that fibroblasts were more likely to communicate with HNSCC cells, which is in accord with the results from iTALK. We anticipate that this work can contribute to the diagnosis and treatment of cancers.

Single-Cell Heterogeneity Restorative Chimeric Engineering Nanoparticles for Alleviating Antibody-Mediated Allograft Injury

Disturbance of single-cell transcriptional heterogeneity is an inevitable consequence of persistent donor-specific antibody (DSA) production and allosensitization. However, identifying and efficiently clearing allospecific antibody repertoires to restore single-cell transcriptional profiles remain challenging. Here, inspired by the high affinity of natural bacterial proteins for antibodies, a genetic engineered membrane-coated nanoparticle termed as DSA trapper by the engineering chimeric gene of protein A/G with phosphatidylserine ligands for macrophage phagocytosis was reported. It has been shown that DSA trappers adsorbed alloreactive antibodies with high saturation and activated the heterophagic clearance of antibody complexes, alleviating IgG deposition and complement activation. Remarkably, DSA trappers increased the endothelial protective lineages by 8.39-fold, reversed the highly biased cytotoxicity, and promoted the proliferative profiles of Treg cells, directly providing an obligate immune tolerant niche for single-cell heterogeneity restoration. In the mice of allogeneic transplantation, the DSA trapper spared endothelial from inflammatory degenerative rosette, improved the glomerular filtration rate, and prolonged the survival of allogeneic mice from 23.6 to 78.3 days. In general, by identifying the lineage characteristics of rejection-related antibodies, the chimeric engineered DSA trapper realized immunoadsorption and further phagocytosis of alloantibody complexes to restore the single-cell genetic architecture of the allograft, offering a promising prospect for the treatment of alloantibody-mediated immune injury.

The Single-Cell Landscape of Intratumoral Heterogeneity and The Immunosuppressive Microenvironment in Liver and Brain Metastases of Breast Cancer

Distant metastasis remains the major cause of morbidity for breast cancer. Individuals with liver or brain metastasis have an extremely poor prognosis and low response rates to anti-PD-1/L1 immune checkpoint therapy compared to those with metastasis at other sites. Therefore, it is urgent to investigate the underlying mechanism of anti-PD-1/L1 resistance and develop more effective immunotherapy strategies for these patients. Using single-cell RNA sequencing, a high-resolution map of the entire tumor ecosystem based on 44 473 cells from breast cancer liver and brain metastases is depicted. Identified by canonical markers and confirmed by multiplex immunofluorescent staining, the metastatic ecosystem features remarkable reprogramming of immunosuppressive cells such as FOXP3+ regulatory T cells, LAMP3+ tolerogenic dendritic cells, CCL18+ M2-like macrophages, RGS5+ cancer-associated fibroblasts, and LGALS1+ microglial cells. In addition, PD-1 and PD-L1/2 are barely expressed in CD8+ T cells and cancer/immune/stromal cells, respectively. Interactions of the immune checkpoint molecules LAG3-LGALS3 and TIGIT-NECTIN2 between CD8+ T cells and cancer/immune/stromal cells are found to play dominant roles in the immune escape. In summary, this study dissects the intratumoral heterogeneity and immunosuppressive microenvironment in liver and brain metastases of breast cancer for the first time, providing insights into the most appropriate immunotherapy strategies for these patients.

Aged microglia promote peripheral T cell infiltration by reprogramming the microenvironment of neurogenic niches

BACKGROUND

The immune cell compartment of the mammalian brain changes dramatically and peripheral T cells infiltrate the brain parenchyma during normal aging. However, the mechanisms underlying age-related T cell infiltration in the central nervous system remain unclear.

RESULTS

Chronic inflammation and peripheral T cell infiltration were observed in the subventricular zone of aged mice. Cell-cell interaction analysis revealed that aged microglia released CCL3 to recruit peripheral CD8⁺ memory T cells. Moreover, the aged microglia shifted towards a pro-inflammation state and released TNF-α to upregulate the expression of VCAM1 and ICAM1 in brain venous endothelial cells, which promoted the transendothelial migration of peripheral T cells. In vitro experiment reveals that human microglia would also transit to a chemotactic phenotype when treated with CSF from the elderly.

CONCLUSIONS

Our research demonstrated that microglia play an important role in the aging process of brain by shifting towards a pro-inflammation and chemotactic state. Aged microglia promote T cell infiltration by releasing chemokines and upregulating adhesion molecules on venous brain endothelial cells.