Dynamic network biomarker factors orchestrate cell-fate determination at tipping points during hESC differentiation

Mesenchymal stem cell-derived extracellular vesicles targeting irradiated intestine exert therapeutic effects

Background: Radiation-induced intestinal injuries are common in patients with pelvic or abdominal cancer. However, these injuries are currently not managed effectively. Mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) have been extensively used in regenerative medicine. However, the results of MSC-EVs in the repair of radiation-induced intestinal damage have been unsatisfactory. We here investigated the nanotherapeutic functions of MSC-EVs in radiation-induced intestinal injury. Methods: We visualized the biodistribution and trend of MSC-EVs through in vivo imaging. A radiation-induced intestinal injury model was constructed, and the therapeutic effect of MSC-EVs was explored through in vivo and in vitro experiments. Immunofluorescence and qRT-PCR assays were conducted to explore the underlying mechanisms. Results: MSC-EVs exhibited a dose-dependent tendency to target radiation-injured intestines while providing spatiotemporal information for the early diagnosis of the injury by quantifying the amount of MSC-EVs in the injured intestines through molecular imaging. Meanwhile, MSC-EVs displayed superior nanotherapeutic functions by alleviating apoptosis, improving angiogenesis, and ameliorating the intestinal inflammatory environment. Moreover, MSC-EVs-derived miRNA-455-5p negatively regulated SOCS3 expression, and the activated downstream Stat3 signaling pathway was involved in the therapeutic efficacy of MSC-EVs in radiation-induced intestinal injuries. Conclusion: MSC-EVs can dose-dependently target radiation-injured intestinal tissues, allow a spatiotemporal diagnosis in different degrees of damage to help guide personalized therapy, offer data for designing EV-based theranostic strategies for promoting recovery from radiation-induced intestinal injury, and provide cell-free treatment for radiation therapy.

CPMI: comprehensive neighborhood-based perturbed mutual information for identifying critical states of complex biological processes

BACKGROUND

There exists a critical transition or tipping point during the complex biological process. Such critical transition is usually accompanied by the catastrophic consequences. Therefore, hunting for the tipping point or critical state is of significant importance to prevent or delay the occurrence of catastrophic consequences. However, predicting critical state based on the high-dimensional small sample data is a difficult problem, especially for single-cell expression data.

RESULTS

In this study, we propose the comprehensive neighbourhood-based perturbed mutual information (CPMI) method to detect the critical states of complex biological processes. The CPMI method takes into account the relationship between genes and neighbours, so as to reduce the noise and enhance the robustness. This method is applied to a simulated dataset and six real datasets, including an influenza dataset, two single-cell expression datasets and three bulk datasets. The method can not only successfully detect the tipping points, but also identify their dynamic network biomarkers (DNBs). In addition, the discovery of transcription factors (TFs) which can regulate DNB genes and nondifferential 'dark genes' validates the effectiveness of our method. The numerical simulation verifies that the CPMI method is robust under different noise strengths and is superior to the existing methods on identifying the critical states.

CONCLUSIONS

In conclusion, we propose a robust computational method, i.e., CPMI, which is applicable in both the bulk and single cell datasets. The CPMI method holds great potential in providing the early warning signals for complex biological processes and enabling early disease diagnosis.

Revealing cell-cell communication pathways with their spatially coupled gene programs

Inference of cell-cell communication (CCC) provides valuable information in understanding the mechanisms of many important life processes. With the rise of spatial transcriptomics in recent years, many methods have emerged to predict CCCs using spatial information of cells. However, most existing methods only describe CCCs based on ligand-receptor interactions, but lack the exploration of their upstream/downstream pathways. In this paper, we proposed a new method to infer CCCs, called Intercellular Gene Association Network (IGAN). Specifically, it is for the first time that we can estimate the gene associations/network between two specific single spatially adjacent cells. By using the IGAN method, we can not only infer CCCs in an accurate manner, but also explore the upstream/downstream pathways of ligands/receptors from the network perspective, which are actually exhibited as a new panoramic cell-interaction-pathway graph, and thus provide extensive information for the regulatory mechanisms behind CCCs. In addition, IGAN can measure the CCC activity at single cell/spot resolution, and help to discover the CCC spatial heterogeneity. Interestingly, we found that CCC patterns from IGAN are highly consistent with the spatial microenvironment patterns for each cell type, which further indicated the accuracy of our method. Analyses on several public datasets validated the advantages of IGAN.