Visualization, benchmarking and characterization of nested single-cell heterogeneity as dynamic forest mixtures.

A major topic of debate in developmental biology centers on whether development is continuous, discontinuous, or a mixture of both. Pseudo-time trajectory models, optimal for visualizing cellular progression, model cell transitions as continuous state manifolds and do not explicitly model real-time, complex, heterogeneous systems and are challenging for benchmarking with temporal models. We present a data-driven framework that addresses these limitations with temporal single-cell data collected at discrete time points as inputs and a mixture of dependent minimum spanning trees (MSTs) as outputs, denoted as dynamic spanning forest mixtures (DSFMix). DSFMix uses decision-tree models to select genes that account for variations in multimodality, skewness and time. The genes are subsequently used to build the forest using tree agglomerative hierarchical clustering and dynamic branch cutting. We first motivate the use of forest-based algorithms compared to single-tree approaches for visualizing and characterizing developmental processes. We next benchmark DSFMix to pseudo-time and temporal approaches in terms of feature selection, time correlation, and network similarity. Finally, we demonstrate how DSFMix can be used to visualize, compare and characterize complex relationships during biological processes such as epithelial-mesenchymal transition, spermatogenesis, stem cell pluripotency, early transcriptional response from hormones and immune response to coronavirus disease. Our results indicate that the expression of genes during normal development exhibits a high proportion of non-uniformly distributed profiles that are mostly right-skewed and multimodal; the latter being a characteristic of major steady states during development. Our study also identifies and validates gene signatures driving complex dynamic processes during somatic or germline differentiation.

Development and validation of a differentiation-related signature based on single-cell RNA sequencing data of immune cells in spinal cord injury.

Background: After spinal cord injury (SCI), the native immune surveillance function of the central nervous system is activated, resulting in a substantial infiltration of immune cells into the affected tissue. While numerous studies have explored the transcriptome data following SCI and revealed certain diagnostic biomarkers, there remains a paucity of research pertaining the identification of immune subtypes and molecular markers related to the immune system post-spinal cord injury using single-cell sequencing data of immune cells. Methods: The researchers conducted an analysis of spinal cord samples obtained at three time points (3,10, and 21 days) following SCI using the GSE159638 dataset. The SCI subsets were delineated through pseudo-time analysis, and differentiation related genes were identified after principal component analysis (PCA), cell clustering, and annotation techniques. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were employed to assess the differentiation-related genes (DRGs) across different subsets. The molecular subtypes of SCI were determined using consensus clustering analysis. To further explore and validate the correlation between the molecular subtypes and the immune microenvironment, the CIBERSORT algorithm was employed. High-value diagnostic gene markers were identified using LASSO regression, and their diagnostic sensitivity was assessed using receiver operating characteristic curves (ROC) and quantitative real-time polymerase chain reaction (qRT-PCR). Results: Three SCI subsets were obtained, and differentiation-related genes were characterized. Within these subsets, two distinct molecular subtypes, namely C1 and C2, were identified. These subtypes demonstrated significant variations in terms of immune cell infiltration levels and the expression of immune checkpoint genes. Through further analysis, three candidate biomarkers (C1qa, Lgals3 and Cd63) were identified and subsequently validated. Conclusions: Our study revealed a diverse immune microenvironment in SCI samples, highlighting the potential significance of C1qa, Lgals3 and Cd63 as immune biomarkers for diagnosing SCI. Moreover, the identification of immune checkpoints corresponding to the two molecular subtypes suggests their potential as targets for immunotherapy to enhance SCI repair in future interventions.