Single cell technologies provide an unprecedented opportunity to explore the heterogeneity in a biological process at the level of single cells. One major challenge in analyzing single cell data is to identify cell subpopulations, stable cell states, and cells in transition between states. To elucidate the transition mechanisms in cell fate dynamics, it is highly desirable to quantitatively characterize cellular states and intermediate states. Here, we present scRCMF, an unsupervised method that identifies stable cell states and transition cells by adopting a nonlinear optimization model that infers the latent substructures from a gene-cell matrix. We incorporate a random coefficient matrix-based regularization into the standard nonnegative matrix decomposition model to improve the reliability and stability of estimating latent substructures. To quantify the transition capability of each cell, we propose two new measures: single-cell transition entropy (scEntropy) and transition probability (scTP). When applied to two simulated and three published scRNA-seq datasets, scRCMF not only successfully captures multiple subpopulations and transition processes in large-scale data, but also identifies transition states and some known marker genes associated with cell state transitions and subpopulations. Furthermore, the quantity scEntropy is found to be significantly higher for transition cells than other cellular states during the global differentiation, and the scTP predicts the "fate decisions" of transition cells within the transition. The present study provides new insights into transition events during differentiation and development.

Single cell technologies provide an unprecedented opportunity to explore the heterogeneity in a biological process at the level of single cells. One major challenge in analyzing single cell data is to identify cell subpopulations, stable cell states, and cells in transition between states. To elucidate the transition mechanisms in cell fate dynamics, it is highly desirable to quantitatively characterize cellular states and intermediate states. Here, we present scRCMF, an unsupervised method that identifies stable cell states and transition cells by adopting a nonlinear optimization model that infers the latent substructures from a gene-cell matrix. We incorporate a random coefficient matrix-based regularization into the standard nonnegative matrix decomposition model to improve the reliability and stability of estimating latent substructures. To quantify the transition capability of each cell, we propose two new measures: single-cell transition entropy (scEntropy) and transition probability (scTP). When applied to two simulated and three published scRNA-seq datasets, scRCMF not only successfully captures multiple subpopulations and transition processes in large-scale data, but also identifies transition states and some known marker genes associated with cell state transitions and subpopulations. Furthermore, the quantity scEntropy is found to be significantly higher for transition cells than other cellular states during the global differentiation, and the scTP predicts the "fate decisions" of transition cells within the transition. The present study provides new insights into transition events during differentiation and development.

Neurotropic strains of mouse hepatitis virus (MHV), a coronavirus, cause acute and chronic demyelinating encephalomyelitis with similarities to the human disease multiple sclerosis. Here, using a lineage-tracking system, we show that some cells, primarily oligodendrocytes (OLs) and oligodendrocyte precursor cells (OPCs), survive the acute MHV infection, are associated with regions of demyelination, and persist in the central nervous system (CNS) for at least 150 d. These surviving OLs express major histocompatibility complex (MHC) class I and other genes associated with an inflammatory response. Notably, the extent of inflammatory cell infiltration was variable, dependent on anatomic location within the CNS, and without obvious correlation with numbers of surviving cells. We detected more demyelination in regions with larger numbers of T cells and microglia/macrophages compared to those with fewer infiltrating cells. Conversely, in regions with less inflammation, these previously infected OLs more rapidly extended processes, consistent with normal myelinating function. Together, these results show that OLs are inducers as well as targets of the host immune response and demonstrate how a CNS infection, even after resolution, can induce prolonged inflammatory changes with CNS region-dependent impairment in remyelination.

Microglia are considered both pathogenic and protective during recovery from demyelination, but their precise role remains ill defined. Here, using an inhibitor of colony stimulating factor 1 receptor (CSF1R), PLX5622, and mice infected with a neurotropic coronavirus (mouse hepatitis virus [MHV], strain JHMV), we show that depletion of microglia during the time of JHMV clearance resulted in impaired myelin repair and prolonged clinical disease without affecting the kinetics of virus clearance. Microglia were required only during the early stages of remyelination. Notably, large deposits of extracellular vesiculated myelin and cellular debris were detected in the spinal cords of PLX5622-treated and not control mice, which correlated with decreased numbers of oligodendrocytes in demyelinating lesions in drug-treated mice. Furthermore, gene expression analyses demonstrated differential expression of genes involved in myelin debris clearance, lipid and cholesterol recycling, and promotion of oligodendrocyte function. The results also demonstrate that microglial functions affected by depletion could not be compensated by infiltrating macrophages. Together, these results demonstrate that microglia play key roles in debris clearance and in the initiation of remyelination following infection with a neurotropic coronavirus but are not necessary during later stages of remyelination.