Journal: Frontiers in Pharmacology

Article Title: Rifampicin Induces Gene, Protein, and Activity of P-Glycoprotein (ABCB1) in Human Precision-Cut Intestinal Slices

doi: 10.3389/fphar.2021.684156

Figure Lengend Snippet: ABCB1 , CYP3A4 , PXR , and RXRA gene expression in hPCIS determined by qRT-PCR. Fold changes in ABCB1 and CYP3A4 mRNA levels in hPCISs incubated with RIF (30 µM) for 4, 8, 12, 16, 20, 24, and 48 h (A) , relative to expression in RIF-free controls, showing that RIF significantly increased mRNA levels of both genes in samples from all donors at all tested time points (one-sample t -tests, three slices from one donor: *, p < 0.05; **, p < 0.01; ***, p < 0.001). PXR (B) and RXRA (C) were demonstrated to be expressed in hPCISs incubated with RIF (30 µM) at all tested time points. Amounts of PXR and RXRA transcripts are presented in arbitrary units (a.u.), calculated as 2 −∆Ct × 10 6 . (D) The matrix of Pearson’s coefficients of correlation was used to assess associations among all targeted transcripts in RIF-treated hPCISs (positive in blue and negative in red, with color intensity proportional to the magnitude of the coefficients). Asterisks indicate significant differences: *, p < 0.05; **, p < 0.01; ***, p < 0.001.

Article Snippet: Correlograms displaying Pearson’s correlation coefficient gene expression in hPCISs (considers all times points, in which gene expressions were analyzed) were generated using R-Studio 1.3.959.

Techniques: Gene Expression, Quantitative RT-PCR, Incubation, Expressing

Journal: Cell Reports Methods

Article Title: A statistical approach for systematic identification of transition cells from scRNA-seq data

doi: 10.1016/j.crmeth.2024.100913

Figure Lengend Snippet: Identifying transition cells based on gene pairwise Pearson’s correlation coefficients (A) Stable cells and transition cells are separated through their intrinsic gene pairwise Pearson’s correlation coefficients (GPPCCs). In a Waddington’s landscape illustrating developmental processes, there are “valleys” and “ridges.” Valleys correspond to stable cellular states, and ridges represent barriers separating these stable states. During developmental processes, cells may transit from one stable state to another due to the change in the local landscape. We modeled these transitions as a result of the change in gene regulatory relations using stochastic differential equations (SDEs). Based on our mathematical derivations, gene pairwise correlation coefficients for transition cells are closer to ±1 compared with stable cells (illustrative heatmap: x axis, cells; y axis, gene pairs; color, values of GPPCCs) (see ). We further defined a transition index, which is proportional to the transition probability, to identify transition cells. (B) Transition cells identification workflow. To identify transition cells, we developed an analytical workflow containing several steps. We first did data preprocessing, including quality control, finding neighbors of each cell and obtaining the gene list with the largest expression variations. Then, GPPCCs were calculated for each cell by using the expression profiles of the cell and its nearest neighbors. Based on the empirical distribution of coefficients from all cells, a transition index, which is proportional to the transitioning probability, was calculated for each cell. (C)–(F) Identifying transition index using a simulation dataset. The simulation dataset is generated using SERGIO containing three steady states with linear transitioning structure. There are 5,000 stable cells in each steady state and 1,000 transition cells transitioning from state 1 to state 2 and state 2 to state 3. (C) UMAP of all cells with transition cells highlighted in red. (D) UMAP colored by transition index. (E and F) Evaluation with doublets. A total of 1,000 stable cells from state 1 and state 2 are randomly selected to generate doublets. (E) UMAP colored by the state of cells. (F) UMAP colored by transition index.

Article Snippet: This is because the strengths and connections of regulatory networks can change during cellular state transitions, , while CellTran requests gene pairwise Pearson’s correlation coefficients (GPPCCs) to be calculated from cells that have similar regulatory profiles.

Techniques: Control, Expressing, Generated

Journal: Cell Reports Methods

Article Title: A statistical approach for systematic identification of transition cells from scRNA-seq data

doi: 10.1016/j.crmeth.2024.100913

Figure Lengend Snippet: Transition index can accurately separate transition cells and stable cells (A) UMAP colored by cell types. Approximately 7,000 MuSCs from the mouse muscle regeneration dataset were used to validate the capability of CellTran to identify transition cells. Cell-type annotations are obtained from the original publication. (B) UMAP colored by transition index. Gray dots on the top right indicate inadequate observation of cells in the cluster to calculate transition indices. (C) eCDF of GPPCCs for transition cells (red) and stable cells (black) in the mouse muscle regeneration dataset. (D) Violin plot of transition index for stable cells, and transition cells in the mouse muscle regeneration dataset. Transition indices of transition cells are significantly higher than those of stable cells (Wilcoxon test; p < 0.01). (E and F) Performance comparison of CellTran, CellRank, and MuTrans in terms of (E) AUROC and (F) PRAUC using the mouse muscle regeneration dataset.

Article Snippet: This is because the strengths and connections of regulatory networks can change during cellular state transitions, , while CellTran requests gene pairwise Pearson’s correlation coefficients (GPPCCs) to be calculated from cells that have similar regulatory profiles.

Techniques: Comparison