Journal: Molecular and Cellular Endocrinology

Article Title: Stem/progenitor cells in normal physiology and disease of the pancreas

doi: 10.1016/j.mce.2021.111459

Figure Lengend Snippet: Transcriptomic profiling of human and embryonic tissues, experimental animal models and patient-derived cell lines via scRNA-seq enables the study of pancreas progenitors. (1) Single-cell suspensions enter high throughput microfluidic systems (e.g. 10X Chromium, Drop-Seq) allowing thousands of cells to be processed and sequenced. (2) Cells undergo dimensionality reduction and are clustered based upon expression profiles – represented via UMAP or t-SNE - enabling the identification of novel cell types and investigation of cellular heterogeneity. (3) Following clustering, differential expression analysis reveals changes in gene expression across cell types. (4) Prediction of cell trajectories can be inferred based upon changes in gene expression over a ‘pseudo’ time-course. Cells are ordered in a 2D space based upon the closeness of their expression pro_les. Overlay of a minimal spanning tree (MST) identifies the longest continual path linking these cells – uncovering cell lineages. (5) Individual trajectories can be dissected and changes in specific gene expression changes plotted in both a supervised and unsupervised manner (6) The development of algorithms (e.g. StemID, SCENT) has enabled the prediction of cell clusters with high potency, stem-like features. Used in conjunction with pseudotime analysis, these algorithms can infer a starting point of differentiation trajectories, as well as identifying novel stem cells in adult tissues.

Article Snippet: Transcriptomic profiling of human and embryonic tissues, experimental animal models and patient-derived cell lines via scRNA-seq enables the study of pancreas progenitors. (1) Single-cell suspensions enter high throughput microfluidic systems (e.g. 10X Chromium, Drop-Seq) allowing thousands of cells to be processed and sequenced. (2) Cells undergo dimensionality reduction and are clustered based upon expression profiles – represented via UMAP or t-SNE - enabling the identification of novel cell types and investigation of cellular heterogeneity. (3) Following clustering, differential expression analysis reveals changes in gene expression across cell types. (4) Prediction of cell trajectories can be inferred based upon changes in gene expression over a ‘pseudo’ time-course.

Techniques: Derivative Assay, High Throughput Screening Assay, Expressing