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Sphetcher: Spherical Thresholding Improves Sketching of Single-Cell Transcriptomic Heterogeneity.

ABSTRACT: The massive size of single-cell RNA sequencing datasets often exceeds the capability of current computational analysis methods to solve routine tasks such as detection of cell types. Recently, geometric sketching was introduced as an alternative to uniform subsampling. It selects a subset of cells (the sketch) that evenly cover the transcriptomic space occupied by the original dataset, to accelerate downstream analyses and highlight rare cell types. Here, we propose algorithm Sphetcher that makes use of the thresholding technique to efficiently pick representative cells within spheres (as opposed to the typically used equal-sized boxes) that cover the entire transcriptomic space. We show that the spherical sketch computed by Sphetcher constitutes a more accurate representation of the original transcriptomic landscape. Our optimization scheme allows to include fairness aspects that can encode prior biological or experimental knowledge. We show how a fair sampling can inform the inference of the trajectory of human skeletal muscle myoblast differentiation.

SUBMITTER: Do VH

PROVIDER: S-EPMC7235285 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Sphetcher: Spherical Thresholding Improves Sketching of Single-Cell Transcriptomic Heterogeneity.

Do Van Hoan VH Elbassioni Khaled K Canzar Stefan S

iScience 20200504 6

The massive size of single-cell RNA sequencing datasets often exceeds the capability of current computational analysis methods to solve routine tasks such as detection of cell types. Recently, geometric sketching was introduced as an alternative to uniform subsampling. It selects a subset of cells (the sketch) that evenly cover the transcriptomic space occupied by the original dataset, to accelerate downstream analyses and highlight rare cell types. Here, we propose algorithm Sphetcher that make ...[more]

PMID: 32438285

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Project description:OBJECTIVES:The streptozotocin (STZ) model is widely used in diabetes research. However, the cellular and molecular states of pancreatic endocrine cells in this model remain unclear. This study explored the molecular characteristics of islet cells treated with STZ and re-evaluated ?-cell dysfunction and regeneration in the STZ model. METHODS:We performed single-cell RNA sequencing of pancreatic endocrine cells from STZ-treated mice. High-quality sequencing data from 2,999 cells were used to identify clusters via Louvain clustering analysis. Principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), force-directed layout (FDL), and differential expression analysis were used to define the heterogeneity and transcriptomic changes in islet cells. In addition, qPCR and immunofluorescence staining were used to confirm findings from the sequencing data. RESULTS:Untreated ?-cells were divided into two populations at the transcriptomic level, a large high-Glut2 expression (Glut2high) population and a small low-Glut2 expression (Glut2low) population. At the transcriptomic level, Glut2low ?-cells in adult mice did not represent a developmentally immature state, although a fraction of genes associated with ?-cell maturation and function were downregulated in Glut2low cells. After a single high-dose STZ treatment, most Glut2high cells were killed, but Glut2low cells survived and over time changed to a distinct cell state. We did not observe conversion of Glut2low to Glut2high ?-cells up to 9 months after STZ treatment. In addition, we did not detect transcriptomic changes in the non-? endocrine cells or a direct trans-differentiation pathway from the ?-cell lineage to the ?-cell lineage in the STZ model. CONCLUSIONS:We identified the heterogeneity of ?-cells in both physiological and pathological conditions. However, we did not observe conversion of Glut2low to Glut2high ?-cells, transcriptomic changes in the non-? endocrine cells, or direct trans-differentiation from the ?-cell lineage to the ?-cell lineage in the STZ model. Our results clearly define the states of islet cells treated with STZ and allow us to re-evaluate the STZ model widely used in diabetes studies.

Dataset Information

Sphetcher: Spherical Thresholding Improves Sketching of Single-Cell Transcriptomic Heterogeneity.

Publications

Sphetcher: Spherical Thresholding Improves Sketching of Single-Cell Transcriptomic Heterogeneity.

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