Scientists are finding that they can understand organ function (and dysfunction in the case of disease) better if they know where the cells that make up these organs came from.
New single-cell technologies allow all cell states within a differentiating tissue to be identified. However, the relationships between cell states that lead to formation of tissues have been difficult to define from the large volumes of data generated.
Now Ken Lau, Ph.D., and colleagues have overcome that challenge with an algorithm they developed called p-Creode. When applied to single-cell RNA-seq and imaging data of a poorly understood population of chemosensory cells in the gut called “tuft cells,” p-Creode uncovered alternative routes of development in the small intestine and colon.
The p-Creode Algorithm for Analyzing Single-Cell Data
(i) Synthetic dataset representing single cells in two-dimensional expression space with five end states and three branch points. Overlay represents density of cells. (ii) Density-normalized representation of the original dataset from downsampling. Overlay represents the density after downsampling. (iii) Density-based k-nearest neighbor (d-kNN) network constructed from downsampled data. Overlay represents the graph measure of closeness centrality derived from the d-kNN network, which is a surrogate for cell state (low, end state; high, transition state). (iv) End states identified by K-means clustering and silhouette scoring of cells with low closeness values (<mean). The number of end-state clusters is doubled to allow for rare cell types. End-state clusters are colored, and open circles represent the centroid per cluster. (v) Topology constructed with a hierarchical placement strategy of cells on path nodes between end states (red), which allows for the placement of data points along an ancestral continuum. Overlay represents the original density of cells. (vi) Aligned topology (red) with maximal consensus though iterative assignment and repositioning of path nodes using neighborhood cell densities. (vii) Representative topology extracted using p-Creode scoring from an ensemble of N topologies. Node size in the output graph represents the original density of cells.
Because tuft cells play a role in immune responses against helminth (parasitic worm) infections, understanding and potentially modulating tuft cell development may be important in controlling inflammatory diseases in the gut, the researchers reported in the journal Cell Systems.
Source – Vanderbilt University