Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B cell Development

By Sean Bendall, K. Davis, E. Amir, Michelle D. Tadmor, Erin F. Simonds, Tiffany J. Chen, Daniel K. Shenfeld, Garry P. Nolan, Dana Pe'er. Cell, 2014

905 citations recorded by the source corpus.

Summary Tissue regeneration is an orchestrated progression of cells from an immature state to a mature one, conventionally represented as distinctive cell subsets. A continuum of transitional cell states exists between these discrete stages. We combine the depth of single-cell mass cytometry and an algorithm developed to leverage this continuum by aligning single cells of a given lineage onto a unified trajectory that accurately predicts the developmental path de novo. Applied to human B cell lymphopoiesis, the algorithm (termed Wanderlust) constructed trajectories spanning from hematopoietic stem cells through to naïve B cells. This trajectory revealed nascent fractions of B cell progenitors and aligned them with developmentally-cued regulatory signaling including IL-7/STAT5 and cellular events such as immunoglobulin rearrangement, highlighting checkpoints across which regulatory signals are rewired paralleling changes in cellular state. This study provides a comprehensive analysis of human B lymphopoiesis, laying a foundation to apply this approach to other tissues and “corrupted” developmental processes including cancer.

Subfields: Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0ee90949240503e5a002feb86df17bd0049da9b5

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Citations in the corpus

The technology and biology of single-cell RNA sequencing. (2015, methodology): “Placing cells on a temporal trajectory can also be done using Wanderlust, which was developed using CyToF single-cell proteomic data, but similar approaches can also be applied to transcriptomic data (Bendall et al., 2014).”
The Human Cell Atlas (2017, positive): “Computationally ordering cells along a time-course of development provides another way to infer regulators – a strategy that has been successful in, for example, differentiating B cells (Bendall et al., 2014), myoblasts (Trapnell et al.”
Deep Learning for Imaging Flow Cytometry: (2016, methodology): “For example, in the 231 analysis of single-cell transcriptomic and proteomic data, much research effort has been 232 applied to solve precisely this task [19,21,22].”
Identification of spatial expression trends in single-cell gene expression data (2018, neutral): “…has high power, whereas spatial methods have the additional ability to identify continuous gradients or spatial expression patterns defined by a fewer number of genes which would be hard to identify by cell-cell expression profile correlation based clustering of cells (Supplementary Figure 13).”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, background): “Since Monocle (Trapnell et al , 2014) and Wanderlust (Bendall et al , 2014) established the TI field, the number of available meth-ods has exploded.”
Design and computational analysis of single-cell RNA-sequencing experiments (2016, background): “Wanderlust [93] Cells are represented as nodes in an ensemble of k-nearest neighbor graphs.”
CellRank for directed single-cell fate mapping (2020, neutral): “The main assumption underlying all pseudotime algorithms that faithfully capture trajectories 1,7–10 is that cell states change in small steps with many transitional populations.”
Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours (2018, background): “Cosine distances have been widely used for measuring cell similarities based on their expression profiles [11] [13] [14] [15] and are appealing as they are scale-independent [15], which makes them robust to technical differences in sequencing depth and capture efficiency between batches.”