Computational Methods for Single-Cell RNA Sequencing

Hie, Brian; Peters, Joshua; Nyquist, Sarah K.; Shalek, Alex K.; Berger, Bonnie; Bryson, Bryan

doi:10.1146/annurev-biodatasci-012220-100601

Cited by 84 publications

(72 citation statements)

References 198 publications

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“…Single-cell RNA-sequencing (scRNA-seq) has transformed our view of the airway epithelium, unveiling a level of cellular diversity that had not been documented using microscopy for phenotyping 10 . This technology profiles the transcriptome of individual cells and therefore facilitates an unbiased characterization of different cell subsets in a heterogenous cell population, driving the discovery of unidentified cell types and states 11 – 13 . The Human Cell Atlas Consortium aims to comprehensively chart — at single-cell resolution with integration of spatial data — the changes that occur in lung cell composition and molecular phenotype in health and disease 14 .…”

Section: Mapping the Airway Epithelial Landscapementioning

confidence: 99%

Regulation of immune responses by the airway epithelial cell landscape

2021

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The community of cells lining our airways plays a collaborative role in the preservation of immune homeostasis in the lung and provides protection from the pathogens and pollutants in the air we breathe. In addition to its structural attributes that provide effective mucociliary clearance of the lower airspace, the airway epithelium is an immunologically active barrier surface that senses changes in the airway environment and interacts with resident and recruited immune cells. Single-cell RNA-sequencing is illuminating the cellular heterogeneity that exists in the airway wall and has identified novel cell populations with unique molecular signatures, trajectories of differentiation and diverse functions in health and disease. In this Review, we discuss how our view of the airway epithelial landscape has evolved with the advent of transcriptomic approaches to cellular phenotyping, with a focus on epithelial interactions with the local neuronal and immune systems.

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Section: Mapping the Airway Epithelial Landscapementioning

confidence: 99%

Regulation of immune responses by the airway epithelial cell landscape

2021

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“…The advent of scRNAseq transcriptome analysis has enabled the exploration of retinogenesis with unprecedented resolution. Several analytical methods have been developed for reconstructing developmental trajectories and pseudotime relationships (the position of the cell along a time trajectory) (discussed by Hie et al, 2020 ). These methods enable the study of cell developmental lineages and their transition between different cell states.…”

Section: The Development Of Cell Types Within Retinal Organoidsmentioning

confidence: 99%

Retinal organoids: a window into human retinal development

2020

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Retinal development and maturation are orchestrated by a series of interacting signalling networks that drive the morphogenetic transformation of the anterior developing brain. Studies in model organisms continue to elucidate these complex series of events. However, the human retina shows many differences from that of other organisms and the investigation of human eye development now benefits from stem cell-derived organoids. Retinal differentiation methods have progressed from simple 2D adherent cultures to self-organising micro-physiological systems. As models of development, these have collectively offered new insights into the previously unexplored early development of the human retina and informed our knowledge of the key cell fate decisions that govern the specification of light-sensitive photoreceptors. Although the developmental trajectories of other retinal cell types remain more elusive, the collation of omics datasets, combined with advanced culture methodology, will enable modelling of the intricate process of human retinogenesis and retinal disease in vitro.

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“…We found similar variability in the clustering solutions of these methods. There has been an explosion of methods developed and published to analyze scRNAseq data, which makes it difficult to select the ideal method [13] . Instead of providing "benchmarking" between different methods, the purpose of this paper is to provide guidance to setting parameters using the Seurat R package when analyzing scRNAseq generated from fresh tumor samples.…”

Section: Introductionmentioning

confidence: 99%