Unmixing cell lineage states with single-cell RNA sequencing

doi:10.1038/nmeth.4047

Using single-cell RNA sequencing to unravel cell lineage relationships in the respiratory tract

Biochemical Society Transactions ◽

10.1042/bst20191010 ◽

2020 ◽

Vol 48 (1) ◽

pp. 327-336 ◽

Cited By ~ 8

Author(s):

L.E. Zaragosi ◽

M. Deprez ◽

P. Barbry

Keyword(s):

Respiratory Tract ◽

Single Cell ◽

Rna Sequencing ◽

Cell Lineage ◽

Neuroendocrine Cells ◽

Basal Cells ◽

Multiciliated Cells ◽

Single Cell Rna Sequencing ◽

Rare Cells ◽

Genetic Labeling

The respiratory tract is lined by a pseudo-stratified epithelium from the nose to terminal bronchioles. This first line of defense of the lung against external stress includes five main cell types: basal, suprabasal, club, goblet and multiciliated cells, as well as rare cells such as ionocytes, neuroendocrine and tuft/brush cells. At homeostasis, this epithelium self-renews at low rate but is able of fast regeneration upon damage. Airway epithelial cell lineages during regeneration have been investigated in the mouse by genetic labeling, mainly after injuring the epithelium with noxious agents. From these approaches, basal cells have been identified as progenitors of club, goblet and multiciliated cells, but also of ionocytes and neuroendocrine cells. Single-cell RNA sequencing, coupled to lineage inference algorithms, has independently allowed the establishment of comprehensive pictures of cell lineage relationships in both mouse and human. In line with genetic tracing experiments in mouse trachea, studies using single-cell RNA sequencing (RNAseq) have shown that basal cells first differentiate into club cells, which in turn mature into goblet cells or differentiate into multiciliated cells. In the human airway epithelium, single-cell RNAseq has identified novel intermediate populations such as deuterosomal cells, ‘hybrid’ mucous-multiciliated cells and progenitors of rare cells. Novel differentiation dynamics, such as a transition from goblet to multiciliated cells have also been discovered. The future of cell lineage relationships in the respiratory tract now resides in the combination of genetic labeling approaches with single-cell RNAseq to establish, in a definitive manner, the hallmarks of cellular lineages in normal and pathological situations.

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Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data

BMC Genomics ◽

10.1186/s12864-020-06938-8 ◽

2020 ◽

Vol 21 (S9) ◽

Author(s):

Siamak Zamani Dadaneh ◽

Paul de Figueiredo ◽

Sing-Hoi Sze ◽

Mingyuan Zhou ◽

Xiaoning Qian

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Data Augmentation ◽

Negative Binomial ◽

Cell Lineage ◽

Simulated Data ◽

Molecular Heterogeneity ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Zero Counts

Abstract Background Single-cell RNA sequencing (scRNA-seq) is a powerful profiling technique at the single-cell resolution. Appropriate analysis of scRNA-seq data can characterize molecular heterogeneity and shed light into the underlying cellular process to better understand development and disease mechanisms. The unique analytic challenge is to appropriately model highly over-dispersed scRNA-seq count data with prevalent dropouts (zero counts), making zero-inflated dimensionality reduction techniques popular for scRNA-seq data analyses. Employing zero-inflated distributions, however, may place extra emphasis on zero counts, leading to potential bias when identifying the latent structure of the data. Results In this paper, we propose a fully generative hierarchical gamma-negative binomial (hGNB) model of scRNA-seq data, obviating the need for explicitly modeling zero inflation. At the same time, hGNB can naturally account for covariate effects at both the gene and cell levels to identify complex latent representations of scRNA-seq data, without the need for commonly adopted pre-processing steps such as normalization. Efficient Bayesian model inference is derived by exploiting conditional conjugacy via novel data augmentation techniques. Conclusion Experimental results on both simulated data and several real-world scRNA-seq datasets suggest that hGNB is a powerful tool for cell cluster discovery as well as cell lineage inference.

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Deciphering cell lineage specification during male sex determination with single-cell RNA sequencing

10.1101/190264 ◽

2017 ◽

Cited By ~ 2

Author(s):

Isabelle Stévant ◽

Yasmine Neirjinck ◽

Christelle Borel ◽

Jessica Escoffier ◽

Lee B. Smith ◽

...

Keyword(s):

Sex Determination ◽

Single Cell ◽

Progenitor Cells ◽

Rna Sequencing ◽

Cell Fate ◽

Time Course ◽

Cell Lineage ◽

Lineage Specification ◽

Cell Fate Decisions ◽

Single Cell Rna Sequencing

SummaryThe gonad is a unique biological system for studying cell fate decisions. However, major questions remain regarding the identity of somatic progenitor cells and the transcriptional events driving cell differentiation. Using time course single cell RNA sequencing on XY mouse gonads during sex determination, we identified a single population of somatic progenitor cells prior sex determination. A subset of these progenitors differentiate into Sertoli cells, a process characterized by a highly dynamic genetic program consisting of sequential waves of gene expression. Another subset of multipotent cells maintains their progenitor state but undergo significant transcriptional changes that restrict their competence towards a steroidogenic fate required for the differentiation of fetal Leydig cells. These results question the dogma of the existence of two distinct somatic cell lineages at the onset of sex determination and propose a new model of lineage specification from a unique progenitor cell population.

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Deciphering Cell Lineage Specification during Male Sex Determination with Single-Cell RNA Sequencing

Cell Reports ◽

10.1016/j.celrep.2018.01.043 ◽

2018 ◽

Vol 22 (6) ◽

pp. 1589-1599 ◽

Cited By ~ 54

Author(s):

Isabelle Stévant ◽

Yasmine Neirijnck ◽

Christelle Borel ◽

Jessica Escoffier ◽

Lee B. Smith ◽

...

Keyword(s):

Sex Determination ◽

Single Cell ◽

Rna Sequencing ◽

Cell Lineage ◽

Lineage Specification ◽

Single Cell Rna Sequencing ◽

Male Sex

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Using Single-Cell and Spatial Transcriptomes to Understand Stem Cell Lineage Specification During Early Embryo Development

Annual Review of Genomics and Human Genetics ◽

10.1146/annurev-genom-120219-083220 ◽

2020 ◽

Vol 21 (1) ◽

pp. 163-181

Author(s):

Guangdun Peng ◽

Guizhong Cui ◽

Jincan Ke ◽

Naihe Jing

Keyword(s):

Stem Cell ◽

Single Cell ◽

Rna Sequencing ◽

Cell Fate ◽

Cell Lineage ◽

Stem Cell Differentiation ◽

Early Embryo ◽

Stem Cell Lineage ◽

Single Cell Rna Sequencing ◽

Cell Organization

Embryonic development and stem cell differentiation provide a paradigm to understand the molecular regulation of coordinated cell fate determination and the architecture of tissue patterning. Emerging technologies such as single-cell RNA sequencing and spatial transcriptomics are opening new avenues to dissect cell organization, the divergence of morphological and molecular properties, and lineage allocation. Rapid advances in experimental and computational tools have enabled researchers to make many discoveries and revisit old hypotheses. In this review, we describe the use of single-cell RNA sequencing in studies of molecular trajectories and gene regulation networks for stem cell lineages, while highlighting the integratedexperimental and computational analysis of single-cell and spatial transcriptomes in the molecular annotation of tissue lineages and development during postimplantation gastrulation.

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Rainbow-Seq: Combining Cell Lineage Tracing with Single-Cell RNA Sequencing in Preimplantation Embryos

iScience ◽

10.1016/j.isci.2018.08.009 ◽

2018 ◽

Vol 7 ◽

pp. 16-29 ◽

Cited By ~ 3

Author(s):

Fernando H. Biase ◽

Qiuyang Wu ◽

Riccardo Calandrelli ◽

Marcelo Rivas-Astroza ◽

Shuigeng Zhou ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Lineage ◽

Lineage Tracing ◽

Preimplantation Embryos ◽

Single Cell Rna Sequencing

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Stromal Heterogeneity in the Human Proliferative Endometrium—A Single-Cell RNA Sequencing Study

Journal of Personalized Medicine ◽

10.3390/jpm11060448 ◽

2021 ◽

Vol 11 (6) ◽

pp. 448

Author(s):

Suzanna Queckbörner ◽

Carolina von Grothusen ◽

Nageswara Rao Boggavarapu ◽

Roy Mathew Francis ◽

Lindsay C. Davies ◽

...

Keyword(s):

Menstrual Cycle ◽

Single Cell ◽

Rna Sequencing ◽

Cell Lineage ◽

Bioinformatic Analysis ◽

Matrix Composition ◽

Cellular Interactions ◽

Novel Therapy ◽

Single Cell Rna Sequencing ◽

Endometrial Regeneration

The endometrium undergoes regular regeneration and stromal proliferation as part of the normal menstrual cycle. To better understand cellular interactions driving the mechanisms in endometrial regeneration we employed single-cell RNA sequencing. Endometrial biopsies were obtained during the proliferative phase of the menstrual cycle from healthy fertile women and processed to single-cell suspensions which were submitted for sequencing. In addition to known endometrial cell types, bioinformatic analysis revealed multiple stromal populations suggestive of specific stromal niches with the ability to control inflammation and extracellular matrix composition. Ten different stromal cells and two pericyte subsets were identified. Applying different R packages (Seurat, SingleR, Velocyto) we established cell cluster diversity and cell lineage/trajectory, while using external data to validate our findings. By understanding healthy regeneration in the described stromal compartments, we aim to identify points of further investigation and possible targets for novel therapy development for benign gynecological disorders affecting endometrial regeneration and proliferation such as endometriosis and Asherman’s syndrome.

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