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

2020 ◽  
Vol 48 (1) ◽  
pp. 327-336 ◽  
Author(s):  
L.E. Zaragosi ◽  
M. Deprez ◽  
P. Barbry

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.

BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Adam Cornish ◽  
Shrabasti Roychoudhury ◽  
Krishna Sarma ◽  
Suravi Pramanik ◽  
Kishor Bhakat ◽  
...  

Abstract Background Single-cell sequencing enables us to better understand genetic diseases, such as cancer or autoimmune disorders, which are often affected by changes in rare cells. Currently, no existing software is aimed at identifying single nucleotide variations or micro (1-50 bp) insertions and deletions in single-cell RNA sequencing (scRNA-seq) data. Generating high-quality variant data is vital to the study of the aforementioned diseases, among others. Results In this study, we report the design and implementation of Red Panda, a novel method to accurately identify variants in scRNA-seq data. Variants were called on scRNA-seq data from human articular chondrocytes, mouse embryonic fibroblasts (MEFs), and simulated data stemming from the MEF alignments. Red Panda had the highest Positive Predictive Value at 45.0%, while other tools—FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, Monovar, and Platypus—ranged from 5.8–41.53%. From the simulated data, Red Panda had the highest sensitivity at 72.44%. Conclusions We show that our method provides a novel and improved mechanism to identify variants in scRNA-seq as compared to currently existing software. However, methods for identification of genomic variants using scRNA-seq data can be still improved.


BMC Genomics ◽  
2020 ◽  
Vol 21 (S9) ◽  
Author(s):  
Siamak Zamani Dadaneh ◽  
Paul de Figueiredo ◽  
Sing-Hoi Sze ◽  
Mingyuan Zhou ◽  
Xiaoning Qian

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.


2022 ◽  
Vol 7 (1) ◽  
Author(s):  
Li Zhang ◽  
Yiming Zhang ◽  
Chengdi Wang ◽  
Ying Yang ◽  
Yinyun Ni ◽  
...  

AbstractLung adenocarcinoma (LUAD) and squamous carcinoma (LUSC) are two major subtypes of non-small cell lung cancer with distinct pathologic features and treatment paradigms. The heterogeneity can be attributed to genetic, transcriptional, and epigenetic parameters. Here, we established a multi-omics atlas, integrating 52 single-cell RNA sequencing and 2342 public bulk RNA sequencing. We investigated their differences in genetic amplification, cellular compositions, and expression modules. We revealed that LUAD and LUSC contained amplifications occurring selectively in subclusters of AT2 and basal cells, and had distinct cellular composition modules associated with poor survival of lung cancer. Malignant and stage-specific gene analyses further uncovered critical transcription factors and genes in tumor progression. Moreover, we identified subclusters with proliferating and differentiating properties in AT2 and basal cells. Overexpression assays of ten genes, including sub-cluster markers AQP5 and KPNA2, further indicated their functional roles, providing potential targets for early diagnosis and treatment in lung cancer.


2017 ◽  
Author(s):  
Isabelle Stévant ◽  
Yasmine Neirjinck ◽  
Christelle Borel ◽  
Jessica Escoffier ◽  
Lee B. Smith ◽  
...  

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.


Cell Reports ◽  
2018 ◽  
Vol 22 (6) ◽  
pp. 1589-1599 ◽  
Author(s):  
Isabelle Stévant ◽  
Yasmine Neirijnck ◽  
Christelle Borel ◽  
Jessica Escoffier ◽  
Lee B. Smith ◽  
...  

2017 ◽  
Author(s):  
Diego R. Revinski ◽  
Laure-Emmanuelle Zaragosi ◽  
Camille Boutin ◽  
Sandra Ruiz-Garcia ◽  
Marie Deprez ◽  
...  

AbstractMulticiliated cells (MCCs) harbour dozens to hundreds of motile cilia, which beat in a synchronized and directional manner, thus generating hydrodynamic forces important in animal physiology. In vertebrates, MCC differentiation critically depends on the synthesis and release of numerous centrioles by specialized structures called deuterosomes. Little is known about the composition, organization and regulation of deuterosomes. Here, single-cell RNA sequencing reveals that human deuterosome-stage MCCs are characterized by the expression of many cell cycle-related genes. We further investigated the uncharacterized vertebrate-specific cell division cycle 20B (CDC20B) gene, the host gene of microRNA-449abc. We show that the CDC20B protein associates to deuterosomes and is required for the release of centrioles and the subsequent production of cilia in mouse and Xenopus MCCs. CDC20B interacts with PLK1, which has been shown to coordinate centriole disengagement with the protease Separase in mitotic cells. Strikingly, over-expression of Separase rescued centriole disengagement and cilia production in CDC20B-deficient MCCs. This work reveals the shaping of a new biological function, deuterosome-mediated centriole production in vertebrate MCCs, by adaptation of canonical and recently evolved cell cycle-related molecules.


2020 ◽  
Vol 21 (1) ◽  
pp. 163-181
Author(s):  
Guangdun Peng ◽  
Guizhong Cui ◽  
Jincan Ke ◽  
Naihe Jing

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.


Cell Reports ◽  
2020 ◽  
Vol 30 (12) ◽  
pp. 4250-4265.e6 ◽  
Author(s):  
Allison M. Greaney ◽  
Taylor S. Adams ◽  
Micha Sam Brickman Raredon ◽  
Elise Gubbins ◽  
Jonas C. Schupp ◽  
...  

iScience ◽  
2018 ◽  
Vol 7 ◽  
pp. 16-29 ◽  
Author(s):  
Fernando H. Biase ◽  
Qiuyang Wu ◽  
Riccardo Calandrelli ◽  
Marcelo Rivas-Astroza ◽  
Shuigeng Zhou ◽  
...  

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