scholarly journals A high-density lineage tree reveals dynamics of expression differences accumulation in nondifferentiating clonal expansion

2021 ◽  
Author(s):  
Feng Chen ◽  
Zizhang Li ◽  
Xiaoyu Zhang ◽  
Peng Wu ◽  
Wenjing Yang ◽  
...  
Keyword(s):  
Single Cell ◽  
Cellular Proliferation ◽  
Single Cells ◽  
Cell Divisions ◽  
Expression Noise ◽  
Lineage Tree ◽  
Gradual Accumulation ◽  
Novel Method ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Differences in gene expression levels among genetically identical cells naturally accumulate during cellular proliferation, forming the basis of expression noise or differentiation. Nevertheless, how transcriptome-wide noise accumulation is constrained to maintain homeostasis during continuous cell divisions has remained largely unresolved. We developed a novel method named single-cell transcriptome and dense tree (STADT) to simultaneously determines the transcriptomes and lineage tree of >50% single cells in a single-cell-seeded colony. This lineage tree revealed gradual accumulation of transcriptome differences that became saturated upon four cell divisions, reduced expression noise for sub-tree/sub-colonies closer to inferred expression boundaries, and transcriptionally modulated co-fluctuations among genes. These results collectively showed, for the first time, constrained dynamics of expression noise in the context of cell division.

scholarly journals Single cell transcriptome atlas of mouse mammary epithelial cells across development

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Bhupinder Pal ◽  
Yunshun Chen ◽  
Michael J. G. Milevskiy ◽  
François Vaillant ◽  
Lexie Prokopuk ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Single Cell ◽  
Developmental Stages ◽  
Mammary Epithelial Cells ◽  
Expression Profiles ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Mammary Epithelial ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Background Heterogeneity within the mouse mammary epithelium and potential lineage relationships have been recently explored by single-cell RNA profiling. To further understand how cellular diversity changes during mammary ontogeny, we profiled single cells from nine different developmental stages spanning late embryogenesis, early postnatal, prepuberty, adult, mid-pregnancy, late-pregnancy, and post-involution, as well as the transcriptomes of micro-dissected terminal end buds (TEBs) and subtending ducts during puberty. Methods The single cell transcriptomes of 132,599 mammary epithelial cells from 9 different developmental stages were determined on the 10x Genomics Chromium platform, and integrative analyses were performed to compare specific time points. Results The mammary rudiment at E18.5 closely aligned with the basal lineage, while prepubertal epithelial cells exhibited lineage segregation but to a less differentiated state than their adult counterparts. Comparison of micro-dissected TEBs versus ducts showed that luminal cells within TEBs harbored intermediate expression profiles. Ductal basal cells exhibited increased chromatin accessibility of luminal genes compared to their TEB counterparts suggesting that lineage-specific chromatin is established within the subtending ducts during puberty. An integrative analysis of five stages spanning the pregnancy cycle revealed distinct stage-specific profiles and the presence of cycling basal, mixed-lineage, and 'late' alveolar intermediates in pregnancy. Moreover, a number of intermediates were uncovered along the basal-luminal progenitor cell axis, suggesting a continuum of alveolar-restricted progenitor states. Conclusions This extended single cell transcriptome atlas of mouse mammary epithelial cells provides the most complete coverage for mammary epithelial cells during morphogenesis to date. Together with chromatin accessibility analysis of TEB structures, it represents a valuable framework for understanding developmental decisions within the mouse mammary gland.

scholarly journals BAMM-SC: A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies

2018 ◽  
Author(s):  
Zhe Sun ◽  
Li Chen ◽  
Hongyi Xin ◽  
Qianhui Huang ◽  
Anthony R Cillo ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
R Package ◽  
Clustering Methods ◽  
Model Framework ◽  
Bayesian Hierarchical ◽  
Bayesian Mixture ◽  
Population Scale ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractThe recently developed droplet-based single cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we have developed a BAyesiany Mixture Model for Single Cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. Specifically, BAMM-SC takes raw data as input and can account for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulations and application of BAMM-SC to in-house scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrated that BAMM-SC outperformed existing clustering methods with improved clustering accuracy and reduced impact from batch effects. BAMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/~Cwec47/singlecell.html.

scholarly journals Droplet-based Single-cell Total RNA-seq Reveals Differential Non-Coding Expression and Splicing Patterns during Mouse Development

2021 ◽  
Author(s):  
Fredrik Salmen ◽  
Joachim De Jonghe ◽  
Tomasz S. Kaminski ◽  
Anna Alemany ◽  
Guillermo Parada ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Single Cell ◽  
Single Cells ◽  
Droplet Microfluidics ◽  
Mammalian Development ◽  
Mouse Development ◽  
Protein Coding ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

In recent years, single-cell transcriptome sequencing has revolutionized biology, allowing for the unbiased characterization of cellular subpopulations. However, most methods amplify the termini of polyadenylated transcripts capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts. Additionally, most workflows do not sequence the full transcript hindering the analysis of alternative splicing. We therefore developed VASA- seq to detect the total transcriptome in single cells. VASA-seq is compatible with both plate- based formats and droplet microfluidics. We applied VASA-seq to over 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. The dynamics of the total single-cell transcriptome result in the discovery of novel cell type markers many based on non-coding RNA, an in vivo cell cycle analysis and an improved RNA velocity characterization. Moreover, it provides the first comprehensive analysis of alternative splicing during mammalian development.

scholarly journals Pooled CRISPR screening with single-cell transcriptome read-out

2016 ◽  
Author(s):  
Paul Datlinger ◽  
Christian Schmidl ◽  
André F Rendeiro ◽  
Peter Traxler ◽  
Johanna Klughammer ◽  
...  
Keyword(s):  
Single Cell ◽  
Selectable Marker ◽  
Single Cells ◽  
Transcriptome Profiling ◽  
Genetic Screens ◽  
Biological Mechanisms ◽  
Guide Rna ◽  
New Gene ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractCRISPR-based genetic screens have revolutionized the search for new gene functions and biological mechanisms. However, widely used pooled screens are limited to simple read-outs of cell proliferation or the production of a selectable marker protein. Arrayed screens allow for more complex molecular read-outs such as transcriptome profiling, but they provide much lower throughput. Here we demonstrate CRISPR genome editing together with single-cell RNA sequencing as a new screening paradigm that combines key advantages of pooled and arrayed screens. This approach allowed us to link guide-RNA expression to the associated transcriptome responses in thousands of single cells using a straightforward and broadly applicable screening workflow.

scholarly journals Single Cell Transcriptome Conservation in Cryopreserved Cells and Tissues

2016 ◽  
Author(s):  
Amy Guillaumet-Adkins ◽  
Gustavo Rodríguez-Esteban ◽  
Elisabetta Mereu ◽  
Alberto Villanueva ◽  
August Vidal ◽  
...  
Keyword(s):  
Single Cell ◽  
Future Study ◽  
Paradigm Shift ◽  
Single Cells ◽  
Sample Preservation ◽  
Preservation Method ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Study Designs ◽  
Processing Steps

AbstractA variety of single cell RNA preparation procedures have been described. So far these protocols require fresh starting material, hindering complex study designs. We describe a sample preservation method that maintains transcripts in viable single cells and so allows to disconnect time and place of sampling from subsequent processing steps. To demonstrate the potential, we sequenced single cell transcriptomes from >1,000 fresh and conserved cells. Our results confirmed that the conservation process did not alter transcriptional profiles. This substantially broadens the scope of applications in single cell transcriptomics and could lead to a paradigm shift in future study designs.

scholarly journals Targeting individual cells by barcode in pooled sequence libraries

2017 ◽  
Author(s):  
Navpreet Ranu ◽  
Alexandra-Chloé Villani ◽  
Nir Hacohen ◽  
Paul C. Blainey
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Single Cells ◽  
Dna Barcodes ◽  
Rna Seq ◽  
Enrichment Method ◽  
Peripheral Blood Mononuclear ◽  
Efficient Sampling ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

There is rising interest in applying single-cell transcriptome analysis and other single-cell sequencing methods to resolve differences between cells. Pooled processing of thousands of single cells is now routinely practiced by introducing cell-specific DNA barcodes early in cell processing protocols1-5. However, researchers must sequence a large number of cells to sample rare subpopulations6-8, even when fluorescence-activated cell sorting (FACS) is used to pre-enrich rare cell populations. Here, a new molecular enrichment method is used in conjunction with FACS enrichment to enable efficient sampling of rare dendritic cell (DC) populations, including the recently identified AXL+SIGLEC6+ (AS DCs) subset7, within a 10X Genomics single-cell RNA-Seq library. DC populations collectively represent 1-2% of total peripheral blood mononuclear cells (PBMC), with AS DC representing only 1-3% of human blood DCs and 0.01-0.06% of total PBMCs.

scholarly journals Single-cell transcriptome profiling of the Ciona larval brain

2018 ◽  
Author(s):  
Sarthak Sharma ◽  
Wei Wang ◽  
Alberto Stolfi
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Expression Patterns ◽  
Transcriptome Profiling ◽  
Specific Gene ◽  
Larval Brain ◽  
Rnaseq Data ◽  
Cell Type Specific ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractThe tadpole-type larva of Ciona has emerged as an intriguing model system for the study of neurodevelopment. The Ciona intestinalis connectome has been recently mapped, revealing the smallest central nervous system (CNS) known in any chordate, with only 177 neurons. This minimal CNS is highly reminiscent of larger CNS of vertebrates, sharing many conserved developmental processes, anatomical compartments, neuron subtypes, and even specific neural circuits. Thus, the Ciona tadpole offers a unique opportunity to understand the development and wiring of a chordate CNS at single-cell resolution. Here we report the use of single-cell RNAseq to profile the transcriptomes of single cells isolated by fluorescence-activated cell sorting (FACS) from the whole brain of Ciona robusta (formerly intestinalis Type A) larvae. We have also compared these profiles to bulk RNAseq data from specific subsets of brain cells isolated by FACS using cell type-specific reporter plasmid expression. Taken together, these datasets have begun to reveal the compartment- and cell-specific gene expression patterns that define the organization of the Ciona larval brain.

scholarly journals Expanding the CITE-seq tool-kit: Detection of proteins, transcriptomes, clonotypes and CRISPR perturbations with multiplexing, in a single assay

2018 ◽  
Author(s):  
Eleni Mimitou ◽  
Anthony Cheng ◽  
Antonino Montalbano ◽  
Stephanie Hao ◽  
Marlon Stoeckius ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Single Cells ◽  
Surface Markers ◽  
Protein Levels ◽  
Cellular Processes ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Lymphoma Sample

ABSTRACTRapid technological progress in the recent years has allowed the high-throughput interrogation of different types of biomolecules from single cells. Combining several of these readouts into integrated multi-omic assays is essential to comprehensively understand and model cellular processes. Here, we report the development of Expanded CRISPR-compatible Cellular Indexing of Transcriptomes and Epitopes by sequencing (ECCITE-seq) for the high-throughput characterization of at least five modalities of information from each single cell: transcriptome, immune receptor clonotypes, surface markers, sample identity and sgRNAs. We demonstrate the use of ECCITE-seq to directly and efficiently capture sgRNA molecules and measure their effects on gene expression and protein levels, opening the possibility of performing high throughput single cell CRISPR screens with multimodal readout using existing libraries and commonly used vectors. Finally, by utilizing the combined phenotyping of clonotype and cell surface markers in immune cells, we apply ECCITE to study a lymphoma sample to discriminate cells and define molecular signatures of malignant cells within a heterogeneous population.

scholarly journals A flexible microfluidic system for single-cell transcriptome profiling elucidates phased transcriptional regulators of cell cycle

2020 ◽  
Author(s):  
Karen Davey ◽  
Daniel Wong ◽  
Filip Konopacki ◽  
Eugene Kwa ◽  
Heike Fiegler ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Regulatory Networks ◽  
Single Cells ◽  
Transcriptome Profiling ◽  
Cost Effective ◽  
Microfluidic System ◽  
Cellular Systems ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

SummarySingle cell transcriptome profiling has emerged as a breakthrough technology for the high-resolution understanding of complex cellular systems. Here we report a flexible, cost-effective and user-friendly droplet-based microfluidics system, called the Nadia Instrument, that can allow 3’ mRNA capture of ∼50,000 single cells or individual nuclei in a single run. The precise pressure-based system demonstrates highly reproducible droplet size, low doublet rates and high mRNA capture efficiencies that compare favorably in the field. Moreover, when combined with the Nadia Innovate, the system can be transformed into an adaptable setup that enables use of different buffers and barcoded bead configurations to facilitate diverse applications. Finally, by 3’ mRNA profiling asynchronous human and mouse cells at different phases of the cell cycle, we demonstrate the system’s ability to readily distinguish distinct cell populations and infer underlying transcriptional regulatory networks. Notably this identified multiple transcription factors that had little or no known link to the cell cycle (e.g. DRAP1, ZKSCAN1 and CEBPZ). In summary, the Nadia platform represents a promising and flexible technology for future transcriptomic studies, and other related applications, at cell resolution.

scholarly journals Single cell transcriptome profiling of mouse and hESC-derived pancreatic progenitors

2018 ◽  
Author(s):  
Nicole A. J. Krentz ◽  
Michelle Lee ◽  
Eric E. Xu ◽  
Shugo Sasaki ◽  
Francis C. Lynn
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Differential Expression Analysis ◽  
Embryonic Stem ◽  
Transcriptome Profiling ◽  
Β Cells ◽  
Pancreatic Progenitors ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Endocrine Progenitors

SummaryHuman embryonic stem cells (hESCs) are a potential unlimited source of insulin-producing β-cells for diabetes treatment. A greater understanding of how β-cells form during embryonic development will improve current hESC differentiation protocols. As β-cells are formed from NEUROG3-expressing endocrine progenitors, this study focused on characterizing the single-cell transcriptomes of mouse and hESC-derived endocrine progenitors. To do this, 7,223 E15.5 and 6,852 E18.5 single cells were isolated from Neurog3-Cre; Rosa26mT/mG embryos, allowing for enrichment of endocrine progenitors (yellow; tdTomato + EGFP) and endocrine cells (green; EGFP). From a NEUROG3-2A-eGFP CyT49 hESC reporter line (N5-5), 4,497 hESC-derived endocrine progenitor cells were sequenced. Differential expression analysis reveals enrichment of markers that are consistent with progenitor, endocrine, or novel cell-state populations. This study characterizes the single-cell transcriptomes of mouse and hESC-derived endocrine progenitors and serves as a resource (https://lynnlab.shinyapps.io/embryonic_pancreas/) for improving the formation of functional β-like cells from hESCs.

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