scholarly journals Single-cell proteomics reveals downregulation of TMSB4X to drive actin release for stereocilia assembly

2019 ◽  
Author(s):  
Ying Zhu ◽  
Mirko Scheibinger ◽  
Daniel C. Ellwanger ◽  
Jocelyn F. Krey ◽  
Dongseok Choi ◽  
...  
Keyword(s):  
Single Cell ◽  
Progenitor Cells ◽  
Hair Cells ◽  
De Novo ◽  
Single Cells ◽  
Transcriptome Profiling ◽  
Developmental Trajectory ◽  
Sensory Hair ◽  
Hair Cell Differentiation ◽  
Cell Transcriptome

AbstractHearing and balance rely on small sensory hair cells that reside in the inner ear. To explore dynamic changes in the abundant proteins present in differentiating hair cells, we used nanoliter-scale shotgun mass spectrometry of single cells, each ∼1 picoliter, from utricles of embryonic day 15 chickens. We identified unique constellations of proteins or protein groups from presumptive hair cells and from progenitor cells. The single-cell proteomes enabled the de novo reconstruction of a developmental trajectory. Inference of protein expression dynamics revealed that the actin monomer binding protein thymosin β4 (TMSB4X) was present in progenitors but dropped precipitously during hair-cell differentiation. Complementary single-cell transcriptome profiling showed downregulation of TMSB4X mRNA during maturation of hair cells. We propose that most actin is sequestered by TMSB4X in progenitor cells, but upon differentiation to hair cells, actin is released to build the sensory hair bundle.

scholarly journals Single-cell proteomics reveals changes in expression during hair-cell development

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Ying Zhu ◽  
Mirko Scheibinger ◽  
Daniel Christian Ellwanger ◽  
Jocelyn F Krey ◽  
Dongseok Choi ◽  
...  
Keyword(s):  
Single Cell ◽  
Hair Cells ◽  
De Novo ◽  
Single Cells ◽  
Transcriptome Profiling ◽  
Developmental Trajectory ◽  
Proteomics Data ◽  
Sensory Hair Cells ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Hearing and balance rely on small sensory hair cells that reside in the inner ear. To explore dynamic changes in the abundant proteins present in differentiating hair cells, we used nanoliter-scale shotgun mass spectrometry of single cells, each ~1 picoliter, from utricles of embryonic day 15 chickens. We identified unique constellations of proteins or protein groups from presumptive hair cells and from progenitor cells. The single-cell proteomes enabled the de novo reconstruction of a developmental trajectory using protein expression levels, revealing proteins that greatly increased in expression during differentiation of hair cells (e.g., OCM, CRABP1, GPX2, AK1, GSTO1) and those that decreased during differentiation (e.g., TMSB4X, AGR3). Complementary single-cell transcriptome profiling showed corresponding changes in mRNA during maturation of hair cells. Single-cell proteomics data thus can be mined to reveal features of cellular development that may be missed with transcriptomics.

scholarly journals Single-cell transcriptome analysis reveals cell lineage specification in temporal-spatial patterns in human cortical development

2020 ◽  
Vol 6 (34) ◽  
pp. eaaz2978 ◽  
Author(s):  
Xiaoying Fan ◽  
Yuanyuan Fu ◽  
Xin Zhou ◽  
Le Sun ◽  
Ming Yang ◽  
...  
Keyword(s):  
Single Cell ◽  
Neural Progenitor Cells ◽  
Neuronal Development ◽  
Single Cell Analysis ◽  
Cortical Development ◽  
Cell Lineage ◽  
Transcriptome Profiling ◽  
Developmental Trajectory ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Neurogenesis processes differ in different areas of the cortex in many species, including humans. Here, we performed single-cell transcriptome profiling of the four cortical lobes and pons during human embryonic and fetal development. We identified distinct subtypes of neural progenitor cells (NPCs) and their molecular signatures, including a group of previously unidentified transient NPCs. We specified the neurogenesis path and molecular regulations of the human deep-layer, upper-layer, and mature neurons. Neurons showed clear spatial and temporal distinctions, while glial cells of different origins showed development patterns similar to those of mice, and we captured the developmental trajectory of oligodendrocyte lineage cells until the human mid-fetal stage. Additionally, we verified region-specific characteristics of neurons in the cortex, including their distinct electrophysiological features. With systematic single-cell analysis, we decoded human neuronal development in temporal and spatial dimensions from GW7 to GW28, offering deeper insights into the molecular regulations underlying human neurogenesis and cortical 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 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 BREM-SC: A Bayesian Random Effects Mixture Model for Joint Clustering Single Cell Multi-omics Data

2020 ◽  
Author(s):  
Xinjun Wang ◽  
Zhe Sun ◽  
Yanfu Zhang ◽  
Zhongli Xu ◽  
Heng Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Mixture Model ◽  
Random Effects ◽  
Single Cells ◽  
Real Data ◽  
Transcriptome Profiling ◽  
Surface Expression ◽  
Surface Proteins ◽  
Cell Surface Expression ◽  
Cell Transcriptome

ABSTRACTDroplet-based single cell transcriptome sequencing (scRNA-seq) technology, largely represented by the 10X Genomics Chromium system, is able to measure the gene expression from tens of thousands of single cells simultaneously. More recently, coupled with the cutting-edge Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), the droplet-based system has allowed for immunophenotyping of single cells based on cell surface expression of specific proteins together with simultaneous transcriptome profiling in the same cell. Despite the rapid advances in technologies, novel statistical methods and computational tools for analyzing multi-modal CITE-Seq data are lacking. In this study, we developed BREM-SC, a novel Bayesian Random Effects Mixture model that jointly clusters paired single cell transcriptomic and proteomic data. Through simulation studies and analysis of public and in-house real data sets, we successfully demonstrated the validity and advantages of this method in fully utilizing both types of data to accurately identify cell clusters. In addition, as a probabilistic model-based approach, BREM-SC is able to quantify the clustering uncertainty for each single cell. This new method will greatly facilitate researchers to jointly study transcriptome and surface proteins at the single cell level to make new biological discoveries, particularly in the area of immunology.

scholarly journals BREM-SC: a bayesian random effects mixture model for joint clustering single cell multi-omics data

2020 ◽  
Vol 48 (11) ◽  
pp. 5814-5824 ◽  
Author(s):  
Xinjun Wang ◽  
Zhe Sun ◽  
Yanfu Zhang ◽  
Zhongli Xu ◽  
Hongyi Xin ◽  
...  
Keyword(s):  
Single Cell ◽  
Mixture Model ◽  
Random Effects ◽  
Single Cells ◽  
Real Data ◽  
Transcriptome Profiling ◽  
Surface Expression ◽  
Surface Proteins ◽  
Cell Surface Expression ◽  
Cell Transcriptome

Abstract Droplet-based single cell transcriptome sequencing (scRNA-seq) technology, largely represented by the 10× Genomics Chromium system, is able to measure the gene expression from tens of thousands of single cells simultaneously. More recently, coupled with the cutting-edge Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), the droplet-based system has allowed for immunophenotyping of single cells based on cell surface expression of specific proteins together with simultaneous transcriptome profiling in the same cell. Despite the rapid advances in technologies, novel statistical methods and computational tools for analyzing multi-modal CITE-Seq data are lacking. In this study, we developed BREM-SC, a novel Bayesian Random Effects Mixture model that jointly clusters paired single cell transcriptomic and proteomic data. Through simulation studies and analysis of public and in-house real data sets, we successfully demonstrated the validity and advantages of this method in fully utilizing both types of data to accurately identify cell clusters. In addition, as a probabilistic model-based approach, BREM-SC is able to quantify the clustering uncertainty for each single cell. This new method will greatly facilitate researchers to jointly study transcriptome and surface proteins at the single cell level to make new biological discoveries, particularly in the area of immunology.

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.

scholarly journals Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low cost microfluidic instrumentation

2017 ◽  
Author(s):  
William Stephenson ◽  
Laura T. Donlin ◽  
Andrew Butler ◽  
Cristina Rozo ◽  
Ali Rashidfarrokhi ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Single Cell ◽  
Synovial Tissue ◽  
Low Cost ◽  
Single Cells ◽  
Transcriptome Profiling ◽  
Massively Parallel ◽  
Rna Seq ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractDroplet-based single cell RNA-seq has emerged as a powerful technique for massively parallel cellular profiling. While these approaches offer the exciting promise to deconvolute cellular heterogeneity in diseased tissues, the lack of cost-effective, reliable, and user-friendly instrumentation has hindered widespread adoption of droplet microfluidic techniques. To address this, we have developed a microfluidic control instrument that can be easily assembled from 3D printed parts and commercially available components costing approximately $540. We adapted this instrument for massively parallel scRNA-seq and deployed it in a clinical environment to perform single cell transcriptome profiling of disaggregated synovial tissue from a rheumatoid arthritis patient. We sequenced 8,716 single cells from a synovectomy, revealing 16 transcriptomically distinct clusters. These encompass a comprehensive and unbiased characterization of the autoimmune infiltrate, including inflammatory T and NK subsets that contribute to disease biology. Additionally, we identified fibroblast subpopulations that are demarcated via THY1 (CD90) and CD55 expression. Further experiments confirm that these represent synovial fibroblasts residing within the synovial intimal lining and subintimal lining, respectively, each under the influence of differing microenvironments. We envision that this instrument will have broad utility in basic and clinical settings, enabling low-cost and routine application of microfluidic techniques, and in particular single-cell transcriptome profiling.

scholarly journals Single cell RNA-Seq reveals distinct stem cell populations that drive sensory hair cell regeneration in response to loss of Fgf and Notch signaling

2018 ◽  
Author(s):  
Mark E. Lush ◽  
Daniel C. Diaz ◽  
Nina Koenecke ◽  
Sungmin Baek ◽  
Helena Boldt ◽  
...  
Keyword(s):  
Hair Cell ◽  
Notch Signaling ◽  
Single Cell ◽  
Lateral Line ◽  
Hair Cells ◽  
Developmental Trajectory ◽  
Cell Regeneration ◽  
Hair Cell Regeneration ◽  
Specific Expression ◽  
Sensory Hair

AbstractLoss of sensory hair cells leads to deafness and balance deficiencies. In contrast to mammalian hair cells, zebrafish ear and lateral line hair cells regenerate from poorly characterized, proliferating support cells. Equally ill-defined is the gene regulatory network underlying the progression of support cells to cycling hair cell progenitors and differentiated hair cells. We used single cell RNA-Sequencing (scRNA-Seq) of lateral line sensory organs and uncovered five different support cell types, including quiescent and activated stem cells. In silico ordering of support cells along a developmental trajectory identified cells that self-renew and new groups of genes required for hair cell differentiation. scRNA-Seq analyses of fgf3 mutants, in which hair cell regeneration is increased, demonstrates that Fgf and Notch signaling inhibit proliferation of support cells in parallel by inhibiting Wnt signaling. Our scRNA-Seq analyses set the foundation for mechanistic studies of sensory organ regeneration and is crucial for identifying factors to trigger hair cell production in mammals. As a resource, we implemented a shiny application that allows the community to interrogate cell type specific expression of genes of interest.

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