scholarly journals Decoding neuronal diversity by single-cell Convert-seq

2019 ◽  
Author(s):  
Joachim Luginbühl ◽  
Tsukasa Kouno ◽  
Rei Nakano ◽  
Thomas E Chater ◽  
Divya M Sivaraman ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Regulatory Networks ◽  
Developmental Trajectories ◽  
Single Cells ◽  
Human Fibroblasts ◽  
Neuronal Diversity ◽  
Cell Fates ◽  
New Strategy ◽  
Research Challenge

SummaryThe conversion of cell fates is controlled by hierarchical gene regulatory networks (GRNs) that induce remarkable alterations in cellular and transcriptome states. The identification of key regulators within these networks from myriad of candidate genes, however, poses a major research challenge. Here we present Convert-seq, combining single-cell RNA sequencing (scRNA-seq) and pooled ectopic gene expression with a new strategy to discriminate sequencing reads derived from exogenous and endogenous transcripts. We demonstrate Convert-seq by associating hundreds of single cells during reprogramming of human fibroblasts to induced neurons (iN) with exogenous and endogenous transcriptional signatures. Convert-seq identified GRNs modulating the emergence of developmental trajectories and predicted combinatorial activation of exogenous transcription factors controlling iN subtype specification. Functional validation of iN subtypes generated by novel combinations of exogenous transcription factors establish Convert-seq as a broadly applicable workflow to rapidly identify key transcription factors and GRNs orchestrating the reprogramming of virtually any cell type.

scholarly journals Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

2019 ◽  
Author(s):  
Ning Wang ◽  
Andrew E. Teschendorff
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Large Scale ◽  
Single Cells ◽  
Differential Expression Analysis ◽  
Dropout Rate ◽  
Rna Seq ◽  
Regulatory Activity

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

scholarly journals Comprehensive network modeling from single cell RNA sequencing of human and mouse reveals well conserved transcription regulation of hematopoiesis

BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Shouguo Gao ◽  
Zhijie Wu ◽  
Xingmin Feng ◽  
Sachiko Kajigaya ◽  
Xujing Wang ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Transcription Factors ◽  
Single Cell ◽  
Rna Sequencing ◽  
Transcription Regulation ◽  
Regulatory Networks ◽  
Stem Cell Differentiation ◽  
Hematopoietic Stem ◽  
Single Cell Rna Sequencing ◽  
Human And Mouse

Abstract Background Presently, there is no comprehensive analysis of the transcription regulation network in hematopoiesis. Comparison of networks arising from gene co-expression across species can facilitate an understanding of the conservation of functional gene modules in hematopoiesis. Results We used single-cell RNA sequencing to profile bone marrow from human and mouse, and inferred transcription regulatory networks in each species in order to characterize transcriptional programs governing hematopoietic stem cell differentiation. We designed an algorithm for network reconstruction to conduct comparative transcriptomic analysis of hematopoietic gene co-expression and transcription regulation in human and mouse bone marrow cells. Co-expression network connectivity of hematopoiesis-related genes was found to be well conserved between mouse and human. The co-expression network showed “small-world” and “scale-free” architecture. The gene regulatory network formed a hierarchical structure, and hematopoiesis transcription factors localized to the hierarchy’s middle level. Conclusions Transcriptional regulatory networks are well conserved between human and mouse. The hierarchical organization of transcription factors may provide insights into hematopoietic cell lineage commitment, and to signal processing, cell survival and disease initiation.

scholarly journals Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

2020 ◽  
Author(s):  
Feng Tian ◽  
Fan Zhou ◽  
Xiang Li ◽  
Wenping Ma ◽  
Honggui Wu ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Human Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Cell Types ◽  
List Type ◽  
Cell Type ◽  
Genomic Architecture ◽  
Gene Modules

SummaryBy circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq (METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent. We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs.HighlightsGenomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetusDetermining correlated gene modules (CGMs) in different cell types by MALBAC-DTMeasuring chromatin open regions in single cells with high detectability by METATACIntegrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM

scholarly journals Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the prenatal human hypothalamus

2021 ◽  
Author(s):  
Brian Herb ◽  
Hannah J Glover ◽  
Aparna Bhaduri ◽  
Alex M Casella ◽  
Tracy L Bale ◽  
...  
Keyword(s):  
Developmental Trajectories ◽  
Single Cells ◽  
Neuronal Diversity ◽  
Cell Type ◽  
Human Hypothalamus ◽  
Paired Samples ◽  
Cell Type Specific ◽  
Gene Regulatory ◽  
Stem Cell Populations ◽  
Temporal Trajectory

The hypothalamus is critically important for regulating most autonomic, metabolic, and behavioral functions essential for life and species propagation, yet a comprehensive understanding of neuronal subtypes and their development in the human brain is lacking. Here, we characterized the prenatal human hypothalamus by sequencing the transcriptomes of 45,574 single-cells from 12 embryos, spanning gestational weeks 4 through 25. These cells describe a temporal trajectory from proliferative stem cell populations to maturing neurons and glia, including 38 distinct excitatory and inhibitory neuronal subtypes. Merging these data with paired samples from the cortex and ganglionic eminences (GE) revealed two distinct neurogenesis pathways, one shared between GE and hypothalamus and a second unique to cortex. Gene regulatory network modeling predicted that these distinct maturation trajectories involve the activation of region- and cell type-specific transcription factor networks. These results provide the first comprehensive transcriptomic view of human hypothalamus development at cellular resolution.

scholarly journals Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data

2020 ◽  
Author(s):  
Junpeng Zhang ◽  
Lin Liu ◽  
Taosheng Xu ◽  
Wu Zhang ◽  
Chunwen Zhao ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Single Cells ◽  
Small Scale ◽  
Mirna Regulation ◽  
Sequencing Data ◽  
Resolution Level ◽  
Novel Strategy ◽  
Cell Cell

AbstractBackgroundExisting computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation.ResultsIn this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to use single-cell miRNA-mRNA co-sequencing data to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks to understand miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. Finally, through exploring cell-cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells to help understand cell-cell crosstalk.ConclusionsTo the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

scholarly journals Phenotypic convergence in the brain: distinct transcription factors regulate common terminal neuronal characters

2018 ◽  
Author(s):  
Nikos Konstantinides ◽  
Katarina Kapuralin ◽  
Chaimaa Fadil ◽  
Luendreo Barboza ◽  
Rahul Satija ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Large Scale ◽  
Single Cells ◽  
Deep Understanding ◽  
Cell Types ◽  
Marker Genes ◽  
Cell Type ◽  
Functional Specification ◽  
Phenotypic Convergence

SummaryTranscription factors regulate the molecular, morphological, and physiological characters of neurons and generate their impressive cell type diversity. To gain insight into general principles that govern how transcription factors regulate cell type diversity, we used large-scale single-cell mRNA sequencing to characterize the extensive cellular diversity in the Drosophila optic lobes. We sequenced 55,000 single optic lobe neurons and glia and assigned them to 52 clusters of transcriptionally distinct single cells. We validated the clustering and annotated many of the clusters using RNA sequencing of characterized FACS-sorted single cell types, as well as marker genes specific to given clusters. To identify transcription factors responsible for inducing specific terminal differentiation features, we used machine-learning to generate a ‘random forest’ model. The predictive power of the model was confirmed by showing that two transcription factors expressed specifically in cholinergic (apterous) and glutamatergic (traffic-jam) neurons are necessary for the expression of ChAT and VGlut in many, but not all, cholinergic or glutamatergic neurons, respectively. We used a transcriptome-wide approach to show that the same terminal characters, including but not restricted to neurotransmitter identity, can be regulated by different transcription factors in different cell types, arguing for extensive phenotypic convergence. Our data provide a deep understanding of the developmental and functional specification of a complex brain structure.

scholarly journals Single cells and transposable element heterogeneity in stem cells and development

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Andrew P. Hutchins
Keyword(s):  
Stem Cells ◽  
Transcription Factors ◽  
Single Cell ◽  
Human Disease ◽  
Single Cells ◽  
Chromatin Modifications ◽  
Single Cell Sequencing ◽  
Insight Into ◽  
Expression And Activity ◽  
Host Complex

AbstractRecent innovations in single cell sequencing-based technologies are shining a light on the heterogeneity of cellular populations in unprecedented detail. However, several cellular aspects are currently underutilized in single cell studies. One aspect is the expression and activity of transposable elements (TEs). TEs are selfish sequences of DNA that can replicate, and have been wildly successful in colonizing genomes. However, most TEs are mutated, fragmentary and incapable of transposition, yet they are actively bound by multiple transcription factors, host complex patterns of chromatin modifications, and are expressed in mRNAs as part of the transcriptome in both normal and diseased states. The contribution of TEs to development and cellular function remains unclear, and the routine inclusion of TEs in single cell sequencing analyses will potentially lead to insight into stem cells, development and human disease.

scholarly journals A comprehensive series of temporal transcription factors in the fly visual system

2021 ◽  
Author(s):  
Nikolaos Konstantinides ◽  
Anthony M. Rossi ◽  
Aristides Escobar ◽  
Liébaut Dudragne ◽  
Yen-Chung Chen ◽  
...  
Keyword(s):  
Stem Cells ◽  
Transcription Factors ◽  
Single Cell ◽  
Neuronal System ◽  
Neuronal Diversity ◽  
Temporal Patterning ◽  
Neuronal Stem Cells ◽  
Mrna Sequencing ◽  
Neuronal Types ◽  
Cell Type Specific

AbstractThe brain consists of thousands of different neuronal types that are generated through multiple divisions of neuronal stem cells. These stem cells have the capacity to generate different neuronal types at different stages of their development. In Drosophila, this temporal patterning is driven by the successive expression of temporal transcription factors (tTFs). While a number of tTFs are known in different animals and across various parts of the nervous system, these have been mostly identified by informed guesses and antibody availability. We used single-cell mRNA sequencing to identify the complete series of tTFs that specify most Drosophila medulla neurons in the optic lobe. We tested the genetic interactions among these tTFs. While we verify the general principle that tTFs regulate the progression of the series by activating the next tTFs in the series and repressing the previous ones, we also identify more complex regulations. Two of the tTFs, Eyeless and Dichaete, act as hubs integrating the input of several upstream tTFs before allowing the series to progress and in turn regulating the expression of several downstream tTFs. Moreover, we show that tTFs not only specify neuronal identity by controlling the expression of cell type-specific genes. Finally, we describe the very first steps of neuronal differentiation and find that terminal differentiation genes, such as neurotransmitter-related genes, are present as transcripts, but not as proteins, in immature larval neurons days before they are being used in functioning neurons; we show that these mechanisms are conserved in humans. Our results offer a comprehensive description of a temporal series of tTFs in a neuronal system, offering mechanistic insights into the regulation of the progression of the series and the regulation of neuronal diversity. This represents a proof-of-principle for the use of single-cell mRNA sequencing for the comparison of temporal patterning across phyla that can lead to an understanding of how the human brain develops and how it has evolved.

scholarly journals Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thoma Itoh ◽  
Takashi Makino
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Rna Seq ◽  
Gene Deletions ◽  
Functional Compensation ◽  
Downstream Effects ◽  
New Strategy ◽  
Single Deletion ◽  
Network Estimation ◽  
Gene Perturbations

AbstractRecent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in “transport”, which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.

scholarly journals IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data

2021 ◽  
Author(s):  
Tiam Heydari ◽  
Matthew A. Langley ◽  
Cynthia Fisher ◽  
Daniel Aguilar-Hidalgo ◽  
Shreya Shukla ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Regulatory Networks ◽  
Developmental Trajectories ◽  
A Priori ◽  
Developmental Trajectory ◽  
Developmental Systems ◽  
Dynamic Simulations ◽  
Versatile Tool ◽  
Gene Perturbations

The increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL , a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs provide an opportunity to inform fundamental hypotheses in developmental programs and help accelerate the design of stem cell-based technologies. We first describe the architecture of IQCELL. Next, we apply IQCELL to a scRNA-seq dataset of early mouse T-cell development and show that it can infer a priori over 75% of causal gene interactions previously reported via decades of research. We will also show that dynamic simulations of the derived GRN qualitatively recapitulate the effects of the known gene perturbations on the T-cell developmental trajectory. IQCELL is applicable to many developmental systems and offers a versatile tool to infer, simulate, and study GRNs in biological systems. (https://gitlab.com/stemcellbioengineering/iqcell)

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