scholarly journals Highly multiplexed spatially resolved gene expression profiling of mouse organogenesis

2020 ◽  
Author(s):  
T. Lohoff ◽  
S. Ghazanfar ◽  
A. Missarova ◽  
N. Koulena ◽  
N. Pierson ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Resolution ◽  
Single Cell ◽  
Cell Fate ◽  
Target Genes ◽  
Single Cells ◽  
Spatial Context ◽  
Sequencing Data ◽  
Cell Fate Decisions ◽  
Spatially Resolved

AbstractTranscriptional and epigenetic profiling of single-cells has advanced our knowledge of the molecular bases of gastrulation and early organogenesis. However, current approaches rely on dissociating cells from tissues, thereby losing the crucial spatial context that is necessary for understanding cell and tissue interactions during development. Here, we apply an image-based single-cell transcriptomics method, seqFISH, to simultaneously and precisely detect mRNA molecules for 387 selected target genes in 8-12 somite stage mouse embryo tissue sections. By integrating spatial context and highly multiplexed transcriptional measurements with two single-cell transcriptome atlases we accurately characterize cell types across the embryo and demonstrate how spatially-resolved expression of genes not profiled by seqFISH can be imputed. We use this high-resolution spatial map to characterize fundamental steps in the patterning of the midbrain-hindbrain boundary and the developing gut tube. Our spatial atlas uncovers axes of resolution that are not apparent from single-cell RNA sequencing data – for example, in the gut tube we observe early dorsal-ventral separation of esophageal and tracheal progenitor populations. In sum, by computationally integrating high-resolution spatially-resolved gene expression maps with single-cell genomics data, we provide a powerful new approach for studying how and when cell fate decisions are made during early mammalian development.

scholarly journals Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis

2021 ◽  
Author(s):  
T. Lohoff ◽  
S. Ghazanfar ◽  
A. Missarova ◽  
N. Koulena ◽  
N. Pierson ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Target Genes ◽  
Single Cells ◽  
Cell Types ◽  
Spatial Context ◽  
Cell Fate Decisions ◽  
Spatially Resolved ◽  
Regulatory Processes ◽  
Cell Transcriptome

AbstractMolecular profiling of single cells has advanced our knowledge of the molecular basis of development. However, current approaches mostly rely on dissociating cells from tissues, thereby losing the crucial spatial context of regulatory processes. Here, we apply an image-based single-cell transcriptomics method, sequential fluorescence in situ hybridization (seqFISH), to detect mRNAs for 387 target genes in tissue sections of mouse embryos at the 8–12 somite stage. By integrating spatial context and multiplexed transcriptional measurements with two single-cell transcriptome atlases, we characterize cell types across the embryo and demonstrate that spatially resolved expression of genes not profiled by seqFISH can be imputed. We use this high-resolution spatial map to characterize fundamental steps in the patterning of the midbrain–hindbrain boundary (MHB) and the developing gut tube. We uncover axes of cell differentiation that are not apparent from single-cell RNA-sequencing (scRNA-seq) data, such as early dorsal–ventral separation of esophageal and tracheal progenitor populations in the gut tube. Our method provides an approach for studying cell fate decisions in complex tissues and development.

scholarly journals Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst

2010 ◽  
Vol 18 (4) ◽  
pp. 675-685 ◽  
Author(s):  
Guoji Guo ◽  
Mikael Huss ◽  
Guo Qing Tong ◽  
Chaoyang Wang ◽  
Li Li Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Cell Fate Decisions ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals Maximal STAT5-Induced Proliferation and Self-Renewal at Intermediate STAT5 Activity Levels

2008 ◽  
Vol 28 (21) ◽  
pp. 6668-6680 ◽  
Author(s):  
Albertus T. J. Wierenga ◽  
Edo Vellenga ◽  
Jan Jacob Schuringa
Keyword(s):  
Gene Expression ◽  
Cell Fate ◽  
Target Genes ◽  
Expression Patterns ◽  
Activity Levels ◽  
Dependent Manner ◽  
Self Renewal ◽  
Hematopoietic Stem ◽  
Cell Fate Decisions

ABSTRACT The level of transcription factor activity critically regulates cell fate decisions, such as hematopoietic stem cell (HSC) self-renewal and differentiation. We introduced STAT5A transcriptional activity into human HSCs/progenitor cells in a dose-dependent manner by overexpression of a tamoxifen-inducible STAT5A(1*6)-estrogen receptor fusion protein. Induction of STAT5A activity in CD34+ cells resulted in impaired myelopoiesis and induction of erythropoiesis, which was most pronounced at the highest STAT5A transactivation levels. In contrast, intermediate STAT5A activity levels resulted in the most pronounced proliferative advantage of CD34+ cells. This coincided with increased cobblestone area-forming cell and long-term-culture-initiating cell frequencies, which were predominantly elevated at intermediate STAT5A activity levels but not at high STAT5A levels. Self-renewal of progenitors was addressed by serial replating of CFU, and only progenitors containing intermediate STAT5A activity levels contained self-renewal capacity. By extensive gene expression profiling we could identify gene expression patterns of STAT5 target genes that predominantly associated with a self-renewal and long-term expansion phenotype versus those that identified a predominant differentiation phenotype.

scholarly journals Shaping development by stochasticity and dynamics in gene regulation

Open Biology ◽  
2017 ◽  
Vol 7 (5) ◽  
pp. 170030 ◽  
Author(s):  
Peng Dong ◽  
Zhe Liu
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Cell Fate ◽  
Single Molecule ◽  
Large Scale ◽  
Single Cells ◽  
Individual Gene ◽  
Functional Specification ◽  
Spatio Temporal

Animal development is orchestrated by spatio-temporal gene expression programmes that drive precise lineage commitment, proliferation and migration events at the single-cell level, collectively leading to large-scale morphological change and functional specification in the whole organism. Efforts over decades have uncovered two ‘seemingly contradictory’ mechanisms in gene regulation governing these intricate processes: (i) stochasticity at individual gene regulatory steps in single cells and (ii) highly coordinated gene expression dynamics in the embryo. Here we discuss how these two layers of regulation arise from the molecular and the systems level, and how they might interplay to determine cell fate and to control the complex body plan. We also review recent technological advancements that enable quantitative analysis of gene regulation dynamics at single-cell, single-molecule resolution. These approaches outline next-generation experiments to decipher general principles bridging gaps between molecular dynamics in single cells and robust gene regulations in the embryo.

scholarly journals Solo: doublet identification via semi-supervised deep learning

2019 ◽  
Author(s):  
Nicholas Bernstein ◽  
Nicole Fong ◽  
Irene Lam ◽  
Margaret Roy ◽  
David G. Hendrickson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
High Resolution ◽  
Single Cell ◽  
Single Cells ◽  
Detection Methods ◽  
Learning Approach ◽  
Rna Seq ◽  
Previous Approach ◽  
Cell Technology

AbstractSingle cell RNA-seq (scRNA-seq) measurements of gene expression enable an unprecedented high-resolution view into cellular state. However, current methods often result in two or more cells that share the same cell-identifying barcode; these “doublets” violate the fundamental premise of single cell technology and can lead to incorrect inferences. Here, we describe Solo, a semi-supervised deep learning approach that identifies doublets with greater accuracy than existing methods. Solo can be applied in combination with experimental doublet detection methods to further purify scRNA-seq data to true single cells beyond any previous approach.

scholarly journals unc-37/Groucho and lsy-22/AES repress Wnt target genes in C. elegans asymmetric cell divisions

2022 ◽  
Author(s):  
Kimberly N. Bekas ◽  
Bryan T. Phillips
Keyword(s):  
Gene Expression ◽  
Cell Fate ◽  
Target Gene ◽  
Target Genes ◽  
Intermediate Cell ◽  
Beta Catenin ◽  
Cell Fate Decisions ◽  
C Elegans ◽  
Somatic Gonad ◽  
Seam Cell

Asymmetric cell division (ACD) is a fundamental mechanism of developmental cell fate specification and adult tissue homeostasis. In C. elegans, the Wnt/beta-catenin asymmetry (WβA) pathway regulates ACDs throughout embryonic and larval development. Under control of Wnt ligand-induced polarity, the transcription factor TCF/POP-1 functions with the coactivator beta-catenin/SYS-1 to activate gene expression in the signaled cell or, in absence of the coactivator, to repress Wnt target genes in the nascent unsignaled daughter cell. To date, a broad investigation of Groucho function in WβA is lacking and the function of the short Groucho AES homolog, lsy-22 has only been evaluated in C. elegans neuronal cell fate decisions. Further, there is conflicting evidence showing TCF utilizing Groucho-mediated repression may be either aided or repressed by addition of AES subfamily of Groucho proteins. Here we demonstrate a genetic interaction between Groucho repressors and TCF/POP-1 in ACDs in the somatic gonad, the seam hypodermal stem cell lineage and the early embryo. Specifically, in the somatic gonad lineage, the signaled cell fate increases after individual and double Groucho loss of function, representing the first demonstration of Groucho function in wild-type WβA ACD. Further, WβA target gene misexpression occurs at a higher rate than DTC fate changes, suggesting derepression generates an intermediate cell fate. In seam cell ACD, loss of Groucho unc-37 or Groucho-like lsy-22 in a pop-1(RNAi) hypomorphic background enhances a pop-1 seam cell expansion and target gene misregulation. Moreover, while POP-1 depletion in lsy-22 null mutants yielded an expected increase in seam cells we observed a surprising seam cell decrease in the unc-37 null subjected to POP-1 depletion. This phenotype may be due to UNC-37 regulation of pop-1 expression in this tissue since we find misregulation of POP-1 in unc-37 mutants. Lastly, Groucho functions in embryonic endoderm development since we observe ectopic endoderm target gene expression in lsy-22(ot244) heterozygotes and unc-37(tm4649) heterozygotes subjected to intermediate levels of hda-1(RNAi). Together, these data indicate Groucho repressor modulation of cell fate via regulation of POP-1/TCF repression is widespread in asymmetric cell fate decisions and suggests a novel role of LSY-22 as a bona fide TCF repressor. As AES Grouchos are well-conserved, our model of combinatorial TCF repression by both Gro/TLE and AES warrants further investigation. 

scholarly journals Bfimpute: A Bayesian factorization method to recover single-cell RNA sequencing data

2021 ◽  
Author(s):  
Zi-Hang Wen ◽  
Jeremy L. Langsam ◽  
Lu Zhang ◽  
Wenjun Shen ◽  
Xin Zhou
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Cell Group ◽  
Specific Gene ◽  
Cell Type ◽  
Sequencing Data ◽  
Gene Expression Matrix ◽  
Study Gene Expression ◽  
Expression Matrix

AbstractSingle-cell RNA-seq (scRNA-seq) offers opportunities to study gene expression of tens of thousands of single cells simultaneously, to investigate cell-to-cell variation, and to reconstruct cell-type-specific gene regulatory networks. Recovering dropout events in a sparse gene expression matrix for scRNA-seq data is a long-standing matrix completion problem. We introduce Bfimpute, a Bayesian factorization imputation algorithm that reconstructs two latent gene and cell matrices to impute final gene expression matrix within each cell group, with or without the aid of cell type labels or bulk data. Bfimpute achieves better accuracy than other six publicly notable scRNA-seq imputation methods on simulated and real scRNA-seq data, as measured by several different evaluation metrics. Bfimpute can also flexibly integrate any gene or cell related information that users provide to increase the performance. Availability: Bfimpute is implemented in R and is freely available at https://github.com/maiziezhoulab/Bfimpute.

scholarly journals True time series of gene expression from multinucleate single cells reveal essential information on the regulatory dynamics of cell differentiation

2020 ◽  
Author(s):  
Anna Pretschner ◽  
Sophie Pabel ◽  
Markus Haas ◽  
Monika Heiner ◽  
Wolfgang Marwan
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Cell Differentiation ◽  
Markov Chains ◽  
Cell Fate ◽  
Single Cells ◽  
Expression Patterns ◽  
Essential Information ◽  
Cell Fate Decisions ◽  
True Time

AbstractDynamics of cell fate decisions are commonly investigated by inferring temporal sequences of gene expression states by assembling snapshots of individual cells where each cell is measured once. Ordering cells according to minimal differences in expression patterns and assuming that differentiation occurs by a sequence of irreversible steps, yields unidirectional, eventually branching Markov chains with a single source node. In an alternative approach, we used multinucleate cells to follow gene expression taking true time series. Assembling state machines, each made from single-cell trajectories, gives a network of highly structured Markov chains of states with different source and sink nodes including cycles, revealing essential information on the dynamics of regulatory events. We argue that the obtained networks depict aspects of the Waddington landscape of cell differentiation and characterize them as reachability graphs that provide the basis for the reconstruction of the underlying gene regulatory network.

scholarly journals Multiplexed biochemical imaging reveals caspase activation patterns underlying single cell fate

2018 ◽  
Author(s):  
Maximilian W. Fries ◽  
Kalina T. Haas ◽  
Suzan Ber ◽  
John Saganty ◽  
Emma K. Richardson ◽  
...  
Keyword(s):  
Cell Death ◽  
Single Cell ◽  
Cell Fate ◽  
Genetic Heterogeneity ◽  
Cellular Response ◽  
Single Cells ◽  
Caspase Activation ◽  
Cell Fate Decisions ◽  
Network Activation ◽  
Biochemical Imaging

The biochemical activities underlying cell-fate decisions vary profoundly even in genetically identical cells. But such non-genetic heterogeneity remains refractory to current imaging methods, because their capacity to monitor multiple biochemical activities in single living cells over time remains limited1. Here, we deploy a family of newly designed GFP-like sensors (NyxBits) with fast photon-counting electronics and bespoke analytics (NyxSense) in multiplexed biochemical imaging, to define a network determining the fate of single cells exposed to the DNA-damaging drug cisplatin. By simultaneously imaging a tri-nodal network comprising the cell-death proteases Caspase-2, -3 and -92, we reveal unrecognized single-cell heterogeneities in the dynamics and amplitude of caspase activation that signify survival versus cell death via necrosis or apoptosis. Non-genetic heterogeneity in the pattern of caspase activation recapitulates traits of therapy resistance previously ascribed solely to genetic causes3,4. Chemical inhibitors that alter these patterns can modulate in a predictable manner the phenotypic landscape of the cellular response to cisplatin. Thus, multiplexed biochemical imaging reveals cellular populations and biochemical states, invisible to other methods, underlying therapeutic responses to an anticancer drug. Our work develops widely applicable tools to monitor the dynamic activation of biochemical networks at single-cell resolution. It highlights the necessity to resolve patterns of network activation in single cells, rather than the average state of individual nodes, to define, and potentially control, mechanisms underlying cellular decisions in health and disease.

scholarly journals A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Simon Haile ◽  
Richard D. Corbett ◽  
Veronique G. LeBlanc ◽  
Lisa Wei ◽  
Stephen Pleasance ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput Sequencing ◽  
Single Cells ◽  
Cell Types ◽  
Full Length ◽  
Sequencing Data ◽  
Total Rna ◽  
Specific Protocol

RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3′ or 5′ termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.

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