scholarly journals Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
David W. McKellar ◽  
Lauren D. Walter ◽  
Leo T. Song ◽  
Madhav Mantri ◽  
Michael F. Z. Wang ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Muscle Repair ◽  
Transcriptomic Data ◽  
Mouse Skeletal Muscle ◽  
Wide Range ◽  
Large Scale Integration ◽  
Scale Integration

AbstractSkeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro-adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation, and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

scholarly journals Strength in numbers: Large-scale integration of single-cell transcriptomic data reveals rare, transient muscle progenitor cell states in muscle regeneration

2020 ◽  
Author(s):  
David W. McKellar ◽  
Lauren D. Walter ◽  
Leo T. Song ◽  
Madhav Mantri ◽  
Michael F.Z. Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Muscle Repair ◽  
Transcriptomic Data ◽  
Wide Range ◽  
Large Scale Integration ◽  
Scale Integration

ABSTRACTSkeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not provide spatial information that is needed to understand the context in which myogenic differentiation occurs. Here, we demonstrate how large-scale integration of new and public single-cell and spatial transcriptomic data can overcome these limitations. We created a large-scale single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 79 public single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting compendium includes nearly 350,000 cells and spans a wide range of ages, injury, and repair conditions. Combined, these data enabled identification of the predominant cell types in skeletal muscle with robust, consensus gene expression profiles, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors marked by stem potential, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem-cell quiescence to myofiber maturation and identified rare, short-lived transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. This analysis identified the temporal variation in the colocalization of immune cell subtype interactions with myogenic progenitors during injury recovery. We provide a public web tool to enable interactive exploration and visualization of this rich single-cell transcriptomic resource. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

Study on Electronic Industry with an Application of Rectilinear Embedding in VLSI Placement

2013 ◽  
Vol 345 ◽  
pp. 355-358
Author(s):  
Li Yan Pan ◽  
Yan Pei Liu
Keyword(s):  
Large Scale ◽  
Outerplanar Graph ◽  
Electronic Industry ◽  
Lagrangian Inversion ◽  
Multiple Parameters ◽  
Vlsi Physical Design ◽  
Wide Range ◽  
Large Scale Integration ◽  
Scale Integration ◽  
The Relationship

The electronic industry has developed quickly in last few years, with the rapid growth of Very Large Scale Integration technology. Placement layout is considered as the original step in VLSI physical design. The rectilinear embedding, which originates from graph theory, has wide range of application in VLSI placement. In this paper, we constructed a mathematical model for VLSI placement. Firstly, the VLSI placement was converted to quadrangulation by using rectilinear embedding speculative knowledge. Then we provided generating functions for two types of quadrangulations with graph multiple parameters. And the explicit formulae were obtained by employing Lagrangian inversion. Furthermore, we found the relationship between outerplanar graph and Hamilton graph, so the counting result of Hamilton quadrangulation was derived. The quadrangulation calculation can be applied to the establishment of arithmetical algorithms, which can be widely used in the optimization of VLSI placement.

Blanket and Selective Copper CVD from Cu (Fod)2 For Multilevel Metallization

1990 ◽  
Vol 181 ◽  
Author(s):  
Alain E. Kaloyeros ◽  
Arjun N. Saxena ◽  
Kenneth Brooks ◽  
Sumanta Ghosh ◽  
Eric Eisenbraun
Keyword(s):  
Large Scale ◽  
Chemical Vapor ◽  
Precursor Chemistry ◽  
Limiting Factors ◽  
Organic Chemical ◽  
Working Pressure ◽  
X Ray ◽  
Wide Range ◽  
Large Scale Integration ◽  
Scale Integration

ABSTRACTAs the focus of integration technology inevitably shifts from the present very large scale integration (VLSI) to ultra large scale integration (ULSI) schemes, thus leading to continuous decrease in circuit dimensions, the limitations of present multilevel metallization technologies become increasingly important. Because of the appreciably higher speeds and more complex multi-functional layering involved in the newest ULSI circuits, the electrical resistance and capacitance of presently used interconnects and their electromigration and stress resistance stand as major limiting factors to signal processing throughput. In this paper, some recent results achieved by the present investigators in their studies of blanket and selective low-temperature metal-organic chemical vapor deposition (LTMOCVD) of copper for potential use in multilevel metallizations in ULSIC’s are presented. The films were produced at 300–400°C in atmospheres of pure H2 or Ar from the β-diketonate precursor bis(6, 6, 7,7, 8, 8, 8-heptafluoro-2, 2-dimethy1-3, 5-octanediono)copper(II), Cu(fod)2. The films were analyzed by x-ray diffraction (XRD), Rutherford Backscattering (RBS), Auger electron spectroscopy (AES), scanning electron microscopy (SEM), energy-dispersive x-ray spectroscopy (EDXS), and four-point resistivity probe. The results of these studies showed that films deposited on metallic substrates were uniform, continuous, adherent, highly pure, and exhibited very low resistivity, as low as 1.8 μΩcm for films deposited in pure H2 atmosphere. Preliminary investigations of selective LTMOCVD of copper showed that selectivity is indeed possible, but is a function of a wide range of parameters that include reactor geometry, substrate type and temperature, working pressure, type of carrier gas, and precursor chemistry.

scholarly journals Adversarial domain translation networks enable fast and accurate large-scale atlas-level single-cell data integration

2021 ◽  
Author(s):  
Jia Zhao ◽  
Gefei Wang ◽  
Jingsi Ming ◽  
Zhixiang Lin ◽  
Yang Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Superior Performance ◽  
Brain Atlas ◽  
Unified Framework ◽  
Fine Grained ◽  
Wide Range ◽  
Shared Information ◽  
Single Nucleus

The rapid emergence of large-scale atlas-level single-cell RNA-sequencing (scRNA-seq) datasets from various sources presents remarkable opportunities for broad and deep biological investigations through integrative analyses. However, harmonizing such datasets requires integration approaches to be not only computationally scalable, but also capable of preserving a wide range of fine-grained cell populations. We created Portal, a unified framework of adversarial domain translation to learn harmonized representations of datasets. With innovation in model and algorithm designs, Portal achieves superior performance in preserving biological variation during integration, while having significantly reduced running time and memory compared to existing approaches, achieving integration of millions of cells in minutes with low memory consumption. We demonstrate the efficiency and accuracy of Portal using diverse datasets ranging from mouse brain atlas projects, the Tabula Muris project, and the Tabula Microcebus project. Portal has broad applicability and in addition to integrating multiple scRNA-seq datasets, it can also integrate scRNA-seq with single-nucleus RNA-sequencing (snRNA-seq) data. Finally, we demonstrate the utility of Portal by applying it to the integration of cross-species datasets with limited shared-information between them, and are able to elucidate biological insights into the similarities and divergences in the spermatogenesis process between mouse, macaque, and human.

scholarly journals Timing Optimization During the Physical Synthesis of Cell-Based VLSI Circuits

2017 ◽  
Author(s):  
Vinícius Dos Santos Livramento ◽  
José Luís Güntzel
Keyword(s):  
Integrated Circuits ◽  
Large Scale ◽  
Cmos Technology ◽  
Vlsi Circuits ◽  
Consumer Electronics ◽  
Clock Frequency ◽  
Wide Range ◽  
Large Scale Integration ◽  
On Chip ◽  
Scale Integration

The evolution of CMOS technology made possible integrated circuits with billions of transistors assembled into a single silicon chip, giving rise to the jargon Very-Large-Scale Integration (VLSI). VLSI circuits span a wide range class of applications, including Application Specific Circuits and Systems-On-Chip. The latter are responsible for fueling the consumer electronics market, especially in the segment of smartphones and tablets, which are responsible for pushing hardware performance requirements every new generation. The required clock frequency affects the performance of a VLSI circuit and induces timing constraints that must be properly handled by synthesis tools. This thesis focuses on techniques for timing closure of cellbased VLSI circuits, i.e. techniques able to iteratively reduce the number of timing violations until the synthesis of the synchronous digital system reaches the specified target frequency.

scholarly journals CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA-sequencing

2018 ◽  
Author(s):  
Kenta Sato ◽  
Koki Tsuyuzaki ◽  
Kentaro Shimizu ◽  
Itoshi Nikaido
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
State Of The Art ◽  
Cell Types ◽  
Cell Search ◽  
Wide Range ◽  
Single Cell Rna Sequencing ◽  
Comparable Accuracy ◽  
Multicellular Organisms

AbstractRecent technical improvements in single-cell RNA sequencing (scRNA-seq) have enabled massively parallel profiling of transcriptomes, thereby promoting large-scale studies encompassing a wide range of cell types of multicellular organisms. With this background, we propose CellFishing.jl, a new method for searching atlas-scale datasets for similar cells and detecting noteworthy genes of query cells with high accuracy and throughput. Using multiple scRNA-seq datasets, we validate that our method demonstrates comparable accuracy to and is markedly faster than the state-of-the-art software. Moreover, CellFishing.jl is scalable to more than one million cells, and the throughput of the search is approximately 1,600 cells per second.

Plant hormones, plant growth regulators

2014 ◽  
Vol 155 (26) ◽  
pp. 1011-1018 ◽  
Author(s):  
György Végvári ◽  
Edina Vidéki
Keyword(s):  
Nervous System ◽  
Growth And Development ◽  
Large Scale ◽  
Essential Role ◽  
Higher Plants ◽  
Structure And Function ◽  
Wide Sense ◽  
Large Scale Integration ◽  
Scale Integration ◽  
And Function

Plants seem to be rather defenceless, they are unable to do motion, have no nervous system or immune system unlike animals. Besides this, plants do have hormones, though these substances are produced not in glands. In view of their complexity they lagged behind animals, however, plant organisms show large scale integration in their structure and function. In higher plants, such as in animals, the intercellular communication is fulfilled through chemical messengers. These specific compounds in plants are called phytohormones, or in a wide sense, bioregulators. Even a small quantity of these endogenous organic compounds are able to regulate the operation, growth and development of higher plants, and keep the connection between cells, tissues and synergy beween organs. Since they do not have nervous and immume systems, phytohormones play essential role in plants’ life. Orv. Hetil., 2014, 155(26), 1011–1018.

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