Intraclonal Plasticity in Mammary Tumors Revealed through Large-Scale Single-Cell Resolution 3D Imaging

AbstractThe broad application of single-cell RNA profiling in plants has been hindered by the prerequisite of protoplasting that requires digesting the cell walls from different types of plant tissues. Here, we present a protoplasting-free approach, flsnRNA-seq, for large-scale full-length RNA profiling at a single-nucleus level in plants using isolated nuclei. Combined with 10x Genomics and Nanopore long-read sequencing, we validate the robustness of this approach in Arabidopsis root cells and the developing endosperm. Sequencing results demonstrate that it allows for uncovering alternative splicing and polyadenylation-related RNA isoform information at the single-cell level, which facilitates characterizing cell identities.

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Performance Improvement in Large-Scale MU-MIMO System with Multiple Antennas on User Side in a Single-Cell Downlink System

Arabian Journal for Science and Engineering ◽

10.1007/s13369-020-04714-0 ◽

2020 ◽

Vol 45 (8) ◽

pp. 6769-6789

Author(s):

Jagtar Singh ◽

Deepak Kedia

Keyword(s):

Single Cell ◽

Performance Improvement ◽

Large Scale ◽

Mimo System ◽

Multiple Antennas ◽

Downlink System

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Vacuum-Driven Micropump with Support Columns: Toward Large Scale Single-Cell RNA-Sequencing

2018 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS) ◽

10.1109/marss.2018.8481157 ◽

2018 ◽

Author(s):

Kento Hisa ◽

Musashi Kakugawa ◽

Takayuki Shibata ◽

Moeto Nagai

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Single Cell Rna Sequencing

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Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

Genome Medicine ◽

10.1186/s13073-021-01000-y ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Chayaporn Suphavilai ◽

Shumei Chia ◽

Ankur Sharma ◽

Lorna Tu ◽

Rafael Peres Da Silva ◽

...

Keyword(s):

Single Cell ◽

Large Scale ◽

Drug Response ◽

Drug Repurposing ◽

High Accuracy ◽

Molecular Heterogeneity ◽

Precision Oncology ◽

Scale Analysis ◽

Rna Seq ◽

Large Scale Analysis

AbstractWhile understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc’s monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc.

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

10.1101/553040 ◽

2019 ◽

Cited By ~ 1

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.

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Single-cell RNA counting at allele- and isoform-resolution using Smart-seq3

10.1101/817924 ◽

2019 ◽

Cited By ~ 6

Author(s):

Michael Hagemann-Jensen ◽

Christoph Ziegenhain ◽

Ping Chen ◽

Daniel Ramsköld ◽

Gert-Jan Hendriks ◽

...

Keyword(s):

Single Cell ◽

Large Scale ◽

Cell Types ◽

Mouse Strains ◽

Rna Molecules ◽

Counting Strategy ◽

Long Read ◽

Sequencing Strategy ◽

Transcriptome Coverage ◽

Scale Characterization

AbstractLarge-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states1. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells2,3. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5’ unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.

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EpiScanpy: integrated single-cell epigenomic analysis

10.1101/648097 ◽

2019 ◽

Cited By ~ 4

Author(s):

Anna Danese ◽

Maria L. Richter ◽

David S. Fischer ◽

Fabian J. Theis ◽

Maria Colomé-Tatché

Keyword(s):

Dna Methylation ◽

Single Cell ◽

Large Scale ◽

Feature Space ◽

Rna Seq ◽

Computational Framework ◽

Learning Techniques ◽

Multiple Feature ◽

The Many ◽

Cell Data

ABSTRACTEpigenetic single-cell measurements reveal a layer of regulatory information not accessible to single-cell transcriptomics, however single-cell-omics analysis tools mainly focus on gene expression data. To address this issue, we present epiScanpy, a computational framework for the analysis of single-cell DNA methylation and single-cell ATAC-seq data. EpiScanpy makes the many existing RNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities. We introduce and compare multiple feature space constructions for epigenetic data and show the feasibility of common clustering, dimension reduction and trajectory learning techniques. We benchmark epiScanpy by interrogating different single-cell brain mouse atlases of DNA methylation, ATAC-seq and transcriptomics. We find that differentially methylated and differentially open markers between cell clusters enrich transcriptome-based cell type labels by orthogonal epigenetic information.

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Functional Pituitary Networks in Vertebrates

Frontiers in Endocrinology ◽

10.3389/fendo.2020.619352 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yorgui Santiago-Andres ◽

Matan Golan ◽

Tatiana Fiordelisio

Keyword(s):

Large Scale ◽

3D Imaging ◽

Vertebrate Evolution ◽

Endocrine Gland ◽

Pituitary Cells ◽

Future Directions ◽

Physiological Demands ◽

Large Scale Networks ◽

Hormone Pulses ◽

Cell Networks

The pituitary is a master endocrine gland that developed early in vertebrate evolution and therefore exists in all modern vertebrate classes. The last decade has transformed our view of this key organ. Traditionally, the pituitary has been viewed as a randomly organized collection of cells that respond to hypothalamic stimuli by secreting their content. However, recent studies have established that pituitary cells are organized in tightly wired large-scale networks that communicate with each other in both homo and heterotypic manners, allowing the gland to quickly adapt to changing physiological demands. These networks functionally decode and integrate the hypothalamic and systemic stimuli and serve to optimize the pituitary output into the generation of physiologically meaningful hormone pulses. The development of 3D imaging methods and transgenic models have allowed us to expand the research of functional pituitary networks into several vertebrate classes. Here we review the establishment of pituitary cell networks throughout vertebrate evolution and highlight the main perspectives and future directions needed to decipher the way by which pituitary networks serve to generate hormone pulses in vertebrates.

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Assessing the measurement transfer function of single-cell RNA sequencing

10.1101/045450 ◽

2016 ◽

Author(s):

Hannah R. Dueck ◽

Rizi Ai ◽

Adrian Camarena ◽

Bo Ding ◽

Reymundo Dominguez ◽

...

Keyword(s):

Transfer Function ◽

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Transfer Functions ◽

Single Cells ◽

Biological Information ◽

Gene Detection ◽

Single Cell Rna Sequencing ◽

Scale Control

AbstractRecently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known. Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate the measurement transfer functions to be linear above ~5-10 molecules. Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

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