Topokaryotyping demonstrates single cell variability and stress dependent variations in nuclear envelope associated domains

ABSTRACTAnalysis of large-scale interphase genome positioning with reference to a nuclear landmark has recently been studied using sequencing-based single cell approaches. However, these approaches are dependent upon technically challenging, time consuming and costly high throughput sequencing technologies, requiring specialized bioinformatics tools and expertise. Here, we propose a novel, affordable and robust microscopy-based single cell approach, termed Topokaryotyping, to analyze and reconstruct the interphase positioning of genomic loci relative to a given nuclear landmark, detectable as banding pattern on mitotic chromosomes. This is accomplished by proximity-dependent histone labeling, where biotin ligase BirA fused to nuclear envelope marker Emerin was coexpressed together with Biotin Acceptor Peptide (BAP)-histone fusion followed by (i) biotin labeling, (ii) generation of mitotic spreads, (iii) detection of the biotin label on mitotic chromosomes and (iv) their identification by karyotyping. Using Topokaryotyping, we identified both cooperativity and stochasticity in the positioning of emerin-associated chromatin domains in individual cells. Furthermore, the chromosome-banding pattern showed dynamic changes in emerin-associated domains upon physical and radiological stress. In summary, Topokaryotyping is a sensitive and reliable technique to quantitatively analyze spatial positioning of genomic regions interacting with a given nuclear landmark at the single cell level in various experimental conditions.

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Normalisr: normalization and association testing for single-cell CRISPR screen and co-expression

10.1101/2021.04.12.439500 ◽

2021 ◽

Author(s):

Lingfei Wang

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Large Scale ◽

High Sensitivity ◽

Statistical Hypothesis ◽

P Value ◽

Statistical Hypothesis Testing ◽

Experimental Conditions ◽

Library Size ◽

Association Testing

AbstractSingle-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity. Here we present Normalisr, a linear-model-based normalization and statistical hypothesis testing framework that unifies single-cell differential expression, co-expression, and CRISPR scRNA-seq screen analyses. By systematically detecting and removing nonlinear confounding from library size, Normalisr achieves high sensitivity, specificity, speed, and generalizability across multiple scRNA-seq protocols and experimental conditions with unbiased P-value estimation. We use Normalisr to reconstruct robust gene regulatory networks from trans-effects of gRNAs in large-scale CRISPRi scRNA-seq screens and gene-level co-expression networks from conventional scRNA-seq.

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Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr

Nature Communications ◽

10.1038/s41467-021-26682-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Lingfei Wang

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Large Scale ◽

Linear Models ◽

P Value ◽

Statistical Association ◽

Experimental Conditions ◽

Library Size ◽

Association Testing ◽

Crispr Screen

AbstractSingle-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity. Furthermore, statistical association testing remains difficult for scRNA-seq. Here we present Normalisr, a normalization and statistical association testing framework that unifies single-cell differential expression, co-expression, and CRISPR screen analyses with linear models. By systematically detecting and removing nonlinear confounders arising from library size at mean and variance levels, Normalisr achieves high sensitivity, specificity, speed, and generalizability across multiple scRNA-seq protocols and experimental conditions with unbiased p-value estimation. The superior scalability allows us to reconstruct robust gene regulatory networks from trans-effects of guide RNAs in large-scale single cell CRISPRi screens. On conventional scRNA-seq, Normalisr recovers gene-level co-expression networks that recapitulated known gene functions.

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Topokaryotyping demonstrates single cell variability and stress dependent variations in nuclear envelope associated domains

Nucleic Acids Research ◽

10.1093/nar/gky818 ◽

2018 ◽

Vol 46 (22) ◽

pp. e135-e135 ◽

Cited By ~ 1

Author(s):

Anamarija Jurisic ◽

Chloé Robin ◽

Pavel Tarlykov ◽

Lee Siggens ◽

Brigitte Schoell ◽

...

Keyword(s):

Nuclear Envelope ◽

Single Cell ◽

Stress Dependent

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Transferable representations of single-cell transcriptomic data

10.1101/2021.04.13.439707 ◽

2021 ◽

Author(s):

Ethan Weinberger ◽

Su-In Lee

Keyword(s):

Single Cell ◽

Large Scale ◽

Future Research ◽

Rna Seq ◽

Cell Type ◽

Experimental Conditions ◽

Technical Noise ◽

Transcriptomic Data ◽

Low Dimensional ◽

Computational Resources

Advances in single-cell RNA-seq (scRNA-seq) technologies are enabling the construction of large-scale, human-annotated reference cell atlases, creating unprecedented opportunities to accelerate future research. However, effectively leveraging information from these atlases, such as clustering labels or cell type annotations, remains challenging due to substantial technical noise and sparsity in scRNA-seq measurements. To address this problem, we present HD-AE, a deep autoencoder designed to extract integrated low-dimensional representations of scRNA-seq measurements across datasets from different labs and experimental conditions. Unlike previous approaches, HD-AE's representations successfully transfer to new query datasets without needing to retrain the model. Researchers without substantial computational resources or machine learning expertise can thus leverage the robust representations learned by pretrained HD-AE models to compare embeddings of their own data with previously generated sets of reference embeddings.

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FlsnRNA-seq: protoplasting-free full-length single-nucleus RNA profiling in plants

Genome Biology ◽

10.1186/s13059-021-02288-0 ◽

2021 ◽

Vol 22 (1) ◽

Cited By ~ 2

Author(s):

Yanping Long ◽

Zhijian Liu ◽

Jinbu Jia ◽

Weipeng Mo ◽

Liang Fang ◽

...

Keyword(s):

Single Cell ◽

Cell Walls ◽

Large Scale ◽

Full Length ◽

Cell Level ◽

Root Cells ◽

Rna Profiling ◽

Different Types ◽

Long Read ◽

Single Nucleus

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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A practical solution to pseudoreplication bias in single-cell studies

Nature Communications ◽

10.1038/s41467-021-21038-1 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Kip D. Zimmerman ◽

Mark A. Espeland ◽

Carl D. Langefeld

Keyword(s):

Single Cell ◽

Mixed Models ◽

Random Effect ◽

Cell Types ◽

Error Rates ◽

Specific Cell ◽

Experimental Conditions ◽

Type 1 Error ◽

Sample Correlation ◽

Inflated Type

AbstractCells from the same individual share common genetic and environmental backgrounds and are not statistically independent; therefore, they are subsamples or pseudoreplicates. Thus, single-cell data have a hierarchical structure that many current single-cell methods do not address, leading to biased inference, highly inflated type 1 error rates, and reduced robustness and reproducibility. This includes methods that use a batch effect correction for individual as a means of accounting for within-sample correlation. Here, we document this dependence across a range of cell types and show that pseudo-bulk aggregation methods are conservative and underpowered relative to mixed models. To compute differential expression within a specific cell type across treatment groups, we propose applying generalized linear mixed models with a random effect for individual, to properly account for both zero inflation and the correlation structure among measures from cells within an individual. Finally, we provide power estimates across a range of experimental conditions to assist researchers in designing appropriately powered studies.

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Tree Diversity Reduces Fungal Endophyte Richness and Diversity in a Large-Scale Temperate Forest Experiment

Diversity ◽

10.3390/d11120234 ◽

2019 ◽

Vol 11 (12) ◽

pp. 234 ◽

Cited By ~ 2

Author(s):

Eric A. Griffin ◽

Joshua G. Harrison ◽

Melissa K. McCormick ◽

Karin T. Burghardt ◽

John D. Parker

Keyword(s):

Phylogenetic Diversity ◽

Large Scale ◽

High Throughput Sequencing ◽

Spatial Scales ◽

Fungal Endophytes ◽

Fungal Endophyte ◽

Tree Diversity ◽

Trophic Levels ◽

Community Diversity ◽

And Function

Although decades of research have typically demonstrated a positive correlation between biodiversity of primary producers and associated trophic levels, the ecological drivers of this association are poorly understood. Recent evidence suggests that the plant microbiome, or the fungi and bacteria found on and inside plant hosts, may be cryptic yet important drivers of important processes, including primary production and trophic interactions. Here, using high-throughput sequencing, we characterized foliar fungal community diversity, composition, and function from 15 broadleaved tree species (N = 545) in a recently established, large-scale temperate tree diversity experiment using over 17,000 seedlings. Specifically, we tested whether increases in tree richness and phylogenetic diversity would increase fungal endophyte diversity (the “Diversity Begets Diversity” hypothesis), as well as alter community composition (the “Tree Diversity–Endophyte Community” hypothesis) and function (the “Tree Diversity–Endophyte Function” hypothesis) at different spatial scales. We demonstrated that increasing tree richness and phylogenetic diversity decreased fungal species and functional guild richness and diversity, including pathogens, saprotrophs, and parasites, within the first three years of a forest diversity experiment. These patterns were consistent at the neighborhood and tree plot scale. Our results suggest that fungal endophytes, unlike other trophic levels (e.g., herbivores as well as epiphytic bacteria), respond negatively to increasing plant diversity.

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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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Compartment and hub definitions tune metabolic networks for metabolomic interpretations

GigaScience ◽

10.1093/gigascience/giz137 ◽

2020 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

T Cameron Waller ◽

Jordan A Berg ◽

Alexander Lex ◽

Brian E Chapman ◽

Jared Rutter

Keyword(s):

Large Scale ◽

Metabolic Networks ◽

Shortest Paths ◽

Large Data ◽

Differential Regulation ◽

Large Data Sets ◽

Data Sets ◽

Human Metabolism ◽

Experimental Conditions ◽

Systemic Model

Abstract Background Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism’s cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments. Results We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications. Conclusions Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.

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