scholarly journals Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq

2021 ◽  
Author(s):  
Joseph M Replogle ◽  
Reuben A Saunders ◽  
Angela N Pogson ◽  
Jeffrey A Hussmann ◽  
Alexander Lenail ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Function ◽  
Rna Processing ◽  
Ribosome Biogenesis ◽  
Genomic Mapping ◽  
Cellular Behavior ◽  
Biological Discovery ◽  
Specific Regulation ◽  
Genome Scale ◽  
Mapping Information

A central goal of genetics is to define the relationships between genotypes and phenotypes. High-content phenotypic screens such as Perturb-seq (pooled CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional genomic mapping but, to date, have been used at limited scales. Here, we perform genome-scale Perturb-seq targeting all expressed genes with CRISPR interference (CRISPRi) across >2.5 million human cells and present a framework to power biological discovery with the resulting genotype-phenotype map. We use transcriptional phenotypes to predict the function of poorly-characterized genes, uncovering new regulators of ribosome biogenesis (including CCDC86, ZNF236, and SPATA5L1), transcription (C7orf26), and mitochondrial respiration (TMEM242). In addition to assigning gene function, single-cell transcriptional phenotypes allow for in-depth dissection of complex cellular phenomena - from RNA processing to differentiation. We leverage this ability to systematically identify the genetic drivers and consequences of aneuploidy and to discover an unanticipated layer of stress-specific regulation of the mitochondrial genome. Our information-rich genotype-phenotype map reveals a multidimensional portrait of gene function and cellular behavior.

scholarly journals HMGB1 Protein Interactions in Prostate and Ovary Cancer Models Reveal Links to RNA Processing and Ribosome Biogenesis through NuRD, THOC and Septin Complexes

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4686
Author(s):  
Aida Barreiro-Alonso ◽  
Mónica Lamas-Maceiras ◽  
Lidia Lorenzo-Catoira ◽  
Mercedes Pardo ◽  
Lu Yu ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Protein Interactions ◽  
Rna Processing ◽  
Ribosome Biogenesis ◽  
Affinity Purification ◽  
Regulation Of Transcription ◽  
Nurd Complex ◽  
Hmgb1 Protein ◽  
Ovary Cancer ◽  
Public Data

This study reports the HMGB1 interactomes in prostate and ovary cancer cells lines. Affinity purification coupled to mass spectrometry confirmed that the HMGB1 nuclear interactome is involved in HMGB1 known functions such as maintenance of chromatin stability and regulation of transcription, and also in not as yet reported processes such as mRNA and rRNA processing. We have identified an interaction between HMGB1 and the NuRD complex and validated this by yeast-two-hybrid, confirming that the RBBP7 subunit directly interacts with HMGB1. In addition, we describe for the first time an interaction between two HMGB1 interacting complexes, the septin and THOC complexes, as well as an interaction of these two complexes with Rab11. Analysis of Pan-Cancer Atlas public data indicated that several genes encoding HMGB1-interacting proteins identified in this study are dysregulated in tumours from patients diagnosed with ovary and prostate carcinomas. In PC-3 cells, silencing of HMGB1 leads to downregulation of the expression of key regulators of ribosome biogenesis and RNA processing, namely BOP1, RSS1, UBF1, KRR1 and LYAR. Upregulation of these genes in prostate adenocarcinomas is correlated with worse prognosis, reinforcing their functional significance in cancer progression.

scholarly journals Transcriptome Analysis of FEN1 Knockdown HEK293T Cell Strain Reveals Alteration in Nucleic Acid Metabolism, Virus Infection, Cell Morphogenesis and Cancer Development

2019 ◽  
Vol 22 (6) ◽  
pp. 379-386
Author(s):  
Song-Bai Liu ◽  
Xiu-Qin Qiu ◽  
Wei-Qiang Guo ◽  
Jin-Li Li ◽  
Qian Su ◽  
...  
Keyword(s):  
Virus Infection ◽  
Expression Profile ◽  
Rna Processing ◽  
Ribosome Biogenesis ◽  
Hek293t Cell ◽  
Repair Process ◽  
Nucleic Acid Metabolism ◽  
Cell Morphogenesis ◽  
Functional Sites

Aim and Objective: Flap endonuclease-1 (FEN1) plays a central role in DNA replication and DNA damage repair process. In mammals, FEN1 functional sites variation is related to cancer and chronic inflammation, and supports the role of FEN1 as a tumor suppressor. However, FEN1 is overexpressed in multiple types of cancer cells and is associated with drug resistance, supporting its role as an oncogene. Hence, it is vital to explore the multi-functions of FEN1 in normal cell metabolic process. This study was undertaken to examine how the gene expression profile changes when FEN1 is downregulated in 293T cells. Materials and Methods: Using the RNA sequencing and real-time PCR approaches, the transcript expression profile of FEN1 knockdown HEK293T cells have been detected for the next step evaluation, analyzation, and validation. Results: Our results confirmed that FEN1 is important for cell viability. We showed that when FEN1 downregulation led to the interruption of nucleic acids related metabolisms, cell cycle related metabolisms are significantly interrupted. FEN1 may also participate in non-coding RNA processing, ribosome RNA processing, transfer RNA processing, ribosome biogenesis, virus infection and cell morphogenesis. Conclusion: These findings provide insight into how FEN1 nuclease might regulate a wide variety of biological processes, and laid the foundation for understanding the role of other RAD2 family nucleases in cell growth and metabolism.

scholarly journals scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets

2019 ◽  
Vol 116 (20) ◽  
pp. 9775-9784 ◽  
Author(s):  
Yingxin Lin ◽  
Shila Ghazanfar ◽  
Kevin Y. X. Wang ◽  
Johann A. Gagnon-Bartsch ◽  
Kitty K. Lo ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Data Integration ◽  
Single Cell ◽  
Rna Seq ◽  
Cell Type ◽  
Large Collection ◽  
Single Cell Rna Sequencing ◽  
Development Trajectory ◽  
Biological Discovery ◽  
Public Datasets

Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors; scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.

Plant genome-scale reconstruction: from single cell to multi-tissue modelling and omics analyses

2018 ◽  
Vol 49 ◽  
pp. 42-48 ◽  
Author(s):  
Cristiana Gomes de Oliveira Dal’Molin ◽  
Lars Keld Nielsen
Keyword(s):  
Single Cell ◽  
Plant Genome ◽  
Genome Scale ◽  
Tissue Modelling

scholarly journals The SZS is an efficient statistical method to identify regulated splicing events in droplet-based RNA sequencing

2020 ◽  
Author(s):  
Julia Eve Olivieri ◽  
Roozbeh Dehghannasiri ◽  
Julia Salzman
Keyword(s):  
Single Cell ◽  
Statistical Method ◽  
Rna Seq ◽  
Computationally Efficient ◽  
Small Set ◽  
Biological Discovery ◽  
Cell Type Specific ◽  
Human Spermatogenesis ◽  
Splicing Patterns ◽  
Cell Data

AbstractTo date, the field of single-cell genomics has viewed robust splicing analysis as completely out of reach in droplet-based platforms, preventing biological discovery of single-cell regulated splicing. Here, we introduce a novel, robust, and computationally efficient statistical method, the Splicing Z Score (SZS), to detect differential alternative splicing in single cell RNA-Seq technologies including 10x Chromium. We applied the SZS to primary human cells to discover new regulated, cell type-specific splicing patterns. Illustrating the power of the SZS method, splicing of a small set of genes has high predictive power for tissue compartment in the human lung, and the SZS identifies un-annotated, conserved splicing regulation in the human spermatogenesis. The SZS is a method that can rapidly identify regulated splicing events from single cell data and prioritize genes predicted to have functionally significant splicing programs.

scholarly journals A quantitative framework for evaluating single-cell data structure preservation by dimensionality reduction techniques

2019 ◽  
Author(s):  
Cody N. Heiser ◽  
Ken S. Lau
Keyword(s):  
Data Structure ◽  
Dimensionality Reduction ◽  
Single Cell ◽  
Structure Preservation ◽  
Reduction Techniques ◽  
Reduction Methods ◽  
Biological Interpretation ◽  
Low Dimensional ◽  
Genome Scale ◽  
Global And Local

SummaryHigh-dimensional data, such as those generated using single-cell RNA sequencing, present challenges in interpretation and visualization. Numerical and computational methods for dimensionality reduction allow for low-dimensional representation of genome-scale expression data for downstream clustering, trajectory reconstruction, and biological interpretation. However, a comprehensive and quantitative evaluation of the performance of these techniques has not been established. We present an unbiased framework that defines metrics of global and local structure preservation in dimensionality reduction transformations. Using discrete and continuous scRNA-seq datasets, we find that input cell distribution and method parameters are largely determinant of global, local, and organizational data structure preservation by eleven published dimensionality reduction methods. Code available atgithub.com/KenLauLab/DR-structure-preservationallows for rapid evaluation of further datasets and methods.

scholarly journals Genome-scale oscillations in DNA methylation during exit from pluripotency

2018 ◽  
Author(s):  
Steffen Rulands ◽  
Heather J Lee ◽  
Stephen J Clark ◽  
Christof Angermueller ◽  
Sébastien A Smallwood ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Epigenetic Mark ◽  
List Type ◽  
Dna Hypomethylation ◽  
Cell Fate Decisions ◽  
Oscillatory Dynamics ◽  
Genome Scale

SummaryPluripotency is accompanied by the erasure of parental epigenetic memory with naïve pluripotent cells exhibiting global DNA hypomethylation both in vitro and in vivo. Exit from pluripotency and priming for differentiation into somatic lineages is associated with genome-wide de novo DNA methylation. We show that during this phase, coexpression of enzymes required for DNA methylation turnover, DNMT3s and TETs, promotes cell-to-cell variability in this epigenetic mark. Using a combination of single-cell sequencing and quantitative biophysical modelling, we show that this variability is associated with coherent, genome-scale, oscillations in DNA methylation with an amplitude dependent on CpG density. Analysis of parallel single-cell transcriptional and epigenetic profiling provides evidence for oscillatory dynamics both in vitro and in vivo. These observations provide fresh insights into the emergence of epigenetic heterogeneity during early embryo development, indicating that dynamic changes in DNA methylation might influence early cell fate decisions.HighlightsCo-expression of DNMT3s and TETs drive genome-scale oscillations of DNA methylationOscillation amplitude is greatest at a CpG density characteristic of enhancersCell synchronisation reveals oscillation period and link with primary transcriptsMultiomic single-cell profiling provides evidence for oscillatory dynamics in vivo

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