scholarly journals Exploring genetic interaction manifolds constructed from rich single-cell phenotypes

Science ◽  
2019 ◽  
Vol 365 (6455) ◽  
pp. 786-793 ◽  
Author(s):  
Thomas M. Norman ◽  
Max A. Horlbeck ◽  
Joseph M. Replogle ◽  
Alex Y. Ge ◽  
Albert Xu ◽  
...  
Keyword(s):  
Single Cell ◽  
Genetic Interaction ◽  
Erythroid Differentiation ◽  
Analytical Framework ◽  
Regulatory Pathways ◽  
Synergistic Interactions ◽  
Expression Of Genes ◽  
Organismal Complexity ◽  
Cell Phenotypes

How cellular and organismal complexity emerges from combinatorial expression of genes is a central question in biology. High-content phenotyping approaches such as Perturb-seq (single-cell RNA-sequencing pooled CRISPR screens) present an opportunity for exploring such genetic interactions (GIs) at scale. Here, we present an analytical framework for interpreting high-dimensional landscapes of cell states (manifolds) constructed from transcriptional phenotypes. We applied this approach to Perturb-seq profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g., identifying suppressors), and mechanistic elucidation of synergistic interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we applied recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.

scholarly journals Exploring genetic interaction manifolds constructed from rich phenotypes

2019 ◽  
Author(s):  
Thomas M. Norman ◽  
Max A. Horlbeck ◽  
Joseph M. Replogle ◽  
Alex Y. Ge ◽  
Albert Xu ◽  
...  
Keyword(s):  
Single Cell ◽  
Genetic Interaction ◽  
Erythroid Differentiation ◽  
Analytical Framework ◽  
Genetic Interactions ◽  
Synthetic Lethal ◽  
Regulatory Pathways ◽  
Synergistic Interactions ◽  
Dimensional Interaction ◽  
Synthetic Lethal Interactions

AbstractSynergistic interactions between gene functions drive cellular complexity. However, the combinatorial explosion of possible genetic interactions (GIs) has necessitated the use of scalar interaction readouts (e.g. growth) that conflate diverse outcomes. Here we present an analytical framework for interpreting manifolds constructed from high-dimensional interaction phenotypes. We applied this framework to rich phenotypes obtained by Perturb-seq (single-cell RNA-seq pooled CRISPR screens) profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g. identifying true suppressors), and mechanistic elucidation of synthetic lethal interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we apply recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.One Sentence SummaryPrinciples and mechanisms of genetic interactions are revealed by rich phenotyping using single-cell RNA sequencing.

scholarly journals Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

2021 ◽  
Vol 12 (2) ◽  
pp. 317-334
Author(s):  
Omar Alaqeeli ◽  
Li Xing ◽  
Xuekui Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Classification Tree ◽  
Area Under The Curve ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Tree Algorithms ◽  
R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

scholarly journals Single-cell map of diverse immune phenotypes in the acute myeloid leukemia microenvironment

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Rongqun Guo ◽  
Mengdie Lü ◽  
Fujiao Cao ◽  
Guanghua Wu ◽  
Fengcai Gao ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Single Cell ◽  
Myeloid Leukemia ◽  
Immune Cell ◽  
Immune Surveillance ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Significant Difference ◽  
Cell Phenotypes ◽  
Acute Myeloid

Abstract Background Knowledge of immune cell phenotypes, function, and developmental trajectory in acute myeloid leukemia (AML) microenvironment is essential for understanding mechanisms of evading immune surveillance and immunotherapy response of targeting special microenvironment components. Methods Using a single-cell RNA sequencing (scRNA-seq) dataset, we analyzed the immune cell phenotypes, function, and developmental trajectory of bone marrow (BM) samples from 16 AML patients and 4 healthy donors, but not AML blasts. Results We observed a significant difference between normal and AML BM immune cells. Here, we defined the diversity of dendritic cells (DC) and macrophages in different AML patients. We also identified several unique immune cell types including T helper cell 17 (TH17)-like intermediate population, cytotoxic CD4+ T subset, T cell: erythrocyte complexes, activated regulatory T cells (Treg), and CD8+ memory-like subset. Emerging AML cells remodels the BM immune microenvironment powerfully, leads to immunosuppression by accumulating exhausted/dysfunctional immune effectors, expending immune-activated types, and promoting the formation of suppressive subsets. Conclusion Our results provide a comprehensive AML BM immune cell census, which can help to select pinpoint targeted drug and predict efficacy of immunotherapy.

scholarly journals Biology of Heme in Mammalian Erythroid Cells and Related Disorders

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Tohru Fujiwara ◽  
Hideo Harigae
Keyword(s):  
Biosynthetic Pathway ◽  
Iron Acquisition ◽  
Protoporphyrin Ix ◽  
Erythroid Differentiation ◽  
Heme Biosynthesis ◽  
Erythroid Cells ◽  
Prosthetic Group ◽  
Expression Of Genes ◽  
Human Disorders ◽  
Pathway Regulation

Heme is a prosthetic group comprising ferrous iron (Fe2+) and protoporphyrin IX and is an essential cofactor in various biological processes such as oxygen transport (hemoglobin) and storage (myoglobin) and electron transfer (respiratory cytochromes) in addition to its role as a structural component of hemoproteins. Heme biosynthesis is induced during erythroid differentiation and is coordinated with the expression of genes involved in globin formation and iron acquisition/transport. However, erythroid and nonerythroid cells exhibit distinct differences in the heme biosynthetic pathway regulation. Defects of heme biosynthesis in developing erythroblasts can have profound medical implications, as represented by sideroblastic anemia. This review will focus on the biology of heme in mammalian erythroid cells, including the heme biosynthetic pathway as well as the regulatory role of heme and human disorders that arise from defective heme synthesis.

Functional Identification of Cell Phenotypes Differentiating from Mice Retinal Neurospheres Using Single Cell Calcium Imaging

2011 ◽  
Vol 31 (6) ◽  
pp. 835-846 ◽  
Author(s):  
R. A. De Melo Reis ◽  
C. S. Schitine ◽  
A. Kofalvi ◽  
S. Grade ◽  
L. Cortes ◽  
...  
Keyword(s):  
Single Cell ◽  
Calcium Imaging ◽  
Functional Identification ◽  
Cell Calcium ◽  
Cell Phenotypes

Category encoding method to select feature genes for the classification of bulk and single‐cell RNA‐seq data

2021 ◽  
Author(s):  
Yan Zhou ◽  
Li Zhang ◽  
Jinfeng Xu ◽  
Jun Zhang ◽  
Xiaodong Yan
Keyword(s):  
Single Cell ◽  
Rna Seq ◽  
Encoding Method

scholarly journals Single-Cell Developmental Classification of B-Cell Precursor Acute Lymphoblastic Leukemia at Diagnosis Reveals Predictors of Relapse

2018 ◽  
Vol 64 ◽  
pp. S33-S34 ◽  
Author(s):  
Kara Davis ◽  
Zinaida Good ◽  
Jolanda Sarno ◽  
Astraea Jager ◽  
Nikolay Samusik ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
B Cell ◽  
Single Cell ◽  
Lymphoblastic Leukemia ◽  
Cell Precursor
Sign in / Sign up

Export Citation Format

Share Document