Volume 25, Issue 6, Pages e3 (November 2018)

Slides:



Advertisements
Similar presentations
Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders  Ariel Feiglin, Bryce K. Allen,
Advertisements

M. Fu, G. Huang, Z. Zhang, J. Liu, Z. Zhang, Z. Huang, B. Yu, F. Meng 
Volume 44, Issue 1, Pages (January 2016)
Volume 21, Issue 11, Pages (December 2017)
Low Dimensionality in Gene Expression Data Enables the Accurate Extraction of Transcriptional Programs from Shallow Sequencing  Graham Heimberg, Rajat.
A bioinformatic analysis of microRNAs role in osteoarthritis
Volume 4, Issue 4, Pages e5 (April 2017)
Spatial Memory Engram in the Mouse Retrosplenial Cortex
A Single-Cell Transcriptome Atlas of the Human Pancreas
Volume 6, Issue 5, Pages e5 (May 2018)
Volume 1, Issue 5, Pages (November 2015)
Rhythmic Working Memory Activation in the Human Hippocampus
Volume 21, Issue 9, Pages (November 2017)
Volume 23, Issue 7, Pages (May 2018)
Volume 9, Issue 3, Pages (September 2017)
Zhifei Luo, Suhn Kyong Rhie, Fides D. Lay, Peggy J. Farnham 
Revealing Global Regulatory Perturbations across Human Cancers
Pejman Mohammadi, Niko Beerenwinkel, Yaakov Benenson  Cell Systems 
Volume 23, Issue 4, Pages (April 2018)
A Call for Systematic Research on Solute Carriers
Volume 25, Issue 6, Pages e4 (November 2018)
Transcriptional Landscape of Cardiomyocyte Maturation
Understanding Tissue-Specific Gene Regulation
The Translational Landscape of the Mammalian Cell Cycle
Volume 33, Issue 4, Pages e6 (April 2018)
Volume 11, Issue 5, Pages (May 2015)
Volume 68, Issue 5, Pages e7 (December 2017)
Volume 3, Issue 1, Pages (July 2016)
Identification of Alpha-Adrenergic Agonists as Potential Therapeutic Agents for Dermatomyositis through Drug-Repurposing Using Public Expression Datasets 
Volume 16, Issue 8, Pages (August 2016)
Agustí Emperador, Oliver Carrillo, Manuel Rueda, Modesto Orozco 
Volume 10, Issue 8, Pages (March 2015)
Integrative Multi-omic Analysis of Human Platelet eQTLs Reveals Alternative Start Site in Mitofusin 2  Lukas M. Simon, Edward S. Chen, Leonard C. Edelstein,
Genome-wide Reconstruction of OxyR and SoxRS Transcriptional Regulatory Networks under Oxidative Stress in Escherichia coli K-12 MG1655  Sang Woo Seo,
Volume 17, Issue 8, Pages (November 2016)
Volume 21, Issue 11, Pages (December 2017)
Revealing Global Regulatory Perturbations across Human Cancers
Volume 24, Issue 4, Pages (November 2006)
Volume 14, Issue 4, Pages (February 2016)
Volume 22, Issue 3, Pages (January 2018)
Volume 5, Issue 4, Pages e4 (October 2017)
Volume 19, Issue 2, Pages (August 2016)
Volume 28, Issue 18, Pages e2 (September 2018)
Volume 6, Issue 1, Pages e4 (January 2018)
Volume 11, Issue 11, Pages (June 2015)
Volume 37, Issue 6, Pages (December 2012)
Volume 23, Issue 10, Pages (June 2018)
Volume 23, Issue 10, Pages (June 2018)
Volume 5, Issue 4, Pages e4 (October 2017)
Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders  Ariel Feiglin, Bryce K. Allen,
Volume 29, Issue 5, Pages (May 2016)
Predicting Gene Expression from Sequence
Volume 35, Issue 2, Pages (August 2011)
R.H. Brophy, B. Zhang, L. Cai, R.W. Wright, L.J. Sandell, M.F. Rai 
Volume 1, Issue 1, Pages (July 2015)
Brandon Ho, Anastasia Baryshnikova, Grant W. Brown  Cell Systems 
Volume 26, Issue 12, Pages e5 (March 2019)
Volume 6, Issue 1, Pages e4 (January 2018)
Regulatory Genomic Data Cubism
Volume 27, Issue 4, Pages e3 (April 2019)
Shani Marom, Amit Blumberg, Anshul Kundaje, Dan Mishmar  iScience 
Experience in Early Infancy Is Indispensable for Color Perception
Genome-wide Functional Analysis Reveals Factors Needed at the Transition Steps of Induced Reprogramming  Chao-Shun Yang, Kung-Yen Chang, Tariq M. Rana 
Volume 26, Issue 11, Pages e3 (March 2019)
Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery  Chen-Tsung Huang, Chiao-Hui Hsieh, Yun-Hsien Chung, Yen-Jen Oyang, Hsuan-Cheng.
Distinct subtypes of CAFs are detected in human PDAC
Volume 25, Issue 5, Pages e4 (May 2017)
Volume 27, Issue 7, Pages e4 (May 2019)
Volume 28, Issue 4, Pages e6 (July 2019)
Composition and Function of Mutant Swi/Snf Complexes
Presentation transcript:

Volume 25, Issue 6, Pages 1436-1445.e3 (November 2018) Revealing the Critical Regulators of Cell Identity in the Mouse Cell Atlas  Shengbao Suo, Qian Zhu, Assieh Saadatpour, Lijiang Fei, Guoji Guo, Guo-Cheng Yuan  Cell Reports  Volume 25, Issue 6, Pages 1436-1445.e3 (November 2018) DOI: 10.1016/j.celrep.2018.10.045 Copyright © 2018 The Author(s) Terms and Conditions

Cell Reports 2018 25, 1436-1445.e3DOI: (10.1016/j.celrep.2018.10.045) Copyright © 2018 The Author(s) Terms and Conditions

Figure 1 Mapping Mouse Cell Network Atlas with Regulon Activity (A) Schematic overview of the computational approach in this study. A modified SCENIC pipeline is used to infer cell-type-specific gene regulatory networks. (B–D) t-SNE map for all sampled single cells (∼61 k) based on regulon activity scores (RAS), each cell is color-coded based on major cell-type assignment. (B) All sampled cells (~61 k) are highlighted. (C) Liver cells are highlighted. (D) Stromal cells are highlighted. See also Figures S1 and S2 and Tables S1, S2, and S3. Cell Reports 2018 25, 1436-1445.e3DOI: (10.1016/j.celrep.2018.10.045) Copyright © 2018 The Author(s) Terms and Conditions

Figure 2 Cell-Type-Specific Regulon Activity Analysis (A–D) Erythroblast. (A) Rank for regulons in erythroblast cell based on regulon specificity score (RSS). (B) Erythroblast cells are highlighted in the t-SNE map (red dots). (C) Binarized regulon activity scores (RAS) (do Z score normalization across all samples, and set 2.5 as cutoff to convert to 0 and 1) for top regulon Lmo2 on t-SNE map (dark green dots). (D) SEEK co-expression result for target genes of top regulon Lmo2 in different GEO datasets. The x axis represents different datasets, and the y axis represents the co-expression significance of target genes in each dataset. Erythroblast related datasets with significant correlation (p value < 0.01) are highlighted by yellow dots. (E–H) Same as (A)–(D) but for B cells. (I–L) Same as (A)–(D) but for oligodendrocytes. (M–P) Same as (A)–(D) but for alveolar type II cells. See also Figure S2 and Table S4. Cell Reports 2018 25, 1436-1445.e3DOI: (10.1016/j.celrep.2018.10.045) Copyright © 2018 The Author(s) Terms and Conditions

Figure 3 Identification of Combinatorial Regulon Modules (A) Identified regulon modules based on regulon connection specificity index (CSI) matrix, along with representative transcription factors, corresponding binding motifs, and associated cell types. (B) Zoomed-in view of module M7 identifies sub-module structures. (C) Different sub-modules in M7 are associated with distinct immune cell types and regulon activities. See also Figure S3. Cell Reports 2018 25, 1436-1445.e3DOI: (10.1016/j.celrep.2018.10.045) Copyright © 2018 The Author(s) Terms and Conditions

Figure 4 A Summary View of the Mouse Cell Network Atlas (A) Relatedness network for the 818 cell types based on similarity of regulon activities. Each group represents a set of highly related cell types. (B) Sankey plot shows relationship between cell-type groups G1–G9 and regulon modules M1–M8, the thematic cell type composition within each cluster is indicated by the corresponding wordcloud plot. (C) A representative screenshot of the web portal obtained by querying “cumulus cells.” See also Figure S4 and Tables S2 and S3. Cell Reports 2018 25, 1436-1445.e3DOI: (10.1016/j.celrep.2018.10.045) Copyright © 2018 The Author(s) Terms and Conditions