Volume 22, Issue 3, Pages (January 2018)

Slides:



Advertisements
Similar presentations
Volume 44, Issue 1, Pages (January 2016)
Advertisements

Volume 54, Issue 6, Pages (June 2007)
Volume 16, Issue 5, Pages (August 2016)
Volume 21, Issue 11, Pages (December 2017)
A Single-Cell Transcriptome Atlas of the Human Pancreas
Volume 2, Issue 4, Pages (April 2008)
Volume 11, Issue 11, Pages (June 2015)
Volume 20, Issue 11, Pages (September 2017)
Shiran Bar, Maya Schachter, Talia Eldar-Geva, Nissim Benvenisty 
Kobe C. Yuen, Baoshan Xu, Ian D. Krantz, Jennifer L. Gerton 
Volume 13, Issue 5, Pages (November 2015)
Spatiotemporal Brassinosteroid Signaling and Antagonism with Auxin Pattern Stem Cell Dynamics in Arabidopsis Roots  Juthamas Chaiwanon, Zhi-Yong Wang 
Volume 23, Issue 4, Pages (April 2018)
Jason M. Rizzo, Rose-Anne Romano, Jonathan Bard, Satrajit Sinha 
Transcriptional Landscape of Cardiomyocyte Maturation
Understanding Tissue-Specific Gene Regulation
Widespread Inhibition of Posttranscriptional Splicing Shapes the Cellular Transcriptome following Heat Shock  Reut Shalgi, Jessica A. Hurt, Susan Lindquist,
Volume 4, Issue 3, Pages (August 2013)
Dynamic Gene Regulatory Networks of Human Myeloid Differentiation
Volume 68, Issue 5, Pages e7 (December 2017)
Volume 3, Issue 1, Pages (July 2016)
Transcriptional Profiling of Quiescent Muscle Stem Cells In Vivo
Volume 85, Issue 4, Pages (February 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,
Volume 18, Issue 1, Pages (January 2017)
Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons by Naomi Habib, Yinqing Li, Matthias Heidenreich, Lukasz Swiech, Inbal Avraham-Davidi,
Volume 17, Issue 9, Pages (November 2016)
Volume 14, Issue 7, Pages (February 2016)
Volume 39, Issue 4, Pages (November 2016)
Volume 21, Issue 11, Pages (December 2017)
Volume 16, Issue 8, Pages (August 2016)
Taming Human Genetic Variability: Transcriptomic Meta-Analysis Guides the Experimental Design and Interpretation of iPSC-Based Disease Modeling  Pierre-Luc.
Analysis of Microarray Data Using Z Score Transformation
Volume 18, Issue 2, Pages (January 2017)
Volume 22, Issue 12, Pages (March 2018)
Revisiting Global Gene Expression Analysis
Michal Levin, Tamar Hashimshony, Florian Wagner, Itai Yanai 
Volume 21, Issue 5, Pages (October 2017)
Large Differences in Small RNA Composition Between Human Biofluids
Saskia Hemmers, Alexander Y. Rudensky  Cell Reports 
Volume 16, Issue 6, Pages (August 2016)
Volume 10, Issue 3, Pages (March 2018)
Volume 22, Issue 8, Pages (February 2018)
Diego Calderon, Anand Bhaskar, David A
Shiran Bar, Maya Schachter, Talia Eldar-Geva, Nissim Benvenisty 
Volume 20, Issue 5, Pages (August 2017)
Volume 14, Issue 6, Pages (June 2014)
Volume 35, Issue 2, Pages (August 2011)
Volume 23, Issue 10, Pages (June 2018)
Volume 22, Issue 3, Pages (January 2018)
Directed Differentiation of Human Pluripotent Stem Cells to Microglia
Varying Intolerance of Gene Pathways to Mutational Classes Explain Genetic Convergence across Neuropsychiatric Disorders  Shahar Shohat, Eyal Ben-David,
Volume 11, Issue 11, Pages (June 2015)
Volume 122, Issue 6, Pages (September 2005)
R.H. Brophy, B. Zhang, L. Cai, R.W. Wright, L.J. Sandell, M.F. Rai 
Volume 14, Issue 6, Pages (June 2014)
Volume 21, Issue 4, Pages (October 2017)
Volume 7, Issue 2, Pages (August 2010)
Volume 9, Issue 3, Pages (November 2014)
Volume 26, Issue 7, Pages e4 (February 2019)
Volume 16, Issue 11, Pages (September 2016)
Volume 27, Issue 4, Pages e3 (April 2019)
Genome-wide Functional Analysis Reveals Factors Needed at the Transition Steps of Induced Reprogramming  Chao-Shun Yang, Kung-Yen Chang, Tariq M. Rana 
Volume 27, Issue 7, Pages e5 (May 2019)
Volume 28, Issue 3, Pages e7 (July 2019)
Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery  Chen-Tsung Huang, Chiao-Hui Hsieh, Yun-Hsien Chung, Yen-Jen Oyang, Hsuan-Cheng.
Volume 27, Issue 7, Pages e4 (May 2019)
Volume 28, Issue 4, Pages e6 (July 2019)
Composition and Function of Mutant Swi/Snf Complexes
Fig. 3 Gene expression analysis in 48-plex drug treatment experiments.
Presentation transcript:

Volume 22, Issue 3, Pages 832-847 (January 2018) Diverse Brain Myeloid Expression Profiles Reveal Distinct Microglial Activation States and Aspects of Alzheimer’s Disease Not Evident in Mouse Models  Brad A. Friedman, Karpagam Srinivasan, Gai Ayalon, William J. Meilandt, Han Lin, Melanie A. Huntley, Yi Cao, Seung-Hye Lee, Patrick C.G. Haddick, Hai Ngu, Zora Modrusan, Jessica L. Larson, Joshua S. Kaminker, Marcel P. van der Brug, David V. Hansen  Cell Reports  Volume 22, Issue 3, Pages 832-847 (January 2018) DOI: 10.1016/j.celrep.2017.12.066 Copyright © 2017 The Author(s) Terms and Conditions

Cell Reports 2018 22, 832-847DOI: (10.1016/j.celrep.2017.12.066) Copyright © 2017 The Author(s) Terms and Conditions

Figure 1 CNS Myeloid Gene Expression Datasets Reveal Common Co-regulated Gene Modules Reflecting Proliferative State and Other Aspects of CNS Myeloid Biology (A) Heatmap of within-study-normalized gene expression (Z score) for the 777 genes (rows) differentially expressed in at least 7 comparisons in 18 different studies (columns). Hierarchical clustering identified 45 modules of co-regulated genes. (See Figure S1 for higher magnification.) (B) Like (A), but only the 82 genes of module 26, which are enriched for proliferation-associated genes. (C) Like (B), but only the samples of study GSE67858, from mice injected with PBS, lipopolysaccharide (LPS), or lymphocytic choriomeningitis virus (LCMV). Proliferation genes were induced by LCMV but not by LPS. (D) Expression levels of two genes from the module for individual samples in the three experimental groups. As typical for this module, both genes show elevated expression in LCMV but not LPS group. Bars above plots represent fold-change comparisons displayed in the next panel. (E) Differential expression of each gene in the module, in LPS- or LCMV-treated animals relative to PBS. Fold changes of the four comparisons represented in (D) are shown in the indicated colors. (F) Like (E), but for comparisons in many more conditions in the database, as well as embryonic and perinatal compared to adult brain myeloid cells. Each point represents the differential expression of one gene in the module for one comparison. Control groups indicated by “Compared to” across the top, with more details in Experimental Procedures and Data S2, S3, S4, and S5. See also Figure S1. Cell Reports 2018 22, 832-847DOI: (10.1016/j.celrep.2017.12.066) Copyright © 2017 The Author(s) Terms and Conditions

Figure 2 Interferon- and LPS-Related Genes Show Distinct Responses in CNS Myeloid Cells (A and J) Like Figure 1F, differential expression of individual genes in the Interferon-Related (A) and LPS-Related (J) modules in response to various stimuli. (Gene sets indicated in Figure S1 and full lists available in Data S4.) (B–I) Expression of two genes from the interferon-related module in three neurodegeneration-related datasets (B–D and F–H) and one infectious model dataset (E and I). Points represent samples, and bars indicate fold changes relative to controls, with green color indicating significance at p ≤ 0.05. (K–R) Like (B)–(I), but for genes in the LPS-Related modules rather than the Interferon-Related module. See also Figure S1. Cell Reports 2018 22, 832-847DOI: (10.1016/j.celrep.2017.12.066) Copyright © 2017 The Author(s) Terms and Conditions

Figure 3 Neurodegeneration-Related Genes Are Elevated in Myeloid Cells from Models of Neurodegenerative Disease but Not after LPS Treatment (A–I) Like Figure 2 (A)–(I) but for the Neurodegeneration-Related gene set rather than the Interferon-Related module. (J and K) Expression of neurodegeneration-related module genes in hMAPT-P301S (J), hMAPT-P301L (K), and control hippocampal myeloid cells. Points correspond to module genes and x and y axes represent expression levels in control and transgenic myeloid cells. Red: significant differential expression at p ≤ 0.05 and fold change ≥2. See also Figure S1. Cell Reports 2018 22, 832-847DOI: (10.1016/j.celrep.2017.12.066) Copyright © 2017 The Author(s) Terms and Conditions

Figure 4 Three Modules Are Associated with Specific Subtypes of Myeloid Cells Like Figures 2A and 2J and 3A but with different sets of genes. See also Figures S1 and S2. Cell Reports 2018 22, 832-847DOI: (10.1016/j.celrep.2017.12.066) Copyright © 2017 The Author(s) Terms and Conditions

Figure 5 CNS Myeloid Profile Is a Superposition of Multiple Transcriptional Responses (A) Trem2 dependence of 5XFAD brain myeloid response by activation module. Top: total number of genes in each gene set (“Gene Set”), genes available in GSE65067 (“in GSE65067”), and genes increased (or, for microglia gene set, decreased) in TremWT mice at adjusted p ≤ 0.1 (“DE in 5XFAD,” DE, differentially expressed). y axis shows the percentage by which the log-fold induction of these differentially expressed is decreased in Trem2KO mice (see Experimental Procedures). Genes less than 12% Trem2 dependent are indicated. Box and whisker plots depict the median, interquartile, and minimum/maximum values. (B) Expression patterns of selected Trem2-dependent and -independent genes. (C) Summary of gene set changes across the database. Rows correspond to gene sets and columns to comparisons, in the same order as Figures 1F, 2, 3, and 4. Cell Reports 2018 22, 832-847DOI: (10.1016/j.celrep.2017.12.066) Copyright © 2017 The Author(s) Terms and Conditions

Figure 6 Myeloid Gene Modules Represent Distinct Cell Types and Activation States (A–I) tSNE projections of single-cell RNA-seq data from 5XFAD and control brains distinguish microglia (big cluster on top) from non-microglia cells (clusters 5, 7, 8, 9, 11, 12, 14 below), as well as activation states within the microglia (GEO: GSE98969). (A) Seurat cluster. (B) Genotype. Disease-associated/neurodegeneration-related cell cluster 4 is highly enriched in 5XFAD cells. (C–I) Gene set scores: average Log2(Normalized UMI+1), with relevant gene set noted in lower left of each panel. Note in particular that different cells show high Neurodegeneration-Related scores (cell cluster 4, F) and Interferon-Related scores (cell cluster 13, D). (A′) Number of cells within each cluster. (B′) Fold enrichment of 5XFAD genotype within each cell cluster (see Experimental Procedures). (C′–I′) Corresponding to C-I, distribution of gene set scores for cells within each cluster. Color for (A′)–(I′) corresponds to (A), by cell cluster. See also Figure S3. Cell Reports 2018 22, 832-847DOI: (10.1016/j.celrep.2017.12.066) Copyright © 2017 The Author(s) Terms and Conditions

Figure 7 Orthologs of Core Neurodegeneration-Related Genes Identified in Mouse Myeloid Cells Are Elevated in AD Fusiform Gyrus (A) Z score expression of mouse genes in the core neurodegeneration-related module in a representative dataset of sorted mouse CNS myeloid cells. Genes are shown in the same order as their human orthologs in (B). Genes without human orthologs, and genes with high neuronal expression are omitted (see Experimental Procedures). The entire set is elevated in purified cells. (B and C) Z score expression of human orthologs of core neurodegeneration-related genes on the rows, and samples from “myeloid-balanced” control or AD tissue (fusiform gyrus, B, or temporal cortex, C) on the columns (see Experimental Procedures and Figure S6). Selected genes labeled; full list in Data S4 (“Neurodegeneration” in the “Myeloid Activation (Coarse)” column). Below, gene set score for each sample (color scale not shown). (D and E) AD cohort has significantly higher Neurodegeneration-Related gene set scores than controls (t test p value) both in fusiform gyrus dataset (D) and temporal cortex dataset (E). Points correspond to samples. (F) Z score gene expression in a dataset of sorted human brain cell types. Genes are shown in the same order as (A–C). (G–I) Like (D) and (E) but for Neutrophil/Monocyte (G), LPS-Related (H), and LPS-Specific (I) other myeloid activation modules. See also Figures S4–S7. Cell Reports 2018 22, 832-847DOI: (10.1016/j.celrep.2017.12.066) Copyright © 2017 The Author(s) Terms and Conditions