Volume 92, Issue 1, Pages (October 2016)

Slides:



Advertisements
Similar presentations
Xiaoshu Chen, Jianzhi Zhang  Cell Systems 
Advertisements

Volume 62, Issue 5, Pages (June 2009)
Maturation of a Recurrent Excitatory Neocortical Circuit by Experience-Dependent Unsilencing of Newly Formed Dendritic Spines  Michael C. Ashby, John T.R.
Mark E.J. Sheffield, Michael D. Adoff, Daniel A. Dombeck  Neuron 
Morphological Substrates for Parallel Streams of Corticogeniculate Feedback Originating in Both V1 and V2 of the Macaque Monkey  Farran Briggs, Caitlin W.
Christian Mayer, Rachel C. Bandler, Gord Fishell  Neuron 
PDF Has Found Its Receptor
Araceli Ramirez-Cardenas, Maria Moskaleva, Andreas Nieder 
Volume 87, Issue 5, Pages (September 2015)
Volume 81, Issue 4, Pages (February 2014)
Volume 64, Issue 3, Pages (November 2009)
Chenguang Zheng, Kevin Wood Bieri, Yi-Tse Hsiao, Laura Lee Colgin 
Volume 66, Issue 6, Pages (June 2010)
Volume 89, Issue 6, Pages (March 2016)
The Branching Point in Erythro-Myeloid Differentiation
Volume 62, Issue 5, Pages (June 2009)
Sensitivity to Complex Statistical Regularities in Rat Auditory Cortex
Vincent B. McGinty, Antonio Rangel, William T. Newsome  Neuron 
Hongbo Yu, Brandon J. Farley, Dezhe Z. Jin, Mriganka Sur  Neuron 
Volume 86, Issue 3, Pages (May 2015)
Volume 96, Issue 4, Pages e5 (November 2017)
CA3 Retrieves Coherent Representations from Degraded Input: Direct Evidence for CA3 Pattern Completion and Dentate Gyrus Pattern Separation  Joshua P.
A Switching Observer for Human Perceptual Estimation
Volume 82, Issue 5, Pages (June 2014)
Adaptive Surround Modulation in Cortical Area MT
Volume 89, Issue 5, Pages (March 2016)
Volume 87, Issue 5, Pages (September 2015)
Talia Konkle, Aude Oliva  Neuron  Volume 74, Issue 6, Pages (June 2012)
Shuijin He, Zhizhong Li, Shaoyu Ge, Yong-Chun Yu, Song-Hai Shi  Neuron 
Volume 85, Issue 4, Pages (February 2015)
Volume 45, Issue 4, Pages (February 2005)
Lineage Tracing Using Cux2-Cre and Cux2-CreERT2 Mice
Volume 88, Issue 3, Pages (November 2015)
Human Orbitofrontal Cortex Represents a Cognitive Map of State Space
Volume 5, Issue 4, Pages e4 (October 2017)
Volume 80, Issue 1, Pages (October 2013)
Ju Tian, Naoshige Uchida  Neuron 
A. Saez, M. Rigotti, S. Ostojic, S. Fusi, C.D. Salzman  Neuron 
Volume 88, Issue 3, Pages (November 2015)
A Switching Observer for Human Perceptual Estimation
BOLD fMRI Correlation Reflects Frequency-Specific Neuronal Correlation
Benjamin Scholl, Daniel E. Wilson, David Fitzpatrick  Neuron 
Redundancy in the Population Code of the Retina
Volume 88, Issue 4, Pages (November 2015)
Volume 74, Issue 2, Pages (April 2012)
Volume 89, Issue 6, Pages (March 2016)
Morphological Substrates for Parallel Streams of Corticogeniculate Feedback Originating in Both V1 and V2 of the Macaque Monkey  Farran Briggs, Caitlin W.
James M. Jeanne, Tatyana O. Sharpee, Timothy Q. Gentner  Neuron 
Volume 75, Issue 5, Pages (September 2012)
Grid and Nongrid Cells in Medial Entorhinal Cortex Represent Spatial Location and Environmental Features with Complementary Coding Schemes  Geoffrey W.
Christian Mayer, Rachel C. Bandler, Gord Fishell  Neuron 
Volume 93, Issue 4, Pages e6 (February 2017)
The Normalization Model of Attention
Volume 72, Issue 6, Pages (December 2011)
Peter H. Rudebeck, Andrew R. Mitz, Ravi V. Chacko, Elisabeth A. Murray 
Benjamin Scholl, Daniel E. Wilson, David Fitzpatrick  Neuron 
Dario Maschi, Vitaly A. Klyachko  Neuron 
Giulia Quattrocolo, Gord Fishell, Timothy J. Petros  Cell Reports 
Gene Density, Transcription, and Insulators Contribute to the Partition of the Drosophila Genome into Physical Domains  Chunhui Hou, Li Li, Zhaohui S.
David P. Doupé, Allon M. Klein, Benjamin D. Simons, Philip H. Jones 
Dynamic Shape Synthesis in Posterior Inferotemporal Cortex
Volume 58, Issue 1, Pages (April 2008)
Cell Assemblies of the Superficial Cortex
Perceptual Classification in a Rapidly Changing Environment
Volume 75, Issue 5, Pages (September 2012)
Giulia Quattrocolo, Gord Fishell, Timothy J. Petros  Cell Reports 
Spatial Representation along the Proximodistal Axis of CA1
A Hippocampal Marker of Recollection Memory Ability among Healthy Young Adults: Contributions of Posterior and Anterior Segments  Jordan Poppenk, Morris.
Volume 75, Issue 1, Pages (July 2012)
Xiaoshu Chen, Jianzhi Zhang  Cell Systems 
Presentation transcript:

Volume 92, Issue 1, Pages 31-44 (October 2016) Clonally Related GABAergic Interneurons Do Not Randomly Disperse but Frequently Form Local Clusters in the Forebrain  Khadeejah T. Sultan, Zhi Han, Xin-Jun Zhang, Anjin Xianyu, Zhizhong Li, Kun Huang, Song-Hai Shi  Neuron  Volume 92, Issue 1, Pages 31-44 (October 2016) DOI: 10.1016/j.neuron.2016.09.033 Copyright © 2016 Elsevier Inc. Terms and Conditions

Figure 1 Interneuron Output of the MGE/PoA Progenitors in the Forebrain (A) Coronal sections of representative P21 Nkx2.1-Cre/Ai9-tdTomato brains showing tdTomato+ interneuron populations in the cortex (Ctx), hippocampus (Hip), and striatum (Str). Scale bar, 100 μm. (B and C) Quantification of the total number (B) and relative distribution (C) of tdTomato+ interneurons in the Ctx, Hip, Str, and globus pallidus (GP). Bars represent mean ± SD (n = 3 brains). (D) Three-dimensional stereological reconstructions of representative P21 brains that received intraventricular injection of low-titer RCAS retrovirus expressing EGFP at E12.5. Green, yellow, red, and white dots represent EGFP-labeled interneurons in the Ctx, Hip, Str, and GP, respectively. (E and F) Quantification of the total number (E) and relative distribution (F) of EGFP+ interneurons in the Ctx, Hip, Str, and GP labeled using in utero intraventricular RCAS-EGFP injection at E12.5. Data are presented as mean ± SD (n = 7 brains). See also Figures S1 and S2. Neuron 2016 92, 31-44DOI: (10.1016/j.neuron.2016.09.033) Copyright © 2016 Elsevier Inc. Terms and Conditions

Figure 2 MGE/PoA-Derived Interneuron Clones in Mayer et al. Do Not Randomly Disperse in Different Forebrain Structures but Are Mostly Located in the Cortex or the Cortex and Hippocampus (A) Comparison of the fractions of clones located in the four brain structures (Ctx, Hip, Str, and GP) in the experimental data (red bars) with the predicted probabilities of clone distribution based on a random dispersion and the relative ratio of total MGE/PoA interneuron output to the four structures in P21 Nkx2.1-Cre/Ai9-tdTomato brains (black bars). Data are presented as mean ± SD (∗p < 0.05; ∗∗∗p < 0.001). (B) Comparison of the fractions of clones located in the four brain structures (Ctx, Hip, Str, and GP) in the experimental data (red bars) with the predicted probabilities of clone distribution based on a random dispersion and the relative ratio of the interneuron output of E12.5 dividing MGE/PoA VZ progenitor cells to the four structures labeled using in utero intraventricular RCAS-EGFP injection (black bars). Data are presented as mean ± SD (∗∗p < 0.01). (C) Quantification of the percentage of interneuron clones that are restricted to one brain structure (blue) or span more than one brain structure (green). Data are presented as mean ± SD (n = 3 brains). (D) Quantification of the percentage of interneuron clones restricted to the cortex, hippocampus, or striatum (n = 3 brains). (E) Quantification of the percentage of interneuron clones spanning different brain structures (n = 3 brains). Ctx, cortex; Hip, hippocampus; Str, striatum; GP, globus pallidus; OB, olfactory bulb. See also Figures S2 and S3 and Table S1. Neuron 2016 92, 31-44DOI: (10.1016/j.neuron.2016.09.033) Copyright © 2016 Elsevier Inc. Terms and Conditions

Figure 3 MGE/PoA-Derived Interneuron Clones in Harwell et al. Are Mostly Restricted to the Cortex (A) Quantification of the percentage of clones that are either restricted to one brain structure (blue) or span more than one brain structure (green). (B) Quantification of the percentage of clones restricted to one brain structure (n = 2 hemispheres). (C) Quantification of the percentage of clones spanning more than one brain structure (n = 2 hemispheres). Ctx, cortex; Hip, hippocampus; Str, striatum. See also Table S2. Neuron 2016 92, 31-44DOI: (10.1016/j.neuron.2016.09.033) Copyright © 2016 Elsevier Inc. Terms and Conditions

Figure 4 Interneuron Clones in Mayer et al. Reliably Form Local Clusters (A) Dendrograms of individual datasets containing multi-cell clones in the same brain structures according to their Euclidean distances. Numbers and colors indicate the lineal relationship between two or more cells based on the recovered barcodes. Colored lines below the numbers indicate spatially isolated clonal clusters. Colored dots mark a sibling neuron located away from the corresponding clonal cluster. Broken black lines indicate local clonal clusters occupying the same or nearby space. The superscript letters indicate the location of the same clone in different brain structures (C, cortex; H, hippocampus; O, olfactory bulb; C/O, cortex or olfactory bulb, uncertain based on the x-y-z spatial coordinates). (B) Quantification of the percentage of local clusters (i.e., the lowest hierarchical branch in the dendrogram) that are clonally related in the experimental dataset (red bars) compared with the percentage estimated from random permutations (i.e., reshuffling) of the clonal identity in the same dendrograms repeated for 100 rounds (black bars). Data are shown as the total percentage in the whole dataset as well as mean ± SD (n = 3 brains) (∗∗∗p < 0.001). (C) Quantification of the percentage of clones that form local clusters in the experimental dataset (red bars) compared with the percentage estimated from random permutations of the clonal identity in the same dendrograms repeated for 100 rounds (black bars). Data are shown as the total percentage in the whole dataset as well as mean ± SD (n = 3 brains) (∗∗∗p < 0.001). (D) Histograms showing the frequency of local clonal clustering estimated from random permutations of clones. Red arrows indicate the number of local clonal clusters observed in each of the three experimental datasets. See also Figure S5. Neuron 2016 92, 31-44DOI: (10.1016/j.neuron.2016.09.033) Copyright © 2016 Elsevier Inc. Terms and Conditions

Figure 5 Interneuron Clones in Mayer et al. Are Spatially Segregated in the Brain (A) Quantification of the average intra-clonal and inter-clonal Euclidean distances for individual clones in each experimental brain dataset. The “inter-clonal” and “intra-clonal” distances for single cells represent the average distance between individual clones and all single barcoded cells as a “clone.” Each black dot represents the average distance for an individual clone. Red lines represent mean ± SD (∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001; n.s., not significant). (B) Quantification of the average inter-clonal and intra-clonal distances for all clones and single cells. Data are presented as mean ± SD (n = 3 brains; ∗∗∗∗p < 0.0001; n.s., not significant). (C) Histograms of inter-clonal and intra-clonal distances for clones and single cells in all experimental brains (n = 3 brains; chi-square test, ∗∗∗∗p < 0.0001). See also Figures S5 and S6. Neuron 2016 92, 31-44DOI: (10.1016/j.neuron.2016.09.033) Copyright © 2016 Elsevier Inc. Terms and Conditions

Figure 6 Interneuron Clones in Harwell et al. Exhibit a Clustering Feature in Spatial Distribution (A) Dendrograms of the left and right hemispheres of the single barcoded experimental dataset according to the Euclidean distances of multi-cell clones in the same brain structure. Numbers and colors indicate the lineal relationship between two or more cells based on the recovered barcodes. Colored lines below the numbers indicate spatially isolated clonal clusters. Colored dots mark a sibling neuron located away from the corresponding clonal cluster. Broken black lines indicate local clonal clusters occupying the same or nearby space. (B) Quantification of the percentage of local clusters (i.e., the lowest hierarchical branch in the dendrogram) that are clonally related in the experimental dataset (red bars) compared with the percentage estimated from random permutations of the clonal identity in the same dendrograms repeated for 100 rounds (black bars). Data are shown as the total percentage in the whole dataset as well as mean ± SD (n = 2 hemispheres). (C) Quantification of the percentage of clones that form local clusters in the experimental dataset (red bars) compared with the percentage estimated from random permutations of the clonal identity in the same dendrograms for 100 rounds (black bars). Data are shown as the total percentage in the whole dataset as well as mean ± SD (n = 2 hemispheres). (D) Histograms showing the frequency of local clonal clustering estimated from random permutations of clones. Red arrows indicate the number of local clonal clusters observed in the experimental dataset. (E) Quantification of the average intra-clonal and inter-clonal Euclidean distances for individual clones in each experimental dataset. The “inter-clonal” and “intra-clonal” distances for single cells represent the average distance between individual clones and all single barcoded cells as a “clone.” Each black dot represents the average distance for an individual clone. Red lines represent mean ± SD (∗p < 0.05; ∗∗∗p < 0.001; n.s., not significant). (F) Quantification of the average inter-clonal and intra-clonal distances for all clones and single cells. Data are presented as mean ± SD (n = 2 hemispheres; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; n.s., not significant). See also Figure S7 and Table S2. Neuron 2016 92, 31-44DOI: (10.1016/j.neuron.2016.09.033) Copyright © 2016 Elsevier Inc. Terms and Conditions