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Viral-genetic tracing of the input-output organization of a central noradrenaline circuit.

Schwarz LA, Miyamichi K, Gao XJ, Beier KT, Weissbourd B, DeLoach KE, Ren J, Ibanes S, Malenka RC, Kremer EJ, Luo L - Nature (2015)

Bottom Line: Thus, the LC-NE circuit overall integrates information from, and broadcasts to, many brain regions, consistent with its primary role in regulating brain states.At the same time, we uncovered several levels of specificity in certain LC-NE sub-circuits.More broadly, our viral-genetic approaches provide an efficient intersectional means to target neuronal populations based on cell type and projection pattern.

View Article: PubMed Central - PubMed

Affiliation: Howard Hughes Medical Institute and Department of Biology, Stanford University, Stanford, California 94305, USA.

ABSTRACT
Deciphering how neural circuits are anatomically organized with regard to input and output is instrumental in understanding how the brain processes information. For example, locus coeruleus noradrenaline (also known as norepinephrine) (LC-NE) neurons receive input from and send output to broad regions of the brain and spinal cord, and regulate diverse functions including arousal, attention, mood and sensory gating. However, it is unclear how LC-NE neurons divide up their brain-wide projection patterns and whether different LC-NE neurons receive differential input. Here we developed a set of viral-genetic tools to quantitatively analyse the input-output relationship of neural circuits, and applied these tools to dissect the LC-NE circuit in mice. Rabies-virus-based input mapping indicated that LC-NE neurons receive convergent synaptic input from many regions previously identified as sending axons to the locus coeruleus, as well as from newly identified presynaptic partners, including cerebellar Purkinje cells. The 'tracing the relationship between input and output' method (or TRIO method) enables trans-synaptic input tracing from specific subsets of neurons based on their projection and cell type. We found that LC-NE neurons projecting to diverse output regions receive mostly similar input. Projection-based viral labelling revealed that LC-NE neurons projecting to one output region also project to all brain regions we examined. Thus, the LC-NE circuit overall integrates information from, and broadcasts to, many brain regions, consistent with its primary role in regulating brain states. At the same time, we uncovered several levels of specificity in certain LC-NE sub-circuits. These tools for mapping output architecture and input-output relationship are applicable to other neuronal circuits and organisms. More broadly, our viral-genetic approaches provide an efficient intersectional means to target neuronal populations based on cell type and projection pattern.

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Simulation of input convergence in Dbh-Cre tracing experimentsIn the sparsest Dbh-Cre trans-synaptic tracing brain, 4 starter cells received input from 43 distinct input regions (309 input neurons, see Supplementary Table 2, Sample #8). In the second sparsest sample, 22 starter cells received input from 66 distinct input regions (756 input neurons; see Supplementary Table 2, Sample #9). a, The relation between the number of input regions for each LC-NE starter cell and the probability of observing > 42 (left) or > 65 (right) input regions in simulation, assuming that each starter cell receives input from a given region with the same probability. As the number of input regions per starter cell increases, the probability of observing inputs from > 42 or > 65 regions also increases. Based on a threshold of p value < 0.001, these simulations suggest that, to account for the total number of observed input areas in each brain sample, there must be individual LC-NE neurons that receive input from more than 15 regions for the sparsest sample (red dot, left) or more than 9 regions for 2nd sparsest sample (red dot, right). b, Detailed view of the distribution of simulation results corresponding to the red dots in (a). Assuming that each cell receives input from 15 (left) or 9 (right) distinct regions, only 5 (left) or 6 (right) out of 10,000 simulations label > 42 (left) or > 65 (right) input regions. Note that if the assumption that each starter cell receives input from the same number of regions does not apply, then there must be at least one cell receiving input from more regions than the number specified in the simulation.
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Figure 13: Simulation of input convergence in Dbh-Cre tracing experimentsIn the sparsest Dbh-Cre trans-synaptic tracing brain, 4 starter cells received input from 43 distinct input regions (309 input neurons, see Supplementary Table 2, Sample #8). In the second sparsest sample, 22 starter cells received input from 66 distinct input regions (756 input neurons; see Supplementary Table 2, Sample #9). a, The relation between the number of input regions for each LC-NE starter cell and the probability of observing > 42 (left) or > 65 (right) input regions in simulation, assuming that each starter cell receives input from a given region with the same probability. As the number of input regions per starter cell increases, the probability of observing inputs from > 42 or > 65 regions also increases. Based on a threshold of p value < 0.001, these simulations suggest that, to account for the total number of observed input areas in each brain sample, there must be individual LC-NE neurons that receive input from more than 15 regions for the sparsest sample (red dot, left) or more than 9 regions for 2nd sparsest sample (red dot, right). b, Detailed view of the distribution of simulation results corresponding to the red dots in (a). Assuming that each cell receives input from 15 (left) or 9 (right) distinct regions, only 5 (left) or 6 (right) out of 10,000 simulations label > 42 (left) or > 65 (right) input regions. Note that if the assumption that each starter cell receives input from the same number of regions does not apply, then there must be at least one cell receiving input from more regions than the number specified in the simulation.

Mentions: The largely indiscriminant input–output relationship revealed by TRIO can in principle be accounted for by input convergence (Fig. 1b, left), output divergence (Fig. 1b, right), or both. A simulation analysis of the two sparsest input tracing samples (Supplementary Table 2) suggested that individual LC-NE neurons must receive input from more than 15 or 9 brain regions, respectively (Extended Data Fig. 9). This is most likely a lower bound as rabies tracing efficiency is far from 100%. However, such extensive integration is not entirely homogenous (Supplementary Table 4). Thus, individual LC-NE neurons integrate inputs from many regions, yet exhibit heterogeneity with respect to brain regions from which they receive input.


Viral-genetic tracing of the input-output organization of a central noradrenaline circuit.

Schwarz LA, Miyamichi K, Gao XJ, Beier KT, Weissbourd B, DeLoach KE, Ren J, Ibanes S, Malenka RC, Kremer EJ, Luo L - Nature (2015)

Simulation of input convergence in Dbh-Cre tracing experimentsIn the sparsest Dbh-Cre trans-synaptic tracing brain, 4 starter cells received input from 43 distinct input regions (309 input neurons, see Supplementary Table 2, Sample #8). In the second sparsest sample, 22 starter cells received input from 66 distinct input regions (756 input neurons; see Supplementary Table 2, Sample #9). a, The relation between the number of input regions for each LC-NE starter cell and the probability of observing > 42 (left) or > 65 (right) input regions in simulation, assuming that each starter cell receives input from a given region with the same probability. As the number of input regions per starter cell increases, the probability of observing inputs from > 42 or > 65 regions also increases. Based on a threshold of p value < 0.001, these simulations suggest that, to account for the total number of observed input areas in each brain sample, there must be individual LC-NE neurons that receive input from more than 15 regions for the sparsest sample (red dot, left) or more than 9 regions for 2nd sparsest sample (red dot, right). b, Detailed view of the distribution of simulation results corresponding to the red dots in (a). Assuming that each cell receives input from 15 (left) or 9 (right) distinct regions, only 5 (left) or 6 (right) out of 10,000 simulations label > 42 (left) or > 65 (right) input regions. Note that if the assumption that each starter cell receives input from the same number of regions does not apply, then there must be at least one cell receiving input from more regions than the number specified in the simulation.
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Figure 13: Simulation of input convergence in Dbh-Cre tracing experimentsIn the sparsest Dbh-Cre trans-synaptic tracing brain, 4 starter cells received input from 43 distinct input regions (309 input neurons, see Supplementary Table 2, Sample #8). In the second sparsest sample, 22 starter cells received input from 66 distinct input regions (756 input neurons; see Supplementary Table 2, Sample #9). a, The relation between the number of input regions for each LC-NE starter cell and the probability of observing > 42 (left) or > 65 (right) input regions in simulation, assuming that each starter cell receives input from a given region with the same probability. As the number of input regions per starter cell increases, the probability of observing inputs from > 42 or > 65 regions also increases. Based on a threshold of p value < 0.001, these simulations suggest that, to account for the total number of observed input areas in each brain sample, there must be individual LC-NE neurons that receive input from more than 15 regions for the sparsest sample (red dot, left) or more than 9 regions for 2nd sparsest sample (red dot, right). b, Detailed view of the distribution of simulation results corresponding to the red dots in (a). Assuming that each cell receives input from 15 (left) or 9 (right) distinct regions, only 5 (left) or 6 (right) out of 10,000 simulations label > 42 (left) or > 65 (right) input regions. Note that if the assumption that each starter cell receives input from the same number of regions does not apply, then there must be at least one cell receiving input from more regions than the number specified in the simulation.
Mentions: The largely indiscriminant input–output relationship revealed by TRIO can in principle be accounted for by input convergence (Fig. 1b, left), output divergence (Fig. 1b, right), or both. A simulation analysis of the two sparsest input tracing samples (Supplementary Table 2) suggested that individual LC-NE neurons must receive input from more than 15 or 9 brain regions, respectively (Extended Data Fig. 9). This is most likely a lower bound as rabies tracing efficiency is far from 100%. However, such extensive integration is not entirely homogenous (Supplementary Table 4). Thus, individual LC-NE neurons integrate inputs from many regions, yet exhibit heterogeneity with respect to brain regions from which they receive input.

Bottom Line: Thus, the LC-NE circuit overall integrates information from, and broadcasts to, many brain regions, consistent with its primary role in regulating brain states.At the same time, we uncovered several levels of specificity in certain LC-NE sub-circuits.More broadly, our viral-genetic approaches provide an efficient intersectional means to target neuronal populations based on cell type and projection pattern.

View Article: PubMed Central - PubMed

Affiliation: Howard Hughes Medical Institute and Department of Biology, Stanford University, Stanford, California 94305, USA.

ABSTRACT
Deciphering how neural circuits are anatomically organized with regard to input and output is instrumental in understanding how the brain processes information. For example, locus coeruleus noradrenaline (also known as norepinephrine) (LC-NE) neurons receive input from and send output to broad regions of the brain and spinal cord, and regulate diverse functions including arousal, attention, mood and sensory gating. However, it is unclear how LC-NE neurons divide up their brain-wide projection patterns and whether different LC-NE neurons receive differential input. Here we developed a set of viral-genetic tools to quantitatively analyse the input-output relationship of neural circuits, and applied these tools to dissect the LC-NE circuit in mice. Rabies-virus-based input mapping indicated that LC-NE neurons receive convergent synaptic input from many regions previously identified as sending axons to the locus coeruleus, as well as from newly identified presynaptic partners, including cerebellar Purkinje cells. The 'tracing the relationship between input and output' method (or TRIO method) enables trans-synaptic input tracing from specific subsets of neurons based on their projection and cell type. We found that LC-NE neurons projecting to diverse output regions receive mostly similar input. Projection-based viral labelling revealed that LC-NE neurons projecting to one output region also project to all brain regions we examined. Thus, the LC-NE circuit overall integrates information from, and broadcasts to, many brain regions, consistent with its primary role in regulating brain states. At the same time, we uncovered several levels of specificity in certain LC-NE sub-circuits. These tools for mapping output architecture and input-output relationship are applicable to other neuronal circuits and organisms. More broadly, our viral-genetic approaches provide an efficient intersectional means to target neuronal populations based on cell type and projection pattern.

Show MeSH