Abstract
Prefrontal cortex (PFC) is the cognitive center that integrates and regulates global brain activity. However, the whole-brain organization of PFC axon projections remains poorly understood. Using single-neuron reconstruction of 6,357 mouse PFC projection neurons, we identified 64 projectome-defined subtypes. Each of four previously known major cortico-cortical subnetworks was targeted by a distinct group of PFC subtypes defined by their first-order axon collaterals. Further analysis unraveled topographic rules of soma distribution within PFC, first-order collateral branch point-dependent target selection and terminal arbor distribution-dependent target subdivision. Furthermore, we obtained a high-precision hierarchical map within PFC and three distinct functionally related PFC modules, each enriched with internal recurrent connectivity. Finally, we showed that each transcriptome subtype corresponds to multiple projectome subtypes found in different PFC subregions. Thus, whole-brain single-neuron projectome analysis reveals organization principles of axon projections within and outside PFC and provides the essential basis for elucidating neuronal connectivity underlying diverse PFC functions.
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Data availability
All data can be visualized and downloaded from the website https://mouse.braindatacenter.cn. Source data are provided with this paper.
Code availability
The FNT software and the codes used in the data analysis are available at https://zenodo.org/record/5981001.
References
Gerfen, C. R., Economo, M. N. & Chandrashekar, J. Long distance projections of cortical pyramidal neurons. J. Neurosci. Res. 96, 1467–1475 (2018).
Harris, K. D. & Shepherd, G. M. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015).
Zingg, B. et al. Neural networks of the mouse neocortex. Cell 156, 1096–1111 (2014).
Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).
Daigle, T. L. et al. A suite of transgenic driver and reporter mouse lines with enhanced brain-cell-type targeting and functionality. Cell 174, 465–480 (2018).
Harris, J. A. et al. Hierarchical organization of cortical and thalamic connectivity. Nature 575, 195–202 (2019).
Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).
Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241 (2021).
Lui, J. H. et al. Differential encoding in prefrontal cortex projection neuron classes across cognitive tasks. Cell 184, 489–506 (2021).
Kuwabara, M., Kang, N., Holy, T. E. & Padoa-Schioppa, C. Neural mechanisms of economic choices in mice. eLife 9, e49669 (2020).
Riceberg, J. S. & Shapiro, M. L. Orbitofrontal cortex signals expected outcomes with predictive codes when stable contingencies promote the integration of reward history. J. Neurosci. 37, 2010–2021 (2017).
Liu, D. et al. Medial prefrontal activity during delay period contributes to learning of a working memory task. Science 346, 458–463 (2014).
Inagaki, H. K., Fontolan, L., Romani, S. & Svoboda, K. Discrete attractor dynamics underlies persistent activity in the frontal cortex. Nature 566, 212–217 (2019).
Uddin, L. Q. Salience processing and insular cortical function and dysfunction. Nat. Rev. Neurosci. 16, 55–61 (2015).
Giustino, T. F. & Maren, S. The role of the medial prefrontal cortex in the conditioning and extinction of fear. Front. Behav. Neurosci. 9, 298 (2015).
Xie, Y., Nie, C. & Yang, T. Covert shift of attention modulates the value encoding in the orbitofrontal cortex. eLife 7, e31507 (2018).
Gogolla, N., Takesian, A. E., Feng, G., Fagiolini, M. & Hensch, T. K. Sensory integration in mouse insular cortex reflects GABA circuit maturation. Neuron 83, 894–905 (2014).
Zhang, S. et al. Selective attention. Long-range and local circuits for top-down modulation of visual cortex processing. Science 345, 660–665 (2014).
Carlen, M. What constitutes the prefrontal cortex? Science 358, 478–482 (2017).
Kim, C. K. et al. Molecular and circuit-dynamical identification of top-down neural mechanisms for restraint of reward seeking. Cell 170, 1013–1027 (2017).
Otis, J. M. et al. Prefrontal cortex output circuits guide reward seeking through divergent cue encoding. Nature 543, 103–107 (2017).
Xu, N. L. et al. Nonlinear dendritic integration of sensory and motor input during an active sensing task. Nature 492, 247–251 (2012).
Gong, H. et al. High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level. Nat. Commun. 7, 12142 (2016).
Wang, X. et al. Chemical sectioning fluorescence tomography: high-throughput, high-contrast, multicolor, whole-brain imaging at subcellular resolution. Cell Rep. 34, 108709 (2020).
Wang, Q. et al. The Allen Mouse Brain Common Coordinate Framework: a 3D reference atlas. Cell 181, 936–953 e920 (2020).
van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Cuntz, H., Mathy, A. & Hausser, M. A scaling law derived from optimal dendritic wiring. Proc. Natl Acad. Sci. USA 109, 11014–11018 (2012).
West, G. B., Brown, J. H. & Enquist, B. J. A general model for the origin of allometric scaling laws in biology. Science 276, 122–126 (1997).
Jhang, J. et al. Anterior cingulate cortex and its input to the basolateral amygdala control innate fear response. Nat. Commun. 9, 2744 (2018).
Shipman, M. L., Johnson, G. C., Bouton, M. E. & Green, J. T. Chemogenetic silencing of prelimbic cortex to anterior dorsomedial striatum projection attenuates operant responding. eNeuro 6, ENEURO.0125-19.2019 (2019).
Murugan, M. et al. Combined social and spatial coding in a descending projection from the prefrontal cortex. Cell 171, 1663–1677 (2017).
Groman, S. M. et al. Orbitofrontal circuits control multiple reinforcement-learning processes. Neuron 103, 734–746 (2019).
White, M. G. et al. Anterior cingulate cortex input to the claustrum is required for top-down action control. Cell Rep. 22, 84–95 (2018).
Ottenheimer, D. J. et al. Reward activity in ventral pallidum tracks satiety-sensitive preference and drives choice behavior. Sci. Adv. 6, eabc9321 (2020).
Song, C., Ehlers, V. L. & Moyer, J. R. Jr. Trace fear conditioning differentially modulates intrinsic excitability of medial prefrontal cortex-basolateral complex of amygdala projection neurons in infralimbic and prelimbic cortices. J. Neurosci. 35, 13511–13524 (2015).
Bloodgood, D. W., Sugam, J. A., Holmes, A. & Kash, T. L. Fear extinction requires infralimbic cortex projections to the basolateral amygdala. Transl. Psychiatry 8, 60 (2018).
Hintiryan, H. et al. The mouse cortico-striatal projectome. Nat. Neurosci. 19, 1100–1114 (2016).
Chon, U., Vanselow, D. J., Cheng, K. C. & Kim, Y. Enhanced and unified anatomical labeling for a common mouse brain atlas. Nat. Commun. 10, 5067 (2019).
Hunnicutt, B. J. et al. A comprehensive excitatory input map of the striatum reveals novel functional organization. eLife 5, e19103 (2016).
Shepherd, G. M. Corticostriatal connectivity and its role in disease. Nat. Rev. Neurosci. 14, 278–291 (2013).
Economo, M. N. et al. Distinct descending motor cortex pathways and their roles in movement. Nature 563, 79–84 (2018).
Guo, Z. V. et al. Maintenance of persistent activity in a frontal thalamocortical loop. Nature 545, 181–186 (2017).
Hu, F. et al. Prefrontal corticotectal neurons enhance visual processing through the superior colliculus and pulvinar thalamus. Neuron 104, 1141–1152 (2019).
Bolkan, S. S. et al. Thalamic projections sustain prefrontal activity during working memory maintenance. Nat. Neurosci. 20, 987–996 (2017).
Blondel, V. D., Guillaume, J. L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).
Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31, 1116–1128 (2006).
Rohlfing, T. & Maurer, C. R. Jr. Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees. IEEE Trans. Inf. Technol. Biomed. 7, 16–25 (2003).
Charrad, M., Ghazzali, N., Boiteau, V. & Niknafs, A. NbClust: an R package for determining the relevant number of clusters in a data set. J. Stat. Softw. 61, 1–36 (2014).
Gittleman, J. L. & Kot, M. Adaptation: statistics and a null model for estimating phylogenetic effects. Syst. Biol. 39, 227–241 (1990).
Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).
O’Leary, D. D. et al. Target selection by cortical axons: alternative mechanisms to establish axonal connections in the developing brain. Cold Spring Harb. Symp. Quant. Biol. 55, 453–468 (1990).
Deck, M. et al. Pathfinding of corticothalamic axons relies on a rendezvous with thalamic projections. Neuron 77, 472–484 (2013).
Kalil, K. & Dent, E. W. Branch management: mechanisms of axon branching in the developing vertebrate CNS. Nat. Rev. Neurosci. 15, 7–18 (2014).
Han, Y. et al. The logic of single-cell projections from visual cortex. Nature 556, 51–56 (2018).
Brown, K. M., Gillette, T. A. & Ascoli, G. A. Quantifying neuronal size: summing up trees and splitting the branch difference. Semin. Cell Dev. Biol. 19, 485–493 (2008).
Simari, P., Picciau, G. & De Floriani, L. Fast and scalable mesh superfacets. Computer Graphics Forum 33, 181–190 (2014).
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).
Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).
Acknowledgements
We thank M. Poo (Institute of Neuroscience, Chinese Academy of Sciences) for reading the manuscript and helpful discussions; J. Xian (Shanghai JiGuang Polytechnic College), Y. Qiao and many volunteers for testing FNT; and J. Huang, C. Jin, J. Wei and Z. Yue for establishing the website for data browsing. This work was supported by a Shanghai Municipal Science and Technology Major Project Grant (2018SHZDZX05, to J.Y.), National Science and Technology Innovation 2030 Grants (2021ZD0200204 and 2021ZD0204402, to J.Y.), the Lingang Laboratory Grant (LG202104-01-06, to J.Y.), a Strategic Priority Research Program of Chinese Academy of Sciences Grant (XDB32040104, to J.Y.) and National Natural Science Foundation of China Grants (61890953, 81827901, and 32192412, to H.G.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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Contributions
Design of the study: Jun Yan, Ninglong Xu and Hui Gong; development of FNT: Lingfeng Gou; virus injection: Yanhe Liu, Li Deng, Qingxu Liu, Zhaoqin Chen, Tianzhi Chen, Dechen Liu and Shou Qiu; smFISH and scRNA-seq analysis: Danyi Ma and Haifang Wang; project management: Yangang Sun and Danying Wang; data processing, quality control and management: Xiaofei Wang, Xinran Wang, Biyu Ren and Xiaoxue Shi; fMOST imaging: Qingming Luo and Hui Gong; image pre-processing and quality control: Anan Li; image registration: Yachuang Hu; data analysis and interpretation and generation of figures: Le Gao, Sang Liu and Qiaoqiao Yang; writing, reviewing and editing the manuscript: Jun Yan, Ninglong Xu, Hui Gong, Le Gao and Sang Liu; scientific direction and funding: Jun Yan, Ninglong Xu, Hui Gong, Qingming Luo, Yangang Sun, Haishan Yao, Chun Xu and Chengyu T. Li.
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Nature Neuroscience thanks Charles Gerfen, Julie Harris and Z. Josh Huang for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Design and application of FNT and image registration.
a, FNT workflow. b, Diagram of FNT tracing procedure. Red color denotes the operations that require precise mouse clicks. Orange color denotes the operations that require rotation, zooming in and out and users’ observation. Green color denotes the operations that require a single key stroke. Green operations are much faster than the other operations. c, Pipeline of neurite tracing by multiple tracers. Each neuron was first traced by two independent annotators and then merged by a third annotator (see Methods for detailed information). d, Scoring scheme to evaluate the quality of neurite tracing. e, Significant improvement of quality score after merging results from two independent annotators. 35 neurons were independently annotated by two annotators (n = 35, NS P > 0.05, ***P < 0.001, two-sided Wilcoxon signed-rank test). Boxplot: edges, 25th and 75th percentiles; central line, the median; whiskers, 1.5×the interquartile range of the edges; dots, outliers. f, Distribution of quality scores of merged results for 600 randomly sampled neurons. g, Accumulative numbers of traced neurons over time. h, Pipeline of image registration. Step 1 involves affine registration and step 2 involves non-rigid registration (see also Methods). i, Validation of image registration. Distance of mass center between 4 brain structures (fr, fasciculus; mtt, mammillothalamic tract; IPN, interpeduncular nucleus; AQ, cerebral aqueduct) from different fMOST samples (n = 5) were examined. Data are presented as mean ± s.e.m.
Extended Data Fig. 2 Somata distribution of neurons of each neuron classes and comparison with bulk labeling experiments.
a, Soma distribution of L2/3 IT, L5 IT, PT, CT, and L6 IT neurons in PFC flatmap. Bar plots show the number of reconstructed neurons in 11 CCFv3 annotated PFC subregions. b, Comparison with bulk labeling experiments in example PFC subregions. For L2/3IT neurons in ACA/PL, L5 IT neurons in ORBl/vl, PT neurons in ORBl/vl, and CT neurons in ORBm/vl, the projection similarity between whole-brain projection of bulk labeling experiments and that of reconstructed single-neuron projectomes in matched spatial locations were calculated (see Methods). First column, the projection similarity between whole-brain projection of bulk labeling experiment and combined single-neuron projectomes from selected number of reconstructed neurons. The red arrow denotes the number of sampled neurons that can largely recapitulate the bulk labeling experiment. Data are presented as mean ± s.e.m. Second column, visualization of single-neuron projectomes. Neurons were randomly colored. Third column, visualization of related bulk labeling experiments from Allen Mouse Brain Connectivity Atlas.
Extended Data Fig. 3 Relationship between soma distance and projectome dissimilarity, consensus neuron model.
a, A positive correlation of soma distance and projectome dissimilarity (PT neurons: R2 = 0.45, P < 2.2 × 10−16; CT neurons: R2 = 0.51, P < 2.2 × 10−16; IT neurons: R2 = 0.29, P < 2.2 × 10−16). Each dot denotes a pair of neurons. Soma distance was computed as the Euclidean distance between two somata. b, A positive correlation of mean soma distance and mean projectome dissimilarity for each subtype pair (IT subtypes: R2 = 0.396, P = 1.7 × 10−105; CT neurons: R2 = 0.828, P = 2 × 10−11; PT neurons: R2 = 0.483, P = 9.3 × 10−11). Each dot denotes a pair of neuron subtypes. The shaded areas around regression line indicate 95% confidence intervals. An example subtype pair of neuron subtypes deviated from the linear correlation was denoted as red points for IT, CT, and PT neurons respectively (IT: subtypes 25 and 12; CT: subtypes 52 and 49; PT: subtypes 63 and 55). Their morphology and somata distribution were shown in c. Soma distance and projectome dissimilarity was averaged between each neuron pairs within subtypes. c, Spatial analysis of soma distributions among neuron subtypes. For each pair of IT, PT, and CT neuron subtypes, Moran’s I was computed based on their spatial distribution of somata respectively (see Methods). Two example pairs of neuron subtypes for IT, PT, and CT neurons, with their soma distribution overlapped (IT: subtypes 25 and 12; CT: subtypes 52 and 49; PT: subtypes 63 and 55) and segregated (IT: subtypes 25 and 3; CT: subtypes 52 and 49; PT: subtypes 61 and 53), were shown. d, Construction of consensus neuron model. For each neuron subtype, the consensus neuron model was generated using FNT-dist and FNT-merge (see also Supplementary Methods 2). The number in each segment of the consensus neuron is the consensus score, which measures the degree of consensus. Then each segment of each neuron would also have a consensus score according to their alignment with the consensus neuron. e, Consensus score of axons in different branch orders. The primary axon (zero-order) is largely conserved for neurons in the same neuron subtype, whereas axons in high order are less conserved, as exemplified by neurons (n = 27) in subtype 60 in f. Data are presented as mean ± s.e.m.
Extended Data Fig. 4 Comparison of morphological features among 64 projectome subtypes.
a, Different morphological features of IT, PT and CT neurons. As calculated by the method illustrated by the schematic diagram (top, see also Methods), morphological features of CT, IT and PT neurons showed significant difference (****P < 1 × 10−12, two-sided Wilcoxon signed-rank test). b and c, The difference of morphological features of 64 neuron subtypes. In b, a heatmap showing the difference of 64 neuron subtypes based on their morphological features. Subtype 26 and subtype 8 were denoted by black rectangles as examples and their distinct morphology was shown in c. d, The power law between axon length and number of branch points of IT, PT and CT neurons. The formula of the power law showed in left top corner. The fitted power by linear regression is 0.9, 0.3 and 0.5, and R2 is 0.8, 0.4 and 0.6 for IT, CT, and PT respectively. The grey shade around the regression line represents 95% confidence interval around the means. e, The power law between axon length and number of branch points in different neuron subtypes. The number of neurons in each of 64 neuron subtypes can be found in Supplementary Table 4. Dot and error bar correspond to the mean value and 95% confidence intervals of power. The horizontal lines represent the 2/3 exponent observed in dendrite and the 3/4 exponent observed in vascular system.
Extended Data Fig. 5 Cortical projection of IT neurons.
a, Ipsi- and contra- lateral cortical projection patterns of 44 IT subtypes. b, Symmetry of projection patterns to ipsi- and contra- lateral cortical areas for IT subtypes with contralateral projection. Symmetry index was quantified as the Spearman’s rank correlation coefficient of ipsi- and contra- lateral projection patterns. Four example neuron subtypes were denoted. Projection patterns of IT subtypes 23 and 34 are highly symmetry that project to bilateral agranular insular areas (AI), whereas IT subtypes 7 and 30 are highly asymmetry with subtype 7 projecting extensively to ipsilateral cortex and subtype 30 projecting specifically to contralateral cortical areas (for example, ECT and ENT). Note that those IT neuron subtypes with only ipsilateral projection are not included. c, Over- and under- represented patterns, indicated by magenta arrowheads and blue arrowheads in the bar plot, of single neurons projecting to cortical areas in lateral subnetwork (see Methods). Top, a diagram and an example neuron with the over-represented pattern of projections to AIp, ECT, ENT, PERI, and TEa. d, Over- and under- represented patterns, indicated by magenta arrowheads and blue arrowheads in the bar plot, of single neurons projecting to cortical areas in central subnetwork (see Methods). Top, a diagram and an example neuron with the over-represented pattern of projections to MOp, SSp, and SSs.
Extended Data Fig. 6 Diversity of projections to lateral and central cortical subnetworks.
a, Soma distribution of 7 IT subtypes projecting to lateral cortical subnetwork. Their somata were color-coded based on their subtypes showed distinct regional (left) and laminar (right) distributions. b, IT subtypes projecting to specific subsets of areas in lateral cortical subnetwork. Their axons were color-coded as in a. The specific combination of their targets was marked. c, Laminar distribution of axon terminals for 7 IT subtypes projecting to lateral subnetwork. Top, a bar plot showing the mean arbor length in cortical areas within lateral subnetwork. Bottom, a heatmap showing preferential distribution of terminal arbors in different cortical targets among lateral subnetwork-projecting subtypes. Right, example axon terminal arbors in different cortical areas. d, Soma distribution of 8 IT subtypes projecting to central cortical subnetwork. Their somata color-coded based on their subtypes showed distinct regional (left) and laminar (right) distributions. e, IT subtypes projecting to specific subsets of areas in central cortical subnetwork. Their axons were color-coded as in d. The specific combination of their targets was marked. f, Laminar distribution of axon terminals for 8 IT subtypes projecting to central subnetwork. Top, a bar plot showing the mean arbor length in cortical areas within central subnetwork. Bottom, a heatmap showing preferential distribution of terminal arbors in different cortical targets among central subnetwork-projecting subtypes. Right, example axon terminal arbors in different cortical areas.
Extended Data Fig. 7 Striatal subdomains defined by PFC inputs and comparison with previous studies.
a, 3D visualization of nine striatal subdomains. b, Quantitative comparison with striatal parcellations from Hintiryan et al.38. Left, Coronal sections showing the striatal subdomains defined by PFC inputs and parcellations from Hintiryan et al. Right, the proportion of each parcel from Hintiryan et al. in nine striatal subdomains. Dots indicate significant enrichment in that striatal subdomain (P < 0.0001, one-sided Fisher’s exact test). c, Projections to striatal subdomains for 44 IT neuron subtypes and non-PFC regions. Left, projection map to striatal subdomains of 44 IT neuron subtypes. Right, projection map to striatal subdomains of 14 bulk labeling experiments in 7 non-PFC regions (see Methods). d, Left, IT neuron in PL-ILA-ORBm (subtype 21) project to both STRa/va and ENT. Right, IT neuron in ACAd (subtype 11) project to both STRdm and VIS.
Extended Data Fig. 8 Mutually exclusive projection of PT neurons in PL/ORB, AId/v, and MOs.
a, Proportion of PT subtypes in PL-ILA-ORB. Correlation of projection to selected target brain regions with PAG vs. PCG highlighted in yellow box (Spearman’s r = −0.148, P = 3.817 × 10−4). b, Coronal view of PL-ORB showing the soma distribution of subtypes 57 and 62 projecting to PAG and subtype 61 projecting to PCG. Morphology of example neurons are shown in 3D with PAG and PCG marked. c, Proportion of PT subtypes in AId/v. Correlation of projection to selected target brain regions with ACB vs. MY highlighted in yellow box (Spearman’s r = −0.546, P = 9.884 × 10−10). d, Sagittal view of AId/v showing the soma distribution of subtype 58 projecting to MY and subtype 59 projecting to ACB. Morphology of example neurons are shown in 3D with ACB and medulla marked. e, Proportion of PT subtypes in MOs. Correlation of projection to selected target brain regions with TH vs. MY highlighted in yellow box (Spearman’s r = −0.637, P = 3.675 × 10−10). f, Horizontal view of MOs showing the soma distribution of subtypes 56 and 58 projecting to MY and subtype 53 projecting to TH. Morphology of example neurons are shown in 3D with TH and MY marked.
Extended Data Fig. 9 Association of transcriptome subtypes and projectome subtypes.
a, Association of PT transcriptome subtypes (Lypd1+ vs. Npnt+) with projectome subtypes (53, 54, 55, 57) in ACA. Sagittal section of ACA showing the soma distribution of subtypes 54 and 57 projecting to PCG and subtypes 53 and 55 projecting to SCm. Dual-color retrograde labeling in PCG and SCm validated our single-neuron projectome results. smFISH experiment showed enriched Npnt+ neurons in SCm-projecting neurons (***P = 3.5 × 10−4, Fisher’s exact test). Scale bar, 20 μm. b, Retro-seq data in ventromedial prefrontal cortex (vmPFC) from Lui et al.10 was mapped to the 9 transcriptome subtypes described in Fig. 7a. This data consists of results from six target brain regions (DS, dorsal striatum; Hypo, hypothalamus; NAc, nucleus accumbence; PAG, periaqueductal gray; cPFC, contralateral prefrontal cortex; Amyg, basolateral amygdala). Projection patterns to those targets for 18 projectome subtypes that reside in vmPFC were shown in violin plot. c, Association of transcriptome subtypes and projectome subtypes based on the pattern in b. Four associated subtype pairs are colored: Lypd1+ L2/3 IT-subtype 19 (red), Deptor+ L5/6 IT-subtype 26 (yellow), Nnat+ L5/6 IT-subtype 38 (green), and Lypd1+ PT-subtypes 57 and 62 (blue). Axon projections of associated projectome subtypes were shown with characteristic targets highlighted by red dashed line. d, Retro-seq data in anterolateral motor (ALM) from Tasic et al.7 was mapped to the 9 transcriptome subtypes described in Fig. 7a. This data consists of results from 21 target brain regions. Projection patterns to those targets for 9 projectome subtypes that reside in ALM were shown in violin plot. e, Association of transcriptome subtypes and projectome subtypes based on the pattern in d. Three associated subtype pairs are colored: Rorb+ L5/6 IT-subtype 27 (red), Lypd1+ PT-subtype 56 (yellow), and Npnt+ PT-subtype 53 (green). Axon projections of associated projectome subtypes were shown with characteristic targets highlighted by red dashed line.
Supplementary information
Supplementary Information
Supplementary Figs. 1–4 and Supplementary Methods 1 and 2
Supplementary Tables 1–4
Supplementary Table 1 Information of virus injection for single-neuron projectome Supplementary Table 2 Abbreviation of brain structures. Supplementary Table 3 Information of viral injection for bulk labeling Supplementary Table 4 Distribution of 64 neuron subtypes in PFC subregions and laminar layers
Supplementary Video 1
FNT tracing procedures
Supplementary Video 2
Website manual
Supplementary Video 3
3D visualization of 64 neuron subtypes
Source data
Source Data Fig. 1
Dissimilarity matrix among 6,357 PFC neurons
Source Data Extended Data Fig. 1
Statistical Source Data
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Gao, L., Liu, S., Gou, L. et al. Single-neuron projectome of mouse prefrontal cortex. Nat Neurosci 25, 515–529 (2022). https://doi.org/10.1038/s41593-022-01041-5
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DOI: https://doi.org/10.1038/s41593-022-01041-5
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