Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Integrating barcoded neuroanatomy with spatial transcriptional profiling enables identification of gene correlates of projections

Abstract

Functional circuits consist of neurons with diverse axonal projections and gene expression. Understanding the molecular signature of projections requires high-throughput interrogation of both gene expression and projections to multiple targets in the same cells at cellular resolution, which is difficult to achieve using current technology. Here, we introduce BARseq2, a technique that simultaneously maps projections and detects multiplexed gene expression by in situ sequencing. We determined the expression of cadherins and cell-type markers in 29,933 cells and the projections of 3,164 cells in both the mouse motor cortex and auditory cortex. Associating gene expression and projections in 1,349 neurons revealed shared cadherin signatures of homologous projections across the two cortical areas. These cadherins were enriched across multiple branches of the transcriptomic taxonomy. By correlating multigene expression and projections to many targets in single neurons with high throughput, BARseq2 provides a potential path to uncovering the molecular logic underlying neuronal circuits.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: In situ sequencing of endogenous mRNAs using BARseq2.
Fig. 2: Multiplexed detection of mRNAs using BARseq2.
Fig. 3: Cadherin expression across transcriptomic neuronal types in motor cortex.
Fig. 4: Correlating gene expression to projections using BARseq2.
Fig. 5: Differential cadherin expression across major classes and cortical areas.
Fig. 6: Cadherins correlate with diverse projections of IT neurons.
Fig. 7: Gene coexpression modules correlate with diverse projections of IT neurons.

Data availability

Raw target area sequencing data (Fig. 4c; SRR12247894, SRR12245390 and SRR12245389) and scRNA-seq data (Fig. 2g–i) are deposited at the Sequence Read Archive (SRR13716225). Raw in situ sequencing images (Figs. 24) are deposited at the Brain Image Library (https://download.brainimagelibrary.org/06/35/0635a0b3b0954c7e/). Example annotated images from the dissected brain slices and other data and intermediate processed sequencing data are deposited at Mendeley Data (https://doi.org/10.17632/jnx89bmv4s.2).

Code availability

Processing scripts are deposited at Mendeley Data (https://doi.org/10.17632/jnx89bmv4s.2).

References

  1. Winnubst, J. et al. Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain. Cell 179, 268–281 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Muñoz-Castañeda, R. et al. Cellular anatomy of the mouse primary motor cortex. Preprint at bioRxiv https://doi.org/10.1101/2020.10.02.323154 (2020).

  3. Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Han, Y. et al. The logic of single-cell projections from visual cortex. Nature 556, 51–56 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Chen, X. et al. High-throughput mapping of long-range neuronal projection using in situ sequencing. Cell 179, 772–786 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Kim, D. W. et al. Multimodal analysis of cell types in a hypothalamic node controlling social behavior. Cell 179, 713–728 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Economo, M. N. et al. Distinct descending motor cortex pathways and their roles in movement. Nature 563, 79–84 (2018).

    CAS  PubMed  Google Scholar 

  9. Zhang, M. et al. Molecular, spatial and projection diversity of neurons in primary motor cortex revealed by in situ single-cell transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2020.06.04.105700 (2020).

  10. Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).

    CAS  PubMed  Google Scholar 

  11. Qian, X. et al. Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat. Methods 17, 101–106 (2020).

    CAS  PubMed  Google Scholar 

  12. Kebschull, J. M. et al. High-throughput mapping of single-neuron projections by sequencing of barcoded RNA. Neuron 91, 975–987 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Huang, L. et al. BRICseq bridges brain-wide interregional connectivity to neural activity and gene expression in single animals. Cell 182, 177–188 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    PubMed  PubMed Central  Google Scholar 

  15. Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Hayano, Y. et al. The role of T-cadherin in axonal pathway formation in neocortical circuits. Development 141, 4784–4793 (2014).

    CAS  PubMed  Google Scholar 

  17. Friedman, L. G. et al. Cadherin-8 expression, synaptic localization, and molecular control of neuronal form in prefrontal corticostriatal circuits. J. Comp. Neurol. 523, 75–92 (2015).

    CAS  PubMed  Google Scholar 

  18. Paul, A. et al. Transcriptional architecture of synaptic communication delineates GABAergic neuron identity. Cell 171, 522–539 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Matsunaga, E., Nambu, S., Oka, M. & Iriki, A. Complex and dynamic expression of cadherins in the embryonic marmoset cerebral cortex. Dev. Growth Differ. 57, 474–483 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Redies, C. Cadherins and the formation of neural circuitry in the vertebrate CNS. Cell Tissue Res. 290, 405–413 (1997).

    CAS  PubMed  Google Scholar 

  21. Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    CAS  PubMed  Google Scholar 

  22. Terakawa, Y. W., Inoue, Y. U., Asami, J., Hoshino, M. & Inoue, T. A sharp cadherin-6 gene expression boundary in the developing mouse cortical plate demarcates the future functional areal border. Cereb. Cortex 23, 2293–2308 (2013).

    PubMed  Google Scholar 

  23. Yao, Z. et al. An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types. Preprint at bioRxiv https://doi.org/10.1101/2020.02.29.970558 (2020).

  24. Fros, J. J. & Pijlman, G. P. Alphavirus infection: Host cell shut-off and inhibition of antiviral responses. Viruses https://doi.org/10.3390/v8060166 (2016).

  25. Klingler, E. et al. Single-cell molecular connectomics of intracortically projecting neurons. Preprint at bioRxiv https://doi.org/10.1101/378760 (2018).

  26. Wang, Y. et al. Complete single-neuron reconstruction reveals morphological diversity in molecularly defined claustral and cortical neuron types. Preprint at bioRxiv https://doi.org/10.1101/675280 (2019).

  27. Harris, K. D. & Shepherd, G. M. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Duan, X., Krishnaswamy, A., De la Huerta, I. & Sanes, J. R. Type II cadherins guide assembly of a direction-selective retinal circuit. Cell 158, 793–807 (2014).

    CAS  PubMed  Google Scholar 

  29. Friedman, L. G., Benson, D. L. & Huntley, G. W. Cadherin-based transsynaptic networks in establishing and modifying neural connectivity. Curr. Top. Dev. Biol. 112, 415–465 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Jontes, J. D. The cadherin superfamily in neural circuit assembly. Cold Spring Harb. Perspect. Biol. 10, a029306 (2018).

    PubMed  PubMed Central  Google Scholar 

  31. Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719–720 (2008).

    CAS  PubMed  Google Scholar 

  32. Lee, D. D. & Seung, H. S. Learning the parts of objects by nonnegative matrix factorization. Nature 401, 788–791 (1999).

    CAS  PubMed  Google Scholar 

  33. Ballouz, S., Verleyen, W. & Gillis, J. Guidance for RNA-seq coexpression network construction and analysis: safety in numbers. Bioinformatics 31, 2123–2130 (2015).

    CAS  PubMed  Google Scholar 

  34. Crow, M., Paul, A., Ballouz, S., Huang, Z. J. & Gillis, J. Exploiting single-cell expression to characterize coexpression replicability. Genome Biol. 17, 101 (2016).

    PubMed  PubMed Central  Google Scholar 

  35. Chen, X., Sun, Y. C., Church, G. M., Lee, J. H. & Zador, A. M. Efficient in situ barcode sequencing using padlock probe-based BaristaSeq. Nucleic Acids Res. 46, e22 (2018).

    CAS  PubMed  Google Scholar 

  36. Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Chen, S. et al. BARcode DEmixing through Non-negative Spatial Regression (BarDensr). PLoS Comput. Biol. 17, e1008256 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Ding, J. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat. Biotechnol. 38, 737–746 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Harris, K. D. et al. Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics. PLoS Biol. 16, e2006387 (2018).

    PubMed  PubMed Central  Google Scholar 

  40. Duan, X. et al. Cadherin combinations recruit dendrites of distinct retinal neurons to a shared interneuronal scaffold. Neuron 99, 1145–1154 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Li, H. et al. Classifying Drosophila olfactory projection neuron subtypes by single-cell RNA sequencing. Cell 171, 1206–1220 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Custo Greig, L. F., Woodworth, M. B., Galazo, M. J., Padmanabhan, H. & Macklis, J. D. Molecular logic of neocortical projection neuron specification, development and diversity. Nat. Rev. Neurosci. 14, 755–769 (2013).

    Google Scholar 

  43. Bagri, A. et al. Slit proteins prevent midline crossing and determine the dorsoventral position of major axonal pathways in the mammalian forebrain. Neuron 33, 233–248 (2002).

    CAS  PubMed  Google Scholar 

  44. Shu, T., Sundaresan, V., McCarthy, M. M. & Richards, L. J. Slit2 guides both precrossing and postcrossing callosal axons at the midline in vivo. J. Neurosci. 23, 8176–8184 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Yoshida, Y. Semaphorin signaling in vertebrate neural circuit assembly. Front Mol. Neurosci. 5, 71 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Berns, D. S., DeNardo, L. A., Pederick, D. T. & Luo, L. Teneurin-3 controls topographic circuit assembly in the hippocampus. Nature 554, 328–333 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Zador, A. M. et al. Sequencing the connectome. PLoS Biol. 10, e1001411 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Peikon, I. D. et al. Using high-throughput barcode sequencing to efficiently map connectomes. Nucleic Acids Res. 45, e115 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Marblestone, A. H., et al Rosetta brains: a strategy for molecularly-annotated connectomics. Preprint at arXiv https://arxiv.org/abs/1404.5103 (2014).

  50. Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Edelstein, A. D. et al. Advanced methods of microscope control using μManager software. J. Biol. Methods 1, e10 (2014).

    PubMed  Google Scholar 

  53. Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Evangelidis, G. D. & Psarakis, E. Z. Parametric image alignment using enhanced correlation coefficient maximization. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1858–1865 (2008).

    PubMed  Google Scholar 

  55. Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).

    CAS  PubMed  Google Scholar 

  56. Rock, C., Zurita, H., Wilson, C. & Apicella, A. J. An inhibitory corticostriatal pathway. Elife 5, e15890 (2016).

    PubMed  PubMed Central  Google Scholar 

  57. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).

    Google Scholar 

Download references

Acknowledgements

The authors thank members of the MAPseq core facility, H. Zhan, Y. Li and N. Gemmill, for MAPseq data production; K. Matho and Z. J. Huang for dissection coordinates in motor cortex; H. Zhan, L. Yuan, H. L. Gilbert, K. Matho, J. Kebschull and D. Fürth for useful discussions; and W. Wadolowski, B. Burbach, K. Lucere and E. Fong for technical support. This work was supported by the National Institutes of Health (5RO1NS073129, 5RO1DA036913, RF1MH114132 and U01MH109113 to A.M.Z; R01MH113005 and R01LM012736 to J.G.; and U19MH114821 to A.M.Z. and J.G.), the Brain Research Foundation (BRF-SIA-2014-03 to A.M.Z.), IARPA MICrONS (D16PC0008 to A.M.Z.), Paul Allen Distinguished Investigator Award (to A.M.Z.), Simons Foundation (350789 to X.C.), Chan Zuckerberg Initiative (2017-0530 ZADOR/ALLEN INST (SVCF) SUB to A.M.Z.) and Robert Lourie award (to A.M.Z.). This work was additionally supported by the Assistant Secretary of Defense for Health Affairs endorsed by the Department of Defense, through the FY18 PRMRP Discovery Award Program (W81XWH1910083 to X.C.) Opinions, interpretations, conclusions and recommendations are those of the authors and are not necessarily endorsed by the U.S. Army. In conducting research using animals, the investigators adhered to the laws of the United States and regulations of the Department of Agriculture.

Author information

Authors and Affiliations

Authors

Contributions

Y.-C.S., X.C. and A.M.Z. conceived the study. Y.-C.S. and X.C. optimized and performed BARseq2. Y.-C.S., X.C. and H.Z collected BARseq2 data. X.C., S.F. and J.G. analyzed data. Y.-C.S., X.C. and S.F. selected gene panels. X.C. and S.L. compared gene expression between BARseq2 and Allen ISH. Y.-C.S. and X.C. performed retrograde tracing combined with FISH validations. Y.-C.S., X.C., S.F. and A.M.Z. wrote the paper.

Corresponding authors

Correspondence to Xiaoyin Chen or Anthony M. Zador.

Ethics declarations

Competing interests

A.M.Z. is a founder and equity owner of Cajal Neuroscience and a member of its scientific advisory board. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Kenneth Harris and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Optimization of BARseq2 for detecting endogenous mRNAs.

a, Relative sensitivity (means and individual data points) of BARseq2 in detecting Slc17a7 using the indicated fixation times, normalized to that achieved with 5 mins of fixation. n = 3 for 480 mins and n = 4 for other conditions. b, Rolony counts for Slc17a7 using either random primers or specific primers at two different concentrations. The two concentrations used were 5 µM (low) and 50 µM (high) for random primers, and 0.5 µM (low) and 5 µM (high) for specific primers. Lines indicate means and dots/crosses represent individual samples. n = 2 slices for each condition. c,d, BARseq2 sensitivity compared to RNAscope. c, Spot density detected by BARseq2 or RNAscope in each 100 µm bin along the laminar axis in auditory cortex. Error bars indicate standard errors. The dashed line indicates linear fit for Slc30a3 and Cdh13. Slope = 1.65 and R2 = 0.73. n = 5 slices for both BARseq2 and RNAscope. d, shows the means and individual samples for each gene. e,f, Positions of rolonies across five sequencing cycles using the original (e) or the optimized (f) sequencing protocol. Scale bars = 10 µm. g, The distribution of minimum distance between rolonies imaged in the first cycle and in the fifth cycle using the original or the optimized protocol. h, Median distance between rolonies imaged in the indicated cycles and the closest rolonies imaged in the first cycle using the original or the optimized protocol. Error bars indicate standard errors. For both (g) and (h), n = 148,708 rolonies for optimized condition and n = 12,114 for original condition. i,j, The distribution of absolute rolony intensities for the first sequencing cycle (i) and relative rolony intensities after 6 sequencing cycles and one stripping step, normalized to the intensities in the first sequencing cycle (j). Amino-allyl dUTP concentrations used are indicated. In (i), n = 63,852 rolonies for 0.08 µM and n = 4,286 rolonies for 0.5 µM; in (j), n = 128,976 rolonies for 0.08 µM and n = 113,235 rolonies for 0.5 µM.

Extended Data Fig. 2 Laminar distribution of cadherins in auditory cortex (green) and motor cortex (brown).

In both cortical areas, cortical depth is normalized so that the bottom and the top of the cortex match between M1 and A1.

Extended Data Fig. 3 Comparison between BARseq2 and Allen gene expression atlas.

Gene expression patterns in auditory cortex identified by BARseq2 are plotted next to in situ hybridization images of the same genes in Allen gene expression atlas (ABA) and the quantified laminar distribution of the gene in both datasets. Only genes that had coronal images in the Allen gene expression atlas are shown. Blue lines indicate the boundaries of the cortex in both BARseq2 and ABA images. In the laminar distribution plots, dots represent values from two BARseq2 samples (purple) and one ABA sample (blue) per gene. Lines indicate means across samples.

Extended Data Fig. 4 The distribution of read counts per cell for the indicated genes in auditory cortex (green) and motor cortex (brown).

Asterisks indicate genes with significant difference in expression between the two areas (p < 0.05 using two-tailed rank sum test after Bonferroni correction). p values after Bonferroni correction are indicated on top.

Extended Data Fig. 5 Transcriptomic typing using BARseq2.

a,b, Slc30a3 expression in excitatory neurons with or without Cdh24 expression in single-cell RNAseq (a) from Tasic, et al.3 or in BARseq2 (b). A cell is considered expressing Cdh24 if the expression is higher than 10 RPKM in RNAseq or 1 count in BARseq2. Red crosses indicate means and green squares indicate medians. c, Expression density (means and individual data points) across laminar positions for the indicated genes. n = 3 slices for the three-gene panel and n = 5 slices for the 65-gene panel. d, Precision and recall of cell typing using the marker gene panel across nine single cell datasets. N = 9 independent datasets shown in (e). In each box, the center shows the median, the bounds of the box show the 1st and 3rd quartiles, the whiskers show the range of the data, and points further than 1.5 IQR (Inter-Quartile Range) from the box are shown as outliers. e, Breakdown of average performance for each cell type in each dataset. The datasets are: scSSALM and scSSV1 are single cell SmartSeq datasets from ALM and V1 respectively3. All other datasets are BICCN M1 datasets23 and the name indicates the technology used (sc = single cell, sn = single nuclei, Cv2/3 = Chromium v2/3, SS = SmartSeq). f, Average cell typing performance for six normalization strategies. N = 9 independent datasets shown in (e). The box plots are generated in the same way as (d). g, Confusion matrix showing overlap between prediction and annotations, normalized by predictions. This plot emphasizes precision; it indicates the probability that a given prediction was correct. h, Confusion matrix showing overlap between prediction and annotations, normalized by annotations. This plot emphasizes recall; it indicates the probability that a given annotation was recovered.

Extended Data Fig. 6 Correlating gene expression to projections using BARseq2.

a, Relative sensitivity of BARseq2 to barcodes (solid line) and endogenous mRNAs (dashed line) using the indicated concentration of Phusion DNA polymerase. Sensitivities are normalized to the original BARseq condition (Ctrl). Circles and crosses show individual data points across n = 2 slices. b, Correlation between pairs of genes in barcoded cells (y-axis) and in non-barcoded cells (x-axis) as determined by BARseq2. Shuffled data (yellow) are also plotted for comparison. c,d, Slc17a7 (x-axes) and Gad1 (y-axes) expression in barcoded neurons in auditory (c) or motor cortex (d). Only neurons with more than 10 counts in either gene are shown. e, The distributions of read counts per barcoded neuron (solid lines) or non-barcoded neuron (dashed lines) in auditory (green) and motor (brown) cortex. f, Slc30a3 expression in barcoded excitatory neurons with or without Cdh24 expression in BARseq2. A cell is considered expressing Cdh24 if the expression is higher than 1 count. Red crosses indicate means and green squares indicate median. g,h, Slc17a7 (x-axes) and Gad1 (y-axes) expression in barcoded projection neurons in motor (g) or auditory cortex (h). Excitatory and inhibitory neurons are color-coded as indicated.

Extended Data Fig. 7 BARseq2 reveals projection and gene expression differences across major classes and IT subtypes.

a, Differential gene expression across major classes (IT, PT, and CT) observed using BARseq2 and single-cell RNAseq. Each dot shows the difference in mean expression of a gene across a pair of major classes observed using BARseq2 (y-axis) or single-cell RNAseq (x-axis). Differences in expression that were statistically significant (FDR < 0.05 using two-tailed rank sum tests) in both A1 and M1 as shown by BARseq2 are labeled purple; otherwise they are labeled yellow. The single-cell RNAseq data used were collected in the visual cortex and anterior-lateral motor cortex3. b, The fraction of ITi-Ctx neurons in four transcriptomic types of IT neurons in auditory cortex. ITi-Ctx neurons have only ipsilateral cortical projections and no striatal projections or contralateral projections6. The number of ITi-Ctx neurons and neurons with other projection patterns for each transcriptomic type are labeled on top of the pie charts. c, The projection strengths for contralateral (y-axis) and ipsilateral (x-axis) cortical projections for each IT neuron in auditory cortex. IT1/IT2 neurons are labeled blue and IT3/IT4 neurons are labeled red.

Extended Data Fig. 8 Variance in projections explained by cadherins and laminar positions.

Box plots of variance in each projection modules explained by the indicated predictors after 100 iterations of 10-fold cross validation. Boxes indicate second and third quartiles and whiskers indicate minimum and maximum values excluding outliers. Outliers are shown in red.

Extended Data Fig. 9 Validation of correlation between cadherins and IT projections.

a, Representative images of in situ hybridization in A1 (top) and M1 (bottom) slices with CTB labeling in the caudal striatum. Three marker genes and CTB labeling are shown in the indicated colors. Scale bars = 100 µm. Arrows and arrowheads indicate example CTB + and CTB- neurons, respectively. Experiments for each combination of targeted gene and CTB labeling condition (Cdh12 with contralateral labeling, Cdh8 with ipsilateral labeling, and Pcdh19 with striatal labeling) were performed in slices from two animals. b, Crops of the indicated individual channels of example neurons from (a). Scale bars = 10 µm. c,d,e, Cumulative probability distribution of the expression of Cdh12 (c), Cdh8 (d), and Pcdh19 (e) in neurons with or without retrograde labeling of contralateral (c), ipsilateral (d), or caudal striatal (e) projections. p values from two-tailed rank sum tests after Bonferroni correction and numbers of neurons used for each experiment are indicated. N = 2 animals for each experiment.

Extended Data Fig. 10 Cadherin co-expression modules correlate with IT projections.

a, Correlation among cadherins in IT neurons in motor cortex identified in the indicated single-cell RNAseq datasets3,23. The datasets included are: tasic_alm and tasic_v1 are single cell SmartSeq datasets from ALM and V1 respectively3; all other datasets are BICCN M1 datasets23; the name indicates the technology used (sc = single cell, sn = single nuclei, Cv2/3 = Chromium v2/3, SS = SmartSeq). b, Modularity (EGAD AUROC) of co-expression modules in BARseq2 M1 against null distribution of modularity (node permutation). BARseq2 modularity is shown by the blue lines with the corresponding p-values. P values are calculated using a one-sided non-parametric node permutation test without multiple comparison correction. c, Association (AUROC) between cadherin co-expression modules and the indicated projections. Significant associations are marked by asterisks (* FDR < 0.1, ** FDR < 0.05). d, Fractions of neurons with the indicated projections as a function of co-expression module expression. e, Distribution of associations of the indicated projection modules with gene expression. Association with significant gene module is shown by a blue line; association with single genes from that module is shown by orange lines; association with all other genes is shown by a gray density. f, Association of the three co-expression modules in transcriptomic IT neurons in the indicated datasets (AUROC, significance shown as in c).

Supplementary information

Supplementary Information

Supplementary Fig. 1 and Supplementary Tables 1–3.

Reporting Summary

Supplementary Table 4

The sequences of primers, probes and GII codes used in the study.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sun, YC., Chen, X., Fischer, S. et al. Integrating barcoded neuroanatomy with spatial transcriptional profiling enables identification of gene correlates of projections. Nat Neurosci 24, 873–885 (2021). https://doi.org/10.1038/s41593-021-00842-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-021-00842-4

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing