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.
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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. 2–4) 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).
Processing scripts are deposited at Mendeley Data (https://doi.org/10.17632/jnx89bmv4s.2).
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).
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).
Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).
Han, Y. et al. The logic of single-cell projections from visual cortex. Nature 556, 51–56 (2018).
Chen, X. et al. High-throughput mapping of long-range neuronal projection using in situ sequencing. Cell 179, 772–786 (2019).
Kim, D. W. et al. Multimodal analysis of cell types in a hypothalamic node controlling social behavior. Cell 179, 713–728 (2019).
Economo, M. N. et al. Distinct descending motor cortex pathways and their roles in movement. Nature 563, 79–84 (2018).
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).
Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).
Qian, X. et al. Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat. Methods 17, 101–106 (2020).
Kebschull, J. M. et al. High-throughput mapping of single-neuron projections by sequencing of barcoded RNA. Neuron 91, 975–987 (2016).
Huang, L. et al. BRICseq bridges brain-wide interregional connectivity to neural activity and gene expression in single animals. Cell 182, 177–188 (2020).
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).
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).
Hayano, Y. et al. The role of T-cadherin in axonal pathway formation in neocortical circuits. Development 141, 4784–4793 (2014).
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).
Paul, A. et al. Transcriptional architecture of synaptic communication delineates GABAergic neuron identity. Cell 171, 522–539 (2017).
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).
Redies, C. Cadherins and the formation of neural circuitry in the vertebrate CNS. Cell Tissue Res. 290, 405–413 (1997).
Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).
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).
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).
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).
Klingler, E. et al. Single-cell molecular connectomics of intracortically projecting neurons. Preprint at bioRxiv https://doi.org/10.1101/378760 (2018).
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).
Harris, K. D. & Shepherd, G. M. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015).
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).
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).
Jontes, J. D. The cadherin superfamily in neural circuit assembly. Cold Spring Harb. Perspect. Biol. 10, a029306 (2018).
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).
Lee, D. D. & Seung, H. S. Learning the parts of objects by nonnegative matrix factorization. Nature 401, 788–791 (1999).
Ballouz, S., Verleyen, W. & Gillis, J. Guidance for RNA-seq coexpression network construction and analysis: safety in numbers. Bioinformatics 31, 2123–2130 (2015).
Crow, M., Paul, A., Ballouz, S., Huang, Z. J. & Gillis, J. Exploiting single-cell expression to characterize coexpression replicability. Genome Biol. 17, 101 (2016).
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).
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).
Chen, S. et al. BARcode DEmixing through Non-negative Spatial Regression (BarDensr). PLoS Comput. Biol. 17, e1008256 (2021).
Ding, J. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat. Biotechnol. 38, 737–746 (2020).
Harris, K. D. et al. Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics. PLoS Biol. 16, e2006387 (2018).
Duan, X. et al. Cadherin combinations recruit dendrites of distinct retinal neurons to a shared interneuronal scaffold. Neuron 99, 1145–1154 (2018).
Li, H. et al. Classifying Drosophila olfactory projection neuron subtypes by single-cell RNA sequencing. Cell 171, 1206–1220 (2017).
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).
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).
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).
Yoshida, Y. Semaphorin signaling in vertebrate neural circuit assembly. Front Mol. Neurosci. 5, 71 (2012).
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).
Zador, A. M. et al. Sequencing the connectome. PLoS Biol. 10, e1001411 (2012).
Peikon, I. D. et al. Using high-throughput barcode sequencing to efficiently map connectomes. Nucleic Acids Res. 45, e115 (2017).
Marblestone, A. H., et al Rosetta brains: a strategy for molecularly-annotated connectomics. Preprint at arXiv https://arxiv.org/abs/1404.5103 (2014).
Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).
Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).
Edelstein, A. D. et al. Advanced methods of microscope control using μManager software. J. Biol. Methods 1, e10 (2014).
Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).
Evangelidis, G. D. & Psarakis, E. Z. Parametric image alignment using enhanced correlation coefficient maximization. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1858–1865 (2008).
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).
Rock, C., Zurita, H., Wilson, C. & Apicella, A. J. An inhibitory corticostriatal pathway. Elife 5, e15890 (2016).
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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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
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