Imaging the transcriptome in situ with high accuracy has been a major challenge in single-cell biology, which is particularly hindered by the limits of optical resolution and the density of transcripts in single cells1,2,3,4,5. Here we demonstrate an evolution of sequential fluorescence in situ hybridization (seqFISH+). We show that seqFISH+ can image mRNAs for 10,000 genes in single cells—with high accuracy and sub-diffraction-limit resolution—in the cortex, subventricular zone and olfactory bulb of mouse brain, using a standard confocal microscope. The transcriptome-level profiling of seqFISH+ allows unbiased identification of cell classes and their spatial organization in tissues. In addition, seqFISH+ reveals subcellular mRNA localization patterns in cells and ligand–receptor pairs across neighbouring cells. This technology demonstrates the ability to generate spatial cell atlases and to perform discovery-driven studies of biological processes in situ.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
RNA-seq data were obtained from GEO accession number GSE98674. RNA SPOTs data were obtained from a previous study8. Source data from this study are available at https://github.com/CaiGroup/seqFISH-PLUS. All data obtained during this study are available from the corresponding author upon reasonable request.
Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M. & Cai, L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–361 (2014).
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).
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).
Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).
Femino, A. M., Fay, F. S., Fogarty, K. & Singer, R. H. Visualization of single RNA transcripts in situ. Science 280, 585–590 (1998).
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).
Eng, C. L., Shah, S., Thomassie, J. & Cai, L. Profiling the transcriptome with RNA SPOTs. Nat. Methods 14, 1153–1155 (2017).
Shah, S. et al. Dynamics and spatial genomics of the nascent transcriptome by intron seqFISH. Cell 174, 363–376.e16 (2018).
Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).
Lubeck, E. & Cai, L. Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nat. Methods 9, 743–748 (2012).
Betzig, E. et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science 313, 1642–1645 (2006).
Rust, M. J., Bates, M. & Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–795 (2006).
Zhu, Q., Shah, S., Dries, R., Cai, L. & Yuan, G.-C. Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data. Nat. Biotechnol. 36, 1183–1190 (2018).
Thompson, R. E., Larson, D. R. & Webb, W. W. Precise nanometer localization analysis for individual fluorescent probes. Biophys. J. 82, 2775–2783 (2002).
Yildiz, A., Tomishige, M., Vale, R. D. & Selvin, P. R. Kinesin walks hand-over-hand. Science 303, 676–678 (2004).
Chen, F., Tillberg, P. W. & Boyden, E. S. Expansion microscopy. Science 347, 543–548 (2015).
Yang, B. et al. Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158, 945–958 (2014).
Chen, F. et al. Nanoscale imaging of RNA with expansion microscopy. Nat. Methods 13, 679–684 (2016).
Moffitt, J. R. et al. High-performance multiplexed fluorescence in situ hybridization in culture and tissue with matrix imprinting and clearing. Proc. Natl Acad. Sci. USA 113, 14456–14461 (2016).
Antebi, Y. E. et al. Combinatorial signal perception in the BMP pathway. Cell 170, 1184–1196 (2017).
Mili, S., Moissoglu, K. & Macara, I. G. Genome-wide screen reveals APC-associated RNAs enriched in cell protrusions. Nature 453, 115–119 (2008).
Wang, T., Hamilla, S., Cam, M., Aranda-Espinoza, H. & Mili, S. Extracellular matrix stiffness and cell contractility control RNA localization to promote cell migration. Nat. Commun. 8, 896 (2017).
McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).
Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).
Shah, P. T. et al. Single-cell transcriptomics and fate mapping of ependymal cells reveals an absence of neural stem cell function. Cell 173, 1045–1057 (2018).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 (2018).
Takei, Y., Shah, S., Harvey, S., Qi, L. S. & Cai, L. Multiplexed dynamic imaging of genomic loci by combined CRISPR imaging and DNA sequential FISH. Biophys. J. 112, 1773–1776 (2017).
Wang, G., Moffitt, J. R. & Zhuang, X. Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci. Rep. 8, 4847 (2018).
Edelstein, A., Amodaj, N., Hoover, K., Vale, R. & Stuurman, N. Computer control of microscopes using µManager. Curr. Protoc. Mol. Biol. 92, 14.20.1–14.20.17 (2010).
Parthasarathy, R. Rapid, accurate particle tracking by calculation of radial symmetry centers. Nat. Methods 9, 724–726 (2012).
Chung, N. C. & Storey, J. D. Statistical significance of variables driving systematic variation in high-dimensional data. Bioinformatics 31, 545–554 (2015).
Huang, H., Liu, Y., Yuan, M. & Marron, J. S. Statistical significance of clustering using soft thresholding. J. Comput. Graph. Stat. 24, 975–993 (2015).
Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).
Ramilowski, J. A. et al. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat. Commun. 6, 7866 (2015).
Eng, C.-H. L. & Cai, L. RNA seqFISH+ supplementary protocol. Protoc. Exch. https://doi.org/10.1038/protex.2019.019 (2019).
Lein, E.S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).
We thank L. Sanchez-Guardado from the Lois laboratory and the Thanos laboratory for providing mouse samples; S. Schindler for sectioning the tissue slices; J. Thomassie for help with data analysis; S. Shah for help with image analysis and input on the manuscript; K. Frieda for advice on the manuscript and help with making figures; and M. Thomsons, S. Chen and C. Lois for discussions. This project is funded by NIH TR01 OD024686, NIH HubMAP UG3HL145609, Paul G. Allen Frontiers Foundation Discovery Center and a Chan-Zuckerberg Initiative pilot grant.
Nature thanks Samantha Morris, Arjun Raj and the other anonymous reviewer(s) for their contribution to the peer review of this work.
C.-H.L.E and L.C. filed a patent on the pseudocolour-encoding scheme in seqFISH+.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, b, seqFISH+ experiments in NIH/3T3 cells (a) and the mouse brain slices (b).
a, Raw images of a NIH/3T3 cell before and after clearing. A marked decrease in background is observed in the cleared sample. The image was acquired on a spinning disk confocal microscope. b, In each round of hybridization for the 10,000-gene experiment, diffraction-limited dots are clearly separated, indicating that the pseudocolour scheme effectively dilutes the density of the sample. The signal is completely removed between different rounds of hybridization, with no ‘cross-talk’ between the pseudocolours. Stripping is accomplished by a 55% formamide wash, which is highly efficient. c, After the completion of each seqFISH+ experiment, the readout probes used in hybridization 1 are rehybridized in round 81. The colocalization rates between rounds 1 and 81 are 76% (647-nm channel), 73% (561-nm channel) and 80% (488-nm channel) within a two-pixel radius. The colocalization between the two images indicates that most of the primary probes remain bound through 80 rounds of hybridization and imaging, although some loss of RNA and signal is seen across 80 rounds of hybridization (n = 227 cells).
Extended Data Fig. 3 seqFISH+ works efficiently across all three fluorescent channels and identifies localization patterns of transcripts in NIH/3T3 cells.
a, Correlation plots between seqFISH+ and bulk RNA-seq in three fluorescent channels. Barcodes are coded entirely within each channel, with n = 3,334, 3,333 and 3,333 barcodes in each channel, respectively. Barcodes in all channels are decoded and called out efficiently. b, seqFISH+ result correlates strongly with RNA SPOTs measurement in NIH/3T3 cells. SPM, spots per million. c, Correlation between seqFISH+ and smFISH for each fluorescent channel (from left to right, n = 24, 18 and 18 genes). All correlations were computed by Pearson’s R coefficient correlation, with two-tailed P values reported. d, The callout frequency of on-target 10,000 barcodes versus the remaining 14,000 off-target barcodes. Off-target barcodes are called out at a rate of 0.22 ± 0.07 (mean ± s.d.) per barcode. e, Histogram of the total number of mRNAs detected per NIH/3T3 cell. On average, 35,492 ± 12,222 transcripts are detected per cell. f, Genes are clustered on the basis of co-occurrence in a 10 × 10-pixel window. Three major clusters are nuclear–perinuclear, cytoplasmic and protrusions. g, mRNAs show preferential spatial localization patterns: nuclear, cytoplasm and protrusions (n = 227 cells). The image is binned into 1 μm × 1 μm windows and coloured on the basis of the genes enriched in each bin (scale bar, 10 μm). h, Examples of genes enriched in each spatial cluster. i, Genes in the subclusters within the nuclear-localized group. Subcluster 1 contains genes that encode for extracellular matrix proteins. Genes in subcluster 2 are involved in the actin cytoskeleton, whereas genes in subcluster 3 are involved in microtubule networks. j, Representative smFISH image (single z-slice) of three genes in subcluster 1 shows nuclear–perinuclear localization (n = 20 FOVs, 40× objective). Scale bar, 10 μm.
a, b, Histogram of the number of genes (a) and total RNA barcodes (b) detected per cell by seqFISH+ in the cortex. c, Unsupervised clustering of seqFISH+ correlates well with scRNA-seq (n = 1,857 genes; Pearson’s R coefficient correlation)25. d, Supervised mapping of seqFISH+-analysed cortex cell clusters with those from scRNA-seq clusters (n = 1,253 genes; P < 0.005). e, The number of genes was downsampled from the 2,511 genes that expressed at least five copies in a cell. For each downsampled dataset, the cell-to-cell correlation matrix is calculated and correlated with the cell-to-cell correlation matrix for the 2,511-gene dataset. Five trials are simulated for each downsampled gene level. Data are mean ± s.d. Even when downsampled to 100 genes, about 40% of the cell-to-cell correlation is retained, because the expression patterns of many genes are correlated. f, Scatter plots of seqFISH+ with scRNA-seq in different cell types. Each dot represents a gene and its mean expression z-score value in either seqFISH+ or scRNA-seq, in astrocytes, oligodendrocytes and excitatory neurons. In general, seqFISH+ and scRNA-seq are in good agreement (n = 598 genes each). g, HMRF detects spatial domains that contain cells with similar expression patterns regardless of cell type. Domain-specific genes are shown. h, Spatial domains in the cortex. i, Mapping of the hierarchical clusters onto the cortex. Coordinates are in units of one pixel (103 nm per pixel). Each camera FOV is 2,000 pixels.
a, b, Expression measured by seqFISH+ (a) and scRNA-seq (b). The expression patterns of seqFISH+ clusters are similar to those shown by scRNA-seq clusters (n = 143 genes).
a, b, seqFISH+ data (a) versus the Allen Brain Atlas (ABA)41 (b). Coordinates are in units of one pixel (103 nm per pixel). Layers 1–6 are shown from left to right.
Extended Data Fig. 7 Additional analysis of cortex and subcellular localization patterns in different cell types.
a, Slide explorer image of the cortex and SVZ FOVs imaged in the first brain slice (n = 913 cells). Schematic is shown in Fig. 3a. b, UMAP representation of cortex and SVZ cells. c, Mapping of the choroid plexus cells, which are exclusively present in the ventricle (n = 109 cells). d, Frequency of contacts between the different cell classes in the cortex, normalized for the abundances of cells in each cluster. e, Each strip represents cells that cluster together, which breaks into layers in the cortex, consistent with expectations, as cells within a layer preferentially interact with each other (n = 523 cells). f, Htra1 transcripts are preferentially localized to the periphery of the astrocytes in the cortex. Left, reconstructed image from the 10,000-gene seqFISH+ experiment. Htra1 transcripts are shown in cyan, and all other transcripts are shown in black. Scale bar, 2 μm. Middle and right, a single z-slice of smFISH images of Htra1 in cortical astrocytes (scale bars, 5 μm). g, Atp1b2 localization in seqFISH+ (left; scale bar, 2 μm) and single z-slice smFISH images (middle and right; scale bars, 5 μm). Many Htra1 and Atp1b2 transcripts are localized to astrocytic processes (f, g; n = 62 astrocytes). smFISH images were background subtracted for better display of RNA molecules (n = 10 FOVs, 40× objective). h, Nr4a1 localization patterns are distinct from Htra1 and Atp1b2 and are more nuclear localized across different cell types. An excitatory neuron is shown from the seqFISH+ reconstructions (n = 337 excitatory neurons; scale bars, 2 μm). i, Kif5a, a kinesin, also exhibits periphery and process localizations in different cell types (n = 60 interneurons; scale bars, 2 μm).
a, Expression of individual genes in the SVZ in UMAP representation (n = 281 cells). b, Violin plots showing z-scored gene expression patterns for Louvain clusters corresponding to NSCs for neuroblasts in the SVZ (n = 281 cells). c, Spatial proximity analysis of the cell clusters in the mouse SVZ. Frequency of contacts between the different cell classes in the SVZ, normalized for the abundances of cells in each cluster. d, Neural progenitors appear to be in spatial proximity with each other. n = 281 cells (c, d). e, Two neuroblast cell clusters are found to be in spatial proximity in the SVZ. f, Subclusters of cells from cluster 7 in the cortex (left). Medium spiny neurons that express Adora2, Pde10a and Rasd2 marker genes form a separate cluster that is detected only in the striatum (right) (n = 42 cells in cluster 7).
a, Slide explorer image of the olfactory bulb FOVs imaged in the second brain slice. b, UMAP analysis of olfactory bulb cells. c, Heat map of z-scored gene expression patterns of cells in the olfactory bulb. d, Violin plots show z-scored marker gene expression patterns in the different classes of cells detected in the olfactory bulb. n = 2,050 cells (a–d). e, Representative smFISH images of Th and Trh. Images were maximum z-projected. In the glomeruli layer, cluster 3 cells express both Th and Trh, whereas in the GCL, cells express Th but not Trh (clusters 5 and 22). n = 10 FOVs, 40× objective. Scale bars, 13 μm (left images), 6.5 μm (right images). f, Frequency of contacts between the different cell classes in the glomerulus, normalized for the abundances of cells in each cluster. g, Cell clusters 3 (Th+ interneurons) and 23 (neuroblast) are in close proximity in the mapped image. Scale bars, 20 μm (f, g).
a, Schematics of the FOVs imaged in the olfactory bulb. b–f, Spatial mapping of the cell clusters in the glomeruli layer (b) and GCL (c–f) in the olfactory bulb. Note the neuroblast cells tend to reside in the interior of the GCL (upper parts of c and d and lower parts of e and f), whereas more mature interneurons are present in the outer layer. This is consistent with the migration of neuroblasts from the SVZ through the rostral migratory stream into the GCL. Scale bars, 20 μm.
Codebook for 10,000 genes. Base 20 pseudocolour coding scheme for each of the 10,000 genes in the three fluorescent channels.
Genes enriched in each of the cell clusters identified in the cortex and olfactory bulb data. The top 20 genes by z-score are shown. Cluster annotations are also listed. The same cluster numbers are used in the main and Extended Data figures.
mRNA localization patterns in the cortex. Cells are divided up into the annotated clusters. In each cluster, mRNAs that are periphery localized or near-nuclear localized are tabulated.
Ligand–receptor pairs and gene enrichments in neighbouring cells. Ligand–receptor pairs that are expressed above a z-score of 1 are shown in the cortex and the olfactory bulb. P values are determined from randomly permuting cell labels (n =1,000). The enrichment tab shows genes that are expressed more strongly in cluster 1 cells that are neighbouring cluster 2 cells than in all cluster 1 cells. The expression values are z-scores and P values are determined from permuting cell labels (n =100).
About this article
Cite this article
Eng, CH.L., Lawson, M., Zhu, Q. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568, 235–239 (2019). https://doi.org/10.1038/s41586-019-1049-y
This article is cited by
Deciphering the molecular and cellular atlas of immune cells in septic patients with different bacterial infections
Journal of Translational Medicine (2023)
Genome Biology (2023)
Journal of Hematology & Oncology (2023)
Genome Biology (2023)
Cell Regeneration (2023)