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Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues

Abstract

RNA-sequencing (RNA-seq) measures the quantitative change in gene expression over the whole transcriptome, but it lacks spatial context. In contrast, in situ hybridization provides the location of gene expression, but only for a small number of genes. Here we detail a protocol for genome-wide profiling of gene expression in situ in fixed cells and tissues, in which RNA is converted into cross-linked cDNA amplicons and sequenced manually on a confocal microscope. Unlike traditional RNA-seq, our method enriches for context-specific transcripts over housekeeping and/or structural RNA, and it preserves the tissue architecture for RNA localization studies. Our protocol is written for researchers experienced in cell microscopy with minimal computing skills. Library construction and sequencing can be completed within 14 d, with image analysis requiring an additional 2 d.

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Figure 1: Schematic overview of FISSEQ library construction and sequencing.
Figure 2: Comparing single-cell RNA-seq with FISSEQ.
Figure 3: Counting resolution-limited amplicons using partition sequencing.
Figure 4: Schematic overview of the SOLiD color-coding and decoding scheme.
Figure 5: Example of image analysis, registration and sequence clustering.
Figure 6: Schematic overview of FISSEQ experimental and analysis steps.

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Acknowledgements

This study was funded by US National Institutes of Health (NIH) Centers of Excellence in Genomic Sciences (CEGS) grant no. P50 HG005550. J.H.L. and co-workers were funded by National Heart, Blood and Lung Institute (NHBLI) grant no. RC2HL102815, by the Allen Institute for Brain Science and by National Institute of Mental Health (NIMH) grant no. MH098977. E.R.D. was funded by NIH grant no. GM080177 and by National Science Foundation (NSF) Graduate Research Fellowship grant no. DGE1144152.

Author information

Authors and Affiliations

Authors

Contributions

J.H.L. and E.R.D. conceived FISSEQ library construction, sequencing, image analysis and bioinformatics. J.S., R.K., J.L.Y., B.M.T., H.S.L. and J.A. provided key feedbacks during the FISSEQ method development. R.T. and T.C.F. assisted with automated microscopy and image analysis. K.Z. and G.M.C. oversaw the project. J.H.L. wrote the paper, and E.R.D. wrote the FISSEQ software.

Corresponding authors

Correspondence to Je Hyuk Lee or George M Church.

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Competing interests

Potential conflicts of interest for G.M.C. are listed on http://arep.med.harvard.edu/gmc/tech.html. Other authors have no conflicts of interest.

Integrated supplementary information

Supplementary Figure 1 The altered distribution of poly A-associated transcripts at different fixation temperatures.

(a) Primary fibroblasts were fixed at room temperature or at 37°C for 10 minutes. The cells were then hybridized to the Cy3 Poly dT(50) or 18S rRNA probe. At room temperature a large fraction of the mRNA is retained in the nucleus. (b) The cold shock-induced nuclear retention of poly A-associated transcripts is cell type-specific. Highly transformed cell lines and iPS cell lines do not appear to be affected by the fixation temperature.

Supplementary Figure 2 Acid-treatment improves cell permeabilization for FISSEQ.

(a) Low amplicon counts are frequently due to poor permeabilization, which can be observed in the axial view of human iPS cells. (b) A brief treatment with 0.1 HCl can significantly improve the uniformity of FISSEQ amplicons throughout the cell, especially in iPS cells.

Supplementary Figure 3 Degradation of residual RNA is essential for FISSEQ amplicon generation.

Far more amplicons are observed after the RNase-treatment before circularization in numerous human iPS cell colonies (labeled with mCherry; bar: 1 μm).

Supplementary Figure 4 Image deconvolution of FISSEQ amplicons in fibroblasts.

(a) A high quality confocal image requires minimal image deconvolution, but doing so can dramatically increase the signal to noise ratio by removing many spurious pixels; however, over-deconvolution can actually lead to more background noise or artifacts (bar: 1 μm). (b) A low quality epifluorescence image can contain a significant amount of out of focus light. Using 3D deconvolution to remove such signal can dramatically improve the image and speed up base calling (bar: 1 μm).

Supplementary Figure 5 Image alignment of FISSEQ amplicons in fibroblasts.

(a) One of the adjustable parameter is the block size for local alignment. For large images (i.e. 4000-by-4000 pixels) dividing images into a 10-by-10 grid is essential. Even for small images one can appreciate the effect of increasing the block number. (b) When using local alignment it is important to specify the degree of block overlap for image stitching. A large image correction with insufficient or no overlap will result in image fragmentation. (c) In general sub-pixel registration accuracy will not affect alignment significantly; however, increasing the upsampling factor can lead to better registration in certain instances.

Supplementary Figure 6 Data analysis using RStudio.

(a) RStudio provides a straightforward GUI for importing, analyzing and visualizing FISSEQ data. We provide several sample datasets and R sessions for FISSEQ data analysis. One can view the history window and click through commands to reproduce the following plots. (b) Correlation plot of gene expression frequency in region 1 and region 2. (c) Frequency histogram of RNA class instances. (d) Distribution of cluster sizes. (e) Area plot of non-ribosomal reads and their location from 5 different wound healing regions.

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Lee, J., Daugharthy, E., Scheiman, J. et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat Protoc 10, 442–458 (2015). https://doi.org/10.1038/nprot.2014.191

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