SiT – spatial isoform transcriptomics

In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length sequence heterogeneity cannot be characterized at spatial resolution with current transcriptome profiling methods. To that end, researchers at the CNRS have developed spatial isoform transcriptomics (SiT), an explorative method for characterizing spatial isoform variation and sequence heterogeneity using long-read sequencing. The researchers show in mouse brain how SiT can be used to profile isoform expression and sequence heterogeneity in different areas of the tissue. SiT reveals regional isoform switching of Plp1 gene between different layers of the olfactory bulb, and the use of external single-cell data allows the nomination of cell types expressing each isoform. Furthermore, SiT identifies differential isoform usage for several major genes implicated in brain function (Snap25, Bin1, Gnas) that are independently validated by in situ sequencing. SiT also provides for the first time an in-depth A-to-I RNA editing map of the adult mouse brain. Data exploration can be performed through an online resource (https://www.isomics.eu), where isoform expression and RNA editing can be visualized in a spatial context.

SiT methodology and datasets

SiT methodology and datasets. (A) Experimental and computational steps for SiT analysis. Right side shows unsupervised gene expression clustering and gene- (short-read) and isoform-level (SiT) expression of Snap25 in a mouse coronal brain section (CBS2). (B) Nanopore sequencing saturation curves for three Visium samples showing the number of UMIs observed as a function of the number of Nanopore reads. Labels indicate sequencing saturations obtained with all flow cells (CBS1, CBS2, MOB) and with just one latest generation Promethion flow cell per sample (vertical dotted lines, CBS1, CBS2). (C) Mean read number (RN) per molecule (UMI) observed for each of the three samples. (D) Percentage of assignment at each step of the workflow: Reads with polyA tail (PolyA) expressed as percentage of total reads; Assigned spatial barcode (SpatialBC) expressed as percentage of reads with PolyA tail found; UMI assigned reads (UMI) as percentage of reads with spatial barcode. Details about the spatial barcode/UMI assignment strategy are in (15). (E) Normalized transcript coverage plot for Nanopore and for Illumina sequencing.

(A) Experimental and computational steps for SiT analysis. Right side shows unsupervised gene expression clustering and gene- (short-read) and isoform-level (SiT) expression of Snap25 in a mouse coronal brain section (CBS2). (B) Nanopore sequencing saturation curves for three Visium samples showing the number of UMIs observed as a function of the number of Nanopore reads. Labels indicate sequencing saturations obtained with all flow cells (CBS1, CBS2, MOB) and with just one latest generation Promethion flow cell per sample (vertical dotted lines, CBS1, CBS2). (C) Mean read number (RN) per molecule (UMI) observed for each of the three samples. (D) Percentage of assignment at each step of the workflow: Reads with polyA tail (PolyA) expressed as percentage of total reads; Assigned spatial barcode (SpatialBC) expressed as percentage of reads with PolyA tail found; UMI assigned reads (UMI) as percentage of reads with spatial barcode. Details about the spatial barcode/UMI assignment strategy are in (15). (E) Normalized transcript coverage plot for Nanopore and for Illumina sequencing.

Lebrigand K, Bergenstråhle J, Thrane K, Mollbrink A, Meletis K, Barbry P, Waldmann R, Lundeberg J. (2023) The spatial landscape of gene expression isoforms in tissue sections. Nucleic Acids Res [Epub ahead of print]. [article].

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