The established ISS method based on barcoded padlock probes (PLPs) and amplification through rolling circle amplification (RCA) has shown robust detection of RNA for various applications, however, further improvements are needed to meet the upscaling demands to explore cellular diversity of scRNA-seq data across large tissue areas of various origins. As an example, our lab has developed in situ sequencing (ISS) to be used to detect RNA isoforms ( 19), transcriptomic distribution ( 20), and cell typing across tissue sections ( 21). Multiplexed in situ hybridization offers the possibility to explore cellular diversity at subcellular resolution in an upscaled approach. Spatial distribution of RNA transcripts for cell-type mapping can give further understanding to arrangement of complex tissues ( 16–18).

Image-based transcriptomics have shown the possibility to detect a various range of gene numbers within tissue sections to different degrees of sensitivity and accuracy ( 12–14), some used to explore data driven discoveries of biological studies such as behavior based cellular changes ( 15, 16). In situ hybridization technologies have come a long way and large consortium efforts are realizing the importance of not only being able to define all the cell types within a human being but also giving them a spatial position in the form of a human cell atlas ( 10, 11). The field of spatial transcriptomics, assigning transcripts a positional location in situ, has emerged as a vital tool in the validation of scRNA-seq data and exploring the transcriptomic profiles and cellular architecture across tissues. However, one major drawback to scRNA-seq methods that would be difficult to solve computationally is that it requires the cellular dissociation of a tissue to collect cells which could lead to under sampling of vulnerable cell types and more importantly, any spatial context of a cell within a given tissue being lost. Capturing only static states of cells has been one drawback to sequencing methods and implementation of computational methods trying to overcome this are currently being used, even with in situ hybridization data ( 7–9).

The exponential growth of sequencing large numbers of cells within the last decade has allowed for a systematic assignment to defining cell types, cell states or transitions between them more clear ( 4–6). In the era of single-cell transcriptomics, large quantities of data from single-cell RNA-sequencing (scRNA-seq) has revealed the true transcriptomic diversity of cell types within organisms in high detail with such technologies becoming more accessible to laboratories around the world ( 1–3). The field of spatially resolved transcriptomic technologies is emerging as a vital tool to profile gene-expression, continuously pushing current methods to accommodate larger gene panels and larger areas without compromising throughput efficiency. Overall, HybISS technology works as a targeted amplification detection method for improved spatial transcriptomic visualization, and importantly, with an ease of implementation. Moreover, the current protocol is demonstrated to work on human brain tissue samples, a source that has proven to be difficult to work with image-based spatial analysis techniques. HybISS design allows for increased flexibility and multiplexing, increased signal-to-noise, all without compromising throughput efficiency of imaging large fields of view. Due to the amplification of probes, amplicons can be visualized with standard epifluorescence microscopes for high-throughput efficiency and the new sequencing chemistry removes limitations bound by sequence-by-ligation chemistry of ISS. Modifications in probe design allows for a new barcoding system via sequence-by-hybridization chemistry for improved spatial detection of RNA transcripts.

Here, we describe the next iteration of ISS, HybISS, hybridization-based in situ sequencing. In situ sequencing (ISS) method based on padlock probes and rolling circle amplification has been used to spatially resolve gene transcripts in tissue sections of various origins. Visualization of the transcriptome in situ has proven to be a valuable tool in exploring single-cell RNA-sequencing data, providing an additional spatial dimension to investigate multiplexed gene expression, cell types, disease architecture or even data driven discoveries.

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