Hybridization-based in situ sequencing (HybISS) for spatially resolved transcriptomics in human and mouse brain tissue

Abstract

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. 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. Here, we describe the next iteration of ISS, HybISS, hybridization-based in situ sequencing. Modifications in probe design allows for a new barcoding system via sequence-by-hybridization chemistry for improved spatial detection of RNA transcripts. 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. HybISS design allows for increased flexibility and multiplexing, increased signal-to-noise, all without compromising throughput efficiency of imaging large fields of view. 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. Overall, HybISS technology works as a targeted amplification detection method for improved spatial transcriptomic visualization, and importantly, with an ease of implementation.

Comprehensive in situ mapping of human cortical transcriptomic cell types

“…HybISS. Technical details and description of the HybISS method can be found published in Gyllborg et al (7) as well as transcript identity and coordinates for Sections A and B. The same gene panel and experimental procedures were used in the generation of Section C. The genes targeted in the HybISS experiments were manually and computationally curated as a part of the Chan Zuckerberg Initiative SpaceTx Consortium.…”

“…However, the precise laminar locations and abundances of many of these cell types have not yet been described, and beyond coarse layers most of these types are at low proportions and intermingled with one another. Hybridization-based In Situ Sequencing (HybISS) is an image-based multi-targeted gene expression profiling technique that allows the precise mapping of individual cells in human brain tissues (7) . Various analytical approaches can assign detected transcripts to segmented cells and subsequently, cells to cell types.…”

BARcode DEmixing through Non-negative Spatial Regression (BarDensr)

“…Both work well in ideal conditions. One school of thought (‘blobs-first’) begins by trying to identify regions in the tissue where a rolony may be present, and then tries to use the imaging data to guess the barcode identity of each rolony [ 4 , 5 , 7 – 9 ]. Another school of thought (‘barcodes-first’) begins by looking at each voxel and trying to determine whether the fluorescence signal emitted in that voxel over all the rounds is consistent with one of the barcodes [ 6 , 10 , 11 ].…”

“…Both of these features—high density and insufficient optical resolution—lead to cases where different signals are mixed together into the same voxel. In this situation, to correctly identify rolony positions and identities it is necessary to perform some kind of ‘demixing.’ Because of this challenge, many current methods simply discard any blobs in regions where strong mixing occurs [ 3 , 8 , 9 ].…”

Nonnegative matrix factorization integrates single-cell multi-omic datasets with partially overlapping features

“…In particular, emerging techniques such as the Multiome assay from 10X Genomics can measure gene expression and chromatin accessibility from the same cell 1-6 , but do not generally capture methylation or spatial features. Spatial transcriptomics, named Method of the Year 2020 by Nature Methods 7 , encompasses a rapidly growing suite of techniques [8][9][10][11] that interrogate gene expression patterns within intact tissue. However, protocols for spatial measurements of epigenomic state are not widely available.The different types of features measured by different single-cell technologies create unique computational data integration challenges.…”