Probabilistic cell typing enables fine mapping of closely related cell types in situ

Abstract

Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related neuronal types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages prior scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method by mapping the inhibitory neurons of hippocampal area CA1, for which ground truth is available from extensive prior work identifying their laminar organization. Our method identified these closely related classes in a spatial arrangement matching ground truth, and further identified multiple classes of isocortical pyramidal Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

Hybridization-based In Situ Sequencing (HybISS): spatial transcriptomic detection in human and mouse brain tissue

“…This has not been possible in previous ISS methods in that every PLP would be detected every round and problematic genes such as high expressers would have to be run separately. Decoding is performed through sequential combinatorial decoding scheme similar to previous ISS methods, where each barcode has a hamming distance of at least two, allowing for correction and probabilistic assignment of barcodes with better accuracy (Figure 1c) 20,21 . Moreover, with bridgeprobes, this now also allows the possibility for further error correction by including 'zeroes' in the decoding scheme where bridge-probes are simply excluded for a subset of genes in different rounds of probing.…”

“…The simplicity and flexibility of HybISS makes it a versatile image-based spatial transcriptomic method that can be easily implemented and adapted for a wide variety of scientific questions. Customizing gene lists will be vital as it is a targeted approach and curating custom gene panels from scRNA-seq data, such as probabilistic cell typing by in situ sequencing (pciSeq) 21 , to create cell-type atlases will provide a useful tool in the HybISS method.…”

“…We see the great potential of implementing HybISS in spatial cell atlas projects. SBL ISS has been demonstrated to assign cell types in mouse hippocampus 21 and neural crest development 18 . ISS has also been applied and analyzed together with scRNA-seq data and data from untargeted transcriptome-wide Spatial Transcriptomics 31 to develop a spatiotemporal atlas of the developing human heart 32 .…”

“…Multiplexed in situ hybridization offers the possibility to explore cellular diversity at subcellular resolution in a upscaled approach. 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 . 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 scRNAseq data across large tissue areas of various origins.…”

Whole-body integration of gene expression and single-cell morphology

“…Other techniques based on probe hybridisation or local sequencing provide expression information in situ, recovering the position of the cells interrogated (reviewed in (Lein et al, 2017)). Such spatial transcriptomics techniques can be correlated and complemented with single-cell transcriptomics Phillips et al, 2019;Qian et al, 2020), and recent developments using viral barcoding have also enabled the integration of long-range projection information in a brain-wide manner (Chen et al, 2019). However, these approaches only retrieve multimodal information for small subsets of cells and/or their scalability is not straight-forward beyond a set of tissue slices (Ortiz et al, 2019), though this could suffice for small organisms (Ebbing et al, 2018).…”