Spatial Transcriptomics to define transcriptional patterns of zonation and structural components in the liver

Abstract

Reconstruction of heterogeneity through single-cell transcriptional profiling has greatly advanced our understanding of the spatial liver transcriptome in recent years. However, global transcriptional differences across lobular units remain elusive in physical space. Here, we implement Spatial Transcriptomics to perform transcriptomic analysis across sectioned liver tissue. We confirm that the heterogeneity in this complex tissue is predominantly determined by lobular zonation. By introducing novel computational approaches, we enable transcriptional gradient measurements between tissue structures, including several lobules in a variety of orientations. Further, our data suggests the presence of previously transcriptionally uncharacterized structures within liver tissue, contributing to the overall spatial heterogeneity of the organ. This study demonstrates how comprehensive spatial transcriptomic technologies can be used to delineate extensive spatial gene expression patterns in the liver, indicating its future impact for studies of liver function, development and regeneration as well as its potential in pre-clinical and clinical pathology.

Microscopic examination of spatial transcriptome using Seq-Scope

“…These gene expression patterns are consistent with the previous RNA in situ hybridization (Aizarani et al, 2019;Halpern et al, 2017) and immunostaining results (Park et al, 2021). However, previous studies using original ST (Hildebrandt et al, 2021) or Slide-Seq (Rodriques et al, 2019) were not able to uncover this level of detail (Figures S4L and S4M), possibly due to the limitations in resolution (Figures 2D and 2E) and RNA capture efficiency (Figures S4N and S4O).…”

“…The performance of Seq-Scope in liver and colon experiments were benchmarked against publicly available datasets produced by 10X VISIUM (https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Human_Brain_Section_1), DBiT-Seq (GEO: GSM4096261 in GSE137986) (Liu et al, 2020), Slide-Seq (Single Cell Portal: 180819_11 in SCP354) (Rodriques et al, 2019), Slide-SeqV2 (Single Cell Portal: 190921_19 in SCP815) (Stickels et al, 2021), and HDST (GEO: GSM4067523 in GSE130682) (Vickovic et al, 2019). Liver Seq-Scope dataset was separately benchmarked against former liver datasets produced using original ST (Zenodo: 10.5281/zenodo.4399655) (Hildebrandt et al, 2021) and Slide-Seq (Single Cell Portal: 1808038_8 in SCP354) (Rodriques et al, 2019). The Seq-Scope dataset had a large area that was not covered by tissues, so we isolated the tissue-overlaid HDMI pixels and used them for the benchmark analysis.…”

Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics

“…We generalize these approaches by modeling continuous variation in expression within a layer; e.g. due to gradients of gene expression [43,27,12,16]. Since current SRT technologies have limited dynamic range and spatial resolution, inference of complicated expression functions may be prone to overfitting.…”

“…Piecewise functions allow for discontinuities in expression where there are sharp changes in cell type composition in space, such as between tissue layers, while continuous functions model gradients of gene expression within a tissue layer, e.g. [43,27]. To reduce overfitting with sparse SRT data, we model gene expression using piecewise linear functions, which are specified by a small number of parameters.…”

Automated Detection of Portal Fields and Central Veins in Whole-Slide Images of Liver Tissue

“…1 Heterogeneity of biological properties of the cells along the PF–CV axis is denoted as zonation. 10 , 11 , 12 , 13 , 14 Physiological metabolic processes exhibit zonation mainly due to zonated gene and protein expression of the hepatocytes, 15 , 16 , 17 , 18 and gradients of compounds in the blood being metabolized along the PF–CV axis. Additionally, pathological alterations of hepatic tissue can be zonated, e.g., steatosis 13 , 19 , 20 , 21 or toxic damage, as after administration of carbon tetrachloride.…”