Artifacts in spatial transcriptomics data: their detection, importance, prevalence, and prevention

Abstract

Data artifacts may induce errors in findings from any spatial transcriptomics platform. To provide protection from these errors, we have developed Border, Location, and edge Artifact DEtection (BLADE). BLADE is a novel collection of automated cross-platform statistical methods for detecting and removing three types of artifacts: (i) border effects, where total gene reads is modified at the border of the capture area; (ii) tissue edge effects, where total gene reads is modified at the edge of the tissue; (iii) location batch malfunctions, where there is a zone in the same location on all slides in a batch with substantially decreased sequencing depth. These artifacts are not mutually exclusive. BLADE has been applied to both Visium and CosMx data, and was used to evaluate our library of 37 10x Visium samples of liver and adipose tissue from humans and mice. Artifacts were found to be both common and impactful in those samples, indicating that artifact detection methods are critical for spatial transcriptomics quality control. Our BLADE software is publicly available.

Keywords: Visium; cellular senescence; modeling; quality control; spatial transcriptomics.

Figure 1

Tissue sample color-coded to indicate which spots are on the interior (yellow), tissue edge (blue), capture area border (red), or on both the edge and border (purple), or have no or insufficient tissue (gray).

Figure 2

Two heatmaps of the sequencing depth of two of our tissue samples. Sequencing depth ranges from red (most) to blue (least). Border effects and edge effects are present in both tissue samples. Border effects can be seen where spots along the image border have elevated sequencing depth. Edge effects can be seen where spots along the tissue edges have reduced sequencing depth.

Figure 3

Three visualizations of tissue images with the same batch location malfunction artifact. Each image has a red arrow pointing towards a group of spots at the bottom center area of the image. Those spots are in the same location in all three images. These spots always stand out from the surrounding spots because of their extremely low number of gene reads.

Figure 4

This figure contains visualizations illustrating how artifacts influence data analysis. (a) and (b) show two visualizations of the spatial distribution of UMAP clusters on the same tissue sample. (a) Shows the results when artifacts were injected into the image. (b) Shows the results when artifacts were not injected into the image. While there are some similarities, there are also substantial differences, such as the number of clusters that were identified. (c), (d), and (e) show the same Visium tissue image: (c) without modification, with visible border artifacts; (d) with the border spots removed; (e) after removing the border spots and then rescaling the remaining read counts (using z-scores) based on their values in the remaining spots. Note that the variability in reads across space is much more visually apparent in (e). The extreme values that occurred due to border artifacts in (c) are compressing the information in the rest of the tissue image, thereby suppressing the substantial amount of potentially informative spatial variation in gene expression visible in (e).