STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping

Abstract

Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we develop STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit at https://github.com/JEFworks-Lab/STalign and as Supplementary Software with additional documentation and tutorials available at https://jef.works/STalign.

Gene count normalization in single-cell imaging-based spatially resolved transcriptomics

“…Many brain regions are enriched for specific cell types and in turn differentially express specific genes when compared to other brain regions. To achieve this, we annotated mouse brain regions by aligning and transferring region annotations from the Allen Brain Atlas using STalign [21], [22]. Then, for a brain region of interest, we randomly sample 100 genes that are significantly overexpressed in this region, thereby simulating a 100-gene gene panel skewed to our brain region of interest.…”

“…Vizgen MERFISH Mouse Brain Receptor Map data was downloaded from the Vizgen website and data from slice 2 replicate 2 was used [20]. Brain anatomical region annotations for each cell were obtained by transferring region annotations from the Allen Brain Atlas Common Coordinate Framework (CCF) using STalign [21], [22]. STalign was applied using the 3D reconstructed Nissl image from the Allen CCF atlas as a source and the MERFISH cell position data as a target.…”

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis

“…Further, while we have focused on applying rasterization on individual spatial omics datasets, SEraster can also be applied to multiple tissue sections in order to create shared pixels in the same coordinate framework (Supplementary Information S1). For instance, such analysis facilitates spatial molecular comparisons after structural alignment (Supplementary Figure 3, (Clifton et al, 2023)).…”

STEP: Spatial Transcriptomics Embedding Procedure for Multi-scale Biological Heterogeneities Revelation in Multiple Samples

“…Not only are these assumptions incompletely correct, they are actually unnecessary because cell types can be well distinguished at the level of gene expression regardless of spatial locations. On the other hand, when it comes to the integration of multiple sections, current methods often require the spatial contiguity between sections, whether vertically or horizontally (Clifton et al, 2023;Dong & Zhang, 2022;Gao et al, 2024;Long et al, 2023;Wang et al, 2023;Yu et al, 2023;Yuan, 2024;Zhou et al, 2023).Though, in concept, spatial domains demonstrate the spatial variations and the lower resolution compared to the cell types, the most fundamental variations that segment the regions are still transcriptional. Although one Bayesian model (Li & Zhou, 2022) and a cell-type annotation based method (Yuan, 2024) can handle both the multi-scale or multi-section (non-contiguous) SRT dataset, there is another limitation unique to these methods: they are unable to provide embeddings that are consistent to their identified spatial domains for the downstream analyses, like pseudotime analysis, where the PCA and other batch-effect removal algorithms specific for scRNA-seq data (Haghverdi et al, 2018;Korsunsky et al, 2019) are conducted, instead.…”

“…Not only are these assumptions incompletely correct, they are actually unnecessary because cell types can be well distin-guished at the level of gene expression regardless of spatial locations. On the other hand, when it comes to the integration of multiple sections, current methods often require the spatial contiguity between sections, whether vertically or horizontally (Clifton et al, 2023; Dong & Zhang, 2022; Gao et al, 2024; Long et al, 2023; Wang et al, 2023; Yu et al, 2023; Yuan, 2024; Zhou et al, 2023). Though, in concept, spatial domains demon-strate the spatial variations and the lower resolution compared to the cell types, the most fundamental variations that segment the regions are still transcriptional.…”