Bayesian segmentation of spatially resolved transcriptomics data

Abstract

Spatial transcriptomics is an emerging stack of technologies, which adds spatial dimension to conventional single-cell RNA-sequencing. New protocols, based on in situ sequencing or multiplexed RNA fluorescent in situ hybridization register positions of single molecules in fixed tissue slices. Analysis of such data at the level of individual cells, however, requires accurate identification of cell boundaries. While many existing methods are able to approximate cell center positions using nuclei stains, current protocols do not report robust signal on the cell membranes, making accurate cell segmentation a key barrier for downstream analysis and interpretation of the data. To address this challenge, we developed a tool for Bayesian Segmentation of Spatial Transcriptomics Data (Baysor), which optimizes segmentation considering the likelihood of transcriptional composition, size and shape of the cell. The Bayesian approach can take into account nuclear or cytoplasm staining, however can also perform segmentation based on the detected transcripts alone. We show that Baysor segmentation can in some cases nearly double the number of the identified cells, while reducing contamination. Importantly, we demonstrate that Baysor performs well on data acquired using five different spatially-resolved protocols, making it a useful general tool for analysis of high-resolution spatial data.

Museum of Spatial Transcriptomics

“…However, without visualizing the plasma membrane, accuracy of cell segmentation is limited. Some analyses, such as identification of tissue regions, can be performed without cell segmentation [115]. Until 2019, image processing was typically performed with poorly documented and technique specific code written in the proprietary language MATLAB, but more recently such code is increasingly written in the open source language Python.…”

Sparcle: assigning transcripts to cells in multiplexed images

“…Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM) (16) and Bayesian Segmentation of Spatial Transcriptomics Data (Baysor) (17) are recent quantification approaches that aim to circumvent the need for cell segmentation. Like Sparcle, Baysor works by assigning transcripts to artificially generated mock cells.…”

Squidpy: a scalable framework for spatial single cell analysis

“…On-disk files are loaded lazily using 6 through 7 , meaning content is only read in memory when requested. The object can be saved as a zarr store 8 . This allows handling very large files that do not fit in memory.…”

“…Hence, existing analysis frameworks for spatial data focus either on pre-processing 5–8 or on one particular aspect of spatial data analysis 9–13 . Due to the lack of a unified data representation and modular API, users so far cannot perform comprehensive analyses leveraging the full spatial modality, e.g.…”