Robust decomposition of cell type mixtures in spatial transcriptomics

Abstract

Spatial transcriptomic technologies measure gene expression at increasing spatial resolution, approaching individual cells. However, a limitation of current technologies is that spatial measurements may contain contributions from multiple cells, hindering the discovery of cell type-specific spatial patterns of localization and expression. Here, we develop Robust Cell Type Decomposition (RCTD, https://github.com/dmcable/RCTD), a computational method that leverages cell type profiles learned from single-cell RNA sequencing data to decompose mixtures, such as those observed in spatial transcriptomic technologies. Our approach accounts for platform effects introduced by systematic technical variability inherent to different sequencing modalities. We demonstrate RCTD provides substantial improvement in cell type assignment in Slide-seq data by accurately reproducing known cell type and subtype localization patterns in the cerebellum and hippocampus.We further show the advantages of RCTD by its ability to detect mixtures and identify cell types on an assessment dataset. Finally, we show how RCTD's recovery of cell type localization uniquely enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD has the potential to enable the definition of spatial components of cellular identity, uncovering new principles of cellular organization in biological tissue..

Transcriptome-wide spatial RNA profiling maps the cellular architecture of the developing human neocortex

“…WTA and scRNA-seq integration maps cell types at scale Spatial mapping of resident cell types across complex tissues can offer new perspectives into cellular signalling, tissue development and function. The integration of single-cell and spatial transcriptomic data, where cell types are defined using scRNA-seq and then resolved across spatially assayed tissue locations, offers a scalable workflow to map cell types in situ [26][27][28] .…”

Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

“…There are many models that can deconvolve cell types from an mRNA mixture of an entire tissue sample, as from bulk RNA-seq 17,[93][94][95][96][97][98][99][100][101] ; however, given that spatial barcoding methods have only recently emerged as an accessible technique, a limited number of models are specifically tailored towards deconvolving mRNA mixtures from capture spots [90][91][92]101,102 . Regardless of which approach is taken, the first step in deconvolution is establishing which cellular subtypes exist in a given tissue sample (as described in the section 'Establishing discrete cell subtypes through scRNA-seq').…”

“…A complementary approach to estimating exact proportions of each cell type in a given capture spot is through a bayesian statistical framework that fits a probability distribution, often a negative binomial distribution 92,103 (alternatively a Poisson distribution 90 ),…”

“…In cases where two samples of matched cell-type composition can be obtained, the efficacy between one processed by scRNA-seq and the other through bulk RNA-seq followed by deconvolution can be compared 96,99,100 . However, a more convenient strategy for assessing efficacy is to create mixtures in silico by combining the transcript measurements from multiple well-characterized scRNA-seq cells [90][91][92]95,96,100 or bulk RNA-seq mixtures of known cell-type composition 97,99 . The model's deconvolution output can be compared with the ground-truth cell subtype proportion values to assess efficacy.…”

Reference-free cell-type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data

“…This lack of single-cell resolution hinders the characterization of cell-type specific spatial organization. To address this challenge, supervised deconvolution approaches such as SPOTlight 3 and RCTD 4 have recently been developed to predict the proportion of cell-types within ST pixels. However, these supervised deconvolution approaches rely on the availability of a suitable single-cell reference, which may present limitations if such a reference does not exist due to budgetary, technical 5 , or biological limitations 6 .…”