Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

Abstract

Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.

Squidpy: a scalable framework for spatial omics analysis

“…The cell-dense pyramidal layer can be easily distinguished with this view of the data, showcasing the richness and interpretability of information that can be extracted from tissue images when brought in a spot-based format. In addition, we can leverage segmented nuclei to inform cell-type deconvolution (or decomposition/mapping) methods such as Tangram 39 or Cell2Location 40 . In Supplementary Fig.…”

Statistical and machine learning methods for spatially resolved transcriptomics data analysis

“…Since the cell types in scRNA-seq data are already known, this problem can be formulated as a semi-supervised learning problem, in which DSTG [ 27 ] predicts unknown cell proportions for each capture location. Other approaches have been proposed for spatial decomposition, for example, a recent method named Tangram [ 57 ]. Tangram [ 57 ] is an optimization-based approach to align scRNA-seq data onto different spatial transcriptomics data by enforcing the similarity between the two data types.…”

“…Other approaches have been proposed for spatial decomposition, for example, a recent method named Tangram [ 57 ]. Tangram [ 57 ] is an optimization-based approach to align scRNA-seq data onto different spatial transcriptomics data by enforcing the similarity between the two data types. It is worth noting that Tangram [ 57 ] is compatible with capture-based and image-based spatial transcriptomics data.…”

“…Tangram [ 57 ] is an optimization-based approach to align scRNA-seq data onto different spatial transcriptomics data by enforcing the similarity between the two data types. It is worth noting that Tangram [ 57 ] is compatible with capture-based and image-based spatial transcriptomics data. A recent approach, Cell2location [ 58 ], is a hierarchical Bayesian model that maps the spatial distribution of cell types by leveraging information from scRNA-seq data.…”

“…Most cells can be accurately mapped with only a small number of genes 2. Can identify intercellular gene-gene regulatory information Does not consider the time delay (including the diffusion time of ligand or the reacting time of the intracellular cascades) that may take place in cell-cell communication novoSpaRc [ 56 ] Generalized optimal-transport model Spatial location reconstruction for scRNA-seq data Does not need to specify landmark genes for alignment The accuracy can be promoted by using different loss functions Tangram [ 57 ] Non-convex optimization by deep learning methods for spatial alignment 1. Spatial location reconstruction for scRNA-seq data 2.…”