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1. Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor–immune hubs

   https://doi.org/10.1038/s41587-024-02173-8  

   He, Siyu; Jin, Yinuo; Nazaret, Achille; Shi, Lingting; Chen, Xueer; Rampersaud, Sham; Dhillon, Bahawar S; Valdez, Izabella; Friend, Lauren E; Fan, Joy Linyue; et al (February 2025, Nature Biotechnology)

   Abstract Spatially resolved gene expression profiling provides insight into tissue organization and cell–cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC. 

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2. Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease

   https://doi.org/10.1038/s41467-022-35233-1  

   Zhang, Xinyi; Wang, Xiao; Shivashankar, G_V; Uhler, Caroline (December 2022, Nature Communications)

   Abstract Tissue development and disease lead to changes in cellular organization, nuclear morphology, and gene expression, which can be jointly measured by spatial transcriptomic technologies. However, methods for jointly analyzing the different spatial data modalities in 3D are still lacking. We present a computational framework to integrate Spatial Transcriptomic data using over-parameterized graph-based Autoencoders with Chromatin Imaging data (STACI) to identify molecular and functional alterations in tissues. STACI incorporates multiple modalities in a single representation for downstream tasks, enables the prediction of spatial transcriptomic data from nuclear images in unseen tissue sections, and provides built-in batch correction of gene expression and tissue morphology through over-parameterization. We apply STACI to analyze the spatio-temporal progression of Alzheimer’s disease and identify the associated nuclear morphometric and coupled gene expression features. Collectively, we demonstrate the importance of characterizing disease progression by integrating multiple data modalities and its potential for the discovery of disease biomarkers. 

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3. A model-based constrained deep learning clustering approach for spatially resolved single-cell data

   https://doi.org/10.1101/gr.276477.121  

   Lin, Xiang; Gao, Le; Whitener, Nathan; Ahmed, Ashley; Wei, Zhi (October 2022, Genome Research)

   Spatially resolved scRNA-seq (sp-scRNA-seq) technologies provide the potential to comprehensively profile gene expression patterns in tissue context. However, the development of computational methods lags behind the advances in these technologies, which limits the fulfillment of their potential. In this study, we develop a deep learning approach for clustering sp-scRNA-seq data, named Deep Spatially constrained Single-cell Clustering (DSSC). In this model, we integrate the spatial information of cells into the clustering process in two steps: (1) the spatial information is encoded by using a graphical neural network model, and (2) cell-to-cell constraints are built based on the spatial expression pattern of the marker genes and added in the model to guide the clustering process. Then, a deep embedding clustering is performed on the bottleneck layer of autoencoder by Kullback–Leibler (KL) divergence along with the learning of feature representation. DSSC is the first model that can use information from both spatial coordinates and marker genes to guide cell/spot clustering. Extensive experiments on both simulated and real data sets show that DSSC boosts clustering performance significantly compared with the state-of-the-art methods. It has robust performance across different data sets with various cell type/tissue organization and/or cell type/tissue spatial dependency. We conclude that DSSC is a promising tool for clustering sp-scRNA-seq data. 

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4. Spatially Resolved Transcriptomic Analysis of the Germinating Barley Grain

   https://doi.org/10.1101/2023.01.24.525109  

   Marta Peirats-Llobet, Changyu Yi (January 2023, bioRxiv)

   Seeds, which provide a major source of calories for humans, are a unique stage of a flowering plant’s lifecycle. During seed germination the embryo reactivates rapidly and goes through major developmental transitions to become a seedling. This requires extensive and complex spatiotemporal coordination of cell and tissue activity. Existing gene expression profiling methods, such as laser capture microdissection followed by RNA-seq and single-cell RNA7 seq, suffer from either low throughput or the loss of spatial information about the cells analysed. Spatial transcriptomics methods couple high throughput analysis of gene expression simultaneously with the ability to record the spatial location of each individual region analysed. We developed a spatial transcriptomics workflow for germinating barley grain to better understand the spatiotemporal control of gene expression within individual seed cell types. More than 14,000 genes were differentially regulated across 0, 1, 3, 6 and 24 hours after imbibition. This approach enabled us to observe that many functional categories displayed specific spatial expression patterns that could be resolved at a sub-tissue level. Individual aquaporin gene family members, important for water and ion transport, had specific spatial expression patterns over time, as well as genes related to cell wall modification, membrane transport and transcription factors. Using spatial autocorrelation algorithms, we were able to identify auxin transport genes that had increasingly focused expression within subdomains of the embryo over germination time, suggestive of a role in establishment of the embryo axis. Together, our data provides an unprecedented spatially resolved cellular map for barley grain germination and specific genes to target for functional genomics to define cellular restricted processes in tissues during germination. The data can be viewed at https://spatial.latrobe.edu.au/. 

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5. Three-dimensional intact-tissue sequencing of single-cell transcriptional states.

   Wang, X. (June 2018, Science)

   The mammalian brain consists of an intricate tapestry of cell types, with diversity crucial for function that arises from both differential gene expression and circuit-specific anatomy. Yet, retrieving high-content gene-expression information while retaining 3D positional anatomy at cellular resolution has been difficult, limiting integrative understanding of brain structure and function. Here we introduce and apply a technology for 3D intact-tissue RNA sequencing, termed STARmap (Spatially-resolved Transcript Amplicon Readout Mapping), which integrates highly-specific signal amplification, novel hydrogel-tissue chemistry, and an error-reduction sequencing process. The capabilities of STARmap were tested by mapping from 160 to 1,020 distinct genes simultaneously in sections of mouse brain at single-cell resolution with unprecedented efficiency, accuracy and reproducibility. These experiments led to the discovery of multiple new neocortical cell types, with gene markers and spatial patterns of organization not previously described, by comparison of the molecularly-defined architectures of sensory versus cognitive neocortex, and by quantification of expression of activity-regulated genes as a function of stimulation condition, spatial position, and cell typology. By adapting STARmap to thick tissue blocks, we observed and confirmed a novel molecularly-defined gradient distribution of excitatory neuron subtypes across cubic millimeter-scale volumes (>30,000 cells), and discovered a short-range 3D pattern of self-clustering shared by many inhibitory neuron subtypes that was accurately identifiable with a 3D STARmap approach. 

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