High-density generation of spatial transcriptomics with STAGE.

Spatial transcriptome technologies have enabled the measurement of gene expression while maintaining spatial location information for deciphering the spatial heterogeneity of biological tissues. However, they were heavily limited by the sparse spatial resolution and low data quality. To this end, we develop a spatial location-supervised auto-encoder generator STAGE for generating high-density spatial transcriptomics (ST). STAGE takes advantage of the customized supervised auto-encoder to learn continuous patterns of gene expression in space and generate high-resolution expressions for given spatial coordinates. STAGE can improve the low quality of spatial transcriptome data and smooth the generated manifold of gene expression through the de-noising function on the latent codes of the auto-encoder. Applications to four ST datasets, STAGE has shown better recovery performance for down-sampled data than existing methods, revealed significant tissue structure specificity, and enabled robust identification of spatially informative genes and patterns. In addition, STAGE can be extended to three-dimensional (3D) stacked ST data for generating gene expression at any position between consecutive sections for shaping high-density 3D ST configuration.

STAMarker: determining spatial domain-specific variable genes with saliency maps in deep learning.

Spatial transcriptomics characterizes gene expression profiles while retaining the information of the spatial context, providing an unprecedented opportunity to understand cellular systems. One of the essential tasks in such data analysis is to determine spatially variable genes (SVGs), which demonstrate spatial expression patterns. Existing methods only consider genes individually and fail to model the inter-dependence of genes. To this end, we present an analytic tool STAMarker for robustly determining spatial domain-specific SVGs with saliency maps in deep learning. STAMarker is a three-stage ensemble framework consisting of graph-attention autoencoders, multilayer perceptron (MLP) classifiers, and saliency map computation by the backpropagated gradient. We illustrate the effectiveness of STAMarker and compare it with serveral commonly used competing methods on various spatial transcriptomic data generated by different platforms. STAMarker considers all genes at once and is more robust when the dataset is very sparse. STAMarker could identify spatial domain-specific SVGs for characterizing spatial domains and enable in-depth analysis of the region of interest in the tissue section.

Spatiotemporal transcriptomic atlas reveals the dynamic characteristics and key regulators of planarian regeneration.

Whole-body regeneration of planarians is a natural wonder but how it occurs remains elusive. It requires coordinated responses from each cell in the remaining tissue with spatial awareness to regenerate new cells and missing body parts. While previous studies identified new genes essential to regeneration, a more efficient screening approach that can identify regeneration-associated genes in the spatial context is needed. Here, we present a comprehensive three-dimensional spatiotemporal transcriptomic landscape of planarian regeneration. We describe a pluripotent neoblast subtype, and show that depletion of its marker gene makes planarians more susceptible to sub-lethal radiation. Furthermore, we identified spatial gene expression modules essential for tissue development. Functional analysis of hub genes in spatial modules, such as plk1, shows their important roles in regeneration. Our three-dimensional transcriptomic atlas provides a powerful tool for deciphering regeneration and identifying homeostasis-related genes, and provides a publicly available online spatiotemporal analysis resource for planarian regeneration research.

SmartGate is a spatial metabolomics tool for resolving tissue structures.

Imaging mass spectrometry (IMS) is one of the powerful tools in spatial metabolomics for obtaining metabolite data and probing the internal microenvironment of organisms. It has dramatically advanced the understanding of the structure of biological tissues and the drug treatment of diseases. However, the complexity of IMS data hinders the further acquisition of biomarkers and the study of certain specific activities of organisms. To this end, we introduce an artificial intelligence tool, SmartGate, to enable automatic peak selection and spatial structure identification in an iterative manner. SmartGate selects discriminative m/z features from the previous iteration by differential analysis and employs a graph attention autoencoder model to perform spatial clustering for tissue segmentation using the selected features. We applied SmartGate to diverse IMS data at multicellular or subcellular spatial resolutions and compared it with four competing methods to demonstrate its effectiveness. SmartGate can significantly improve the accuracy of spatial segmentation and identify biomarker metabolites based on tissue structure-guided differential analysis. For multiple consecutive IMS data, SmartGate can effectively identify structures with spatial heterogeneity by introducing three-dimensional spatial neighbor information.

Heterogeneous Fenton oxidation of 2,4-dichlorophenol catalyzed by PEGylated nanoscale zero-valent iron supported by biochar.

The excessive use of herbicides and fungicides containing 2,4-dichlorophenol (2,4-DCP) has led to serious environmental water pollution; 2,4-DCP is chemically stable and difficult to be degraded effectively by biological and physical methods. And the degradation of 2,4-DCP using advanced oxidation techniques has been a hot topic. Biochar, polyethylene glycol, ferrous sulfate, and sodium borohydride were used to synthesize the heterogeneous catalyst PEGylated nanoscale zero-valent iron supported by biochar (PEG-nZVI@BC). The catalyst was characterized using scanning electron microscope (SEM) and other means to determine its physicochemical properties. Catalytic performance and mechanism of this catalyst with hydrogen peroxide for the oxidation of 2,4-DCP were investigated. The results showed that PEG-nZVI@BC had good dispersibility, stability, and inoxidizability; the degradation efficiency of 50 mg/L 2,4-DCP by PEG-nZVI@BC/H2O2 system 92.94%, 1.68 times higher than that of nZVI/H2O2 system; there are both free radical and non-free radical pathways in PEG-nZVI@BC/H2O2 system; the degradation process of 2,4-DCP includes hydroxylation, dechlorination, and ring-opening. Overall, PEG-nZVI@BC is a promising heterogeneous catalyst for the degradation of 2,4-DCP.

Bioremediation of petroleum hydrocarbon contaminated soil by microorganisms immobilized on sludge modified by non-ionic surfactant.

The immobilization of microorganisms on high-quality and inexpensive carriers to remediate oil-contaminated soil is an effective strategy for contaminated soil remediation. Due to the abundance in nutrients, large specific surface area, and fewer pathogens, the composting sludge is considered a high-quality immobilized material. Herein, two non-ionic surfactants, TW-80 and sophorolipid, were used to modify composted sludge. High-efficiency petroleum hydrocarbon-degrading bacteria groups selected in the laboratory were fixed on the modified composting sludge under optimal conditions. The immobilized material was placed in the soil contaminated by petroleum hydrocarbons at an additive amount of 2wt/%, and a simulated remediation experiment was performed for 90 days. Both soil properties and microbial structure were characterized. Surfactant-modified compost sludge enhances the adsorption capacity to petroleum hydrocarbon. The immobilized microorganisms in the modified compost sludge showed a good effect on the remediation of soil contaminated by petroleum hydrocarbons. In addition, immobilized materials also increase the diversity of the microbial community structure in the soil. High-efficiency petroleum hydrocarbon-degrading bacteria immobilized on surfactant-modified compost can effectively promote the degradation of petroleum hydrocarbons in the soil and increase the abundance of microorganisms in the soil. It shows the feasibility of eco-friendly remediation of hydrocarbon-contaminated soil.

Integrating spatial transcriptomics data across different conditions, technologies and developmental stages.

With the rapid generation of spatial transcriptomics (ST) data, integrative analysis of multiple ST datasets from different conditions, technologies and developmental stages is becoming increasingly important. Here we present a graph attention neural network called STAligner for integrating and aligning ST datasets, enabling spatially aware data integration, simultaneous spatial domain identification and downstream comparative analysis. We apply STAligner to ST datasets of the human cortex slices from different samples, the mouse olfactory bulb slices generated by two profiling technologies, the mouse hippocampus tissue slices under normal and Alzheimer's disease conditions, and the spatiotemporal atlases of mouse organogenesis. STAligner efficiently captures the shared tissue structures across different slices, the disease-related substructures and the dynamical changes during mouse embryonic development. In addition, the shared spatial domain and nearest-neighbor pairs identified by STAligner can be further considered as corresponding pairs to guide the three-dimensional reconstruction of consecutive slices, achieving more accurate local structure-guided registration than the existing method.

Deciphering the Spatial Modular Patterns of Tissues by Integrating Spatial and Single-Cell Transcriptomic Data.

Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to analyze the expression level of tissues at a cellular resolution. However, it could not capture the spatial organization of cells in a tissue. The spatially resolved transcriptomics technologies (ST) have been developed to address this issue. However, the emerging STs are still inefficient at single-cell resolution and/or fail to capture the sufficient reads. To this end, we adopted a partial least squares-based method (spatial modular patterns [SpaMOD]) to simultaneously integrate the two data modalities, as well as the networks related to cells and spots, to identify the cell-spot comodules for deciphering the SpaMOD of tissues. We applied SpaMOD to three paired scRNA-seq and ST datasets, derived from the mouse brain, granuloma, and pancreatic ductal adenocarcinoma, respectively. The identified cell-spot comodules provide detailed biological insights into the spatial relationships between cell populations and their spatial locations in the tissue.

Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder.

Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context of the tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we develop a graph attention auto-encoder framework STAGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles. To better characterize the spatial similarity at the boundary of spatial domains, STAGATE adopts an attention mechanism to adaptively learn the similarity of neighboring spots, and an optional cell type-aware module through integrating the pre-clustering of gene expressions. We validate STAGATE on diverse spatial transcriptomics datasets generated by different platforms with different spatial resolutions. STAGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns. Importantly, STAGATE could be extended to multiple consecutive sections to reduce batch effects between sections and extracting three-dimensional (3D) expression domains from the reconstructed 3D tissue effectively.

Joint reconstruction of cis-regulatory interaction networks across multiple tissues using single-cell chromatin accessibility data.

The rapid accumulation of single-cell chromatin accessibility data offers a unique opportunity to investigate common and specific regulatory mechanisms across different cell types. However, existing methods for cis-regulatory network reconstruction using single-cell chromatin accessibility data were only designed for cells belonging to one cell type, and resulting networks may be incomparable directly due to diverse cell numbers of different cell types. Here, we adopt a computational method to jointly reconstruct cis-regulatory interaction maps (JRIM) of multiple cell populations based on patterns of co-accessibility in single-cell data. We applied JRIM to explore common and specific regulatory interactions across multiple tissues from single-cell ATAC-seq dataset containing ~80 000 cells across 13 mouse tissues. Reconstructed common interactions among 13 tissues indeed relate to basic biological functions, and individual cis-regulatory networks show strong tissue specificity and functional relevance. More importantly, tissue-specific regulatory interactions are mediated by coordination of histone modifications and tissue-related TFs, and many of them may reveal novel regulatory mechanisms.