Integrative analysis methods for spatial transcriptomics

Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data

Spatial transcriptomics technology has been an essential and powerful method for delineating tissue architecture at the molecular level. However, due to the limitations of the current spatial techniques, the cellular information cannot be directly measured but instead spatial spots typically varying from a diameter of 0.2 to 100 µm are characterized. Therefore, it is vital to apply computational strategies for inferring the cellular composition within each spatial spot. The main objective of this review is to summarize the most recent progresses in estimating the exact cellular proportions for each spatial spot, and to prospect the future directions of this field.

Platinum Nanoparticles Regulated V2C MXene Nanoplatforms with NIR-II Enhanced Nanozyme Effect for Photothermal and Chemodynamic Anti-Infective Therapy

Given the challenge of multidrug resistance in antibiotics, non-antibiotic-dependent antibacterial strategies show promise for anti-infective therapy. V2C MXene-based nanomaterials have demonstrated strong biocompatibility and photothermal conversion efficiency (PCE) for photothermal therapy (PTT). However, the limitation of V2C MXene's laser irradiation to the near-infrared region I (NIR-I) restricts tissue penetration, making it difficult to achieve complete bacterial eradication with single-effect therapeutic strategies. To address this, Pt nanoparticles (Pt NPs) are attached to V2C, forming artificial nanoplatforms (Pt@V2C). Pt@V2C exhibits enhanced PCE (59.6%) and a longer irradiation laser (NIR-II) due to the surface plasmon resonance effect of Pt NPs and V2C. Notably, Pt@V2C displays dual enzyme-like activity with chemodynamic therapy (CDT) and NIR-II enhanced dual enzyme-like activity. The biocatalytic mechanism of Pt@V2C is elucidated using density functional theory. In an in vivo animal model, Pt@V2C effectively eliminates methicillin-resistant Staphylococcus aureus from deep-seated tissues in subcutaneous abscesses and bacterial keratitis environments, accelerating abscess resolution and promoting wound and cornea healing through the synergistic effects of PTT/CDT. Transcriptomic analysis reveals that Pt@V2C targets inflammatory pathways, providing insight into its therapeutic mechanism. This study presents a promising therapeutic approach involving hyperthermia-amplified biocatalysis with Pt NPs and MXene nanocomposites.

Harnessing computational spatial omics to explore the spatial biology intricacies

Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.

De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data with DeepLinc

Based on a deep generative model of variational graph autoencoder (VGAE), we develop a new method, DeepLinc (deep learning framework for Landscapes of Interacting Cells), for the de novo reconstruction of cell interaction networks from single-cell spatial transcriptomic data. DeepLinc demonstrates high efficiency in learning from imperfect and incomplete spatial transcriptome data, filtering false interactions, and imputing missing distal and proximal interactions. The latent representations learned by DeepLinc are also used for inferring the signature genes contributing to the cell interaction landscapes, and for reclustering the cells based on the spatially coded cell heterogeneity in complex tissues at single-cell resolution.

Emerging artificial intelligence applications in Spatial Transcriptomics analysis

Spatial transcriptomics (ST) has advanced significantly in the last few years. Such advancement comes with the urgent need for novel computational methods to handle the unique challenges of ST data analysis. Many artificial intelligence (AI) methods have been developed to utilize various machine learning and deep learning techniques for computational ST analysis. This review provides a comprehensive and up-to-date survey of current AI methods for ST analysis.