Cell Neural Networks on Spatial Transcriptomics (CellNEST) deciphers patterns of communication between cells in spatially resolved transcriptomics data and can detect both signals between individual c...

SpotSweeper is a spatially aware method for quality control of spatially resolved transcriptomics data that corrects for spatial confounding missed by existing methods, including both local and region...

This review explores how stroke induces changes in gene expression, cell behavior and brain networks, offering insights into recovery. It highlights strategies to enhance cellular reprogramming and br...

Spatial transcriptomics provides researchers with a powerful tool to investigate gene expression patterns within their native positional context in tissues, revealing intricate cellular relationships....

OmiCLIP is a visual–omics foundation model that integrates histology and spatial transcriptomics. The associated Loki platform offers accurate and robust tools for alignment, annotation, cell-type dec...

A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications. Ho...

In this review, Weber, Rust, and Tackenberg discuss the potential mechanisms by which neural stem/progenitor cell therapy can induce brain recovery after ischemic stroke. They argue that neural stem/p...

Proseg is a segmentation approach for single-cell spatially resolved transcriptomics data that uses unsupervised probabilistic modeling of the spatial distribution of transcripts to accurately segment...

Xiang et al. generated and demonstrate the utility of a spatiotemporal transcriptomic atlas of intracerebral hemorrhage (ICH) at single-cell resolution across 9 time points between 3 hours and 28 days...

Currently, there is an urgent need to evaluate the strengths and limitations of various probe-based full transcriptome methods for formalin-fixed paraffin-embedded tumor tissues. Here, the authors ana...

As the next generation of spatial transcriptomics tools hits the market, researchers are uncovering previously unknown interactions that could transform clinical research.

Single-cell sequencing reveals cellular heterogeneity but is challenged by technical noise and batch effects. Here, authors present CellFM, an 800-million-parameter foundation model trained on 100 mil...

Sankowski and Prinz propose a classification framework for microglia states that considers the contextual plasticity of microglia. Their multimodal classification aligns a robust terminology with biological function and cellular context.

Cancer cells display highly heterogeneous and plastic states in glioblastoma, an incurable brain tumour. However, how these malignant states arise and whether they follow defined cellular trajectories...

Single-cell sequencing technologies have revealed the transcriptional diversity of microglia, the brain-resident macrophage population. In this issue, Stevens and colleagues discuss how microglia adap...

Multiplexed error-robust fluorescence in situ hybridization (MERFISH) together with deep-learning-based nucleus segmentation enabled the construction of a highly detailed and informative spatially res...

Cell-type deconvolution methods are often needed to analyse spatial transcriptomic data to recover cell-type distributions. In this Review, the authors describe the process of cell-type deconvolution,...

Large-scale single‑cell atlas efforts have revealed many aging‑ or disease‑associated cell types, yet these populations are often underrepresented in heterogeneous tissues, limiting detailed molecular...

Single-nucleus RNA sequencing can be applied to FFPE tissues, but isolating intact nuclei remains challenging. Here the authors develop a low-temperature enzymatic dissociation strategy for rapid, hig...

The rapid advancement of spatial transcriptomic technologies, particularly in situ hybridization based methods, has enabled the profiling of gene expression at sub cellular resolution across large tis...

Transcript diversity including splicing and alternative 3'end usage is crucial for cellular identity and adaptation, yet its spatial coordination remains poorly understood. Here, we present SPLISOSM (...

Single-cell technologies now span multiple modalities, generating large, complex datasets that challenge analysis and sharing. We present ShinyCell2 , an enhanced R package for interactive visualisati...

Pericytes in the central nervous system are essential for maintaining blood-brain barrier function, regulating blood flow, modulating immune responses, and interacting closely with surrounding cells o...

In this Perspective, Smyth and Kipnis reappraise the concept of immune privilege in the central nervous system. Although immune privilege was originally thought to involve isolation of the central ner...

Developing new medications can take years. But computational biologist Aviv Regev says AI-powered cell mapping is transforming the process—helping to create life-saving drugs in much less time.

Advances in AI are transforming scientific discovery, yet spatial biology, a field that deciphers the molecular organization within tissues, remains constrained by labor-intensive workflows. Here, we ...

Papetti et al. discuss recent advances in understanding human neural development, aging, and disease uncovered through human-rodent chimeric brain models and examine their potential to drive progress ...

Spatial omics technologies offer insights into the organization of cellular and molecular components and their interactions within the tumor ecosystem. Overcoming the key challenges to integrating the...