The molecular mechanism underlying brain regeneration in vertebrates remains elusive. We performed spatial enhanced resolution omics sequencing (Stereo-seq) to capture spatially resolved single-cell transcriptomes of axolotl telencephalon sections during development and regeneration. Annotated cell types exhibited distinct spatial distribution, molecular features, and functions. We identified an injury-induced ependymoglial cell cluster at the wound site as a progenitor cell population for the potential replenishment of lost neurons, through a cell state transition process resembling neurogenesis during development. Transcriptome comparisons indicated that these induced cells may originate from local resident ependymoglial cells. We further uncovered spatially defined neurons at the lesion site that may regress to an immature neuron–like state. Our work establishes spatial transcriptome profiles of an anamniote tetrapod brain and decodes potential neurogenesis from ependymoglial cells for development and regeneration, thus providing mechanistic insights into vertebrate brain regeneration.

An R loop is a unique triple-stranded structure that participates in multiple key biological processes and is relevant to human diseases. Accurate and comprehensive R loop profiling is a prerequisite for R loops studies. However, current R loop mapping methods generate large discrepancies, therefore an independent method is in urgent need. Here, we establish an independent R loop CUT&Tag (Tn5-based cleavage under targets and tagmentation) method by combining CUT&Tag and GST-His₆-2×HBD (glutathione S-transferase–hexahistidine–2× hybrid-binding domain), an artificial DNA-RNA hybrid sensor that specifically recognizes the DNA-RNA hybrids. We demonstrate that the R loop CUT&Tag is sensitive, reproducible, and convenient for native R loop mapping with high resolution, and find that the capture strategies, instead of the specificity of sensors, largely contribute to the disparities among different methods. Together, we provide an independent strategy for genomic profiling of native R loops and help resolve discrepancies among multiple R loop mapping methods.

R-loops play versatile roles in many physiological and pathological processes, and are of great interest to scientists in multiple fields. However, controversy about their genomic localization and incomplete understanding of their regulatory network raise great challenges for R-loop research. Here, we present R-loopBase (https://rloopbase.nju.edu.cn) to tackle these pressing issues by systematic integration of genomics and literature data. First, based on 107 high-quality genome-wide R-loop mapping datasets generated by 11 different technologies, we present a reference set of human R-loop zones for high-confidence R-loop localization, and spot conservative genomic features associated with R-loop formation. Second, through literature mining and multi-omics analyses, we curate the most comprehensive list of R-loop regulatory proteins and their targeted R-loops in multiple species to date. These efforts help reveal a global regulatory network of R-loop dynamics and its potential links to the development of cancers and neurological diseases. Finally, we integrate billions of functional genomic annotations, and develop interactive interfaces to search, visualize, download and analyze R-loops and R-loop regulators in a well-annotated genomic context. R-loopBase allows all users, including those with little bioinformatics background to utilize these data for their own research. We anticipate R-loopBase will become a one-stop resource for the R-loop community.

Glioblastoma (GBM) cells are highly heterogeneous and invasive, leading to treatment resistance and relapse. However, the molecular regulation in and distal to tumors remains elusive. Here, we collected paired tissues from the tumor core (TC) and peritumoral brain (PTB) for integrated snRNA-seq and snATAC-seq analyses. Tumor cells infiltrating PTB from TC behave more like oligodendrocyte progenitor cells than astrocytes at the transcriptome level. Dual-omics analyses further suggest that the distal regulatory regions in the tumor genome and specific transcription factors are potential determinants of regional heterogeneity. Notably, while activator protein 1 (AP-1) is active in all GBM states, its activity declines from TC to PTB, with another transcription factor, BACH1, showing the opposite trend. Combined inhibition of AP-1 and BACH1 more efficiently attenuates the tumor progression in mice and prolongs survival than either single-target treatment. Together, our work reveals marked molecular alterations of infiltrated GBM cells and a synergy of combination therapy targeting intratumor heterogeneity in and distal to GBM.

Single-cell sequencing technologies have advanced our understanding of cellular heterogeneity and biological complexity. However, existing methods face limitations in throughput, capture uniformity, cell size flexibility, and technical extensibility. We present Stereo-cell, a spatial enhanced-resolution single-cell sequencing platform based on high-density DNA nanoball (DNB)–patterned arrays, which enables scalable and unbiased cell capture at a wide input range and supports high-fidelity transcriptome profiling. Stereo-cell further allows integration with imaging-based modalities and multiomics strategies, including immunofluorescence and epitope profiling. This platform is also compatible with profiling extracellular vesicles, microstructures, and large cells, whereas its spatial resolution facilitates in situ analysis of cell-cell interactions, cellular microenvironments, and subcellular transcript localization. Together, Stereo-cell provides a flexible framework for expanding single-cell research applications.