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a, Predicted RNA secondary structures of circXPO1(2,3,4) generated by RNAfold, showing stem-loops flanking the BSJ site (black line). Sequences 50 nt upstream and downstream of BSJ are highlighted in yellow. b, Workflow for identifying RBPs associated with circXPO1(2,3,4). circRNA sequence and RBP binding data were obtained from CIRCpedia, ATtRACT and POSTAR3. Sixteen RBPs from ENCODE eCLIP-seq were selected; cross BSJ reads were mapped to a 100 nt region centered on the BSJ. c, RBPs mapped to the circXPO1(2,3,4) BSJ region (left) and linear XPO1 exon 2 and 4 (right), based on eCLIP-seq data from HepG2 and K562 cell lines, showing less RBP binding on circRNA than linear RNA. d, Schematic of the circFISH probe design for distinguishing circXPO1(2,3,4) from linear XPO1. Gold550-labeled probes target exons 2–4 (shared); Red650-labeled probes hybridize to linear-specific exons. e, Representative images of circXPO1(2,3,4) in HeLa cells. Transcription site (yellow arrow) and circRNA specific focus (white arrow). Nuclear boundary (blue dashed); cellular boundary (white dashed). Magenta, exons 2–4 (Gold550); green, linear specific exons (Red650). f, Statistics of Gold550-only puncta per cell from circFISH shown in e. Data are mean ± s.e.m.; n = 41 cells. g, Comparison of circXPO1(2,3,4) signal numbers between circFISH and abcFISH. abcFISH...

The authors developed spatial NT-seq to map the landscape of RNA turnover and stability regulation in the mouse brain, identifying the dentate gyrus as a turnover hotspot and revealing region-specific...

Nature Reviews Genetics, Published online: 28 October 2026; doi:10.1038/s41576-026-01030-5In this Tools of the Trade article, Yuemeng Jia describes SPACE-seq. By combining lineage tracing with spatial transcriptomics, SPACE-seq connects lineage history to molecular state and tissue context, enabling insight into how tissue niches shape cell fate and clonal behaviour.

Madrid, Spain, November 4, 2026 - MGI Tech Co., Ltd. (“MGI”), a company committed to developing core tools and technologies that drive innovation in life sciences, today announced the Genomics Unit at the Madrid Science Park (Parque Científico de Madrid, PCM) has become the first STOmics Certified Service Provider (CSP) in Madrid and the second at the national level in Spain. This collaboration brings Stereo-seq spatial transcriptomics capabilities to the Spanish R&D ecosystem, offering researchers, hospitals, and deep-tech companies access to unbiased, whole-transcriptome spatial analysis at true cellular and subcellular resolution. Image Credit: MGI Through this new certification, PCM will offer Stereo-seq (Spatial Enhanced Resolution Omics-sequencing) based on DNBSEQ technology, enabling unbiased, whole-transcriptome spatial analysis at true cellular and subcellular resolution(500 nm). By mapping gene expression while preserving tissue context, the platform overcomes a key limitation of traditional genomics and is expected to accelerate advances in precision oncology, neuroscience, embryology, and plant biology. To ensure robustness and reproducibility, the new service is delivered through an end-to-end collaboration with the Histology and Microscopy Services of the National Center for Biotechnology (CNB-CSIC). This integrated workflow spans tissue processing and imaging through to sequencing and bioinformatic analysis, providing a complete solution for research...

In this Tools of the Trade article, Yuemeng Jia describes SPACE-seq. By combining lineage tracing with spatial transcriptomics, SPACE-seq connects lineage history to molecular state and tissue context...

Nature Reviews Genetics, Published online: 28 October 2026; doi:10.1038/s41576-026-01030-5In this Tools of the Trade article, Yuemeng Jia describes SPACE-seq. By combining lineage tracing with spatial transcriptomics, SPACE-seq connects lineage history to molecular state and tissue context, enabling insight into how tissue niches shape cell fate and clonal behaviour.

The Kidney Tissue Atlas is a set of interactive tools built to promote retrieval, exploration, discovery, and analysis of the KPMP data by the greater research community.

We performed snRNA-seq on SN tissue from eight postmortem donors (4 CON, 4 PD), isolated nuclei by FACS and profiled gene expression using the 10x Chromium platform (Fig. 1a, Supplementary Table 1). Complementary spatial transcriptomics using Visium was conducted on six sections (1 CON, 2 iLBD, and 3 PD), which was guided by immunofluorescence imaging for α-Syn and neuronal markers (HuC/D and NeuN) and regional validation with tyrosine hydroxylase (TH), calretinin, calbindin, H&E, and Luxol Fast Blue (LFB) staining (Supplementary Fig. 1a). Unsupervised clustering of the snRNA-seq data revealed eight major cell types (Fig. 1b, Supplementary Table 2). Cells from CON and PD samples were broadly intermingled across clusters (Fig. 1c), and the proportions of cell types were comparable across conditions (Fig. 1d), although PD samples had relatively reduced numbers of endothelial cells and increased numbers of T cells (Fig. 1e). Cell-type annotations were validated via label transfer to a reference human SN dataset17 (Supplementary Fig. 2). Two vascular cell types were resolved, with endothelial cells marked by CLDN5 and mural cells marked by PDGFRB (Fig. 1f). Microglia included a subset of MRC1+ brain-resident macrophages.18 Spatial clustering of the Visium profiles revealed 15 spatial clusters (SC0–SC14) using scVI, providing robust...

We evaluated 11 segmentation methods (Watershed, BIDCell, Cellpose, SCS, SAM, SAM2, mSAM, Baysor, CelloType, Swin-s and Mask R-CNN) across 15 spatial transcriptomics datasets generated using 5 different technologies: Stereo-seq, Xenium, CosMx, STARmap PLUS and MERFISH (Fig. 1a, Supplementary Table 1 and Methods). The datasets span two species (mouse and human), 12 tissue types (brain, breast, embryo, lung, pancreas, skin, colon, stomach, ovarian, kidney, prostate and utero) and a wide range of cell counts, from around 700 cells in an ovarian cancer dataset to large cohorts of more than 800,000 cells in a prostate cancer dataset. We selected this diversity to reflect the breadth of contemporary spatial transcriptomics applications and to challenge segmentation robustness under heterogeneous biological and technical conditions (Supplementary Fig. 1a,b). We also performed cross-technology benchmarking (Fig. 1b) to account for discrepancies between platforms. This design separated algorithm-specific effects from platform-dependent biases. We conducted all the analysis using a unified benchmarking pipeline (Fig. 1c) that applies standardized quality control (QC), patch-based processing and consistent preprocessing across methods to ensure fair and reproducible comparisons. During preprocessing, we established data subsetting criteria for each dataset and method based on technical limitations (Methods). For methods that explicitly integrate transcriptomic information into their...

Single-cell RNA-sequencing (scRNA-seq) atlases of zebrafish development have catalogued marker genes for hundreds of cell types, but transcriptomic data alone cannot reveal their expression patterns i...

Experimental lineage tracing reveals histories and fates of individual cells but is costly and often impractical, particularly in primary human tissues. Gene expression differences that persist across...

Rhizobial nitrogen fixation in legume nodules requires precise spatial and temporal coordination between host and bacterial gene expression. Here, we applied spatial enhanced resolution omics sequencing (Stereo-seq) to profile Medicago nodules across five developmental stages, generating a co-transcriptomic atlas of nodule development. Using a mixed-probe capture strategy, we simultaneously detected host and rhizobial transcripts within the same tissue sections, enabling analysis of host-microbe transcriptional coordination during symbiosis. We identified dynamic changes in host cell composition and developmental trajectories and uncovered two transcription factors, MtERF82 and MtDOF53, required for maintenance of the indeterminate nodule meristem. Rhizobial and host genes exhibited stage- and zone-specific expression patterns along the nodule apex-to-base axis. Notably, reactive oxygen species (ROS)-associated stress responses in both host cells and rhizobia were linked to symbiotic nitrogen fixation. Functional analyses further showed that MtERF71 and MtCBF1 promote nitrogen fixation by regulating ROS homeostasis and activating key symbiotic genes, highlighting stress signaling as an integral component of mutualistic symbiosis.

Read the full paper: Integrated scRNA‐Seq and Spatial Transcriptomics Reveal CHPF and GPC1 as Markers of Cancer‐Associated Fibroblast–Dominant Domains in Colorectal Cancer

The progression from ductal carcinoma in situ (DCIS) to invasive breast carcinoma (IBC) critically determines patient outcomes, yet its mechanisms remain incompletely understood. Integrating single-cell RNA sequencing, spatial transcriptomics, and genomics across 28 patients with synchronous DCIS and IBC, we delineate the spatial-molecular hierarchy of this transition. Invasion is primarily driven by clonal expansion of pre-existing DCIS subclones, emphasizing transcriptional reprogramming and tumor microenvironment (TME) remodeling over acquisition of additional driver alterations. IBC cells exhibit pronounced epithelial-mesenchymal transition and metabolic reprogramming. We uncover dynamic TME remodeling at the invasive front, identifying key ligand–receptor interactions (e.g., PPIA-BSG, MDK-LRP1, CXCL12-CXCR4) facilitating basement membrane disruption, angiogenesis and immunosuppression. Deconvolution of basement membrane breach reveals four molecularly defined stages (NMFT1–NMFT4)