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PubMed2026

SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomics dataset.

SUMMARY: Image-based spatial transcriptomics (iST) deliver gene expression measurements of RNA transcripts in tissue slices with single-molecule resolution and spatial context preserved. Modern Graph Neural Network (GNN) models are promising methods for capturing the complex molecular and cellular phenotypes in tissues at single-transcript and single-cell levels. A key application of GNNs is the detection of spatial domains or niches, that is, groups of molecules and/or cells that collaboratively work together to produce complex phenotypes. Due to the vast number of detected transcripts in (iST) dataset, applying GNNs on RNA molecule graphs is not trivial. We present a Python package, SpatialRNA, for easy (sub)graph generation from tissue samples and provide comprehensive tutorials for convenient and efficient application of Graph Neural Network models under the PyG framework. This highly scalable tool comprehensively segments tissue into spatial domains, aiding in biological interpretation of iST data and its underlying molecular microenvironments. AVAILABILITY AND IMPLEMENTATION: The SpatialRNA package is freely accessible from online repository https://github.com/ruqianl/spatialrna and can be installed via pip. Comprehensive tutorials, guidance on parameter selection, and complete workflows of case studies are available from the documentation website https://ruqianl.github.io/spatialrna_docs/, and uploaded on Zenodo with a DOI 10.5281/zenodo.17339575.

Neural Networks, Computer↗

PubMed2026

Volumetric DNA microscopy for mapping spatial transcriptomes in three dimensions.

The architecture and function of biological systems are inherently three-dimensional, yet most existing spatial transcriptomic technologies remain restricted to thin tissue sections, limiting their capacity to resolve cellular organization and microenvironments within intact tissue volumes. To address this limitation, we developed volumetric DNA microscopy, a scalable, optics-free approach for spatial transcriptome profiling directly within intact biological specimens. The method encodes spatial information into DNA molecules that form a dense intermolecular network in situ, enabling the reconstruction of three-dimensional spatial relationships through short-read sequencing and computational analysis. Here we detail the complete workflow including in situ cDNA synthesis, spatial encoding through DNA nanoball formation, dual-scale proximity bridging between neighboring nanoballs and spatial reconstruction via geodesic spectral embedding. Sequencing libraries can be generated within 7-8 d by a competent graduate-level molecular biologist, followed by standardized downstream computational analysis. Because the workflow requires only routine molecular biology reagents and a benchtop sequencer, volumetric DNA microscopy provides a versatile platform for exploring genetic and morphological features in intact tissues.

Spatial Transcriptomics↗

PubMed2026

A Practical Workflow for Spatial Transcriptomics Data Analysis: From Data Acquisition to Advanced Analyses.

Spatial transcriptomics (ST) profiles genome-wide gene expression while preserving the two-dimensional spatial context of mRNA molecules within tissue sections, enabling studies of tissue architecture and microenvironment-associated biology. However, ST analysis remains challenging because data import, quality control, integration, deconvolution, spatial statistics, and visualization often require multiple software environments and reproducible parameter choices. This protocol presents a practical computational workflow for public ST datasets in R, beginning with data acquisition and software setup and proceeding through Seurat-based data loading, quality control, normalization, multi-sample integration, clustering, and spatially variable gene analysis. The workflow then applies complementary deconvolution strategies, including reference-guided SPOTlight analysis and unsupervised STdeconvolve topic modeling, followed by Giotto-based spatial cell-cell communication analysis and interactive region-of-interest (ROI) selection using a custom Python Dash application. By emphasizing script-based execution, explicit parameter rationales, expected outputs, and troubleshooting checkpoints, the protocol provides an adaptable framework for standard array-based ST datasets and related platforms after dataset- and platform-specific parameter evaluation.

Spatial Transcriptomics↗

PubMed2026

Disentangling the cellular composition of FLCN-mutated tumors in Birt-Hogg-Dubé Syndrome by spatial transcriptomics.

Birt-Hogg-Dubé (BHD) syndrome is a hereditary cancer predisposition syndrome caused by pathogenic variants in the folliculin (FLCN) gene and is associated with an increased risk of multifocal renal tumors. FLCN-mutated tumors (FMTs) often exhibit morphological heterogeneity with mixed morphological features resembling renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC), yet the molecular basis underlying the heterogeneous morphologic features and the morphologic-genomic correlations remain poorly defined. In our prior work, we identified mutually exclusive expressions of L1 cell adhesion molecule (L1CAM) and forkhead box I1 tboxI1 (FOXI1) labeling the two morphologically distinct cellular populations in BHD-associated FMTs, leading to the hypothesis that these two tumor compartments may have distinct molecular features and may reflect different nephron epithelial differentiation states. In this follow-up study, we tested this hypothesis using L1CAM and FOXI1 as morphology-guided markers for spatial transcriptomic profiling of the distinct tumor compartments in FMTs with the NanoString GeoMX Digital Spatial Profiler (DSP). Six FMTs from three patients with BHD and three normal kidney tissues were analyzed. L1CAM+ and FOXI1+ area of interest (AOI) were collected from tumor areas with various tumor compositions, including L1CAM+ dominant, FOXI1+ dominant, and mixed tumor areas. Spatial transcriptomic analysis identified distinct gene expression signatures in L1CAM+ and FOXI1+ FMT compartments independent of the local tumor compositions. FOXI1+ tumor cells showed robust enrichment for intercalated cells (IC)-associated gene signatures. In contrast, L1CAM+ tumor cells exhibited a heterogeneous transcriptional profile, with partial overlap across a spectrum of renal tubular epithelial cell types rather than a definitive principal cell-like identity. Despite this compartment-specific differences, both compartments share expression of a panel of tumor signature genes, including glycoprotein nmb (GPNMB) gene, and a core of cancer related biological functions and signaling pathways. Together, these findings refined the prior dichotomous model of BHD-associated renal tumors and support a model in which L1CAM+ and FOXI1+ tumor compartments represent divergent evolutionary or differentiation states with a common FLCN-mutant neoplastic transcriptional program. This spatial transcriptomic profiling provides molecular evidence for the morphological heterogeneity of FMTs and insights on the tumor biology of BHD-associated FMTs.

Birt-Hogg-Dubé↗

PubMed2025

SpaceBar enables clone tracing in spatial transcriptomic data.

We report a cellular barcoding strategy, SpaceBar, that enables simultaneous clone tracing and spatial transcriptomics profiling. Our approach uses a library of 96 synthetic barcode sequences that can be robustly detected by imaging based spatial transcriptomics (seqFISH), delivered such that each cell is labeled with a combination of barcodes. We used these barcodes to label melanoma cells in a tumor xenograft model and profiled both clone identity and spatial gene expression in situ. We developed a gene scoring metric that quantifies how strongly gene expression is driven by intrinsic cellular cues or extrinsic environmental signals. Our framework distinguishes between clonal dynamics and environmentally-driven transcriptional regulation in complex tissue contexts.

Journal Article↗

PubMed2025

Spatial mutual nearest neighbors for spatial transcriptomics data.

MOTIVATION: Mutual nearest neighbors (MNN) is a widely used computational tool to perform batch correction for single-cell RNA-sequencing data. However, in applications such as spatial transcriptomics, it fails to take into account the 2D spatial information. RESULTS: Here, we present spatialMNN, an algorithm that integrates multiple spatial transcriptomic samples and identifies spatial domains. Our approach begins by building a k-nearest neighbors (kNN) graph based on the spatial coordinates, prunes noisy edges, and identifies niches to act as anchor points for each sample. Next, we construct a MNN graph across the samples to identify similar niches. Finally, the spatialMNN graph can be partitioned using existing algorithms, such as the Louvain algorithm to predict spatial domains across the tissue samples. We demonstrate the performance of spatialMNN using large datasets, including one with N = 31 10x Genomics Visium samples. We also evaluate the computing performance of spatialMNN to other popular spatial clustering methods. AVAILABILITY AND IMPLEMENTATION: Our software package is available on GitHub (https://github.com/Pixel-Dream/spatialMNN). The code is available on Zenodo (https://doi.org/10.5281/zenodo.15073963).

Algorithms↗

PubMed2026

Identification of cryosensitive niches and a targetable FOS/AP‑1 program in the human ovarian cortex by single‑cell and spatial transcriptomics.

BACKGROUND: The ovary is a vital and dynamic reproductive organ. Ovarian tissue cryopreservation (OTC) plays a vital role in preserving female fertility. However, the cellular subtypes most susceptible to cryoinjury and the molecular mechanisms underlying cryopreservation-associated damage remain poorly understood. This study aimed to identify cell populations vulnerable to freezing-thawing and to elucidate the key transcriptomic alterations and signaling pathways associated with ovarian cryoinjury at the single-cell and spatial levels. METHODS: Ovarian cortical tissues from patients undergoing three gender reassignment surgery (GRS) were divided into fresh and vitrification-rapid warming groups. Following collagenase IV digestion, 10x Genomics single-cell RNA-seq was used for dissociated ovarian cell suspensions (27,185 fresh and 25,480 frozen-thawed cells). Eight major cell clusters were identified. Additionally, 110 oocytes (66 fresh, 44 vitrification-rapid warming) were isolated and analyzed using the Smart-seq2 platform. Spatial transcriptomics was performed via BGI Stereo-seq. Molecular validation was performed via β-galactosidase staining, immunofluorescence, and qRT-PCR. RESULTS: Cryopreservation significantly altered the activity of pathways related to focal adhesion, oxidative stress, and apoptosis, particularly in stromal and perivascular cells. The number of FOS-positive perivascular cells was notably increased after vitrification-rapid warming, whereas the number of PTGDS-positive stromal cells decreased. Oocyte analysis revealed that cryopreservation primarily disrupted pathways involved in the cell cycle and meiosis, although the damage was not irreversible, supporting the relative safety of long-term cryostorage. Spatial transcriptomics and functional validation further confirmed the rapid and robust activation of the FOS/AP-1 pathway after vitrification-rapid warming, particularly in perivascular and granulosa cells. Treatment with T-5224 (a FOS/AP-1 inhibitor) significantly rescued the morphology and function of cultured frozen-thawed ovaries. CONCLUSIONS: Stromal and perivascular cells are the main cell types that are sensitive to ovarian cryopreservation. The FOS/AP-1 pathway is markedly activated after, suggesting the exacerbation of metabolic impairment. In oocytes within the ovarian cortex, the cell cycle and meiosis-related physiological processes were the primary processes affected.

Female↗

PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗

PubMed2026

Decoding regional keratinization in human oral mucosa through high-resolution spatial transcriptomics.

Oral mucosa exhibits region-specific keratinization, essential for periodontal health, yet the spatial and molecular mechanisms driving these differences remain poorly understood. This study aimed to generate a high-resolution spatial transcriptomic atlas of the human oral mucosa around the mucogingival junction, to reveal stromal-epithelial interactions, that distinguish keratinized from non-keratinized programs. Formalin-fixed paraffin-embedded specimens from the mucogingival junction area of two healthy donors were analyzed with the 10 × Genomics Visium HD platform, yielding two keratinized and two non-keratinized regions. Spatial clustering, pseudotime trajectory inference, cell-type integration with a single-cell reference, and ligand-receptor network analysis were applied to delineate epithelial and stromal compartments. Sixteen reproducible clusters, recapitulating tissue architecture, were identified and revealed distinct transcriptional signatures, distinguishing gingiva from lining mucosa. Pseudotime analysis revealed bifurcating epithelial lineages, originating from a shared basal progenitor layer into keratinized and non-keratinized programs. Gingival keratinization was driven by stromal collagen ligands (COL1A1, COL1A2, COL6A1, COL6A2) engaging epithelial receptors (CD44, SDC1), further reinforced within the epithelium by desmosomal adhesion via DSG1-DSC2/3. Gingival keratinization emerges from integrated stromal collagen signaling and epithelial adhesion. This spatially resolved framework advances understanding of oral mucosal specialization and provides a foundation for biologically guided regenerative therapies.

Humans↗

PubMed2026

Single-cell and spatial transcriptomics define a progenitor subpopulation and fibroinflammatory niche at the leading edge of parathyroid carcinoma.

Parathyroid carcinoma (PC) is a rare but clinically aggressive endocrine malignancy with limited treatment options and a poorly defined tumor microenvironment (TME). To elucidate its cellular heterogeneity and spatial architecture, we integrated single-cell and spatial transcriptomic profiling with whole-exome sequencing and multiplex immunohistochemistry on eight parathyroid neoplasm specimens, including PC, parathyroid adenoma, and atypical parathyroid tumor. We identified a distinct progenitor-like endocrine subpopulation (Ca-1) enriched in CDC73-mutant PC, exhibiting stem-like properties, elevated cell cycle activity, and pronounced genomic instability. Spatial mapping revealed that Ca-1 cells preferentially localize at the leading edge, forming a fibroinflammatory niche characterized by the enrichment of inflammatory cancer-associated fibroblasts (iCAFs) and SPP1+ macrophages. Within this niche, the dipeptidyl peptidase 4 (DPP4) is selectively expressed in Ca-1 cells and iCAFs, implicating a potential paracrine axis driving stromal remodeling and immunosuppression. These findings suggest that a spatially organized ecosystem may promote PC progression through TME remodeling and highlight the DPP4-CXCL2 axis as a candidate pathway for future investigation in aggressive parathyroid neoplasms.

Humans↗

PubMed2026

Integrated Pan-Cancer, Single-Cell, and Spatial Transcriptomic Analyses Identify ZDHHC12 as a Biomarker Associated with Macrophage Infiltration and the Immune Landscape in Glioma.

BACKGROUND: The tumor immune microenvironment (TME) critically influences cancer progression and therapeutic response. However, the pan-cancer expression landscape, prognostic relevance, and spatial distribution of ZDHHC12 remain incompletely characterized. This study investigated the prognostic value of ZDHHC12 and its associations with immune microenvironmental features and drug sensitivity. METHODS: Data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets were used to evaluate ZDHHC12 expression and prognosis across cancer types. Immune infiltration analyses, single-cell RNA sequencing, and spatial transcriptomics were integrated to characterize the associations of ZDHHC12 with the cancer immunity cycle and the spatial architecture of glioma. Drug sensitivity and immunotherapy-related metrics were assessed using pharmacogenomic databases and computational prediction models. RESULTS: ZDHHC12 was aberrantly expressed across multiple tumors and was associated with patient prognosis. Its expression was broadly correlated with immune cell recruitment- and activation-related signatures. In glioma, single-cell and spatial transcriptomic analyses showed enrichment of ZDHHC12 in monocyte/macrophage populations and spatial co-localization with BAK1, CD68, and CD163. ZDHHC12 expression was also associated with predicted drug sensitivity and immunotherapy-related metrics. CONCLUSION: ZDHHC12 may serve as a candidate pan-cancer prognostic biomarker. In glioma, its expression is associated with macrophage-enriched and immunosuppressive microenvironmental features. Functional studies are required to establish causality and determine its therapeutic relevance.

GBM↗

PubMed2026

Deciphering CD8+ T cell exhaustion in human cancers through single-cell and spatial transcriptomics.

Exhausted CD8+ T cells (Tex) within the tumor microenvironment (TME) represents a critical barrier limiting anti-tumor immune responses. Tex cells are characterized by upregulated inhibitory immune checkpoint receptors, reduced cytotoxicity, and functional heterogeneity. Their genomic features and regulatory networks remain poorly defined, and only a minority of patients respond to immune checkpoint blockade (ICB) therapy. Single-cell RNA sequencing (scRNA-seq), through high-resolution transcriptomic profiling, has revealed diverse Tex subpopulations, identified subpopulation-specific marker genes and regulatory pathways. Spatial transcriptomics has further mapped the spatial distribution of Tex and their interaction networks with immune cells, tumor cells, and stromal cells, elucidating the impact of spatial heterogeneity on Tex functionality. Current studies indicate that the exhausted state of Tex is dynamic and modifiable, with functional differences among subpopulations closely associated with tumor progression and therapeutic response. However, the genomic characteristics, epigenetic regulation, and spatial interaction mechanisms of Tex require further exploration. This review summarizes recent advances in high-resolution omics technologies for precisely dissecting Tex heterogeneity, functional features, and interactions with other cells. It emphasizes the central value of optimizing Tex-targeted tumor immunotherapy strategies, providing theoretical foundations and directional guidance for developing more effective anti-tumor immunotherapies.

Humans↗

PubMed2024

PoweREST: Statistical Power Estimation for Spatial Transcriptomics Experiments to Detect Differentially Expressed Genes Between Two Conditions.

Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.

Differentially expressed genes↗

PubMed2026

A single-nucleus and spatial transcriptomic atlas of poplar leaves reveals the regulation of leaf polarity and cuticle deposition.

Leaf adaxial-abaxial polarity is fundamental for plant morphogenesis and environmental adaptation through asymmetric cell differentiation. Emerging evidence reveals dorsoventral metabolic gradients act downstream of transcriptional networks to fine-tune cellular specialization. While conserved transcription factors (e.g., HD-ZIP III and KANADI) establish initial polarity, the molecular networks driving position-specific cellular differentiation and their integration with metabolic adaptation remain unclear. Leveraging single-nucleus and spatial transcriptomics, we resolve major cell classes (mesophyll, epidermal, and vascular-associated) and their adaxial-abaxial subtypes, revealing dorsoventral polarity in transcriptional profiles and metabolic pathways. Adaxial cells are enriched in phenylpropanoid/flavonoid biosynthesis, while abaxial cells show preferential activation of stress and hormone signaling. Notably, we identify MYC2 as a key regulator of adaxial cuticle biosynthesis, binding to promoters of lipid biosynthetic and transport genes (e.g., CER10 and LTPG1) and promoting cuticle thickening. Our study uncovers how positional identity shapes transcriptional and metabolic polarity in leaves, with MYC2 emerging as a central regulator coordinating organ-specific adaptations. These findings provide insights into the spatial regulation of plant development and stress resilience, offering potential strategies for engineering stress-tolerant woody crops.

Plant Leaves↗

PubMed2026

Genetic and epigenetic underpinnings of biological aging: a multi-omics study integrating Mendelian randomization, spatial transcriptomics, and drug target discovery.

Inflammaging represents a hallmark of biological aging, yet the causal inflammatory mediators driving multi-dimensional epigenetic aging and their effector genes remain poorly characterized at the genetic level. We developed a four-tier analytical framework integrating causal screening, multi-omics effector gene mapping, spatial transcriptomics, and drug target evaluation. Two-sample Mendelian randomization (MR) of 91 circulating inflammatory proteins against six aging phenotypes identified IL-12B, IFNG, and IL-2 as the most robust pro-aging mediators with consistent effects across independent outcomes. Using multi-omics summary-based MR (SMR) as the core analytical engine, we integrated four-layer whole-blood molecular QTL resources eQTL (eQTLGen, n = 31,684), sQTL (GTEx, n = 755), pQTL (INTERVAL + SCALLOP, n = 34,232), and mQTL (McRae et al., n = 1,980) - with GWAS summary statistics for four epigenetic age acceleration measures. At a stringent threshold (P_SMR < 1×10⁻¹²), seven high-confidence effector genes were identified: NHLRC1, TPMT, SELP, and RIPPLY3 for IEAA; ZNF373A and PLDN for HannumAA; and EDARADD for PhenoAA. The chromosome 6p21 NHLRC1-TPMT locus, overwhelmingly driven by methylation QTL signals (-log₁₀P = 26.06), emerged as the dominant genetic node of epigenetic aging. Spatial projection via gsMap onto a mouse E16.5 embryo atlas (121,767 cells) revealed preferential enrichment in smooth muscle and lung, with EDARADD showing marked specificity in mucosal epithelium. Cross-database drug target mining classified TPMT and SELP as repurposable known targets and NHLRC1 as a high-priority novel druggable candidate. This study provides multi-omics convergent causal evidence for inflammation-driven epigenetic aging and delivers genetically anchored targets for precision anti-aging intervention.

Aging↗

PubMed2026

Spatial transcriptomics of primary and metastatic ALK-rearranged NSCLC reveals site-specific adaptations.

INTRODUCTION: Genetic alterations and the tumor microenvironment (TME) influence treatment response in anaplastic lymphoma kinase-rearranged non-small cell lung cancer (ALK+ NSCLC). This study maps site-specific TME adaptations and exploratory risk-associated signatures in lymph node metastases (LNT) to investigate metastatic evolution. METHOD: We applied spatial transcriptomics to profile tumor (PanCK+) and stromal (PanCK-) compartments in a pilot cohort of 16 cases: primary lung tumors (LT, n = 3), LNT (n = 10), and brain metastases (BT, n = 3), with three site-matched non-tumor controls. LNT-derived prognostic signatures were evaluated using The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA LUAD) cohorts. RESULTS: Distinct, site-specific TME features were observed. LNT stroma was enriched in fibroblasts and macrophages, while tumor segments showed increased neutrophils. BT exhibited a macrophage-associated immunosuppressive TME. Tumor cells evolved divergently: LT retained pulmonary identity and showed trend towards translation-associated programs, LNT cells shifted toward senescence and epigenetic remodeling, and BT cells showed activation of Class A/1 (Rhodopsin-like) receptor, GPCR and drug metabolism pathways. In LNT, exploratory risk-associated differences were observed. Low-risk cases (n = 6) showed adaptive immune signatures, whereas high-risk cases (n = 4) showed enrichment for stromal MET signaling and stress-response pathways. Because treatment exposure differed markedly between the risk groups, these observations should be interpreted as hypothesis-generating. TCGA LUAD analysis suggested the broader biological relevance of immune-associated markers, but reflected general LUAD rather than ALK+ specific biology. Discordant associations for GCLC and TIMP1 underscored the importance of spatial context. CONCLUSION: Site-specific microenvironments may influence tumor adaptation across metastatic niches in ALK+ NSCLC. The exploratory risk-associated findings require validation in larger, uniformly treated cohorts.

Humans↗

PubMed2026

An integrated single-cell and spatial transcriptomic atlas of thyroid cancer progression identifies prognostic fibroblast subpopulations.

Although well-differentiated thyroid carcinoma (WDTC) is characterized by a robust treatment response, aggressive subtypes, such as anaplastic thyroid carcinoma (ATC), remain highly lethal. To understand thyroid cancer evolution in both children and adults, we analyzed single-cell transcriptomes of 423,733 cells from 81 samples and spatially resolved key tumor and microenvironment populations across 28 tumors with spatial transcriptomics, including rare and unique composite WDTC/ATC tumors and pediatric diffuse sclerosing thyroid carcinomas. Additionally, we identified gene signatures of stromal cell populations in 5 large thyroid cancer bulk RNA-sequencing cohorts. Through this multi-institutional effort, we defined a population of POSTN+ myofibroblast cancer-associated fibroblasts (myCAFs) that are intimately associated with invasive tumor cells and correlate with poor prognosis, lymph node metastasis, and disease progression in thyroid carcinoma. We also revealed a population of inflammatory CAFs that are distant to tumor cells and are found in the inflammatory stromal microenvironment of autoimmune thyroiditis. Together, our study provides spatial profiling of thyroid cancer evolution in samples with mixed WDTC/ATC histopathology and identifies a prognostic myCAF subtype with potential clinical utility in predicting aggressive disease in both children and adults.

Humans↗

PubMed2025

PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.

Recent advancements in spatial transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost for current ST data generation techniques restricts the large-scale application of ST. Consequently, maximization of the use of available resources to achieve robust statistical power for ST data is a pressing need. One fundamental question in ST analysis is detection of differentially expressed genes (DEGs) under different conditions using ST data. Such DEG analyses are performed frequently, but their power calculations are rarely discussed in the literature. To address this gap, we developed PoweREST, a power estimation tool designed to support the power calculation for DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments and after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application that allows users to interactively calculate and visualize study power along with relevant parameters.

Gene Expression Profiling↗