Encompassing view of spatial and single-cell RNA sequencing renews the role of the microvasculature in human atherosclerosis
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Oral microbiome diversity associates with carotid intima media thickness in middle-aged male subjects
Although there have been significant advancements in reducing the burden of cardiovascular disease (CVD) by modifying traditional CVD risk factors, substantial risks persist, particularly among male subjects who exhibit heightened susceptibility to atherosclerosis. In this context, we aim to study the link between oral microbiome and carotid intima media thickness (cIMT).
Stromal architecture and fibroblast subpopulations with opposing effects on outcomes in hepatocellular carcinoma
Dissecting the spatial heterogeneity of cancer-associated fibroblasts (CAFs) is vital for understanding tumor biology and therapeutic design. By combining pathological image analysis with spatial proteomics, we revealed two stromal archetypes in hepatocellular carcinoma (HCC) with different biological functions and extracellular matrix compositions. Using paired single-cell RNA and epigenomic sequencing with Stereo-seq, we revealed two fibroblast subsets CAF-FAP and CAF-C7, whose spatial enrichment strongly correlated with the two stromal archetypes and opposing patient prognosis. We discovered two functional units, one is the intratumor inflammatory hub featured by CAF-FAP plus CD8_PDCD1 proximity and the other is the marginal wound-healing hub with CAF-C7 plus Macrophage_SPP1 co-localization. Inhibiting CAF-FAP combined with anti-PD-1 in orthotopic HCC models led to improved tumor regression than either monotherapy. Collectively, our findings suggest stroma-targeted strategies for HCC based on defined stromal archetypes, raising the concept that CAFs change their transcriptional program and intercellular crosstalk according to the spatial context.
Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics
The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tumor classification. A limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity, which restrict its feasibility. Here we demonstrate NePSTA (neuropathology spatial transcriptomic analysis) for comprehensive morphological and molecular neuropathological diagnostics from single 5-µm tissue sections. NePSTA uses spatial transcriptomics with graph neural networks for automated histological and molecular evaluations. Trained and evaluated across 130 participants with CNS malignancies and healthy donors across four medical centers, NePSTA predicts tissue histology and methylation-based subclasses with high accuracy. We demonstrate the ability to reconstruct immunohistochemistry and genotype profiling on tissue with minimal requirements, inadequate for conventional molecular diagnostics, demonstrating the potential to enhance tumor subtype identification with implications for fast and precise diagnostic workup.
Rapid brain tumor classification from sparse epigenomic data
Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropathology for the past decade, achieving this within a clinically relevant timeframe of under 1 h after biopsy collection remains elusive. Advances in third-generation sequencing have brought this goal closer, but established machine learning techniques rely on computationally intensive methods, making them impractical for live diagnostic workflows in clinical applications. Here we present MethyLYZR, a naive Bayesian framework enabling fully tractable, live classification of cancer epigenomes. For evaluation, we used nanopore sequencing to classify over 200 brain tumor samples, including 10 sequenced in a clinical setting next to the operating room, achieving highly accurate results within 15 min of sequencing. MethyLYZR can be run in parallel with an ongoing nanopore experiment with negligible computational overhead. Therefore, the only limiting factors for even faster time to results are DNA extraction time and the nanopore sequencer’s maximum parallel throughput. Although more evidence from prospective studies is needed, our study suggests the potential applicability of MethyLYZR for live molecular classification of nervous system malignancies using nanopore sequencing not only for the neurosurgical intraoperative use case but also for other oncologic indications and the classification of tumors from cell-free DNA in liquid biopsies.
Mechanism of expression regulation of head-to-head overlapping protein-coding genes INO80E and HIRIP3
Although the existence of overlapping protein-coding genes in eukaryotic genomes is known for decades, their role in regulating expression remains far from fully understood. Here, the mechanism regulating the expression of head-to-head overlapping genes, a pair of INO80E and HIRIP3 genes is presented. Based on a series of experiments, we show that the expression of these genes is strongly dependent on sense/antisense interactions. The overlapping transcripts form an RNA:RNA duplex that has a stabilizing effect on the mRNAs involved, and this stabilization may be mediated by the ELAVL1 protein. We also show that the transcription factor RARG is important for the transcription of both genes studied. In addition, we demonstrate that the overlapping isoform of INO80E forms an R-loop that may positively regulate HIRIP3 isoforms. We propose that both structures, dsRNA and R-loops, help to keep the DNA loop open to allow the transcription of the remaining variants of both genes. However, experiments suggest that RNA:RNA duplex formation plays a major role, while R-loops play only a complementary one. The absence of this dsRNA structure leads to the loss of a stable DNA opening and consequently to transcriptional interference.
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