Background: Pathogen detection in primary care is crucial not only to identify viruses like SARS-CoV-2 but also for antibiotic-resistant bacteria. While microfluidic devices enable point-of-care diagnostics, they often lack sufficient sensitivity. On-chip isothermal amplification techniques, such as padlock probing-based rolling circle amplification (PLP-RCA), can enhance specificity and sensitivity while keeping device complexity low. However, integrating PLP-RCA on-chip requires precise optimization of enzyme concentrations, flow conditions, and target capture to achieve its full potential. Results: This study demonstrates a microfluidic RCA assay using porous agarose microbeads as a solid-phase capture, packed inside a microfluidic device. Various target capture strategies were systematically compared and quantitatively investigated, progressing from single-stranded synthetic DNA oligonucleotides to double-stranded Staphylococcus aureus genomic DNA. The best strategy for double-stranded Staphylococcus aureus genomic DNA used a primer bound to the beads that capture the PLP and the target genomic DNA. The system integrates an amorphous-hydrogenated silicon (a-Si:H) thin film p-i-n photodiode and a high-pass interference filter, enabling on-chip fluorescence signal acquisition of amplicons. This integration allows for a fully functional PLP-RCA assay on-chip, along with the added merits of device portability and compatibility with clinical demands. Significance and novelty: This study systematically evaluates single- and double-stranded target capture for on-chip PLP-RCA assays. It demonstrates the successful integration of microfluidics with a solid-phase capture medium and fluorescence detection system. The findings highlight the potential of this platform for developing sensitive, portable pathogen detection devices suited for clinical applications.”

Background.  Glioblastoma (GB), particularly IDH-wild type, is the most aggressive brain malignancy with a dismal prognosis. Despite advances in molecular profiling, the complexity of its tumor microenvironment and spatial organization remains poorly understood. This study aimed to create a comprehensive single-cell and spatial atlas of GB to unravel its cellular heterogeneity, spatial architecture, and clinical relevance.

Methods.  We integrated single-cell RNA sequencing data from 26 datasets, encompassing over 1.1 million cells from 240 patients, to construct GBmap, a harmonized single-cell atlas. High-resolution spatial transcriptomics was employed to map the spatial organization of GB tissues. We developed the Tumor Structure Score (TSS) to quantify tumor organization and correlated it with patient outcomes.

Results.  We showcase the applications of GBmap for reference mapping, transfer learning, and biological discoveries. GBmap revealed extensive cellular heterogeneity, identifying rare populations such as tumor-associated neutrophils and homeostatic microglia. Spatial analysis uncovered 7 distinct tumor niches, with hypoxia-dependent niches strongly associated with poor prognosis. The TSS demonstrated that highly organized tumors, characterized by well-defined vasculature and hypoxic niches, correlated with worse survival outcomes.

Conclusions.  This study provides a comprehensive resource for understanding glioblastoma heterogeneity and spatial organization. GBmap and the TSS provide an integrative view of tumor architecture in GB, highlighting hypoxia-driven niches that may represent avenues for further investigation. Our resource can facilitate exploratory analyses and hypothesis generation to better understand disease progression.

Recent advancements in spatial transcriptomics have been largely triggered by two high-resolution technologies: Visium-HD and Xenium in-situ. While sequencing-based Visium HD features a refined bin size of 2 µm and transcriptome wide coverage, Xenium in-situ is a targeted imaging-based detection technology with sub-micron resolution. Herein we use a publicly available lung dataset which contains Visium-HD and Xenium-5K data generated on identical tissue slides to make a bona-fide technical comparison aligned with thorough pathological annotations. Whilst Visium-HD offers a broader gene coverage for detection and likely detects more tumor subclones, Xenium-5K achieves comparable results when robust clustering algorithms are applied. Importantly, from the pathological point of view, the single-cell segmentation accuracy is essential when analyzing irregularly shaped cells, where Xenium may be in favor. At the opposite side, although Xenium-5K based cell segmentation to delineate immune cells, normal lung, and vasculature at cell resolution is decent, it relies on fluorescent signals for transcript detection, which is challenging in heavily pigmented tissues such as melanoma or dust-laden alveolar macrophages, an application scenario for which Visium HD may stand out. From this perspective, pathological derived factors are the prior consideration for selecting an appropriate ST approach under difference research settings including cancer.

The amniote pallium contains sensory circuits that are structurally and functionally equivalent, yet their evolutionary relationship remains unresolved. We used birthdating analysis, single-cell RNA and spatial transcriptomics, and mathematical modeling to compare the development and evolution of known pallial circuits across birds (chick), lizards (gecko), and mammals (mouse). We reveal that neurons within these circuits’ stations are generated at varying developmental times and brain regions across species and found an early developmental divergence in the transcriptomic progression of glutamatergic neurons. Our research highlights developmental distinctions and functional similarities in the sensory circuit between birds and mammals, suggesting the convergence of high-order sensory processing across amniote lineages.

Oligodendroglia are the responsible cells for myelination in the central nervous system and their involvement in Parkinson’s disease (PD) is poorly understood. We performed sn-RNA-seq and image-based spatial transcriptomics of human caudate nucleus and putamen (dorsal striatum) from PD and control brain donors to elucidate the diversity of oligodendroglia and how they are affected by the disease. We profiled a total of ~ 200.000 oligodendroglial nuclei, defining 15 subclasses, from precursor to mature cells, 4 of which are disease-associated. These PD-specific populations are characterized by the overexpression of heat shock proteins, as well as distinct expression signatures related to immune responses, myelination alterations, and disrupted cell signaling pathways. We have also identified impairments in cell communication and oligodendrocyte development, evidenced by changes in neurotransmitter receptors expression and cell adhesion molecules. In addition, we observed significant disruptions in oligodendrocyte development, with aberrant differentiation trajectories and shifts in cell proportions, particularly in the transition from mature oligodendrocytes to disease-associated states. Quantitative immunohistochemical analysis revealed decreased myelin levels in the PD striatum, which correlated with transcriptomic alterations. Furthermore, spatial transcriptomics mapping revealed the distinct localization of disease-associated populations within the striatum, with evidence of impaired myelin integrity. Thus, we uncover oligodendroglia as a critical cell type in PD and a potential new therapeutic target for myelin-based interventions.

The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.

Optical pooled screening is an important tool to study dynamic phenotypes for libraries of genetically engineered cells. However, the desired engineering often requires that the barcodes used for in situ genotyping are expressed from the chromosome. This has not previously been achieved in bacteria. Here we describe a method for in situ genotyping of libraries with genomic barcodes in Escherichia coli. The method is applied to measure the intracellular maturation time of 84 red fluorescent proteins.

Spatial transcriptomics tools enable the detection and localization of hundreds to thousands of transcripts in biological tissues. However, most technologies are not designed to detect microRNAs. Existing technologies should be expanded to incorporate these key molecular regulators and enable more-comprehensive transcriptomic studies that will shape the new field of spatial miRNomics.

Background T-cell activation and clonal expansion are essential to effective immunotherapy responses in non-small cell lung cancer (NSCLC). The distribution of T-cell clones may offer insights into immunogenic mechanisms and imply potential prognostic and predictive information. Methods We analyzed α/β T-cell receptor (TCR) clonality using RNA-sequencing of bulk frozen tumor tissue from 182 patients with NSCLC. The data was integrated with molecular and clinical characteristics, extensive in situ imaging, and spatial sequencing of the tumor immune microenvironment. TCR clonality was also determined in an independent cohort of nine patients with immune checkpoint-treated NSCLC. Results TCR clonality (Gini index) patterns ranged from high T-cell clone diversity with high evenness (low Gini index) to clonal dominance with low evenness (high Gini index). Generally, TCR clonality in cancer was lower than in matched normal lung parenchyma distant from the tumor (p=0.021). The TCR clonality distribution between adenocarcinoma and squamous cell carcinoma was similar; however, smokers showed a higher Gini index. While in the operated patient with NSCLC cohort, TCR clonality was not prognostic, in an immune checkpoint inhibitor-treated cohort, high TCR clonality was associated with better therapy response (p=0.016) and prolonged survival (p=0.003, median survival 13.8 vs 2.9 months). On the genomic level, a higher Gini index correlated strongly with a lower frequency of epidermal growth factor receptor (EGFR) and adenomatous polypsis coli (APC) gene mutations, but a higher frequency of P53 mutations, and a higher tumor mutation burden. In-depth characterization of the tumor tissue revealed that high TCR clonality was associated with an activated, inflamed tumor phenotype (PRF1, GZMA, GZMB, INFG) with exhaustion signatures (LAG3, TIGIT, IDO1, PD-1, PD-L1). Correspondingly, PD-1+, CD3+, CD8A+, CD163+, and CD138+immune cells infiltrated cancer tissue with high TCR clonality. In situ sequencing recovered single dominant T-cell clones within the patient tumor tissue, which were predominantly of the CD8 subtype and localized closer to tumor cells.

As the field of spatial omics continues to expand, the spatially resolved profiling of microRNA (miRNA) expression in tissues, or ‘spatial miRNomics,’ remains in its infancy, with only a few initial pioneering studies to date. MiRNA expression exhibits distinct spatial, temporal, and cell type‐specific patterns, and the dysregulation of these patterns is associated with numerous pathological conditions. This highlights the potential of miRNAs as targets for spatial transcriptomic studies in translational and clinical research. In this review, we examine the current landscape of spatial technologies for miRNA detection, from foundational methods to cutting-edge innovations, and we discuss conceptual and technical challenges. We also outline the biomedical implications of spatial miRNA profiling and set out future directions for exploring the spatial dimension of gene regulation.