BACKGROUND: High-risk neuroblastoma accounts for 15% of pediatric cancer mortality, with survival rates stagnating at 50%. While spatial transcriptomics (ST) offers a path to understanding the tumor microenvironment and therapy resistance, current commercial platforms are either cost-prohibitive, low resolutions and/or lack sensitivity required for precious clinical samples.
AIMS: To develop a high-resolution, cost-effective ST platform (approximately €100/array) tailored for investigating the spatial effects of innovative targeted therapies in neuroblastoma.
METHODS: Our platform utilizes custom-printed microarrays featuring four subarrays, each containing ± 85,000 spots (13.
Somatic mutations accumulate in the genome of dividing cells, occasionally leading to a cellular fitness advantage and positive selection. This fitness advantage results in small clones, driven by point mutations in common cancer-related genes such as TP53 and NOTCH1, similar to those seen in malignant tumors. Determining the role of these clones in human carcinogenesis requires the direct visualization of their mutations in the spatial context.
Existing technologies are restricted to gene expression profiles (spatial transcriptomics), lacking the exact genetic sequence.
Understanding dynamic immune–tumor interactions is essential for improving cancer detection, monitoring treatment response, and identifying early signs of relapse. However, current immune monitoring strategies in oncology rely heavily on venous blood sampling, limiting longitudinal resolution and increasing cost and patient burden. Molecular profiling approaches such as RNA sequencing provide deep biological insight but are not traditionally compatible with frequent, remote sampling. HemoPrint addresses this limitation by enabling scalable, decentralized immune transcriptomic profiling using dried blood microsamples.
Accurate identification of somatic mutations is fundamental for cancer research. While their detection traditionally relies on DNA sequencing with matched tumor-normal samples, RNA sequencing (RNA-seq) offers distinct advantages by restricting analysis to expressed mutations with greater therapeutic potential. However, most RNA-seq datasets, particularly in clinical practice, lack matched normal samples, making reliable discrimination between somatic and germline variants a major challenge. As a result, RNA-based somatic mutation discovery in tumor-only settings remains inconsistent, with substantial variability across existing variant-calling pipelines and no established standard.
Long non-coding RNAs (IncRNAs) are a class of transcripts with lengths exceeding 200 nucleotides that do not encode proteins. Despite their crucial roles in cellular functions and biological processes, only a minority of the over 20,000 annotated IncRNAs have been functionally characterized. Here, we established a high-throughput, CRISPR-Interference (CRISPRi) arrayed screening platform with serial cellular and molecular phenotyping to systematically characterize IncRNA functions. We reasoned that the integration of a comprehensive cellular and molecular phenotype can increase the probability of uncovering cellular functions and pathways controlled by IncRNA transcripts.
Detecting low-frequency mutations within a high-abundance wild-type (WT) background is essential for precision cancer diagnostics. Standard methods like qPCR and NGS, while effective, are hindered by high costs, complexity, and lengthy workflows. Electrochemical biosensors using mutation-specific capture probes offer a simpler, cost-effective alternative but lack the selectivity needed for detecting low-abundance mutations. To address this, we developed a high-throughput platform for systematically evaluating the hybridization affinity between biorecognition elements (capture probes) and target DNA under varied conditions.
Detecting low-frequency mutations within a high-abundant wild-type (WT) background is critical for precision cancer diagnostics. Standard methods, such as qPCR and NGS, face challenges including high costs, workflow complexity, and long turnaround times. Electrochemical biosensors that rely on mutation-specific capture probes present a promising alternative through their simplicity and cost-effectiveness but currently lack the sensitivity required for low-abundant mutation detection. We developed a high-throughput platform to study the affinity between biorecognition elements (i.