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Spalinskas, R.

Publications and source records attributed to Spalinskas, R..

2 recordsLinked to original sources

Efficient murine cardiac phenotyping by combining synchrotron-based phase-contrast micro-CT, histology, immunofluorescence and spatial transcriptomics

1.Congenital heart disease is commonly studied using genetically modified mouse models, but characterization of complex three-dimensional (3D) cardiac anomalies remains technically challenging. Histology of fixed paraffin-embedded samples, typically used for structural and molecular analysis, is limited to two dimensions (2D). Synchrotron radiation-based phase-contrast micro-computed tomography (SRPC-{micro}CT) enables rapid, high-resolution, 3D imaging but had yet to be fully integrated with molecular tissue analysis. Importantly, SRPC-{micro}CT is nondestructive and compatible with paraffin-embedded tissue, potentially allowing integration with downstream molecular analyses such as multiplexed spatial transcriptomic profiling on tissue sections. Here, we present a pipeline combining SRPC-{micro}CT with complementary molecular approaches for rapid and accurate phenotyping of mouse hearts and for investigating disease mechanisms. We demonstrate the successful integration of high-resolution 3D imaging with spatial transcriptomics, fluorescent stainings, and histochemistry. Notably, all 2D modalities were applied sequentially to a single tissue section and registered within the 3D volume. This multimodal framework provides a powerful approach for linking structural and molecular information that is broadly applicable across biomedical research.

developmental biology↗

High-resolution regulatory maps connect cardiovascular risk variants to disease related pathways

Genetic variant landscape of cardiovascular disease (CVD) is dominated by non-coding variants among which many occur within putative enhancers regulating the expression levels of relevant genes. It is crucial to assign the genetic variants to their correct gene both to gain insights into perturbed functions and better assess the risk of disease. In this study, we generated high-resolution genomic interaction maps (~750 bases) in aortic endothelial, smooth muscle and THP-1 macrophages using Hi-C coupled with sequence capture targeting 25,429 features including variants associated with CVD. We detected interactions for 761 CVD risk variants obtained by genome-wide association studies (GWAS) and identified novel as well as established functions associated with CVD. We were able to fine-map 331 GWAS variants using interaction networks, thereby identifying additional genes and functions. We also discovered a subset of risk variants interacting with multiple promoters and the expression levels of such genes were correlated. The presented resource enables functional studies of cardiovascular disease providing novel approaches for its diagnosis and treatment.

genomics↗