Statistical Bioinformatics Seminar: Daniel Leon-Perinan (Berlin Institute for Medical Systems Biology) and Dr Elena Splendiani (Sapienza University of Rome)

Hosted by Sydney Precision Data Science Centre 

Speakers: 

Daniel Leon-Perinan, Berlin Institute for Medical Systems Biology 

Dr Elena Splendiani, Sapienza University of Rome 

Abstract: 

Spatial transcriptomics (ST) has significantly advanced our understanding of the
molecular mechanisms involved in tissue development, homeostasis, and disease.  However,
there is a need for easy-to-use, high-resolution and cost-efficient methods that can be
scaled up for the analysis of tissues in 3D.  To address these challenges, we introduce
Open-ST, a high-resolution, sequencing-based platform, designed for the analysis of
tissue molecular organization in both 2D and 3D.  This experimental and computational
resource is open-source, modular and cost-effective, making it accessible to a broad
range of researchers and facilitates the adaptation of new implementations.  

Open-ST has proven effective in various contexts.  In mouse brain tissue, it captured
transcripts at subcellular resolution and successfully reconstructed cell types.  In a
primary head-and-neck tumor and patient-matched healthy and metastatic lymph nodes,
Open-ST captured the diversity of immune, stromal, and tumor populations, findings that
were corroborated by imaging-based ST.  Notably, distinct cellular states were organized
around cell-cell communication hotspots in the primary tumor.  These transcriptomic
states were maintained in the metastasis, despite the spatial organization being
disrupted.  Reconstructing the metastatic lymph node into a “3D virtual tissue
block” from serial tissue sections, identified spatially contiguous structures that
were not discernible in 2D.  These included potential biomarkers located at the 3D
tumor/lymph node boundary.  

Currently, we are extending Open-ST to increase its applicability to a wider range of
research questions, from small RNAs to long-reads, as well as extending its application
to formalin-fixed paraffin-embedded tissues.  Furthermore, integrating
immunofluorescence staining in the workflow allows a multiomic perspective on the tissue
architecture.  Given its accessibility and versatility, Open-ST can be adopted and
customized by a diverse range of users, enabling its application to increasingly
specialized studies.  

About the speakers: 

Daniel Leon-Perinan is a computational biologist pursuing his PhD in the Rajewsky lab
at the Berlin Institute for Medical Systems Biology (BIMSB-MDC).  His research focuses
on understanding how gene expression patterns, in space and time, can predict disease
progression, by developing methods that generate, analyze and integrate large-scale
data.  With a background in Biotechnology and Computer Science, he works at the
intersection of spatial biology, data visualization, and machine learning, with the aim
to make biological data accessible and searchable.  

Dr Elena Splendiani is a postdoctoral research fellow at the Department of Experimental
Medicine, Sapienza University of Rome.  She received her PhD in Molecular Medicine from
Sapienza University in 2023 and her Master’s degree in Genetics and Molecular Biology
in 2019 from the same institution.  Her research focuses on solid tumors, with a
particular interest in biomarker discovery and RNA-level molecular characterization,
including both coding and non-coding RNAs.  She co-developed a high-spatial-resolution
technology, Open-ST, which she is currently applying to characterize tumors and study
their microenvironments.  

This event will be held online.  

Zoom: https://uni-sydney.zoom.us/j/85114748391