Biology subjects

Vanuytven, S.

Publications and source records attributed to Vanuytven, S..

3 recordsLinked to original sources

bioRxiv2023

Integration of Multiple Spatial-Omics Modalities Reveals Unique Insights into Molecular Heterogeneity of Prostate Cancer

Recent advances in spatial omics methods are revolutionising biomedical research by enabling detailed molecular analyses of cells and their interactions in their native state. As most technologies capture only a specific type of molecules, there is an unmet need to enable integration of multiple spatial-omics datasets. This, however, presents several challenges as these analyses typically operate on separate tissue sections at disparate spatial resolutions. Here, we established a spatial multi-omics integration pipeline enabling co-registration and granularity matching, and applied it to integrate spatial transcriptomics, mass spectrometry-based lipidomics, single nucleus RNA-seq and histomorphological information from human prostate cancer patient samples. This approach revealed unique correlations between lipids and gene expression profiles that are linked to distinct cell populations and histopathological disease states and uncovered molecularly different subregions not discernible by morphology alone. By its ability to correlate datasets that span across the biomolecular and spatial scale, the application of this novel spatial multi-omics integration pipeline provides unprecedented insight into the intricate interplay between different classes of molecules in a tissue context. In addition, it has unique hypothesis-generating potential, and holds promise for applications in molecular pathology, biomarker and target discovery and other tissue-based research fields.

cancer biology↗

bioRxiv2023

A multi-omics genome-and-transcriptome single-cell atlas of human preimplantation embryogenesis reveals the cellular and molecular impact of chromosome instability

The frequent acquisition of genomic abnormalities in human preimplantation embryos is a leading cause of pregnancy loss, but does not necessarily prohibit healthy offspring. However, the impact of genomic abnormalities on cellular states and development of the early human embryo remains largely unclear. Here, we characterise aneuploidy and reconstruct gene regulatory networks in human preimplantation embryos, and investigate gene expression and developmental perturbations instigated by aneuploidy using single-cell genome-and-transcriptome sequencing (G&T-seq). At the genomic level, we show that acquired numerical and structural chromosomal aberrations are frequent across all stages of early embryogenesis and in all cell lineages. At the transcriptome level, we identify regulators of cell identity and uncover a network of 248 transcription factors from 10 major gene regulatory modules that characterise the distinct lineages of human preimplantation embryos. By integrating single-cell DNA-with RNA-information, we unveil how expression levels are affected by losses or gains of the corresponding genes in embryonic cells across human preimplantation development, as well as how copy-number aberrant transcription factor genes perturb the expression of their cognate target genes in euploid regions. Furthermore, we reveal a majority of aneuploid cells show a developmental delay and reduced fitness, indicating cell competition within the mosaic diploid-aneuploid embryo, which may contribute to selection against aneuploid cells and the birth of healthy offspring from mosaic diploid-aneuploid embryos. In summary, our multi-modal analyses provide unprecedented insights into early human embryo development.

developmental biology↗

bioRxiv2023

Single-cell Genome-and-Transcriptome sequencing without upfront whole-genome amplification reveals cell state plasticity of melanoma subclones

Single-cell multi-omics methods are enabling the study of cell state diversity, which is largely determined by the interplay of the genome, epigenome, and transcriptome. Here, we describe Gtag&T-seq, a genome-and-transcriptome sequencing (G&T-seq) protocol of the same single cells that omits whole-genome amplification (WGA) by using direct genomic tagmentation (Gtag). Gtag drastically decreases the cost and improves coverage uniformity at both the single-cell and pseudo-bulk level when compared to WGA-based G&T-seq. We also show that transcriptome-based DNA copy number inference has limited resolution and accuracy, underlining the importance of affordable multi-omic approaches. Moreover, applying Gtag&T-seq to a melanoma xenograft model before treatment and at minimal residual disease revealed differential cell state plasticity and treatment response between cancer subclones. In summary, Gtag&T-seq is a low-cost and accurate single-cell multi-omics method enabling the exploration of genetic alterations and their functional consequences in single cells at scale.

genomics↗