Biology subjects

Ovando, J. A.

Publications and source records attributed to Ovando, J. A..

2 recordsLinked to original sources

bioRxiv2023

scResolve: Recovering single cell expression profiles from multi-cellular spatial transcriptomics

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable from cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

bioinformatics↗

bioRxiv2023

Optimal transport for mapping senescent cells in spatial transcriptomics

Spatial transcriptomics (ST) provides a unique opportunity to study cellular organization and cell-cell interactions at the molecular level. However, due to the low resolution of the sequencing data additional information is required to utilize this technology, especially for cases where only a few cells are present for important cell types. To enable the use of ST to study senescence we developed scDOT, which combines ST and single cell RNA-Sequencing (scRNA-Seq) to improve the ability to reconstruct single cell resolved spatial maps. scDOT integrates optimal transport and expression deconvolution to learn non-linear couplings between cells and spots and to infer cell placements. Application of scDOT to existing and new lung ST data improves on prior methods and allows the identification of the spatial organization of senescent cells, the identification of their neighboring cells and the identification of novel genes involved in cell-cell interactions that may be driving senescence.

cell biology↗