Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly
evolving, making it possible to comprehensively characterize cells and tissues in health and …

Spatial omics technologies enable a deeper understanding of cellular organizations and
interactions within a tissue of interest. These assays can identify specific compartments or …

Despite the emergence of experimental methods for simultaneous measurement of multiple
omics modalities in single cells, most single-cell datasets include only one modality. A major …

A key aspect of nearly all single-cell sequencing experiments is dissociation of intact tissues
into single-cell suspensions. While many protocols have been optimized for optimal cell …

The development of spatial transcriptomics (ST) technologies has transformed genetic
research from a single-cell data level to a two-dimensional spatial coordinate system and …

Single-cell RNA sequencing methods have led to improved understanding of the
heterogeneity and transcriptomic states present in complex biological systems. Recently, the …

Heterogeneous feature spaces and technical noise hinder the cellular data integration and
imputation. The high cost of obtaining matched data across modalities further restricts …

From single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), one can
extract high-dimensional gene expression patterns that can be described by intercellular …

Multi-modal learning is essential for understanding information in the real world. Jointly
learning from multi-modal data enables global integration of both shared and modality …

The rise and fall of estrogen and progesterone across menstrual cycles and during
pregnancy regulates breast development and modifies cancer risk. How these hormones …