Recent advances in single-cell technologies have enabled high-throughput molecular
profiling of cells across modalities and locations. Single-cell transcriptomics data can now …

The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome
from single cells is transforming our understanding of cell biology in health and disease. In …

Single-cell multi-omics technologies and methods characterize cell states and activities by
simultaneously integrating various single-modality omics methods that profile the …

Resolving the spatial distribution of RNA and protein in tissues at subcellular resolution is a
challenge in the field of spatial biology. We describe spatial molecular imaging, a system …

Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …

Spatial omics has been widely heralded as the new frontier in life sciences. This term
encompasses a wide range of techniques that promise to transform many areas of biology …

Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over
the past decade, particularly in developmental biology, cancer, immunology, and …

The formation and maintenance of tissue integrity requires complex, coordinated activities
by thousands of genes and their encoded products. Until recently, transcript levels could …

Transcriptional heterogeneity among malignant cells of a tumor has been studied in
individual cancer types and shown to be organized into cancer cell states; however, it …