Single-cell multi-omics visualization with DNA, RNA, and protein layers

Single-Cell Multi-Omics: The Cellular Resolution Revolution

Introduction: The Cellular Resolution Frontier Biology has always been a story of scale. For decades, we studied organisms, then tissues, then cell populations — averaging signals across thousands or millions of cells. But tissues are not homogeneous. A tumor contains cancer cells, immune cells, fibroblasts, and endothelial cells, each with distinct molecular profiles. The brain contains hundreds of neuronal subtypes, each with unique functions. Even “identical” cells in culture exhibit stochastic variation in gene expression that can determine cell fate. ...

March 6, 2026 · 67 AI Lab
Abstract representation of single-cell transcriptomics and neural networks

AI for Transcriptomics: Understanding Gene Expression at Scale

Introduction: The Language of the Cell While genomics maps the static blueprint of life, transcriptomics captures its dynamic execution. If the genome is the dictionary, the transcriptome is the conversation—the precise subset of genes being expressed by a specific cell, at a specific moment, under specific conditions. For decades, bulk RNA sequencing averaged these conversations across millions of cells, giving us a cacophonous blend that masked individual cellular identities. The advent of single-cell RNA sequencing (scRNA-seq) changed everything, allowing us to listen to individual cellular voices. ...

March 1, 2026 · 67 AI Lab