Singlecell

Singlecell refers to methods and analyses that measure molecular information at the level of individual cells, rather than in bulk samples. This approach reveals cellular heterogeneity within tissues, helps trace developmental lineages, and can identify rare cell populations that are masked in averaged data.

Technologies used in single-cell analysis include single-cell RNA sequencing (scRNA-seq) for transcriptomes, single-cell DNA sequencing (scDNA-seq) for genome and copy-number variation profiling, and assays of chromatin accessibility such as scATAC-seq. High-throughput capture platforms often rely on microfluidics or droplet-based systems with barcoded libraries, while alternative approaches use microwell arrays or fluorescence-activated cell sorting. Spatially resolved methods add information about a cell’s location within a tissue, providing context for expression patterns.

Applications span developmental biology, immunology, neuroscience, and oncology. Researchers construct cellular atlases, map cell lineages, study tumor heterogeneity and clonal evolution, and profile immune repertoires. In neuroscience, single-cell analysis contributes to refined cell-type taxonomy and circuit-level understanding. In clinical research, it supports discovery of biomarkers and can inform strategies for diagnosis and personalized therapy.

Data analysis in single-cell studies involves preprocessing and quality control, normalization, and handling sparsity and dropout events. Downstream work includes dimensionality reduction, clustering, and trajectory or pseudotime analyses, followed by cell-type annotation and integration across samples or platforms. Ongoing methodological advances aim to improve multi-omics integration, data standardization, and reproducibility in the field.