singelcellprofilering

Singelcellprofilering refers to the study of molecular features at the level of individual cells, enabling the analysis of cellular heterogeneity within tissues, organs, and tumors. It encompasses methods that profile genomes, transcriptomes, epigenomes, proteomes, or spatial context at single-cell resolution.

Prominent data modalities include single-cell RNA sequencing (scRNA-seq) for gene expression, single-cell ATAC sequencing (scATAC-seq) for chromatin accessibility, and single-cell methylation assays for epigenetic states. Multi-omic approaches such as CITE-seq and REAP-seq combine RNA measurements with measurements of surface proteins. Spatial profiling methods, including spatial transcriptomics and in situ hybridization techniques like MERFISH and seqFISH, add the geographic context of cells within tissues.

A typical workflow involves isolating viable single cells using methods such as fluorescence-activated cell sorting or microfluidic platforms, followed by library preparation and sequencing. Data analysis includes quality control, normalization, and dimensionality reduction, then clustering to identify cell types or states. Downstream steps often cover cell-type annotation, differential expression analysis, trajectory or pseudotime analysis to study lineage relationships, and integration across batches or studies to mitigate technical variation.

Applications span developmental biology, cancer research, immunology, neuroscience, and organoid modeling, where researchers seek to map cell-type diversity, track dynamic cellular programs, and understand interactions within complex microenvironments.

Challenges include technical noise and dropout events in single-cell data, doublets, batch effects, data sparsity, and the need for advanced computational resources and standardized best practices. Ethical considerations arise in the handling of human-derived single-cell data and related privacy concerns.