BulkTranscriptomik

BulkTranscriptomik refers to the study of gene expression in bulk tissue samples, measuring the average expression levels of thousands of transcripts across many cells. It is a central approach in transcriptomics, distinct from single-cell transcriptomics which resolves expression at the level of individual cells. Bulk transcriptomics commonly uses RNA sequencing or microarray platforms. RNA-seq provides digital readouts of transcript abundance and can quantify novel transcripts and splice variants; microarrays measure predefined probes.

Sample processing aims to minimize technical variation; data are normalized to account for sequencing depth and gene length, using metrics such as TPM, RPKM/FPKM, or counts per million, followed by differential expression analysis with tools like DESeq2, edgeR, or limma. Batch effects and confounding factors are important considerations. Downstream analyses include clustering, gene ontology and pathway enrichment, and network analyses.

A key limitation of BulkTranscriptomik is that signals from different cell types within a tissue are averaged, obscuring cell-type specific changes. Deconvolution methods attempt to estimate cell-type proportions or cell-type–specific expression by leveraging reference datasets from single-cell RNA-seq or known marker genes. This area remains active for improving resolution in complex tissues.

Applications span biomedical research, including cancer, neurobiology, aging, and response to treatment, as well as large-scale projects like tissue-expression atlases. Data generated are deposited in public repositories such as GEO and ArrayExpress.

In sum, BulkTranscriptomik offers a cost-effective, robust framework for measuring gene expression at tissue level, while ongoing methodological advances seek to address its inability to resolve cellular heterogeneity.