Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (619 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
The determination of the pattern of genes expressed at the level of genetic transcription, under specific circumstances or in a specific cell.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Gene Expression Profilings; Profiling, Gene Expression; Profilings, Gene Expression; Gene Expression Monitoring; Gene Expression Monitorings; Monitoring, Gene Expression; Monitorings, Gene Expression; Gene Expression Pattern Analysis.
Where it fits in the MeSH classification
- Broader topics
- Genetic Techniques
- Narrower topics
- Epitranscriptomics; Ribosome Profiling; RNA-Seq; Single-Cell Gene Expression Analysis; Spatial Transcriptomics; Subtractive Hybridization Techniques
- See also
- Gene Expression; Oligonucleotide Array Sequence Analysis; Transcriptome; Subtractive Hybridization Techniques
Related concepts
- Genetic Transcription
Research landscape
PubMed indexes 293,289 records tagged with the MeSH heading “Gene Expression Profiling” (counts retrieved 2026-10-29). 72,616 of them (24.8%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 109,587 records.
Reviews account for 14,792 records (5.0%) and meta-analyses for 1,067. Randomized controlled trials number 843 (0.3%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 25,808 |
| 2000-2009 | 85,278 |
| 2010-2019 | 109,587 |
| 2020 onward | 72,616 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 14,792 | 5.0% |
| Meta-analyses | 1,067 | 0.4% |
| Randomized controlled trials | 843 | 0.3% |
Method: PubMed query "Gene Expression Profiling"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in genetics and genomics
Genetic findings vary widely in strength. A variant linked to a condition in one study may not replicate in other ancestries, and most common variants have small effects that are meaningful only in aggregate. Studies of rare, high-impact variants can support strong causal claims about a gene, while association studies describe statistical links that need functional and independent replication. Check the sample size, the ancestry of participants, whether results were replicated, and whether a laboratory or clinical outcome was actually measured.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Efficient differential expression analysis of large-scale single-cell transcriptomics data using Dreamlet | 2026 | Research article Design not recorded in structured metadata | 28 | cc by PMID 42778542 · DOI · PMC13601538 |
| CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data | 2026 | Research article Design not recorded in structured metadata | 8 | cc by PMID 42225598 · DOI · PMC13242928 |
| Single-cell atlas of transcriptomic vulnerability across brain disorders | 2026 | Research article Design not recorded in structured metadata | 5 | cc by PMID 42778699 · DOI · PMC13600946 |
Efficient differential expression analysis of large-scale single-cell transcriptomics data using Dreamlet [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells.
- Closing excerpt
- 4 M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (150 words)
Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet ( DiseaseNeurogenomics.github.io/dreamlet ) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4 M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42778542 · DOI · PMC13601538
CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data [2]
Abstract reading summary — source excerpts
- Opening excerpt
- MotivationSingle-cell sequencing technologies allow researchers to study cell-cell variation within a cell population.
- Closing excerpt
- com/PeterZZQ/CeSpGRN.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (197 words)
MotivationSingle-cell sequencing technologies allow researchers to study cell-cell variation within a cell population. Variations between cells are driven by the underlying biological network, particularly gene regulatory networks (GRNs). GRNs rewire as cells evolve, and different cells can have different GRNs. However, while single-cell RNA-sequencing (scRNA-seq) and single-cell multi-omics data have been used to reconstruct GRNs, the output GRNs are rarely cell-specific, but rather, most existing methods infer population-level or cell-type-level GRNs.ResultsWe propose CeSpGRN (Cell-Specific Gene Regulatory Network inference), a method that infers cell-specific GRNs from scRNA-seq, paired scRNA-seq and scATAC-seq, or spatial transcriptomic data. In particular, existing methods that use matching scRNA-seq and scATAC-seq data incorporate population-level region information in GRN inference, whereas CeSpGRN utilizes single-cell resolution region information. CeSpGRN infers cell-specific GRNs using a kernel-weighted Gaussian Copula Graphical Model, and incorporates multi-omic or spatial location information when constructing the objective function. We tested CeSpGRN on both simulated and real datasets, and the results show that CeSpGRN has a superior performance compared to baseline methods in reconstructing GRNs and detecting regulatory interactions that differ between cells. CeSpGRN uncovered regulatory interactions that rewire during biological processes on real datasets.Availability and implementationCeSpGRN is a Python package available at https://github.com/PeterZZQ/CeSpGRN.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42225598 · DOI · PMC13242928
Single-cell atlas of transcriptomic vulnerability across brain disorders [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Neurodegenerative and neuropsychiatric diseases impose a considerable societal and public health burden.
- Closing excerpt
- Our disease atlas provides a perspective of the transcriptomic landscape in neurodegenerative and neuropsychiatric disorders, shedding light on shared and distinct processes involving the neurological-immune-vascular systems, and identifying potential targets for therapeutic intervention.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (272 words)
Neurodegenerative and neuropsychiatric diseases impose a considerable societal and public health burden. However, our understanding of the molecular mechanisms underlying these highly complex conditions remains limited1,2. Here, to gain deeper insights into the aetiology of different brain diseases, we used specimens from 1,494 unique donors to generate a population-scale single-cell transcriptomic atlas of the human dorsolateral prefrontal cortex, comprising over 6.3 million individual nuclei. The cohort includes neurotypical controls, as well as donors affected by eight common and complex brain disorders: Alzheimer's disease (AD), diffuse Lewy body disease (DLBD), vascular dementia (Vas), Parkinson's disease (PD), tauopathy, frontotemporal dementia, schizophrenia, and bipolar disorder. We show that interindividual variation accounts for a substantial portion of gene expression variation. By comparing transcriptomic variation across diseases, we reveal universal signatures enriched in basic cellular functions such as mRNA processing and protein localization. After discounting these cross-disease signatures, we show stronger genetic and transcriptomic concordance among AD, DLBD, Vas and PD. Furthermore, we characterize transcriptomic variation among different AD phenotypes, distinct from those observed in healthy ageing, revealing a reduction in neuronal abundance in individuals with more severe AD, coupled with an increase in immune and vascular cell populations. Exploring the neuropsychiatric symptoms (NPSs) that frequently accompany AD, we find an increased abundance of deep-layer excitatory neurons associated with a broad range of NPSs. By constructing transcriptome trajectories that capture AD progression, we implicate cell-type-specific responses in the early and late stages of AD. Our disease atlas provides a perspective of the transcriptomic landscape in neurodegenerative and neuropsychiatric disorders, shedding light on shared and distinct processes involving the neurological-immune-vascular systems, and identifying potential targets for therapeutic intervention.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42778699 · DOI · PMC13600946
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures [4]
- Research article Interpretation of T cell states from single-cell transcriptomics data using reference atlases [5]
- Research article Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics [6]
- Research article Meta-analysis and meta-review of thyroid cancer gene expression profiling studies identifies important diagnostic biomarkers [7]
- Research article Novel reference genes for quantifying transcriptional responses of Escherichia coli to protein overexpression by quantitative PCR [8]
- Research article A transcriptional profiling meta-analysis reveals a core EWS-FLI gene expression signature [9]
References
- Hoffman GE, Lee D, Bendl J, Prashant NM, Hong A, Casey C, et al.. Efficient differential expression analysis of large-scale single-cell transcriptomics data using Dreamlet. . 2026. PMID 42778542 · DOI · PMC13601538
- Zhang Z, Han J, Song L, Zhang X. CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data. . 2026. PMID 42225598 · DOI · PMC13242928
- Lee D, Koutrouli M, Masse NY, Hoffman GE, Kinrot S, Wang X, et al.. Single-cell atlas of transcriptomic vulnerability across brain disorders. . 2026. PMID 42778699 · DOI · PMC13600946
- Wirapati P, Sotiriou C, Kunkel S, Farmer P, Pradervand S, Haibe-Kains B, et al.. Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res. 2008. PMID 18662380 · DOI · PMC2575538
- Andreatta M, Corria-Osorio J, Müller S, Cubas R, Coukos G, Carmona SJ. Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nat Commun. 2021. PMID 34017005 · DOI · PMC8137700
- Gulati GS, D'Silva JP, Liu Y, Wang L, Newman AM. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol. 2025. PMID 39169166 · DOI
- Griffith OL, Melck A, Jones SJ, Wiseman SM. Meta-analysis and meta-review of thyroid cancer gene expression profiling studies identifies important diagnostic biomarkers. J Clin Oncol. 2006. PMID 17075124 · DOI
- Zhou K, Zhou L, Lim Q', Zou R, Stephanopoulos G, Too HP. Novel reference genes for quantifying transcriptional responses of Escherichia coli to protein overexpression by quantitative PCR. BMC Mol Biol. 2011. PMID 21513543 · DOI · PMC3110127
- Hancock JD, Lessnick SL. A transcriptional profiling meta-analysis reveals a core EWS-FLI gene expression signature. Cell Cycle. 2008. PMID 18256529 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.