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 (456 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 pattern of gene expression at the level of genetic transcription in a specific organism or under specific circumstances in specific cells.
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: Transcriptomes; Gene Expression Profiles; Expression Profile, Gene; Expression Profiles, Gene; Gene Expression Profile; Profile, Gene Expression; Profiles, Gene Expression; Gene Expression Signatures.
Where it fits in the MeSH classification
- Broader topics
- Transcription, Genetic; Genetic Structures
- Narrower topics
- Epitranscriptome
- See also
- Gene Expression Profiling; Exome
Related concepts
Research landscape
PubMed indexes 96,093 records tagged with the MeSH heading “Transcriptome” (counts retrieved 2026-10-29). 57,180 of them (59.5%) were published from 2020 onward, and the busiest period so far is 2020 onward with 57,180 records.
Reviews account for 5,164 records (5.4%) and meta-analyses for 730. Randomized controlled trials number 336 (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 | 0 |
| 2000-2009 | 24 |
| 2010-2019 | 39,825 |
| 2020 onward | 57,180 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 5,164 | 5.4% |
| Meta-analyses | 730 | 0.8% |
| Randomized controlled trials | 336 | 0.3% |
Method: PubMed query "Transcriptome"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in molecular biology
Molecular biology establishes mechanisms through controlled experiments, often in simplified systems such as purified proteins, cultured cells or model organisms. Such work can show what is possible in a defined system, but whether a pathway matters in a whole organism or in human disease needs further evidence. Check the experimental system, whether findings were reproduced by independent groups, and whether the authors tested the mechanism directly or inferred it from correlation.
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 |
| A spatial and temporal transcriptomic atlas of mouse intestinal regeneration in the jejunum | 2026 | Research article Design not recorded in structured metadata | 1 | cc by PMID 42830316 · DOI · PMC13635233 |
| PPGLomics: an interactive platform for pheochromocytoma and paraganglioma transcriptomics | 2026 | Research article Design not recorded in structured metadata | 1 | cc by PMID 42690223 · DOI · PMC13641061 |
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
A spatial and temporal transcriptomic atlas of mouse intestinal regeneration in the jejunum [2]
Abstract reading summary — source excerpts
- Opening excerpt
- The intestinal epithelium exhibits a remarkable capacity for regeneration following injury, yet the spatial and temporal dynamics of the damage-repair cycle remain incompletely understood.
- Closing excerpt
- In this work, we provide a spatiotemporal atlas of mouse intestinal regeneration at subcellular 2-µm resolution that will inform future therapeutic strategies for inflammatory bowel diseases and gastrointestinal pathologies arising from injury or cancer therapy.
- 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 (155 words)
The intestinal epithelium exhibits a remarkable capacity for regeneration following injury, yet the spatial and temporal dynamics of the damage-repair cycle remain incompletely understood. We present a spatial transcriptomics atlas of ionizing radiation-induced injury and repair in the mouse jejunum, capturing mRNA from 16,921 genes across approximately 26 million bins at 2-µm resolution in 23 biological samples spanning six days post-irradiation. Analysis reveals transcriptional patterns and niche signals undetectable by bulk or single-cell approaches, including spatially restricted interferon-target gene activation, an altered differentiation trajectory in the lower villus during early repair, two transcriptionally distinct crypt regenerative cell populations, and global transcriptional upregulation consistent with hypertranscription as a hallmark of intestinal regeneration, with evidence for hypertranscription extending to human Crohn's disease. In this work, we provide a spatiotemporal atlas of mouse intestinal regeneration at subcellular 2-µm resolution that will inform future therapeutic strategies for inflammatory bowel diseases and gastrointestinal pathologies arising from injury or cancer therapy.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42830316 · DOI · PMC13635233
PPGLomics: an interactive platform for pheochromocytoma and paraganglioma transcriptomics [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors with unique biological behavior and remarkably high heritability, yet dedicated bioinformatics resources for these diagnoses remain limited.
- Closing excerpt
- io/PPGLomics.
- 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 (151 words)
Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors with unique biological behavior and remarkably high heritability, yet dedicated bioinformatics resources for these diagnoses remain limited. Existing cancer multi-omics platforms are pan-cancer in scope, often lacking the disease-specific annotations, granularity, and cross-database harmonization required for meaningful stratification and hypothesis generation. Here, we introduce PPGLomics, an interactive Web-based platform designed for comprehensive PPGL transcriptomics analysis. PPGLomics v1.0 integrates two major datasets, the TCGA-PCPG cohort (n = 160) spanning multiple molecular subtypes and the A5 Consortium SDHB cohort (n = 91) with detailed clinicopathological and molecular annotations. The platform provides basic and clinical scientists, as well as a broad range of healthcare professionals, with tools for differential expression analysis, correlation analysis, survival analysis, and visualization, including boxplots, heatmaps, volcano plots, and Kaplan-Meier survival plots, enabling exploration of gene expression patterns across PPGL subtypes without requiring bioinformatics expertise. PPGLomics v1.0 is freely available at https://alkaissilab.shinyapps.io/PPGLomics.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 42690223 · DOI · PMC13641061
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 The landscape of long noncoding RNAs in the human transcriptome [4]
- Research article Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas [5]
- Research article Epigenetic Signatures of Cigarette Smoking [6]
- Research article The transcriptional landscape of age in human peripheral blood [7]
- Research article Induction of a common microglia gene expression signature by aging and neurodegenerative conditions: a co-expression meta-analysis [8]
- Research article Meta-signature of human endometrial receptivity: a meta-analysis and validation study of transcriptomic biomarkers [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
- Ofer R, Tran T, He P, Qiu X, Hu X, Zachariah E, et al.. A spatial and temporal transcriptomic atlas of mouse intestinal regeneration in the jejunum. . 2026. PMID 42830316 · DOI · PMC13635233
- Alkaissi H, Gordon CM, Pacak K. PPGLomics: an interactive platform for pheochromocytoma and paraganglioma transcriptomics. . 2026. PMID 42690223 · DOI · PMC13641061
- Iyer MK, Niknafs YS, Malik R, Singhal U, Sahu A, Hosono Y, et al.. The landscape of long noncoding RNAs in the human transcriptome. Nat Genet. 2015. PMID 25599403 · DOI · PMC4417758
- Kool M, Korshunov A, Remke M, Jones DT, Schlanstein M, Northcott PA, et al.. Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas. Acta Neuropathol. 2012. PMID 22358457 · DOI · PMC3306778
- Joehanes R, Just AC, Marioni RE, Pilling LC, Reynolds LM, Mandaviya PR, et al.. Epigenetic Signatures of Cigarette Smoking. Circ Cardiovasc Genet. 2016. PMID 27651444 · DOI · PMC5267325
- Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, et al.. The transcriptional landscape of age in human peripheral blood. Nat Commun. 2015. PMID 26490707 · DOI · PMC4639797
- Holtman IR, Raj DD, Miller JA, Schaafsma W, Yin Z, Brouwer N, et al.. Induction of a common microglia gene expression signature by aging and neurodegenerative conditions: a co-expression meta-analysis. Acta Neuropathol Commun. 2015. PMID 26001565 · DOI · PMC4489356
- Altmäe S, Koel M, Võsa U, Adler P, Suhorutšenko M, Laisk-Podar T, et al.. Meta-signature of human endometrial receptivity: a meta-analysis and validation study of transcriptomic biomarkers. Sci Rep. 2017. PMID 28855728 · DOI · PMC5577343
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.