Transcriptomic Studies
Transcriptomic Studies - Related Videos
10:18
A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae
0 Views •
Cited by 15 •
2015
This manuscript describes the use of state-of-the-art technology provided by DNA-microarrays. Microarrays provide an overview of the transcriptomic changes in bacteria incurred under a specific condition. Moreover, we highlight the ease by which large amounts of data can be analyzed by using convenient in-house developed software packages.
10:16
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
0 Views •
2025
This article introduces a protocol for using DeepSpaceDB, a dynamic, interactive database for spatial transcriptomics, offering analysis workflows and examples to explore tissue organization and disease-related gene expression.
13:02
The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
0 Views •
Cited by 10 •
2016
This article describes the application of untargeted metabolomics, transcriptomics and multivariate statistical analysis to grape berry transcripts and metabolites in order to gain insight into the terroir concept, i.e., the impact of the environment on berry quality traits.
06:02
An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level
0 Views •
Cited by 4 •
2020
This paper presents methods for growing cardiac myocytes with different shapes, which represent different pathologies, and sorting these adherent cardiac myocytes based on their morphology at a single cell level. The proposed platform provides a novel approach to high throughput and drug screening for different types of heart failure.
07:27
Transcriptome Analysis of Single Cells
0 Views •
Cited by 72 •
2011
In this article we describe a simple method for the harvesting of single cells from rat primary neuronal cultures and subsequent transcriptome analysis using aRNA amplification. This approach is generalizable to any cell type.
FAQs
Transcriptomic studies compare quantified RNA levels from samples exposed to different biological conditions. A transcript that is more or less abundant in one sample can be classified as differentially expressed relative to another. Examining many transcripts together also reveals coordinated regulatory patterns, helping relate gene activity to cellular responses rather than interpreting isolated measurements.
Interpreting groups of changing transcripts can link gene activity with cellular states and biological pathways. This systems-level view helps researchers move from a list of expression differences toward a testable model of how cells respond, develop, or become associated with disease. The resulting model remains grounded in measured RNA patterns across the analyzed samples.
The biological condition selected for sampling strongly shapes the patterns that become visible. Developmental stage, disease-associated state, cell or tissue identity, and exposure to an environmental or experimental stimulus can each produce different transcript profiles. Consequently, conclusions apply to the sampled context and are most informative when comparisons match the biological question.
Researchers first isolate RNA from the cells or tissues of interest, then convert it into a form that can be measured. Sequencing or another expression-profiling approach is used to quantify transcripts, after which samples can be compared. The resulting measurements support identification of differentially expressed genes and broader regulatory patterns.
Both sequencing and other expression-profiling methods provide ways to measure transcript abundance after RNA has been converted into a measurable form. The central analytical task is to quantify transcripts consistently across samples and examine their differences. This makes the data useful for detecting expression changes without limiting the study to a single gene.
They are useful when researchers need to characterize cell types, distinguish developmental stages, investigate disease-associated changes, or examine responses to environmental or experimental stimuli. At a broader level, the results support systems biology, biomarker discovery, and development of testable models of cellular function. These applications connect measured transcript patterns with biological interpretation.