Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Regulated mRNA Transport02:22

Regulated mRNA Transport

6.2K
In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
6.2K
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.5K
2.5K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

2.8K
2.8K
Ribosome Profiling02:24

Ribosome Profiling

3.5K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Robust Non-Invasive Cardiac Index Prediction via Feature Integration and Data-Augmented Neural Networks.

Bioengineering (Basel, Switzerland)·2026
Same author

Three-Dimensional Nanopatterning Using Extreme Ultraviolet Colloidal Talbot Lithography.

Nano letters·2026
Same author

Exciplex-Forming Co-Host Systems for Efficient TADF and Phosphorescent Organic Light-Emitting Diodes.

Chemistry, an Asian journal·2026
Same author

Investigating the contributions of electrostatic and capillary effects in anti-dust nanostructures.

Nanotechnology·2026
Same author

Comparison of Mycobacterial Culture Yield from Pleural Tissue Using Pleuroscopic Cryobiopsy versus Forceps Biopsy in Patients with Tuberculous Pleurisy.

Respiration; international review of thoracic diseases·2026
Same author

Circadian Dysregulation in Aging Alters Senescence and Inflammatory Pathways in a Sex- and Time-of-Day-Dependent Manner.

bioRxiv : the preprint server for biology·2026
Same journal

Genetic Impacts on Variability of Body Fat Distribution Uncover Gene-Environment and Gene-Gene Interactions.

bioRxiv : the preprint server for biology·2026
Same journal

16S ribosomal RNA modification drives transcript-specific translation efficiency.

bioRxiv : the preprint server for biology·2026
Same journal

FlcE latches onto the FliL-stator complex to turbocharge flagellar motility in <i>Borrelia burgdorferi</i>.

bioRxiv : the preprint server for biology·2026
Same journal

Synaptic pruning, myelination and the emergence of psychiatric disorders in late adolescence.

bioRxiv : the preprint server for biology·2026
Same journal

Structural and functional insights into the Rcs phosphorelay.

bioRxiv : the preprint server for biology·2026
Same journal

The structural basis of RanGAP1 regulation and catalysis in nuclear transport.

bioRxiv : the preprint server for biology·2026
See all related articles

Related Experiment Video

Updated: Jun 5, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

500

Tackling the Complexity of Spatial Transcriptomics Data Interpretation with Large Language Models.

Taushif Khan, Colleen M Farley, John J Wilson

    Biorxiv : the Preprint Server for Biology
    |December 16, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Large Language Models (LLMs) show promise in analyzing complex spatial transcriptomics data for cancer research. Claude 3.5 Sonnet effectively interprets tumor immune landscapes, accelerating biological insights from gene expression patterns.

    More Related Videos

    Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
    09:19

    Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

    Published on: July 6, 2022

    4.8K
    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    637

    Related Experiment Videos

    Last Updated: Jun 5, 2025

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    500
    Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
    09:19

    Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

    Published on: July 6, 2022

    4.8K
    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    637

    Area of Science:

    • Computational Biology
    • Genomics
    • Immunology

    Background:

    • Spatial transcriptomics provides detailed cellular insights into tissue microenvironments, crucial for cancer research.
    • Interpreting the large datasets from spatial transcriptomics is a significant analytical challenge.

    Purpose of the Study:

    • To explore the utility of Large Language Models (LLMs) in analyzing and interpreting spatial transcriptomic data.
    • To develop and validate a workflow for LLM-assisted spatial transcriptomics analysis in a murine melanoma model.

    Main Methods:

    • Benchmarking multiple LLMs for spatial gene expression pattern description and quantification.
    • Developing a systematic workflow using Claude 3.5 Sonnet for tumor immune landscape analysis.
    • Integrating spatial expression data with immunological knowledge for interpretation.

    Main Results:

    • Most LLMs struggled with spatial transcriptomics data interpretation; Claude 3.5 Sonnet showed high accuracy in spot quantification and pattern recognition.
    • Claude 3.5 Sonnet successfully identified macrophage markers and elucidated local immune organization.
    • The LLM identified coordinated immunosuppressive mechanisms within tumor regions, extending current immunological understanding.

    Conclusions:

    • LLMs, particularly Claude 3.5 Sonnet, can serve as powerful assistive tools for spatial transcriptomics data interpretation.
    • LLM-assisted analysis combines pattern recognition with knowledge integration to enhance biological insights.
    • This proof of concept demonstrates LLMs' potential to accelerate the translation of spatial transcriptomics data into actionable findings.