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

Two-Dimensional Microscopy in Microbiology01:29

Two-Dimensional Microscopy in Microbiology

Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field microscopy...
Introduction to GIS01:28

Introduction to GIS

Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

You might also read

Related Articles

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

Sort by
Same author

Inflammation With a Twist: A Rare Urologic Case Report.

Cureus·2026
Same author

Rediscovering the Ciucci pathway: in-vivo evidence of a palmar lymphatic channel.

Breast cancer research and treatment·2026
Same author

Superficial and Functional Imaging of the Posterior Upper Arm Pathway in the Healthy Population.

Journal of surgical oncology·2026
Same author

Control of Charge Separation and Recombination Processes by Selection and Connectivity of Bridges in Donor-Chiral Bridge-Acceptor (D-χ-A) Molecules.

The journal of physical chemistry. B·2026
Same author

Acid stress modulates metabolo-inflammatory pathways in oral epithelial cells.

bioRxiv : the preprint server for biology·2026
Same author

STiLE: Automated Tissue Microarray Dearraying for Spatial Transcriptomics.

bioRxiv : the preprint server for biology·2026
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

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

Related Experiment Video

Updated: May 19, 2026

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

spatiAlytica: Viewer-Grounded Multimodal Agentic System for Interactive Spatial Omics Analysis.

Arun Das1,2, Kexun Zhang3, Jifeng Song1,4

  • 1Cancer Virology Program, UPMC Hillman Cancer Center, Pittsburgh, PA, USA.

Biorxiv : the Preprint Server for Biology
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

spatiAlytica empowers biologists to analyze spatial omics data using natural language. This AI system, integrated into Napari, simplifies complex analyses and uncovers biological insights, outperforming existing methods.

Related Experiment Videos

Last Updated: May 19, 2026

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

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Spatial Omics

Background:

  • Spatial transcriptomics and proteomics offer insights into tissue architecture and cellular interactions.
  • Current analysis methods are hindered by programming complexity and limitations of text-based AI.

Purpose of the Study:

  • To introduce spatiAlytica, a viewer-centric AI system for non-programmer biologists to conduct spatial omics analysis.
  • To enable hypothesis-driven exploration and interpretation of spatial omics data through natural language interaction.

Main Methods:

  • spatiAlytica integrates with the Napari viewer, featuring viewer-state serialization, agentic memory, and biological concept mapping.
  • The system supports code generation, debugging, Spatial Visual Question Answering (VQA), and grounded interpretation.
  • A benchmark, spatiAlyticaBench, was created for evaluating spatial analytical capabilities.

Main Results:

  • spatiAlytica demonstrated superior performance compared to baseline agents, utilizing less time and computational resources.
  • Case studies on Kaposi's sarcoma, colorectal cancer, and ovarian cancer successfully identified known spatial patterns.
  • Progressive CD8 T-cell dysfunction during Kaposi's sarcoma progression was uncovered.

Conclusions:

  • spatiAlytica provides an accessible and efficient platform for spatial omics data analysis.
  • The system facilitates exploratory analysis and interpretive reasoning for biologists without extensive programming skills.
  • spatiAlytica has the potential to advance discoveries in cancer research and other fields utilizing spatial omics data.