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

Flow Cytometry01:23

Flow Cytometry

13.1K
The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
In...
13.1K

You might also read

Related Articles

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

Sort by
Same author

From uric acid to tophi: multistage molecular and cellular mechanisms of tophi formation.

Frontiers in immunology·2026
Same author

Tailoring the Cu Local Microenvironment to Create Formate Conversion-Desorption Equilibrium for Industrial Level Formaldehyde Electrooxidation.

Angewandte Chemie (International ed. in English)·2026
Same author

Reference Ranges of Maximum Diameter and Volume of the Fetal Large Intestine During the Second-to Third-Trimester: An MRI Study.

Prenatal diagnosis·2026
Same author

Programmed cell death in gouty nephropathy: molecular mechanisms and therapeutic implications.

Frontiers in immunology·2026
Same author

Tunable acoustic rotation for deep biophysical phenotyping of preclinical Alzheimer's disease.

Materials today. Bio·2026
Same author

Closed-Loop Iterative Self-Calibration of Initial Phase in Phased Arrays.

Sensors (Basel, Switzerland)·2026
Same journal

Mechanistic insights into cellular deformation enable enhanced extensional-flow cytometry for label-free classification and sorting.

Microsystems & nanoengineering·2026
Same journal

AlGaN/GaN HEMT Hâ‚‚ sensor with integrated Wheatstone bridge and on-chip microheater for 0.1-ppm detection.

Microsystems & nanoengineering·2026
Same journal

Fully flexible large-area MEMS-based triaxial force sensor compatible with flat panel display manufacturing.

Microsystems & nanoengineering·2026
Same journal

Self-aligned assembly of piezoelectric nanorods for 6G wireless communications.

Microsystems & nanoengineering·2026
Same journal

Wearable ultrasound: a review of core technologies and clinical applications in cardiovascular monitoring.

Microsystems & nanoengineering·2026
Same journal

Microfluidic encapsulation of the human gut microbiota-a tool for research and beyond.

Microsystems & nanoengineering·2026
See all related articles

Related Experiment Video

Updated: Jul 15, 2025

A Microfluidic Chip for the Versatile Chemical Analysis of Single Cells
15:41

A Microfluidic Chip for the Versatile Chemical Analysis of Single Cells

Published on: October 15, 2013

15.0K

Computer vision meets microfluidics: a label-free method for high-throughput cell analysis.

Shizheng Zhou1, Bingbing Chen1, Edgar S Fu2

  • 1State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228 China.

Microsystems & Nanoengineering
|September 25, 2023
PubMed
Summary
This summary is machine-generated.

Integrating microfluidic chips and computer vision enhances life science research by enabling rapid, noninvasive single-cell analysis. This powerful combination offers high-throughput screening for drug discovery and diagnostics.

Keywords:
Electrical and electronic engineeringOptical sensors

More Related Videos

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions
09:09

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

Published on: November 23, 2015

8.6K
Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments
07:46

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Published on: April 30, 2021

4.8K

Related Experiment Videos

Last Updated: Jul 15, 2025

A Microfluidic Chip for the Versatile Chemical Analysis of Single Cells
15:41

A Microfluidic Chip for the Versatile Chemical Analysis of Single Cells

Published on: October 15, 2013

15.0K
A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions
09:09

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions

Published on: November 23, 2015

8.6K
Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments
07:46

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Published on: April 30, 2021

4.8K

Area of Science:

  • Life Sciences
  • Biology
  • Biotechnology

Background:

  • Microfluidic chips generate extensive single-cell imaging data.
  • Computer vision (CV) analyzes this data for cellular health and function insights.
  • Existing methods can be invasive or damage delicate cells.

Purpose of the Study:

  • To review the integration of microfluidic chips and computer vision for advanced biological analysis.
  • To highlight the benefits of this combined approach for cell imaging and characterization.
  • To explore future applications in drug discovery, diagnostics, and personalized medicine.

Main Methods:

  • Utilizing microfluidic chips for controlled cell culture and manipulation.
  • Applying computer vision algorithms for image processing and data extraction.
  • Leveraging artificial intelligence (AI) for enhanced in situ cell analysis.
  • Integrating microelectromechanical devices (MEMS) for advanced functionality.

Main Results:

  • Achieved noninvasive and low-damage cellular characterization, crucial for fragile cells.
  • Enabled accurate recognition and analysis of target species in microbial populations.
  • Demonstrated potential for label-free, automated, and high-throughput cellular analysis.
  • Facilitated the study of cellular responses to various compounds.

Conclusions:

  • The synergy between microfluidics and computer vision significantly advances single-cell analysis in life sciences.
  • This integrated approach promises automated, cost-effective, and rapid cellular information recognition.
  • Future developments in AI and hardware will further expand applications in medicine and biotechnology.