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Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

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Updated: Apr 26, 2026

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

A contact-imaging based microfluidic cytometer with machine-learning for single-frame super-resolution processing.

Xiwei Huang¹, Jinhong Guo², Xiaolong Wang¹

¹School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.

|August 12, 2014

Summary

This summary is machine-generated.

This study presents a novel single-frame super-resolution imaging technique for microfluidic cytometers. This advancement significantly improves cell analysis throughput for point-of-care diagnostics.

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Area of Science:

Biomedical Engineering
Microfluidics
Optical Imaging

Background:

Conventional flow cytometers are bulky and unsuitable for point-of-care applications.
Existing lensless microfluidic imaging with multi-frame super-resolution has limited cell flow rates and throughput.
High-resolution cell imaging is crucial for accurate diagnostics.

Purpose of the Study:

To develop a miniaturized, high-throughput microfluidic cytometer for point-of-care use.
To introduce a single-frame super-resolution processing method for improved imaging.
To enable on-line machine learning for real-time cell analysis.

Main Methods:

Development of a contact-imaging based microfluidic cytometer prototype.
Implementation of single-frame super-resolution processing with on-line machine learning.
Utilized captured contact images of cells for analysis.

Main Results:

Achieved less than 8% error in absolute microbead counting compared to commercial flow cytometers.
Demonstrated a coefficient of variation of 0.10 for cell-ratio analysis of mixed red blood cells (RBC) and HepG2 cells.
Successfully demonstrated cell recognition and counting using the prototype.

Conclusions:

The developed single-frame super-resolution microfluidic cytometer offers a promising alternative for point-of-care diagnostics.
The system achieves high accuracy and throughput, overcoming limitations of previous methods.
On-line machine learning enhances the efficiency of cell analysis in microfluidic devices.