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Updated: May 7, 2026

A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

Published on: September 22, 2011

A Modular and Scalable FPGA Platform for Intelligent, High-Throughput Image-Activated Cell Sorting.

Yan Ding¹, Jiehua Zhou^1,2, Ruiqi Xi¹

¹School of Integrated Circuits, Wuhan University, Wuhan, China.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|May 6, 2026

Summary

This summary is machine-generated.

This study presents a stable, low-latency image processing solution for image-activated cell sorting (IACS) using field-programmable gate arrays (FPGAs). The system achieves rapid AI inference for enhanced cell classification and sorting accuracy.

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Last Updated: May 7, 2026

A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

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Purification of Specific Cell Population by Fluorescence Activated Cell Sorting FACS

Purification of Specific Cell Population by Fluorescence Activated Cell Sorting FACS

Published on: July 10, 2010

Area of Science:

Biotechnology
Artificial Intelligence
Microfluidics

Background:

Image-activated cell sorting (IACS) links cell morphology to physiology for high-throughput classification.
Integrating artificial intelligence (AI) enhances morphological analysis but increases computational complexity, leading to latency issues in IACS.
High latency and instability in AI-driven IACS analysis can result in longer processing times and inaccurate cell sorting.

Purpose of the Study:

To develop a stable, low-latency image processing system for IACS using field-programmable gate arrays (FPGAs).
To enhance real-time AI inference for cellular data acquisition and classification within IACS.
To overcome the computational challenges and latency problems associated with AI in IACS.

Main Methods:

Developed a two-stage FPGA processing system leveraging the high-level synthesis (HLS) framework for cellular data acquisition and AI inference.
Deployed a customized U-Net model on an AMD-Xilinx accelerator card, incorporating hardware acceleration for activation functions and convolutions.
Implemented hardware acceleration modules, including activation function approximation and pixel-level convolution acceleration, for optimized performance.

Main Results:

Achieved a stable image segmentation latency of 3.06 milliseconds (ms) at a 272 MHz clock frequency.
Delivered a high processing throughput of up to 16,601 frames per second (fps).
Successfully separated deformed HeLa cells and distinguished various cell types (colorectal cells, red blood cells, HeLa cells) and microspheres based on morphological parameters.

Conclusions:

The developed FPGA-based system provides a stable and low-latency image processing solution for IACS.
This approach significantly improves the efficiency and accuracy of AI-driven cell sorting.
The system demonstrates the potential for real-time, high-throughput cell analysis in biological and medical applications.