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Measuring and modeling macrophage proliferation in a lab-on-CMOS capacitance sensing microsystem.

Kyle Smith¹, Ching-Yi Lin², Yann Gilpin²

¹Department of Chemical Engineering, Pittsburgh, PA, United States.

Frontiers in Bioengineering and Biotechnology

|May 30, 2023

Summary

This study introduces a lab-on-CMOS biosensor for tracking macrophage proliferation. The biosensor quantifies cell growth by measuring capacitance changes over time, enabling accurate cell counting.

Keywords:

CMOS biosensors capacitance cell proliferation lab-on-CMOS lab-on-chip macrophage-cell model

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Mesenchymal Stem Cell Regulation of Macrophage Phagocytosis; Quantitation and Imaging

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Published on: July 16, 2021

Area of Science:

Biomedical Engineering
Cell Biology
Biosensor Technology

Background:

Accurate monitoring of cell proliferation is crucial in biological research and drug development.
Existing methods for cell counting can be labor-intensive and time-consuming.
Need for high-throughput, quantitative cell proliferation tracking platforms.

Purpose of the Study:

To develop and validate a lab-on-CMOS biosensor for quantitative tracking of macrophage proliferation.
To establish a correlation between biosensor capacitance measurements and cell numbers.
To create a temporal model for predicting cell population dynamics.

Main Methods:

Utilized a lab-on-CMOS biosensor platform with multiple electrodes.
Measured capacitance changes in response to RAW 264.7 macrophage proliferation.
Developed a temporal model linking capacitance growth factor to cell number evolution over 30 hours.

Main Results:

Demonstrated a linear correlation between macrophage proliferation and average capacitance growth factor.
Successfully modeled cell number evolution over extended periods using capacitance data.
The biosensor platform provides quantitative and real-time insights into cell growth.

Conclusions:

The lab-on-CMOS biosensor offers a robust method for quantitative cell proliferation monitoring.
Capacitance measurements serve as a reliable indicator of macrophage cell numbers.
The developed temporal model accurately captures and predicts cell population dynamics.