Issue 13, 2016

Computational cell analysis for label-free detection of cell properties in a microfluidic laminar flow

Abstract

Although a flow cytometer, being one of the most popular research and clinical tools for biomedicine, can analyze cells based on the cell size, internal structures such as granularity, and molecular markers, it provides little information about the physical properties of cells such as cell stiffness and physical interactions between the cell membrane and fluid. In this paper, we propose a computational cell analysis technique using cells’ different equilibrium positions in a laminar flow. This method utilizes a spatial coding technique to acquire the spatial position of the cell in a microfluidic channel and then uses mathematical algorithms to calculate the ratio of cell mixtures. Most uniquely, the invented computational cell analysis technique can unequivocally detect the subpopulation of each cell type without labeling even when the cell type shows a substantial overlap in the distribution plot with other cell types, a scenario limiting the use of conventional flow cytometers and machine learning techniques. To prove this concept, we have applied the computation method to distinguish live and fixed cancer cells without labeling, count neutrophils from human blood, and distinguish drug treated cells from untreated cells. Our work paves the way for using computation algorithms and fluidic dynamic properties for cell classification, a label-free method that can potentially classify over 200 types of human cells. Being a highly cost-effective cell analysis method complementary to flow cytometers, our method can offer orthogonal tests in companion with flow cytometers to provide crucial information for biomedical samples.

Graphical abstract: Computational cell analysis for label-free detection of cell properties in a microfluidic laminar flow

Article information

Article type
Paper
Submitted
05 févr. 2016
Accepted
25 avr. 2016
First published
10 mai 2016

Analyst, 2016,141, 4142-4150

Computational cell analysis for label-free detection of cell properties in a microfluidic laminar flow

A. C. Zhang, Y. Gu, Y. Han, Z. Mei, Y. Chiu, L. Geng, S. H. Cho and Y. Lo, Analyst, 2016, 141, 4142 DOI: 10.1039/C6AN00295A

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements