An Optofluidic Platform for Cell-Counting Applications

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Date

2023

Journal Title

Journal ISSN

Volume Title

Publisher

Royal Soc Chemistry

Open Access Color

Green Open Access

Yes

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No
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Average
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Average
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Top 10%

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Abstract

Cell-counting is critical for a wide range of applications, e.g., life sciences, medicine, or pharmacology. Hemocytometry is a classical method that requires manual counting of cells under a microscope. This methodology is low-cost but manual counting is slow, and the test accuracy is limited by the operator experience. Accuracy and throughput of such application could be improved with the use of automated cell-counting devices. Possessing the ability of recording and processing cell images, devices employing these technologies could dramatically improve the accuracy of the counting results. However, accuracy of these devices still requires further improvement as the counting results rely only on 100-200 cells. Furthermore, the test cost of these devices increases due to the need for a counting chamber or consumables compatible with their hardware settings. Herein, in order to address these drawbacks, we introduced an optofluidic cell-counting platform that could scan more than 2000 cells, which dramatically improves the test accuracy. Our technology could yield an error rate below 1% for cell viability, and below 5% for cell concentration. The platform could deliver the count results within only similar to 1 minute, including sample loading, autofocusing, recording images, and image processing. The presented platform also benefits from a built-in fluidic component that eliminates the need for an external counting chamber, and allows fully automated sample loading and self-cleaning modality compatible with any solutions that are typically used for cell-counting tests. Providing an easy-to-use and rapid feature from sample loading to image analyses, our optofluidic platform could be a critical asset for accurate and low cost cell-counting applications.

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Keywords

Proteomic Analysis, Assays, Microscopy, Image Processing, Computer-Assisted, Medicine, Cell Count

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OpenCitations Citation Count
5

Source

Analytical Methods

Volume

15

Issue

18

Start Page

2244

End Page

2252
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CrossRef : 3

Scopus : 6

PubMed : 3

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Mendeley Readers : 6

SCOPUS™ Citations

6

checked on Feb 20, 2026

Web of Science™ Citations

6

checked on Feb 20, 2026

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1.42990245

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