Real-time red blood cell counting and osmolarity analysis using a photoacoustic-based microfluidic system.


Journal

Lab on a chip
ISSN: 1473-0189
Titre abrégé: Lab Chip
Pays: England
ID NLM: 101128948

Informations de publication

Date de publication:
29 06 2021
Historique:
pubmed: 20 5 2021
medline: 6 7 2021
entrez: 19 5 2021
Statut: ppublish

Résumé

Counting the number of red blood cells (RBCs) in blood samples is a common clinical diagnostic procedure, but conventional methods are unable to provide the size and other physical properties of RBCs at the same time. In this work, we explore photoacoustic (PA) detection as a rapid label-free and noninvasive analysis technique that can potentially be used for single RBC characterization based on their photoabsorption properties. We have demonstrated an on-chip PA flow cytometry system using a simple microfluidic chip combined with a PA imaging system to count and characterize up to ∼60 RBCs per second. Compared with existing microfluidic-based RBC analysis methods, which typically use camera-captured image sequences to characterize cell morphology and deformation, the PA method discussed here requires only the processing of one-dimensional time-series data instead of two- or three-dimensional time-series data acquired by computer vision methods. Therefore, the PA method will have significantly lower computational requirements when large numbers of RBCs are to be analyzed. Moreover, we have demonstrated that the PA signals of RBCs flowing in a microfluidic device could be directly used to acquire the osmolarity conditions (in the range of 124 to 497 mOsm L-1) of the medium surrounding the RBCs. This finding suggests a potential extension of applicability to blood tests via PA-based biomedical detection.

Identifiants

pubmed: 34008680
doi: 10.1039/d1lc00263e
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2586-2593

Auteurs

Wenxiu Zhao (W)

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. lqliu@sia.cn and Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China and University of Chinese Academy of Sciences, Beijing 100049, China.

Haibo Yu (H)

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. lqliu@sia.cn and Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China.

Yangdong Wen (Y)

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. lqliu@sia.cn and Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China and University of Chinese Academy of Sciences, Beijing 100049, China.

Hao Luo (H)

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. lqliu@sia.cn and Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China and University of Chinese Academy of Sciences, Beijing 100049, China.

Boliang Jia (B)

Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, SAR, China. wenjli@cityu.edu.hk.

Xiaoduo Wang (X)

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. lqliu@sia.cn and Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China.

Lianqing Liu (L)

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. lqliu@sia.cn and Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China.

Wen Jung Li (WJ)

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. lqliu@sia.cn and Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China and Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, SAR, China. wenjli@cityu.edu.hk.

Articles similaires

Arabidopsis Arabidopsis Proteins Osmotic Pressure Cytoplasm RNA, Messenger

Detailing organelle division and segregation in Plasmodium falciparum.

Julie M J Verhoef, Cas Boshoven, Felix Evers et al.
1.00
Plasmodium falciparum Mitochondria Apicoplasts Humans Animals

Low-cost portable sensor for rapid and sensitive detection of Pb

Niloufar Amin, Jiangang Chen, Qing Cao et al.
1.00
Lead Electric Capacitance Limit of Detection Electrodes Electrochemical Techniques
Deep Learning Humans Erythrocytes Erythrocytes, Abnormal Image Processing, Computer-Assisted

Classifications MeSH