Label-free microfluidic cell sorting and detection for rapid blood analysis. Issue 5 (19th January 2023)

• Record Type: Journal Article
• Authors: Lu, Nan; Tay, Hui Min; Petchakup, Chayakorn; He, Linwei; Gong, Lingyan; Maw, Kay Khine; Leong, Sheng Yuan; Lok, Wan Wei; Ong, Hong Boon; Guo, Ruya; Li, King Ho Holden; Hou, Han Wei
• Is Part Of: Lab on a chip. Volume 23:Issue 5(2023)
• Page Start: 1226
• Page End: 1257
• Publication Date: 2023-01-19
• Subjects: Miniature electronic equipment -- Periodicals; Combinatorial chemistry -- Periodicals; Biotechnology -- Periodicals; 543.0813
• DOI: 10.1039/d2lc00904h
• Languages: English
• ISSNs: 1473-0197

Abstract

Abstract : This review focuses on recent microfluidic technologies for label-free blood cell separation, detection and coupling with machine learning approaches. Abstract : Blood tests are considered as standard clinical procedures to screen for markers of diseases and health conditions. However, the complex cellular background (>99.9% RBCs) and biomolecular composition often pose significant technical challenges for accurate blood analysis. An emerging approach for point-of-care blood diagnostics is utilizing "label-free" microfluidic technologies that rely on intrinsic cell properties for blood fractionation and disease detection without any antibody binding. A growing body of clinical evidence has also reported that cellular dysfunction and their biophysical phenotypes are complementary to standard hematoanalyzer analysis (complete blood count) and can provide a more comprehensive health profiling. In this review, we will summarize recent advances in microfluidic label-free separation of different blood cell components including circulating tumor cells, leukocytes, platelets and nanoscale extracellular vesicles. Label-free single cell analysis of intrinsic cell morphology, spectrochemical properties, dielectric parameters and biophysical characteristics as novel blood-based biomarkers will also be presented. Next, we will highlight research efforts that combine label-free microfluidics with machine learning approaches to enhance detection sensitivity and specificity in clinical studies, as well as innovative microfluidic solutions which are capable of fully integrated and label-free blood cell sorting and analysis. Lastly, we will envisage the current challenges and future outlook of label-free microfluidics platforms for high throughput multi-dimensional blood cell analysis to identify non-traditional circulating biomarkers for clinical diagnostics.