Microfluidic chip with reversible interface for noninvasive remission status monitoring and prognosis prediction of acute myeloid leukemia.

Biosens Bioelectron

Discipline of Intelligent Instrument and Equipment, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China; The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, The Key Laboratory for Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China; Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen, 361005, China. Electronic address:

Published: January 2023

Acute myeloid leukemia (AML) requires close monitoring of remission status for timely disease management. Liquid biopsy serves as a noninvasive approach for evaluating treatment response and guiding therapeutic modifications. Herein, we designed a non-invasive Leukemic stem cell Specific Capture Chip (LSC-Chip) with reversible recognition interface for AML remission status monitoring and prognosis prediction. A stem cell marker CD34 antibody coated herringbone chip with disulfide linkers was designed to capture and release leukemic stem cells (LSCs) in peripheral blood for efficient LSC enumeration and downstream single-cell analysis. Samples from 32 AML patients and 10 healthy donors were recruited for LSC enumeration and prognosis-associated subtyping with panels of official LSC markers (CD34/CD123/CD38) and (Tie2/CD34/CD123/CD38), respectively. A cutoff value of 2.5 LSCs per milliliters of peripheral blood can be used to precisely distinguish non-remission AML patients from complete remission group. Moreover, single-cell RNA-seq of LSCs was performed to check different transcriptional profiles of LSC subtypes. Overall, the LSC-Chip with reversible recognition interface enabled reliable detection of LSCs from AML patient samples for noninvasive remission status monitoring and prognosis prediction in clinical AML management.

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http://dx.doi.org/10.1016/j.bios.2022.114803DOI Listing

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