Label-free biophysical markers from whole blood microfluidic immune profiling reveals severe immune response signatures

Kerwin K. Zeming, Rohan Vernekar, Mui Teng Chua, Kai Yun Quek, Greg Sutton, Timm Krüger, Win Sen Kuan, Jongyoon Han

Research output: Contribution to journalArticlepeer-review

Abstract

Disease manifestation and severity from acute infections are often due to hyper-aggressive host immune responses which changes within minutes. Current methods for early diagnosis of infections focus on detecting low abundance pathogens, which are time-consuming, of low sensitivity, and does not reflect the severity of the pathophysiology appropriately. The approach here focuses on profiling the rapidly changing host inflammatory response, which in its over-exuberant state, leads to sepsis and death. A 15-min label-free immune profiling assay from 20 µL of unprocessed blood using unconventional L and Inverse-L shaped pillars of deterministic lateral displacement (DLD) microfluidic technology was developed. The 10 hydrodynamic interactions of deformable immune cells enable simultaneous sorting and immune response profiling in whole blood. Preliminary clinical study of 85 donors in emergency department with a spectrum of immune response states from healthy to severe inflammatory response shows correlation with biophysical markers of immune cell size, deformability, distribution, and cell counts. The speed of patient stratification demonstrated
here has promising impact in deployable point-of-care systems for acute infections triage, risk management and resource allocation at emergency departments, where clinical manifestation of infections severity may not be clinically evident as compared to inpatients in the wards or intensive care units.
Original languageEnglish
Article number2006123
Number of pages53
JournalSmall
Early online date16 Feb 2021
DOIs
Publication statusE-pub ahead of print - 16 Feb 2021

Keywords

  • immune profiling
  • deterministic lateral displacement
  • cell deformability
  • inflammation biomarkers
  • infection

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