Multi-modal sensing with integrated machine learning to differentiate specific leukocytes targeted by electrically sensitive hybrid particles

Brandon K Ashley; Jianye Sui; Mehdi Javanmard; Umer Hassan

doi:10.1016/j.bios.2023.115661

Multi-modal sensing with integrated machine learning to differentiate specific leukocytes targeted by electrically sensitive hybrid particles

Biosens Bioelectron. 2023 Dec 1:241:115661. doi: 10.1016/j.bios.2023.115661. Epub 2023 Sep 3.

Authors

Brandon K Ashley¹, Jianye Sui², Mehdi Javanmard³, Umer Hassan⁴

Affiliations

¹ Department of Biomedical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA.
² Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA.
³ Department of Biomedical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA; Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA.
⁴ Department of Biomedical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA; Department of Electrical and Computer Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA; Global Health Institute, Rutgers, the State University of New Jersey, New Brunswick, NJ, 08901, USA. Electronic address: umer.hassan@rutgers.edu.

PMID: 37690356
PMCID: PMC10977608 (available on 2024-12-01)
DOI: 10.1016/j.bios.2023.115661

Abstract

The growing need for personalized, accurate, and non-invasive diagnostic technology has resulted in significant advancements, from pushing current mechanistic limitations to innovative modality developments across various disease-related biomarkers. However, there still lacks clinical solutions for analyzing multiple biomarkers simultaneously, limiting prognosis for patients suffering with complicated diseases or comorbidities. Here, we conceived, fabricated, and validated a multifrequency impedance cytometry apparatus with novel frequency-sensitive barcoded metal oxide Janus particles (MOJPs) as cell-receptor targeting agents. These microparticles are modulated by a metal oxide semi-coating which exhibit electrical property changes in a multifrequency electric field and are functionalized to target CD11b and CD66b membrane proteins on neutrophils. A multi-modal system utilizing supervised machine learning and simultaneous high-speed video microscopy classifies immune-specific surface receptors targeted by MOJPs as they form neutrophil-MOJP conjugates, based on multivariate multifrequency electrical recordings. High precision and sensitivity were determined based on the type of MOJPs conjugated with cells (>90% accuracy between neutrophil-MOJP conjugates versus cells alone). Remarkably, the design could differentiate the number of MOJPs conjugated per cell within the same MOJP class (>80% accuracy); which also improved comparing electrical responses across different MOJP types (>75% accuracy) as well. Such trends were consistent in individual blood samples and comparing consolidated data across multiple samples, demonstrating design robustness. The configuration may further expand to include more MOJP types targeting critical biomarker receptors in one sample and increase the modality's multiplexing potential.

Keywords: Biomarkers; Biosensor; Disease diagnostics; Impedance cytometry; Machine learning; Microfluidics.

MeSH terms

Biomarkers
Biosensing Techniques* / methods
Electric Impedance
Humans
Leukocytes
Machine Learning
Oxides

Substances

Biomarkers
Oxides

Grants and funding

T32 GM135141/GM/NIGMS NIH HHS/United States