Point-of-critical-care diagnostics for sepsis enabled by multiplexed micro and nanosensing technologies

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

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.

Digital filtering dissemination for optimizing impedance cytometry signal quality and counting accuracy

Improving biosensor performance which utilize impedance cytometry is a highly interested research topic for many clinical and diagnostic settings. During development, a sensor's design and external factors are rigorously optimized, but improvements in signal quality and interpretation are usually still necessary to produce a sensitive and accurate product. A common solution involves digital signal processing after sample analysis, but these methods frequently fall short in providing meaningful signal outcome changes. This shortcoming may arise from a lack of investigative research into selecting and using signal processing functions, as many choices in current sensors are based on either theoretical results or estimated hypotheses. While a ubiquitous condition set is improbable across diverse impedance cytometry designs, there lies a need for a streamlined and rapid analytical method for discovering those conditions for unique sensors. Herein, we present a comprehensive dissemination of digital filtering parameters applied on experimental impedance cytometry data for determining the limits of signal processing on signal quality improvements. Various filter orders, cutoff frequencies, and filter types are applied after data collection for highest achievable noise reduction. After designing and fabricating a microfluidic impedance cytometer, 9 µm polystyrene particles were measured under flow and signal quality improved by 6.09 dB when implementing digital filtering. This approached was then translated to isolated human neutrophils, where similarly, signal quality improved by 7.50 dB compared to its unfiltered original data. By sweeping all filtering conditions and devising a system to evaluate filtering performance both by signal quality and object counting accuracy, this may serve as a framework for future systems to determine their appropriately optimized filtering configuration.

MACHINE LEARNING ENABLES QUANTIFYING CELL-JANUS PARTICLE CONJUGATES THROUGH MICROFLOWING IMPEDANCE SIGNALS

In this work, we demonstrate the differentiation of demodulated multifrequency signals from impedance sensitive microparticles when targeting surface receptors on neutrophils in a microfluidic impedance cytometer. These scheme uses a single signal input and detection configuration, and machine learning can differentiate particle types with up to 82% accuracy.

Antibody-functionalized aluminum oxide-coated particles targeting neutrophil receptors in a multifrequency microfluidic impedance cytometer

Personalized diagnostics of infectious diseases require monitoring disease progression due to their ever-changing physiological conditions and the multi-faceted organ system mechanisms involved in disease pathogenesis. In such instances, the recommended clinical strategies involve multiplexing data collection from critical biomarkers related to a patient's conditions along with longitudinal frequent patient monitoring. Numerous detection technologies exist both in research and commercial settings to monitor these conditions, however, they fail to provide biomarker multiplexing ability with design and data processing simplicity. For a recently conceived multiplexing biomarker modality, this work demonstrates the use of electrically sensitive microparticles targeting and identifying membrane receptors on leukocytes using a single detection source, with a high potential for multiplexing greater than any existing impedance-based single-detection scheme. Here, polystyrene microparticles are coated with varying thicknesses of metal oxides, which generate quantifiable impedance shifts when exposed to multifrequency electric fields depending on the metal oxide thickness. Using multifrequency impedance cytometry, these particles can be measured and differentiated rapidly across one coplanar electrode scheme. After surface-functionalizing particles with antibodies targeting CD11b and CD66b receptors, the particles are combined with isolated neutrophils to measure receptor expression. A combination of data analysis techniques including multivariate analysis, supervised machine learning, and unsupervised machine learning was able to accurately differentiate samples with up to 91% accuracy. This proof-of-concept study demonstrates the potential for these oxide-coated particles for enumerating specific leukocytes enabling multiplexing. Further, additional coating thicknesses or different metal oxide materials can enable a compendium of multiplexing targeting resource to be used to develop a high-multiplexing sensor for targeting membrane receptor expression.

Aluminum Oxide-Coated Particle Differentiation Employing Supervised Machine Learning and Impedance Cytometry

This article uses a supervised machine learning (ML) system for identifying groups of nanoparticles coated with metal oxides of varying thicknesses using a microfluidic impedance cytometer. These particles generate unique impedance signatures when probed with a multifrequency electric field and finds applications in enabling many multiplexed biosensing technologies. However, current experimental and data processing techniques are unable to sensitively differentiate different metal oxide coated particle types. Here, we employ various machine learning models and collect multiple particle metrics measured. In reported experiments, a 75% accuracy was determined to separate aluminum oxide coated (10nm and 30nm), which is significantly greater than observing only univariate data between different microparticle types. This approach will enable ML models to differentiate such particles with greater accuracies.

Simultaneous detection of sepsis host response biomarkers in whole blood using electrochemical biosensor

Sepsis is a silent killer, caused by a syndromic reaction of the body's immune system to an infection that is typically the ultimate pathway to mortality due to numerous infectious diseases, including COVID‐19 across the world. In the United States alone, sepsis claims 220,000 lives, with a dangerously high fatality rate between 25% and 50%. Early detection and treatment can avert 80% of sepsis mortality which is currently unavailable in most healthcare institutions. The novelty in this work is the ability to simultaneously detect eight (IL‐6, IL‐8, IL‐10, IP‐10, TRAIL, d‐dimer, CRP, and G‐CSF) heterogeneous immune response biomarkers directly in whole blood without the need for dilution or sample processing. The DETecT sepsis (Direct Electrochemical Technique Targeting Sepsis) 2.0 sensor device leverages electrochemical impedance spectroscopy as a technique to detect subtle binding interactions at the metal/semi‐conductor sensor interface and reports results within 5 min using only two drops (~100 μl) of blood. The device positively (r >0.87) correlated with lab reference standard LUMINEX for clinical translation using 40 patient samples. The developed device showed diagnostic accuracy greater than 80% (AUC >0.8) establishing excellent specific and sensitive response. Portable handheld user‐friendly feature coupled with precise quantification of immune biomarkers makes the device amenable in a versatile setting providing insights on patient's immune response. This work highlights an innovative solution of enhancing sepsis care and management in the absence of a decision support device in the continuum of sepsis care.

Faster infection diagnostics for intensive care unit (ICU) patients

INTRODUCTION

: The patient admitted to intensive care units (ICU) is critically ill, to some extent immunosuppressed, with a high risk of infection, sometimes by multidrug-resistant microorganisms. In this context, the intensivist expects from the microbiology service quick and understandable information so that appropriate antimicrobial treatment for that particular patient and infection can be initiated.

AREAS COVERED

: In this review of recent literature (2015-2021), we identified diagnostic methods for the most prevalent infections in these patients through a search of the databases Pubmed, evidence-based medicine online, York University reviewers group, Cochrane, MBE-Trip, and Sumsearch using the terms: adult, clinical laboratory techniques, critical care, early diagnosis, microbiology, molecular diagnostic techniques, spectrometry and metagenomics.

EXPERT OPINION

: There has been an exponential surge in diagnostic systems used directly on blood and other samples to expedite microbial identification and antimicrobial susceptibility testing of pathogens. Few studies have thus far assessed their clinical impact; final outcomes will also depend on preanalytical and post-analytical factors. Besides, many of the resistance mechanisms cannot yet be detected with molecular techniques, which impairs the prediction of the actual resistance phenotype.

Nonetheless, this is an exciting field with much yet to explore.

Rapid Detection of Sepsis: Recent Advances in Biomarker Sensing Platforms

Sepsis is a major cause of mortality among hospitalized patients worldwide. Rapid diagnosis is critical as early treatments have been demonstrated to improve survival. Despite the importance of early detection, current technologies and clinical methods are often insufficient due to their lack of the necessary speed, selectivity, or sensitivity. The development of rapid sensing platforms that target sepsis-related biomarkers could significantly improve the outcomes of patients. This Mini-Review focuses on the recent advances in rapid diagnosis of soluble biomarkers in blood with the emphasis on different configurations of point-of-care (POC) instruments. Specifically, it first describes the commonly targeted biomarkers and the mechanisms by which they are detected. Then, it highlights the recently developed sensors that aim to reduce the total time of diagnosis without sacrificing selectivity and limit of detection. These sensors are categorized based on their distinct sensing and transduction mechanisms. Finally, it concludes with a brief outlook over future developments of multiplexed sensors.

Investigating Cell-Particle Conjugate Orientations in a Microfluidic Channel to Ameliorate Impedance-based Signal Acquisition and Detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:7233-7236. [PMID: 34892768 PMCID: PMC8767423 DOI: 10.1109/embc46164.2021.9630171]

Many biomedical experimental assays rely on cell-to-microparticle conjugation and their subsequent detection to quantify disease-related biomarkers. In this report, we investigated the effect of particle attachment position on a cell's surface to a signal acquired using impedance cytometry. We also present a novel configuration of independent coplanar microelectrodes positioned at the bottom and top of the microfluidic channel. In simulation results, our configuration accurately identifies different particle positions around the cell. We implemented a channel design with focusing regions between electrodes, and considered external factors around the channel such as polydimethylsiloxane (PDMS) interacting with the electric field and physical constraints of top electrodes placed farther away from the channel which improves detection accuracy.