Rapid and Automated Sepsis Diagnosis Using Centrifugal Devices and Microfluidics

Sepsis is a life-threatening condition with high mortality, claiming over 13 million lives annually. In patients with septic shock, each hour of delayed treatment increases the risk of death by 8%. Current diagnostic methods rely heavily on blood culture, a time-consuming process involving multiple culture steps that can take several days, delaying targeted antibiotic therapy. This underscores the urgent need for faster, culture-free diagnostic approaches. In this work, we developed methods to detect bacteria directly from blood at low, clinically relevant concentrations ( 10 CFU/mL), enabling the potential to bypass the traditional culture process entirely. We further established technologies for direct bacterial identification and antimicrobial susceptibility testing (AST) from blood, bypassing subculture or solid culture steps, thereby facilitating targeted antibiotic therapy. For detection at clinically relevant concentrations, we combined a centrifugation-based method for efficient bacterial isolation and concentration from blood with microfluidic trapping and automated bacterial detection using deep learning applied to microscopy images. We also designed two novel centrifugal devices to automate the above sample preparation in a single step: (1) a One-step device and (2) an inclined filter device. Both are fully automated, plug-and-play, centrifuge-only systems that isolate and concentrate bacteria while selectively lysing blood cells—without manual intervention and using only standard laboratory centrifuges. The One-step device employs trapped air to control fluid movement during centrifugation, integrating seamlessly into clinical workflows and supporting three key applications: (a) direct subculturing from low bacterial concentrations, eliminating the need for initial blood culture; (b) species identification via matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS); and (c) microfluidic single-cell detection from moderate bacterial loads, along with in-device colorimetric AST. The inclined filter device uses size-based filtration during centrifugation to isolate and concentrate bacteria, lyse blood cells, and enable subculture-based detection. We further demonstrated AST from positive blood cultures using the One-step device in combination with an impedance-based cytometer, delivering results within 2 hours. Additionally, we adapted the inclined filter device for stool sample preparation, enabling bacterial detection using Optical DNA Mapping and demonstrating the platform’s versatility. Together, these technologies provide a rapid, comprehensive, and automated solution for pathogen detection and antibiotic profiling, laying the foundation for same-shift, culture-free sepsis diagnostics.

Publication Details

Journal
KTH Publication Database DiVA (KTH Royal Institute of Technology)
Published
2025-01-01
Primary Topic
Geochemistry and Geologic Mapping
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article
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article

Rapid and Automated Sepsis Diagnosis Using Centrifugal Devices and Microfluidics

KTH Publication Database DiVA (KTH Royal Institute of Technology)
Geochemistry and Geologic Mapping
article

Rapid and Automated Sepsis Diagnosis Using Centrifugal Devices and Microfluidics

article en

Abstract

Sepsis is a life-threatening condition with high mortality, claiming over 13 million lives annually. In patients with septic shock, each hour of delayed treatment increases the risk of death by 8%. Current diagnostic methods rely heavily on blood culture, a time-consuming process involving multiple culture steps that can take several days, delaying targeted antibiotic therapy. This underscores the urgent need for faster, culture-free diagnostic approaches. In this work, we developed methods to detect bacteria directly from blood at low, clinically relevant concentrations ( 10 CFU/mL), enabling the potential to bypass the traditional culture process entirely. We further established technologies for direct bacterial identification and antimicrobial susceptibility testing (AST) from blood, bypassing subculture or solid culture steps, thereby facilitating targeted antibiotic therapy. For detection at clinically relevant concentrations, we combined a centrifugation-based method for efficient bacterial isolation and concentration from blood with microfluidic trapping and automated bacterial detection using deep learning applied to microscopy images. We also designed two novel centrifugal devices to automate the above sample preparation in a single step: (1) a One-step device and (2) an inclined filter device. Both are fully automated, plug-and-play, centrifuge-only systems that isolate and concentrate bacteria while selectively lysing blood cells—without manual intervention and using only standard laboratory centrifuges. The One-step device employs trapped air to control fluid movement during centrifugation, integrating seamlessly into clinical workflows and supporting three key applications: (a) direct subculturing from low bacterial concentrations, eliminating the need for initial blood culture; (b) species identification via matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS); and (c) microfluidic single-cell detection from moderate bacterial loads, along with in-device colorimetric AST. The inclined filter device uses size-based filtration during centrifugation to isolate and concentrate bacteria, lyse blood cells, and enable subculture-based detection. We further demonstrated AST from positive blood cultures using the One-step device in combination with an impedance-based cytometer, delivering results within 2 hours. Additionally, we adapted the inclined filter device for stool sample preparation, enabling bacterial detection using Optical DNA Mapping and demonstrating the platform’s versatility. Together, these technologies provide a rapid, comprehensive, and automated solution for pathogen detection and antibiotic profiling, laying the foundation for same-shift, culture-free sepsis diagnostics.

KTH Publication Database DiVA (KTH Royal Institute of Technology)
Openalex Percentile: Top 67%
Geochemistry and Geologic Mapping
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