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Efficient filter-in-centrifuge separation of low-concentration bacteria from blood
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Micro and Nanosystems.ORCID iD: 0000-0001-8248-6670
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Micro and Nanosystems.ORCID iD: 0000-0003-0477-6300
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Micro and Nanosystems.
2023 (English)In: Lab on a Chip, ISSN 1473-0197, E-ISSN 1473-0189, Vol. 23, no 19, p. 4334-4342Article in journal (Refereed) Published
Abstract [en]

Separating bacteria from infected blood is an important step in preparing samples for downstream bacteria detection and analysis. However, the extremely low bacteria concentration and extremely high blood cell count make efficient separation challenging. In this study, we introduce a method for separating bacteria from blood in a single centrifugation step, which involves sedimentation velocity-based differentiation followed by size-based cross-flow filtration over an inclined filter. Starting from 1 mL spiked whole blood, we recovered 32 ± 4% of the bacteria (Escherichia coli, Klebsiella pneumonia, or Staphylococcus aureus) within one hour while removing 99.4 ± 0.1% of the red blood cells, 98.4 ± 1.4% of the white blood cells, and 90.0 ± 2.6% of the platelets. Changing the device material could further increase bacteria recovery to >50%. We demonstrated bacterial recovery from blood spiked with 10 CFU mL−1. Our simple hands-off efficient separation of low-abundant bacteria approaches clinical expectations, making the new method a promising candidate for future clinical use.

Place, publisher, year, edition, pages
Royal Society of Chemistry, 2023. Vol. 23, no 19, p. 4334-4342
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-336628DOI: 10.1039/D3LC00594AISI: 001067116400001PubMedID: 37712252Scopus ID: 2-s2.0-85172200692OAI: oai:DiVA.org:kth-336628DiVA, id: diva2:1797648
Note

QC 20231031

Available from: 2023-09-15 Created: 2023-09-15 Last updated: 2025-12-15Bibliographically approved
In thesis
1. Rapid and Automated Sepsis Diagnosis Using Centrifugal Devices and Microfluidics
Open this publication in new window or tab >>Rapid and Automated Sepsis Diagnosis Using Centrifugal Devices and Microfluidics
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Abstract [sv]

Sepsis är ett livshotande tillstånd med hög dödlighet som orsakar över 13 miljoner dödsfall årligen. Hos patienter med septisk chock ökar risken för dödlig utgång med 8 % varje timme som behandlingen fördröjs. Nuvarande diagnostiska metoder är starkt beroende av blododling – en tidskrävande process som innefattar flera odlingssteg och kan ta flera dagar, vilket fördröjer riktad antibiotikabehandling. Detta understryker det akuta behovet av snabbare, odlingsfria diagnostiska metoder.

I detta arbete har vi utvecklat metoder för att direkt detektera bakterier i blod vid låga, kliniskt relevanta koncentrationer ( 10 CFU/mL), vilket möjliggör att helt kringgå den traditionella odlingsprocessen. Vi har dessutom utvecklat teknologier för direkt bakteriell identifiering och resistensbestämning (AST) från blod, utan behov av subkultivering eller fasta odlingssteg, vilket underlättar riktad antibiotikabehandling.

För detektion vid kliniskt relevanta koncentrationer kombinerade vi en centrifugeringsbaserad metod för effektiv isolering och koncentrering av bakterier från blod med mikrofluidisk infångning och automatiserad bakteriedetektering med hjälp av djupinlärning applicerad på mikroskopibilder. Vi utvecklade även två nya centrifu genheter för att automatisera ovanstående provberedning i ett enda steg: (1) en One-step-enhet och (2) en lutande filterenhet. Båda är fullt automatiserade, plug-and-play-system som enbart använder centrifugering och som isolerar och koncentrerar bakterier samtidigt som blodceller selektivt lyseras – helt utan manuella moment och med endast standard-laboratoriecentrifuger. One-step-enheten använder luft instängd i enheten för att styra vätskeflödet under centrifugering och kan sömlöst integreras i kliniska arbetsflöden. Enheten har tre huvudsakliga tillämpningar: (a) direkt subkultur från låga bakteriekoncentrationer, vilket eliminerar behovet av initial blododling; (b) artidentifiering via matrix-assisted laser desorption ionization time-of-flight masspektrometri (MALDI-TOF-MS); och (c) mikrofluidisk detektion av enskilda celler vid måttliga bakterielaster, samt färgmetrisk AST i enheten. Den lutande filterenheten använder storleksbaserad filtrering under centrifugering för att isolera och koncentrera bakterier, lysera blodceller och möjliggöra subkultur-baserad detektion.

Vi har även demonstrerat AST från positiva blododlingar med hjälp av One-step-enheten i kombination med en impedansbaserad cytometer, vilket ger resultat inom 2 timmar. Dessutom anpassade vi den lutande filterenheten för provberedning av avföringsprover, vilket möjliggjorde bakteriedetektering med Optical DNA Mapping och visade plattformens mångsidighet.

Tillsammans skapar dessa teknologier en snabb, heltäckande och automatiserad lösning för patogendetektion och antibiotikaprofilering, och lägger grunden för odlingsfri sepsisdiagnostik.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2025. p. 67
Series
TRITA-EECS-AVL ; 2025:71
Keywords
sepsis, bloodstream infections, diagnostics, precision medicine, automatic bacteria detection, antibiotic susceptibility, deep learning, AI driven microscopy
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-368646 (URN)978-91-8106-337-0 (ISBN)
Public defence
2025-09-19, F3, Lindstedtsvägen 26 & 28, Stockholm, 13:00 (English)
Supervisors
Note

QC 20250822

Available from: 2025-08-22 Created: 2025-08-20 Last updated: 2025-08-26Bibliographically approved

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van der Wijngaart, WouterOsaid, MohammadZeng, Kaiyang

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