<p>Heteroresistance (HR) is an antibiotic resistance phenotype characterized by the presence of rare resistant subpopulations (frequency ≈ 10<sup>−7</sup> to 10<sup>−4</sup>) within a main susceptible bacterial population. During antibiotic exposure, these subpopulations can be enriched and cause treatment failure. Standard antibiotic susceptibility testing (AST) often fails to detect HR, and the current gold-standard population analysis profile (PAP) test is labor-intensive and time-consuming. We present a digital phenotyping approach combining droplet microfluidics with image texture to detect HR from clinical isolates, including Gram-negative (<i>Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii</i>) and Gram-positive (<i>Staphylococcus aureus</i>) bacteria isolated from bloodstream infections. Our method achieves detection at subpopulation frequencies as low as 10<sup>−6</sup> in 12 to 30 h, depending on bacterial species, which is faster than the PAP test, together with single-cell resolution and high-throughput. This computationally assisted microfluidic platform enables rapid and accurate identification of HR, representing a step toward targeted antibiotic therapy in critical infections.</p>

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Droplet microfluidics with image texture quantification for detection of rare antibiotic-resistant subpopulations from bloodstream infections

  • Sagar N. Agnihotri,
  • Nikos Fatsis-Kavalopoulos,
  • Emma Vikdahl,
  • Jonas Windhager,
  • Agustin A. Corbat,
  • Dan I. Andersson,
  • Maria Tenje

摘要

Heteroresistance (HR) is an antibiotic resistance phenotype characterized by the presence of rare resistant subpopulations (frequency ≈ 10−7 to 10−4) within a main susceptible bacterial population. During antibiotic exposure, these subpopulations can be enriched and cause treatment failure. Standard antibiotic susceptibility testing (AST) often fails to detect HR, and the current gold-standard population analysis profile (PAP) test is labor-intensive and time-consuming. We present a digital phenotyping approach combining droplet microfluidics with image texture to detect HR from clinical isolates, including Gram-negative (Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii) and Gram-positive (Staphylococcus aureus) bacteria isolated from bloodstream infections. Our method achieves detection at subpopulation frequencies as low as 10−6 in 12 to 30 h, depending on bacterial species, which is faster than the PAP test, together with single-cell resolution and high-throughput. This computationally assisted microfluidic platform enables rapid and accurate identification of HR, representing a step toward targeted antibiotic therapy in critical infections.