Rapid culture-free analysis of bloodstream pathogens for transforming sepsis care

The National Institute of Allergy and Infectious Diseases (NIAID) awarded Massachusetts Institute of Technology $424,265 under the Allergy and Infectious Diseases Research program (CFDA 93.855) on August 7, 2025, for a project period extending through July 31, 2027. The project develops an integrated Density-shift Immunocapture Separation-Raman (DIMS-RAMAN) platform designed to rapidly isolate and analyze bacterial pathogens from blood samples within approximately 1-2 hours. This culture-free diagnostic system combines a novel fluidic separation platform that isolates microbial cells directly from whole blood with Raman spectroscopy analysis and machine learning-based classification to enable rapid enumeration, bacterial strain identification, and antibiotic resistance assessment—critical capabilities for guiding timely sepsis treatment and reducing inappropriate broad-spectrum antibiotic use.

The research addresses a significant public health challenge, as sepsis kills over 10 million people globally annually and represents the most expensive condition treated in U.S. hospitals at over $60 billion yearly. By providing actionable diagnostic information within 1-2 hours rather than relying on traditional culture-dependent methods that require 24-48 hours, the DIMS-RAMAN platform has potential to transform sepsis care through early pathogen identification and targeted antimicrobial selection. Preliminary studies demonstrated the ability to isolate and concentrate Salmonella at single-cell resolution from blood samples within 3 hours, establishing the technical feasibility of the proposed approach.

Abstract

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to an infection. Majority of sepsis is bacterial, and is typically accompanied by bacteremia, i.e., the presence of bacteria in blood at 1-104 CFU/ML. Each year, sepsis kills over 10 million people globally, and is the most expensive condition treated in US hospitals costing over $60 billion annually. IT is a medical emergency requiring early recognition and immediate treatment, but the lack of rapid and actionable tests leads to delays in recognition and treatment of sepsis, non-optimal treatment, and overuse of broad-spectrum antibiotics resulting in emergence of drug- resistant pathogens. We propose an integrated "dims-raman" platform to rapidly isolate and analyze bacterial cells from blood samples by machine learning-enabled raman spectroscopy within approximately 1-2 h to provide actionable information to guide treatment in a timely manner. The proposed system combines a novel yet simple fluidic platform called density-shift immunocapture separation (dims) that enables direct separation of microbial cells from whole blood, with raman spectroscopy - an analytical technique that generates molecular fingerprints of targets from the inelastic scattering of light in a sample - combined with machine learning-based spectral analysis and classification. We envision that target cells isolated by dims are concentrated into micro/nanoliter volumes for direct microscopic observation and raman spectroscopy at the single cell level, enabling culture-free enumeration, and machine learning-enabled identification of bacterial strains and assessment of antibiotic resistance. Our preliminary study using dims was able to isolate, concentrate by 10,000X, and image salmonella (~30 CFU/ML) at the single cell level from blood within 3 h. Bacterial cells have unique raman signatures, which, using machine learning based spectral analysis have enabled differentiation of the 30 most common sepsis related pathogens (including methicillin resistant and susceptible s. Aureus) with a 99% classification accuracy using just 10 cells. Moreover, our work demonstrated applications in heterogeneous mixtures of bacteria species and blood cells in liquid droplets with 92% accuracy. In addition, we further demonstrated antibiotic CO-incubation free susceptibility testing across the four major anti-tuberculosis drugs with 98% accuracy. The proposed work aims to develop an integrated dims-raman platform for sepsis by demonstrating integrated isolation, concentration, detection, and raman analysis at the single cell level of the most common sepsis-causing bacteria with a sensitivity of 1 CFU/ML from whole blood within 1-2 h. If successful, the proposed work could eventually lead to a platform capable of returning pathogen type, concentration, and antibiotic susceptibility of clinically-relevant pathogens from blood without relying on culture, thus transforming sepsis detection and informing appropriate antibiotic treatment in a timely manner, saving human lives and reducing the cost of care.