Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.

Background: Flow cytometry is a powerful tool for identifying and quantifying various cell markers, such as viability, vitality, and individual cell age, at single-cell stages. However, cell autofluorescence and marker fluorophore signals overlap at low fluorescence intensities. Thus, these signals must be unmixed before determining the age fraction. Methods and Results:A comparison was made between principal component regression (PCR) and random forest (RF) to predict autofluorescence signals of Saccharomyces pastorianus var. carlsbergensis in a flow cytometer. RF provided better prediction results than the PCR and was therefore determined to be better suited for unmixing signals. In the subsequent application for unmixing the autofluorescence signal from the marker fluorophore signal, the Gaussian mixture analysis based on RF was in better agreement with the microscopy-determined replicative age distribution than the PCR-based method. Conclusion:The proposed approach of single-laser spectral unmixing and subsequent Gaussian mixture analysis showed that the microscopy data was consistent with the unmixed fluorescence spectra. The demonstrated approach enables fast and reliable unmixing of flow cytometric spectral data using a single-laser spectral unmixing method. This analysis method enables age determination of cells in industrial processes. This age determination allows for quantifying the yeast cell's age fractions, providing a detailed view of age-related changes. Additionally, the bud scar labeling technique can be used to determine age-related changes in Pichia pastoris yeast for biotechnological applications or recombinant protein expression.

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