Beyond the Billion: Dose-Response Immunophenotyping and Machine Learning Classification of Live versus Heat-Inactivated Gram-Positive Probiotic Strains in Human Peripheral Blood Mononuclear Cells

Probiotic research is constrained by three pervasive yet insufficiently challenged assumptions: the requirement for a minimum of one billion colony-forming units for efficacy, the necessity for gut colonization, and the inherent superiority of live over inactivated preparations. This study addresses these gaps through a fully factorial experimental design evaluating ten Gram-positive probiotic strains in both viable (Active Fluorescent Units, AFU) and heat-inactivated (Total Fluorescent Units, TFU) forms across three flow cytometry-verified concentrations (105, 106, 107 cells/well per ISO 19344:2015) in primary human peripheral blood mononuclear cells (PBMC) from a single healthy male Caucasian donor (58 years), with simultaneous quantification of 17 cytokines by BioPlex suspension array. Viable preparations induced profoundly greater absolute cytokine responses than heat-inactivated preparations across 14 of 17 analytes, heat-inactivated preparations demonstrated stronger dose-response correlations (mean within-strain Spearman {rho} up to 1.00) for 13 of 17 cytokines, a finding we attribute to the uncontrolled proliferation of live bacteria during 24-hour co-culture compressing the effective concentration range. Six of ten viable strains exhibited monotonically increasing profiles; two strains displayed non-monotonic bell-shaped kinetics with peak activity at 106 AFU/well and significant attenuation at 107, directly falsifying the assumption that dose escalation uniformly increases immunological activity. MCP-1 was the sole cytokine showing no significant difference between viability states (p = 0.61, fold-change 1.1), providing an internal methodological control. In this single-donor model, unsupervised hierarchical clustering identified three immunological phenotype clusters, requiring multi-donor validation before these groupings can be treated as generalizable biological phenotypes, with Random Forest classification achieving 86.7% internal partition-recovery consistency (clusters derived from the same data; not an estimate of generalization to novel strains) versus 33.3% chance; In this single-donor experiment, IL-13, IL-12p70, and IFN-{gamma}, not IL-6 or IL-1{beta}, were the primary discriminators of strain identity; generalizability of this ranking requires multi-donor validation. Heat-inactivated preparations achieved [≥]70% functional equivalence relative to viable preparations at 107 TFU/well for the majority of responsive strains (Functional Equivalence Dose, FED70), while one strain remained immunologically inert in heat-inactivated form across all concentrations, a finding subject to the caveat that no positive control stimulus was included to formally verify PBMC functional competence on the experimental day. These findings establish a methodological framework integrating flow cytometric standardization, multiplex immunophenotyping, and machine learning for evidence-based dose characterization, postbiotic functional equivalence assessment, and data-driven strain classification in probiotic research (all p-values are descriptive within a single-donor experimental context). CONTRIBUTION TO THE FIELDThe probiotic field faces a structural paradox: products are commercialized at doses spanning three orders of magnitude without any functional evidence anchoring a specific concentration to a specific immunological outcome, and existing colony-forming unit (CFU)-based quantification methodology is inherently inapplicable to the growing category of heat-inactivated postbiotic preparations. This study addresses both gaps simultaneously and introduces a third layer of novelty -- machine learning-based functional classification of probiotic strains by their cytokine-induction phenotype -- for which no published precedent exists in the literature. By systematically characterizing ten Gram-positive probiotic strains in both viable and heat-inactivated forms across three concentration levels (105, 106, 107 cells/well) using flow cytometric input standardization (Active Fluorescent Units/Total Fluorescent Units per ISO 19344:2015) and a 17-plex BioPlex cytokine readout in primary human PBMC from a single healthy donor, this work provides the first systematic multi-strain dose-response immunophenotyping dataset for Gram-positive probiotic strains. Three distinct immunological phenotype clusters are identified, including non-monotonic bell-shaped kinetics in two strains and near-complete immunological inertness in the heat-inactivated form of one strain, establishing that dose-response shape and thermal inactivation sensitivity are strain-intrinsic properties that cannot be generalized across species or formulations. Data-driven hierarchical clustering of cytokine fingerprints identified three candidate functional phenotype groups, internally re-recovered by Random Forest with 86.7% Leave-One-Out consistency -- an internal partition-recovery metric that requires external validation on independent strain cohorts before the groups can be treated as generalizable biological phenotypes. Seventeen cytokines were quantified simultaneously via Bio-Plex multiplex immunoassay. Live bacteria induced significantly greater responses across 14 of 17 cytokines, with tumor necrosis factor-alpha showing a 356.9-fold increase compared to heat-inactivated preparations. However, heat-inactivated bacteria exhibited stronger dose-dependency (Spearman rho up to 0.70), suggesting a more faithful representation of true dose-response relationships unconfounded by bacterial proliferation during co-culture. Strain-specific profiling revealed three distinct immunomodulatory phenotypes emerging entirely from data-driven analysis without prior functional annotation.