Immune profiling of <i>Mycobacterium tuberculosis</i>-specific T cells in recent and remote infection

Mpande, Cheleka A M; Rozot, Virginie; Mosito, Boitumelo; Musvosvi, Munyaradzi; Dintwe, One; Bilek, Nicole; Hatherill, Mark; Scriba, Thomas J.; Nemes, Elisa

doi:10.1101/2020.11.13.20230946

Cited by 3 publications

(4 citation statements)

References 43 publications

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“…The multiple functions of B cells provide several avenues for leveraging B cells as a platform for cell-based therapies, including antigen-presenting B cells as a cancer immunotherapy and protein production for rare genetic diseases (6,9). Immune profiling of activation of NK, B, and T cells to a stimulus (such as an antigen from a virus or bacterium) can be used to identify the potency of a cell therapy and potentially predict outcome (10)(11)(12), but current methods are destructive of blood or tissue and require labor-intensive techniques that take hours to days to complete and interpret. New labelfree and non-destructive tools are needed to assess lymphocyte activation and subtype in single cells in a more rapid manner.…”

Section: Introductionmentioning

confidence: 99%

Autofluorescence lifetime imaging classifies human lymphocyte activation and subtype

Schmitz

Tweed

Rehani

et al. 2023

Preprint

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New non-destructive tools are needed to reliably assess lymphocyte function for immune profiling and adoptive cell therapy. Optical metabolic imaging (OMI) is a label-free method that measures the autofluorescence intensity and lifetime of metabolic cofactors NAD(P)H and FAD to quantify metabolism at a single-cell level. Here, we investigate whether OMI can resolve metabolic changes between human quiescent versus IL4/CD40 activated B cells and IL12/IL15/IL18 activated memory-like NK cells. We found that quiescent B and NK cells were more oxidized compared to activated cells. Additionally, the NAD(P)H mean fluorescence lifetime decreased and the fraction of unbound NAD(P)H increased in the activated B and NK cells compared to quiescent cells. Machine learning classified B cells and NK cells according to activation state (CD69+) based on OMI parameters with up to 93.4% and 92.6% accuracy, respectively. Leveraging our previously published OMI data from activated and quiescent T cells, we found that the NAD(P)H mean fluorescence lifetime increased in NK cells compared to T cells, and further increased in B cells compared to NK cells. Random forest models based on OMI classified lymphocytes according to subtype (B, NK, T cell) with 97.8% accuracy, and according to activation state (quiescent or activated) and subtype (B, NK, T cell) with 90.0% accuracy. Our results show that autofluorescence lifetime imaging can accurately assess lymphocyte activation and subtype in a label-free, non-destructive manner.

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Section: Introductionmentioning

confidence: 99%

Autofluorescence lifetime imaging classifies human lymphocyte activation and subtype

Schmitz

Tweed

Rehani

et al. 2023

Preprint

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“…Innate and adaptive effector responses were measured in stimulated PBMCs using flow cytometry (Supplementary Methods S1 and [16]). Five stimulations were used to induce M .…”

Section: Methodsmentioning

confidence: 99%

“…Innate and adaptive effector responses were measured in stimulated PBMCs using flow cytometry (Supplementary Methods S1 and [16]). Five stimulations were used to induce M.tb and nonspecific T cell responses in the adaptive dataset: peptide pools spanning E6C10; peptide pools spanning EspC, EspF and Rv2348c (collectively termed Esp), which are both are specific for M.tb; M.tb-lysate, which is a mixture of M.tb-specific antigens, some of which are cross-reactive with other mycobacteria; Staphylococcus Enterotoxin B (SEB), the positive control; or the cells were left unstimulated as negative control.…”

Section: Immune Measurementsmentioning

confidence: 99%

Multidimensional analysis of immune response identified biomarkers of recentMycobacterium tuberculosisinfection

Lloyd

Steigler

Mpande

et al. 2021

Preprint

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The risk of tuberculosis (TB) disease is higher in individuals with recent Mycobacterium tuberculosis (M.tb) infection compared to individuals with more remote, established infection. We aimed to define blood-based biomarkers to distinguish between recent and remote infection, which would allow targeting of recently infected individuals for preventive TB treatment. We hypothesized that integration of multiple immune measurements would outperform the diagnostic performance of a single biomarker. Analysis was performed on different components of the immune system, including adaptive and innate responses to my-cobacteria, measured on recently and remotely M.tb infected adolescents. The datasets were standardized using variance stabilizing (vast) scaling and missing values were imputed using a multiple factor analysis-based approach. For data integration, we compared the performance of a Multiple Tuning Parameter Elastic Net (MTP-EN) to a standard EN model, which was built to the single datasets. Biomarkers with non-zero coefficients from the optimal single data EN models were then isolated to build logistic regression models. A decision tree and random forest model were used for statistical validation. We found no difference in the predictive performances of the optimal MTP-EN model and the EN model [average area under the receiver operating curve (AUROC)=0.93]. EN models built to the integrated dataset and the adaptive dataset yielded identically high AUROC values (average AUROC=0.91), while the innate data EN model performed poorly (average AUROC=0.62). Results also indicated that integration of adaptive and innate biomarkers did not outperform the adaptive biomarkers alone (Likelihood Ratio Test χ2=6.09, p=0.808). From a total of 193 variables, the level of HLA-DR on ESAT6/CFP10-specific Th1 cytokine-expressing CD4 cells was the strongest biomarker for recent M.tb infection. The discriminatory ability of this variable was confirmed in both tree-based models.A single biomarker measuring M.tb-specific T cell activation yielded excellent diagnostic potential to distinguish between recent and remote M.tb infection.

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“…They selected a subset of available blood samples from a large prospective adolescent cohort study that were serially tested with QuantiFERON-TB Gold In-Tube (QFT) to define "recent" (QFT conversion < 6 months) and "remote" (persistent QFT+ for > 1 year) TB infection reactivity. They identified and defined the ΔHLA-DR median fluorescence intensity (MFI) biomarker as the difference in HLA-DR expression between IFN-γ+TNF+ Mtb-specific and total CD3+ T cells (12,13). The diagnostic performance of this composite FC biomarker was assessed by blinded prediction in test cohorts with "recent" versus "remote" TB infection reactivity.…”

Section: Accurate Diagnosis and Estimation Of Risk In Latent Tuberculmentioning

confidence: 99%

New Diagnostics to Infer Risk in Tuberculosis: Is the Term “Latent Tuberculosis Infection” Obsolete?

Escalante

Wilson

2021

Am J Respir Crit Care Med

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Immune profiling of Mycobacterium tuberculosis-specific T cells in recent and remote infection

Cited by 3 publications

References 43 publications

Autofluorescence lifetime imaging classifies human lymphocyte activation and subtype

Autofluorescence lifetime imaging classifies human lymphocyte activation and subtype

Multidimensional analysis of immune response identified biomarkers of recentMycobacterium tuberculosisinfection

New Diagnostics to Infer Risk in Tuberculosis: Is the Term “Latent Tuberculosis Infection” Obsolete?

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