Researchers Use Flow Cytometry to Distinguish Between Active and Latent TB
Method identifies subset of T cells as indicator of disease status.!--h2>
Researchers in Switzerland and South Africa claim using flow cytometry to identify the relative proportion of a subset of CD4+ T cells in patients with Mycobacterium tuberculosis (Mtb) infection can help accurately and rapidly distinguish between individuals with latent disease and those with active infection. Their studies suggest that substantial increases in the proportion of single-positive TNF-α+ Mtb-specific CD4+ T cells in infected patients correlate with active, rather than latent disease.
The work, by scientists from the Centre Hospitalier Universitaire Vaudois at the University of Lausanne, the Swiss Vaccine Research Institute, and the University of Cape Town’s South African Tuberculosis Vaccine Initiative, is published in Nature Medicine in a paper titled “Dominant TNF-α+ Mycobacterium tuberculosis–specific CD4+ T cell responses discriminate between latent infection and active disease.”
The rapid diagnosis of active Mtb infection remains a complex clinical and laboratory challenge that requires several clinical, radiological, histopathological, bacteriological, and molecular parameters, explain the scientists. They point out that although IFN-γ release assays measure responses to antigens expressed by Mtb and can discriminate between infection by the organism and immunity induced by the BCG vaccine, such assays can’t distinguish between active disease and latent infection.
Studies in the field of antiviral immunity have shown that polyfunctional profiles of virus-specific T cell responses, and not IFN-γ production alone, do correlate with disease activity. Building on such observations, the investigators used polychromatic flow cytometry to functionally characterize Mtb-specific T cells in infected subjects. The aim was to test the hypothesis that different cytokine profiles of pathogen-specific T cells may help discriminate between active disease and latent infection.
Using the polychromatic flow cytometry technique they showed that individuals with latent disease demonstrated largely polyfunctional Mtb-specific CD4+ T-cell responses, whereas individuals with active disease have largely monofunctional T-cell responses. More specifically, the relative number of single-positive TNF-α–producing CD4+ T cells was much higher in individuals with active disease.
The technique was then used to diagnose either latent or active Mtb in another 101 individuals to see how well the approach correlated with diagnosis based on existing techniques. Overall, the sensitivity and specificity of the flow cytometry-based assay was 67% and 92%, respectively, the positive predictive value was 80%, and the negative predictive value was 92.4%.
In their test cohort, a cutoff of 37.4% (or higher) of single-positive TNF-α-producing CD4+ T cells equated to a sensitivity of 100% and specificity of 96% when it came to diagnosing active disease. Further analyses of data suggested that a cutoff of 38.8% monofunctional T cells actually represents the value allowing the best separation between latent infection and active disease.