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Nienałtowski, K.

Publications and source records attributed to Nienałtowski, K..

2 recordsLinked to original sources

Single-cell profiling quantifies the dose gap between population-average and complete pY-STAT1/3 suppression by JAK inhibitors

Inhibitor potency is commonly assessed from population-average responses, but how much single-cell signaling remains under strong average suppression is less clear. We measured nuclear STAT1 and STAT3 tyrosine phosphorylation in two human monocytic cell lines stimulated with IFN-{gamma}; and IL-10 and treated with six JAK-pathway inhibitors. Across 24 profiles, we compared arithmetic-mean inhibition and inhibition of the mean of log10-transformed single-cell responses (log-mean) with the fractions of cells leaving the stimulated range (partial inhibition) or entering the non-stimulated range (complete inhibition). At the lowest tested doses where arithmetic-mean or log-mean inhibition reached at least 80%, median fractions remaining above the non-stimulated range were 76% and 37%, respectively. Thus, strong average suppression frequently left substantial fractions of cells with tangibly elevated pY-STAT responses. Log-mean inhibition generally approximated partial-inhibition occupancy, whereas the apparent IC50 for complete-inhibition occupancy was a median 2.4-fold higher than the log-mean IC50 and 7.4-fold higher than the arithmetic-mean IC50. These relationships provide an empirical guide for estimating cell fractions from conventional inhibition curves.

systems biology↗

Fractional response analysis reveals logarithmic cytokine responses in cellular populations

Although we can now measure single-cell signaling responses with multivariate, high-throughput techniques our ability to interpret such measurements is still limited. Even interpretation of dose-response based on single-cell data is not straightforward: signaling responses can differ significantly between cells, encompass multiple signaling effectors, and have dynamic character. Here, we use probabilistic modeling and information-theory to introduce fractional response analysis (FRA), which quantifies changes in fractions of cells with given response levels. FRA can be universally performed for heterogeneous, multivariate, and dynamic measurements and, as we demonstrate, uncovers otherwise hidden patterns in single-cell data. In particular, we show that fractional responses to type I interferon in human peripheral blood mononuclear cells are very similar across different cell types, despite significant differences in mean or median responses and degrees of cell-to-cell heterogeneity. Further, we demonstrate that fractional responses to cytokines scale linearly with the log of the cytokine dose, which uncovers that cellular populations are sensitive to fold-changes in the dose, as opposed to additive changes.

systems biology↗