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Summary Cells receive time-varying signals from the environment and generate functional responses by secreting their own signaling molecules. Characterizing dynamic input-output relationships in single cells is crucial for understanding and modeling cellular systems. We developed an automated microfluidic system that delivers precisely defined dynamical inputs to individual living cells and simultaneously measures key immune parameters dynamically. Our system combines nanoliter immunoassays, microfluidic input generation, and time-lapse microscopy, enabling study of previously untestable aspects of immunity by measuring time-dependent cytokine secretion and transcription factor activity from single cells stimulated with dynamic inflammatory inputs. Employing this system to analyze macrophage signal processing under pathogen inputs, we found that the dynamics of TNF secretion are highly heterogeneous and surprisingly uncorrelated with the dynamics of NF-κB, the transcription factor controlling TNF production. Computational modeling of the LPS/TLR4 pathway shows that post-transcriptional regulation by TRIF is a key determinant of noisy and uncorrelated TNF secretion dynamics in single macrophages., Graphical Abstract, Highlights • Dynamic stimulation of single immune cells with a versatile microfluidic device • Coupled longitudinal measurements of NF-κB localization and TNF secretion on the same cell • Single-cell harvesting, staining, and mRNA quantification on the same device • High-content dataset, and modeling of TRIF-based noise in TNF secretion, Junkin et al. have developed a method to quantitatively probe single-cell input-output dynamics with an automated microfluidic system. They conduct coupled measurements of transcription factor and cytokine secretion dynamics from the same single cells to enable modeling, which uncovers dynamic and noise-based roles of TRIF in the NF-κB-TNF pathway.

We describe a new cell-free protein synthesis (CFPS) method for site-specific incorporation of non-natural amino acids (nnAAs) into proteins in which the orthogonal tRNA (o-tRNA) and the modified protein (i.e. the protein containing the nnAA) are produced simultaneously. Using this method, 0.9-1.7 mg/ml of modified soluble super-folder green fluorescent protein (sfGFP) containing either p-azido-l-phenylalanine (pAzF) or p-propargyloxy-l-phenylalanine (pPaF) accumulated in the CFPS solutions; these yields correspond to 50-88% suppression efficiency. The o-tRNA can be transcribed either from a linearized plasmid or from a crude PCR product. Comparison of two different o-tRNAs suggests that the new platform is not limited by Ef-Tu recognition of the acylated o-tRNA at sufficiently high o-tRNA template concentrations. Analysis of nnAA incorporation across 12 different sites in sfGFP suggests that modified protein yields and suppression efficiencies (i.e. the position effect) do not correlate with any of the reported trends. Sites that were ineffectively suppressed with the original o-tRNA were better suppressed with an optimized o-tRNA (o-tRNA(opt)) that was evolved to be better recognized by Ef-Tu. This new platform can also be used to screen scissile ribozymes for improved catalysis.

Absolute quantification of macromolecules in single cells is critical for understanding and modeling biological systems that feature cellular heterogeneity. Here we show extremely sensitive and absolute quantification of both proteins and mRNA in single mammalian cells by a very practical workflow that combines proximity ligation assay (PLA) and digital PCR. This digital PLA method has femtomolar sensitivity, which enables the quantification of very small protein concentration changes over its entire 3-log dynamic range, a quality necessary for accounting for single-cell heterogeneity. We counted both endogenous (CD147) and exogenously expressed (GFP-p65) proteins from hundreds of single cells and determined the correlation between CD147 mRNA and the protein it encodes. Using our data, a stochastic two-state model of the central dogma was constructed and verified using joint mRNA/protein distributions, allowing us to estimate transcription burst sizes and extrinsic noise strength and calculate the transcription and translation rate constants in single mammalian cells.