Expanded Multiplexing on Sensor-Constrained Microfluidic Partitioning Systems.

Microfluidics can split samples into thousands or millions of partitions, such as droplets or nanowells. Partitions capture analytes according to a Poisson distribution, and in diagnostics, the analyte concentration is commonly inferred with a closed-form solution via maximum likelihood estimation (MLE). Here, we present a new scalable approach to multiplexing analytes. We generalize MLE with microfluidic partitioning and extend our previously developed Sparse Poisson Recovery (SPoRe) inference algorithm. We also present the first in vitro demonstration of SPoRe with droplet digital PCR (ddPCR) toward infection diagnostics. Digital PCR is intrinsically highly sensitive, and SPoRe helps expand its multiplexing capacity by circumventing its channel limitations. We broadly amplify bacteria with 16S ddPCR and assign barcodes to nine pathogen genera by using five nonspecific probes. Given our two-channel ddPCR system, we measured two probes at a time in multiple groups of droplets. Although individual droplets are ambiguous in their bacterial contents, we recover the concentrations of bacteria in the sample from the pooled data. We achieve stable quantification down to approximately 200 total copies of the 16S gene per sample, enabling a suite of clinical applications given a robust upstream microbial DNA extraction procedure. We develop a new theory that generalizes the application of this framework to many realistic sensing modalities, and we prove scaling rules for system design to achieve further expanded multiplexing. The core principles demonstrated here could impact many biosensing applications with microfluidic partitioning.

Structural Features and Nonlinear Rheology of Self-Assembled Networks of Cross-Linked Semiflexible Polymers.

Disordered networks of semiflexible filaments are common support structures in biology. Familiar examples include fibrous matrices in blood clots, bacterial biofilms, and essential components of cells and tissues of plants, animals, and fungi. Despite the ubiquity of these networks in biomaterials, we have only a limited understanding of the relationship between their structural features and their highly strain-sensitive mechanical properties. In this work, we perform simulations of three-dimensional networks produced by the irreversible formation of cross-links between linker-decorated semiflexible filaments. We characterize the structure of networks formed by a simple diffusion-dependent assembly process and measure their associated steady-state rheological features at finite temperature over a range of applied prestrains that encompass the strain-stiffening transition. We quantify the dependence of network connectivity on cross-linker availability and detail the associated connectivity dependence of both linear elasticity and nonlinear strain-stiffening behavior, drawing comparisons with prior experimental measurements of the cross-linker concentration-dependent elasticity of actin gels.