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.

Extreme Compressed Sensing of Poisson Rates from Multiple Measurements.

Compressed sensing (CS) is a signal processing technique that enables the efficient recovery of a sparse high-dimensional signal from low-dimensional measurements. In the multiple measurement vector (MMV) framework, a set of signals with the same support must be recovered from their corresponding measurements. Here, we present the first exploration of the MMV problem where signals are independently drawn from a sparse, multivariate Poisson distribution. We are primarily motivated by a suite of biosensing applications of microfluidics where analytes (such as whole cells or biomarkers) are captured in small volume partitions according to a Poisson distribution. We recover the sparse parameter vector of Poisson rates through maximum likelihood estimation with our novel Sparse Poisson Recovery (SPoRe) algorithm. SPoRe uses batch stochastic gradient ascent enabled by Monte Carlo approximations of otherwise intractable gradients. By uniquely leveraging the Poisson structure, SPoRe substantially outperforms a comprehensive set of existing and custom baseline CS algorithms. Notably, SPoRe can exhibit high performance even with one-dimensional measurements and high noise levels. This resource efficiency is not only unprecedented in the field of CS but is also particularly potent for applications in microfluidics in which the number of resolvable measurements per partition is often severely limited. We prove the identifiability property of the Poisson model under such lax conditions, analytically develop insights into system performance, and confirm these insights in simulated experiments. Our findings encourage a new approach to biosensing and are generalizable to other applications featuring spatial and temporal Poisson signals.

To Petabytes and beyond: recent advances in probabilistic and signal processing algorithms and their application to metagenomics.

As computational biologists continue to be inundated by ever increasing amounts of metagenomic data, the need for data analysis approaches that keep up with the pace of sequence archives has remained a challenge. In recent years, the accelerated pace of genomic data availability has been accompanied by the application of a wide array of highly efficient approaches from other fields to the field of metagenomics. For instance, sketching algorithms such as MinHash have seen a rapid and widespread adoption. These techniques handle increasingly large datasets with minimal sacrifices in quality for tasks such as sequence similarity calculations. Here, we briefly review the fundamentals of the most impactful probabilistic and signal processing algorithms. We also highlight more recent advances to augment previous reviews in these areas that have taken a broader approach. We then explore the application of these techniques to metagenomics, discuss their pros and cons, and speculate on their future directions.

Ultrasound molecular imaging of ovarian cancer with CA-125 targeted nanobubble contrast agents.

Ultrasound is frequently utilized in diagnosis of gynecologic malignancies such as ovarian cancer. Because epithelial ovarian cancer (EOC) is often characterized by overexpression of cancer antigen 125 (CA-125), ultrasound contrast agents able to target this molecular signature could be a promising complementary strategy. In this work, we demonstrate application of CA-125-targeted echogenic lipid and surfactant-stabilized nanobubbles imaged with standard clinical contrast harmonic ultrasound for imaging of CA-125 positive OVCAR-3 tumors in mice. Surface functionalization of the nanobubbles with a CA-125 antibody achieved rapid significantly (P < 0.05) enhanced tumor accumulation, higher peak ultrasound signal intensity and slower wash out rates in OVCAR-3 tumors compared to CA-125 negative SKOV-3 tumors. Targeted nanobubbles also exhibited increased tumor retention and prolonged echogenicity compared to untargeted nanobubbles. Data suggest that ultrasound molecular imaging using CA-125 antibody-conjugated nanobubbles may contribute to improved diagnosis of EOC.

Lactate Sensors on Flexible Substrates.

Lactate detection by an in situ sensor is of great need in clinical medicine, food processing, and athletic performance monitoring. In this paper, a flexible, easy to fabricate, and low-cost biosensor base on lactate oxidase is presented. The fabrication processes, including metal deposition, sol-gel IrOx deposition, and drop-dry enzyme loading method, are described in detail. The loaded enzyme was examined by scanning electron microscopy. Cyclic voltammetry was used to characterize the sensors. Durability, sensibility, and selectivity of the biosensors were examined. The comparison for different electrode sizes and different sensing film materials was conducted. The sensor could last for four weeks with an average surface area normalized sensitivity of 950 nA/(cm² mM) and 9250 nA/(cm² mM) for Au-based electrodes, and IrOx-modified electrodes respectively, both with an electrode size of 100 × 50 µm. The self-referencing method to record noises simultaneously with the working electrode greatly improved sensor sensitivity and selectivity. The sensor showed little response to interference chemicals, such as glutamate and dopamine.

Wireless Power Transfer for Autonomous Wearable Neurotransmitter Sensors.

In this paper, we report a power management system for autonomous and real-time monitoring of the neurotransmitter L-glutamate (L-Glu). A low-power, low-noise, and high-gain recording module was designed to acquire signal from an implantable flexible L-Glu sensor fabricated by micro-electro-mechanical system (MEMS)-based processes. The wearable recording module was wirelessly powered through inductive coupling transmitter antennas. Lateral and angular misalignments of the receiver antennas were resolved by using a multi-transmitter antenna configuration. The effective coverage, over which the recording module functioned properly, was improved with the use of in-phase transmitter antennas. Experimental results showed that the recording system was capable of operating continuously at distances of 4 cm, 7 cm and 10 cm. The wireless power management system reduced the weight of the recording module, eliminated human intervention and enabled animal experimentation for extended durations.

Ultrasound imaging beyond the vasculature with new generation contrast agents.

Current commercially available ultrasound contrast agents are gas-filled, lipid- or protein-stabilized microbubbles larger than 1 µm in diameter. Because the signal generated by these agents is highly dependent on their size, small yet highly echogenic particles have been historically difficult to produce. This has limited the molecular imaging applications of ultrasound to the blood pool. In the area of cancer imaging, microbubble applications have been constrained to imaging molecular signatures of tumor vasculature and drug delivery enabled by ultrasound-modulated bubble destruction. Recently, with the rise of sophisticated advancements in nanomedicine, ultrasound contrast agents, which are an order of magnitude smaller (100-500 nm) than their currently utilized counterparts, have been undergoing rapid development. These agents are poised to greatly expand the capabilities of ultrasound in the field of targeted cancer detection and therapy by taking advantage of the enhanced permeability and retention phenomenon of many tumors and can extravasate beyond the leaky tumor vasculature. Agent extravasation facilitates highly sensitive detection of cell surface or microenvironment biomarkers, which could advance early cancer detection. Likewise, when combined with appropriate therapeutic agents and ultrasound-mediated deployment on demand, directly at the tumor site, these nanoparticles have been shown to contribute to improved therapeutic outcomes. Ultrasound's safety profile, broad accessibility and relatively low cost make it an ideal modality for the changing face of healthcare today. Aided by the multifaceted nano-sized contrast agents and targeted theranostic moieties described herein, ultrasound can considerably broaden its reach in future applications focused on the diagnosis and staging of cancer.