Method and Apparatus for Automated Isolation of Nucleic Acids from Small Cell Samples

RNA isolation is a ubiquitous need, driven by current emphasis on microarrays and miniaturization. With commercial systems requiring 100,000 to 1,000,000 cells for successful isolation, there is a growing need for a small-footprint, easy-to-use device that can harvest nucleic acids from much smaller cell samples (1,000 to 10,000 cells). The process of extraction of RNA from cell cultures is a complex, multi-step one, and requires timed, asynchronous operations with multiple reagents/buffers. An added complexity is the fragility of RNA (subject to degradation) and its reactivity to surface. A novel, microfluidics-based, integrated cartridge has been developed that can fully automate the complex process of RNA isolation (lyse, capture, and elute RNA) from small cell culture samples. On-cartridge cell lysis is achieved using either reagents or high-strength electric fields made possible by the miniaturized format. Traditionally, silica-based, porous-membrane formats have been used for RNA capture, requiring slow perfusion for effective capture. In this design, high efficiency capture/elution are achieved using a microsphere-based "microfluidized" format. Electrokinetic phenomena are harnessed to actively mix microspheres with the cell lysate and capture/elution buffer, providing important advantages in extraction efficiency, processing time, and operational flexibility. Successful RNA isolation was demonstrated using both suspension (HL-60) and adherent (BHK-21) cells. Novel features associated with this development are twofold. First, novel designs that execute needed processes with improved speed and efficiency were developed. These primarily encompass electric-field-driven lysis of cells. The configurations include electrode-containing constructs, or an "electrode-less" chip design, which is easy to fabricate and mitigates fouling at the electrode surface; and the "fluidized" extraction format based on electrokinetically assisted mixing and contacting of microbeads in a shape-optimized chamber. A secondary proprietary feature is in the particular layout integrating these components to perform the desired operation of RNA isolation. Apart from a novel functional capability, advantages of the innovation include reduced or eliminated use of toxic reagents, and operator-independent extraction of RNA

Computationally restoring the potency of a clinical antibody against Omicron.

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs1-3 and revealed how quickly viral escape can curtail effective options4,5. When the SARS-CoV-2 Omicron variant emerged in 2021, many antibody drug products lost potency, including Evusheld and its constituent, cilgavimab4-6. Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination4 and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign and renew the efficacy of COV2-2130 against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and subsequent variants of concern, and provides protection in vivo against the strains tested: WA1/2020, BA.1.1 and BA.5. Deep mutational scanning of tens of thousands of pseudovirus variants reveals that 2130-1-0114-112 improves broad potency without increasing escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Our computational approach does not require experimental iterations or pre-existing binding data, thus enabling rapid response strategies to address escape variants or lessen escape vulnerabilities

Microfluidic device for generating neural cells to simulate post-stroke conditions

This application provides devices for modeling ischemic stroke conditions. The devices can be used to culture neurons and to subject a first population of the neurons to low-oxygen conditions and a second population of neurons to normoxic conditions. The neurons are cultured on a porous barrier, and on the other side of the barrier run one or more fluid-filled channels. By flowing fluid with different oxygen levels through the channels, one can deliver desired oxygen concentrations to the cells nearest those channels

Microfluidic device for generating neural cells to simulate post-stroke conditions

This application provides devices for modeling ischemic stroke conditions. The devices can be used to culture neurons and to subject a first population of the neurons to low-oxygen conditions and a second population of neurons to normoxic conditions. The neurons are cultured on a porous barrier, and on the other side of the barrier run one or more fluid-filled channels. By flowing fluid with different oxygen levels through the channels, one can deliver desired oxygen concentrations to the cells nearest those channels

Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients' recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients

Perspectives for self-driving labs in synthetic biology.

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed toward solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology