Biology subjects

Reuel, N. F.

Publications and source records attributed to Reuel, N. F..

11 recordsLinked to original sources

bioRxiv2026

High-Throughput FRET Affinity Screening Technique (HTFAST) For Cell-Free Expressed Binding Protein Characterization

The rapid engineering of high-affinity binding proteins, such as nanobodies and single-domain antibodies (sdAbs), is increasingly driven by cell-free, machine-learning-guided optimization. However, high-throughput, quantitative characterization of binding affinity remains a major bottleneck, particularly for proteins expressed in cell-free systems without purification. Here, we present High-Throughput FRET Affinity Screening Technique (HTFAST) for rapid affinity characterization of binders expressed directly in crude E. coli cell-free protein synthesis reactions. HTFAST leverages Forster resonance energy transfer (FRET) between fluorescent-protein-fused binders and dye-labeled antigens to enable real-time, quantitative measurement of equilibrium dissociation constants. We systematically optimized fluorophore pairs used and labeling parameters using the SpyTag003-SpyCatcher003 model system. Using donor-quenching and acceptor-emission FRET analyses, HTFAST reliably quantified nanomolar binding affinities in crude lysates for SpyTag003-SpyCatcher003 model system. We validated the platform for nanobodies by characterizing a CD4-binding nanobody, Nb457, and benchmarking multiple SARS-CoV-2 receptor-binding domain sdAbs, demonstrating HTFASTs ability to rank binding strengths across a range of affinities. Finally, we demonstrate that both binding partners can be expressed directly in CFPS, further streamlining screening workflows. Overall, HTFAST provides a scalable, quantitative, and cell-free-compatible approach for high-throughput affinity screening, well suited for DBTL campaigns aimed at accelerating the development of next-generation binding proteins.

bioengineering↗

bioRxiv2025

SMART Sensors for Cell Growth Monitoring in Closed System G-Rex for Scalable, Cost-Conscious Cell and Gene Therapy Manufacturing

Cell and gene-modified cell therapy (CGT), commonly referred to as the fourth pillar of cancer treatment, includes creating advanced cancer killing cells ex vivo and then subsequently putting them into the patients body to selectively attack cancerous cells. Typically, immune cells from a patients body are removed, genetically modified to find and kill cancer, grown to a large army of cancer killing cells, and put back into the patient. This complex process is being simplified through innovation, some of which we describe here. One key innovation is an immune cell manufacturing device configuration called G-Rex (for "gas permeable rapid expansion"), which is now widely used to create armies of cancer killing cells for clinical trials and for commercially approved drug products on a worldwide basis. G-Rex is uniquely simple and popular because it is the only technology that eliminates the need for pumps and flow circuits to deliver oxygen and nutrients. Instead, the material and geometry of G-Rex allow cancer killing cells to get access to oxygen and nutrients on demand. This greatly minimizes complexity and sets the stage for high throughput manufacturing. To further simplify G-Rex relative to all CGT drug product manufacturing alternatives, it would be ideal to eliminate any need of physical intervention to assess cell quantity. With that goal, we conceived a novel approach to determining cell quantity in a non-invasive manner. More specifically, we invented and built a sensor into G-Rex that continuously reports the quantity of cells in G-Rex without any intervention or sampling to an external reader. Herein, we call the sensor a "single-use metabolite absorbing resonant transducer in G-Rex (SMART G-Rex)" and the reader a "SMART Reader." The SMART G-Rex is wirelessly interrogated by the SMART Reader to determine the extent of cell expansion in the G-Rex at any given time. More specifically, as cell quantity increases, the SMART G-Rex absorbs cell secreted metabolites (organic compounds such as eicosanoids) which cause a change in resonant frequency (measured as % of frequency change, or Skroot Growth Index (SGI)). By placing SMART G-Rex in proximity of the SMART Reader, whether in or out of an incubator, changes in resonant frequency are detected and translated into cell quantity with high reliability. For example, using 45 sensors across multiple donor cell samples, we established 99% confidence in SMARTs accuracy at signaling a >60X expansion (3B cells at harvest). The SMART G-Rex provides a non-invasive way of instantly quantifying the number of cells in a CGT drug production process and informing manufacturers when a CGT drug product has reached the desired quantity of cells necessary to proceed with formulation, fill, and finish. By integrating SMART technology, G-Rex is the only technology for CGT manufacturing that can avoid intervention when determining cells quantity or allow real-time knowledge of cell quantity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/676768v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1557118org.highwire.dtl.DTLVardef@740652org.highwire.dtl.DTLVardef@14b0430org.highwire.dtl.DTLVardef@ac0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2025

Rapid Cell-Free Combinatorial Mutagenesis Workflow Using Small Oligos Suitable for High-Iteration, Active Learning-Guided Protein Engineering

Active learning-guided protein engineering e2iciently navigates the challenging fitness landscape by screening designs iteratively in a model-guided design-build-test-learn cycle. However, while high iterations boost performance, current workflows reliance on tedious and costly cell-based cloning and expression steps limits the iterations they can practically implement. To address this problem, we present a novel combinatorial mutagenesis workflow that uses small ([~]20-40 bp) mutagenic annealed-oligo fragments and cell-free expression to rapidly and conveniently screen protein variants in <9 hours. Using bulk-prepared mutagenic oligos eliminates the need for cloning, PCR-based mutagenesis, or ordering costly genes each screening round. Their >80% size reduction from current fragment-based shu2ling strategies also helps avoid including multiple mutations on the same fragment, reducing the number one must order to cover the design space. By screening 3-10 fragment assemblies for two di2erent proteins, we show our approach is a general, scalable, and cost-e2ective platform for high-iteration protein engineering.

bioengineering↗

bioRxiv2025

Single-Walled Carbon Nanotube Probes for Protease Characterization Directly in Cell-Free Expression Reactions

Proteins can be rapidly prototyped with cell-free expression (CFE) but in most cases there is a lack of probes or assays to measure their function directly in the cell lysate, thereby limiting the throughput of these screens. Increased throughput is needed to build standardized, sequence to function data sets to feed machine learning guided protein optimization. Herein, we describe the use of fluorescent single-walled carbon nanotubes (SWCNT) as effective probes for measuring protease activity directly in cell-free lysate. Substrate proteins were conjugated to carboxymethyl cellulose-wrapped SWCNT, yielding stable and sensitive probes for protease detection with a detection limit of 6.4 ng/mL for bacterial protease from Streptomyces griseus. These probes successfully measured subtilisin activity in unpurified CFE reactions, surpassing commercial assays. Furthermore, they enabled continuous monitoring of activity during synthesis of subtilisin in both purified and lysate-based CFE systems without compromising protein expression. Surface passivation techniques, such as pre-incubation with cell lysate and supplement components, reduced the initial signal loss and improved probe signal stability in the complex cell lysate environment. These modular probes can be used, as described, for high-throughput screening and optimization of proteases and, with the change of conjugated substrate, a wider range of other hydrolases.

bioengineering↗

bioRxiv2024

Amplified DNA Heterogeneity Assessment with Oxford Nanopore Sequencing Applied to Cell Free Expression Templates

In this work, Oxford Nanopore sequencing is tested as an accessible method for quantifying heterogeneity of amplified DNA. This method enables rapid quantification of deletions, insertions, and substitutions, the probability of each mutation error, and their locations in the replicated sequences. Amplification techniques tested were conventional polymerase chain reaction (PCR) with varying levels of polymerase fidelity (OneTaq, Phusion, and Q5) as well as rolling circle amplification (RCA) with Phi29 polymerase. Plasmid amplification using bacteria was also assessed. By analyzing the distribution of errors in a large set of sequences for each sample, we examined the heterogeneity and mode of errors in each sample. This analysis revealed that Q5 and Phusion polymerases exhibited the lowest error rates observed in the amplified DNA. As a secondary validation, we analyzed the emission spectra of sfGFP fluorescent proteins synthesized with amplified DNA using cell free expression. Error-prone polymerase chain reactions confirmed the dependency of reporter protein emission spectra peak broadness to DNA error rates. The presented nanopore sequencing methods serve as a roadmap to quantify the accuracy of other gene amplification techniques, as they are discovered, enabling more homogenous cell-free expression of desired proteins.

bioinformatics↗

bioRxiv2024

Single-use, Metabolite Absorbing, Resonant Transducer (SMART) culture vessels for label-free, continuous cell culture progression monitoring

Secreted metabolites are an important class of bio-process analytical technology (PAT) targets that can correlate to cell condition. However, current strategies for measuring metabolites are limited to discrete measurements, resulting in limited understanding and ability for feedback control strategies. Herein, we demonstrated a continuous metabolite monitoring strategy using a single-use metabolite absorbing resonant transducer (SMART) to correlate with cell growth. Polyacrylate was shown to absorb secreted metabolites from living cells containing hydroxyl and alkenyl groups such as terpenoids, that act as a plasticizer. Upon softening, the polyacrylate irreversibly conformed into engineered voids above a resonant sensor, changing the local permittivity which is interrogated, contact-free, with a vector network analyzer. Compared to sensing using the intrinsic permittivity of cells, the SMART approach yields a 20-fold improvement in sensitivity. Tracking growth of many cell types such as Chinese hamster ovary, HEK293, K562, HeLa, and E. coli cells as well as perturbations in cell proliferation during drug screening assays were demonstrated. The sensor was benchmarked to show continuous measurement over six days, ability to track different growth conditions, selectivity to transducing active cell growth metabolites against other components found in the media, and feasibility to scale out for high throughput campaigns.

bioengineering↗

bioRxiv2023

Reinforcement learning-guided control strategies for CAR T-cell activation and expansion: Simulation study with a virtual cell culture environment

Reinforcement learning (RL), a subset of machine learning (ML), can potentially optimize and control biomanufacturing processes, such as improved production of therapeutic cells. Here, the process of CAR-T cell activation by antigen presenting beads and their subsequent expansion is formulated in-silico. The simulation is used as an environment to train RL-agents to dynamically control the number of beads in culture with the objective of maximizing the population of robust effector cells at the end of the culture. We make periodic decisions of incremental bead addition or complete removal. The simulation is designed to operate in OpenAI Gym which enables testing of different environments, cell types, agent algorithms and state-inputs to the RL-agent. Agent training is demonstrated with three different algorithms (PPO, A2C and DQN) each sampling three different state input types (tabular, image, mixed); PPO-tabular performs best for this simulation environment. Using this approach, training of the RL-agent on different cell types is demonstrated, resulting in unique control strategies for each type. Sensitivity to input noise (sensor performance), number of control step interventions, and advantage of pre-trained agents are also evaluated. Therefore, we present a general computational framework to maximize the population of robust effector cells in CAR-T cell therapy production. Author SummaryCustom control strategies for expansion and activation of patient-specific CAR T-cell therapies resolved by reinforcement learning using a simulation environment and updatable cell growth parameters.

bioengineering↗

bioRxiv2023

Characterizing the Interactions of Cell Membrane-Disrupting Peptides with Lipid-Functionalized Single-Walled Carbon Nanotube Systems for Antimicrobial Screening

Lipid-functionalized single-walled carbon nanotubes (SWNTs) have garnered significant interest for their potential use in a wide range of biomedical applications. In this work, we used molecular dynamics simulations to study the equilibrium properties of SWNTs surrounded by the phosphatidylcholine (POPC) corona phase, and their interactions with three cell membrane disruptor peptides: colistin, TAT peptide, and crotamine-derived peptide. Our results show that SWNTs favor asymmetrical positioning within the POPC corona, so that one side of the SWNT, covered by the thinnest part of the corona, comes in contact with charged and polar functional groups of POPC and water. We also observed that colistin and TAT insert deeply into POPC corona, while crotamine-derived peptide only adsorbs to the corona surface. Compared to crotamine-derived peptide, colistin and TAT also induce larger perturbations in the thinnest region of the corona, by allowing more water molecules to directly contact the SWNT surface. In separate simulations, we show that three examined peptides exhibit similar insertion and adsorption behaviors when interacting with POPC bilayers, confirming that peptide-induced perturbations to POPC in conjugates and bilayers are similar in nature and magnitude. Furthermore, we observed correlations between the peptide-induced structural perturbations and the near-infrared emission of the lipid-functionalized SWNTs, which suggest that the optical signal of the conjugates transduces the morphological changes in the lipid corona. Overall, our findings indicate that lipid-functionalized SWNTs could serve as simplified cell membrane model systems for pre-screening of new antimicrobial compounds that disrupt cell membranes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/525557v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@4c5645org.highwire.dtl.DTLVardef@1b3cd30org.highwire.dtl.DTLVardef@1643c6corg.highwire.dtl.DTLVardef@1bb096e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2020

Automated Classification of Bacterial Cell Sub-Populations with Convolutional Neural Networks.

Quantification of phenotypic heterogeneity present amongst bacterial cells can be a challenging task. Conventionally, classification and counting of bacteria sub-populations is achieved with manual microscopy, due to the lack of alternative, high-throughput, autonomous approaches. In this work, we apply classification-type convolutional neural networks (cCNN) to classify and enumerate bacterial cell sub-populations (B. subtilis clusters). Here, we demonstrate that the accuracy of the cCNN developed in this study can be as high as 86% when trained on a relatively small dataset (81 images). We also developed a new image preprocessing algorithm, specific to fluorescent microscope images, which increases the amount of training data available for the neural network by 72 times. By summing the classified cells together, the algorithm provides a total cell count which is on parity with manual counting, but is 10.2 times more consistent and 3.8 times faster. Finally, this work presents a complete solution framework for those wishing to learn and implement cCNN in their synthetic biology work.

synthetic biology↗

bioRxiv2020

Design and Validation of a Frugal, Automated, Solid-Phase Peptide Synthesizer

Solid phase peptide synthesis (SPPS) has enabled widespread use of synthetic peptides in applications ranging from pharmaceuticals to materials science. The demand for synthetic peptides has driven recent efforts to produce automated SPPS synthesizers which utilize fluid-handling components common to chemistry laboratories to drive costs down to several thousand dollars. Herein, we describe the design and validation of a more frugal SPPS synthesizer that uses inexpensive, consumer-grade fluid-handling components to achieve a prototype price point between US$300 and $600. We demonstrated functionality by preparing and characterizing peptides with a variety of distinct properties including binding functionality, nanoscale self-assembly, and oxidation-induced fluorescence. This system yielded micromoles of peptide at a cost of approximately $1/residue, a cost which may be further reduced by optimization and bulk purchasing.

biochemistry↗

bioRxiv2019

One-Pot E. coli Cell-Free Extract for in vitro Expression of Disulfide Bonded Proteins

In vitro expression of proteins from E. coli extract is a useful method for prototyping and production of cytotoxic or unnatural products. However, proteins that have multiple disulfide bonds require custom extract that, to date, requires careful addition of exogenous isomerase enzymes or the use of expensive commercial kits. This cost and complexity currently limit access to some groups who wish to rapidly prototype proteins with disulfide bonds. Herein, we present a simple solution that does not require addition of supplemental enzymes. We use a commercially available SHuffle T7 Express lysY strain of E. coli that expresses both T7 RNAP and DsbC isomerase enzymes. We experimentally determine optimal growth conditions (IPTG induction and harvest times) to balance overall productivity and efficiency of disulfide bond formation using a luciferase (from Gaussia princeps) that contains five disulfide bonds as our reporter protein. We also demonstrate the ability for rapid prototyping by screening the activity of four luciferase candidates against ten luciferin analogues. To display the broad applicability of the extract, three other enzymes containing [≥]3 disulfide bonds (hevamine, endochitinase A, and periplasmic AppA) were also expressed from minimal genetic templates that had undergone rolling circle amplification and confirmed via activity assays.

bioengineering↗