Fast diagnostic methods are crucial to reduce the burden on healthcare systems. Currently, detection of diabetes complications such as neuropathy requires time-consuming approaches to observe the correlated red blood cells (RBCs) morphological changes. To tackle this issue, an optical analysis of RBCs in air was conducted in the 250-2500 nm range. The distinct oscillations present in the scattered and direct transmittance spectra have been analyzed with both Mie theory and anomalous diffraction approximation. The results provide information about the swelling at the ends of RBCs and directly relate the optical data to RBCs morphology and deformability. Both models agree on a reduction in the size and deformability of RBCs in diabetic patients, thus opening the way to diabetes diagnosis and disease progression assessment.

It is of great importance to study the detachment/attachment behaviors of cells (cancer cell, immune cell and epithelial cell), as they are closely related with tumor metastasis, immunoreaction and tissue development at variety scales. To characterize the detachment/attachment during the interaction between cells and substrate, some researchers proposed using cell traction force (CTF) as the indicator. To date, various strategies have been developed to measure the CTF. However, these methods only realize the measurements of cell passive forces on flat cases. In order to quantify the active CTF on non-flat surfaces, which can better mimic the in vivo case, we employed elastic hydrogel microspheres as a force sensor. The microspheres were fabricated by microfluidic chips with controllable size and mechanical properties to mimic substrate. Cells were cultured on microsphere and the CTF led to the deformation of microsphere. By detecting the morphology information, the CTF exerted by attached cells can be calculated by the in-house numerical code. Using this microspheres, the CTF of various cells (including tumor cell, immunological cell, and epithelium cell) were successfully obtained on non-flat surfaces with different curvature radii. The proposed method provides a versatile platform to measure the CTF with high precision and to understand the detachment/attachment behaviors during physiology processes.

Biosensors are valuable tools in accelerating the test phase of the design-build-test-learn cycle of cell factory development, as well as in bioprocess monitoring and control. G protein-coupled receptor (GPCR)-based biosensors enable cells to sense a wide array of molecules and environmental conditions in a specific manner. Due to the extracellular nature of their sensing, GPCR-based biosensors require compartmentalization of distinct genotypes when screening production levels of a strain library to ensure that detected levels originate exclusively from the strain under assessment. Here, we explore the integration of production and sensing modalities into a single Saccharomyces cerevisiae strain and compartmentalization using three different methods: (1) cultivation in microtiter plates, (2) spatial separation on agar plates, and (3) encapsulation in water-in-oil-in-water double emulsion droplets, combined with analysis and sorting via a fluorescence-activated cell sorting (FACS) machine. Employing tryptamine and serotonin as proof-of-concept target molecules, we optimize biosensing conditions and demonstrate the ability of the autocrine screening method to enrich for high producers, showing the enrichment of a serotonin-producing strain over a non-producing strain. These findings illustrate a workflow that can be adapted to screening for a wide range of complex chemistry at high throughput using commercially available microfluidic systems.

The fifth modeling workshop (5MW) was held in June 2023 in Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multi-phase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized data sets. A potential solution would be the use of specific descriptors (ex. patch sizes and types mapped to a homology model) in a database that would allow for detailed analyses without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high-level guidance in ICHQ8-Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and Artificial Intelligence (AI). Recent requests for feedback from health authorities on use of ML and AI may provide an opportunity for more clarity regarding expectations for use and deployment. Two specific additional outcomes of the workshop were alignment on a key definition for “mechanistic modeling” and the acknowledgement/realization that modeling can have a significant impact on improving sustainability of bioprocessing through elimination of experiments both during development and manufacturing. Feedback from the participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g. biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter-disciplinary subject matter experts (SMEs) (e.g. data science, chromatography) is essential to advancing and maintaining the modeling community.

Sodium butyrate (NaBu), well-known as a histone deacetylase inhibitor and for its capacity to impede cell growth, can enhance the production of a specific protein, such as an antibody, in recombinant Chinese hamster ovary (CHO) cell cultures. In this study, two CHO cell lines, namely K1 and DG44, along with their corresponding mAb-producing lines, K1-Pr and DG44-Pr, were cultivated with or without NaBu. A SWATH-based profiling method was employed to analyze the proteome. Cells cultured in the presence of NaBu exhibited a reduction in mitosis and gene expression, supported by their culture data demonstrating growth inhibition. The presence of NaBu corresponded to an upregulation of intracellular trafficking and secretion pathways, aligned with an observed increase in mAb production. This upregulation was associated with an elevated glycosylation pathway and a slight alteration in the glycosylation profile of the mAbs. The observed increases in fatty acid oxidation, redox interactions, and lipid biosynthesis are likely attributable to the metabolic effects of NaBu. A comprehensive understanding of the systemic effects of NaBu will facilitate the discovery of strategies to enhance or prolong the productivity of CHO cells.

Synechococcus elongatus PCC 11801 is a fast-growing cyanobacterium, exhibiting high tolerance to environmental stresses. We have earlier characterized its genome and analysed its transcriptome and proteome. However, to deploy it as a potential cell factory, it is necessary to expand its synthetic biology toolbox, including promoter elements and ribosome binding sites (RBSs). Here, based on the global transcriptome analysis, 48 native promoters of the genes with high transcript count were characterized using a fluorescent reporter system. P cpcB, P psbA1, P 11770 promoters exhibited consistently higher fluorescence under all the cultivation conditions. Similarly, from the genome data and proteome analysis, 534 operons were identified. Fifteen intergenic regions exhibiting higher protein expression from the downstream gene were systematically characterized for RBSs, using an operon construct comprising of fluorescent protein genes eyfp and mTurq under P cpcB (P cpcB: eyfp:RBS: mTurq:T rrnB). Overall, the work presents promoter and RBS sequence libraries, with varying strengths, to expedite bioengineering of PCC 11801.

Acetogenic Clostridia are obligate anaerobes that have emerged as promising microbes for the renewable production of biochemicals owing to their ability to efficiently metabolize sustainable single-carbon feedstocks. Additionally, Clostridia are increasingly recognized for their biosynthetic potential, with recent discoveries of diverse secondary metabolites ranging from antibiotics to pigments to modulators of the human gut microbiota. Lack of efficient methods for genomic integration and expression of large heterologous DNA constructs remains a major challenge in studying biosynthesis in Clostridia and using them for metabolic engineering applications. To overcome this problem, we harnessed chassis-independent recombinase-assisted genome engineering (CRAGE) to develop a workflow for facile integration of large gene clusters (>10 kB) into the human gut acetogen Eubacterium limosum. We then integrated a non-ribosomal peptide synthetase gene cluster from the gut anaerobe Clostridium leptum, which previously produced no detectable product in traditional heterologous hosts. Chromosomal expression in E. limosum without further optimization led to production of phevalin at 2.4 mg/L. These results further expand the molecular toolkit for a highly tractable member of the Clostridia, paving the way for sophisticated pathway engineering efforts, and highlighting the potential of E. limosum as a Clostridial chassis for exploration of anaerobic natural product biosynthesis.

Microfiltration is an essential step during biopharmaceutical manufacturing. However, unexpected flux decay can occur. Although the flux decay profile and initial flux are important factors determining microfiltration filterability, predicting them accurately is challenging since the root cause of unexpected flux decay remains elusive. In this study, the methodology for developing a prediction model of flux decay profiles was established. First, the filtration profiles of different monodisperse polystyrene latex and silica beads of various sizes were evaluated. These results revealed that the size and surface electrostatic properties of the beads affect the flux decay profile. Taking the size and surface electrostatic properties of protein aggregates into account, we constructed a predictive model using model bead filtration profiles. We showed that this methodology was applicable to two different microfiltration filters to predict the flux decay profile of therapeutic proteins. Since this prediction model is based on normalized flux, the initial flux must be predicted. We therefore successfully developed a method to predict initial flux based on the Hagen-Poiseuille equation using sample viscosity values for both filters. These prediction models can be used for effective microfiltration scale-up assessment and can be applied during early stage of process development.

Governments and biopharmaceutical organizations aggressively leveraged expeditious communication capabilities, decision models and global strategies to make a COVID-19 vaccine happen within a period of 12 months. This was an unusual effort and cannot be transferred to normal times. However, this focus on a single vaccine has also led to other treatments and drug developments being sidelined. Society expects the pharmaceutical industry to provide an uninterrupted supply of medicines. However, it is often overlooked how complex the manufacture of these compounds is and what logistics are required, not to mention the time needed to develop new drugs. The overarching theme, therefore, is patient access and how we can help ensure access and extend it to low- and middle-income countries. Despite unceasing efforts to make medications available to all patient populations, this must never be done at the expense of patient safety. A major fraction of the costs in biopharmaceutical manufacturing are for drug discovery, process development, and clinical studies. Infrastructure costs are very difficult to quantify because they often depend on whether a greenfield facility or an existing, depreciated facility is used or adapted for a new product. To accelerate process development concepts of platform process and prior knowledge are increasingly leveraged. While more traditional protein therapeutics continue to dominate the field, we are also experiencing the exciting emergence and evolution of other therapeutic formats (bispecifics, tetravalent mAbs, antibody-drug conjugates, enzymes, peptides, etc.) that offer unique treatment options for patients. Protein modalities are still dominant, but new modalities are being developed that can be learned from including advanced therapeutics like cell and gene therapies. The industry must develop a model-based strategy for process development and technologies such as continuous integrated biomanufacturing must be adopted. The overall conclusion is that the pandemic pace was unsustainable, focused on vaccine delivery at the expense of other modalities/disease targets, and had implications for professional and personal life (work-life balance). Routinely reducing development time from 10 years to 1 year is nearly impossible to achieve. Environmental aspects of sustainable downstream processing are also described.

Spinal muscular atrophy (SMA) is a devastating neuromuscular disease caused by mutations in the survival motor neuron 1 ( SMN1) gene. Gene editing technology repairs the conversion of the 6th base T to C in exon 7 of the paralogous SMN2 gene, compensating for the SMN protein expression and promoting the survival and function of motor neurons. However, low editing efficiency and unintended off-target effects limit the application of this technology. Here, we optimized a TaC9-adenine base editor (ABE) system by combining Cas9 nickase with the transcription activator-like effector (TALE)-adenosine deaminase fusion protein to effectively and precisely edit SMN2 without detectable Cas9 dependent off-target effects in human cell lines. We also generated human SMA-induced pluripotent stem cells (SMA-iPSCs) through the mutation of the splice acceptor or deletion of the exon 7 of SMN1. TaC9-R10 induced 45% SMN2 T6>C conversion in the SMA-iPSCs. The SMN2 T6>C splice-corrected SMA-iPSCs were directionally differentiated into motor neurons, exhibiting SMN protein recovery and anti-apoptosis ability. Therefore, the TaC9-ABE system with dual guides from the combination of Cas9 with TALE could be a potential therapeutic strategy for SMA with high efficacy and safety.

Monoclonal antibodies (MAbs) are powerful therapeutic tools in modern medicine and represent a rapidly expanding multi-billion USD market. While bioprocesses are generally well understood and optimized for MAbs, online quality control remains challenging. Notably, N-glycosylation is a critical quality attribute of MAbs as it affects binding to Fcγ receptors (FcγR), impacting the efficacy and safety of MAbs. Traditional N-glycosylation characterization methods are ill-suited for online monitoring of a bioreactor; in contrast, surface plasmon resonance (SPR) represents a promising avenue, as SPR biosensors can record MAb-FcγR interactions in real-time and without labelling. In this study, we produced five lots of differentially glycosylated Trastuzumab (TZM) and finely characterized their glycosylation profile by HILIC-UPLC chromatography. We then compared the interaction kinetics of these MAb lots with four FcγRs including FcγRIIA and FcγRIIB at 5 oC and 25 oC. When interacting with FcγRIIA/B at low temperature, the differentially glycosylated MAb lots exhibited distinct kinetic behaviours, contrary to room-temperature experiments. Galactosylated TZM (1) and core fucosylated TZM (2) could be discriminated and even quantified using an analytical technique based on the area under the curve (AUC) of the signal recorded during the dissociation phase of a SPR sensorgram describing the interaction with FcγRIIA (1) or FcγRII2B (2). Because of the rapidity of the proposed method (less than 5 minutes per measurement) and the small sample concentration it requires (as low as 30 nM, exact concentration not required), it could be a valuable process analytical technology for MAb glycosylation monitoring.

Sonification, or the practice of generating sound from data, is a promising alternative or complement to data visualization for exploring research questions in the life sciences. Expressing or communicating data in the form of sound rather than graphs, tables, or renderings can provide a secondary information source for multitasking or remote monitoring purposes or make data accessible when visualizations cannot be used. While popular in astronomy, neuroscience, and geophysics as a technique for data exploration and communication, its potential in the biological and biotechnological sciences has not been fully explored. In this review, we introduce sonification as a concept, some examples of how sonification has been used to address areas of interest in biology, and the history of the technique. We then highlight a selection of biology-related publications that involve sonifications of DNA datasets and protein datasets, sonifications for data collection and interpretation, and sonifications aimed to improve science communication and accessibility. Through this review, we aim to show how sonification has been used both as a discovery tool and a communication tool and to inspire more life-science researchers to incorporate sonification into their own studies.

A high rate upflow anaerobic polyfoam-based bioreactor (UAPB) was developed for lab-scale in-situ biogas upgrading. The use of a porous diffuser, alongside high gas recirculation, led to a higher H 2 liquid mass transfer, and subsequently to a better uptake for high CH 4 content (up to 90%). Although our attempts to optimize both operational parameters (H 2 flow rate and gas recirculation ratio) were not successful, at a very high recirculation ratio (32) and flow rate (54 mL/h), a significant improvement of the hydrogen consumption was achieved. These operational conditions have in turn driven the methanogenic community towards the dominance of Methanosaetaceae, which out-competed Methanosarcinaceae. Nevertheless, a highly stable methane production rate was observed despite the methanogenic turnover. During the different applied operational conditions, the bacterial community was especially impacted, resulting in substantial shifts of taxonomic groups. Notably, Aeromonadaceae was the only bacterial group positively correlated with increasing hydrogen consumption rates. The capacity of Aeromonadaceae to extracellularly donate electrons suggests that direct interspecies electron transfer (DIET) enhanced biogas upgrading. Overall, the proposed innovative biological in-situ biogas upgrading technology using the UAPB configuration shows promising results for stable, simple and effective biological biogas upgrading.

Synthetic mRNA is currently produced in standardised in vitro transcription systems. However, this one-size-fits-all approach has associated drawbacks in supply chain shortages, high reagent costs, complex product-related impurity profiles and limited design options for molecule-specific optimisation of product yield and quality. Herein, we describe for the first time development of an in vivo mRNA manufacturing platform, utilising an E. coli cell chassis. Coordinated mRNA, DNA, cell and media engineering, primarily focussed on disrupting interactions between synthetic mRNA molecules and host cell RNA degradation machinery, increased product yields >40-fold compared to standard ‘unengineered’ E. coli expression systems. Mechanistic dissection of cell factory performance showed that product mRNA accumulation levels approached theoretical limits, accounting for ~30% of intracellular total RNA mass, and that this was achieved via host-cell’s reallocating biosynthetic capacity away from endogenous RNA and cell biomass generation activities. We demonstrate that varying sized functional mRNA molecules can be produced in this system and subsequently purified in large- or small-scale processes. Accordingly, this study introduces a new mRNA production technology, expanding the solution space available for mRNA manufacturing.

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.

The in vitro transcription (IVT) reaction used in the production of mRNA vaccines and therapies remains poorly quantitatively understood. Mechanistic modeling of IVT could inform reaction design, scale up, optimization, and control. In this work, we develop a mechanistic model of IVT to include nucleation and growth of magnesium pyrophosphate crystals and subsequent agglomeration of crystals and DNA. A novel quantitative description is included for the rate of transcription as a function of target sequence length, DNA concentration, and T7 polymerase concentration. The model explains previously unexplained trends in IVT data and quantitatively predicts the effect of adding the pyrophosphatase enzyme to the reaction system. The model is validated on additional literature data showing an ability to predict transcription rates as a function of RNA sequence length.

The recent uptick in the approval of ex vivo cell therapies highlight the relevance of Lentivirus (LV) as an enabling viral vector of modern medicine. As labile biologics, however, LVs pose critical challenges to industrial biomanufacturing. In particular, LV purification – currently reliant on filtration and anion-exchange or size-exclusion chromatography – suffers from long process times and low yield of transducing particles, which translate in high waiting time and cost to patients. Seeking to improve LV downstream processing, this study introduces peptides targeting the enveloped protein Vesicular stomatitis virus G (VSV-G) to serve as affinity ligands for the chromatographic purification of LV particles. An ensemble of candidate ligands was initially discovered by implementing a dual-fluorescence screening technology and a targeted in silico approach designed to identify sequences with high selectivity and tunable affinity. The selected peptides were conjugated on Poros resin and their LV binding-and-release performance was optimized by adjusting the flow rate, composition, and pH of the chromatographic buffers. Ligands GKEAAFAA and SRAFVGDADRD were selected for their high product yield (50-60% of viral genomes; 40-50% of HT1080 cell-transducing particles) upon elution in PIPES buffer with 0.65 M NaCl at pH 7.4. The peptide-based adsorbents also presented remarkable values of binding capacity (up to 3·10 9 TU per mL of resin at the residence time of 1 min) and clearance of host cell proteins (up to 220-fold reduction of HEK293 HCPs). Additionally, GKEAAFAA demonstrated high resistance to caustic cleaning-in-place (0.5 M NaOH, 30 min) with no observable loss in product yield and quality.

In recent years, environmental DNA (eDNA) has received attention from biologists due to its sensitivity, convenience, labor and material efficiency, and lack of damage to organisms. The extensive application of eDNA has opened avenues for the monitoring and biodiversity assessment of amphibians, which are frequently small and difficult to observe in the field, in areas such as biodiversity survey assessment and detection of specific, rare and endangered, or alien invasive species. However, the accuracy of eDNA can be influenced by factors such as ambient temperature, pH, and false positives or false negatives, which makes eDNA an adjunctive tool rather than a replacement for traditional surveys. This review provides a concise overview of the eDNA method and its workflow, summarizes the differences between applying eDNA for detecting amphibians and other organisms, reviews the research progress in eDNA technology for amphibian monitoring, identifies factors influencing detection efficiency, and discusses the challenges and prospects of eDNA. It aims to serve as a reference for future research on the application of eDNA in amphibian detection.