Deep Learning-Assisted Automated Single Cell Electroporation Platform for Effective Genetic Manipulation of Hard-to-Transfect Cells

Nanoneedle-Based Electroporation for Efficient Manufacturing of Human Primary Chimeric Antigen Receptor Regulatory T-Cells

Regulatory T cells (Tregs) play a crucial role in moderating immune responses offering promising therapeutic options for autoimmune diseases and allograft rejection. Genetically engineering Tregs with chimeric antigen receptors (CARs) enhances their targeting specificity and efficacy. With non-viral transfection methods suffering from low efficiency and reduced cell viability, viral transduction is currently the only viable approach for GMP-compliant CAR-Treg production. However, viral transduction raises concerns over immunogenicity, insertional mutagenesis risk, and high costs, which limit clinical scalability. This study introduces a scalable nanoneedle electroporation (nN-EP) platform for GMP-compatible transfection of HLA-A2-specific CAR plasmids into primary human Tregs. The nN-EP system achieves 43% transfection efficiency, outperforming viral transduction at multiplicity of infection 1 by twofold. Importantly, nN-EP preserves Treg viability, phenotype and proliferative capacity. HLA-A2-specific CAR-Tregs generated using nN-EP show specific activation and superior suppressive function compared to polyclonal or virally transduced Tregs in the presence of HLA-A2 expressing antigen presenting cells. These findings underscore the potential of nN-EP as a GMP-suitable method for CAR-Treg production, enabling broader clinical application in immune therapies.

Innovations and challenges in collagen and gelatin production through precision fermentation

Collagen and gelatin are essential biomaterials widely used in industries such as food, cosmetics, healthcare, and pharmaceuticals. Traditionally derived from animal tissues, these proteins are facing growing demand for more sustainable and ethical production methods. Precision fermentation (PF) offers a promising alternative by using genetically engineered microorganisms to produce recombinant collagen and gelatin. This technology not only reduces environmental impact but also ensures consistent quality and higher yields. In this review, we provide a comprehensive overview of collagen and gelatin production through PF destined for the food sector, exploring key advances in recombinant technologies, synthetic biology, and bioprocess optimization. Challenges such as scaling production, cost-efficiency, and market integration are addressed, alongside emerging solutions for enhancing industrial competitiveness. We also highlight leading companies leveraging PF to drive innovation in the food industry. As PF continues to evolve, future developments are expected to improve efficiency, reduce costs, and expand the applications of recombinant collagen and gelatin, particularly in the food and supplement sectors.

Mechanically mediated cargo delivery to cells using microfluidic devices

Drug delivery technologies, which are a crucial area of research in the field of cell biology, aim to actively or passively deliver drugs to target cells to enhance therapeutic efficacy and minimize off-target effects. In recent years, with advances in drug development, particularly, the increasing demand for macromolecular drugs (e.g., proteins and nucleic acids), novel drug delivery technologies and intracellular cargo delivery systems have emerged as promising tools for cell and gene therapy. These systems include various viral- and chemical-mediated methods as well as physical delivery strategies. Physical methods, such as electroporation and microinjection, have shown promise in early studies but have not been widely adopted due to concerns regarding efficiency and cellular viability. Recently, microfluidic technologies have provided new opportunities for cargo delivery by allowing for precise control of fluid dynamic parameters to achieve efficient and safe penetration of cell membranes, as well as for foreign material transport. Microfluidics-based mechanical delivery methods utilize biophysical phenomena, such as cell constriction and fluid shear, and are associated with high throughput and high transfection efficiency. In this review, we summarize the latest advancements in microfluidic mechanical delivery technologies, and we discuss constriction- and fluid shear-induced delivery strategies. Furthermore, we explore the potential application of artificial intelligence in optimizing cargo delivery technologies, aiming to provide theoretical support and practical guidance for the future development of novel cellular drug delivery technologies.

Well Plate-Based Localized Electroporation Workflow for Rapid Optimization of Intracellular Delivery

Efficient and nontoxic delivery of foreign cargo into cells is a critical step in many biological studies and cell engineering workflows with applications in areas such as biomanufacturing and cell-based therapeutics. However, effective molecular delivery into cells involves optimizing several experimental parameters. In the case of electroporation-based intracellular delivery, there is a need to optimize parameters like pulse voltage, duration, buffer type, and cargo concentration for each unique application. Here, we present the protocol for fabricating and utilizing a high-throughput multi-well localized electroporation device (LEPD) assisted by deep learning-based image analysis to enable rapid optimization of experimental parameters for efficient and nontoxic molecular delivery into cells. The LEPD and the optimization workflow presented herein are relevant to both adherent and suspended cell types and different molecular cargo (DNA, RNA, and proteins). The workflow enables multiplexed combinatorial experiments and can be adapted to cell engineering applications requiring in vitro delivery. Key features • A high-throughput multi-well localized electroporation device (LEPD) that can be optimized for both adherent and suspended cell types. • Allows for multiplexed experiments combined with tailored pulse voltage, duration, buffer type, and cargo concentration. • Compatible with various molecular cargoes, including DNA, RNA, and proteins, enhancing its versatility for cell engineering applications. • Integration with deep learning-based image analysis enables rapid optimization of experimental parameters.

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

BACKGROUND

Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research.

METHODS

In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing.

RESULTS

This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies.

CONCLUSIONS

Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.

Molecular Simulations Reveal the Free Energy Landscape and Transition State of Membrane Electroporation

The formation of pores over lipid membranes by the application of electric fields, termed membrane electroporation, is widely used in biotechnology and medicine to deliver drugs, vaccines, or genes into living cells. Continuum models for describing the free energy landscape of membrane electroporation were proposed decades ago, but they have never been tested against spatially detailed atomistic models. Using molecular dynamics (MD) simulations with a recently proposed reaction coordinate, we computed potentials of mean force of pore nucleation and pore expansion in lipid membranes at various transmembrane potentials. Whereas the free energies of pore expansion are compatible with previous continuum models, the experimentally important free energy barrier of pore nucleation is at variance with established models. The discrepancy originates from different geometries of the transition state; previous continuum models assumed the presence of a membrane-spanning defect throughout the process, whereas, according to the MD simulations, the transition state of pore nucleation is typically passed before a transmembrane defect has formed. A modified continuum model is presented that qualitatively agrees with the MD simulations. Using kinetics of pore opening together with transition state theory, our free energies of pore nucleation are in excellent agreement with previous experimental data.

Single-cell nanobiopsy enables multigenerational longitudinal transcriptomics of cancer cells

Single-cell RNA sequencing has revolutionized our understanding of cellular heterogeneity, but routine methods require cell lysis and fail to probe the dynamic trajectories responsible for cellular state transitions, which can only be inferred. Here, we present a nanobiopsy platform that enables the injection of exogenous molecules and multigenerational longitudinal cytoplasmic sampling from a single cell and its progeny. The technique is based on scanning ion conductance microscopy (SICM) and, as a proof of concept, was applied to longitudinally profile the transcriptome of single glioblastoma (GBM) brain tumor cells in vitro over 72 hours. The GBM cells were biopsied before and after exposure to chemotherapy and radiotherapy, and our results suggest that treatment either induces or selects for more transcriptionally stable cells. We envision the nanobiopsy will contribute to transforming standard single-cell transcriptomics from a static analysis into a dynamic assay.

High-Precision Single-Cell microRNA Therapy by a Functional Nanopipette with Sensitive Photoelectrochemical Feedback

This work proposes the concept of single-cell microRNA (miR) therapy and proof-of-concept by engineering a nanopipette for high-precision miR-21-targeted therapy in a single HeLa cell with sensitive photoelectrochemical (PEC) feedback. Targeting the representative oncogenic miR-21, the as-functionalized nanopipette permits direct intracellular drug administration with precisely controllable dosages, and the corresponding therapeutic effects can be sensitively transduced by a PEC sensing interface that selectively responds to the indicator level of cytosolic caspase-3. The experimental results reveal that injection of ca. 4.4 × 10-20 mol miR-21 inhibitor, i.e., 26488 copies, can cause the obvious therapeutic action in the targeted cell. This work features a solution to obtain the accurate knowledge of how a certain miR-drug with specific dosages treats the cells and thus provides an insight into futuristic high-precision clinical miR therapy using personalized medicine, provided that the prerequisite single-cell experiments are courses of personalized customization.

A paintbrush for delivery of nanoparticles and molecules to live cells with precise spatiotemporal control

Delivery of very small amounts of reagents to the near-field of cells with micrometer spatial precision and millisecond time resolution is currently out of reach. Here we present μkiss as a micropipette-based scheme for brushing a layer of small molecules and nanoparticles onto the live cell membrane from a subfemtoliter confined volume of a perfusion flow. We characterize our system through both experiments and modeling, and find excellent agreement. We demonstrate several applications that benefit from a controlled brush delivery, such as a direct means to quantify local and long-range membrane mobility and organization as well as dynamical probing of intercellular force signaling.

Massively Parallel High-Throughput Single-Cell Patterning and Large Biomolecular Delivery in Mammalian Cells Using Light Pulses

The recent advancements of single-cell analysis have significantly enhanced the ability to understand cellular physiology when compared to bulk cellular analysis. Here a massively parallel single-cell patterning and very large biomolecular delivery is reported. Micro-pillar polydimethyl siloxane stamp with different diameters (40-100 µm with 1 cm × 1 cm patterning area) is fabricated and then imprint distinct proteins and finally pattern single-cell to small clusters of cells depending on the micro-pillar diameters. The maximum patterning efficiency is achieved 99.7% for SiHa, 96.75% for L929, and 98.6% for MG63 cells, for the 100 µm micro-pillar stamp. For intracellular delivery of biomolecules into the patterned cells, a titanium micro-dish device is aligned on top of the cells and exposed by infrared light pulses. The platform successfully delivers small to very large biomolecules such as PI dyes (668 Da), dextran 3000 Da, siRNA (20-24 bp), and large size enzymes (464 KDa) in SiHa, L929 and MG63 cells. The delivery efficiency for PI dye, Dextran 3000, siRNA, and enzyme for patterned cells are ≈95 ± 3%, 97 ± 1%, 96 ± 1% and 94 ± 3%, with cell viability of 98 ± 1%. Thus, the platform is compact, robust, easy for printing, and potentially applicable for single-cell therapy and diagnostics.

Cellular Delivery of Large Functional Proteins and Protein-Nucleic Acid Constructs via Localized Electroporation

Delivery of proteins and protein-nucleic acid constructs into live cells enables a wide range of applications from gene editing to cell-based therapies and intracellular sensing. However, electroporation-based protein delivery remains challenging due to the large sizes of proteins, their low surface charge, and susceptibility to conformational changes that result in loss of function. Here, we use a nanochannel-based localized electroporation platform with multiplexing capabilities to optimize the intracellular delivery of large proteins (β-galactosidase, 472 kDa, 75.38% efficiency), protein-nucleic acid conjugates (protein spherical nucleic acids (ProSNA), 668 kDa, 80.25% efficiency), and Cas9-ribonucleoprotein complex (160 kDa, ∼60% knock-out and ∼24% knock-in) while retaining functionality post-delivery. Importantly, we delivered the largest protein to date using a localized electroporation platform and showed a nearly 2-fold improvement in gene editing efficiencies compared to previous reports. Furthermore, using confocal microscopy, we observed enhanced cytosolic delivery of ProSNAs, which may expand opportunities for detection and therapy.

Multiplexed high-throughput localized electroporation workflow with deep learning-based analysis for cell engineering

Manipulation of cells for applications such as biomanufacturing and cell-based therapeutics involves introducing biomolecular cargoes into cells. However, successful delivery is a function of multiple experimental factors requiring several rounds of optimization. Here, we present a high-throughput multiwell-format localized electroporation device (LEPD) assisted by deep learning image analysis that enables quick optimization of experimental factors for efficient delivery. We showcase the versatility of the LEPD platform by successfully delivering biomolecules into different types of adherent and suspension cells. We also demonstrate multicargo delivery with tight dosage distribution and precise ratiometric control. Furthermore, we used the platform to achieve functional gene knockdown in human induced pluripotent stem cells and used the deep learning framework to analyze protein expression along with changes in cell morphology. Overall, we present a workflow that enables combinatorial experiments and rapid analysis for the optimization of intracellular delivery protocols required for genetic manipulation.

High-Throughput Microfluidics Platform for Intracellular Delivery and Sampling of Biomolecules from Live Cells

Nondestructive cell membrane permeabilization systems enable the intracellular delivery of exogenous biomolecules for cell engineering tasks as well as the temporal sampling of cytosolic contents from live cells for the analysis of dynamic processes. Here, we report a microwell array format live-cell analysis device (LCAD) that can perform localized-electroporation induced membrane permeabilization, for cellular delivery or sampling, and directly interfaces with surface-based biosensors for analyzing the extracted contents. We demonstrate the capabilities of the LCAD via an automated high-throughput workflow for multimodal analysis of live-cell dynamics, consisting of quantitative measurements of enzyme activity using self-assembled monolayers for MALDI mass spectrometry (SAMDI) and deep-learning enhanced imaging and analysis. By combining a fabrication protocol that enables robust assembly and operation of multilayer devices with embedded gold electrodes and an automated imaging workflow, we successfully deliver functional molecules (plasmid and siRNA) into live cells at multiple time-points and track their effect on gene expression and cell morphology temporally. Furthermore, we report sampling performance enhancements, achieving saturation levels of protein tyrosine phosphatase activity measured from as few as 60 cells, and demonstrate control over the amount of sampled contents by optimization of electroporation parameters using a lumped model. Lastly, we investigate the implications of cell morphology on electroporation-induced sampling of fluorescent molecules using a deep-learning enhanced image analysis workflow.