Deep Learning and Computer Vision Strategies for Automated Gene Editing with a Single-Cell Electroporation Platform

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.

Artificial Intelligence and Computational Biology in Gene Therapy: A Review

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

Deep learning in CRISPR-Cas systems: a review of recent studies

In genetic engineering, the revolutionary CRISPR-Cas system has proven to be a vital tool for precise genome editing. Simultaneously, the emergence and rapid evolution of deep learning methodologies has provided an impetus to the scientific exploration of genomic data. These concurrent advancements mandate regular investigation of the state-of-the-art, particularly given the pace of recent developments. This review focuses on the significant progress achieved during 2019-2023 in the utilization of deep learning for predicting guide RNA (gRNA) activity in the CRISPR-Cas system, a key element determining the effectiveness and specificity of genome editing procedures. In this paper, an analytical overview of contemporary research is provided, with emphasis placed on the amalgamation of artificial intelligence and genetic engineering. The importance of our review is underscored by the necessity to comprehend the rapidly evolving deep learning methodologies and their potential impact on the effectiveness of the CRISPR-Cas system. By analyzing recent literature, this review highlights the achievements and emerging trends in the integration of deep learning with the CRISPR-Cas systems, thus contributing to the future direction of this essential interdisciplinary research area.

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.

Robotic Intracellular Electrochemical Sensing for Adherent Cells

Nanopipette-based observation of intracellular biochemical processes is an important approach to revealing the intrinsic characteristics and heterogeneity of cells for better investigation of disease progression or early disease diagnosis. However, the manual operation needs a skilled operator and faces problems such as low throughput and poor reproducibility. This paper proposes an automated nanopipette-based microoperation system for cell detection, three-dimensional nonovershoot positioning of the nanopipette tip in proximity to the cell of interest, cell approaching and proximity detection between nanopipette tip and cell surface, and cell penetration and detection of the intracellular reactive oxygen species (ROS). A robust focus algorithm based on the number of cell contours was proposed for adherent cells, which have sharp peaks while retaining unimodality. The automated detection of adherent cells was evaluated on human umbilical cord vein endothelial cells (HUVEC) and NIH/3T3 cells, which provided an average of 95.65% true-positive rate (TPR) and 7.59% false-positive rate (FPR) for in-plane cell detection. The three-dimensional nonovershoot tip positioning of the nanopipette was achieved by template matching and evaluated under the interference of cells. Ion current feedback was employed for the proximity detection between the nanopipette tip and cell surface. Finally, cell penetration and electrochemical detection of ROS were demonstrated on human breast cancer cells and zebrafish embryo cells. This work provides a systematic approach for automated intracellular sensing for adherent cells, laying a solid foundation for high-throughput detection, diagnosis, and classification of different forms of biochemical reactions within single cells.

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.

Recent Advances in Microscale Electroporation

Electroporation (EP) is a commonly used strategy to increase cell permeability for intracellular cargo delivery or irreversible cell membrane disruption using electric fields. In recent years, EP performance has been improved by shrinking electrodes and device structures to the microscale. Integration with microfluidics has led to the design of devices performing static EP, where cells are fixed in a defined region, or continuous EP, where cells constantly pass through the device. Each device type performs superior to conventional, macroscale EP devices while providing additional advantages in precision manipulation (static EP) and increased throughput (continuous EP). Microscale EP is gentle on cells and has enabled more sensitive assaying of cells with novel applications. In this Review, we present the physical principles of microscale EP devices and examine design trends in recent years. In addition, we discuss the use of reversible and irreversible EP in the development of therapeutics and analysis of intracellular contents, among other noteworthy applications. This Review aims to inform and encourage scientists and engineers to expand the use of efficient and versatile microscale EP technologies.

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.

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

Genome engineering of cells using CRISPR/Cas systems has opened new avenues for pharmacological screening and investigating the molecular mechanisms of disease. A critical step in many such studies is the intracellular delivery of the gene editing machinery and the subsequent manipulation of cells. However, these workflows often involve processes such as bulk electroporation for intracellular delivery and fluorescence activated cell sorting for cell isolation that can be harsh to sensitive cell types such as human-induced pluripotent stem cells (hiPSCs). This often leads to poor viability and low overall efficacy, requiring the use of large starting samples. In this work, a fully automated version of the nanofountain probe electroporation (NFP-E) system, a nanopipette-based single-cell electroporation method is presented that provides superior cell viability and efficiency compared to traditional methods. The automated system utilizes a deep convolutional network to identify cell locations and a cell-nanopipette contact algorithm to position the nanopipette over each cell for the application of electroporation pulses. The automated NFP-E is combined with microconfinement arrays for cell isolation to demonstrate a workflow that can be used for CRISPR/Cas9 gene editing and cell tracking with potential applications in screening studies and isogenic cell line generation.