A new integrated platform developed by researchers is set to break crucial bottlenecks in cellular research, diagnostics, and personalized medicine. A team from the Indian Institute of Technology Madras and Toyohashi University of Technology in Japan has engineered a system that combines a massively parallel microdevice for delivering materials into cells with a sophisticated, AI-powered analysis pipeline. This dual-module system dramatically accelerates the pace of both intracellular delivery and the subsequent evaluation of its success, achieving a throughput that was previously impractical for most laboratories.
The core challenge in fields like drug discovery, gene modulation, and cell engineering has been twofold: administering biomolecules to a vast number of cells efficiently and then analyzing the outcome of that delivery at a comparable speed. This new platform tackles both problems simultaneously by pairing a high-throughput cell-squeezing device with an automated image-cytometry process driven by deep learning. By unifying delivery with rapid, reliable quality control, the technology moves complex cell manipulation from a proof-of-concept stage toward practical, scalable workflows that could one day enable on-site patient cell therapies.
Mechanical Delivery at Massive Scale
The first component of the platform is a microfluidic chip designed for unprecedented throughput. This device facilitates a process known as mechanoporation, a technique that uses mechanical force to create temporary openings in a cell’s membrane. The chip’s base contains a porous membrane integrating up to 62,000 vertical through-holes, each smaller than the diameter of a typical cell. As cells are guided across this array, they are gently squeezed through the sub-cell-sized apertures.
This brief squeezing action forms transient nanopores in the cell membrane, allowing cargo such as biomolecules to enter the cell’s interior from the surrounding fluid. The technique has been successfully used to deliver materials like small interference RNAs (siRNAs) and plasmid DNA. Immediately after passing through the hole, the cell membrane reseals, and the cell exits the device for collection and analysis. This method is gentle enough to ensure high cell viability post-delivery while being capable of processing as many as 3 million cells per minute, representing a significant leap in scale for intracellular delivery systems.
AI-Powered Rapid Analysis
Once the cells are treated, the second module of the platform takes over to provide rapid, automated analysis. Traditionally, evaluating the success of intracellular delivery involves manual, time-consuming microscopy and cell counting. This new system replaces that laborious process with a powerful AI-driven pipeline. After collection, the cells are imaged under a microscope using both bright-field and fluorescence channels. These images are then fed into an advanced AI model for automated single-cell image cytometry.
The system is built on a Mask R-CNN–based instance-segmentation model, a sophisticated type of neural network capable of identifying and isolating individual objects within an image. The AI processes the image set in one pass, performing multiple tasks simultaneously. It uses the bright-field image for cell localization and measures cell size from the instance segmentation masks. Concurrently, it analyzes fluorescence channels to quantify results: a green fluorescent signal indicates successful cargo delivery, while a red signal is used to identify dying or non-viable cells. This allows the system to automatically classify each cell into one of four states—such as “delivered-live” or “undelivered-dead”—and calculates precise metrics for delivery efficiency and overall cell viability. The entire analysis can be completed in under 83 seconds.
Bridging Throughput and Quality Control
The true innovation of the platform lies in its seamless integration of high-speed delivery with equally fast and automated quality control. In many research settings, high throughput alone is not enough; the results must be trustworthy and reproducible. By developing an AI analysis module that keeps pace with the physical cell-processing device, the researchers have created a balanced workflow where evaluation no longer lags behind experimentation. This synergy ensures that the vast datasets generated by the device are immediately useful for making high-confidence decisions.
This approach addresses a critical gap in the field, where the ability to manipulate cells has often outpaced the ability to analyze them effectively. The automated pipeline does the counting and measuring of cell size, delivery success, and viability, removing the potential for human error and drastically reducing the time to results. According to Professor Moeto Nagai of Toyohashi University of Technology, unifying high-throughput delivery with automated quality control is what moves these technologies from theoretical concepts to practical workflows. This leap is essential for developing systems that could one day be used for clinical applications, such as preparing a patient’s cells for therapy at the point of care.
Transforming Cellular Research and Medicine
The implications of this technology are broad, promising to revolutionize any field that relies on large-scale cell screening and manipulation. Its capabilities are particularly relevant for drug discovery, where researchers need to test the effects of countless compounds on large cell populations, and for advanced diagnostics that rely on single-cell analysis. Furthermore, it stands to accelerate the development of next-generation treatments in cell engineering and gene modulation, including personalized therapies that require modifying a patient’s own cells.
By significantly reducing the manual labor and time involved in complex cellular experiments, the platform expands what a research team can accomplish in a single day. The ability to perform large-scale screening and receive quantitative feedback almost instantly opens the door to more complex experimental designs and faster iteration. The developers note that the device’s efficacy across diverse cell types highlights its potential for wide-ranging applications. Ultimately, this integrated system represents a key step toward a future where sophisticated cell and gene-editing workflows are not confined to specialized facilities but can be deployed rapidly in a variety of clinical and research settings.