Transforming microfluidics for single-cell analysis with robotics and artificial intelligence

Abstract

Single-cell analysis has advanced biomedical research by revealing cellular heterogeneity with unprecedented resolution, identifying rare subpopulations that drive disease progression and therapeutic resistance. Microfluidics is central to this advancement, enabling precise single-cell isolation, manipulation, and cellular profiling. However, limitations in automation, reliability, and technical barriers hinder the widespread adoption of microfluidic single-cell analysis. This review highlights key innovations in experimental methods and deep learning-driven data analysis to overcome these challenges. Operating microfluidics with robotic operation, digital microfluidics, or microrobots enhances experimental precision and scalability. Beyond experimental automation, deep learning revolutionizes data interpretation through label-free image processing and cell status classification and regression. Generative models further refine analysis by correcting batch effects and generating synthetic datasets, improving accuracy and reproducibility in single-cell studies. Considering the complexity of integrating these technologies, remote shared cloud labs represent a potential pathway toward standardized and high-throughput single-cell analysis, facilitating broader access to advanced experimental workflows. Overall, the convergence of robotics and artificial intelligence in single-cell analysis will change data acquisition, hypothesis testing, and model refinement, driving breakthroughs in drug discovery and personalized medicine. While implementation remains challenging, this paradigm shift is transforming biomedical research, enabling unprecedented precision, scalability, and data-driven innovation.

Fig. 1. Milestones in microfluidics, robotics and AI in two decades. This timeline highlights key milestones in microfluidics, from PDMS prototyping to the emergence of AI-guided microfluidics. Notable advances include the Quake valve, cell culture integration, digital, paper-based, and droplet microfluidics, along with acoustofluidics, magnetofluidics, 3D printing, and optofluidics. Recent breakthroughs encompass Drop-seq, organ-on-a-chip, CITE-seq, POCT for COVID-19, and single-cell multi-omics. Parallel progress in robotics and AI includes early consumer robotics (AIBO, Roomba), autonomous navigation through the DARPA Grand Challenge, and interactive systems such as PR2, ROS, and Kinect. The rise of deep learning with AlexNet and achievements like AlphaGo and GPT-2/3/4 (ref. 197) enabled smarter, perception-driven automation. By the 2020s, the introduction of RT-2 (ref. 198) and Tesla Optimus marked a shift toward intelligent robotic workflows. Key robotic scientist milestones include EXPO (2006), Adam (2009), Maholo (2017), Robot Chemist (2020), and ChemAgents (2024). Created using BioRender, based on references.

Fig. 2. Lab-built robotic systems for microfluidic operations in single-cell and microscale biological analysis. (A) The nanoPOTS platform integrates a high-precision syringe pump, nanowell chip, droplet capture frame, and stereomicroscope for picoliter-scale liquid dispensing in single-cell proteomics workflows. (B) A robot-assisted acoustofluidic end effector system, integrating a robotic arm, glass capillary, piezo transducer, and acoustofluidic device to enable high-precision fluid mixing, particle manipulation, and biological sample handling. (C) The PiSPA (pick-up single-cell proteomics analysis) system couples a capillary probe with a droplet-array-based liquid handling platform (SODA: sequential operation droplet array) to isolate and process individual cells through sequential in situ lysis, digestion, and LC–MS injection. (D) A compact robotic platform combining a pipette, syringe pump, onboard camera, and motion control system to automate microtissue handling in 384-well plates under sterile culture hood conditions. (E) The RoboCulture system for yeast cultivation integrates real-time optical feedback and force sensing with robotic tip exchange and liquid handling to enable fully autonomous, long-duration culture and monitoring. Created using BioRender, based on references.

Fig. 3. Applications of commercial liquid-handling robots in microfluidic single-cell analysis. (A) A droplet microfluidics system coupled with a commercial liquid handling robot (Freedom EVO, Tecan) and automated syringe pumps. (B) A nanoliter-scale single-cell proteomics workflow based on the proteoCHIP platform, integrated with the cellenONE robotic system. Single cells are dispensed into nanowells, followed by nanoliter-scale (nL) cell lysis and tryptic digestion, tandem mass tag (TMT) labeling for multiplexed quantification, and sample pooling for liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis. (C) A high-throughput single-cell migration platform capable of tracking tens of thousands of individual cells. The system combines a liquid handling robot (OT-2, Opentrons) with autonomous image analysis software for microfluidic single-cell analysis. Created using BioRender, based on references.

Fig. 4. On-chip microrobots for single-cell manipulation and analysis. (A) A digital microfluidic ferrobotics system enables high-throughput, programmable fluidic operations for biomedical diagnostics, including SARS-CoV-2 amplification and detection. (B) An optoelectronic microrobot within an OET system achieves precise single-cell manipulation with minimal cellular damage. Light-controlled actuation allows targeted cell collection and transfer. (C) Optically actuated, TPP-fabricated soft microrobots enable gentle, high-precision single-cell manipulation using optical tweezers. The designs illustrate deformation under trapping beam-induced forces, leveraging wireframe structures for controlled cell manipulation. Created using BioRender, based on references.

Fig. 5. AI models for single-cell analysis. (A) CNNs capture spatial hierarchies of cellular features, enabling precise cell classification and regression. Classification models distinguish cells by morphology and molecular traits, while regression models predict continuous variables like gene expression and phenotypic responses. (B) GANs consist of a generator that creates synthetic data and a discriminator that differentiates real from fake, engaging in an adversarial process to refine data generation. This framework enables high-fidelity single-cell data synthesis, correcting batch effects, and simulating cellular states under diverse conditions. Images were created using BioRender.

Fig. 6. Cloud Lab synergizing microfluidics, robotics, and AI for accelerated single-cell discovery. The Cloud Lab concept connects laboratory protocol management, single-cell microfluidics, robotic operation, and artificial intelligence into a unified workflow. Laboratory modules manage experimental protocols, while microfluidic systems enable high-throughput single-cell data collection. Robotic platforms execute automated workflows, and AI modules generate and refine hypotheses based on acquired data. Images were created using BioRender.

Jinxiong Cheng

Rajiv Anne

Yu-Chih Chen