Tiny Wells, Big Data: Microwell Chips Meet Artificial Intelligence

Every cell in the human body carries its own story, and understanding those individual stories has become one of the most powerful ideas in modern biology. A new review published in Biomedical Microdevices examines how an unassuming piece of engineering—arrays of microscopic wells, each capable of trapping a single cell—has matured into a foundational platform for single-cell analysis, and how artificial intelligence is now poised to transform these devices from measurement tools into engines of prediction and even autonomous discovery. Written by Hoi Lam Cheung, Dinh-Nguyen Nguyen, My Thi Tra Ngo and Ngoc-Duy Dinh, the review systematically connects the physics of microwell design to the biology that can be extracted from isolated cells, and then maps out the computational future that awaits the field.

The central premise is deceptively simple. Single-cell analysis matters because populations of supposedly identical cells are anything but identical. Differences in gene expression, protein secretion, morphology and behaviour drive disease progression, immune responses and therapeutic outcomes in ways that bulk measurements average away. Microwell platforms address this heterogeneity by spatially confining individual cells within miniature compartments while preserving access for imaging, perturbation and downstream molecular measurements. Unlike droplet-based systems, which encapsulate cells in aqueous pockets within oil, microwells keep cells physically addressable and visually observable, allowing researchers to link what a cell looks like, what it does and what it expresses within a single workflow.

The review pays close attention to how engineering choices determine scientific outcomes. Cell-loading strategies—whether gravitational settling, centrifugation-assisted deposition, hydrodynamic focusing or immunomagnetic capture—shape how efficiently wells are occupied and whether the same cell can be tracked across multiple measurement modalities. Well geometry is equally consequential: truncated cone-shaped arrays improve trapping efficiency, L-shaped and paired wells allow controlled encounters between two different cells, and nested nanowell-in-microwell architectures enable motility studies inside confined arenas. Platform architecture and material selection matter too. Polydimethylsiloxane, the workhorse polymer of microfluidics, can absorb small-molecule drugs and distort drug-response assays, while polymer films and hydrogel coatings extend culture lifetimes and support organoid growth. Phototoxicity during live fluorescence imaging and the need to support cell spreading and proliferation impose further constraints on design. The authors argue that these parameters collectively determine the information available for computational analysis—experimental design and analytical power are inseparable.

From this engineering foundation, the review surveys the major application domains where microwells have proven their worth. In cellular behaviour and cell–cell interactions, microwell arrays have enabled researchers to follow lymphocyte cytotoxicity dynamics, serial killing by immune cells, and the activation profiles of paired T cells in real time. Time-resolved cell-pairing arrays have revealed multiple activation states among individual T cells, while single-cell arrays of hematological cancer cells have allowed quantitative assessment of how immune killers engage, destroy and move on to successive targets. Such assays turn immunology from a population statistic into a choreography of individual cellular encounters.

Secretome analysis represents another signature strength. Microengraving methods, first demonstrated for rapidly selecting cells producing antigen-specific antibodies, imprint the proteins secreted by each trapped cell onto an antibody-coated surface, allowing frequencies and rates of cytokine secretion to be measured across thousands of cells simultaneously. Hierarchical loading microwell chips have evaluated single-cell cytokine secretion and cell–cell interactions, while nanoplasmonic microwell arrays now monitor secretion in real time without labels. The field has even extended to single extracellular vesicles and particles, with oil-sealed hydrogel microwell arrays capturing the nanoscale output of individual cells—a frontier the review identifies as rapidly expanding.

Genomic and transcriptomic profiling is arguably where microwells achieved their greatest visibility. Massively parallel polymerase cloning in nanoliter wells enabled genome sequencing of single cells, and Microwell-Seq platforms mapped the mouse cell atlas at unprecedented scale. Seq-Well brought portable, low-cost RNA sequencing to the field, while later generations such as Microwell-seq 2.0 and Microwell-seq3 extended the approach to multiplexed chemical perturbation screens and joint profiling of chromatin accessibility and RNA expression. Addressable microwell arrays support dual-indexed workflows and permit imaging to be integrated with sequencing, so that morphology and transcript identity can be recovered from the same cell. Comparative studies summarized in the review suggest microwell-based methods perform robustly even with cryopreserved clinical samples, an important consideration as single-cell genomics moves toward routine clinical use.

Drug screening and precision medicine complete the application landscape. Microwell chips have measured drug sensitivity in individual prostate cancer cells, supported miniaturized multiplexed high-content screening of drug and immune responses in multichambered formats, and enabled high-throughput generation of patient-derived cancer stem cells for precision medicine. Deep learning frameworks for in silico screening of anticancer drugs at the single-cell level hint at a future where candidate compounds are triaged computationally before ever touching a laboratory plate. Throughout these domains, the authors stress a common advantage: microwells preserve cell identity, allowing spatial, temporal, functional and molecular data to be linked for the same individual cell rather than inferred across disconnected measurements.

That linkage, however, comes at a cost. The datasets generated are enormous, multidimensional and difficult to analyse with conventional approaches, and this is where artificial intelligence enters the story. The review provides a careful, even sceptical accounting, distinguishing AI methods that have been directly demonstrated in microwell-based studies from those developed in the broader single-cell field that remain prospective. Demonstrated applications include automated image analysis, cell tracking, phenotype classification and behavioural analysis: deep learning models have profiled single-cell migration and proliferation on addressable dual-nested microwell arrays, automated the analysis of natural killer cell cytotoxicity in single cancer cell arrays, and enabled stain-free cell viability screening from regularized single-cell imaging. Deep learning has also revealed how cell–cell interactions shape single-cell behaviour in high-throughput coculture systems, and studies of single-cell morphodynamics now predict cell fate decisions during epithelial differentiation—evidence that image-derived features can carry predictive molecular information.

Looking further ahead, the authors outline an ambitious computational roadmap built on multimodal AI, foundation models, large language model agents and autonomous laboratory systems. Foundation models pretrained on cross-species single-cell landscapes have already identified conserved regulatory programs underlying cell types, suggesting transferable biological knowledge that could be fine-tuned for microwell data. Large language model agents have recently been shown to design droplet microfluidic experiments autonomously and to mine the microwell microfluidics literature, and the review extends this vision to microwell platforms themselves: agentic AI systems that could plan experiments, adjust loading and imaging parameters, interpret results in closed loops and iteratively refine hypotheses without human intervention. Autonomous microfluidic labs, combining machine learning with robotic liquid handling and microfluidic control, are identified as the natural endpoint of this trajectory—moving microwell-based single-cell research from measurement toward prediction and, ultimately, self-driving discovery.

The significance of this synthesis lies in its insistence that hardware and software must be co-designed. If the geometry of a well determines which cells are captured, and the imaging regime determines what is visible, then the AI models trained on that data inherit the assumptions and limitations of the platform. By explicitly linking microwell engineering to the information available for computational analysis, the review offers the field a blueprint: researchers who want predictive, AI-ready single-cell biology must build platforms that capture the right cells, preserve their identity, and generate the multimodal, well-annotated data that modern machine learning demands. As microwell arrays and artificial intelligence converge, the humble microscopic well may become the standardized test tube of the autonomous biology era.

Subject of Research: Microwell-based single-cell analysis platforms and their emerging integration with artificial intelligence

Article Title: Microwell platform for single-cell applications and future integration with artificial intelligence (AI)

Article References: Cheung, H. L., Nguyen, D.-N., Ngo, M. T. T., & Dinh, N.-D. (2026). Microwell platform for single-cell applications and future integration with artificial intelligence (AI). Biomedical Microdevices, 28(3), Article 66. https://doi.org/10.1007/s10544-026-00852-8

Image Credits: AI Generated

DOI: 10.1007/s10544-026-00852-8

Keywords: microwell platforms, single-cell analysis, artificial intelligence, microfluidics, cell heterogeneity, secretome analysis, single-cell RNA sequencing, drug screening, precision medicine, deep learning, large language model agents, autonomous laboratories