Speaker
Description
Single-cell technologies have become a crucial tool for investigating biological systems by enabling detailed characterisation of cellular heterogeneity. Beyond measurements of individual modalities such as the transcriptome or proteome, multi-omic approaches capturing multiple molecular identities from the same cell, have provided further insights into dissecting complex biological processes. However, in contrast to these advances in molecular profiling, the physical dimension such as cell size, shape, and mechanics is almost entirely lacking. Here, we present a high-throughput microfluidic platform, im-seq, to directly connect single-cell imaging with gene expression.
To date, efforts to link optical phenotype data with sequencing readouts at the single-cell level have been limited in throughput, relying on imaging cells either under static conditions or in fully deterministic flow-based systems. Static, array-based approaches are inherently limited in throughput and do not scale easily. Deterministic flow-based systems are more scalable, but the need for highly precise control limits their analysis rate.
Im-seq overcomes these limitations by deliberately harnessing randomness, rather than enforcing determinism. We generate millions of multimodal barcodes by encapsulating random combinations of hydrogel beads, each of which is uniquely identifiable both optically and by sequencing, in droplets. This approach enables simultaneous capture of physical phenotypes and transcriptomic profiles from individual cells in flow, combining the versatility of imaging-based characterisation with the scalability of droplet sequencing workflows. Currently, im-seq can record linked imaging and mRNA sequencing data for single cells at a rate of hundreds per cell per minute. We have applied im-seq to investigate the relationship between gene expression and physical phenotype in haematopoietic cell lines, demonstrating the power of large, multimodal datasets in interrogating cellular function.
We thus provide a general and scalable strategy for multimodal single-cell characterisation at high throughput, enabling gene expression–phenotype studies not feasible under existing workflows. Moreover, the implementation is relatively simple, requiring only a commercially available microscope and syringe pumps, without complex liquid handling such as valves and controlled cell/droplet dispensing. We anticipate that im-seq will open new avenues for studying the relationship between cell phenotypes and gene expression programmes, and for leveraging multimodal signatures to understand cell behaviour in health and disease.