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Linking single-cell transcriptomes with secretion using secretion-encoded single-cell sequencing (SEC-seq)

Langerman, J.; Baghdasarian, S.; Cheng, R. Y.-H.; Plath, K.; James, R. G.; Di Carlo, D.

2024-05-20 bioengineering
10.1101/2024.05.17.594711 bioRxiv
Show abstract

Cells secrete numerous proteins and other biomolecules into their surroundings to achieve critical functions - from communicating with other cells to blocking the activity of pathogens. Secretion of cytokines, growth factors, extracellular vesicles, and even recombinant biologic drugs defines the therapeutic potency of many cell therapies. However, gene expression states that drive specific secretory phenotypes are largely unknown. We provide a protocol that enables linking the Secretion amount of a target protein EnCoded (SEC) by thousands of single cells with transcriptional sequencing (seq). SEC-seq leverages microscale hydrogel particles called Nanovials to isolate cells and capture their secretions in close proximity, oligonucleotide-labeled antibodies to tag secretions on Nanovials, and flow cytometry and single-cell RNA-sequencing platforms for readout. Cells on Nanovials can be sorted based on viability, secretion amount, or other surface markers without fixation or permeabilization, and cell and secretion-containing Nanovials are directly introduced into microfluidic droplets-in-oil emulsions for single-cell barcoding of cell transcriptomes and secretions. We have used SEC-seq to link T-cell receptor sequences to the relative amount of associated cytokine secretions, surface marker gene expression with a highly secreting and potential regenerative population of mesenchymal stromal cells, and the transcriptome with high immunoglobulin secretion from plasma cells. Nanovial modification and cell loading takes under 4 hours, and once the desired incubation time is over, staining, cell sorting, and emulsion generation for scRNA-seq can also be completed in under 4 hours. By linking gene expression and secretory strength, SEC-seq can expand our understanding of cell secretion, how it is regulated, and how it can be engineered to make better therapies.

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