Sign in
Systems BiologyNature Communications
Peer-reviewed journal article

Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting

James M. FulcheriD, Lye Meng MarkillieiD, Hugh D. MitchelliD, Sarah WilliamsiD, Kristin M. EngbrechtiD, David J. DegnaniD, Lisa BrameriD, Ronald Jay MooreiD, William ChrisleriD, Joshua Cantlon-BruceiD, Johannes BagnoliiD, Weijun QianiD, Anjali SethiD, Ljiljana Paša‐TolićiD, Ying ZhuiD

Abstract

Single-cell multiomics provides comprehensive insights into gene regulatory networks, cellular diversity, and temporal dynamics. Here, we introduce nanoSPLITS (nanodroplet SPlitting for Linked-multimodal Investigations of Trace Samples), an integrated platform that enables global profiling of the transcriptome and proteome from same single cells using RNA sequencing and mass spectrometry-based proteomics, respectively. Benchmarking of nanoSPLITS demonstrated excellent measurement precision, with deep proteomic and transcriptomic profiling of single-cells. We applied nanoSPLITS to cyclin-dependent kinase 1 inhibited cells and found phospho-signaling events could be quantified alongside global protein and mRNA measurements, providing new insights into cell cycle regulation. We also extended nanoSPLITS to single-cells isolated from human pancreatic islets, introducing an efficient approach for facile identification of unknown cell types, and detecting their protein markers by mapping transcriptomic data to existing large-scale single-cell RNA sequencing reference databases. Herein, we establish nanoSPLITS as a new multiomic technology incorporating global proteomics and anticipate the approach will be critical to furthering our understanding of single-cell systems.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISDawar '26Xu '26Leduc '24Jiang '22Davey '21Hyman '20
79% semantic matchSystems Biology
Pranav Dawar, Lye Meng Markillie, Sarah M. Williams+8 more·Lab on a Chip·Jan 1, 2026· 3 citations