Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP.
Andrew LeducLuke KhouryJoshua CantlonSaad KhanNikolai SlavovPublished in: Nature protocols (2024)
Single-cell proteomics by mass spectrometry (MS) allows the quantification of proteins with high specificity and sensitivity. To increase its throughput, we developed nano-proteomic sample preparation (nPOP), a method for parallel preparation of thousands of single cells in nanoliter-volume droplets deposited on glass slides. Here, we describe its protocol with emphasis on its flexibility to prepare samples for different multiplexed MS methods. An implementation using the plexDIA MS multiplexing method, which uses non-isobaric mass tags to barcode peptides from different samples for data-independent acquisition, demonstrates accurate quantification of ~3,000-3,700 proteins per human cell. A separate implementation with isobaric mass tags and prioritized data acquisition demonstrates analysis of 1,827 single cells at a rate of >1,000 single cells per day at a depth of 800-1,200 proteins per human cell. The protocol is implemented by using a cell-dispensing and liquid-handling robot-the CellenONE instrument-and uses readily available consumables, which should facilitate broad adoption. nPOP can be applied to all samples that can be processed to a single-cell suspension. It takes 1 or 2 d to prepare >3,000 single cells. We provide metrics and software (the QuantQC R package) for quality control and data exploration. QuantQC supports the robust scaling of nPOP to higher plex reagents for achieving reliable and scalable single-cell proteomics.
Keyphrases
- single cell
- mass spectrometry
- rna seq
- induced apoptosis
- cell cycle arrest
- high throughput
- multiple sclerosis
- endothelial cells
- electronic health record
- randomized controlled trial
- healthcare
- ms ms
- primary care
- endoplasmic reticulum stress
- liquid chromatography
- cell therapy
- oxidative stress
- high resolution
- ionic liquid
- stem cells
- big data
- quality control
- label free
- cell proliferation
- quality improvement
- pi k akt
- bone marrow
- deep learning
- artificial intelligence