# Droplet sample preparation

## Abstract

Mass spectrometry methods have enabled quantifying thousands of proteins at the single cell level. These methods open the door to tackling many biological challenges, such as characterizing heterogeneity in the tumor micro-environment and better understanding signaling pathways driving stem cell differentiation. To further advance single-cell MS analysis, we developed an automated nano-ProteOmic sample Preparation (nPOP).

## Raw Data from experiments benchmarking nPOP

• MassIVE Repository:

## Processed Data from experiments benchmarking nPOP

• Peptides-raw.csv
• Peptides x single cells at 1% FDR. The first columns list the peptide sequences and each subsequent column corresponds to a single cell. Peptide identification is based on spectra analyzed by MaxQuant and is enhanced by using DART-ID to incorporate retention time information. See Specht et al., 2019 for details.

• Proteins-processed.csv
• Proteins x single cells at 1% FDR, imputed and batch corrected.

• Cells.csv
• Annotation x single cells. Each column corresponds to a single cell and the rows include relevant metadata, such as, cell type, measurements from the isolation of the cell, and derivative quantities, i.e., rRI, CVs, reliability. This file corresponds to Proteins-processed.csv and Peptides-raw.csv files.

• HeLa-proteins.csv
• Proteins x single cells for HeLa cells at 1% FDR, unimputed and zscored.

• U-937-proteins.csv
• Proteins x single cells for U-937 cells at 1% FDR, unimputed and zscored.