# Single and few cell analysis for correlative light microscopy, metabolomics, and targeted proteomics

## TL;DR

This paper introduces a method to study single or few cells by combining microscopy with metabolomics and proteomics to understand cellular processes.

## Contribution

A new method for correlative single-cell analysis combining light microscopy, metabolomics, and proteomics.

## Key findings

- The method enables correlative measurement of cellular structures and metabolites.
- It allows for targeted protein detection on the single-cell level using RPPA.
- The approach combines LC-MS and RPPA for few-cell analysis.

## Abstract

The interactions of proteins, membranes, nucleic acid, and metabolites shape a cell's phenotype. These interactions are stochastic, and each cell develops differently, making it difficult to synchronize cell populations. Consequently, studying biological processes at the single- or few-cell level is often necessary to avoid signal dilution below the detection limit or averaging over many cells. We have developed a method to study metabolites and proteins from a small number of or even a single adherent eukaryotic cell. Initially, cells are lysed by short electroporation and aspirated with a microcapillary under a fluorescent microscope. The lysate is placed on a carrier slide for further analysis using liquid-chromatography mass spectrometry (LC-MS) and/or reverse-phase protein (RPPA) approach. This method allows for a correlative measurement of (i) cellular structures and metabolites and (ii) cellular structures and proteins on the single-cell level. The correlative measurement of cellular structure by light-microscopy, metabolites by LC-MS, and targeted protein detection by RPPA was possible on the few-cell level. We discuss the method, potential applications, limitations, and future improvements.

We combined a single-cell lysis and handover system with mass spectrometry and reverse-phase protein arrays, allowing correlative single- and few-cell analysis combining microscopy with metabolomics and targeted proteomics.