Single cell inductively coupled plasma mass spectrometry (SC-ICP-MS) has gained attention within the atomic spectrometry community during the last decade since it is a powerful and innovative tool to study cell-to-cell variations. Using this approach cells are individually introduced into the ICP, and a cloud of ions is generated when each cell is vaporised and ionised. Applying mathematical and statistical data processing, information about the mass of an element per cell, per number of cells could be retrieved [1].

As in single particle-ICP-MS, transport efficiency (TE) is a crucial parameter but its accurate determination for SC-ICP-MS is still a difficult task [2]. Different approaches have been reported for TE determination in SC-ICP-MS. Examples include the use of reference nanoparticles (NPs), microdroplets, or carbon-based microbeads tagged with an element visible to ICP-MS (typically employed in cytometry) [2, 3]. Despite the progress made so far, there are still remaining challenges posed by differences in the inherent physicochemical properties (e.g. size, shape, density, robustness) between NPs and cells and the uncertainty associated with bead tagging efficiency and with techniques commonly used for cell counting.

This work proposes a strategy based on the automated accurate isolation and counting of carbon microbeads to metrologically characterise (e.g. for measurement uncertainty) and validate TE determination in SC-ICP-MS. To achieve this, carbon-based microbeads with sizes and density similar to those of cells (5-10 µm) were automatically isolated and dispensed into a vial with subsequent measurement of their 12C/13C signal (depending on their size) in SC-ICP-MS mode. The number concentration of beads in the same vial was confirmed using complementary techniques like optical particle counting and coulter counting. The difference between the number of accurately dispensed/counted beads and that of the ICP-MS measured beads was used to accurately determine TE for a total consumption nebuliser. Additionally, the optimization of critical instrumental parameters, such as the bead concentration range, sample flow rate, nebulizer and make-up gas conditions will be presented. Critical aspects of sample preparation and their impact on the quality of data will also be discussed.