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Alexander Xu1 2 Qianhe Liu1 Kaitlyn Takata1 Sarah Jeoung1 Yapeng Su1 Igor Antoschechkin1 Sisi Chen1 Matthew Thomson1 James Heath1 2

1, California Institute of Technology, Pasadena, California, United States
2, Institute for Systems Biology, Seattle, Washington, United States

Single cell measurements have revamped our understanding of biological tissues by resolving their single cell heterogeneity. New technologies such as droplet microfluidic transcriptomics and microchip proteomics provide better insights on how healthy tissues function, and how aberrant cells in the tissue cause disease. However, our understanding of how the single cells in a tissue perform their functions is limited by the types of information that can be captured from each cell.
Here we present a method for measuring the whole transcriptome and cytosolic proteins in single cells on a microfluidic chip. By coupling single cell barcode chip (SCBC) proteomics and bead-based sequencing technologies, we capture multiple measurements of a single cell without splitting cell contents, and we use gold standard measurement modes – fluorescence sandwich immunoassays for proteins and sequencing for transcripts. This technique is enabled by a DNA labeling strategy that allows measurements to be taken independently and robustly, and linked after data processing.
We measure two cell types, showing that there is a unique molecular signature for both cell types in both protein and transcript data. The microfluidic chip is designed such that location barcodes scale geometrically and the chip can be readily scaled to produce larger single cell integrated datasets. This strategy for taking multiple measurements from single cells is unique in that it does not require either the protein or transcript signal to be converted from its native measurement mode. This is done in an effort to produce the best possible data using established technology. By using established technology, we are also able to further modify the integrated SCBC to capture metabolomic measurements in the future. Ultimately, this technology presents a generalizable DNA encoding strategy to augment sequencing methods, as well as a powerful tool to better understand single cell biology by expanding the scope of their measurements.

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