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2017 MRS Spring Meeting

WE09 - A DNA Encoding Strategy for Integrated Single Cell Proteomics and Transcriptomics

Apr 19, 2017 2:00pm ‐ Apr 19, 2017 2:45pm


Single cell measurements, including transcriptomics and proteomics, are transforming the study of biology. Single cell techniques leverage a multitude of materials technologies including microfluidics and DNA-functionalized microbeads, enabling studies of the true heterogeneity of complex tissues and therefore potential disease treatments. However, it is still difficult to gather multiple types of biological measurements from single cells, which is necessary to determine how the myriad of biomolecules communicate and interact with each other. Here we present a strategy that enables the measurement of the whole transcriptome and selected proteins from a single cell using a microfluidic chip device. The challenge lies in how single cell resolution is obtained for each technique. Single cell proteomic resolution is obtained either temporally through flow cytometry and mass spectrometry, or spatially on antibody array chips. On the other hand, transcriptomic single cell resolution requires DNA-based barcodes to be placed on capture beads, which are extracted after the final sequencing step. By modifying the single cell barcode chip platform used for single cell proteomics, we have developed a DNA labeling strategy that bridges spatial single cell proteomic resolution with DNA-barcoded single cell transcriptomic resolution. Our method, called Spatially Correlated Sequencing (SpaCeSeq), allows RNA capture beads positioned in microfluidic chamber arrays to be tagged with location-encoding DNA strands. Therefore, after the RNA of a single cell is sequenced, it can be traced back to a microwell containing protein measurements taken by an antibody array, thus correlating sequenced transcripts and measured proteins from a single cell. SpaCeSeq can also be applied to label single cell transcriptomes with DNA corresponding to information such as treatments or culture conditions, and the proteomics chip can be further modified to capture metabolomic measurements. Ultimately, this technology can contribute to answers for a range of biological questions, from fundamental studies of the relationship between transcription and translation in single cells, to a better understanding of tumor biology and targeted treatments.


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