Image-Seq: A high-density microfluidic trap array for single cell transcriptome analysis coupled with image based phenotyping

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ABSTRACT The ability to combine single cell RNA sequencing and image-based phenotyping in a massively parallel format would enable direct correlations to be made between the function and gene expression of single cells. However, the fundamental limitations of existing technologies have prevented the realization of this goal in a high-throughput automated platform. Here we propose to solve this systems design challenge by creating a high-density trap array that contains unique DNA barcodes printed at known addresses in the trap array. The proposed technology, which we refer to as Image-Seq, will involve organizing single cells in a high-density array, imaging each cell/well at high resolution at multiple wavelengths, and finally preparing the single cells for RNA-seq using the locally printed DNA barcodes as cellular identifiers that can be traced in NGS datasets back to specific live cell images. To achieve this goal, the Duke team will partner with Applied Microarray, who will print an array of DNA barcodes at unique spatial addresses. Aim 1 will focus on demonstrating that DNA barcodes can be printed with high fidelity and used as templates for reverse transcription of cellular mRNA. Aim 2 will demonstrate the ability to achieve high throughput trapping of single cells, automated imaging, and lysis of single cells directly inside the microfluidic chips. Aim 3 will demonstrate the ability to obtain both a live cell image and a transcriptome profile of each single cell in mixed human and mouse cell lines and also in dissociated tissue samples. This project has many potential applications both in basic research and in follow- on clinical applications. One application is in making better use of limited samples where only hundreds to thousands of dissociated single cells can be obtained from a tissue, which is not currently possible by other single cell analysis workflows. Another potential application is in drug sensitivity testing, which involves time- lapse imaging to quantify single cell growth rates and compare these to the gene expression analysis of those same cells in order to implicate the signaling pathways invoked by drug resistant cells.