Image-seq: A new technology for spatially-resolved single-cell RNA sequencing

• DOI: 10.5281/zenodo.7067661
• Publication Date: 2022-09-11
• Authors: Christa Haase; Karin Gustafsson; Shenglin Mei; Shu‐Chi A. Yeh; Dmitry Richter; Jelena Milosevic; Raphaël Turcotte; Peter V. Kharchenko; David B. Sykes; David T. Scadden; Charles P. Lin
• Keywords: RNA-Seq

Data and code used to produce the analysis in "Image-seq: A new technology for spatially-resolved single-cell RNA sequencing" by Christa et al. (2022). Here, we present Image-seq, a technology that provides single-cell transcriptional data on cells that are isolated from specific spatial locations under image guidance, thus preserving the spatial information of the target cells. Additionally, the technique is compatible with intravital microscopy, making it possible to document the temporal history of the cells being analyzed. Cell samples are processed using state-of-the-art library preparation protocols, and therefore combine spatial information with highly sensitive RNA sequencing readouts from individual, intact cells. We have used both high-throughput, droplet-based sequencing, as well as Smartseq library preparation to demonstrate its application to bone marrow and leukemia biology, uncovering DPP4 as a highly upregulated gene in early AML progression that marks a more proliferative subpopulation. We provided count matrixes for 10X single cell data (count.matrices.tar) and Smartseq v4 data (Image-seq-analysis.zip).