TY - JOUR TI - Automated cell-type classification in intact tissues by single-cell molecular profiling AU - Nagendran, Monica AU - Riordan, Daniel P AU - Harbury, Pehr B AU - Desai, Tushar J A2 - Rajagopal, Jay VL - 7 PY - 2018 DA - 2018/01/10 SP - e30510 C1 - eLife 2018;7:e30510 DO - 10.7554/eLife.30510 UR - https://doi.org/10.7554/eLife.30510 AB - A major challenge in biology is identifying distinct cell classes and mapping their interactions in vivo. Tissue-dissociative technologies enable deep single cell molecular profiling but do not provide spatial information. We developed a proximity ligation in situ hybridization technology (PLISH) with exceptional signal strength, specificity, and sensitivity in tissue. Multiplexed data sets can be acquired using barcoded probes and rapid label-image-erase cycles, with automated calculation of single cell profiles, enabling clustering and anatomical re-mapping of cells. We apply PLISH to expression profile ~2900 cells in intact mouse lung, which identifies and localizes known cell types, including rare ones. Unsupervised classification of the cells indicates differential expression of ‘housekeeping’ genes between cell types, and re-mapping of two sub-classes of Club cells highlights their segregated spatial domains in terminal airways. By enabling single cell profiling of various RNA species in situ, PLISH can impact many areas of basic and medical research. KW - in situ hybridization KW - single cell expression profiling KW - lung cell type classification KW - Idiopathic Pulmonary Fibrosis JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -