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Methods. 2015 Sep 1;85:44-53. doi: 10.1016/j.ymeth.2015.05.016. Epub 2015 May 23.

Computer vision for image-based transcriptomics.

Author information

1
Faculty of Sciences, Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland; Life Science Zurich Graduate School, Ph.D. program in Systems Biology, Switzerland.
2
Faculty of Sciences, Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.
3
Faculty of Sciences, Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland. Electronic address: lucas.pelkmans@imls.uzh.ch.

Abstract

Single-cell transcriptomics has recently emerged as one of the most promising tools for understanding the diversity of the transcriptome among single cells. Image-based transcriptomics is unique compared to other methods as it does not require conversion of RNA to cDNA prior to signal amplification and transcript quantification. Thus, its efficiency in transcript detection is unmatched by other methods. In addition, image-based transcriptomics allows the study of the spatial organization of the transcriptome in single cells at single-molecule, and, when combined with superresolution microscopy, nanometer resolution. However, in order to unlock the full power of image-based transcriptomics, robust computer vision of single molecules and cells is required. Here, we shortly discuss the setup of the experimental pipeline for image-based transcriptomics, and then describe in detail the algorithms that we developed to extract, at high-throughput, robust multivariate feature sets of transcript molecule abundance, localization and patterning in tens of thousands of single cells across the transcriptome. These computer vision algorithms and pipelines can be downloaded from: https://github.com/pelkmanslab/ImageBasedTranscriptomics.

KEYWORDS:

FISH; High-throughput; Image-based transcriptomics; In situ hybridization; Localization; Segmentation; Single-cell; Single-molecule; Subcellular

PMID:
26014038
DOI:
10.1016/j.ymeth.2015.05.016
[Indexed for MEDLINE]
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