Platform for Single Cell Genomics and Epigenomics
Prof. Dr. Joachim L. Schultze
Group Leader
Sigmund-Freud-Str. 27
53127 Bonn
 +49 228 43302-410

Areas of investigation/research focus

PRECISE (Platform foR SinglECell GenomIcS and Epigenomics) is a joint venture between DZNE and the University of Bonn. We focus on developing and applying new single-cell high-throughput genomics technologies and making them available to internal as well as external cooperation partners. Additionally, we work on adapting existing genomics applications to novel computational architectures (memory-driven computing) – more Infos.

Some of the technologies offered at PRECISE are listed below:

  • SMART-Seq2: SMART-Seq2 (Picelli et al., Nat. Methods, 2013) is the single cell RNA-seq (scRNA-seq) method with the highest sensitivity, allows the sequencing of the entire transcript and entirely relies on off-the-shelf reagents. SMART-seq2 is the best choice when it comes to the study of splice variants, SNPs or monoallelic gene expression. Library preparation costs are significantly reduced by using an in-house version of Tn5 transposase (Picelli et al., Genome Research, 2014).
  • SeqWell: The SeqWell method allows the sequencing of few thousand cells per experiment (Gierahn et al., Nat. Methods, 2017). SeqWell captures and counts only the 3´-end of each transcript and is most suitable in the initial (discovery) phase of an experiment due to its higher throughput and significantly lower cost per cell as compared to Smart-seq2.  It combines the benefits of performing reactions on a nanoliter scale with the compartmentalization of individual cells in microwells.
  • BD Rhapsody: PRECISE was the only alpha-tester in Europe for this technology, recently introduced on the market by BD Genomics. Rhapsody is based on the CytoSeq method (Fan et al., Science, 2015) and allows the 3´-end sequencing of up to 20K cells/experiment on (partly) customizable gene panels (500 or 1000 genes). Rhapsody uses microwell arrays and is equipped with an imaging system for cell visualization and counting. The price for sequencing is considerably lower since it doesn´t require the analysis of the entire transcriptome.
 more Infos

Single-cell transcriptomics is a rapid moving field, where new library preparation and computational methods are introduced every few weeks. It is therefore vital to keep up with the pace of technological development, in order to always offer cutting-edge analysis to our collaborators. We are investing important amounts of time, human and technical resources in testing and implementing recently released new single-cell technologies as well as developing better, faster and inexpensive methods to tackle ever more complex biological questions.

Some of the new technologies in the pipeline are listed below:

  • scATAC-seq, to characterize open chromatin genomic regions, a proxy for finding transcriptionally active gene regions.
  • sciRNA-seq, a plate-based 3´-sequencing method based on a “split-and-pool” approach, which makes use of combinatorial indexing and Tn5 transposase (Cao et al., 2017). SciRNA-seq works on fixed cells, does not require single cell sorting and is easily scalable to thousands of cells, thus reducing the library preparation cost.
  • FRISCR (Fixed and Recovered Intact Single Cell RNA, Thomsen et al., 2016). A method capable of isolating RNA from fixed, permeabilized, stained, and sorted cells which is later converted to sequencing library with the Smart-seq2 method. Currently the method works with >400 cells but the goal is to achieve single-cell resolution in 2018.

Key Publications

Camell CD*, Sander J*, Spadaro O, Lee A, Nguyen KY, Wing A, Goldberg EL, Youm YH, Brown CW, Elsworth J, Rodeheffer MS, Schultze JL, Dixit VD. (* shared first). Inflammasome-driven catecholamine catabolism in macrophages blunts lipolysis during ageing. Nature. 2017 Oct 05; 550:119-123. doi: 10.1038/nature24022
See P, Dutertre CA, Chen J, Günther P, McGovern N, Irac SE, Gunawan M, Beyer M, Händler K, Duan K, Sumatoh HRB, Ruffin N, Jouve M, Gea-Mallorquí E, Hennekam RCM, Lim T, Yip CC, Wen M, Malleret B, Low I, Shadan NB, Fen CFS, Tay A, Lum J, Zolezzi F, Larbi A, Poidinger M, Chan JKY, Chen Q, Rénia L, Haniffa M, Benaroch P, Schlitzer A, Schultze JL, Newell EW, Ginhoux F. Mapping the human DC lineage through the integration of high-dimensional techniques. Science. 2017 Jun 09; 356:pii: eaag3009.
Beyer M, Abdullah Z, Chemnitz JM, Maisel D, Sander J, Lehmann C, Thabet Y, Shinde PV, Schmidleithner L, Köhne M, Trebicka J, Schierwagen R, Hofmann A, Popov A, Lang KS, Oxenius A, Buch T, Kurts C, Heikenwalder M, Fätkenheuer G, Lang PA, Hartmann P, Knolle PA, Schultze JL. Tumor-necrosis factor impairs CD4(+) T cell-mediated immunological control in chronic viral infection. Nat Immunol. 2016 May 01; 17:593-603. doi: 10.1038/ni.3399
Jia Xue, Susanne V. Schmidt, Jil Sander, Astrid Draffehn, Wolfgang Krebs, Inga Quester, Dominic DeNardo, Trupti D. Gohel, Martina Emde, Lisa Schmidleithner, Hariharasudan Ganesan, Andrea Nino-Castro, Michael R. Mallmann, Larisa Labzin, Heidi Theis, Michael Kraut, Marc Beyer, Eicke Latz, Tom C. Freeman, Thomas Ulas, Joachim L. Schultze. Transcriptome-Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation. Immunity. 2014 Feb 19; 40:274-288. doi: 10.1016/j.immuni.2014.01.006
De Nardo D, Labzin LI, Kono H, Seki R, Schmidt SV, Beyer M, Xu D, Zimmer S, Lahrmann C, Schildberg FA, Vogelhuber J, Kraut M, Ulas T, Kerksiek A, Krebs W, Bode N, Grebe A, Fitzgerald ML, Hernandez NJ, Williams BR, Knolle P, Kneilling M, Röcken M, Lütjohann D, Wright SD, Schultze JL, Latz E. High-density lipoprotein mediates anti-inflammatory reprogramming of macrophages via the transcriptional regulator ATF3. Nat Immunol. 2014 Feb 01; 15:152-60. doi: 10.1038/ni.2784


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