Selected Publications
- Costa Jordão MJ, Brendecke SM, Sankowski R, Sagar, Locatelli G, Tai Y-H, Tay TL, Schramm E, Armbruster S, Hagemeyer N, Mai D, Çiçek Ö, Falk T, Kerschensteiner M, Grün D, PrinzM (2019) Single-cell profiling of the myeloid compartment identifies new cell populations with distinct fates during neuroinflammation. Science, 363: eaat7554.
- Falk T, Mai D, Bensch R, Çiçek Ö, Abdulkadir A, Marrakchi Y, Böhm A, Deubner J, Jäckel Z, Seiwald K, Dovzhenko A, Tietz O, Dal Bosco C, Walsh S, Saltukoglu D, Tay TL, Prinz M, Palme K, Simons M, Diester I, Brox T, Ronneberger O (2019) U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods, 16: 67-70.
- Shemer A#, Grozovski J#, Tay TL#, Tao J, Süß P, Volaski A, Gross M, Kim J-S, David E, Chappell-Maor L, Thielecke L, Glass CK, Cornils K, Prinz M, Jung S (2018) Engrafted parenchymal brain macrophages differ from host microglia in transcriptome, epigenome and responsiveness to challenge. Nat Commun, 9: 5206 (#shared first author)
- Tay TL*#, Sagar#, Dautzenberg J, Grün D*, Prinz M* (2018) Unique microglia recovery population revealed by single-cell RNAseq following neurodegeneration. Acta Neuropathol Commun, 6: 87. (*corresponding author, #shared first author)
- Tay TL*, Mai D, Dautzenberg J, Fernandez-Klett F, Lin G, Sagar, Datta M, Drougard A, Stempfl T, Ardura-Fabregat A, Staszewski O, Margineanu A, Sporbert A, Steinmetz L, Pospisilik JA, Jung S, Priller J, Grün D, Ronneberger O, Prinz M* (2017) A new fate mapping system reveals context-dependent random or clonal expansion of microglia. Nat Neurosci, 20(6): 793-803. (*corresponding author)
FRIAS Project
CellSign − an interactive meta-analysis web tool for contextual classification of cellular phenotypes based on gene expression
There has been tremendous progress in gene sequencing technologies for the study of cellular properties, organ formation and disease pathogenesis in recent years. However life science researchers are mostly focused on limited datasets restricted to their specialisations. Here I propose a meta-analysis framework to leverage information across vast gene expression datasets by noise reduction and transcript alignment. This strategy could improve experimental design, reveal consensus gene regulatory mechanisms of cellular processes, consolidate our knowledge on transcriptional changes, and unveil new targets for detailed mechanistic studies. My work in brain immune cells (microglia) have suggested rapid context-dependent changes in microglial subpopulations and unveiled disease-linked gene signature in several mouse models. In this project I want to identify core gene regulatory modules by comparing gene datasets of proliferative and plastic cell types, to uncover novel genes, motifs or physiological contexts that promote these cellular characteristics. As these cells exert positive (e.g., organ growth, recovery) or destructive outcomes in various circumstances, the results may contribute towards precision therapy for wound healing, chronic inflammation and cancer. “CellSign” will be an interactive website for motif or pathway analyses to allow diverse researchers to understand their genes, cells or datasets of interest from the fresh perspective of other specialties.