C.1 Live Cell Imaging and Tissue Imaging
Principal Investigators
… S. Dooley ..
Dr. rer. nat. Ursula Klingmüller (*03.06.69)
Phone: 06221/56-2675, Mail: kai.breuhahn@med.uni-heidelberg.de
Dr. Ing. Niels Grabe (*07.09.71)
Phone: 06221/54-51248, Mail: niels.grabe@bioquant.uni-heidelberg.de
Hamamatsu Tissue Imaging and Analysis Center (TIGA) at the University Heidelberg
BIOQUANT, Im Neuenheimer Feld 267, 69120 Heidelberg
Prof. Dr. med. Peter Schirmacher (*04.11.61)
Phone: 06221/56-2601, Mail: peter.schirmacher@med.uni-heidelberg,de
Institute of Pathology (head: Prof. Dr. P. Schirmacher)
University Hospital Heidelberg, Im Neuenheimer Feld 220/221, 69120 Heidelberg
State of the art and own contributions
Quantitative cell and tissue imaging represents a central component for the acquisition of
primary spatial data and for validating the results of selected parts of different WPs. Hereby,
histology is considered the gold standard. Therefore, in this WP assays based on ‘microscopic
slides’ are transferred onto an automatic staining robot and an automatic slide scanner (virtual
microscopy). This ensures reproducibility, standardization and accessibility of according
results. Automatic slide scanning is a relative novel technique, increasingly present in
pathology (digital pathology). For both technologies, already existing automatic staining and
automatic scanning, the respective protocols have to be adapted. The resulting ‘virtual slides’
are further processed using dedicated image processing algorithms. Here novel technical
approaches are needed, as virtual slides enable contextual analyses in a large spatial
dimensions and also deliver large data sets (in compressed format up to 2 GB). Generally, the
integration of automatic slide staining and virtual microscopy with gene microarray
expression data is considered to pave the way for systems pathology).
Relevant existing methods and data: Together with Hamamatsu Photonics the fluorescence
capable Hamamatsu NDP Nanozoomer was established at the University Heidelberg in the
Hamamatsu Tissue Imaging and Analysis Centre (TIGA). Brightfield and fluorescence
images can be acquired in Z-stacks and are stored in a special high-performance image data
base. The resulting large images can be freely studied in different levels of magnification over
the internet by all collaboration partners. In addition to conventional manual staining, an
automatic staining robot (Leica Biovision Bond max) is established at the TIGA Centre. The
robot is capable of integrated dewaxing, colorimetric/fluorescence staining, antibody
application, as well as integrated in-situ RNA hybridization by slide heating. During the last 2
years an own software development environment dedicated to the image processing of virtual
slides has been established on the basis of MatLab and commercial software. The software
development is directly coupled to the NDP Nanozoomer.
Description of planned work for five years, including clearly formulated milestones after
three years
Quantitative Spatiotemporal Profiling of Liver Regeneration: The underlying experimental
validation works are done by the respective collaboration partners (primarily
Breuhahn/Schirmacher). In dependence of the experimental results, selected protocols are
transferred onto imaging and image analysis and/or automatic staining. This WP therefore
focuses on image processing and subsequent spatial modeling. Quantitative spatial profiling
during time-resolved liver regeneration yields quantitative spatiotemporal expression profiles
concerning signaling pathways and structural proteins (antibody, in-situs from collaboration
partners). Results will be integrated with further modeling (Timmer, Kummer, Drasdo). For
each task the respective slides are scanned, made available via internet, and image processed.
According image processing algorithms for quantitative image evaluation are developed using
own and commercial software. Imaged histological sections are stored in a central database.
Three years milestones
 Established fluorescence staining and virtual slide based imaging protocols
 Image database as well as quantitative spatiotemporal profiles of selected pathways (e.g.
TNF-α/NF-κB) in murine liver regeneration
Role of this project in the consortium
The project conceptually provides essential and unique spatially and temporally resolved
expression data sets of key signalling pathways during liver regeneration. We will also
strongly contribute to the methodical portfolio of the initiative, since we have established a
unique integrated histological, image processing and spatial modelling pipeline we offer to
integrate in the consortium.
Requested funding
1 Ph.D. (75% E13) (bioinformatician)
Consumables: 8.000 p.a. (e.g. antibodies, probes, stains, materials)
Investment: 6.000 (computer, software)
Travel: 1.500 p.a. (conferences, project meetings)
References
1.
Halama N, Michel S, Kloor M, Zoernig I, Pommerencke T, von Knebel Doeberitz M, Schirmacher P,
Weitz J, Grabe N, Jager D. The localization and density of immune cells in primary tumors of human
metastatic colorectal cancer shows an association with response to chemotherapy. Cancer Immun
2009;9:1.
2.
Pommerencke T, Steinberg T, Dickhaus H, Tomakidi P, Grabe N. Nuclear staining and relative
distance for quantifying epidermal differentiation in biomarker expression profiling. BMC
Bioinformatics 2008;9:473.
3.
Michel S, Benner A, Tariverdian M, Wentzensen N, Hoefler P, Pommerencke T, Grabe N, von Knebel
Doeberitz M, Kloor M. High density of FOXP3-positive T cells infiltrating colorectal cancers with
microsatellite instability. Br J Cancer 2008;99:1867-1873.
4.
Grabe N. [Virtual microscopy in systems pathology]. Pathologe 2008;29 Suppl 2:259-263.
5.
Grabe N, Pommerencke T, Steinberg T, Dickhaus H, Tomakidi P. Reconstructing protein networks of
epithelial differentiation from histological sections. Bioinformatics 2007;23:3200-3208.
6.
Grabe N, Neuber K. Simulating psoriasis by altering transit amplifying cells. Bioinformatics
2007;23:1309-1312.
7.
Steinberg T, Schulz S, Spatz JP, Grabe N, Kohl A, Komposch G, Tomakidi P. Early Keratinocyte
Differentiation on Micropillar Interfaces. . Nano Lett. 2007 Feb;7(2):287-94..
8.
Grabe N, Pommerencke T, Muller D, Huber S, Neuber K, Dickhaus H. Modelling epidermal
homeostasis as an approach for clinical bioinformatics. Stud Health Technol Inform 2006;124:105-110.
9.
Grabe N, Neuber K. A multicellular systems biology model predicts epidermal morphology, kinetics
and Ca2+ flow. Bioinformatics 2005;21:3541-3547.