Functional Genome Analysis  (B070)
Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 580
D-69120 Heidelberg, Germany.




  Affinity Proteomics  -  Antibody Microarrays
Technical Aspects
Diagnostics / Biomedical Studies
   - Detailed protocols
   - Nanobody aggregation

   - PDAC microenvironment
   - Detection of circulating cells 
   - Sample preparation
   - Effective binder selection
   - Pancreatic ductal
adenocarcinoma (PDAC)
   - Stellate cell secretome
Protein Enantiomers
   - Mass transport

   - PDAC cell secretome
   - Single-molecule detection

   - Other tumour entities
   - PDAC cell proteome


As a consequence of large-scale genomic sequencing, strong interest has emerged in analysing the function of the DNA-encoded information on a similarly global scale. However, many aspects of modulation and regulation of cellular activity cannot be investigated at the level of nucleic acids but require an analysis of the proteome. Post-transcriptional control of protein translation, post-translational modifications as well as protein degradation by proteolysis, for example, have profound effects at the functional level. Estimations suggest that there are more than 200 types of protein modification. Its proportion and importance is reflected by the fact that 5% to 10% of mammalian genes encode for proteins that modify other proteins.
The complexity in the human proteome is expected to range from one hundred thousand to several million different protein molecules, all based on the about 22,000 protein-encoding genes. The situation is additionally complicated by the facts that not for every protein of multicellular organisms the function is known and that a protein may have different functions dependent on structure variations, interacting partners, location and time of expression. Also, the dynamic range of protein expression is very large indeed.
Various mass-spectrometry-based processes exist for a powerful analysis of proteins of an organism or tissue. Also, assays such as yeast-two-hybrid analyses in all their facets permit global studies for the identification of interaction partners. As a third approach, affinity proteomics has an enormous potential in a global characterisation of molecule mixtures at the protein level. Knowledge of genomic sequences and transcriptional profiles do not suffice for a reliable description of actual protein expression, let alone an analysis of protein structures and biochemical activities or a quantitative examination of protein-protein interactions. This kind of information, however, is crucial for an understanding of the molecular biology of cells, tissues or whole organisms and has a broad biotechnical and medical potential. We perfom such analyses on a relatively large-scale with nevertheless high reproducibility, a near-single-molecule sensitivity, and an accuracy that is superior to ELISA-based assays.

Antibody microarrays:
Utilising antibody microarrays that currently consist of some 3,000 antibodies, we pursue the analysis of studying variations in actual protein abundance, isoform occurrence and other structural variations. Basic technical processes are understood in much detail, such as the choice of appropriate surfaces, the effect of kinetics and mass transport and labelling procedures as well as many other aspects. Detailed protocols are available that allow reproducible and reliable analysis of expression variations on complex protein extracts from tissues, cells or body liquids down to attomolar concentrations. Antibody generation and selection was and is performed in collaborations with companies as well as academic partners, partly within consortia that aim at the creation of well-characterised and specific antibodies or other binders (e.g., Affinomics). In addition, improved preparation of protein extracts proved crucial for success. The current set-up was and is used in various projects, frequently combining the information on protein levels with other data. Also, quantification of the results is performed, either by actual counting of individual molecules or by an analysis of dissociation parameters.

Protein microarrays:
We utilise protein microarrays containing mostly full-length molecules for the investigation of protein-interactions in a quantitative manner. Microarray production is done by in situ synthesis by an in vitro transcription and translation process on the microarrays, starting from full-length cDNAs or gene-specific PCR-products. Protein interaction of all kinds as well as the influence of co-factors such as small molecules are studied this way. The most complex protein array produced so far contained some 14,000 individual proteins. The set-up is used in various projects and on the proteome of several organisms.
Personalised proteomics:
In a recently completed technical development, we added to the in situ protein production a process that allows to present on the microarray the proteins in exactly the conformation as they occur in tissues or other samples of individual patients, reflecting all mutations or splice variants that are specific for the particular sample. Thereby, particularly the effects of individual variations on protein interaction - with other proteins, nucleic acids or small compounds - can be studied in a quantitative manner.

Weidmann et al. (2019) CELL Chem. Biol., in press. pdf icon
Schröder et al. (2013) Proteomics Clin. Appl. 7, 802. pdf icon

Marzoq et al. (2019) Sci. Rep. 9, 5303. pdf icon
Hoheisel et al. (2013) Proteomics Clin. Appl. 7, 8. pdf icon

Hufnagel et al. (2019) Bio-protocol 9, e3152. pdf icon
Alhamdani et al. (2012) J. Proteomics 75, 3747. pdf icon

Goerke et al. (2018) NMR Biomed. 31, e3920. pdf icon
Friedrich et al. (2011) Proteomics 11, 3757. pdf icon

Kunz et al. (2018) Sci. Rep. 8, 7934. pdf icon
Schmidt et al. (2011) J. Prot. Res. 10, 1316. pdf icon

Hufnagel et al. (2018) Sci. Rep. 8, 7503. pdf icon
Schröder et al. (2011) Protein Micoarrays - Meth. Mol. Biol., 203. pdf icon

Kunz et al. (2017) BBA-Gen. Subjects 1861, 2196-2205. pdf icon
Alhamdani & Hoheisel (2011) Mol. Anal. & Genome Disc., Wiley, 219.

Mustafa et al. (2017) Oncotarget 8, 11963-11976. pdf icon
Sill et al. (2010) BMC Bioinformatics 11, 556. pdf icon

Syafrizayanti et al. (2017) Sci. Rep. 7, 39756. pdf icon
Alhamdani et al. (2010) Proteomics 10, 3203. pdf icon

Kamhieh-Milz et al. (2016) J. Proteomics 150, 74-85. pdf icon
Schröder et al. (2010) Antibody Engineer., Vol. 2, Springer, 429. pdf icon

Bakdash et al. (2016) Cancer Res. 76, 4332-4346. pdf icon
Alhamdani et al. (2010) J. Prot. Res. 9, 963. pdf icon

Loeffler et al. (2016) Nature Comm. 7, 11844. pdf icon
Schröder et al. (2010) Mol. Cell. Prot. 9, 1271. pdf icon

Sill et al. (2016) Microarrays 5, 19. pdf icon
Gloriam et al. (2010) Mol. Cell. Prot. 9, 1. pdf icon

Kibat et al. (2016) New Biotechnol. 33, 574-581. pdf icon
Alhamdani et al. (2009) Genome Med. 1, 68. pdf icon

Bal et al. (2016) Br. J. Haematology 4, 602-615. pdf icon
Börner et al. (2009) BioTechniques 46, 297. pdf icon

Nijaguna et al. (2015) J. Proteomics 128, 251-261. pdf icon
Taussig et al. (2007) Nature Meth. 4, 13. pdf icon

Mock et al. (2015) Oncotarget 6, 13579-13590. pdf icon
Kusnezow et al. (2007) Proteomics 7, 1786. pdf icon

Betzen et al. (2015) Proteomics Clin. Appl. 9, 342.
pdf icon
Kusnezow et al. (2006) Mol. Cell. Prot. 5, 1681. pdf icon

Bradbury et al. (2015) Nature 518, 27. pdf icon
Angenendt et al. (2006) Mol. Cell. Prot. 5, 1658. pdf icon

Hoheisel (2014) labor&more 10/14, 10. pdf icon
Kusnezow et al. (2006) Proteomics 6, 794. pdf icon

Srinivasan et al. (2014) Proteomics 14, 1333. pdf icon
Kersten et al. (2005) Expert Rev. Proteomics 2, 499. pdf icon

Syafrizayanti et al. (2014) Exp. Rev. Prot. 11, 107.
pdf icon
Kusnezow & Hoheisel .(2003) J. Mol. Recognit. 16, 165. pdf icon

Marzoq et al. (2013) J. Biol. Chem. 288, 32517. pdf icon
Kusnezow et al. (2003) Proteomics 3, 254. pdf icon

Lueong et al. (2013) J. Prot. Bioinf. 07, 004. pdf icon
Kusnezow & Hoheisel (2002) BioTechniques 33, 14. pdf icon

The impact of the secretome of activated pancreatic stellate cells on growth and differentiation of pancreatic tumour cells

Pancreatic ductal adenocarcinoma (PDAC) exists in a complex desmoplastic microenvironment. As part of it, pancreatic stellate cells (PSCs) provide a fibrotic niche, stimulated by a dynamic communication between activated PSCs and tumour cells. Investigating how PSCs contribute to tumour development and for identifying proteins that the cells secrete during cancer progression, we studied by means of complex antibody microarrays the secretome of activated PSCs. A large number of secretome proteins were associated with cancer-related functions, such as cell apoptosis, cellular growth, proliferation and metastasis.
Their effect on tumour cells could be confirmed by growing tumour cells in medium conditioned with activated PSC secretome. Analyses of the tumour cells’ proteome and mRNA revealed a strong inhibition of tumour cell apoptosis, but promotion of proliferation and migration.
Many cellular proteins that exhibited variations were found to be under the regulatory control of eukaryotic translation initiation factor 4E (eIF4E), whose expression was triggered in tumour cells grown in the secretome of activated PSCs. Inhibition by an eIF4E siRNA blocked the effect, inhibiting tumour cell growth in vitro.
Our findings show that activated PSCs acquire a pro-inflammatory phenotype and secret proteins that stimulate pancreatic cancer growth in an eIF4E-dependent manner, providing further insight into the role of stromal cells in pancreatic carcinogenesis and cancer progression.
Marzoq et al. (2019) Sci. Rep. 9, 5303. pdf icon

Figure legend: Scheme of the overall experimental set-up. First, the protein content of the secretome of activated PSCs was analysed and predictions were made about the functional consequences, which the secreted proteins would have in recipient cells. Second, tumour cells were grown in media conditioned with secretome. The intracellular proteome was studied and used for functional predictions. The predictions from secretome and intracellular proteome were compared and validated by investigating the actual functional variations observed and by identifying relevant regulative factors.

Scheme of the approach taken

The structural basis of nanobody unfolding reversibility and thermoresistance

Nanobodies represent the variable binding domain of camelid heavy-chain antibodies and are employed in a rapidly growing range of applications in biotechnology and biomedicine. Their success is based on unique properties including their assumed ability to reversibly refold after denaturation. By characterizing nearly 70 nanobodies, we show that, opposed to common assumption, irreversible aggregation does occur for many binders upon heat denaturation, potentially affecting application-relevant parameters like stability, affinity and immunogenicity. However, by deriving aggregation propensities from apparent melting temperatures, we show that an optional disulfide bond suppresses nanobody aggregation. This effect is further enhanced by increasing the length of a complementarity determining loop which, although expected to destabilize, contributes to nanobody stability. The effect of such variations depends on environmental conditions, however. Nanobodies with two disulfide bonds, for example, are prone to lose their functionality in the cytosol. Our study suggests strategies to engineer nanobodies that exhibit optimal performance parameters and gives insights into general mechanisms which evolved to prevent protein aggregation.

Figure legend: Parameters determined in the nanobody analysis. (A) A typical nanobody scaffold is shown. CDR loops are highlighted: CDR1, blue; CDR2, orange; CDR3, red. Hallmark positions are shown as black sticks, conserved and optional disulfide bonds as yellow sticks. (B & C) Parameters obtained from differential scanning flourimetry and turbidity assays at a temperature range of 25°C to 95°C. (B) Upper panel: The ratio of intrinsic protein fluorescence emission (350 nm/330 nm) reports about the onset temperature of unfolding (Ton) and the melting point (Tm) during the heating phase. A difference of zero between initial and final ratio values after a complete temperature cycle (black arrow) would indicate complete reversibility. Lower panel: The turbidity trace of the heating phase yields the onset temperature of aggregation (Ts) and the turbidity integral (blue shaded area); the latter serves as a qualitative measure of aggregation. If Ts occurs during the cooling phase, the turbidity integral is determined in reverse orientation. (C) Upper panel: Apparent melting temperature (Tm) values yield the ΔTm shift when aggregation is modulated by the nanobody concentration. The ΔTm shift can serve as a measure of aggregation propensity. Lower panel: the directly related turbidity traces are shown.

Kunz et al. (2018) Sci. Rep. 8, 7934. pdf icon
Kunz et al. (2015) BBA-Gen. Subjects 1861, 2196-2205.
pdf icon

AUC values of the 189 individual serum markers. Analysis by Receiver Operating Characteristic (ROC) curves was performed for all identified serum protein markers individually. Panel A shows the result calculated from the training set; the respective AUC values are shown, ranging from 55.2% to 96.0%. In panel B, the AUC values are shown as calculated for the individual marker molecules in the test set. For presentation, the order of the markers along the x-axis was kept as in panel A, highlighting the limited degree of reproducibility for individual markers.
Comparison of the tumour cell secretome and patient sera for an accurate serum-based diagnosis of pancratic ductal adenocarcinoma

Pancreatic cancer is the currently most lethal malignancy. Toward an accurate diagnosis of the disease in body liquids, we studied the protein composition of the secretomes of 16 primary and established cell lines of pancreatic ductal adenocarcinoma (PDAC). Compared to the secretome of non-tumorous cells, 112 proteins exhibited significantly different abundances. Functionally, the proteins were associated with PDAC features, such as decreased apoptosis, better cell survival and immune cell regulation.
The result was compared to profiles obtained from 164 serum samples from two independent cohorts – a training and a test set – of patients with PDAC or chronic pancreatitis and healthy donors. Eight of the 112 secretome proteins exhibited similar variations in their abundance in the serum profile specific for PDAC patients, which was composed of altogether 189 proteins.
The 8 markers shared by secretome and serum yielded a 95.1% accuracy of distinguishing PDAC from healthy in a Receiver Operating Characteristic curve analysis, while any number of serum-only markers produced substantially less accurate results. Utility of the identified markers was confirmed by classical enzyme linked immunosorbent assays (ELISAs). The study highlights the value of cell secretome analysis as a means of defining reliable serum biomarkers.
Mustafa et al. (2017) Oncotarget 8, 11963-11976. pdf icon

images of different incubations
Detailed protocols for expression profiling by antibody microarrays

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As a multiplexing technique, antibody microarrays facilitate the highly parallel detection of thousands of different analytes from very small sample volumes of only few microliters. This is combined with a high sensitivity in the picomolar to femtomolar range, which is similar to the sensitivity of ELISA, the gold standard for protein quantification. In order to obtain such sensitivities in a robust and reproducible manner for complex analytes, it is essential to use an optimised experimental layout, sample handling, labelling and incubation as well as defined data processing steps.
Based on earlier work, we continuously developed the processing of microarrays and protein samples. In the publications listed below, our antibody microarray protocols for multiplexed expression profiling studies are described in detail; they permit the analysis of the abundance of very many proteins in plasma, urine, cell and tissue samples.

Schröder et al. (2010) Antibody Engineering, Vol. 2, SpringerVerlag, 429-445. pdf icon

Schröder et al. (2010) Mol. Cell. Prot. 9, 1271-1280. pdf icon

Alhamdani et al. (2010) Proteomics 10, 3203-3207. pdf icon

Schröder et al. (2011) Protein Micoarrays - Meth. Mol. Biol., Springer, 203-221.


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