Klervi Even-Desrumeaux

Patrick Fourquet

Véronique Secq

Daniel Baty

Patrick Chames
email: patrick.chames@inserm.fr

Single-domain antibodies: a versatile and rich source of binders for breast cancer diagnostic approaches

Keywords: Single domain antibodies, cancer, diagnostic, biomarkers, beads array

Noninvasive early detection of breast cancer through the use of biomarkers is urgently needed since the risk of recurrence, morbidity, and mortality is closely related to disease stage at the time of primary surgery. A crucial issue in this approach is the availability of relevant markers and corresponding monoclonal antibodies suitable for the development of effective immunodiagnostic modalities. The identification of such markers from human pathological lesions and the isolation of specific antibodies using conventional approaches remain major challenges. Camelids produce functional antibodies devoid of light chains of which the single N-terminal domain of the heavy chain is fully capable of antigen binding. When produced as an independent domain, these so-called single-domain antibody fragments (sdAbs) or nanobodies have several advantages for biotechnological applications owing to their unique properties of size (13 kDa), stability, solubility, and expression yield.

In this work, we have generated phage display libraries from animal immunized with breast cancer biopsies. These libraries were used to isolate sdAbs against known and relevant antigens such as HER2, or several cancer-specific sdAbs against unknown targets. We describe the identification of one these targets, cytokeratin 19, using affinity purification in combination with mass spectrometry. Some of these sdAbs were used in several straightforward diagnostic applications such as immunohistochemistry analysis of tumor samples, multiplexed cytometric bead array analysis of crude samples, or an immune enrichment procedure of rare cells. Here, we demonstrate that phage display-based selection of single-domain antibodies is an efficient and high-throughput compatible approach to generate binders with excellent characteristics for the fast development of diagnostic and prognostic modalities.

Introduction

Breast cancer is the most common cancer among women in Western countries. In France, 41,000 new cases are diagnosed each year and 25% of these women are below the age of 50. Breast cancer has a heterogeneous behavior and the frequency of metastasis in regional lymph nodules depends on tumor types 1 . Overall survival in breast cancer patients has been improved by the development of screening and treatment methods. While the 1980s saw a sharp rise in the number of new cases of breast cancer, that number now appears to have stabilized 1 . The drop in the death rate from breast cancer is probably due to the fact that more women are having mammograms. When detected early, the chances for successful treatment of breast cancer are considerably improved.

However, the early diagnosis of breast cancer and an appropriate follow-up after surgical treatment are still major issues for the treatment of breast cancer. Mammographic imaging is the most effective approach to the diagnosis of breast cancer in women older than 50 years of age. Although new improvements are being made in the resolution of these imaging techniques, tumors smaller than 5 mm usually go undiagnosed 2 , and high-grade tumors can evolve very significantly during the interval between two check-up of regular mammography imaging, i.e. one to two years. For these reasons, new approaches are urgently needed to improve the early diagnosis of breast cancer and the overall and disease-free survival rates of patients.

Many studies have demonstrated significant variations of the expression level of several proteins during cancer development. These changes may cause measurable alterations of cancer cells and secretion of marker proteins into body fluids such as serum. Such tumor markers for breast cancer used in the clinic include cancer antigen 15-3 (CA 15-3), carcinoembyonic antigen (CEA) 3 , and HER2 4 . Antibody-based techniques such as immunohistochemistry (IHC), enzyme-linked immunosorbent assay (ELISA) are widely used for diagnostic purposes of several antigens such as HER2, CEA, CA15-3 or hormonal receptors. For example, HER2 is a 185 kDa protein composed of three domains: a cytoplasmic domain, a transmembrane domain, and an extracellular domain (ECD). The ECD of HER2 can be cleaved from the surface of breast cancer cells by matrix metalloproteases and released into the serum. Increased levels of ECD (≥ 15 ng/mL) can be detected in approximately 15% to 30% of unselected presurgical breast cancer samples and in up to 45% of patients with metastatic breast cancer 5 . Measuring HER-2 is important for selecting optimal therapy and predicting prognosis in breast cancer patients. Current methods for evaluating HER-2 include the detection of shed antigen in the serum by enzyme-linked immunosorbent assay (ELISA) 6 , or the analysis of protein overexpression by IHC. IHC is now frequently used in the evaluation of many epithelial proliferations. Improved antigen retrieval techniques and a better understanding of biology have contributed to the broader utility of IHC for solving everyday diagnostic problems in breast pathology 7 .

In addition, new emerging antibody based approaches are now in development for breast cancer diagnosis. For example, microarray multiplex protein measurement of biomarker-based molecular diagnostic and prognostic cancer testing assays is destined to become a large growth segment of the immunodiagnostic industry. Protein microarrays are among the novel class of rapidly emerging proteomic technologies that might lead to efficient and specific diagnosis 8 . Microbead protein arrays use surface-coupled capture molecules and flow cytometry to simultaneously quantify several antigens. Multiplexing is achieved by either using different sized microbeads or color coding that is readable by a laser-induced fluorescent detection system 9 . This latter approach is suitable for the analysis of up to a hundred different bead-bound antibodies and/or proteins in a complex mixture. These assays can be directly compared to ELISA method 10 , but they require smaller volumes of sample material, and can be multiplexed. Thus bead array represents the method of choice for low density antibody array for clinical use such as diagnosis or prognosis 11,12 . This technique has the potential to generate non invasive and high throughput assay for screening and follow-up of large populations of women.

Another emerging and interesting diagnostic approach is the detection of circulating tumor cells (CTC) as an indicator of metastatic disease. Their detection is expected to provide a powerful tool for cancer prognosis, diagnosis of minimal residual disease, assessment of tumor sensitivity to anticancer drugs, and personalization of anticancer therapy 13 . CTC detection in blood samples may also be used to diagnose cancer and monitor a patient's response to therapy. Since CTCs are rare, comprising about one tumor cell among billions of normal blood cells in advanced cancer patients 14 , their detection and enumeration is a difficult task. Breast cancer circulating tumor cells are typically extracted from circulation through positive selection using epithelial cell-adhesion molecule (EpCAM), or HER2 as markers, leading to currently unknown biases when cells are undergoing epithelial-to-mesenchymal transition 14 . To increase the specificity of this assay, one solution could be to use a large panel of antibodies targeting several membrane tumor antigens.

All this approaches are dependent on the availability of a variety of specific monoclonal antibodies (mAb). Unfortunately, it is not always easy to identify non-cross reactive mAbs against a large variety of markers. Moreover, their high production cost and their stability after immobilization are limiting factors for the development of some diagnostic assay 15,16 . The availability of large libraries of recombinant antibody fragments such as single chain Fv (scFv) or Fab is especially suited for these approaches but the stability and production yield of these fragments remain a limiting factor 17 . In 1993, Hamers-Casterman et al 18 discovered that sera of camels, dromedaries and llamas contain a unique type of antibodies devoid of light chains. Camelids produce functional antibodies devoid of light chains (HCAbs). Single domain antibodies (sdAbs), corresponding to the variable domain of these antibodies have several advantages for biotechnological applications 19,20 thanks to their unique properties including a small size (13 kDa), a high stability even in the absence of disulfide bond formation 21 , a high solubility, and high expression yields 22 . Phage display technology has been extensively used to isolate tumor specific antibodies able to bind their cognate antigens in the cellular context [23][24][25][26][27][28][29] . It can be used to isolate binders against tumor specific markers [30][31][32][33][34][35] .

In this study, we have used phage display to isolate several relevant anti tumor markers sdAbs from immunized libraries. The straightforward use of these sdAbs for diagnostic approaches are also presented, including conventional assays such as ELISA or IHC, but also more innovative approaches such as multiplexed analysis of a complex sample by cytometric bead array (CBA) and the enrichment of rare cells in a complex sample by magnetic beads followed by flow cytometry characterization.

Results

Selection of phage antibodies

Phage display was used to isolate antibodies against breast cancer specific targets. Various selection strategies were performed to obtain sdAbs against known and unknown targets.

Selection of anti-HER2 sdAbs

Although breast cancer is a very heterogeneous disease, 20-30% of tumors overexpress the human epidermal growth factor receptor 2 (HER2) 36 . SdAbs against this target are thus highly relevant for a diagnosis approach. To isolate such sdAbs, a llama was immunized with HER2expressing ovarian cancer cell line SKOv3 and a sdAb library was built and used to perform biopannings. The first round of selection was performed on purified recombinant HER2 ectodomain fused to a human Fc portion (HER2-Fc) followed by a second round on HER2positive SKBr3 cell line. After selection, 188 clones were screening and 92 clones were found positive by ELISA on HER2 positive cells (data not shown). Twenty clones were analyzed by sequencing, and all of them corresponded to a unique clone named C7b. The fine specificity of this clone was confirmed by ELISA (Fig. 1A). SdAb C7b specifically bound to HER2-Fc fusion but not to an irrelevant Fc fusion, demonstrating its specificity for the HER2 ectodomain. Next, we used a flow cytometric based assay 37 to determine the affinity of this sdAb on cells naturally overexpressing HER2, and compared to the affinity of a dimeric version of this sdAb (obtained by Fc fusion and called C7b-Fc) and to the FDA-approved anti-HER2 monoclonal antibody (Herceptin/trastuzumab) (Fig. 1B). The precise KD values were determined using the non-linear curve fitting program Prism (GraphPad) The affinity of the monovalent sdAb was 7 nM, 11-fold less than the bivalent herceptin affinity determined in parallel (600 pM). However, the measured affinity of C7b-Fc, a bivalent format of sdAb C7b directly comparable to Herceptin (bivalent IgG), was 200 pM, i.e. threefold higher than the one of Herceptin (Fig. 1B). To confirm the affinity of sdAb C7b using an independent approach, a competitive ELISA using recombinant HER2-Fc antigen in solution was set up.

The calculated K D of 5.3 nM (Fig. 1C) was in close agreement with the flow cytometry data.

Finally, we performed a competition analysis by flow cytometric assay between sdAb C7b and Herceptin (Fig. 1D). A large excess of Herceptin could not compete out sdAb C7b, demonstrating that these two molecules do not share their epitope.

Selection of breast cancer-specific sdAbs of unknown specificity

To select binders against unknown breast cancer markers, llamas were immunized with breast cancer biopsy lysates. Phage libraries were built and panned using various approaches.

Several selections were performed on different samples including a mixture of 9 breast cancer cell lines (BT474, SKBr3, HCC1954, MCF7, MDA-MB-231, T47D, HCC1806, BRCA-Mz-01, HCC1937) and a mixture of 11 biopsy lysates (5801, 5772e, 5766, 5586, 5572i, 5592, 5011, 5712, 5713, 5033, 5627). After two rounds of panning, a primary screening was performed by phage-sdAb ELISA on maxisorp-adsorbed lysates corresponding to each selection. Positive clones were selected to perform a secondary screening step against 11 breast cancer biopsy lysates, a mixture of 9 breast cancer cell line lysates, one healthy breast cell line (HME1) as control of cancer specificity, and one patient PBMC (peripheral blood mononuclear cells) lysate as control of epithelial cell specificity. A final set of 20 unique clones were selected based on the diversity of their phage ELISA profile on breast cancer cell lines and biopsies and for their specificity of cancer sample. To establish the possibility to use sdAbs in diagnostic assay without the need of conventional mAbs, we tested the possibility to step up a sandwich ELISA using a couple of sdAbs targeting different epitopes of the same unknown cancer marker. To determine a relevant sdAb couple, series of sandwich ELISA were performed with all binders previously selected on various lysates. A couple of sdAbs leading to robust signals (KE23 for capture and KE32 for detection) was chosen as a proof of concept (data not show). This couple of sdAbs was used to determine the presence of their antigen in a mix of various breast cancer cell line and biopsy lysates by sandwich sdAb ELISA (Fig. 2B). The results suggest than the targeted antigen is differentially expressed in breast cancers. No signal was obtained using a lysate of healthy breast cell line (HME1) or human PBMC lysates, suggesting a specificity of these sdAbs for epithelial cancers.

For most application, the identification of antigens recognized by these binders is of interest and permits the identification of monoclonal antibodies to be used in a sandwich approach for sensitive and quantitative determination of the antigen concentration in various disease samples. As a proof of concept, an approach was designed to elucidate the nature of the antigen recognized by sdAb KE9. This sdAb was produced in the cytoplasm of E. coli in fusion with the avitag to allow an efficient and directed in vivo enzymatic biotinylation by BirA 38 . The purified in vivo biotinylated sdAb was incubated with magnetic streptavidin beads before being incubated with one cell line lysate leading to strong signal by phage ELISA (T47D). A negative control was performed by using this lysate with an irrelevant sdAb anti-Nef 39 . A SDS-PAGE analysis of the immunoprecipitation revealed several strong specific bands in the 50 kDa range (Fig. 3A). Mass spectrometry analysis of these bands identified the precipitated proteins as cytokeratin 19 (KRT19). The nature of KE9 antigen was confirmed by sandwich ELISA using KE9 as capture antibody, an anti-KRT19 mAb as detecting reagent and purified commercial KRT19 as antigen (Fig. 3B).

Immunohistochemistry assays using single domain antibodies

IHC is a common procedure performed that has acquired an increasingly prominent role in the diagnosis of breast pathology. To evaluate the efficiency of the selected single domain antibodies in IHC, we tested their reactivity on frozen embedded ductal breast cancer tissues (unrelated to biopsies used for immunization). As shown in Fig. 4, four sdAbs yielded intense stainings. Interestingly, various patterns could be obtained. SdAbs KE9, KE23, KE32 stained the cytoplasm of epithelial tumor cells whereas sdAb KE21 yielded a strong signal restricted to the nucleus of tumor cells. No staining was obtained in the absence of sdAb or using an irrelevant sdAb. These results demonstrate that these sdAbs can be efficiently used to detect their antigen within this setting, independently of the cellular localization of their antigen.

Multiplexed analysis using single domain antibodies immobilized on fluorescent beads

This straightforward selection approach by phage display opens the possibility to rapidly select a variety of binders against various cancer samples and use the selected binders as binding unit to establish highly sensitive and quantitative diagnostic approaches. Therefore, we aimed at using the previously characterized binders to elaborate a multiplexed diagnostic assay for complex but precious samples such as biopsy lysates or patient serum.

As a proof of concept, we decided to use previously isolated sdAbs against tumor markers HER2 and CEA 25 , together with the anti-KRT19 sdAb and the couple of sdAbs KE23/32 to build up a cytometric bead array assay. This approach uses the broad dynamic range of fluorescence detection offered by flow cytometry and antibody-coated beads to efficiently capture analytes. Each type of bead in the array is coded with a unique fluorescence intensity, and is functionalized with a different sdAb, so that beads can be mixed and run simultaneously in a single tube. Four types of commercially available fluorescent beads were coated with streptavidin to immobilize in vivo biotinylated sdAbs on their surface in an orientated fashion 38 . For this proof of concept, a complex sample was elaborated by mixing a patient serum containing a previously determined concentration of CEA, a purified recombinant HER2-Fc fusion, and the breast cancer biopsy lysate 5712 containing KRT19 and the unknown target. To evaluate the sensitivity of this approach, the precise concentration of KRT19 contained in this lysate was first established by traditional sandwich ELISA by comparison with a standard curve obtained with the purified antigen (data not show). All four antigens were simultaneously detected using the corresponding mAb for known targets or by phage-sdAb (KE32) for the unknown target. As seen in Fig. 5A, all antigens could be simultaneously detected with high sensitivity using a small amount (50 µL) of complex sample.

Magnetic enrichment of rare cells using single domain antibodies

Streptavidin microbeads (Miltenyi Biotech) compatible with MACSQuant magnetic columns were used to evaluate the potential of biotinylated sdAbs as capture reagent for the enrichment of rare cells. As a proof of concept, anti-HER2 sdAb C7b was used to enrich breast cancer cells (SKBr3) from a large excess of human PBMC. A SKBr3:PBMC ratio of 3x10 3 : 6x10 6 was chosen to mimic the proportion of CTC in blood. The enrichment was monitored by following the percentage of HER2 positive cells using Herceptin. This simple procedure led to 276-fold enrichment of tumor cells compared to the initial ratio (Fig. 5B).

Impressively, more than 97% of HER2 positive cells could be retrieved despite the 2 000-fold initial excess of PBMC.

Discussion

In this paper, we describe the selection of anti-tumor marker sdAbs and their straightforward use in several applications. Using the same library generated by immunization of llama with breast cancer biopsies, we selected sdAbs against known and relevant antigens such as HER2, or various sdAbs against unknown targets. Finally, some of these sdAbs were used to elaborate a sensitive and specific multiplexed assay on CBA to analyze a complex breast cancer representative sample. Other applications such as classical immunohistochemistry and an efficient and specific immune-enrichment of rare cells are also shown.

Surprisingly, a direct coating of cell lysates used for selections on maxisorp 96 well plates could be used to characterize the binding specificity of selected clones against a wide range of cell line and biopsy lysates. The high complexity of these lysates and the possible conformational changes of antigens due to the adsorption process might have prevented the generation of high signal for the monoclonal phage ELISA screening assay. We found that most selected phage clones could properly bind their antigens in these conditions. Bound phage were detected using an HRP-labeled monoclonal antibody directed toward p8, present at around 2800 copies per phage particle and allowing a considerable signal amplification. As such, phage ELISA on immobilized lysates represents a sensitive, robust, inexpensive and HT-compatible reverse phase array assay that can be exploited to test hundreds of binders against dozens of cell lysates. This approach could be used to generate antigenic profiles of various cancer related samples including cell line, biopsies or fluids such as serum samples.

We have shown the possibility to identify the antigen recognized by the selected phage-sdAbs. In a previous work, we had demonstrated the feasibility to produce active sdAbs in the cytoplasm of E. coli, fused to the avitag, a 15 aminoacid sequence which is biotinylated on a single lysine by E. coli enzyme BirA. This approach allows the efficient and inexpensive production of biotinylated sdAbs which can be directly immobilized on streptavidin coated surfaces such as plates or beads, in a near covalent and oriented fashion 38 .

Here we have used this approach to immobilize sdAbs on magnetic streptavidin beads to immunoprecipitate the corresponding antigen. This approach was significantly more efficient than other ones, including the use of in vitro biotinylated sdAbs with streptavidin beads, his 6 -sdAbs with cobalt chelating beads, or direct covalent coupling of sdAb to epoxy beads (data not show). As proof of concept, the target of phage-sdAb KE9 was identified as cytokeratin 19 (KRT19). KRT19 is a relevant antigen for breast cancer analysis and is already used for immunohistochemistry analysis 40 , as tumor marker for serum analysis 41,42 and for circulating tumor cells detection 43 . This result suggests that the proposed approach is capable of leading to the discovery of new relevant cancer related targets and biomarkers.

The in vivo biotinylation approach was also used to generate a sensitive multiplexed cytometry bead array. This assay can be directly compared to ELISA method 10 , but requires smaller volumes of sample material, and can be multiplexed. Thus bead array represents the method of choice for low density antibody array for clinical use such as diagnosis or prognosis 11,12 . SdAbs have a tendency to bind cryptic epitopes normally inaccessible to conventional antibodies 25 . The anti-HER2 sdAb or the anti-KRT19 sdAb selected in this work seem to follow the same trend since they do not compete with trastuzumab or a commercial anti-KRT19 mAb, respectively. This characteristic facilitates the design of sandwich assays once the antigen is identified. Because we used in vivo biotinylation leading to the addition of a single biotin at the C-terminus of sdAbs, we could use fluorescently coded streptavidin beads that were coupled to a single sdAb before being mixed and incubated with the sample. This approach bypasses the need to covalently bind each chosen sdAb to a batch of fluorescent bead, and allows the HT-compatible generation of multiplexed bead arrays.

A necessary step in metastasis is the dissemination of malignant cells into the bloodstream, where cancer cells travel throughout the body as CTC in search of an opportunity to seed a secondary tumor. CTC detection represents a valuable diagnostic tool and can be used as liquid biopsy for prognostic and predictive purposes. There has been a push toward developing technologies to reliably detect CTC using a variety of molecular and immunocytochemical techniques. One of the numerous problems of CTC purification is the heterogeneous nature of CTC, and the difficulty to define appropriate specific antigens for their detection. The availability of several sdAbs targeting new anti-tumor makers might be helpful. Here, as proof of this concept, we describe a sensitive and powerful enrichment procedure of rare tumor cells using an anti-HER2 sdAb. Importantly, this system is highly flexible and only relies in the availability of relevant anti-tumor sdAbs. The use of two different sdAbs for targeting two markers for the capture and the detection is an easy way to enhance the specificity of this assay. The proposed approach to generate anti-tumor sdAbs being fully compatible with high throughput approaches; it becomes feasible to test various sdAb combinations to optimize this CTC assay.

Thus, the combination of phage display and single domain antibody technology appears as an attractive approach to rapidly generate a variety of binder whose characteristics are especially well adapted to the generation of inexpensive but powerful diagnostic tools for breast cancer diagnosis.

Materials and methods

Proteins

sdAb CEA17 was previously described 25 .

Herceptin is a kind gift of Daniel Olive (CRCM, Marseille, France) Plasmid pET-14b was used to produce in vivo enzymatically biotinylated sdAbs. All sdAbs produced in this vector carry a C-terminal his 6 -tag, preceded by the Avitag TM (GLNDIFEAQKIEWHE). For generation of plasmids coding for sdAb-avitag TM -his 6 , see 38 .

To subclone sdAbs isolated by selection in pHEN1-PhoA-6HisGS plasmid 44 in plasmids coding for sdAb-avitag TM -his 6 38 , sdAb was first amplified from pHEN1-sdAb-myc-his 6 using primers pHENpETAvirev (CGTTCAGACCTGCGGCCGCTGAGGAGACAGTGACCTGG) and pHENpETAvifor (CTTTAAGAAGGAGATATACCATGGCCGAGGTGCAGCTGGTG). Vector pET-sdAbaNef -avitag-his 6 was digested with NcoI and NotI and purified. Cloning by recombination was performed with In-Fusion™ Advantage PCR Cloning Kits (clontech) as recommended by the manufacturer. All constructs were verified by nucleotide sequencing.

To subclone sdAbs isolated by selection (in pHEN1 plasmid) in plasmids coding for sdAb-Fc (Fc from human IgG1), sdAbs were first amplified from pHEN1-sdAb-myc-his 6 using primers 5HLsecVHH

(GGTTGCGTAGCTGAAACCGGTGAGGTGCAGCTGGTG) and 3VHHendH

(CGGTGGGCATGTGTGAGTTTTGTCTGAGGAGACGGTGACCTG). Vector pHLsec-sdAb-Fc was digested with AgeI and BstEII and purified. Cloning by recombination was performed using In-Fusion™

Advantage PCR Cloning Kits (clontech) as recommended by the manufacturer. All constructs were verified by nucleotide sequencing.

Serum sample, cells lines, biopsies

Patient sera were kindly provided by Pr. J. Biopsies or cells were lysed with a potter in lysis buffer : 150 mM NaCl, 1% Triton X-100, 50 mM Tris HCl pH8 with protease inhibitor cocktail (Complete, Roche). The lysate was centrifuged 10 min at 13000 g at 4°C. Supernatant was the final cell lysate. Total protein concentration (average between 2-5 mg/ml) was determined spectrophotometrically using a protein assay kit (Bio-Rad Laboratories, Hercules, CA, USA).

Production, purification and in vitro biotinylation of sdAbs

Production and purification procedures of in vivo biotinylated sdAbs was performed as described 38 .

In vitro biotinylation of protein was performed using Ez-link micro NHS-PEO4-biotinylation kit (Perbio science) following the recommendation of the manufacturer.

Llama immunization and library construction

Three young adult llamas (Lama glama) were immunized subcutaneously at days 1, 30, 60, 90 and 120 with breast cancer biopsy lysate (4701, 4952) or with healthy breast biopsy (one biopsy lysate per animal). One llama was immunized with 50 million of SKOv3 cells which strongly express HER2. VHH library construction was performed as described 25,45 . Briefly, genes coding for VHH were amplified by RT-PCR from the total RNA of peripheral blood mononuclear cells isolated by ficoll gradient, and cloned into phagemid pHEN1.

Selection of phage-sdAbs

Phage-sdAb library was produced as described in 25 using KM13 helper phage.

Different strategies of panning were performed. Phage selection were performed using antigen-coated magnetic epoxy beads (Dynabeads, invitrogen, following recommendations of the manufacturer), using 96-well maxisorp plate (Nunc) coated with antigen overnight at 4°C, or using 2x10 6 intact cells.. Beads, plate or cells were washed three times in PBS (using a magnetic particle concentrator for magnetic beads and centrifugation step for cells). Phage-sdAb library (1 ml) and beads, cells or plate were saturated in milk PBS 2%. For selection including a depletion step, phage-sdAb libraries were incubated with depletion support with rotation during 2 h at room temperature or at 4°C for cells. Phage-sdAb libraries (depleted or not) were recovered and incubated with beads or plate with rotation during 2 h at room temperature or at 4°C for cells. Next, beads, cells or plate were washed ten times with 1 ml of PBS 0,1% Tween (without tween for cells) and twice with PBS. Phage fixed on beads, cells or plates were eluted with tryspin solution (Sigma) at 1 mg/ml during 30 min at room temperature with rotation. Eluted phages were treated as described in 25 .

ELISA assays ELISA on epoxy beads

HER2-Fc or human Fc (R & D systems) were immobilized on magnetic epoxy beads (Dynabeads, invitrogen) during 48 h at 4°C following recommendation of the manufacturer.

For ELISA, 2 µl of beads/well were used. Beads were blocked with 5% milk-PBS (MPBS)

for two hours at RT. Beads were washed and incubated for 1h at RT with 50 µl of 2% MPBS containing in vivo biotinylated sdAbs CEA17 or C7b at 10 µg/ml or HRP-conjugated anti-Fc mAb at 1 µg/ml. After three washes with PBS, beads with sdAbs were incubated with HRPconjugated streptavidin (Jackson) (1 µg/ml) in 2% MPBS for one hour at RT. After three washes in PBS, bound antibodies were detected using ABTS. Coloration was followed at 405 nm.

Competitive ELISA on epoxy beads for affinity determination

A fixed concentration of in vivo biotinylated sdAb C7b (2 µg/mL) was added to serial dilutions of soluble HER2-Fc antigen (fivefold dilutions starting from 1.25 µM). After 1 h of incubation at RT, the remaining free fraction of biotinylated sdAb C7b was measured by incubating the mixture with 2 µl of HER2-Fc immobilized beads per well (prepared as above)

for 30 min at RT. After washing, bound sdAbs were detected as above. Dissociation constant was calculated using the non-linear curve fitting program Prism (GraphPad).

Phage-sdAb ELISA with lysate for screening and profiling

Monoclonal phage-sdAbs were produced as described in 25 . Phage-containing supernatants were tested for binding by ELISA.

Fifty µl/well of biopsy (5734, 5772ext, 5712, 5586, 5766, 5801, 5572int, 5592, 5627, 5011, 5033, 5713) or cell (BT474, SKBr3, HCC1954, MCF7, MDA-MB-231, T47D, HCC1806, BRCA-Mz-01, HCC1937, HME1, MC38, PBMC) lysates (200 µg/ml of total proteins) were coated overnight at 4°C on maxisorp 96-well plate (Nunc). After three washes with PBS, plates were blocked with 5% MPBS for two hours at RT. Plates were incubated for 1 h at RT with 25 µl/well of phage-containing supernatants mixed to 25 µl/well of 4% MPBS. After three washes with PBS Tween 0.1% and three washes in PBS, plates were incubated with HRP-conjugated anti-M13 mAb (Pharmacia) diluted 1/5000 for 1 h at RT. After three washes with PBS Tween 0.1% and three washes in PBS, bound secondary antibodies were detected using ABTS. Coloration was followed at 405 nm.

ELISA using a couple of sdAbs

Streptavidin plates (Thermo scientific) were blocked with 5% milk-PBS (MPBS) for two hours at RT. Fifty µl/well of in vivo biotinylated sdAb at 10 µg/ml in 2% MPBS were incubated overnight at 4°C. Wells were washed and incubated for 1h at RT with 50 µl of 2% MPBS containing cell (BT474, SKBr3, HCC1954, MCF7, MDA-MB-231, T47D, HCC1806, BRCA-Mz-01, HCC1937, HME1, MC38, PBMC) or biopsy (5734, 5772ext, 5712, 5586, 5766, 5801, 5572int, 5592, 5627, 5011, 5033, 5713) lysates at 100 µg/ml of total proteins.

After three washes with PBS tween 0.1% and three washes in PBS, plates were incubated 1 h at RT with 25 µl/well of phage-containing supernatants mixed to 25 µl/well of 4% MPBS.

Following three washes with PBS tween 0.1% and three washes in PBS plates were incubated with HRP-conjugated anti-M13 mAb diluted 1/5000 for 1 h at RT. After three washes with PBS Tween 0.1% and three washes in PBS, bound secondary antibodies were detected using ABTS. Coloration was followed at 405 nm.

Flow cytometry analysis

Experiments were performed on ice with rocking in 1% BSA PBS. Typically, 2x10 5 cells resuspended in 50 µl were distributed in 96-well microtiter plate, and incubated for 1 h with various concentrations of in vitro biotinylated antibodies. After washing, binders were detected with PE conjugated streptavidin (1:10) (Beckman Coulter). Fluorescence was measured using a MACSQuant (Miltenyi) and results were analyzed with the MACSQuant software. Negative (secondary antibody only) controls were carried out.

Immunoprecipitation

Cell lysates were depleted by incubation with magnetic streptavidin beads (Dynabeads, Invitrogen) for 2 h at RT. In the same time, in vivo biotinylated sdAbs were incubated with magnetic streptavidin beads during 1 h at RT (4 µg of sdAbs for 100 µl of beads for one immnoprecipitation). Beads were washed 3 times with PBS and incubated with depleted cell lysates during 2 h at RT under agitation. Beads with immune complexes were washed 5 times in lysis buffer and heated to 95°C for 5 min in 10µl of non-reducing protein loading buffer.

In-gel digestion and MALDI-TOF MS.

Protein spots excised from Coomassie blue (Imperial Protein stain, Pierce) stained gels were subjected to in-gel digestion with trypsin (Sequencing grade modified porcine trypsine;

Promega, Madison, WI, USA) according to a modified protocol from Shevchenko et al. 46 .

Tryptic peptides were then extracted from the gel, pooled and dried in a Speedvac evaporator.

Mass analyses were performed on a MALDI-TOF-TOF Bruker Ultraflex spectrometer (Bruker Daltonics, Wissembourg, France) controlled by the Flexcontrol 3.0 package (Build 173.1). A list of contaminant ions was constituted from a blank sample (blank piece of gel treated and analysed exactly as a true sample) and substracted from sample peaklists before database search. The peptide mass lists were used to identify the protein using MASCOT software 2.2 (Matrix Science) available on site.

Immunochemistry assay

in vivo biotinylated nanobodies were tested in immunostaining on 5 µm sections of frozenembedded cancer tissus. Endogenous peroxidase activity was blocked by incubation with 3% H 2 O 2 . Slides were incubated for 1 h with 10 µg/ml of in vivo biotinylated nanobodies at RT.

Detection was performed by incubations at RT for 30 min with streptavidin peroxidase.

Visualization was performed by DAB revelation (Dako) peroxidase reaction with haematoxylin as counterstain.

CBA assay

Three types of CBA Functional Bead system (BD Biosciences) were used for the assay.

The Functional Bead Conjugation Buffer Set was used for conjugation of streptavidin to beads following the recommendation of the manufacturer.

For multiplexed assay, 1.5x10 5 beads of each type were used per assay. The whole procedure was performed in the dark. Beads were coated with sdAbs individually and all types of beads were mixed for the rest of the procedure. Beads were blocked with 3% BSA PBS for 2 h at RT. Beads were incubated with in vivo biotinylated sdAb (C7b, CEA17, KE9) at 10 µg/ml in 1% BSA PBS for 1 h at RT. After two washes with PBS, all bead types were mixed and incubated for 1 h at RT with serial dilutions of sample containing patient serum with CEA, recombinant HER2-Fc (R & D systems) and a lysate of biopsy 5712. After two washes with PBS, beads were incubated for 1 h at RT with 2 µg/ml of anti-HER2 mAb (Santa-Cruz, sc-74241), anti-CK19 mAb (Santa-Cruz, sc-53258) and anti-CEA mAb 35A7 (kind gift of A.

Pelegrin, Montpellier). After two washes with PBS, beads were incubated for 1 h at RT with PE-conjugated goat anti-mouse mAb (Santa cruz) diluted 1/200. Fluorescence was measured using a MACSQuant (Miltenyi) and results were analyzed with the MACSQuant software.

Negative controls (secondary antibody only) were carried out.

Rare cells enrichment assay

The enrichment procedure was performed using Streptavidin Microbeads (Miltenyi Biotech)

following the recommendation of the manufacturer. Capture antibody was in vitro biotinylated sdAb C7b at 10 µg/ml. Detection antibody was Herceptin labeled with Alexa660 at 10 µg/ml. Labeling was performed using Alexa Fluor® 660 Protein Labeling Kit (Molecular Probes, Invitrogen) following the recommendation of the manufacturer. We used a ratio of 3 000 SKBr3 cells mixed with 6 000 000 of human PBMC. In vivo biotinylated sdAb KE23 was immobilized on streptavidin plates at 10 µg/ml. Lysates of biopsies (same as in A), a mixture of breast cancer cell lines (BC cell lines, same as in A), human PBMC and healthy breast cell line HME1 were added. After washing, KE32 phage-sdAb was added and bound phage were detected by HRP-conjugated anti-M13 mAb.

  Fig 2A shows representative examples of this various patterns, including a clone binding to antigen differentially expressed in biopsies and cell lines, but negative on PBMC lysate (KE9), a clone showing a specificity restricted to breast cancer lysates and yielding no signal on biopsy lysates (KE36), or clones able to bind biopsy lysates but not cell line lysates (KE21 and KE15).

Figure 1 :

 1 Figure 1: sdAb C7b specificity and affinity. A) Epoxy beads were coated with HER2-Fc or Fc antigen, and incubated with anti-HER2 (C7b) or anti-CEA (CEA17) in vivo biotinylated sdAbs. Bound sdAbs were detected with HRP-conjugated streptavidin. HRP-conjugated anti-Fc mAb were used as positive control of the presence of Fc fragments on beads. B) SKBr3 cells were incubated with serial dilutions of in vitro biotinylated sdAb C7b (•), Herceptin (■) or C7b-Fc (▲). Captured antibodies were detected by PE-conjugated streptavidin. Cells were analyzed by flow cytometry assay on MACSQuant. Error bars represent the standard deviation of MFI (mean fluorescence intensity) of experiments performed in triplicates. K D values were determining using non-linear fitting program Prism (GraphPad) C) 2 µg/mL of in vitro biotinylated sdAb C7b was incubated with various concentrations of soluble HER2-Fc antigen. The free fraction of biotinylated sdAb was measured by incubation with bead coupled to HER2-Fc followed by washing and detection using streptavidin-HRP. Error bars represent the standard deviation of experiments performed in triplicates. D) SKBr3 cells were incubated with serial dilutions of Herceptin (1 to 0.000001 µM). In vitro biotinylated Herceptin at 0.001 µM (•) or sdAb C7b at 0.01 µM (■) were added. Captured antibodies were detected by PE-conjugated streptavidin. Cells were analyzed by flow cytometry assay on MACSQuant. Error bars represent the standard deviation of experiments performed in triplicates.

Figure 2 :

 2 Figure 2: Phage-ELISA characterization of isolated sdAbs. A) Reverse phase ELISA. Maxisorp plate were coated with soluble protein lysate of biopsies (11 different biopsies), a mixture of breast cancer cell lines, named BC cell lines in the figure, and containing cell line lysates BT474, SKBr3, HCC1954, MCF7, MDA-MB-231, T47D, HCC1806, BRCA-Mz-01, HCC1937, human PBMC and healthy breast cell line HME1. Phage-sdAbs were added. Bound phage were detected with HRP-conjugated anti-M13 mAb. B) Sandwich sdAb ELISA.

Figure 3 :

 3 Figure 3: Targets identification. A) In vivo biotinylated KE9 sdAb or control sdAb (Cont.)were immobilized on streptavidin beads and used to perform immunoprecipitation using

Figure 4 :

 4 Figure 4: Immunohistochemistry assay. Frozen embedded ductal breast cancer tissue slides, corresponding to different samples, were incubated with in vivo biotinylated sdAbs. Bound sdAbs were detected by HRP-conjugated streptavidin. Original magnification, x20.

Figure 5 :

 5 Figure5: Breast cancer diagnosis assay with sdAbs. A) Multiplexed analysis of four breast cancer specific targets in a complex sample using a CBA assay. CBA beads were coated with streptavidin and incubated with in vivo biotinylated sdAb C7b, CEA17, KE9 or KE23. All beads were mixed and incubated with serial dilutions of serum containing CEA (starting concentration: 500 ng/ml), HER2-Fc antigen (starting concentration: 10 µg/ml), and biopsy 5712 lysate (starting concentrations: 100 µg/ml of total protein and 7 µg/ml of KRT19).Beads were incubated with phage-sdAb KE32 , anti-CEA, anti-HER2 and anti-KRT19 mouse antibody. Anti-M13 mAb was added, followed by PE-conjugated goat-anti-mouse mAb. Beads were analyzed by flow cytometry assay on MACSQuant. Error bars represent the standard deviation of MFI (mean fluorescence intensity) of experiments performed in triplicates. B) Enrichment of rare cells. Enrichment of initial mixture of SKBr3 cells (3 000) in human PBMC (6 000 000) yielding an initial ratio of 0.05% of cancer cells. Cancer cells were enriched using in vitro biotinylated anti-HER2 sdAb C7b loaded on streptavidin microbeads, and detected with Alexa660 labeled Herceptin. Enrichment procedure was performed on the MACSQuant cytometer and analyzed by flow cytometry on the same device.

Figure 1 A

 1 Figure 1

  Figure 2

Acknowledgements

This work was supported by the ANR (Agence Nationale de Recherche) program 'Nanosciences and Nanotechnologies' under the grant ANR-07-PNANO-051-01 and by the ARC (Association pour la Recherche contre le Cancer). We would like to thank Matthieu Pophillat for excellent MS expertise.

Competing interests

The author(s) declare that they have no competing interests


Armin Weinberger

Daniel Bodemer

Manu Kapur

Gaëlle Molinari

Nikol Rummel

Nicolas Balacheff

Shaaron Ainsworth

Mireille Betrancourt

Krista Deleeuw

Julia Eberle

Gerhard Fischer

Anniken Furberg

Hannie Gijlers

Anders Kluge

Natasa Lackovic

Inge Molenaar

Jochen Rick

Sascha Schanze

Mike Tissenbaum

Dimitra Tsovaltzi

Katharina Westermann

MUPEMURE -Multiple Perspectives on Multiple Representations White Paper Organizers: The MUPEMURE Theme Team

Introduction and Motivation

Our understanding of STEM (science, technology, engineering, and mathematics) topics such as statistics, photosynthesis, the water cycle, etc. is strongly evoked and guided by how these topics are graphically or textually represented in text books or online environments. Sharing multiple perspectives on multiple representations (MUPEMURE) can affect the course and type of learners' reasoning by disambiguating discourse, fostering self-explanation and elaboration, and by strengthening a shared task focus. Latest educational technology for creating, modifying, and sharing representations in TEL (Technology-Enhanced Learning) scenarios needs to be combined with dedicated instructional approaches such as group awareness and scripting approaches as well as artificial intelligence to investigate the following research questions:



How can we conceptualize knowledge being distributed in multiple (external and internal) presentations and agents across technologyenhanced learning landscapes?

 How do learners coordinate multiple representations and converge (or diverge) upon shared, canonical representations?

 How can learners be facilitated to actively share, process, and acquire multiple perspectives on multiple representations of STEM topics in TEL environments?

A large body of research has been concerned with learners' difficulties to detect and avoid misconceptions and to construct relations between multiple representations for building coherent mental representations of STEM topics. Moreover, much research has been invested in the questions of how to choose the optimal representation of knowledge to optimize cognitive load and how to design multiple representations for learning purposes. MUPEMURE builds on this work and takes it further by asking how learners can be facilitated to actively create and modify multiple representations and acquire multiple perspectives on science topics through specific, collaborative knowledge building activities.

This new perspective on representations is addressing current online scenarios of knowledge construction, e.g., in social networking sites, where learners can create, upload, and discuss pictures and videos.

With the 1 st MUPEMURE workshop at the Alpine Rendez-Vous in La Clusaz, 2011, we aimed to bring together the scientific communities of Multimedia Learning (MML) Research and research on Computer-Supported Collaborative Learning (CSCL) and identify the overlap of these research fields in which MUPEMURE research can be allocated (see Fig. 1).

MML CSCL MUPE MURE

Workshop Description

The workshop covered a number of activities involving keynotes, representing and categorizing the research of the participants as well as breakout groups to better identify and address the emerging field of MUPEMURE (see Tab. 1). Daniel Bodemer next presented a complex CSCL environment in which learners needed to translate between and actively integrate multiple given representations and were made aware about the state of integration of the learning partners, which lead to better individual learning gains.

Julia Eberle and Gerhard Fischer introduced the concept of meta-design for formal and informal learning, i.e. allowing participants in run-time to change the design of an environment for creating multiple (truly unforeseeable) representations. The evolving multi-representation scenarios fundamentally differ in respect of the type of community (community of interest versus community of practice), the boundary objects, and the internal and external scripts involved supporting reflection in or reflection on action.

Anniken Furberg, Anders Kluge, and Sten Ludvigsen investigated how groups are sharing representations and how this is connected to a trajectory of interactions and developing representations. They found that students first reconstruct the information given by teachers and then create hypotheses in a computer-supported inquiry environment.

Hannie Gijlers, Alieke van Dijk and Armin Weinberger presented studies of tablet-supported collaborative drawing in elementary school combining collaboration scripts and awareness tools to highlight and guide learners to resolve cognitive divergences. Their results show that those instructional approaches substantially facilitated processes and outcomes of learning about science phenomena.

Manu Kapur introduced his concept of productive failure: Opportunities of students to "mess around" with the task are pivotal to arriving at more systematic views and approaches.

Anders Kluge and Ingeborg Krange presented the MIRACLE project (Mixed Reality Interactions across Contexts of Learning). Bridging virtual and real spaces as well as school, the web, and the museum, learners can manipulate interactive models concerning energy production and consumption.

Natasa Lackovic laid out how she uses pictorial representations of abstract concepts to shape real classroom discussion. She aims to investigate the relation between multiple representations and different types of discussion these representations mediate.

Inge Molenaar investigated pedagogical agents facilitating meta-cognition. Her results indicate that the interaction between the group members amplifies the effect of scaffolds, especially those in form of questions.

Gaëlle Molinari demonstrated how peers mutually model each other's knowledge with concept maps in CSCL. She orchestrated individual and collaborative learning phases and varied similar and complementary knowledge resources to facilitate transactivity, i.e. the extent to which learners operate on each other's reasoning.

Jochen Rick presented his work with tabletops for learning mathematics, DigiTile, with which elementary students can link multiple representations of mathematics. This work shows how learners can benefit from directly manipulation multiple representations.

Sascha Schanze showed with his work "what words cannot

express", how self-constructed representations, e.g., drawings, concept maps, work sheets, in CSCL for the chemistry class can promote progressive reflection. Schanze stresses the potential of self-constructed representations for revision through peer interaction. Drawings seem to partly meet learners' problems to translate between different representations.

Mike Tissenbaum, Michelle Lui, and Jim Slotta investigated how learners construct personally meaningful artifacts with computer support inside and outside the classroom. Their notion of the smart classroom allows teachers to understand and alter the flow of class activities with computersupported scripts.

Dimitra Tsovaltzi investigated how externalization of (erroneous) internal representations in CSCL could be facilitated with error-awareness prompts, to foster construction of canonical knowledge in turn.

Katharina Westermann and Nikol Rummel follow up on Kapur's productive failure work and complement it with some guidance in form of motivational prompts (e.g. "keep going") or cognitive prompts (e.g. "maybe your solution does not always work, here you have a counterexample"). Small groups of learners use Tablet-PCs to construct multiple representations such as tables, graphs, and formulas to foster mathematical literacy.

Breakout groups

To further integrate the various studies placed within MUPEMURE, groups were formed that consisted of participants covering different areas of the field of MUPEMURE, i.e. approaching MUPEMURE rather from a MML or rather from a CSCL research perspective (see Fig. 1). All participants positioned themselves and each other on this schematic representation of MML-MUPEMURE-CSCL research and heterogeneous dyads were identified ahead of the workshop. Starting from a representation highlighting given and self-constructed representations being produced and processed by the individual as well as shared among peers (Fig. 2), missing dimensions were identified, such as computers and tools for representing something, learners' prerequisites (e.g. representational competencies) or the teacher's role in the way multiple representations are selected and/or in the orchestration of learning activities with multiple representations.

In two larger breakout groups as well as in the plenum, the MUPEMURE model was discussed:

Bringing MML and CSCL together addresses how a group can coordinate itself by external representations taking into account the particular characteristics and capabilities of CSCL. External representations then are needed to externalize and eventually converge on a shared understanding. In this regard, CSCL is fundamentally building on and being scaffolded by various external representations. Externalizing representations that can be interpreted by computers can then create situations, in which computers can be programmed to provide some basic responses and advanced tools dealing with learners' representations.

Moreover, representations and collaboration may have some reciprocal effects. Specific representations may foster specific interactions and vice versa, some forms of collaboration may lend themselves to specific external representations.

Representations can have a strong influence of learners' understanding, especially when they are regarded as being correct and representing canonical knowledge. In CSCL research, the notion of productive failure emerged showing that noncanonical representations may pose far greater learning opportunities under specific circumstances than representations, which are known to be correct. Being able to trace the development of representations may therefore be a smart way to tweak learners' understanding and epistemological beliefs.

 MUPEMURE -Multiple Perspectives on Multiple Representations  Seite 5

Emerging Research Questions

In addition to our motivating questions (see p. 2), the following questions emerged in the plenary discussions on advancing the MUPEMURE model:

How can the temporal dimension of phenomena be represented?

In this respect, snapshots, narrations, video etc. can help and needed to be carefully combined to arrive at a good fit between representing a whole process and being data-economical.

How do we represent collaboration?

Do we start from the individual learners to the group arguments (from MML to CSCL):  external representation  triggers an internal representation  shared and combined between learners that may lead to higher levels. The researcher should make the decision depending on his/her background.

Summing up, starting at the social or at the individual level is an ontological debate; the position of technology depends on its function; the driver to connect CSCL and MER is the construct of externalization; which construct is leading depends on our position as a researcher and our research question.

Grand Challenge Problems

Many technical novelties allow for new cultural practices of dealing with knowledge and representations. Learners are frequently online with mobile devices; they share their reality with friends taking and uploading photos and videos into social networks wherever they go. New (mobile) hardware with multi-touch surfaces, such as iPads or tabletops, allow for the direct manipulation of representations. One of the fundamental challenges for society and education will be to foster an active, smart and responsible use of these knowledge-at-your-fingertips-devices rather than a media consuming only attitude. The learning opportunities have drastically increased. Learners may use formal and informal learning opportunities online, e.g., learn by free lessons shared on YouTube, and be confronted with views and representations diverging from their face-to-face education. Formerly individual learning scenarios, like reading a book, may become a collaborative experience with the use of eBooks. To exploit the benefits and reduce the barriers peer learning entails, instructional approaches need to be translated and created anew for TEL scenarios, such as scripting collaboration or making learners aware. To benefit from MUPEMURE, it may be pivotal to design for smart ways of data reduction to foster comprehension, i.e. to avoid "data obesity". This means to be intrusive at the right time right person in the right way.

The following Grand Challenge Problems have been posed and elaborated by the Provocateur of the workshop, Nicolas Balacheff: A semiotic recommender system to decide which representation can fit learning needs at best Technology offers a large range of possibilities to represent learning objects and content, either in texts, hypertexts, drawings, animations and films. All these representations present advantages and limits which are rarely presented precisely to teachers who have to choose material for their teaching next day. This choice is very complex because of the large number of aspects to take into account: nature of the content to be taught, variety of the available resources (computers, digital tablets, smartphones, phones, pocket calculators, paper & pencil), constraints on the communication (on site, at a distance, with or without shared visualizations), learners' competence and needs. A great scenario would be for the teacher to describe the content and the constraints, and then get advice about the representations which would fit at best the learning needs. Further development would be to express these needs and get the resources which on the net fit at best the requirements.

What problems of the European education system are addressed, and what are the long term benefits for society?

This GCP is not specific to European education systems, it is of an international relevance. Then, the problem is related to the "data deluge" and the difficulty of finding the proper resources within the Internet. The long term benefit is the enhancement of the professional activity of the teachers and a more efficient exploitation of the learning potential of the internet. With learners able to express their needs, one may expect such a system to provide responses to a question by selecting the most adapted representation. This technology may not only impact learning but also the use of the internet within an informal learning context.

What are the main activities to address this Grand Challenge Problem?

From the scientific perspective this challenge calls for a high level collaboration of computer scientists, with researchers having a specific expertise in semantic, learning science, semiotic and epistemology. The main activity should be a project (STREPS type) with a consortium gathering the said competence and as a key production a prototype of such a tool in a well-defined and not too complex domain (esp. a domain in which the issue of representation is not completely open from the learning science and semiotic perspective).

Additionally, related research projects could be designed to explore the following complementary features:

-Indicators to recognize the right moment/time to provide non-intrusive feedback/scaffolding to learners -Indicators on when, how, and what kind structuring the learning process should be provided in a personalized way; -Criteria for choosing the effective order of representation type (self-constructed created vs. pre-constructed given) depending on the expected processing and conceptual understanding of the learner What is the timeframe for the Grand Challenge Problem?

A 3-years STREP

What are measurable progress and success indicators?

To be able to run a demonstrator in a non-trivial knowledge domain would be a good indication of success, together with an acceptable argumentation on its potential generality.

How can funding be attracted?

This idea could attract funding from the knowledge industry and Ministries of Education of EU member states. Such a problem can be the origin of successful European projects (STREP size).

When the representation is not there, how to introduce it and facilitate its adoption?

One of the challenges of teaching is to find a way to meaningfully introduce new representations when the hypothesis is that learners do not have this representation or any premises. This challenge is common to all disciplines in various forms, but it is of a special importance in the case of science where representations are more often than not rigorously codified in semiotic registers and are used for computational purposes. A badly introduced representation can be the source of misconceptions, and also of exclusion of learners from the learning community since adopting a representation is not only a cognitive but also a social process.

Hence the problem of the introduction must be rooted in a two dimensional space: epistemological -understanding the role of the representation in the building and the use of a piece of knowledgeand sociological -understanding the communication dimension of the representation and its contribution to the construction of a community.

Situations of communication associated to problem-solving situations are likely to provide the best context to facilitate the emergence, the sharing and the use of representations. This designates CSCL as the TEL research area in which tools and models could be found to take up this challenge.

This grand challenge problem is tightly related to the connecting learner and contextualizing learning Stellar Grand Challenges.

What problems of the European education system are addressed, and what are the long term benefits for society?

This GCP problem is not specific to European education systems, it as of an international relevance. It raises many issues, among which the following which are of interest for formal and informal education:

-For a given piece of knowledge, what would be the communication and problem-solving characteristics of a situation allowing the emergence of a relevant and efficient shared representation? -In the case of formal education, what are the constraints so that the emergent representation is either compliant to the one socially shared, or stands at a distance which allows the teacher to bridge the gap without "forcing" learners? -In the case of informal learning, which hints would help to construct representations through processes allowing to manage the tensions between the variety of the individual needs and backgrounds and the construction of the community sharing meaning and knowledge.

What are the main activities to address this Grand Challenge Problem?

From a scientific perspective this challenge calls for a high level input from semiology and epistemology in tight relationship with educational research and engineering. It requires from computer scientists a better understanding of the interaction between knowledge representation, interface and their actual implementation. HCI requires epistemological characteristics, either reified or emergent, of the interface and its system of interactions to be revisited to produce models taking into account the "meaning" dimension and not only the functional dimension in computational terms ("meaning beyond affordance" may be the slogan).

The main activity should be a project (STREPS type) with a consortium gathering the said competence and a network of classroom or onthe-field learning communities to implement and evaluate the specifications of the situations. The content domain must be well-defined and not too complex (esp. a domain in which the issue of representation is not completely open from the learning science and semiotic perspective).

What is the timeframe for the Grand Challenge Problem?

This GCP can be associated to long term basic research on meaning, learning and representation, and shorter term research project directly related to the current technology searching for its enhancement possibly targeting first domain specific research projects where educational problems are well identified.

A 3-years STREP would set a standard timeframe for the building of a first demonstrator.

What are measurable progress and success indicators?

The explicit and precise description of the scenario for a successful learning situation fulfilling the mentioned properties and a consensus of the research community would be a first evidence of success. Then, an indicator would be the level of dissemination and the activity of a community of teachers adopting these scenarios and engaging in discussions to understand and revise-improve them. Eventually, the adoption of the specification of scenarios to enhance CSCL environment making use of content specific representations will be a relevant criterion.

How can funding be attracted?

A progress on this issue will have an impact beyond education, on the design of advanced HCI principle of design for environments which will provide services but also evolve with the user understanding of its functioning and its functionalities. This may be of interest for the knowledge industry and fit well in the EC agenda.

How can T(EL) support navigation across media and communities?

Navigating across text-based material is now a familiar practice on the Internet supported either by metadata retrieving or full text explorations of the documents. Even, some techniques (e.g. LSA) allow an exploration beyond the identification of common words, deriving commonalities in content from the recognition of lexical context (e.g. "I drove to Munich", is conjectured to have a close semantic relation to "I took my car to Munich" thanks to a statistical knowledge of the cooccurrence of "driving" and "car" in texts). The last decade has developed the possibility to navigate among multimedia material; the complexity of the technology is of another order, because if it is a common idea that a picture is best than a thousand words, when one come to formalize the semantic of images or video, it appears to be much more complicated than for texts. Though, it happens that in learning contexts graphical representations and images are considered as extremely important tools to facilitate sense making or communicating knowledge. This is actually the core reason why working on multiple representations is so important, even critical for the development of efficient learning, teaching and training strategies. Then, a TEL research challenge is to provide the technology and understand the practices which will allow navigating among these representations taking into account learning requirements What problems of the European education system are addressed, and what are the long term benefits for society?

This GCP problem is not specific to European education systems, it as of an international relevance.

The navigation trail among the representations provides access to a learner representation profile (e.g. better learning curve with certain representations) and to clusters of learners sharing preferences (type of representation, type of treatments). Profile and clusters can be used to optimize group making in the design of CSCL scripts, or networking learners and teachers in a more efficient way so as to facilitate learning. A prerequisite is to specify a learning grammar for each type of representation.

What are the main activities to address this Grand Challenge Problem?

This complex GCP might benefit from a series of seed projects on issues like: identifying the semantic proximity of representations of different types for a given content; identifying the productive differences between representations so that building on them can facilitate meaning making; defining a "measure" of the epistemic complexity of a representation; defining a "measure" of the cognitive complexity of a representation; specifying inter-representations manipulation in support of learning. This could last a period of 2 years, then be followed by a 3-years STREP-like project to integrate these results and propose a navigation toolkit to be used in other applications.

What is the timeframe for the Grand Challenge Problem?

This is a mid-term research problem.

What are measurable progress and success indicators?

Production of quantitative indicators of distance and likeliness of representations, benchmarking of these on a shared bank of learning representations, level of adoption of the toolkit by other projects.

How can funding be attracted?

Representation grammars will provide a ground for tools necessary to navigate efficiently and relevantly the Internet in search of resources. Including tangible representations (objects), this challenge will be in line with the foreseen web3.0 and hence of interest beyond the TEL research area. It can attract interest from researchers in semiology, computer-science, education and providers of Internet services.

Researchers and Communities

The communities involved in MML and CSCL research mainly consist of education and psychology researchers, but clearly also move beyond those disciplines. The concept of the Learning Sciences is indeed multi-and interdisciplinary. Some disciplines have been mentioned above; obviously, some more disciplines need to be involved:

Education is needed to design instruction, such as CSCL scripts or awareness tools, and to develop measurements of relevant processes and outcomes. Psychologists are needed to build this research on existing, sound methods and theories of cognition and motivation. Computer scientists are needed to co-develop new tools to create and share representations. Designers and artists are needed to contribute an understanding of aesthetics and historical backgrounds of representations.

Fig. 1 .

 1 Fig. 1. MUPEMURE in the overlap of MML and CSCL research

  These dyads focused on enhancing transfer with and across multiple representations (Anniken & Katharina), supporting sharing whilst creating solutions in formal and informal learning (Anders & Mike), bringing communities of multiple disciplines together with boundary objects (Gerhard, Julia, & Natasa), how to manipulate representations to support learning (Inge & Jochen), what representations to choose and how to sequence them (Krista and Sascha), how to foster representational competency (Gaëlle, Mireille, & Shaaron), and how to build on heterogeneities in the classroom and supporting learners working with erroneous examples at the right time (Dimitra & Hannie).Towards a MUPEMURE modelOne central goal of the workshop was to advance a model of MUPEMURE.

Fig. 2 .

 2 Fig. 2. Model of MUPEMURE.

  or from the group to individual learners' arguments (from CSCL to MML):  representing individual learners'  knowledge construction in the collaborative moment  does not reside in the individual head  it is an ontological problem with where knowledge resides  but the knowledge creation that happens in the collaboration. Starting with the MML hypotheses (see e.g. Schnotz), (H1) external representations  facilitate the construction of more elaborate internal representations improve the quality of collaboration  results in higher levels of learning outcomes OR (H2) different external representations  lead to different internal representations  result in different patterns of collaboration and learning outcomes. If we start with the CSCL hypothesis: external representations  facilitate the visualization of differences and also potential gaps between learners' knowledge  improve collective cognitive convergence  may result in higher levels of learning outcomes. Which process drives the other, is collaboration driving representations or do representations drive collaboration? Two parts of the cycle  chicken egg problem  collaboration  representation  where do you come from  What do you want to foreground?