Nicolas Beldiceanu

Alexandre Dolgui

Clemens Gonnermann
email: clemens.gonnermann@iwb.tum.de

Gabriel Gonzalez-Castañé

Niki Kousi
email: kousi@lms.mech.upatras.gr

Bart Meyers
email: bart.meyers@flandersmake.be

Julien Prud'homme

Simon Thevenin

Eduard Vyhmeister
email: eduardo.vyhmeister@insight-centre.org

Per-Olov Östberg

ASSISTANT: Learning and Robust Decision Support System for Agile Manufacturing Environments

Keywords: Artificial intelligence, Data analytics, Digital twins, Decision aid, Reconfigurable manufacturing systems, Process and production planning, Scheduling and real-time control

The European project ASSISTANT concept will provide a set of AI-based digital twins that helps process engineers and production planners to operate collaborative mixed-model assembly lines based on the data collected from IoT devices and external data sources. Such a tool will help planners to design the assembly line, plan the production, operate the line, and improve process tuning. In addition, the system monitors the line in real-time, ensures that all required resources are available, and allows fast re-planning when necessary. ASSISTANT aims to make cost-effective decisions while ensuring product quality, safety and wellbeing of the workers, and managing the various sources of uncertainties. The resulting digital twin systems will be data-driven, agile, autonomous, collaborative and explainable, safe but reactive.

INTRODUCTION

We present in this paper the main scientific ideas of our new European project ASSISTANT. With a multidisciplinary consortium combining key skills in AI, manufacturing, edge computing and robotics (Assistant, 2020), the project ASSISTANT aims to create intelligent digital twins through the joint use of machine learning (ML), optimization, simulation and domain models. The resulting tools will help to design and operate complex collaborative and reconfigurable production systems based on data collected from various sources such as IoT devices. ASSISTANT targets a significant increase in flexibility and reactivity, product/process quality, and in the robustness of manufacturing systems, by integrating human and machine intelligence in a sustainable learning relationship.

AI challenges in the context of manufacturing. While AI is already a reality for the GAFA (Google, Apple, Facebook, and Amazon), it is not yet enough present in manufacturing. The GAFAs are using AI to deliver services to a large public, but manufacturers' business models and needs are quite different: they address small markets with dedicated products, which require specialized tools. While AI is already used to make low-level manufacturing decisions such as automated machine tuning or predictive quality, the project relies on predictive and prescriptive analytics to extend the use of AI to higher-level manufacturing decisions such as process planning, production planning and scheduling. As a human must remain responsible for making such high-level decisions, ASSISTANT will rely on the generative design framework to help users find the best plans. Generative design [START_REF] Aameri | Towards an ontology for generative design of mechanical assemblies[END_REF] is an iterative design process allowing, "a user to specify goals expressed as objectives and constraints to a software application and the application returns a set of feasible and/or optimal design solutions". While the generative design was proposed in the context of product design, ASSISTANT aims to extend the use of this technique to other manufacturing decisions.

The use of AI within generative design requires reliable and real-time data. In manufacturing, such data is nowadays available in digital twins. Within ASSISTANT, the classical definition of a digital twin is seen as a data fabric, used by generative design techniques through machine learning to yield data-driven decisions. ASSISTANT will go beyond the traditional trial-and-error use of digital twins and generative design by: 1. Using machine learning to automatically develop part of digital twins from data from IoT/ERP/MES, and to model parameter uncertainties in digital twins. 2. Systematically organizing the cooperation between digital twins and machine learning tools to improve models. 3. Extending the idea of generative design while taking into account the synergy between machine learning and digital twins.

CHALLENGES FOR INDUSTRY

Current situation in the manufacturing industry. The paradigm shift from mass production to mass customization and mass individualization drives companies to launch new products frequently: their survival depends on their reconfigurability, adaptability, and flexibility to face various uncertainties. This has led to technological advances (such as reconfigurable manufacturing systems and adaptable factories), where resources can be rearranged, and replaced quickly. These new production systems must be simple, inexpensive, easy to maintain and update, and robust to hazards. AI is timely for helping manufacturing companies from various sectors to bring down costs, increase quality by reducing product defects, shorten unplanned downtime and transition times, and increase production speed.

ASSISTANT has been designed to tackle the different levels of manufacturing challenges:

1) Complex production process challenge: The large number of end items and components makes production planning extremely difficult. The constant system changes and variability lead to many parameters (e.g. demand, processing times, component arrival dates) that vary significantly and are difficult to forecast accurately.

2) Product and process quality challenge: The lack of regularity in demand makes it difficult to ensure productprocess quality. With frequent system reconfigurations, new tools and resources are frequently acquired or modified, and the performance of these tools and their impact on the production system is difficult to predict.

3) Hyper dynamic worker challenge: As manufacturing systems become more and more robotized, most systems will be a hybrid with humans and machines, where robots handle regular flow and operators bring flexibility. This leads to the development of cobots where humans help machines. In such a system, workers are more exposed to injuries, and it is crucial to account for safety and ergonomics.

4) Unsustainable industrial practices: in the past, industrial practices have focused on economic perspectives. But the manufacturing industry now has to also consider policies, regulations and process restrictions to address environmental and social challenges. Sustainability assessment methods and indicators can be added at different levels of strategic decision-making, altering purely economic-based strategies.

STATE OF THE ART

The adoption of data-driven modeling and the underpinning use of IoT and machine learning technologies are widely considered to have revolutionized the manufacturing industry. However, adoption and market penetration are slow and often incremental. For example, ML techniques for smart manufacturing processes have been demonstrated in shop floor control [START_REF] Wang | Deep neural networks based order completion time prediction by using real-time job shop RFID data[END_REF], predictive maintenance [START_REF] Yam | Intelligent predictive decision support system for condition-based maintenance[END_REF], online scheduling [START_REF] Hammami | On-line self-adaptive framework for tailoring a neural-agent learning model addressing dynamic real-time scheduling problems[END_REF]. Key challenges include the general complexity of aggregating and analyzing data in general yet meaningful ways.

The development of AI requires accurate and real-time data. This data can be taken from Digital twins. The digital twin has been introduced as a virtual representation of real objects [START_REF] Glaessgen | The Digital Twin paradigm for future NASA and US Air Force vehicles[END_REF]. In a manufacturing context, the digital twin is composed of interconnected physical and functional models of resources, products and systems. The purpose is to improve production by carrying out autonomous decisionmaking in planning and operation [START_REF] Boschert | Digital Twin-The Simulation Aspect[END_REF].

The state-of-the-art/baseline tools for high-level decisionmaking in manufacturing (production planning, scheduling) relies on optimization/operation research techniques. With the development of computing technologies, software providers started to propose Advanced Planning Systems (APS) [START_REF] Stadtler | Supply chain management and advanced planning--basics, overview and challenges[END_REF] to model the production process. The future of APS lies in the incorporation of data analytics techniques, to better represent the possible values that can take uncertain data [START_REF] Thevenin | Material requirements planning under demand uncertainty using stochastic optimization[END_REF]. Techniques to include data analytics into the linear program classically used in APS have been developed (Thevenin 2020[START_REF] Bertsimas | Data-driven robust optimization[END_REF]), but their application in manufacturing remains scarce [START_REF] Zhao | A data-driven robust optimization approach to operational optimization of industrial steam systems under uncertainty[END_REF]. While APSs have a large added value for companies, they remain largely under-utilized because of their costs, and because it requires qualified planners to ensure APS receive accurate data, and to analyze the output of the plan.

Similarly, many approaches have been developed to design conventional manufacturing line, they can be adapted or replaced to meet the new challenges posed by reconfigurable manufacturing systems [START_REF] Koren | Reconfigurable manufacturing systems:principles, design, and future trends[END_REF]; Hashemi-Petroodi 2020; Yelles-Chaouche 2020). While different approaches have been developed to use the flexibility of RMS efficiently by automatizing process planning [START_REF] Battaïa | Optimal Cost Design of Flow Lines with Reconfigurable Machines for Batch Production[END_REF], the issues of designing reconfigurable lines are not yet sufficiently studied.

The human factor plays a crucial part in manufacturing systems (Hashemi-Petroodi 2020b). As such, robotic support of humans for a collaborative assembly greatly enhances those systems [START_REF] Michalos | ROBO-PARTNER: Seamless Human-Robot Cooperation for Intelligent, Flexible and Safe Operations in the Assembly Factories of the Future[END_REF].

PROJECT OBJECTIVES

ASSISTANT aims to provide a set of related AI-based, explainable intelligent digital twins that help process engineers and production planners to operate collaborative production systems based on the data collected from IoT devices and external data sources (see Fig 1). ⧫ Such a tool will be based on the synergy between digital twins and machine learning to synthesize models for manufacturing ⧫ In addition, the system monitors the production resources in real-time, ensuring that all required resources are available, and allowing fast re-planning when necessary.

These intelligent digital twins enhance advanced planning systems with machine learning and data analytics not only to predict the values of uncertain parameters based on data available from the shop and from external sources, but also to enhance the accuracy of the decision models by learning the behavior of a constantly changing shop floor, and to estimate conditions outside normality that could cause problems.

Following recent trends, we can undoubtedly predict that future manufacturing systems will be reconfigurable with hyper-dynamic workers, and highly digitalized. Consequently, ASSISTANT focuses on factories with production processes, and assembly lines where multiple variants of a product are processed and where digital twins already partially exist. Such production systems are composed of multiple stations, where the resources (workers/robots/tools) can move from one station to another.

Fig. 1. Set of interrelated twins for decision aid

The following four intelligent twins will be developed:

• The process planning twin helps the process engineer to design a manufacturing system that can be re-configured while maintaining a high level of product/process quality and without escalating costs. This twin computes the possible process plans, and it selects the possible configuration of the shop floor. The goal is to design a factory with enough configurations to deal with the various scenarios of future demands.

• The production planning twin helps the production manager to operate an agile factory. This intelligent twin adjusts the production capacity to the demand (specify the shop floor configuration, set the worker requirement, subcontract labor, set number of functional assembly lines), and place the orders of components to the suppliers. This twin accounts for various sources of uncertainties such as demand, production defects, process durations, etc. • The scheduling twin allocates the operations to the resource, and it assigns a precise moment to perform each operation. • The real-time control twin executes the plans on the manufacturing systems in real-time. This intelligent twin detects anomalies in the process and products, and it updates the parameters of the equipment when required. In addition, the twin detects deviations from the plan and triggers feedback to other twins to update the plans when required. Finally, machine learning tools detect situations that are dangerous for humans and modify the behavior of automated resources when required.

The twins will be data driven, self-adaptive, autonomous, collaborative, explainable, safe, and reactive. (a) The information processing and predictive analytics step learn the values of future parameters (e.g., process durations) using machine learning techniques (e.g., random forest, neural networks). In addition, the user might want to hedge against the uncertainty of some parameters (e.g., demand), and machine learning is used to describe (e.g., as a probability distribution) the uncertainty of these stochastic parameters. Once learned, the values (or the uncertainty description) of these parameters are updated in the domain models. Finally, this step produces useful predictions on the state of the factory (or production line or machine), such as the feasibility and safety (e.g., no robot collisions) of an assembly plan.

(b) The prescriptive reasoning/decision-making is based on an optimization model (e.g., constraint programming) tailored for the decision to be made. The models are automatically built from the data available in the real-time image of the factory, and they automatically adapt to changes in the factory. Consequently, the model is data-driven. To produce explainable decision proposals, the tool will generate a visualization of the prescriptive analytics model. The visualization also allows a non-expert to modify the models. From this model, AI Approaches (genetic algorithms, learning variable neighborhood search, ...), stochastic and robust optimization suggest appropriate decisions in an uncertain environment. The user analyzes the solutions thanks to a graphical representation of the search spaces, and they can filter solutions by tightening the targeted KPI values. The decisions are validated through simulation on the virtual replicate of the shop floor. While the simulation is negative, the models are iteratively enriched through model acquisition techniques to learn to better represent the reality of the shop floor.

(c) The reactive decision-making step designs the policies to implement to react to unforeseen events in a timely manner. These policies can be simple decision rules easily understandable by the human planner, fast re-optimization techniques based on the model designed in step (b), or black box neural network policies.

(3) Actuation: Once the plan is validated by the decisionmaker, the real-time-control module runs the operations of the production schedule and tunes the machines to make sure all parameters stay within predefined intervals. Thanks to continuous monitoring, in case of unexpected events, the actuation modifies the schedule in real-time, and it triggers alerts when the situation cannot be adjusted automatically.

During actuation, the real-time digital twin communicates with workers thanks to wearable devices. Finally, the actuation monitors some data that are inputs to production planning and production system reconfiguration, and it updates their values in the digital image of the factory.

METHODOLOGY

Our methodology is divided into three pillars: research, development, validation.

At the research level (pilar 1) we aim to:

(a) Engage the optimization, machine learning, and manufacturing scientific communities in a dialogue to address key issues of robust, adaptable, and reconfigurable manufacturing systems.

(b) Exploit both statistical and symbolic machine learning approaches. While learning neural networks are critical for finding correlations in data and come up with predictions (finding trends in a stream, doing predictive maintenance), combining machine learning and discrete optimization is crucial to acquire optimization models (from various data sources) that can be directly executed by solvers (e.g. CP solver, MIP solver) to generate production plans.

(c) Go from ad hoc optimization software to synthesized optimization software. Currently, most production scheduling models are developed manually for each type of production environment under some fixed assumptions. Such an approach is not feasible in a reconfigurable environment, and we aim to synthesize the production of models and software components directly from the acquired data.

(d) Use the digital twins as intelligible oracles for machine learning. Digital twins give engineers the ability to check the feasibility of their decisions. A key idea of the ASSISTANT project is to enable the communication between digital twins and machine learning to speed up the convergence of learning algorithms toward operational executable models.

(e) Provide explanation-based interaction with humans. Both digital twins and machine learning should be able to put humans in the loop by providing human interpretable concise explanations.

At the development level (pilar 2), we aim to design a multilevel distributed architecture that goes from the specification of the manufacturing/assembly system down to the execution of a production plan, which organizes the circulation of data to allow machine learning and to provide a feedback loop where humans can interfere.

The validation and application level pilar aims (a) to demonstrate the feasibility and relevance of the approach in relation to three concrete industrial case; (b) Provide a toolbox of components for possible use cases outside of the project.

For the project use cases, the entire ASSISTANT chain will be implemented, starting with data collection, followed by the enrichment of digital twin and leads to the different AIbased decision support tools for process design, production planning/scheduling, and real-time control in manufacturing systems. These decision-support tools (demonstrators) will be tested based on (anonymized) data from "real" business environments within the ASSISTANT framework as standalone prototypes. After successful validation of demonstrators, the use cases, system/process specifications, system architecture, concepts, methods, techniques, algorithms, and software code will be available for ASSISTANT consortium partners. They may be incorporated into the studied manufacturing systems for application in daily operations by the consortium partners at their own cost.

Most of the results of the project will be disseminated as large as possible in the industry to increase the number of their users. This novel methodology will be validated on a significant panel of use cases selected for their relevance in the current context of the digital transformation of production in major manufacturing sectors undergoing rapid transformations like energy (Siemens), industrial equipment (Atlas Copco), and automotive sectors (Stellantis) which already make extensive use of digital twins. 11 academic and industrial partners are involved, see Fig. 3. The expected progress beyond state of the art and innovation potential is as follows:

Recent AI innovations were mostly developed by the GAFA to provide on-line customized software services for the masses. Such early adopters of AI have large business models focusing on fast growth. As AI is their core activity, they have acquired a digital maturity. Moreover, the GAFAs have natural and direct access to a huge amount of data through online services. Manufacturing is focused on the core business with dedicated optimization software to address key problems they face (safety, quality, costs, service level), which must have a rapid return on investment, even if operating at smaller scales. Transferring the GAFA AI approach to manufacturing is a real challenge.

The conception of ASSISTANT's intelligent digital twin network requires the following several breakthroughs:

• The use of machine learning for high level manufacturing decisions.

• Acquire robust optimization parameterized models by combining machine learning and digital twins

• Machine learning to learn digital twin's parameters, and inclusion of uncertainties in domain models.

• The extension of generative design to all manufacturing decisions.

• Data driven optimization to design reconfigurable production systems.

• Optimization under uncertainties for production planning.

• Develop tools for human interaction with AI systems and safe human-robot collaboration.

ASSISTANT contributes to the use of AI methods for highlevel manufacturing decisions such as process and production planning. The resulting approaches will provide the right level of reconfigurability since they hedge against uncertainties well characterized through machine learning.

Unlike most existing black-box AI methods, ASSISTANT relies on the acquisition of explainable models. The main line of researches in ASSISTANT are summarized below.

A real-time picture of the system thanks to the data-fabric. To overcome the challenge of data acquisition, data cleaning, data confidentiality, it is provided a standardized data fabric, building on technology-neutral and highly efficient communication patterns, supports instrumentation and integration of diverse legacy systems, as well as the use of distributed (edge/fog/cloud) computing resources for analysis and training on data. Building on the data fabric and the interconnected digital twins, we will adapt the framework to the manufacturing context, and contribute to improving the design and operation of production systems.

Acquire robust optimization parameterized models by combining machine learning and digital twins. The innovation potential of ASSISTANT intends to advance the state-of-the-art of model acquisition. First, we will provide methods to learn parameterized models that can be transposed to several situations, i.e. a model should not become invalid if we add/remove a machine. In the context of production scheduling, typical parameters are the numbers/type/capacity/speed of machines, task characteristics, cost matrices, and dependency graphs. Second, we will initiate a line of research where model acquisition interacts smoothly with digital twins to converge to more realistic models. Discrete optimization models have a strong structure that allows one to acquire models from a restricted set of samples [START_REF] Beldiceanu | Using finite transducers for describing and synthesising structural time-series constraints[END_REF]. To get more realistic models we aim to integrate digital twins in the learning process in a smart way: rather than considering the huge amount of simulated data generated by a digital twin, we plan to use active learning [START_REF] Bessière | Constraint acquisition[END_REF] to query in a focused way digital twins to converge to more robust models.

Machine learning to learn digital twin's parameters and inclusion of uncertainties in domain models. Contrarily, to existing digital twins who use ML mostly for specific prediction tasks, we will enhance digital twins with machine learning for prescriptive analytics in manufacturing to make decisions leading to flexible and robust manufacturing decisions in the design, operation, and real-time stages. We will use human-understandable AI, by incorporating whitebox modeling techniques in domain modeling, including behavioral models of schedules, machine behavior. Another aspect is that these domain models and AI algorithms need to explicitly take into consideration uncertainties. We will extend domain modeling languages to support the modeling of uncertainties. In addition, we will use digital twins to produce more realistic explainable decision aid models based on reinforcement learning (e.g. learn physical restrictions) that optimize the full process globally.

The extension of generative design to all manufacturing decisions. To the best of our knowledge, generative design has only been used to design products and production systems. We will extend the use of generative design to production planning and scheduling, allowing the user to specify targeted KPI, while the software returns solutions within a specified range.

Data-driven optimization to design reconfigurable production systems. ASSISTANT will provide a method to design reconfigurable production lines with the right level of reconfigurability, while maintaining the required product/process quality. This will be achieved by using data analytics techniques to build the uncertainty sets, and by using machine learning to learn the implication of a process plan on the quality of the products and process.

Optimization under uncertainties for production planning. On the one hand, the discrepancy between the data used by the system and what is happening in the company leads to poor decisions from APS. In ASSISTANT, the intelligent digital twins make decisions based on real-time data. On the other hand, historical data analyzed with machine learning techniques allow one to get predictions about the values of various unknown parameters. We aim to integrate the resulting characterization of the uncertainties of such parameters to create plans that are robust to the uncertainties. Finally, to be aligned with the requirements and changes of the shop floor ASSISTANT aims to develop a tool to automatically build the scheduling models based on data.

Develop tools for human interaction with AI systems and safe human-robot collaboration. ASSISTANT aims to improve the support of the human operator both in the context of indirect and direct interaction with robots: i) we aim at more reliable human recognition than previous applications, extracting the human's status and intentions in order to adapt to their behavior, entering a ii) direct interaction state when required: wearable devices, such as AR glasses or smart watches will provide a set of sensors giving input data for deep learning methods to extract models of humans' status and intentions. ASSISTANT will comply fully with ethics requirements concerning human safety in collaborative manufacturing systems.

CONCLUSION

This paper presents the main object and the concept of the research project ASSISTANT. The project started in November 2020, and it will last three years. The innovative ideas of ASSISTANT aim to provide decision-makers with generative design-based software for the entire cycle of manufacturing decisions from process selection, production system design to planning, scheduling, and real-time control. Rather than writing ad hoc code, it provides a set of intelligent digital twins that self-adapt to the manufacturing environment. ASSISTANT promotes a methodology that enhances the generative design with learning aspects of AI from the data available in manufacturing. The project aims to synthesize predictive/prescriptive models adjusted to the shop floor for each decision level. Digital twins will be used as oracles by ML to converge towards models in phase with reality. In this context, ML is used to predict parameter values, characterize parameter uncertainty, and acquire physical constraints.

Fig. 2

 2 Fig. 2 illustrates the concept of ASSISTANT. The architecture of the intelligent digital twin networks follows the classical three-step framework (perception, reasoning, actuation).

Fig 2 .

 2 Fig 2. Concepts of ASSISTANT (2) Reasoning: The reasoning step has three sub-steps:

Fig

  Fig. 3 Project consortium 6. DISCUSSION

ACKNOWLEDGEMENTS:

This project is funded by the European Commission, under grant agreement number 101000165, H2020 -ICT-38-2020, Artificial intelligence for manufacturing. The authors thank also all academic and industrial participants in the project.


Mireille Chevallet

Sylvie Luche

Hélène Diemer

Jean-Marc Strub

Alain Van Dorsselaer

Thierry Rabilloud
email: thierry.rabilloud@cea.fr

Sweet silver: a formaldehyde-free silver staining using aldoses as developing agents, with enhanced compatibility with mass spectrometry

Protein detection methods after electrophoresis have to be sensitive, homogeneous, and not to impair downstream analysis of proteins by mass spectrometry. Speed, low cost and userfriendliness are also favored features. Silver staining combines many of these features, but its compatibility with mass spectrometry is limited. We describe here a new variant of silver staining that is completely formaldehyde-free. Reducing sugars in alkaline borate buffer are used as developers. While keeping the benefits of silver staining, this method is shown to afford a much better performance in terms of compatibility with silver staining, both in peptide mass fingerprinting by MALDI and in LC/ESI/MS/MS. .

Introduction

Protein detection is still a key step for the proteomics analysis of proteins separated on mono-or bidimensional gels. Besides obvious constraints such as sensitivity, homogeneity from one protein to another and linearity throughout a wide dynamic range, suitable features include compatibility with digestion and mass spectrometry, speed, convenience and low.

Up to now, no protein detection method matches perfectly these prerequisites, in the three families of methods that are of current use in proteomics.

Detection with organic dyes is simple, cheap and rather linear, shows an adequate compatibility with mass spectrometry, but its lacks sensitivity. In its most widely-used version, namely Colloidal Coomassie Blue [START_REF] Neuhoff | Improved staining of proteins in polyacrylamide gels including isoelectric focusing gels with clear background at nanogram sensitivity using Coomassie Brilliant Blue G-250 and R-250[END_REF], it requires long staining times for optimal sensitivity. However, faster protocols in this category are available, such as the dye pair staining technique [START_REF] Choi | Background-free, fast protein staining in sodium dodecyl sulfate polyacrylamide gel using counterion dyes, zincon and ethyl violet[END_REF], but the sensitivity remains moreless the same.

Fluorescent detection methods, on their side, show a convenient linearity and an adequate sensitivity, although the latter varies from the one of colloidal Coomassie, such as sypro orange [START_REF] Steinberg | SYPRO orange and SYPRO red protein gel stains: one-step fluorescent staining of denaturing gels for detection of nanogram levels of protein[END_REF] to the one of silver (sypro ruby, deep purple) [START_REF] Berggren | Background-free, high sensitivity staining of proteins in one-and two-dimensional sodium dodecyl sulfatepolyacrylamide gels using a luminescent ruthenium complex[END_REF], [START_REF] Mackintosh | A fluorescent natural product for ultra sensitive detection of proteins in one-dimensional and two-dimensional gel electrophoresis[END_REF] . Although superior to the one of silver staining, their compatibility with mass spectrometry does not always equal the one of Coomassie Blue, and this has been unfortunately shown to be the case for Sypro Ruby [START_REF] Rabilloud | A comparison between Sypro Ruby and ruthenium II tris (bathophenanthroline disulfonate) as fluorescent stains for protein detection in gels[END_REF] and Deep Purple [START_REF] Chevalier | Different impact of staining procedures using visible stains and fluorescent dyes for large-scale investigation of proteomes by MALDI-TOF mass spectrometry[END_REF], i.e. the most sensitive variants.

In addition to this drawback, optimal sensitivity requires rather long staining times, and the cost of the commercial reagents can become a concern when large series of gels are to be produced. Within this category, the covalent labeling used in DIGE is slightly different [START_REF] Unlu | Difference gel electrophoresis: a single gel method for detecting changes in protein extracts[END_REF], in the sense that the sensitivity in detection is reached by exploiting the very low fluorescent noise, thereby allowing to use the very high signal to noise ratio to achieve sensitive detection through a highperformance hardware. However, the absolute level of signal is very low in this technique, so that protein excision for identification by MS is made on a more heavily loaded gel stained with noncovalent fluorescent probes. Furthermore, spot excision after fluorescent staining is usually carried out on a UV table, with the associated safety problems.

The last family of protein detection methods consists of silver staining [START_REF] Chevallet | Silver staining of proteins in polyacrylamide gels[END_REF]. This method is sensitive, but labor-intensive, and its linearity is limited. However, in the proteomics frame, the most important problem lies in its limited compatibility with mass spectrometry. Although this feature has been improved by destaining of the spots or bands after silver staining [START_REF] Gharahdaghi | Mass spectrometric identification of proteins from silver-stained polyacrylamide gel: a method for the removal of silver ions to enhance sensitivity[END_REF], or by the use of silver-ammonia methods [START_REF] Chevallet | Improved mass spectrometry compatibility is afforded by ammoniacal silver staining[END_REF], the compatibility with mass spectrometry remains far below what can be achieved with fluorescent probes or colloidal Coomassie [START_REF] Winkler | Silver-and Coomassie-staining protocols: detection limits and compatibility with ESI MS[END_REF]. This low compatibility has been attributed to the use of formaldehyde [START_REF] Richert | About the mechanism of interference of silver staining with peptide mass spectrometry[END_REF], which also induces artefactual formylations. [START_REF] Osés-Prieto | Formation of epsilon-formyllysine on silverstained proteins: implications for assignment of isobaric dimethylation sites by tandem mass spectrometry[END_REF]. It would be therefore of great interest to have in hands a sensitive silver staining method totally formaldehyde-free. However, most silver reducers used in silver reduction (e.g. hydroquinone) [START_REF] Merril | Silver stains for proteins in gels[END_REF] also show an important ability to induce protein crosslinks. To date, the only formaldehyde-free silver staining method uses carbohydrazide as the reducing agent [START_REF] Richert | About the mechanism of interference of silver staining with peptide mass spectrometry[END_REF]. While this results in improved compatibility with mass spectrometry, the staining performances are inadequate, both in sensitivity and homogeneity.

We describe here a new family of silver staining protocols, using reducing sugars in alkaline borate buffer as developing agents. These methods combine the classical sensitivity and staining homogeneity of classical silver staining methods, while showing a much improved compatibility with mass spectrometry.

Material and methods

Samples

Molecular weight markers (broad range, Bio-Rad) were diluted down to 10 ng/µl for each band in SDS buffer (Tris-HCl 125mM pH 7.5, containing 2% (w/v) SDS, 5% (v/v) thioglycerol, 20% (v/v) glycerol and 0.005% (w/v) bromophenol blue). The diluted solution was heated in boiling water for 5 minutes. A tenfold dilution in SDS buffer was performed to get a 1ng/µl per protein dilution . J774 cells (mouse macrophage) and WEHI274 cells (mouse monocytes) cells were grown in spinner flasks in DMEM + 5% fetal calf serum up to a density of 1 million cells /ml. The cells were collected by centrifugation (1000g 5 minutes), washed in isotonic wash buffer (10mM Tris HCl pH 7.5, 1mM EDTA and 250mM sucrose) and centrifuged at 100g for 5 minutes . The final pellet was suspended in its volume of isotonic wash buffer, transferred in an ultracentrifuge tube, and 4 volumes of concentrated lysis solution (8.75M urea, 2.5M thiourea, 5% CHAPS, 50mM DTT and 25mM spermine base) were added. After lysis at room temperature for 30 minutes, the viscous lystae was centrifuged at 200,000g for 1 hour at room temperature. The supernatant was collected, the protein concentration was estimated and the solution was made 0.4% (w/v) in carrier ampholytes (Pharmalyte 3-10). The solution was stored frozen at -20°C until use 2.2 Electrophoresis 2.2.1. SDS electrophoresis 10%T gels (160x200x1.5 mm) were used for protein separation. The Tris taurine buffer system was used [START_REF] Tastet | A versatile electrophoresis system for the analysis of high-and low-molecular-weight proteins[END_REF], operated at a ionic strength of 0.1 and a pH of 7.9. The final gel composition is thus Tris 180mM, HCl 100 mM, acrylamide 10% (w/v), bisacrylamide 0.27%. The upper electrode buffer is Tris 50mM, Taurine 200mM, SDS 0.1%. The lower electrode buffer is Tris 50mM, glycine 200mM, SDS 0.1%.

For 1D SDS gels, a 4% stacking gel in Tris 125mM, HCl 100mM was used. No stacking gel was used for 2D electrophoresis.

The gels were run at 25V for 1hour, then 12.5W per gel until the dye front has reached the bottom of the gel.

IEF

Home made 160mm long 4-8 or 3-10.5 linear pH gradient gels were cast according to published procedures [START_REF] Rabilloud | Sample application by in-gel rehydration improves the resolution of two-dimensional electrophoresis with immobilized pH gradients in the first dimension[END_REF]. Four mm-wide strips were cut, and rehydrated overnight with the sample, diluted in a final volume of 0.6ml of rehydration solution (7M urea, 2M thiourea, 4% CHAPS and 100mM dithiodiethanol [START_REF] Rabilloud | Improvement of the solubilization of proteins in two-dimensional electrophoresis with immobilized pH gradients[END_REF], [START_REF] Luche | About thiol derivatization and resolution of basic proteins in two-dimensional electrophoresis[END_REF]).

The strips were then placed in a multiphor plate, and IEF was carried out with the following electrical parameters 100V for 1 hour, then 300V for 3 hours, then 1000V for 1 hour, then 3400 V up to 60-70 kVh.

After IEF, the gels were equilibrated for 20 minutes in Tris 125mM, HCl 100mM, SDS 2.5%, glycerol 30% and urea 6M. They were then transferred on top of the SDS gels and sealed in place with 1% agarose dissolved in Tris 125mM, HCl 100mM, SDS 0.4% and 0.005% (w/v) bromophenol blue. Electrophoresis was carried out as described above.

Detection on gels

Colloidal coomassie blue staining was performed according to the published method [START_REF] Neuhoff | Improved staining of proteins in polyacrylamide gels including isoelectric focusing gels with clear background at nanogram sensitivity using Coomassie Brilliant Blue G-250 and R-250[END_REF].

Fluorescent staining was carried out with a ruthenium complex [START_REF] Rabilloud | A comparison between Sypro Ruby and ruthenium II tris (bathophenanthroline disulfonate) as fluorescent stains for protein detection in gels[END_REF] with the improved protocol previously described [START_REF] Lamanda | Improved Ruthenium II tris (bathophenantroline disulfonate) staining and destaining protocol for a better signal-tobackground ratio and improved baseline resolution[END_REF].

The classical silver staining methods used were an ultrafast method [START_REF] Shevchenko | Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels[END_REF], a silver-ammonia method [START_REF] Chevallet | Improved mass spectrometry compatibility is afforded by ammoniacal silver staining[END_REF] and a classical silver nitrate staining [START_REF] Rabilloud | A comparison between low background silver diammine and silver nitrate protein stains[END_REF] The new staining methods were based on the fast silver nitrate method, and all the steps up to silver impregnation were kept constant (see table 1). Only the developing bath was changed.

Various reducing sugars (hexoses or pentoses) were tested at concentrations carrying from 15 to 150 mM. As development proceeds only under alkaline conditions, various alkaline buffers were tested, including sodium carbonate, sodium borate, sodium phosphate and sodium hydroxide, at pH ranging from 11 to 12.5.

The stop bath was the Tris-acetate buffer used in the silver nitrate method.

Image analysis

The gel images, acquired on an Agfa DuoScan T1200 at 300ppi resolution and grayscale mode, were converted to the TIFF format, and then analyzed with the delta 2D (v 3.5) software (Decodon, Germany). The default detection parameters calculated by the software were used and no manual edition of the spots was performed. For fluorescent stain, spot excision was performed on a UV table operating at 302nm. The spots were collected in microtiter plates. The spots coming from gels stained with organic compounds (dyes or fluorophores) were not destained prior to acetonitrile washing. The spots coming from silver-stained gels were destained with the ferricyanide-thiosulfate protocol [START_REF] Gharahdaghi | Mass spectrometric identification of proteins from silver-stained polyacrylamide gel: a method for the removal of silver ions to enhance sensitivity[END_REF]. The solvent was then removed and the spots were stored at -20°C until use.

In gel digestion :

In gel digestion was performed with an automated protein digestion system, MassPrep Station (Waters Corp., Milford, USA). The gel plugs were washed twice with 50 µL of 25 mM ammonium hydrogen carbonate (NH4HCO3) and 50 µL of acetonitrile. The cysteine residue were reduced by 50 µL of 10 mM dithiothreitol at 57°C and alkylated by 50 µL of 55 mM iodoacetamide. After dehydration with acetonitrile, the proteins were cleaved in gel with 10 µL of 12.5 ng/µL of modified porcine trypsin (Promega, Madison, WI, USA) in 25 mM NH4HCO3.

The digestion was performed overnight at room temperature. The generated peptides were extracted with 60% acetonitrile in 5% acid formic.

MALDI-TOF-MS analysis

MALDI-TOF mass measurements were carried out on UltraflexTM TOF/TOF (Bruker Daltonik GmbH, Bremen, Germany). This instrument was used at a maximum accelerating potential of 25kV in positive mode and was operated in reflectron mode. The samples were prepared by standard dried droplet preparation on stainless steel MALDI targets using alpha-cyano-4hydroxycinnamic acid as matrix.

The external calibration of MALDI mass spectra was carried out using singly charged monoisotopic peaks of a mixture of bradykinin 1-7 (m/z=757.400), human angiotensin II (m/z=1046.542), human angiotensin I (m/z=1296.685), substance P (m/z=1347.735), bombesin (m/z=1619.822), renin (m/z=1758.933), ACTH 1-17 (m/z=2093.087) and ACTH 18-39 (m/z=2465.199). To achieve mass accuracy, internal calibration was performed with tryptic peptides coming from autolysis of trypsin, with respectively monoisotopic masses at m/z = 842.510, m/z = 1045.564 and m/z = 2211.105. Monoisotopic peptide masses were automatically annotated using Flexanalysis 2.4 software. Peaks are automatically collected with a signal to noise ratio above 4 and a peak quality index greater than 30.

LC-MS/MS

Nano-LC-MS/MS analysis was performed either using a nanoAcquity UPLC TM system (Waters Corp., Milford, USA), coupled to a Synapt HDMS TM mass spectrometer (Waters Corp., Milford, USA).

From each sample, 4.5 µL was loaded on a precolumn (Waters, C18, 5µm, 180 µm id, 20 mm length), before chromatographic separation on a C18 column (Waters, C18, 1.7 µm,75 mm id, 200 mm length). The gradient was generated at a flow rate of 400 nL/min. The gradient profile consisted of a linear one from 99% of a water solution acidified by 0.1% HCOOH vol/vol (solution A), to 50% of a solution of CH3CN acidified by 0.1% HCOOH vol/vol (solution B) in 35 min, followed by a second gradient ramp to 90% of B in 1 min. Data acquisition was piloted by MassLynx software V4.1. Calibration was performed using adducts of 0.1% phosphoric acid (Acros, NJ, USA) with a scan range from m/z 50 to 2000. Automatic switching between MS and MS/MS modes was used. The internal parameters of the Synapt HDMS TM were set as follows.

The electrospray capillary voltage was set to 3.2 kV, the cone voltage set to 35 V, and the source temperature set to 80°C. The MS survey scan was m/z 250-1500 with a scan time of 1 s. When the peak intensity of 2+, 3+ or 4+ peptide ions rose above a threshold of 12 counts/s, tandem mass spectra were acquired. The scan range for MS/MS acquisition was from m/z 50 to 2000 with a scan time of 1. Fragmentation was performed using argon as the collision gas and with a collision energy profile optimized for various mass ranges of precursor ions. Data processing was done automatically with the ProteinLynx Global server V.2.3 (Waters Corp., Milford, USA).

MS and MS/MS Data analysis

The MASCOT search algorithm (Version 2.2.04, Matrix Science, London, UK) [START_REF] Perkins | Probability-based protein identification by searching sequence databases using mass spectrometry data[END_REF] was used for protein identification against the Swiss-Prot database (55.1). All proteins present in the database were used without any pI and Mr restrictions. A maximum number of one missed cleavage by trypsin was allowed, and carbamidomethylated cysteine and oxidized methionine were set as variable modifications. For the peptide mass fingerprint, the peptide mass error was limited to 50 ppm. For MS/MS ion search, only doubly and triply peptides were searched. The peptide tolerance was typically set to 50 ppm and MS/MS tolerance was set to 0.1 Da.

Results and discussion

The first step of this study was to build an efficient stain. The basic requisite was to eliminate completely the formaldehyde, and to replace it in the stain development step by another reducer.

However, it was known from prior work that inorganic reducers [START_REF] Heukeshoven | Simplified method for silver staining of proteins in polyacrylamide gels and the mechanism of silver staining[END_REF] and many organic ones [START_REF] Richert | About the mechanism of interference of silver staining with peptide mass spectrometry[END_REF] do not lead to any practical staining. Thus, the choice was restricted to aldehydes However, despite the use of glutaraldehyde in some silver staining protocols [START_REF] Porro | Ultrasensitive silver-stain method for the detection of proteins in polyacrylamide gels and immunoprecipitates on agarose gels[END_REF], this class of chemicals have not been extensively tested. While bifunctional aldehydes were ruled out from their wellknown protein crosslinking behavior, most aliphatic aldehydes either did not give any practical stain or were not soluble enough in water. The only exception to these rules seemed to be aldoses,, i.e. hydroxy aldehydes, which are fairly soluble in water and have a chemical reactivity which is different from the one of aliphatic aldehydes. The use of glucose has been metioned briefly in the literature [START_REF] Heukeshoven | Neue Ergebnisse der Silberfaerbung von proteinen : Untersuchungen zum Mechanismus und Einfluss einiger Parameter auf die Effektivitaet der Silberfaerbung[END_REF], but this idea had to be brought to a stage usable in proteomics. Initial tests made by SDS electrophoresis of molecular weight markers showed us that a practical staining was obtained only if both the concentration of the sugar was in the 100mM range (2% w/v), which is almost 10 times the concentration of formaldehyde in a classical silver stain developer, and if at the same time the pH of the developer was increased to 12 or over. As carbonate solutions do not reach easily such values, we tested other basic solutions, namely sodium hydroxide, and phosphate or borate buffer. As shown on figure 1, a weak staining was obtained with sodium hydroxide (center panel) while phosphate gave absolutely no stain and borate gave the best result, probably because of its well-known binding to the sugar diols structures.

Further corroborating this view of a specific buffer effect, we could not devise any stain using both silver ammonia as the silvering agent and a sugar in the developing agent (data not shown).

This borate-specific effect led us to investigate if some sugars would perform better than others in such a protocol. To this purpose, we tested several hexoses and pentoses. We also increased the pH of the developer up to 12.7 to increase development speed and sensitivity, and the results are shown on figure 2. Except mannose, which did not give any staining (data not shown) all the other aldoses tested gave a positive staining. On the whole, pentoses were more efficient than hexoses, but this may be linked to their greater molar concentration at equal weight. This led us to the final protocol for formaldehyde free silver staining, shown on table 1.

As this stain is intended to be used for proteomics analysis, its reproducibility needed to be verified. To this purpose, an identical sample (200µg of J774 total cell extract was loaded on 6 different 2D gels (immobilized pH gradient pH 4-8). Three were stained by the classical silver staining, and three by the new silver-galactose-borate stain. The resulting images were then analyzed by the Delta 2D software. Approximately 1500 spots were detected on the gels (shown on supplemental figure 1) , independently of the stain used, indicating at the same time i) a close sensitivity between the new stain and the control one, and ii) a good reproducibility of each staining method. The relative standard deviation (i.e. the standard deviation/ mean volume, expressed in percentile) was calculated for each spot. The median rsd (i.e. the value for which half of the spots have a lower rsd and half a higher one) was 16% for the control silver staining and 11% for the silver-galactose-borate stain. This comparable median rsd further documents the reproducibility of the new stain as at least as good as the one of the control stain.

Finally, the practicability of the whole method was assessed by comparison between three variants of the silver-aldose staining with three classical variants of silver staining (ultrafast, standard silver nitrate and silver ammonia) as well as with colloidal Coomassie staining and fluorescent staining (Figure 3) . Homologous spots were excised on each gel and analyzed by mass spectrometry. The results are shown on table 2, from which several trends can be drawn::

(i) as expected, the overall compatibility with mass spectrometry of formaldehyde-free silver staining methods is higher than the one of classical ones, especially in the high molecular weight range.

(ii) among the aldose-silver staining variants, galactose shows the best compromise between staining performances and performances in subsequent MS analysis (iii) the high performance silver staining methods, and especially the improved ones, allow to visualize and analyze spots that escape detection by colloidal Coomassie or fluorescence and even by medium sensitivity silver staining [START_REF] Shevchenko | Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels[END_REF] (e.g. vinculin, plastin or importin).

These results can be seen directly on the mass spectra, as shown on figure 4.

We also checked that the better performances of the sugar developers in terms of mass spectrometry compatibility were not restricted to MALDI mass spectrometry. To this purpose, two spots (vinculin and malate dehydrogenase) were analyzed by LC-ESI/MS/MS, and the summarized results are reported on Table 3. It can be seen that the better MS compatibility of sugar developers applies also with this mass spectrometry method, so that the improvement over formaldehyde developer is likely to take place at the digestion/peptide extraction step.

Finally, we checked the overall performance of the new stain on a wide pH range and at a lower protein load. The results, shown on figure 5, demonstrate that the silver-aldose stain is slightly less sensitive than the classical silver staining, but that there is no gross difference over the complete pH range between the two stains.

Although the better compatibility with silver staining undoubtedly supports a better yield of unmodified peptides, as those ones only are counted positive by our criteria, it cannot be ruled out that the aldoses, being aldehydes, can modify some reactive groups in the proteins, especially the side chain amino group of lysines. However, the missed cleavage at this site, plus the mass of the glucide, are likely to produce heavy peptides that will not easily show up in mass spectrometry. However, if such peptides are detected, this artefactual modification could be mistaken for a glycation, a modification associated with aging [START_REF] Suji | Glucose, glycation and aging[END_REF], [START_REF] Ulrich | Protein glycation, diabetes, and aging[END_REF]. However, glycation has been described to date only with glucose. This is why we tested pentoses as developing agents, as these sugars will induce a completely unnatural modification. Moreover, if a natural modification of the same mass is expected, performing a duplicate experiment where one gel is developed with a hexose and the other gel with a pentose would discriminate between natural and artefactual modifications.

On the point of view of peptide extraction, the combination of a very short fixation, as in the Shevchenko's method, with this sugar-borate developer would probably further enhance peptide recovery. However, for reasons that remain unclear, we could not make a practically usable stain combining both features.

Concluding remarks

We believe that the sugar-borate developer brings silver staining to a happy compromise for proteomics. It keeps the advantages of visible methods, namely the absence of requirement of costly hardware (required for fluorescence). it affords a better sensitivity than colloidal Coomassie, fluorescence and silver staining methods really optimized for downstream mass spectrometry (e.g. the Shevchenko's method). Although the sensitivity of this silver staining method is slightly inferior to the one of the best silver staining methods, this is outweighed by the superior mass spectrometry compatibility, and by the absence of fomaldehyde-linked artifacts. It also allows for a better safety in the laboratory, as this method does not require either a UV table for spot excision (required by many fluorescent methods) nor the noxious formaldehyde chemical.

Acknowledgments.

The support from the Région Rhone Alpes by a grant (analytical chemistry subcall, priority research fields call 2003-2006) is gratefully acknowledged. The better extraction of peptides above 1500 Da, containing more sequence information, is obvious in this case with the silver-aldose methods

2. 5 .

 5 Mass spectrometry 2.5.1. Spot excision:

Figure 1 :Figure 2 Figure 3 :Figure 4 :

 1234 Figure 1: Initial tests of silver staining with aldoses Molecular weight markers (BioRad, broad range) were diluted serially in SDS buffer and separated by SDS electrophoresis. The corresponding gel was stained with standard (formaldehyde) silver staining (panel A), or with glucose as the developing agent in 3.5% potassium carbonate (panel B) or in 100mM boric acid/150mM NaOH (panel C). Protein separated: MYO: myosin (205kDa), GAL: beta galactosidase (116kDa), PHO: glycogen phosphorylase (97kDa) BSA: bovine serum albumin (67kDa), OVA: ovalbumin (46 kDa), CAR: carbonic anhydrase (30 kDa), STI: soybean trypsin inhibitor (21 kDa), LYS: lysozyme (14.5 kDa). Protein content per lane in each panel, from left to right: 100 ng/protein, 100 ng, 50 ng, 20 ng, 10 ng, 5 ng, 2 ng. 1 ng

Figure 5 :

 5 Figure 5: Evaluation of staining performances 75µg of proteins extracted from J774 murine macrophages were separated by 2D gel electrophoresis and detected by various methods. The IEF pH range was 3.5-10.5 linear, and the xecond dimension gel was a 10% continuous gel. A: detection by silver staining, formaldehyde developer. B: detection by silver-galactose-borate.

Table 1 :

 1 flowchart for silver staining

	Step	solution	Time
	Fixation	Ethanol 30% (v/v) Acetic acid 10% (v/v)	overnight
	Rinse	water	4x 10 minutes
	Sensitization	8mM sodium thiosulfate	1 minute
	Rinse	water	2x 1 minute
	Silvering	Silver nitrate 12mM	20-30 minutes
	Rinse	water	5-10 seconds
	development	2% (w/v) sugar, 100mM boric acid, 150mM	20-30 minutes
		NaOH, 50µM sodium thiosulfate
	Stop	Tris 40g/l, acetic acid 20 ml/l	30 minutes
	This table represents the optimized protocol for silver staining with aldoses. In the initial siolver
	staining, the developer contains 3.5% potassium carbonate, 50µM thiosulfate and 3mM
	formaldehyde

Table 2 :

 2 MS analysis of proteins stained by different methods Homologous spots excised from two-dimensional gels (4-8 linear pH gradients, 10% acrylamide) loaded with equal amounts of J774 proteins (200µg) and stained by various methods were digested, and the digests were analysed by MALDI mass spectrometry. The summary of the mass spectrometry data is in the form %coverage / Mascot score. The spot numbers refer to those shown on figure 3. CCB: colloidal -Coomassie blue staining. RuBPS: fluorescent staining with a ruthenium complex NI: not identified. ND: not detected

	Protein names: EF2: elongation factor 2; eIF: eukaryotic initiation factor; IPA: phosphoinositol
	phosphatase; LHCR2: Loss of heterozygosity 11 chromosomal region 2 gene A protein
	homolog ; MDH: malate dehydrohenase; PCTI: prolyl cis-trans isomerase; PDI: protein disulfide
	isomerase; PGK: phosphoglycerate kinase; PGM: phosphoglucomutase; RPSA: Ribosomal
	protein 40S subunit, protein SA;

Table 3 :

 3 summary of MS/MS results obtained on vinculin and malate dehydrogenase