2025Activity​​​‌ reportProject-TeamSAIRPICO

7.1.1 DCT2Net

• Name:‌
  Trained shallow CNN (convolution‌​‌ neural network)-based DCT (Discrete​​ Cosine Transform) denoiser
• Keywords:​​​‌
  Deep learning, Denoising, Convolutional‌ Neural Network, Deconvolution
• Functional‌​‌ Description:

  DCT2net software, based​​​‌ on the well-known DCT​ (Discrete Cosine Transform) image​‌ denoising algorithm, is dedicated​​ to noise removal from​​​‌ images. The traditional DCT​ denoiser can be seen​‌ as a shallow CNN​​ and thereby its original​​​‌ linear transform can be​ tuned through gradient descent​‌ in a supervised manner,​​ improving considerably its performance.​​​‌ Consequently, DCT2net is a​ shallow and interpretable convolution​‌ network, whose parameters optimization​​ allows to improve very​​​‌ significantly the performances of​ the traditional DCT denoiser.​‌ To deal with the​​ remaining artifacts induced by​​​‌ DCT2net, an original hybrid​ solution between DCT and​‌ DCT2net is proposed, combining​​ the best of what​​​‌ these two methods can​ offer. Experiments on artificially​‌ noisy images show that​​ the two-layer DCT2net method​​​‌ provides results comparable to​ the BM3D method and​‌ is as fast as​​ the DnCNN algorithm composed​​​‌ of more than a​ dozen of layers.

  Inter​‌ Deposit Digital Number: IDDN.FR.001.460033.000.S.P.2021.000.21000​​ 21

• URL:
  https://­team.­inria.­fr/­serpico/­software/­dct2net
• Publication:​​​‌
  hal-03511641
• Contact:
  Charles Kervrann​
• Participants:
  Sebastien Herbreteau, Charles​‌ Kervrann, Leo Maury

7.1.2​​ DeepFinder

• Name:
  Deep learning​​​‌ for macromolecule identification within​ 3D cellular cryo-electron tomograms​‌
• Keywords:
  Image analysis, Deep​​ learning, Cryo-electron microscopy, Object​​​‌ detection
• Functional Description:

  DeepFinder​ is a computational approach​‌ that uses artificial neural​​ networks to accurately and​​​‌ jointly localize multiple types​ and/or states of macromolecules​‌ in 3D cellular cryo-electron​​ tomograms. DeepFinder leverages deep​​​‌ learning and outperforms the​ commonly-used template matching method​‌ on ideal data. On​​ synthetic image data (SHREC​​​‌ 2019, 2020, and 2021​ challenges), DeepFinder is very​‌ fast and produces superior​​ detection results when compared​​​‌ to other competitive deep​ learning methods, especially on​‌ small macromolecules. On experimental​​ cryo-ET data depicting ribosomes,​​​‌ the detection results obtained​ by DeepFinder are consistent​‌ with expert annotations. We​​ have got a high​​​‌ overlap of detection (86%)​ and a similar structure​‌ resolution that those determined​​ by subtomogram averaging.

  Inter​​​‌ Deposit Digital Number: IDDN.FR.001.460030.000.S.P.2021.000.21000​

• URL:
  https://­github.­com/­deep-finder/­deep-finder
• Publication:
  hal-03509223​‌
• Contact:
  Emmanuel Moebel
• Participants:​​
  Arthur Masson, Mounir Messaoudi,​​​‌ Emmanuel Moebel, Charles Kervrann​
• Partners:
  Max Planck Institute​‌ Martinsried, Fondation Fourmentin-Guilbert, Helmholtz​​ Pioneer Campus, Université de​​​‌ Strasbourg

7.1.3 ExoDeepFinder

• Name:​
  A Deep learning method​‌ for exocytosis event detection​​ in fluorescence TIRF microscopy​​​‌ movies
• Keywords:
  Image analysis,​ Deep learning, Fluorescence microscopy,​‌ Live-cell microscopy, Anomaly detection​​
• Functional Description:
  ExoDeepFinder is​​​‌ a software for the​ detection of rare dynamic​‌ exocytosis events observed in​​ temporal series of 2D​​​‌ Total Internal Reflection Fluorescent​ Microscopy (TIRFM) images. This​‌ U-net, originally designed for​​ analyzing 3D cryo-electron tomography​​​‌ images (DeepFinder), achieved good​ absolute performances with a​‌ relatively small training dataset​​ of 60 cells/2000 events.​​​‌ ExoDeepFinder method uses hybrid​ annotations performed manually by​‌ experts (more than 10,​​ 000 spatiotemporal coordinates of​​​‌ annotated exocytosis events) and​ automatically annotated bright spots​‌ that are not bona​​ fide exocytosis events, with​​​‌ no data curation. By​ gathering the manual and​‌ automatic datasets, we significantly​​ boosted the performance in​​​‌ order to detect very​ rare events in the​‌ volumes (< 1 event​​ per frame in average),​​​‌ even if the automatic​ spot detector (ATLAS) potential​‌ produces annotation errors. ExoDeepFinder​​ outcompeted the unsupervised conventional​​ methods on a benchmark​​​‌ composed of several dozen‌ experimental movies of one‌​‌ thousand frames with variable​​ signal-to-background ratios, while exhibiting​​​‌ a greater plasticity to‌ the experimental conditions when‌​‌ tested under drug treatments​​ and after changes in​​​‌ cell line or imaged‌ reporter. This robustness to‌​‌ unseen experimental conditions did​​ not require re-training demonstrating​​​‌ generalization capability of ExoDeepFinder.‌ The algorithm, designed for‌​‌ large 2D+time volume processing,​​ takes about 30 seconds​​​‌ to process a video‌ of 300 x 300‌​‌ x 1000 voxels with​​ no parameter adjustment, as​​​‌ compared to 10 to‌ 20 minutes required with‌​‌ the two conventional image​​ analysis algorithms. The method,​​​‌ as well as the‌ annotated training datasets, were‌​‌ made transparent and available​​ through an open-source software​​​‌ as well as a‌ Napari plugin and can‌​‌ directly be applied to​​ custom user data.
• URL:​​​‌
  https://­github.­com/­deep-finder/­tirfm-deepfinder
• Publication:
  hal-04874728
• Contact:‌
  Arthur Masson
• Participants:
  Charles‌​‌ Kervrann, Arthur Masson, Hugo​​ Lachuer, Anne-Sophie Mace, Kristin​​​‌ Schauer, Emmanuel Moebel
• Partners:‌
  Institut Jacques Monod, Institut‌​‌ Gustave Roussy, UMR 144​​ CNRS - Institut Curie​​​‌

7.1.4 LIChI

• Name:
  Linear‌ and Iterative Combinations of‌​‌ patches for Image denoising​​
• Keywords:
  Image analysis, Denoising​​​‌
• Functional Description:
  In the‌ past decade, deep neural‌​‌ networks have revolutionized image​​ denoising in achieving significant​​​‌ accuracy improvements by learning‌ on datasets composed of‌​‌ noisy/clean image pairs. However,​​ this strategy is extremely​​​‌ dependent on training data‌ quality, which is a‌​‌ well-established weakness. To alleviate​​ the requirement to learn​​​‌ image priors externally, single‌ image (a.k.a., self-supervised or‌​‌ zero-shot) methods perform denoising​​ solely based the analysis​​​‌ of the input noisy‌ image without external dictionary‌​‌ or training dataset. This​​ work investigates the effectiveness​​​‌ of linear combinations of‌ patches for denoising under‌​‌ this constraint. Although conceptually​​ very simple, we show​​​‌ that linear combinations of‌ patches are enough to‌​‌ achieve state-of-the-art performance. The​​ proposed parametric approach relies​​​‌ on quadratic risk approximation‌ via multiple pilot images‌​‌ to guide the estimation​​ of the combination weights.​​​‌ Experiments on images corrupted‌ artificially with Gaussian noise‌​‌ as well as on​​ real-world noisy images demonstrate​​​‌ that our method is‌ on par with the‌​‌ very best single-image denoisers,​​ outperforming the recent neural​​​‌ network-based techniques, while being‌ much faster and fully‌​‌ interpretable.
• URL:
  https://­github.­com/­sherbret/­lichi
• Publication:​​
  hal-03894346
• Contact:
  Charles Kervrann​​​‌
• Participants:
  Sebastien Herbreteau, Charles‌ Kervrann
• Partner:
  Airbus Defense‌​‌ and Space

7.1.5 NL-Ridge​​

• Name:
  A unified framework​​​‌ of non-local parametricmethods for‌ image denoising
• Keywords:
  Image‌​‌ analysis, Denoising
• Functional Description:​​
  We propose a unified​​​‌ view of non-localmethods for‌ single-image denoising, for which‌​‌ BM3D is the most​​ popular representative, that operate​​​‌ by gathering noisy patches‌ together according to their‌​‌ similarities in order to​​ process them collaboratively. Our​​​‌ general estimation framework is‌ based on the minimization‌​‌ of the quadratic risk,​​ which is approximated in​​​‌ two steps, and adapts‌ to photon and electronic‌​‌ noises. Relying on unbiased​​ risk estimation (URE) for​​​‌ the first step and‌ on “internal adaptation”, a‌​‌ concept borrowed from deep​​ learning theory, for the​​​‌ second, we show that‌ our approach enables to‌​‌ reinterpret and reconcile previous​​​‌ state-of-the-art non-local methods. Within​ this framework, we propose​‌ a novel denoiser called​​ NL-Ridge that exploits linear​​​‌ combinations of patches. While​ conceptually simpler, we show​‌ that NL-Ridge can outperform​​ well-established state-of-the-art single-image denoisers.​​​‌
• URL:
  https://­github.­com/­sherbret/­NL-Ridge/
• Publication:
  hal-04472406​
• Contact:
  Charles Kervrann
• Participants:​‌
  Sebastien Herbreteau, Charles Kervrann​​
• Partner:
  Airbus Defense and​​​‌ Space

7.1.6 DeepCristae

• Name:​
  A CNN for the​‌ restoration of mitochondria cristae​​ in live microscopy images​​​‌
• Keywords:
  Image analysis, Deep​ learning, Deconvolution, Denoising, Live-cell​‌ microscopy, Fluorescence microscopy, Convolutional​​ Neural Network
• Functional Description:​​​‌
  DeepCristae is a CNN​ specifically developed to restore​‌ mitochondria cristae in low​​ spatial resolution microscopy images.​​​‌ The main specificities of​ the method are 1)​‌ a new training loss​​ dedicated to the restoration​​​‌ of specific pixels of​ interest, 2) a random​‌ image patch sampling focusing​​ on areas of mitochondria​​​‌ to increase the size​ of the training set,​‌ and 3) metrics for​​ objective assessment of cristae​​​‌ restoration. DeepCristae was applied​ to several microscopy modalities​‌ and different biological scenarios​​ capturing live mitochondria at​​​‌ high speed with low​ illumination and thus low​‌ phototoxicity. It allows long-term/fast​​ dynamic observation of cristae​​​‌ behavior and organization.
• URL:​
  https://­gitlab.­inria.­fr/­anbadoua/­DeepCristae
• Publication:
  hal-04295317
• Contact:​‌
  Anais Badoual
• Participants:
  Anais​​ Badoual, Ludovic Leconte, Cesar​​​‌ Augusto Valades Cruz, Jean​ Salamero, Salome Papereux, Charles​‌ Kervrann
• Partner:
  UMR 144​​ CNRS - Institut Curie​​​‌

7.1.7 BDM-Generator4BioImaging

• Name:
  A​ generative "Birth-Death-Move" model to​‌ simulate spatiotemporal dynamics of​​ biomolecules in cells
• Keywords:​​​‌
  Live-cell microscopy, Stochastic models,​ Image analysis, Multi-Object Tracking,​‌ Multi-physics simulation, Marked Point​​ Process
• Functional Description:
  Generators​​​‌ of space-time dynamics in​ bioimaging have become essential​‌ to build ground truth​​ datasets for image processing​​​‌ algorithm evaluation such as​ biomolecule detectors and trackers,​‌ as well as to​​ generate training datasets for​​​‌ deep learning algorithms. In​ this contribution, we leverage​‌ a stochastic model, called​​ birth-death-move (BDM) point process,​​​‌ in order to generate​ joint dynamics of biomolecules​‌ in cells. This particle-based​​ stochastic simulation method is​​​‌ very flexible and can​ be seen as a​‌ generalization of well-established standard​​ particle-based generators. In comparison,​​​‌ our approach allows us:​ (1) to model a​‌ system of particles in​​ motion, possibly in interaction,​​​‌ that can each possibly​ switch from a motion​‌ regime (e.g., Brownian) to​​ another (e.g., a directed​​​‌ motion), (2) to take​ into account finely the​‌ appearance over time of​​ new trajectories and their​​​‌ disappearance, these events possibly​ depending on the cell​‌ regions but also on​​ the current spatial configuration​​​‌ of all existing particles.​ This flexibility enables to​‌ generate more realistic dynamics​​ than standard particle-based simulation​​​‌ procedures, by for example​ accounting for the colocalization​‌ phenomena often observed between​​ intracellular vesicles.
• URL:
  https://­github.­com/­balsollier-lisa/­BDM-generator-for-bioimaging​​​‌
• Publication:
  hal-04021538
• Contact:
  Frédéric​ Lavancier
• Participants:
  Lisa Balsollier,​‌ Frédéric Lavancier, Charles Kervrann​​
• Partners:
  Université de Nantes,​​​‌ ENSAI, UMR 144 CNRS​ - Institut Curie

7.1.8​‌ NAGINI-3D

• Name:
  N-Active shapes​​ for seGmentINg 3D biological​​​‌ Images
• Keywords:
  Image segmentation,​ Active contours, Convolutional Neural​‌ Network
• Functional Description:
  NAGINI-3D​​ is a method for​​​‌ segmenting 3D biological images​ that combines the advantages​‌ of active contours and​​ deep learning. The general​​ idea is to train​​​‌ a convolutional network to‌ estimate the position of‌​‌ objects within images and,​​ for each of them,​​​‌ to predict a set‌ of parameters that can‌​‌ be used to generate​​ a surface that delineates​​​‌ the edges of the‌ object. This method allows‌​‌ one to represent 3D​​ objects as continuous shapes​​​‌ and to compute differential‌ geometry features such as‌​‌ local curvature of object​​ surfaces.
• URL:
  https://­github.­com/­QuentinRapilly/­NAGINI-3D
• Publication:​​​‌
  hal-04978619
• Contact:
  Quentin Rapilly‌
• Participants:
  Quentin Rapilly, Anais‌​‌ Badoual, Charles Kervrann

7.1.9​​ Wetlands

• Keywords:
  Python, Library,​​​‌ Virtual environment
• Functional Description:‌

  Wetlands can create Conda‌​‌ environments on demand, install​​ dependencies, and execute arbitrary​​​‌ code within them. This‌ makes it easy to‌​‌ build plugin systems or​​ integrate external modules into​​​‌ an application without dependency‌ conflicts, as each environment‌​‌ remains isolated.

  For example,​​ if your application needs​​​‌ to use both Stardist‌ and Cellpose, installing them‌​‌ in the same environment​​ may not work due​​​‌ to conflicting dependencies. With‌ Wetlands, you can create‌​‌ a dedicated environment for​​ each library and run​​​‌ them both as needed‌ from your main script.‌​‌

  The name Wetlands comes​​ from the tropical environments​​​‌ where anacondas thrive.

• URL:‌
  https://­arthursw.­github.­io/­wetlands/­latest/
• Contact:
  Arthur Masson‌​‌
• Participant:
  Arthur Masson

BioImageIT for bioimage‌​‌ management and processing –​​

New image acquisition systems​​​‌ generate large number of‌ images and large volume‌​‌ images. Such data sets​​ are hard to store,​​​‌ to process and to‌ analyze for in a‌​‌ workstation. Many solutions exist​​ for data management (e.g.​​​‌ Omero, OpenImadis), image analysis‌ (e.g. Fiji, Icy, CellProfiler)‌​‌ and statistics (e.g R​​ software). Each of them​​​‌ has its specificities and‌ several bridges have been‌​‌ developed between pieces of​​ software. Nevertheless, in many​​​‌ use-cases, we need to‌ perform analysis using tools‌​‌ that are available in​​ different pieces of software​​​‌ and different languages. It‌ is then tedious to‌​‌ create a workflow that​​ brings the data from​​​‌ one tool to another.‌ This process requires programming‌​‌ skills and most of​​ the time, custom scripts​​​‌ are developed to handle‌ data processing management. To‌​‌ overcome these difficulties, we​​ have already developed a​​​‌ framework – BioImageIT (‌bioimageit.github.io) – to‌​‌ create a middleware application​​ that allow any scientist​​​‌ to process, and analyze‌ data using only one‌​‌ single high level application,​​ while keeping track of​​​‌ metadata. This BioImageIT application‌ is based on 3‌​‌ components:

• –
  an interoperability​​ with existing databases;
• –​​​‌
  an image processing and‌ analysis tools integration method‌​‌ based on packaging and​​ wrapping techniques;
• –
  an​​​‌ application with a graphical‌ interface to easily annotate‌​‌ data, run processing tools,​​ and visualize data and​​​‌ results.

This software architecture‌ has three main goals.‌​‌ First, data are annotated​​ with open formats and​​​‌ experiment can then be‌ stored in different architectures‌​‌ or servers. Second, the​​ processing tools are used​​​‌ as binary packages managed‌ by the Conda technology.‌​‌ This enable to gently​​ handle dependencies and several​​​‌ versions of the same‌ tool. Any existing tool‌​‌ can then be integrated​​​‌ in its native programming​ language. Third, using a​‌ single middleware application allows​​ to automatically generate metadata​​​‌ for any processed data,​ improving the traceability and​‌ the repeatability of any​​ experimental result (FAIR principles).​​​‌

We envision to continue​ to promote BioImageIT in​‌ the forthcoming years, initiated​​ in the frame of​​​‌ the France-BioImaging research infrastructure​ (france-bioimaging.org) in​‌ order to provide a​​ standardized image processing tool​​​‌ set and data management​ for the imaging facilities.​‌

Scheme of the BioImageIT​​ components interactions

A unified framework​‌ of non-local parametric methods​​ for image denoising

Participant:​​​‌ Charles Kervrann.

In​ 9, we proposed​‌ a unified view of​​ non-local methods for single-image​​​‌ denoising, for which BM3D​ is the most popular​‌ representative, that operate by​​ gathering noisy patches together​​​‌ according to their similarities​ in order to process​‌ them collaboratively. Our general​​ estimation framework is based​​​‌ on the minimization of​ the quadratic risk, which​‌ is approximated in two​​ steps, and adapts to​​​‌ photon and electronic noises.​ Relying on unbiased risk​‌ estimation (URE) for the​​ first step and on​​​‌ “internal adaptation”, a concept​ borrowed from deep learning​‌ theory, for the second,​​ we show that our​​​‌ approach enables to reinterpret​ and reconcile previous state-of-the-art​‌ non-local methods. Within this​​ framework, we proposed a​​​‌ novel denoiser called NL-Ridge​ that exploits linear combinations​‌ of patches. While conceptually​​ simpler, we showed that​​​‌ NL-Ridge can outperform well-established​ state-of-the-art single-image denoisers. (in​‌ collaboration with S. Herbreteau,​​ ENSAI, Bruz, France; R.​​​‌ Fraisse, AIRBUS Defence and​ Space, Toulouse, France)

S.​‌ Herbreteau, C. Kervrann. A​​ unified framework of non-local​​​‌ parametric methods for image​ denoising. SIAM J.​‌ Imaging Sciences, 18(1): 89-119,​​ 2025, DOI:10.1137/24M1630967,​​​‌ hal-04472406, 9.​ (NL-Ridge software)​‌

Image processing and image​​ analysis in microscopy

Participant:​​​‌ Anaïs Badoual.

In​ recent years, microscopy images​‌ have played a crucial​​ part in the life​​​‌ sciences. They are the​ privileged witnesses for the​‌ observation at the molecular​​ level of the great​​​‌ many and very complex​ intra-cellular interactions. As a​‌ corollary, the quantitative analysis​​ of these images has​​​‌ become essential to improve​ the understanding of this​‌ cellular machinery. While the​​ acquisition of microscopic information​​​‌ varies greatly from one​ microscopy modality to another,​‌ they all now share​​ a common denominator: the​​​‌ digital world. Indeed, since​ the 2000s, behind every​‌ microscope there are now​​ automated systems, digital sensors,​​​‌ digital cameras and of​ course one or more​‌ computers. The latter are​​ present throughout the entire​​​‌ process from image acquisition​ to the interpretation of​‌ their content. As explained​​ in 20, the​​​‌ nature of experiments has​ therefore evolved from purely​‌ qualitative observations to quantitative​​ analyses on computers. (in​​​‌ collaboration with D. Sage,​ EPFL, BIG Group, Lausanne,​‌ Switzerland)

D. Sage, A.​​ Badoual. Image Processing and​​ Image Analysis in Microscopy​​​‌. In Photonic Imaging‌ for Biology: From Conventional‌​‌ Microscopy to Super-Resolution, Chapter​​ 10, 2025-237, John Wiley​​​‌ and Sons Inc., J.-B.‌ Sibarita (editor), 2025, ISBN:‌​‌ 978-1-394-41788-9, hal-05469062, 20​​.

DeepCristae, a CNN​​​‌ for the restoration of‌ mitochondria cristae in live‌​‌ microscopy images

Participants: Anaïs​​ Badoual, Charles Kervrann​​​‌.

Mitochondria play an‌ essential role in the‌​‌ life cycle of eukaryotic​​ cells. However, we still​​​‌ do not know how‌ their ultrastructure, like the‌​‌ cristae of the inner​​ membrane, dynamically evolves to​​​‌ regulate these fundamental functions,‌ in response to external‌​‌ conditions or during interaction​​ with other cell components.​​​‌ Although high-resolution fluorescent microscopy‌ coupled with recently developed‌​‌ innovative probes can reveal​​ this structural organization, their​​​‌ long-term, fast and live‌ 3D imaging remains challenging.‌​‌ To address this problem,​​ we have developed a​​​‌ convolutional neural network (CNN),‌ called DeepCristae 14,‌​‌ to restore mitochondrial cristae​​ in low spatial resolution​​​‌ microscopy images. Our CNN‌ is trained from 2D‌​‌ STED images using a​​ novel loss specifically designed​​​‌ for cristae restoration. Random‌ sampling centered on mitochondrial‌​‌ areas was also developed​​ to improve training efficiency.​​​‌ Quantitative assessments were carried‌ out using metrics we‌​‌ derived to give a​​ meaningful measure of cristae​​​‌ restoration. Depending on the‌ conditions of use indicated,‌​‌ DeepCristae works well on​​ broad microscopy modalities (STED,​​​‌ Live-SR, AiryScan and LLSM).‌ It is ultimately applied‌​‌ in the context of​​ mitochondrial network dynamics during​​​‌ interaction with endo/lysosomes membranes.‌ (in collaboration with J.‌​‌ Salamero, L. Leconte, C.A.​​ Valades-Cruz, CNRS-UMR144, Institut Curie;​​​‌ T. Liu, Z. Chen,‌ PKU University, Institute of‌​‌ Molecular Medicine, Beijing, People​​ Republic of China)

S.​​​‌ Papereux, L. Leconte, C.A.‌ Valades-Cruz, T. Liu, J.‌​‌ Dumont, Z. Chen, J.​​ Salamero, C. Kervrann, A.​​​‌ Badoual. DeepCristae, a CNN‌ for the restoration of‌​‌ mitochondria cristae in live​​ microscopy images, Communications​​​‌ Biology, 8, 320, 2025,‌ DOI:10.1038/s42003-025-07684-x, hal-04295317‌​‌, 14.

Polarization​​ MultiFocus Microscopy for volumetric​​​‌ super-resolution and orientation imaging‌ of biofilaments

Participant: Caio‌​‌ Vaz Rimoli.

Accessing​​ molecular orientation in single​​​‌ molecule localization microscopy (SMLM)‌ offers valuable insights into‌​‌ molecular ordering and organization​​ in biological structures. Conventional​​​‌ single-molecule orientation-localization microscopy (SMOLM)‌ methods typically rely on‌​‌ either engineering the microscope’s​​ point-spread function (PSF) to​​​‌ encode the orientation information‌ or on polarization-resolved detection.‌​‌ While PSF engineering enables​​ detailed orientation analysis, it​​​‌ often requires complex computational‌ analysis and suffers from‌​‌ reduced performance in dense​​ cellular environments due to​​​‌ PSF spreading and overlap.‌ In contrast, polarization-based approaches‌​‌ are easier to implement​​ and are more fit​​​‌ when imaging dense samples‌ but are typically unable‌​‌ to retrieve the axial​​ information of single molecules.​​​‌

To overcome this limitation,‌ we introduced the Polarization‌​‌ MultiFocus Microscope (PolMFM), a​​ novel method for simultaneously​​​‌ retrieving the orientation and‌ 3D position of single‌​‌ molecules 24. PolMFM​​ combines the orientation measurement​​​‌ capabilities of a 4-polarization‌ splitting scheme with a‌​‌ 3-planes multifocus microscope (MFM)​​ enabling the reconstruction of​​​‌ molecular 2D orientation, wobble,‌ and axial localization in‌​‌ a single acquisition. Through​​​‌ simulations, we demonstrated that​ PolMFM accurately recovers both​‌ orientation and 3D position,​​ despite PSF defocusing. Experimental​​​‌ validation with reference samples​ shows that PolMFM matches​‌ the orientation precision of​​ 4-Polar STORM, while uniquely​​​‌ adding axial information.

Moreover,​ we demonstrated the power​‌ of PolMFM by resolving​​ the orientation and 3D​​​‌ positions of molecules in​ actin filaments in fixed​‌ cells, and by revealing​​ that chromatin in crickets​​​‌ undergoes major reorganization and​ increased ordering during spermiogenesis.​‌ These findings highlight the​​ potential of PolMFM for​​​‌ high-precision, multidimensional super-resolution imaging​ in complex and crowded​‌ biological environments. (in collaboration​​ with B. Hajj, CNRS-UMR168,​​​‌ Institut Curie, Paris, France;​ S. Brasselet, Institut Fresnel,​‌ Marseille, France)

L. Régnier,​​ C. V. Rimoli, S.​​​‌ Dey, F.C. Tsai, G.A.​ Orsi, S, Brasselet, B.​‌ Hajj . Polarization MultiFocus​​ Microscopy for volumetric super-resolution​​​‌ and orientation imaging of​ biofilaments, BioRxiv DOI:​‌10.1101/2025.11.19.687997, hal-05392077,​​ 24.

Template Learning:​​ deep learning with domain​​​‌ randomization for particle picking​ in cryo-electron tomography

Participant:​‌ Charles Kervrann.

Cryo-electron​​ tomography (cryo-ET) enables the​​​‌ three-dimensional visualization of biomolecules​ and cellular components in​‌ their near-native state. Particle​​ picking, a crucial step​​​‌ in cryo-ET data analysis,​ is traditionally performed by​‌ template matching—a method utilizing​​ cross-correlations with available biomolecular​​​‌ templates. Despite the effectiveness​ of recent deep learning-based​‌ particle picking approaches, their​​ dependence on initial data​​​‌ annotation datasets for supervised​ training remains a significant​‌ limitation. In this work,​​ we proposed a technique​​​‌ that combines the accuracy​ of deep learning particle​‌ identification with the convenience​​ of the model training​​​‌ on biomolecular templates enabled​ through a tailored domain​‌ randomization approach. Our technique,​​ named Template Learning 8​​​‌, automates the simulation​ of training datasets, incorporating​‌ considerations for molecular crowding,​​ structural variabilities, and data​​​‌ acquisition variations. This reduces​ or even eliminates the​‌ dependence of supervised deep​​ learning on annotated experimental​​​‌ datasets. We demonstrated that​ models trained on simulated​‌ datasets, optionally fine-tuned on​​ experimental datasets, outperform those​​​‌ exclusively trained on experimental​ datasets. Also, we illustrated​‌ that Template Learning used​​ as an alternative to​​​‌ template matching, can offer​ higher precision and better​‌ orientational isotropy, especially for​​ picking small non-spherical particles.​​​‌ Template Learning software is​ open-source, Python-based, and GPU​‌ and CPU parallelized. (in​​ collaboration with M. Eltsov​​​‌ and M. Harastani, IGBMC​ Strasbourg and Institut Pasteur​‌ Paris, France)

M. Harastani,​​ G. Patra, C. Kervrann,​​​‌ M. Eltsov. Template Learning:​ deep learning with domain​‌ randomization for particle picking​​ in cryo-electron tomography,​​​‌ Nature Communications, 16, 8833,​ 2025, DOI:10.1038/s41467-025-63895-0,​‌ hal-04874266, 8.​​

Deep learning detection of​​​‌ dynamic exocytosis events in​ fluorescence TIRF microscopy

Participants:​‌ Charles Kervrann, Arthur​​ Masson.

Segmentation and​​​‌ detection of biological objects​ in fluorescence microscopy is​‌ of paramount importance in​​ cell imaging. Deep learning​​​‌ approaches have recently shown​ promise to advance, automatize​‌ and accelerate analysis. However,​​ most of the interest​​​‌ has been given to​ the segmentation of static​‌ objects of 2D/3D images​​ whereas the segmentation of​​ dynamic processes obtained from​​​‌ time-lapse acquisitions has been‌ less explored. Here we‌​‌ adapted DeepFinder, a U-net​​ originally designed for 3D​​​‌ noisy cryo-electron tomography (cryo-ET)‌ data, for the detection‌​‌ of rare dynamic exocytosis​​ events (termed ExoDeepFinder 10​​​‌) observed in temporal‌ series of 2D Total‌​‌ Internal Reflection Fluorescent Microscopy​​ (TIRFM) images. ExoDeepFinder achieved​​​‌ good absolute performances with‌ a relatively small training‌​‌ dataset of 60 cells/12000​​ events. We rigorously compared​​​‌ deep learning performances with‌ unsupervised conventional methods from‌​‌ the literature. ExoDeepFinder outcompeted​​ the tested methods, but​​​‌ also exhibited a greater‌ plasticity to the experimental‌​‌ conditions when tested under​​ drug treatments and after​​​‌ changes in cell line‌ or imaged reporter. This‌​‌ robustness to unseen experimental​​ conditions did not require​​​‌ re-training demonstrating generalization capability‌ of ExoDeepFinder. ExoDeepFinder, as‌​‌ well as the annotated​​ training datasets, were made​​​‌ transparent and available through‌ an open-source software as‌​‌ well as a Napari​​ plugin and can directly​​​‌ be applied to custom‌ user data. The apparent‌​‌ plasticity and performances of​​ ExoDeepFinder to detect dynamic​​​‌ events open new opportunities‌ for future deep-learning guided‌​‌ analysis of dynamic processes​​ in live-cell imaging. (in​​​‌ collaboration with H. Lachuer,‌ Institut Jacques Monod, Paris,‌​‌ France; K. Schauer, Institut​​ Gustave-Roussy; A.S. Macé, CNRS-UMR144,​​​‌ PICT-IBiSA, Institut Curie, Paris,‌ France)

H. Lachuer, E.‌​‌ Moebel, A.S. Macé, A.​​ Masson, K. Schauer, C.​​​‌ Kervrann. Deep learning detection‌ of dynamic exocytosis events‌​‌ in fluorescence TIRF microscopy​​, PLoS Computational Biology,​​​‌ 21(10): e1013556, 2025, DOI:‌10.1371/journal.pcbi.1013556, hal-04874728,‌​‌ 10.

H. Lachuer,​​ E. Moebel, A.S. Macé,​​​‌ A. Masson, K. Schauer,‌ C. Kervrann. Deep learning‌​‌ detection of dynamic exocytosis​​ events in TIRF microscopy​​​‌, In Colloque Français‌ d'Intelligence Artificielle en Imagerie‌​‌ Biomédicale (IABM), Nice, France,​​ 2025, hal-05470418, 26​​​‌. (poster)

Ensembling Unets‌ for rare chromosomal aberration‌​‌ detection in metaphase images,​​ uncertainty quantification, and ionizing​​​‌ radiation dose estimation

Participant:‌ Charles Kervrann.

In‌​‌ biological dosimetry a radiation​​ dose is estimated using​​​‌ the average number of‌ chromosomal aberrations per peripheral‌​‌ blood lymphocytes 17,​​ 19, 22.​​​‌ This analysis is still‌ manually performed on 2D‌​‌ metaphase images depicting the​​ 23 pairs of chromosomes​​​‌ because the false discovery‌ rate of current automated‌​‌ detection systems is too​​ high and variable because​​​‌ of sensitivity to small‌ variations in image quality‌​‌ (chromosome spread, illumination variations​​ ...). Therefore, the current​​​‌ systems are only used‌ to assist human experts.‌​‌ Designing more performant automatic​​ and reliable chromosomal aberration​​​‌ detection systems has become‌ of paramount importance to‌​‌ improve diagnosis speed and​​ reduce human expertise time.​​​‌ In this work, we‌ proposed a novel deep-learning‌​‌ method for automatic rare​​ chromosomal aberration detection and​​​‌ uncertainty quantification. We formulate‌ the problem as a‌​‌ unique regression problem requiring​​ the minimization of a​​​‌ sparsity-promoting loss to reduce‌ the false alarm rate.‌​‌ Furthermore, we select checkpoints​​ at the end of​​​‌ each epoch during training‌ to form a model‌​‌ ensemble. The resulting artificial​​ experts are further analyzed​​​‌ to derive a consensus‌ voting, similar to an‌​‌ agreement of human annotator​​​‌ rating, to provide trustworthy​ aberration detections and confidence​‌ intervals. We also propose​​ in this work an​​​‌ approach to visualize training​ dynamics using low-dimension representation​‌ to better interpret the​​ relationships between training stochasticity​​​‌ and ensemble diversity 17​. A radiation dose​‌ curve is finally derived​​ from deep learning-assisted counting​​​‌ of dicentrics and fragments​ in metaphase images, in​‌ high agreement with the​​ reference hand-crafted curve in​​​‌ biological dosimetry with a​ promising dose estimation validation.​‌ This approach provided an​​ convenient explainable artificial intelligence​​​‌ tool to understand the​ mechanism of the chromosomal​‌ aberration detection of the​​ model. (in collaboration with​​​‌ M.A. Benadjaoud, ASNR, PSE-SANTE/SERAMED/LRAcc,​ Fontenay-aux-Roses, France)

A. Deschemps,​‌ E. Grégoire, J.S. Martinez,​​ A. Vaurijoux, P. Fernandez,​​​‌ D. Dugue, L. Bobyk,​ M. Valente, G. Gruel,​‌ E. Moebel, M.A. Benadjaoud,​​ C. Kervrann. Ensembling Unets​​​‌ for rare chromosomal aberration​ detection in metaphase images,​‌ uncertainty quantification, and ionizing​​ radiation dose estimation,​​​‌ 2024, hal-04874432. (submitted​ to "Cytometry Part A,​‌ in revision)

A. Deschemps,​​ E. Grégoire, J.S. Martinez,​​​‌ A. Vaurijoux, P. Fernandez,​ D. Dugue, L. Bobyk,​‌ M. Valente, G. Gruel,​​ E. Moebel, M.A. Benadjaoud,​​​‌ C. Kervrann. Explainable artificial​ intelligence approach using low-dimensional​‌ visualization and ensembling uncertainty​​ quantification for rare chromosomal​​​‌ aberration detection in cytogenetic​ imaging, Proc. of​‌ Int. Conf. on Image​​ Processing, Theory, Tools and​​​‌ Applications (IPTA), Istanbul, Turkiye,​ 2025, DOI:10.1109/IPTA66025.2025.11222058,​‌ hal-05446156v1, 17.​​

Prediction of parametric surfaces​​​‌ for multi-object segmentation in​ 3D biological imaging

Participants:​‌ Quentin Rapilly, Anaïs​​ Badoual, Charles Kervrann​​​‌.

Multi-object segmentation algorithms​ are of great interest​‌ in a very large​​ range of fields. Deep​​​‌ learning brought major improvements​ in terms of processing​‌ speed or prediction accuracy.​​ Nevertheless, some traditional methods​​​‌ such as active surfaces​ have features that conventional​‌ deep learning methods cannot​​ provide, especially representing the​​​‌ object in a continuous​ geometrical way and encoding​‌ prior information on the​​ shapes to segment. Those​​​‌ features are of particular​ interest in biology to​‌ efficiently segment noisy and​​ poorly resolved data, and​​​‌ then understand the interactions​ between segmented cells. We​‌ introduced NAGINI-3D (N-Active shapes​​ for seGmentINg 3D biological​​​‌ Images) 18, 21​, a new hybrid​‌ segmentation method dedicated to​​ multi-object segmentation of 3D​​​‌ images that combines the​ efficiency of deep learning​‌ and the powerful representation​​ of active surfaces. We​​​‌ evaluated our method on​ real and synthetic 3D​‌ datasets of fluorescence microscopy.​​ (in collaboration with P.​​​‌ Maindron and G. Bouet,​ SAINBIOSE - Santé Ingénierie​‌ Biologie, Saint-Etienne, France)

Q.​​ Rapilly, A. Badoual, P.​​​‌ Maindron, G. Bouet, C.​ Kervrann. Prediction of parametric​‌ surfaces for multi-object segmentation​​ in 3D biological imaging​​​‌, In Proc. of​ Int. Conf. on Scale​‌ Space and Variational Methods​​ in Computer Vision (SSVM),​​​‌ Totnes, United Kingdom, 2025,​ DOI:10.1007/978-3-031-92366-1_20, hal-04978619​‌, 18.

Q.​​ Rapilly. A hybrid CNN-snake​​​‌ approach for localization, segmentation,​ and shape representation in​‌ 3D biological imaging,​​ PhD Thesis, University of​​​‌ Rennes, December 2025, tel-05502320​, 21.

Q.​‌ Rapilly, P. Maindron, G.​​ Bouet-Chalon, A. Badoual, C.​​ Kervrann. Segmentation multi-objets par​​​‌ prédiction de surfaces paramétriques‌ pour l'imagerie biologique 3D‌​‌, In Colloque Français​​ d'Intelligence Artificielle en Imagerie​​​‌ Biomédicale (IABM), Nice, France,‌ 2025, hal-05467429, 27‌​‌. (poster)

Acidification on‌ the plasma membrane

Participants:‌​‌ Ludger Johannes, Christian​​ Wunder.

The pH​​​‌ balance between extracellular and‌ intracellular space is crucial‌​‌ for a multitude of​​ cellular processes. Real-time observation​​​‌ of pH fluctuations in‌ the range 4-9 in‌​‌ live cells and tissues​​ in a sensitive, non-invasive​​​‌ manner has become feasible‌ with advances in pH‌​‌ quantification by organic dyes,​​ genetically encoded fluorescent proteins,​​​‌ and DNA-based probes. In‌ this work 12,‌​‌ we discussed mechanisms through​​ which pH affects cell​​​‌ cycle, transcription, senescence, neurotransmission,‌ glycolipid-lectin driven endocytosis, tissue‌​‌ remodelling, immune responses, and​​ GPCR signalling. Growth factor-stimulated​​​‌ acidification of the extracellular‌ space notably triggers enzymatic‌​‌ reactions like desialylation at​​ the plasma membrane that​​​‌ control processes involving cell‌ migration and bone resorption.‌​‌ Research into the role​​ of pH in cellular​​​‌ physiology continues to be‌ a fertile ground for‌​‌ discovery that underscores its​​ fundamental importance. (in collaboration​​​‌ with E. MacDonald, CRBM‌ - Centre de recherche‌​‌ en Biologie cellulaire de​​ Montpellier)

E. MacDonald, L.​​​‌ Johannes, C. Wunder. Acidification‌ on the plasma membrane‌​‌, Current Opinion in​​ Cell Biology, 95, 2025,​​​‌ DOI:10.1038/s42003-025-07684-x 10.1016/j.ceb.2025.102531,‌ hal-05328086, 12.‌​‌

Membrane glycoproteins get another​​ go: the GlycoSwitch

Participants:​​​‌ Ludger Johannes, Christian‌ Wunder.

The glycan‌​‌ makeup of membrane glycoproteins​​ and glycosphingolipids at the​​​‌ cell surface is traditionally‌ viewed as mature and‌​‌ static. Recent findings challenge​​ this view, showing that​​​‌ selective glycan remodeling can‌ redirect membrane glycoproteins back‌​‌ to the Golgi for​​ another go. In this​​​‌ review we discussed the‌ glycosylation processes in cells,‌​‌ with a focus on​​ the terminal glycan chains​​​‌ on proteins and lipids‌ that are capped by‌​‌ sialic acid sugars, and​​ that engage the glycan-binding​​​‌ proteins of the galectin‌ family. We highlighted new‌​‌ studies demonstrating that growth​​ factors trigger the removal​​​‌ of sialic acid by‌ endogenous neuraminidases at the‌​‌ cell surface, leading to​​ glycolipid–lectin driven endocytosis and​​​‌ retrograde traffic to the‌ Golgi. This molecular circuit,‌​‌ termed the GlycoSwitch, introduces​​ new perspectives on glycan-mediated​​​‌ regulation of cellular functions.‌ (in collaboration with R.‌​‌ Weigert, CI-NIH Bethesda, USA;​​ H. Clausen, University of​​​‌ Copenhagen, Department of Cellular‌ and Molecular Medicine, Denmark)‌​‌

L. Johannes, R. Weigert,​​ C. Wunder, H. Clausen,​​​‌ K. Schjoldager. Membrane glycoproteins‌ get another go: the‌​‌ GlycoSwitch, Trends in​​ Cell Biology, 2025, DOI:​​​‌10.1016/j.tcb.2025.09.005. (In Press)‌

Galectin-3 mediated endocytosis of‌​‌ the orphan G-protein-coupled receptor​​ GPRC5A

Participants: Ludger Johannes​​​‌, Christian Wunder.‌

Galectins, a family of‌​‌ glycan-binding proteins, play crucial​​ roles in various cellular​​​‌ functions, acting at both‌ intracellular and extracellular levels.‌​‌ Among them, Galectin-3 (Gal-3)​​ stands out as a​​​‌ unique member, possessing an‌ intrinsically unstructured N-terminal oligomerization‌​‌ domain and a canonical​​ carbohydrate-recognition domain (CRD). Gal-3​​​‌ binding to glycosylated plasma‌ membrane cargo leads to‌​‌ its oligomerization and membrane​​​‌ bending, ultimately resulting in​ the formation of endocytic​‌ invaginations. An interactomic assay​​ using proteomic analysis of​​​‌ endogenous Gal-3 immunoprecipitates identified​ the orphan G protein-coupled​‌ receptor GPRC5A as a​​ novel binding partner of​​​‌ Gal-3. GPRC5A, also known​ as Retinoic Acid-Induced protein​‌ 3 (RAI3), is transcriptionally​​ induced by retinoic acid.​​​‌ Our results further demonstrated​ that extracellular recombinant Gal-3​‌ stimulates GPRC5A internalization. In​​ SW480 colorectal cancer cells,​​​‌ glycosylated GPRC5A interacts with​ Gal-3. Interestingly, while GPRC5A​‌ expression was upregulated by​​ the addition of all-trans​​​‌ retinoic acid (ATRA), its​ endogenous internalization in SW480​‌ cells was specifically triggered​​ by extracellular Gal-3, but​​​‌ not by ATRA. This​ study provided new insights​‌ into the endocytic mechanisms​​ of GPRC5A, for which​​​‌ no specific ligand has​ been identified to date.​‌ Further research may uncover​​ additional Gal-3-mediated functions in​​​‌ GPRC5A cellular signaling and​ contribute to the development​‌ of innovative therapeutic strategies.​​ (in collaboration with University​​​‌ of Strasbourg, France and​ University of Jijel, Algeria)​‌

A. Boucheham, J. Mallor​​ Franco, S. Bär, E.​​​‌ MacDonald, S. Zuttion, L.​ Blagec, B. Rinaldi, J.​‌ Chicher, L. Kuhn, P.e​​ Hammann, C Wunder, L.​​​‌ Johannes, H. Recherche, S.​ Friant. Galectin-3 mediated endocytosis​‌ of the orphan G-protein-coupled​​ receptor GPRC5A, Cells,​​​‌ 14(19):1571, 2025, DOI:10.3390/cells14191571​.

Next-generation small molecule​‌ inhibitors of clathrin function​​ acutely inhibit endocytosis

Participants:​​​‌ Ludger Johannes, Massiullah​ Shafaq-Zadah, Estelle Dransart​‌.

Clathrin-mediated endocytosis (CME)​​ is the predominant endocytic​​​‌ pathway in eukaryotic cells​ and a major regulator​‌ of cell physiology as​​ it facilitates the internalization​​​‌ of receptors, channels, and​ transporters and viral entry.​‌ The clathrin terminal domain​​ acts as a central​​​‌ protein interaction hub within​ the endocytic protein network.​‌ Previously described inhibitors of​​ CME display off-target activities​​​‌ that result in cytotoxicity,​ providing limitations to their​‌ use. Here, we reported​​ the development and characterization​​​‌ of next-generation small molecule​ inhibitors of clathrin terminal​‌ domain function. These compounds​​ termed Pitstop 2c and​​​‌ Pitstop 2d occupy the​ binding site within the​‌ clathrin terminal domain for​​ endocytic protein ligands including​​​‌ epsin, resulting in potent​ inhibition of receptor-mediated endocytosis​‌ and reduced entry of​​ vesicular stomatitis virus (VSV)​​​‌ with minimal cytotoxic side​ effects. Next-generation Pitstops thus​‌ provide an improved toolset​​ to address clathrin function​​​‌ in cell physiology with​ potential applications as inhibitors​‌ of virus and pathogen​​ entry. (in collaboration with​​​‌ Leibniz-Forschungsinstitut für Molekulare Pharmakologie​ (FMP), Berlin, Germany; Department​‌ of Biology, Chemistry, Pharmacy,​​ Freie Universität Berlin, Germany;​​​‌ Helmholtz-Zentrum Berlin für Materialien​ und Energie, Macromolecular Crystallography,Berlin,​‌ Germany; Chemistry, School of​​ Environmental and Life Sciences,​​​‌ The University of Newcastle,​ Callaghan, Australia)

A. Horatscheck,​‌ M. Krauß, H. Bulut,​​ V. Chambon, M. Shafaq-Zadah,​​​‌ E. Dransart, K. Peloza,​ K.F. Santos, M.J. Robertson,​‌ K. Prichard, S. Miksche,​​ S. Radetzki, J.-P. von​​​‌ Kries, M.C. Wahl, A.​ McCluskey, L. Johannes, V.​‌ Haucke, M. Nazaré. Next-generation​​ small molecule inhibitors of​​​‌ clathrin function acutely inhibit​ endocytosis, Structure, 33:878-890,​‌ 2025, DOI:10.1016/str.2025.02.011.​​

Growth factor-triggered desialylation controls​​​‌ glycolipid-lectin driven endocytosis

Participants:​ Ludger Johannes, Christian​‌ Wunder, Massiullah Shafaq-Zadah​​, Estelle Dransart.​​

Glycolipid-lectin (GL-Lect) driven endocytosis​​​‌ controls the formation of‌ clathrin-independent carriers (CLICs) and‌​‌ the internalization of various​​ cargos such as integrin.​​​‌ Whether this process is‌ regulated in a dynamic‌​‌ manner remained unexplored. In​​ this work 11,​​​‌ we demonstrated that within‌ minutes, the epidermal growth‌​‌ factor triggers the galectin-driven​​ endocytosis of cell surface​​​‌ glycoproteins, such as integrins,‌ that are key regulators‌​‌ of cell adhesion and​​ migration. The onset of​​​‌ this process, mediated by‌ the Na${}^{+}$+/H‌​‌${}^{+}$ antiporter NHE-1 and​​ the neuraminidases Neu1/3, requires​​​‌ the pH-triggered enzymatic removal‌ of sialic acids whose‌​‌ presence otherwise prevents galectin​​ binding. Desialylated glycoproteins are​​​‌ then retrogradely transported to‌ the Golgi apparatus where‌​‌ their glycan makeup is​​ reset to regulate EGF-dependent​​​‌ invasive cell migration. Further‌ evidence is provided for‌​‌ a role of neuraminidases​​ and galectin-3 in acidification-dependent​​​‌ bone resorption. Glycosylation at‌ the cell surface thereby‌​‌ emerges as a dynamic​​ and reversible regulatory post-translational​​​‌ modification that controls a‌ highly adaptable trafficking pathway.‌​‌ (in collaboration with R.​​ Weigert, CI-NIH Bethesda, USA;​​​‌ H. Clausen, University of‌ Copenhagen, Department of Cellular‌​‌ and Molecular Medicine, Denmark;​​ S. Mayor, National Centre​​​‌ for Biological Sciences, Bangalore,‌ India; H. Leffler, Lund‌​‌ University, Division of Microbiology,​​ Immunology and Glycobiology, Sweden)​​​‌

E. MacDonald, A. Forrester,‌ C.A. Valades-Cruz, T.D. Madsen,‌​‌ J. Hetmanski, E. Dransart,​​ Y. Ng, R. Godbole,​​​‌ A. Akhil Shp, L.‌ Leconte, V. Chambon, D.‌​‌ Ghosh, A. Pinet, D.D.​​ Bhatia, B. Lombard, D.​​​‌ Loew, M.R. Larson, H.‌ Leffler, D.J. Lefeber, H.‌​‌ Clausen, P. Caswell, M.​​ Shafaq-Zadah, S. Mayor, R.​​​‌ Weigert, C. Wunder, L.‌ Johannes. Growth factor-induced desialylation‌​‌ for the fast control​​ of endocytosis, Nature​​​‌ Cell Biology, 27(3), 2025,‌ DOI:10.1038/s41556-025-01616-x, 11‌​‌.

Spatial N-glycan rearrangement​​ on α5β1 integrin nucleates​​​‌ galectin-3 oligomers to determine‌ endocytic fate

Participants: Ludger‌​‌ Johannes, Christian Wunder​​, Massiullah Shafaq-Zadah,​​​‌ Estelle Dransart.

Membrane‌ glycoproteins frequently adopt different‌​‌ conformations when altering between​​ active and inactive states.​​​‌ In this work 15‌, we discovered a‌​‌ molecular switch that exploits​​ dynamic spatial rearrangements of​​​‌ N-glycans during such conformational‌ transitions to control protein‌​‌ function. For the conformationally​​ switchable cell adhesion glycoprotein​​​‌ α5β1 integrin, we found‌ that only the bent-closed‌​‌ state arranges N-glycans to​​ nucleate the formation of​​​‌ up to tetrameric oligomers‌ of the glycan-binding protein‌​‌ galectin-3. We proposed a​​ structural model of how​​​‌ these galectin-3 oligomers are‌ assembled and how they‌​‌ clamp the bent-closed state​​ to prime it for​​​‌ endocytic uptake and subsequent‌ retrograde trafficking to the‌​‌ Golgi for polarized distribution​​ in cells. Our findings​​​‌ highlighted an unexpectedly dynamic‌ regulation of the glycan‌​‌ landscape at the cell​​ surface to achieve oligomerization​​​‌ of galectin-3. Galectin-3 oligomers‌ are thereby identified as‌​‌ decoders of defined spatial​​ patterns of N-glycans and​​​‌ as functional extracellular interactors‌ of specifically the bent-closed‌​‌ conformational state of α5β1​​ integrin and possibly other​​​‌ family members. (in collaboration‌ with H. Leffler, Lund‌​‌ University, Division of Microbiology,​​ Immunology and Glycobiology, Sweden;​​​‌ D. Roderer and S.‌ Raunser, Leibniz-Forschungsinstitut für Molekulare‌​‌ Pharmakologie, Berlin, Germany)

M.​​​‌ Shafaq-Zadah, E. Dransart, C.​ Wunder, V. Chambon, C.A.​‌ Valades-Cruz, L. Leconte, N.K.​​ Sarangi, J. Robinson, S.​​​‌ Bai, R. Regmi, A.D.​ Cicco, A. Hovasse, R.​‌ Bartels, U.J. Nilsson, S.​​ Cianférani-Sanglier, H. Leffler, T.E.​​​‌ Keyes, D. Lévy, S.​ Raunser, D. Roderer, L.​‌ Johannes. N-glycan rearrangement on​​ α5β1integrin nucleates galectin-3 oligomers​​​‌ to determine endocytic fate​, Nature Communications, 16,​‌ 9461, 2025, DOI:10.1038/s41467-025-64523-7​​, hal-05335689, 15​​​‌.

9.1.1​ Contract with ASNR –​‌ Localization of chromosomal aberrations​​ and detection of gene​​​‌ translocation between chromosomes induced​ by nuclear radiation dose​‌ excess in light microscopy​​ images

Participant: Charles Kervrann​​​‌.

Funding: ASNR (Autorité​ de Sûreté Nucléaire et​‌ Radioprotection)

Duration: (2020 –​​ 2028)

Collaborator: M. Benadjaoud​​​‌ (ASNR, Direction de la​ Recherche et de l’Expertise​‌ en Santé, SERAMED/LRAcc, Fontenay-aux-Roses)​​

The first goal of​​​‌ this project was to​ develop statistical and deep-learning​‌ methods for localizing and​​ classifying chromosomal aberrations observed​​​‌ in 2D microscopy images​ (blood test) and estimating​‌ radiation dose following a​​ postulated nuclear reactor accident​​​‌ (PhD thesis of Antonin​ Deschemps).

The second goal​‌ was to develop supervised​​ deep-learning classification and score-based​​​‌ diffusion methods and algorithms​ for the identification of​‌ gene translocation between chromosomes​​ observed in FISH (Fluorescence​​​‌ in situ hybridization) microscopy​ images (PhD thesis of​‌ Quentin Tallon).

The third​​ goal will consist in​​​‌ deploying generative AI and​ transfer learning methods in​‌ three areas: 1) The​​ development of an AI​​​‌ model for the preselection​ of metaphases in Glemsa/Fish3​‌ modalities; 2) the conversion​​ of the automatic aberration​​​‌ counting in 3-Fish imaging​ into radiation dose, taking​‌ into account confounding factors​​ and associated uncertainties; 3)​​​‌ the development of a​ new AI model for​‌ the automatic counting of​​ chromosomal aberrations in M-Fish​​​‌ imaging (PhD theis of​ Amine Banani).

This project​‌ funded by the ASNR,​​ Région-Bretagne, ANR (ASTRID program),​​​‌ and AID (Agence de​ l'Innovation de Defense) concerned​‌ the PhD theses of​​ Antonin Deschemps (2020-2023), Quentin​​​‌ Tallon (2022-2025), and Amine​ Banani (2026-2028) and the​‌ post-doc of Emmanuel Moebel​​ (2022-2023).

10.1.1 Participation in other​‌ International Programs

10.2.1​​ Visits of international scientists​​​‌

10.2.2‌ Visits to international teams‌​‌

10.3.1 Other‌ european programs/initiatives

ESFRI initiative‌​‌ program: EuroBioImaging

Participants: Charles​​​‌ Kervrann, Arthur Masson​.

Coordinator: J. Eriksson​‌ (Turku University, Finland)

Funding:​​ Member states of the​​​‌ European Union

Partners: 18​ European countries in 2022​‌ (+1 observer)

As a​​ member of the National​​​‌ Research Infrastructures (RI) France​ BioImaging, SAIRPICO is involved​‌ in the ESFRI Euro-BioImaging​​ project, and now in​​​‌ the ERIC EuroBioImaging (since​ November 2019), one of​‌ the landmarks of biomedical​​ science Research Infrastructures in​​​‌ the roadmap of the​ European Strategic Forum on​‌ Research Infrastructures (ESFRI 2018).​​ The mission of Euro-BioImaging​​​‌ is to provide access,​ service and training to​‌ state-of-the-art imaging technologies and​​ foster the cooperation and​​​‌ networking at the European​ level including multidisciplinary scientists,​‌ industry, regional, national and​​ European authorities.

10.4.1 France-BioImaging project​

Participants: Charles Kervrann,​‌ Arthur Masson.

Duration:​​ 2011 – 2030

Funding:​​​‌ Investissement d'Avenir, ANR INBS-PIA1​ 2011 and “FBI Next​‌ Generation” (ANR program 2020-2030)​​

Coordinator: R.-M. Mège (Institut​​​‌ Jacques Monod, CNRS)

Partners:​ CNRS, Aix-Marseille Université, Collège​‌ de France, Ecole Normale​​ Supérieure, Ecole Polytechnique, Inria,​​​‌ Institut Curie, Institut Pasteur,​ INSERM, Université de Bordeaux,​‌ Université de Montpellier, Université​​ de Nantes, Université de​​​‌ Paris, Université de Rennes,​ Université de Rouen, Université​‌ de Strasbourg, Université de​​ Lyon, Université de Grenoble,​​​‌ Université de Toulouse.

SAIRPICO​ (previously SERPICO 2010-2023) is​‌ member of the French​​ initiative, the so-called “France-BioImaging”​​​‌ (FBI) National Research Infrastructure​ which gathers several outstanding​‌ cellular imaging centers (microscopy,​​ spectroscopy, probe engineering and​​​‌ signal processing). FBI is​ on the French Roadmap​‌ of Research Infrastructure.​​ The mission of FBI​​​‌ is to build a​ distributed coordinated French infrastructure​‌ for photonic and electronic​​ cellular bioimaging, dedicated to​​​‌ innovation, training and technology​ transfer. High-computing capacities are​‌ needed to exhaustively analyze​​ image flows.

SAIRPICO is​​​‌ head of the IPDM​ (Image Processing and Data​‌ Management) node of the​​ FBI network composed of​​​‌ 11 nodes since Jan​ 2024. In this context,​‌ we address the following​​ scientific problems: i/ exhaustive​​​‌ analysis of bioimaging data​ sets; ii/ deciphering of​‌ key steps of biological​​ mechanisms at organ, tissular,​​​‌ cellular and molecular levels​ through the systematic use​‌ of time-lapse 3D microscopy​​ and image processing methods;​​​‌ iii/ storage and indexing​ of extracted and associated​‌ data and metadata through​​ an intelligent data management​​​‌ system. The team recruited​ R&D engineers to disseminate​‌ image processing software for​​ large scale computing and​​​‌ data sets processing.

This​ project concerned the two​‌ internships of E. Choffat​​ (Master 2) and C.​​​‌ Ntsoumou Lihoula (Master 2)​ (supervised by A. Masson)​‌ in 2025.

10.4.2 ANR​​ POLARISCOPIA project: Next generation​​​‌ information processing of microscopy​ vector-valued images : application​‌ in cell polarized imaging​​

Participants: Charles Kervrann,​​​‌ Luuger Johannes, Caio​ Vaz Rimoli, Arthur​‌ Masson, Vincent Briane​​, Chencheng Gu,​​​‌ Leo Maury, Ferdinand​ Plesse–Costa.

Duration: 48​‌ months (Oct 2022 –​​ Sept 2026)

Funding: ANR​​​‌ (Agence Nationale de la​ Recherche) PRME

Coordinator: Charles​‌ Kervrann

The objective of​​ the project is to​​​‌ create the next generation​ of information processing techniques​‌ required to overcome the​​ three aforementioned barriers, and​​ to solve challenging image​​​‌ processing problems induced by‌ the acquisition of 3D+Time‌​‌ vector-valued images. This will​​ be achieved here by​​​‌ integrating concepts in statistical‌ signal-image processing and machine‌​‌ learning, combined with innovative​​ developments in fluorescence microscopy.​​​‌ The resulting algorithms will‌ serve to characterize the‌​‌ dynamics of biomolecules and​​ to decipher the molecular​​​‌ transport pathways, which are‌ of considerable of interest‌​‌ in fundamental cell biology​​ and for future precision​​​‌ medicine.

This project concerned‌ the postdoc position of‌​‌ Vincent Briane and the​​ PhD positions of Léo​​​‌ Maury and Chencheng Gu‌ (supervised by C. Kervrann)‌​‌ in 2025.

10.4.3 ANR​​ DEEPNER project: Deciphering chromatin​​​‌ rearrangements in response to‌ UV irradiation using new‌​‌ deep learning based cryo-electron​​ tomography data analysis tools​​​‌

Participants: Charles Kervrann,‌ Mounir Messaoudi.

Duration:‌​‌ 48 months (Oct 2023​​ – Sept 2027)

Funding:​​​‌ ANR (Agence Nationale de‌ la Recherche) PRC

Coordinator:‌​‌ Mikhail Eltsov (IGBMC, Strasboug)​​

Partners: Sorbonne University (IMPMC),​​​‌ Inria Rennes (SAIRPICO Team)‌

The goal is to‌​‌ reveal, with unprecedented resolution,​​ chromatin reorganization during genotoxic​​​‌ stress. We will analyze‌ the following three structural‌​‌ levels of the chromatin:​​ (1) spatial organization of​​​‌ chromatin domains (nucleosome distribution,‌ density, local order); (2)‌​‌ geometry of DNA linkers​​ and (3) conformation and​​​‌ disassembly of nucleosomes. This‌ analysis will reveal the‌​‌ chromatin structure-based mechanisms enabling​​ detection and repair of​​​‌ UV-induced lesions in the‌ chromatin context. To enable‌​‌ this analysis, we will​​ develop appropriate DL approaches​​​‌ and software, in particular‌ those for in situ‌​‌ cryo-ET data annotation and​​ analysis. The added value​​​‌ of this research will‌ be cryo-ET data analysis‌​‌ methods and algorithms embedded​​ in software enabling not​​​‌ only an automated in‌ situ annotation and analysis‌​‌ of nucleosomes but also​​ other molecular complexes, in​​​‌ health and disease.

This‌ project concerned the PhD‌​‌ position of Mounir Messaoudi​​ (supervised by C. Kervrann)​​​‌ in 2025.

10.4.4 ANR‌ OMEGA-MEMDO: Orientation microscopy and‌​‌ cryo-electron tomography to study​​ glycandependent assembly of membrane​​​‌ nanodomains

Participants: Ludger Johannes‌, Charles Kervrann,‌​‌ Massiullah Shafaq-Zadah, Caio​​ Vaz Rimoli, Estelle​​​‌ Dransart, Xingyi Cheng‌, Ferdinand Plesse–Costa,‌​‌ Ilyes Hamitouche.

Duration:​​ 48 months (Oct 2025​​​‌ – Sept 2029)

Funding:‌ ANR (Agence Nationale de‌​‌ la Recherche) PRC

Coordinator:​​ Ludger Johannes

Partners: UMR168​​​‌ (B. Hajj, D. Levy,‌ Julien Maufront, Amaury Autric)‌​‌ - Institut Curie

With​​ the OMEGA-MEMDO program, we​​​‌ will use forefront techniques‌ to monitor the membrane‌​‌ nanodomain construction process from​​ cellular to molecular scales.​​​‌ Oligomerization site mutants of‌ Gal3 and glycosylation site‌​‌ mutants of a5b1 integrin​​ will be tested on​​​‌ cells by lattice light‌ sheet microscopy for endocytic‌​‌ uptake, and by Single​​ Molecule Orientation-Localization Microscopy (SMOLM)​​​‌ for orientation and order‌ of lipids and proteins‌​‌ at the nanoscale. Purified​​ Gal3, a5b1 integrin, and​​​‌ mutants will be analyzed‌ by cryo-electron tomography (cryo-ET)‌​‌ on model membrane systems​​ to visualize individual proteins​​​‌ and their conformation, spatial‌ organization, and membrane environment.‌​‌ Developments in image processing​​ and artificial intelligence will​​​‌ be performed to extract‌ and correlate cellular and‌​‌ molecular information from SMOLM​​​‌ and cryo-ET datasets.

This​ project concerned the postdoc​‌ position of Xingyi Cheng​​ and the PhD position​​​‌ of Ferdinand Plesse–Costa (supervised​ by C. Kervrann, B.​‌ Hajj (UMR168 - Insitut​​ Curie), and C. Vaz​​​‌ Rimoli) in 2025.

10.4.5​ Fondation ARC Programmes labellisés​‌ (PGA): Dysregulation of sialoglycans​​ via the GlycoSwitch pathway​​​‌ controls immune modulation during​ tumor progression

Participants: Ludger​‌ Johannes, Christian Wunder​​, Massiullah Shafaq-Zadah,​​​‌ Estelle Dransart, Ilyes​ Hamitouche.

Duration: 36​‌ months (Oct 2025 –​​ Sept 2028)

Funding: Fondation​​​‌ ARC

Coordinator: Ludger Johannes​

Define changes in sialic​‌ acid and GlycoSwitch component​​ expression during tumor progression​​​‌ in a mouse model​ of HNSCC and in​‌ vitro, determine the stage​​ at which the impairment​​​‌ of the GlycoSwitch results​ in inhibition of tumor​‌ progression through blockage of​​ immune evasion, and transpose​​​‌ findings from the mouse​ model to patient samples.​‌

10.5.1​​ Allocations d'Installation Scientifique (AIS)​​​‌

Participant: Anaïs Badoual.​

Funding: Rennes Métropole

Duration:​‌ 36 months (Oct 2022​​ – Sep 2025)

This​​​‌ project concerned the internships​ of two Master 1​‌ students in 2025.

• –​​
  Maelys Hanoire (4 months,​​​‌ from April 2025 to​ August 2025) – "Classification​‌ of astrocytic calcium signals​​ observed with 3D lattice​​​‌ light sheet fluorescence microscopy"​. The goal of​‌ the internship was to​​ develop a method to​​​‌ classify segmented astrocytic calcium​ signals into those that​‌ are localized within microdomains​​ and those that propagate​​​‌ throughout the astrocyte. Due​ to the lack of​‌ reliable labeled annotations, the​​ work primarily focused on​​​‌ unsupervised 3D approaches, using​ either applied mathematics techniques​‌ (such as PCA) or​​ deep learning methods.
• –​​​‌
  Matteo Audigier (3 months​ from June 2025 to​‌ August 2025) – "Development​​ of Python modules for​​​‌ image processing in 3D+time​ fluorescence microscopy images.​‌ The goal of the​​ internship was to rewrite​​​‌ an existing method, originally​ developed in Java by​‌ Anaïs Badoual, for detecting​​ and segmenting calcium signals​​​‌ in 3D+time LLSM images​ into modular Python code.​‌ The work also focused​​ on optimizing the method​​​‌ through parallelization and alternative​ mathematical algorithms, as well​‌ as adding the extraction​​ of new quantitative metrics​​​‌ and improving the visualization​ of the results.

11.1.1 Scientific events:​​​‌ organisation

Participant: Ludger Johannnes​.

11.1.2 Scientific​​ events: selection

Participants: Charles​​​‌ Kervrann.

11.1.3​​ Journal

Participants: Charles Kervrann​​​‌, Ludger Johannes,​ Anaïs Badoual, Christian​‌ Wunder, Frédéric Lavancier​​.

11.1.4 Invited talks​​

Participants: Charles Kervrann,​​​‌ Ludger Johannes, Massiullah‌ Shafaq-Zadah, Estelle Dransart‌​‌, Frédéric Lavancier,​​ Quentin Rapilly.

• –​​​‌
  Charles Kervrann:

  • >
    "Statistical‌ and artificial methods for‌​‌ live-cell fluorescence imaging and​​ cryo-electron tomography", IMPMC–CNRS​​​‌ UMR 7590 Sminar, Sorbonne‌ University, Paris, France, January‌​‌ 2025. (seminar)
  • >
    "Machine​​ learning for molecule identification​​​‌ in 3D cryo-cellular tomograms"‌, Journées de la‌​‌ Société Française des Microscopies​​ (SFμ), Toulouse, France, July​​​‌ 2025. (invited talk)
  • >‌
    "Deep learning to detect‌​‌ and identify molecules in​​ 3D microscopy images",​​​‌ Journées INRAE Scientifiques et‌ Techniques RµI (JST RµI),‌​‌ Versailles, France, November 2025.​​ (invited talk)
  • >
    "Deep​​​‌ learning to detect and‌ identify molecules in 3D‌​‌ microscopy images", INRAE​​ DIGIT-BIO Webinar, October 2025.​​​‌ (seminar)
  • >
    "Deep learning‌ to detect and identify‌​‌ molecules in 3D microscopy​​ images", Interdisciplinary School​​​‌ MIFOBIO, Seignosse, France, October‌ 2025. (invited talk)
  • >‌​‌
    "Deep learning and convolutional​​ neural network for macromolecule​​​‌ detection and identification in‌ 3D cryo-cellular tomograms",‌​‌ FRISBI webinar, November 2025.​​ (seminar)
• –
  Ludger Johannes:​​​‌

  • >
    "GlycoSwitch: a novel‌ signaling circuit to control‌​‌ endocytosis", 5 FEBS​​ Special Meeting on Sphingolipid​​​‌ Biology/XIII International Ceramide Conference,‌ Varna, Bulgaria, May 2025.‌​‌
  • >
    "Glycan-based membrane remodeling​​ for endocytic uptake into​​​‌ cells", 5th Jacques‌ Monod meeting on Membrane‌​‌ Organization and Remodeling in​​ Roscoff, France, May 2025.​​​‌
  • >
    "GlycoSwitch: A novel‌ signaling circuit to control‌​‌ endocytosis", Annual meeting​​ of National Synchrotron Radiation​​​‌ Research Center in Hsinchu,‌ Taiwan, September 2025. (Keynote‌​‌ lecture)
  • >
    "GlycoSwitch: A​​ novel signaling circuit to​​​‌ control endocytosis", Biomembrane‌ Days, biannual conference series‌​‌ of the Max Planck​​ Institute of Colloids and​​​‌ Interfaces, Berlin, Germany, September‌ 2025.
  • >
    "GlycoSwitch —‌​‌ a novel signaling circuit​​ to control endocytosis",​​​‌ Annual GDR APPICOM meeting,‌ Dourdan, France, November 2025.‌​‌
  • >
    "GlycoSwitch: a novel​​ signaling circuit to control​​​‌ endocytosis", Annual meeting‌ of the Department of‌​‌ Cellular and Molecular Medicine​​ of the University of​​​‌ Copenhagen, Denmark, November 2025.‌ (Keynote lecture).
  • >
    "GlycoSwitch:‌​‌ a novel signaling circuit​​​‌ to control endocytosis",​ Université de Namur, Belgium,​‌ March 2025. (seminar)
  • >​​
    "GlycoSwitch: a novel signaling​​​‌ circuit to control endocytosis"​, National Taiwan University,​‌ Taipei, Taiwan, 1st and​​ 8th September 2025. (seminar)​​​‌
  • >
    "GlycoSwitch: a novel​ signaling circuit to control​‌ endocytosis", Chang Gung​​ University, Taoyuan City, Taiwan,​​​‌ September 2025. (seminar)
• –​ Massiullah Shafaq-Zadah and Estelle​‌ Dransart:
  • >
    GlycoMADNESS Symposium,​​ Paris, France, September 2025.​​​‌ (invited talk)
• –
  Frédéric​ Lavancier:

  • >
    "Estimating the​‌ hyperuniformity exponent of spatial​​ point processes", SSIAB,​​​‌ Smögen, Sweden, June 2025.​ (invited talk)
• –
  Quentin​‌ Rapilly:

  • >
    "NAGINI-3D: N-Active​​ shapes for seGmentINg 3D​​​‌ biological images", RTMFM-MAIIA​ workshop webinar, December 2025.​‌ (seminar)

11.1.5 Leadership within​​ the scientific community

Participants:​​​‌ Charles Kervrann, Ludger​ Johannes.

• –
  Charles​‌ Kervrann is head of​​ the "BioImage Informatics" node​​​‌ (ANR France-BioImaging project since​ Janurary 2024, National Research​‌ Infrastructure" for Biology and​​ Health) (co-head since 2011).​​​‌ He is member of​ the IEEE Signal Processing​‌ Society (since 2010).
• –​​
  Ludger Johannes is member​​​‌ of the American Society​ for Cell Biology (since​‌ 1996), Société de Biologie​​ Cellulaire de France (since​​​‌ 1996), French Society for​ Biophysics (SFB) and Membrane​‌ Study Group (GEM) (since​​ 2012), Société de Chimie​​​‌ Thérapeutique (SCT) (since 2014),​ Biophysical Society (since 2015),​‌ Société Française pour l'Etude​​ des Toxines (SFET) (since​​​‌ 2021), Société Française du​ Cancer (SFC) (since 2022),​‌ Société Chimique de France​​ (SCF) (since 2023).

11.1.6​​​‌ Scientific expertise

Participants: Ludger​ Johannes, Christian Wunder​‌, Frédéric Lavancier.​​

• –
  Ludger Johannes is:​​​‌

  • >
    member of the​ scientific council of the​‌ Indo-French Centre for the​​ Promotion of Advanced Research​​​‌ (IFCPAR/CEFIPRA since 2021,
  • >​
    member of the permanent​‌ SVE3 HCERES expert panel​​ (independent agency for the​​​‌ reviewing of research structures​ in France) (2021-2025),
  • >​‌
    HCERES mission IAB, Grenoble​​ (November 2025),
  • >
    member​​​‌ of the Scientific Council​ of the "Membrane Study​‌ Group" (since 2021),
  • >​​
    Member of the scientific​​​‌ council of the Chemical​ Biology Interest Group (GDR​‌ Chémobiologie) since 2021,
  • >​​
    member of the scientific​​​‌ committee of the Cellular​ and Tissular Imaging Platform​‌ (PICT) at CurieCoreTech of​​ Institut Curie since 2021,​​​‌
  • >
    member of the​ scientific council of Doctoral​‌ School BIOSIGNE (ED568) since​​ 2014.
  • >
    scientific expert​​​‌ for the reviewing of​ projects for the European​‌ Innovation Council (EIC), European​​ Research Council (ERC), Cancéropôle​​​‌ PACA, HCERES, CEFIPRA, Fondation​ Maladies Rares, Israel Science​‌ Foundation, and ANR in​​ 2025,
  • >
    member of​​​‌ the reviewing body of​ the Hellenic Foundation for​‌ Research and Innovation (HFRI)​​ since 2021.
• –
  Christian​​​‌ Wunder was :

  • >​
    Vice-chair for Evaluation of​‌ Marie Sklodowska-Curie Actions (MSCA)​​ in HorizonEurope since 2012​​​‌
  • >
    reviewer for CEFIPRA​ calls on DNA-origami in​‌ 2025,
  • >
    reviewer for​​ the proposal evaluation of​​​‌ HORIZON-HLTH-2025-01-TOOL-03 ("Leveraging multimodal data​ to advance Generative Artificial​‌ Intelligence applicability in biomedical​​ research" (GenAI4EU)) in 2025.​​​‌
• –
  Frédéric Lavancier was​ scientific expert for the​‌ reviewing of projects from​​ the Swiss National Science​​​‌ Foundation in 2025.

11.1.7​ Research administration

Participants: Charles​‌ Kervrann, Ludger Johannes​​, Massiullah Shafaq-Zadah.​​

• –
  Charles Kervrann:

  • >​​​‌
    Head of the SAIRPICO‌ Project-Team since 2023.
  • >‌​‌
    Head of the "BioImage​​ Informatics" node (ANR France-BioImaging​​​‌ project, National Research Infrastructure"‌ for Biology and Health)‌​‌ since Jan 2024 (co-head​​ since 2011).
• –
  Ludger​​​‌ Johannes:

  • >
    Deputy director‌ of Chemical Biology of‌​‌ Cancer unit (INSERM-U1339 /​​ CNRS-UMR3666)since January 2025.
  • >​​​‌
    Head of Traffic, Signaling‌ and Delivery team at‌​‌ Curie Institute since 2001.​​
  • >
    Scientific director of​​​‌ the Metabolomics and Lipidomics‌ Platform at CurieCoreTech of‌​‌ Institut Curie since 2021.​​
  • >
    Representative of Doctoral​​​‌ School BIOSIGNE (ED568) in‌ the Doctoral College of‌​‌ PSL University since 2016.​​
  • >
    Institut Curie coordinator​​​‌ of institutional partnership between‌ Institut Curie, CNRS and‌​‌ the National Centre for​​ Biological Sciences (NCBS) à​​​‌ Bangalore (India) since 2012.‌
• –
  Massiullah Shafaq-Zadah:

  • >‌​‌
    Member of the laboratory​​ council of UMR3666.

11.2.1 Supervision

• –
  PhD‌​‌ supervision

  • >
    Quentin Rapilly​​ (PhD defended in December​​​‌ 2025, Inria grant): "Hybrid‌ CNN-Snake algorithms for quantitative‌​‌ analysis of 3D+time live-cell​​ images" (started in December​​​‌ 2022, supervised by A.‌ Badoual and C. Kervrann).‌​‌
  • >
    Léo Maury (PhD​​ in progress, ANR Polariscaopia​​​‌ grant): "Machine learning and‌ optimization methods for 3D‌​‌ vector-valued microscopy image reconstruction"​​ (started in January 2024,​​​‌ supervised by C. Kervrann).‌
  • >
    Chencheng Gu (PhD‌​‌ in progress, ANR Polariscopia​​ grant): "Spatial statistics and​​​‌ machine learning for molecular‌ dynamics analysis in polarized‌​‌ microscopy" (started in March​​ 2024, supervised by C.​​​‌ Kervrann).
  • >
    Mounir Messaoudi‌ (PhD in progress, ANR‌​‌ DeepNer grant): "Machine learning​​ for 3D cryo-electron tomogram​​​‌ analysis: localization, identification, and‌ spatial organization of macromolecules‌​‌ in cells" (started in​​ May 2024, supervised by​​​‌ C. Kervrann).
  • >
    Ferdinand‌ Plesse–Costa (PhD in progress,‌​‌ Inria grant): "Statistical and​​ machine learning for image​​​‌ reconstruction and super-resolution in‌ fluorescence polarized microscopy" (started‌​‌ in November 2024, supervised​​ by C. Kervrann, B.​​​‌ Hajj (CNRS-UMR168 - Institut‌ Curie), and C. Vaz‌​‌ Rimoli).
• –
  Postdoc supervision​​

  • >
    Ilyes Hamitouche (INSERM​​​‌ gant), since June 2023,‌ supervised by M. Shafaq-Zadah,‌​‌ E. Dransart, C. Kervrann,​​ and L. Johannes.
  • >​​​‌
    Xingyi Cheng (ANR Omega-MEMDO),‌ since November 2025, supervised‌​‌ by C. Kervrann.
• –​​
  Master supervision

  • >
    Matteo​​​‌ Audigier, Grenoble INP –‌ ENSIMAG, Master 1, from‌​‌ Jun 2025 until Aug​​ 2025, supervised by A.​​​‌ Badoual.
  • >
    Enzo Choffat,‌ ESIEA – Laval, Master‌​‌ 2, from Apr 2025​​ until Aug 2025, supervised​​​‌ by A. Masson
  • >‌
    Maelys Hanoire, ESIEA –‌​‌ Laval, Master 1, from​​ Apr 2025 until Aug​​​‌ 2025, supervised by A.‌ Badoual.
  • >
    Carel Ntsoumou‌​‌ Lihoula, Ecole Nationale Supérieure​​ d'Ingénieurs du Mans (ENSIM),​​​‌ Master 2, from Mar‌ 2025 until Sep 2025,‌​‌ supervised by A. Masson.​​

11.2.2 Juries

Participants: Charles​​​‌ Kervrann, Ludger Johannes‌.

• –
  Charles Kervrann:‌​‌

  • >
    President of the​​ PhD defense committee of​​​‌ :

    • X. Cheng -‌ "Automated image processing of‌​‌ membranes and membrane proteins​​​‌ in cryo-electron tomography".​ UMR168 Institut Curie, University​‌ PSL, supervised by M.​​ Dezi and D. Levy.​​​‌ (defense in June 2025)​
    • H. Barral - "Self-supervised​‌ learning for infrared video​​ enhancement". Centre Borelli,​​​‌ ENS Paris-Saclay, Unviersíty Paris-Saclay,​ supervised by P. Arias​‌ and A. Davy (defendse​​ in October 2025)
  • >​​​‌
    Reviewer for the PhD​ theses of :

    • E.​‌ Grandgirard - "Simulation-based deep​​ learning for 3D nuclei​​​‌ segmentation and cell type​ classification in label-free imaging"​‌. Unviversity of Toulouse,​​ supervised by C. Sengès​​​‌ and M. Serrurier. (defense​ in November 2025)
    • F.​‌ Robert - "3D semantic​​ cell segmentation via propagation​​​‌ of 2D results and​ integration of intercellular priors"​‌. University of Bordeaux,​​ supervised by B. Denis​​​‌ de Senneville and C.F.​ Grosset. (defense in September​‌ 2025)
  • >
    Member of​​ the PhD defense committee​​​‌ of :

    • T. Bonte​ - "Computer vision for​‌ the phenotypic profyling of​​ the cell cycle".​​​‌ Mines ParisTech, University PSL,​ supervised by T. Walter.​‌ (defense in April 2025)​​
• –
  Ludger Johannes:

  • >​​​‌
    Reviewer and President of​ the PhD thesis of​‌ :

    • S. Salame -​​ "A comprehensive characterization of​​​‌ lysophospholipid acyltransferases reveals the​ regulation and functions of​‌ glycerophospholipid lipid tails".​​ Université Nice Côte d’Azur,​​​‌ supervised by T. Harayama.​ (defense in May 2025)​‌
  • >
    Member of the​​ PhD defense of:

    • S.​​​‌ Ruggiero - "Characterisation of​ the functions of the​‌ protein arginine methyltransferase PRMT4/CARM1​​ in triple-negative breast cancer"​​​‌. Université Paris Sciences​ et Lettres, supervised by​‌ T. Dubois. (defense in​​ November 2025)

11.2.3 Educational​​​‌ and pedagogical outreach

Participants:​ Charles Kervrann, Ludger​‌ Johannes, Anaïs Badoual​​, Frédéric Lavancier,​​​‌ Quentin Rapilly.

• –​
  Charles Kervrann:

  • >
    Master​‌ 2: "From Bioimage Processing​​ to BioImage Informatics",​​​‌ 3 hours (4.5 hours​ TD), coordinator of the​‌ module (30 hours /​​ equiv 45 hours TD),​​​‌ Master 2 Research IRIV,​ Telecom-Physique Strasbourg and University​‌ of Strasbourg.
  • >
    Engineer​​ Degree (3rd year) and​​​‌ Master 2 Statistics and​ Mathematics: "Statistical Models and​‌ Image Analysis", 30​​ hours (45 hours TD),​​​‌ Ecole Nationale de la​ Statistique et de l'Analyse​‌ de l'Information (ENSAI), Bruz.​​
  • >
    "Basics in deep​​​‌ learning and convolutional neural​ networks for microscopy image​‌ analysis", 1 hour,​​ Interdisciplinary School MIFOBIO (300​​​‌ participants), Seignosse, France, October​ 2025.
  • >
    "Statistical and​‌ artificial methods for live-cell​​ fluorescence imaging and cryo-electron​​​‌ tomography", 2 hours,​ Master 2 Research "Digital​‌ Health", University of Rennes,​​ France, November 2025.
• –​​​‌
  Ludger Johannes

  • >
    Master​ 2: "Endocytic trafficking" for​‌ "Chemical Frontiers of Living​​ Cells", 2 hours, PSL​​​‌ Research University.
  • >
    Lecture​ "Molecular Biology of the​‌ Cell", 2 hours,​​ Institut Pasteur/Curie, lectures and​​​‌ lab training.
  • >
    Lecture​ "Lipids and Glycans" for​‌ “Chemical Frontiers of Living​​ Cells” course, 2 hours,​​​‌ PSL Research University.
• –​
  Anaïs Badoual:

  • >
    Master​‌ 2 degree: "Analysis of​​ Image Sequences", 9​​​‌ hours (13.5 hours TD)​ and 3 hours Practical​‌ Courses (2 hours TD),​​ Master 2 Research SiVos,​​​‌ ISTIC & University of​ Rennes.
  • >
    Master 2​‌ degree: "Object Tracking in​​ microscopy", 1.75 hours​​ (2.63 hours TD), Master​​​‌ 2 Research IRIV, Telecom-Physique‌ Strasbourg.
  • >
    Engineer Degree‌​‌ (3rd year) and Master​​ 2 Statistics and Mathematics:​​​‌ "Statistical Models and Image‌ Analysis", 7.5 hours‌​‌ practical course (5 hours​​ TD), Ecole Nationale de​​​‌ la Statistique et de‌ l'Analyse de l'Information (ENSAI),‌​‌ Bruz.
  • >
    "Human-in-the-loop for​​ cell image segmentation",​​​‌ workshop, 2 1h45-sessions, Interdisciplinary‌ School MIFOBIO, Seignosse, France,‌​‌ October 2025.
  • >
    Examiner​​ in a Selection Committee​​​‌ of 1st year-students, Mines-Telecom,‌ 2 days, 2025.
• –‌​‌
  Frédéric Lavancier:

  • >
    Engineer​​ degree (2nd year) :​​​‌ “Generalized and linear regression‌ models”, 24 hours‌​‌ (36 hours TD), coordinator,​​ ENSAI.
  • >
    Engineer degree​​​‌ (2nd year) : “Markovian‌ models”, 21 hours‌​‌ (31.5 hours TD), coordinator,​​ ENSAI.
  • >
    Doctoral course​​​‌ : “A short introduction‌ to models and inference‌​‌ for spatial point processes”​​, 4 hours, Cotonou,​​​‌ Benin, January 2025.
• –‌
  Quentin Rapilly:

  • >
    Licence‌​‌ (L2) degree: "Introduction to​​ probabilities theory", 20​​​‌ hours (TD), INSA Rennes‌
  • >
    Licence (L1) degree:‌​‌ "Introduction to mathematics for​​ engineering", 16 hours​​​‌ (Lecture/TD), INSA Rennes/ENSAB

11.3.1 Productions (articles,‌​‌ videos, podcasts, serious games,​​ ...)

E. MacDonald, C.​​​‌ Nugues, L. Johannes. Casser‌ du sucre sur le‌​‌ dos des cellules cancéreuses​​, 42(1): 36-28, Médecine/Sciences,​​​‌ 2026, DOI: 10.1051/medsci/2025249.‌

International​​​‌ journals

• 8 articleM.​Mohamad Harastani, G.​‌Gurudatt Patra, C.​​Charles Kervrann and M.​​​‌Mikhail Eltsov. Template​ Learning: deep learning with​‌ domain randomization for particle​​ picking in cryo-electron tomography​​​‌.Nature Communications16​8833October 2025,​‌ 1-15HALDOIback​​ to textback to​​​‌ text
• 9 articleS.​Sébastien Herbreteau and C.​‌Charles Kervrann. A​​ unified framework of non-local​​​‌ parametric methods for image​ denoising.SIAM Journal​‌ on Imaging Sciences18​​1January 2025,​​​‌ 89-119HALDOIback​ to textback to​‌ text
• 10 articleH.​​Hugo Lachuer, E.​​​‌Emmanuel Moebel, A.-S.​Anne-Sophie Macé, A.​‌Arthur Masson, K.​​Kristine Schauer and C.​​​‌Charles Kervrann. Deep​ learning detection of dynamic​‌ exocytosis events in fluorescence​​ TIRF microscopy.PLoS​​​‌ Computational Biology2110​October 2025, e1013556​‌HALDOIback to​​ textback to text​​​‌
• 11 articleE.Ewan​ Macdonald, A.Alison​‌ Forrester, C.Cesar​​ Valades-Cruz, T. D.​​​‌Thomas D. Madsen,​ J. H.Joseph H.R.​‌ Hetmanski, E.Estelle​​ Dransart, Y.Yeap​​​‌ Ng, R.Rashmi​ Godbole, A. A.​‌Ananthan Akhil Shp,​​ L.Ludovic Leconte,​​​‌ V.Valérie Chambon,​ D.Debarpan Ghosh,​‌ A.Alexis Pinet,​​ D.Dhiraj Bhatia,​​​‌ B.Bérangère Lombard,​ D.Damarys Loew,​‌ M.Martin Larsen,​​ H.Hakon Leffler,​​​‌ D. J.Dirk J​ Lefeber, H.Henrik​‌ Clausen, A.Anne​​ Blangy, P.Patrick​​​‌ Caswell, M.Massiullah​ Shafaq-Zadah, S.Satyajit​‌ Mayor, R.Roberto​​ Weigert, C.Christian​​​‌ Wunder and L.Ludger​ Johannes. Growth factor-triggered​‌ de-sialylation controls glycolipid-lectin-driven endocytosis​​.Nature Cell Biology​​​‌273February 2025​, 449-463HALDOI​‌back to textback​​ to text
• 12 article​​​‌E.Ewan Macdonald,​ L.Ludger Johannes and​‌ C.Christian Wunder.​​ Acidification on the plasma​​​‌ membrane.Current Opinion​ in Cell Biology95​‌August 2025, 102531​​HALDOIback to​​​‌ textback to text​
• 13 articleE.Etienne​‌ Meunier and P.Patrick​​ Bouthemy. Segmenting the​​ motion components of a​​​‌ video: A long-term unsupervised‌ model.IEEE Transactions‌​‌ on Pattern Analysis and​​ Machine IntelligenceEarly access​​​‌September 2025, 1-12‌HALDOI
• 14 article‌​‌S.Salomé Papereux,​​ L.Ludovic Leconte,​​​‌ C. A.Cesar Augusto‌ Valades-Cruz, T.Tianyan‌​‌ Liu, J.Julien​​ Dumont, Z.Zhixing​​​‌ Chen, J.Jean‌ Salamero, C.Charles‌​‌ Kervrann and A.Anaïs​​ Badoual. DeepCristae, a​​​‌ CNN for the restoration‌ of mitochondria cristae in‌​‌ live microscopy images.​​Communications Biology81​​​‌February 2025, 320‌In press. HALDOI‌​‌back to textback​​ to text
• 15 article​​​‌M.Massiullah Shafaq-Zadah,‌ E.Estelle Dransart,‌​‌ I.Ilyes Hamitouche,​​ C.Christian Wunder,​​​‌ V.Valérie Chambon,‌ C. A.Cesar A‌​‌ Valades-Cruz, L.Ludovic​​ Leconte, N. K.​​​‌Nirod Kumar Sarangi,‌ S.Sarah Cianférani-Sanglier,‌​‌ J.Jack Robinson,​​ R.Raju Regmi,​​​‌ A. D.Aurélie Di‌ Cicco, S.-K.Siau-Kun‌​‌ Bai, R.Richard​​ Bartels, A.Agnès​​​‌ Hovasse, U. J.‌Ulf J Nilsson,‌​‌ H.Hakon Leffler,​​ T. E.Tia E​​​‌ Keyes, D.Daniel‌ Lévy, S.Stefan‌​‌ Raunser, D.Daniel​​ Roderer and L.Ludger​​​‌ Johannes. Spatial N-glycan‌ rearrangement on α₅β₁ integrin‌​‌ nucleates galectin-3 oligomers to​​ determine endocytic fate.​​​‌Nature Communications161‌2025, 9461HAL‌​‌DOIback to text​​back to text
• 16​​​‌ articleP.Pierre Ucla‌, J.Joanne Lê-Chesnais‌​‌, H.Henri Ver​​ Hulst, X.Xingming​​​‌ Ju, I.Isabel‌ Calvente, E.Elnaz‌​‌ Nematollahi, L.Ludovic​​ Leconte, J.Jean​​​‌ Salamero, I.Isabelle‌ Bonnet, C.Catherine‌​‌ Monnot, H. D.​​Hélène D Moreau,​​​‌ J.Jessem Landoulsi,‌ V.Vincent Semetey and‌​‌ S.Sylvie Coscoy.​​ Quantifying cell traction forces​​​‌ at the single-fiber scale‌ in 3D: An approach‌​‌ based on deformable photopolymerized​​ fiber arrays.Proceedings​​​‌ of the National Academy‌ of Sciences of the‌​‌ United States of America​​12242October 2025​​​‌HALDOI

International peer-reviewed‌ conferences

• 17 inproceedingsA.‌​‌Antonin Deschemps, E.​​Eric Grégoire, J.​​​‌Juan Martinez, A.‌Aurelie Vaurijoux, P.‌​‌Pascale Fernandez, D.​​Delphine Dugue, E.​​​‌Emmanuel Moebel, G.‌Gaëtan Gruel, M.‌​‌Mohamed‐amine Benadjaoud and C.​​Charles Kervrann. Explainable​​​‌ Artificial Intelligence Approach Using‌ Low-Dimensional Visualization and Ensembling‌​‌ Uncertainty Quantification for Rare​​ Chromosomal Aberration Detection in​​​‌ Cytogenetic Imaging.2025‌ Fourteenth International Conference on‌​‌ Image Processing, Theory, Tools​​ & Applications (IPTA)IPTA​​​‌ 2025 - Fourteenth International‌ Conference on Image Processing,‌​‌ Theory, Tools &amp; Applications​​Istanbul, TurkeyIEEE2025​​​‌, 1-6HALDOI‌back to textback‌​‌ to textback to​​ text
• 18 inproceedingsQ.​​​‌Quentin Rapilly, A.‌Anaïs Badoual, P.‌​‌Pierre Maindron, G.​​Guenaelle Bouet and C.​​​‌Charles Kervrann. Prediction‌ of Parametric Surfaces for‌​‌ Multi-Object Segmentation in 3D​​ Biological Imaging.Lecture​​​‌ Notes in Computer Science‌ (LNCS)SSVM 2025 -‌​‌ 10th International Conference on​​​‌ Scale Space and Variational​ Methods in Computer Vision​‌15667Totnes, United Kingdom​​Springer2025, 255–268​​​‌HALDOIback to​ textback to text​‌
• 19 inproceedingsQ.Quentin​​ Tallon, J.Juan​​​‌ Martinez Guerrero, E.​Eric Gregoire, P.​‌Pascale Fernandez, D.​​Delphine Dugue, G.​​​‌Gaëtan Gruel, C.​Charles Kervrann and M.​‌Mohamed‐amine Benadjaoud. Diffusion​​ model uniform manifold filtering​​​‌ for classification of small​ datasets with underrepresented classes:​‌ Application to chromosomal aberration​​ microscopy detection.Proceedings​​​‌ Volume Seventeenth International Conference​ on Machine Vision (ICMV​‌ 2024)ICMV 2024 -​​ 17th International Conference on​​​‌ Machine Vision13517Edinburgh,​ Scotland, United KingdomSPIE​‌ Digital LibraryFebruary 2025​​, 135170KHALDOI​​​‌back to text

Scientific​ book chapters

• 20 inbook​‌D.Daniel Sage and​​ A.Anaïs Badoual.​​​‌ Image Processing and Image​ Analysis in Microscopy.​‌Photonic Imaging for Biology:​​ From Conventional Microscopy to​​​‌ Super‐ResolutionChapitre 10Ed.,​ WileyOctober 2025,​‌ 205–237HALback to​​ textback to text​​​‌

Doctoral dissertations and habilitation​ theses

• 21 thesisQ.​‌Quentin Rapilly. A​​ hybrid CNN-snake approach for​​​‌ localization, segmentation, and shape​ representation in 3D biological​‌ imaging.Université de​​ rennesDecember 2025HAL​​​‌back to textback​ to text
• 22 thesis​‌Q.Quentin Tallon.​​ Artificial Intelligence for the​​​‌ automatic detection of chromosomal​ translocations : application to​‌ retrospective dosimetry based on​​ FISH imaging.Université​​​‌ de RennesJanuary 2025​HALback to text​‌

Reports & preprints

• 23​​ miscY.Yasmine Hachani​​​‌, P.Patrick Bouthemy​, E.Elisa Fromont​‌, S.Sylvie Ruffini​​, L.Ludivine Laffont​​​‌ and A.Alline de​ Paula Reis. Supervised​‌ contrastive learning for cell​​ stage classification of animal​​​‌ embryos.2025HAL​
• 24 miscL.Louise​‌ Régnier, C. V.​​Caio Vaz Rimoli,​​​‌ S.Simli Dey,​ F.-C.Feng-Ching Tsai,​‌ G. A.Guillermo A​​ Orsi, S.Sophie​​​‌ Brasselet and B.Bassam​ Hajj. Polarization MultiFocus​‌ Microscopy for volumetric super-resolution​​ and orientation imaging of​​​‌ biofilaments.November 2025​HALDOIback to​‌ textback to text​​

Other scientific publications

• 25​​​‌ inproceedingsA.Anaïs Badoual​, M.Misa Arizono​‌, M.Mathieu Ducros​​, U. V.U​​​‌ Valentin Nägerl and C.​Charles Kervrann. Analysis​‌ of astrocytic Ca2+ signaling​​ revealed by LLSM.​​​‌IABM 2025 - Colloque​ Français d'Intelligence Artificielle en​‌ Imagerie BiomédicaleNice, France​​2025HAL
• 26 inproceedings​​​‌H.Hugo Lachuer,​ E.Emmanuel Moebel,​‌ A.-S.Anne-Sophie Macé,​​ A.Arthur Masson,​​​‌ K.Kristine Schauer and​ C.Charles Kervrann.​‌ Deep learning detection of​​ exocytosis events in TIRF​​​‌ microscopy.IABM 2025​ - Colloque français d'Intelligence​‌ Artificielle pour le Bio-Médical​​Nice, France2025HAL​​​‌back to text
• 27​ inproceedingsQ.Quentin Rapilly​‌, A.Anaïs Badoual​​, P.Pierre Maindron​​​‌, G.Guenaelle Bouet​ and C.Charles Kervrann​‌. Prediction of Parametric​​ Surfaces for Multi-Object Segmentation​​​‌ in 3D Biological Imaging​ (Poster).IABM 2025​‌ - Colloque français d'Intelligence​​ Artificielle pour le Bio-Médical​​Nice, France2025HAL​​​‌back to text