Question

How do B lymphocytes progress through multiple functional states to gain the ability to produce high affinity antibodies?

Challenges

Antibodies are lifesaving biomolecules – major effectors of vaccines efficacy and popular therapeutics for cancer treatment. In natural immunity, highly potent antibodies are produced by the descendants of a single antigen-specific B cell after a process of cellular and molecular maturation. In the aim of designing new therapies for challenging infections and cancers, understanding the distinct steps of B lymphocyte maturation is a major focus of immunology research.

Novel analytical methods enable the parallel analysis of antibody gene sequences and genome-wide gene expression in hundreds to thousands of single B cells, opening new avenues to dissect the mechanisms of B cell maturation. Integrating and analyzing the massive amount of data produced in those experiments will be an exciting challenge in the coming years.

Type of data

For thousands of B cells, sorted from healthy human lymphoid organs, human tumors, and mouse lymphoid organs after vaccination:

- Gene expression data (single-cell RNAseq)

- Cell surface phenotype and size/aspect characteristics (flow cytometry)

- Antibody gene sequence (computed from single-cell RNAseq)

Methods

Fluorescence-activated Cell Sorting (FACS), single-cell RNA sequencing, sequence alignment and mapping on genome, sequence reconstruction from short read RNAseq, dimensionality reduction and clustering, pseudotime analysis, cell – cell interaction networks, gene – gene interaction networks.

Question

How proteins unfold under applied force?

Challenges

The mechanical stability of a protein is determined by the energy landscape which describes how a protein will unfold under forces. Single molecule force spectroscopies such as atomic force microscopy (AFM) allow to measure the forces required to unfold individual proteins at different pulling velocities. These large datasets can be interpreted using different theoretical scenarios to reconstruct the unfolding energy landscape.

Type of data

We have thousands of AFM force measurements of individual unfolding events of an immunoglobulin protein domain obtained at different pulling velocities.

Methods

We will determine the unfolding force of each event at the different velocities, pool them into a dynamic force spectrum and fit the appropriate theoretical model. Data will be compared with available molecular dynamics simulations. We will employ methods and tools of data analysis and optimization and concepts of statistical mechanics. Programming in matlab and/or python.

Question

How much membrane proteins interact with cytoskeleton in the frame of the immune synapse?

In our labs, we use a collection of force based biophysical techniques to study the early T cell mechanotransduction (Atomic Force Microscopy (AFM)^1,2, Biomembrane Force Probe (BFP), Flow Chamber³, Traction Force Microscopy, Optical Tweezers) aside of smart decorated substrates  and advanced quantitative surface microscopies^4–6. We focus on the particular moment where the ligation of the T cell receptors (TCR) with antigens displayed on partner substrates is the very first step in triggering the T cells response. It has been demonstrated that this passes through the formation of the so called 'immune synapse' (IS) that involves additional molecules like integrins and CD45 ⁷.

The mechanosensitive nature of the T-cell/APC as well as mechanics of the TCR-complex is at the moment extensively studied, but the question of the existence, if any, of a direct physical interaction between the TCR and the cytoskeleton is still not known ⁸. Such an interaction may have some profound impact on the motion and recruitment of TCR at the interface between the T cell and the partner cell or substrate. In particular, it may explain complex behavior, like biphasic response to substrate elasticity, which proposed mechanism crucially depends on this coupling^9,10.

In order to explore this link, we used optical tweezers to pull membrane tubes from T cells using different antibodies as handle, each targetting a given protein present at the IS, here, TCR/CD3, LFA1, CD45. We also pulled nanotubes after disrupting the actin cytoskeleton using Latrunculin.

We then develop a theoretical mechanical model that retains the key biophysical properties of the coupled system consisting of the T cell and the bound receptor/ligand pair on one hand and the force transducer on the other.

The model provides a fine description of the force curves, and the fitting strategy we developed allowed us to extract informations about cellular as well as molecular viscoelastic parameters. Our model also furnishes a way to estimate key viscoelastic parameters, which accounts for the stiffness of the ligand/receptor complex, the cell rigidity and viscosity.

The goal here will be to explore the data, by testing hypothesis, to validate the proposed theoretical model.

Challenges

Going from experimental data to publication ready representations

Type of data

For thousands of optical tweezers force curves, data tables of output from mechanical model fitting.

Methods

Cleaning the data, organising it, subsetting it.
Building relevant representations.

Producing advanced statistical analysis.

→ Python programming

Some references

1. Sadoun, A. et al. Controlling T cells spreading, mechanics and activation by micropatterning. Sci. Rep. 11, 6783 (2021).

2. Zak, A. et al. Rapid viscoelastic changes are a hallmark of early leukocyte activation. Biophys. J. 120, 1692–1704 (2021).

3. Limozin, L. et al. TCR-pMHC kinetics under force in a cell-free system show no intrinsic catch bond, but a minimal encounter duration before binding. Proc. Natl. Acad. Sci. U. S. A. 116, 16943–16948 (2019).

4. Sadoun, A. et al. Dissecting T-cell mechanosensing at molecular and cellular scales. Wiley Analytical Science https://analyticalscience.wiley.com/do/10.1002/was.00010013 (2020).

5. Benard, E., Nunès, J. A., Limozin, L. & Sengupta, K. T Cells on Engineered Substrates : The Impact of TCR Clustering Is Enhanced by LFA-1 Engagement. 9, 1–12 (2018).

6. Dejardin, M.-J. et al. Lamellipod Reconstruction by Three-Dimensional Reflection Interference Contrast Nanoscopy (3D-RICN). Nano Lett. (2018) doi:10.1021/acs.nanolett.8b03134.

7. Limozin, L. & Puech, P.-H. Membrane Organization and Physical Regulation of Lymphocyte Antigen Receptors: A Biophysicist’s Perspective. J. Membr. Biol. 252, 397–412 (2019).

8. Puech, P.-H. & Bongrand, P. Mechanotransduction as a major driver of cell behaviour: mechanisms, and relevance to cell organization and future research. Open Biol. 11, 210256 (2021).

9. Dillard, P., Varma, R., Sengupta, K. & Limozin, L. Ligand-Mediated Friction Determines Morphodynamics of Spreading T Cells. Biophys. J. 107, 2629–2638 (2014).

10. Wahl, A. et al. Biphasic mechanosensitivity of T cell receptor-mediated spreading of lymphocytes. Proc. Natl. Acad. Sci. 116, 5908–5913 (2019).

Question

How does neural population dynamics relate to behaviorally-relevant computations?

Challenges

At any given instant, hundreds of billions of cells in our brains are lighting up in a complicated yet highly coordinated manner to give rise to our thoughts, percepts, and movements. A single neuron may be connected to thousands of other cells, sending out and receiving information through electrical impulses called spikes. From an engineering perspective, these spikes form a signal that may be viewed as a series of ones and zeros rapidly unfolding in time. Altogether, these signals reflect the ongoing computations taking place inside the nervous system, and as such, constitute a window into the brain’s inner workings. Recent advances in recording techniques have allowed experimenters to collect data from hundreds to thousands of neurons simultaneously while animals perform simple tasks. Dealing with such high-dimensional data poses important technical challenges that require elaborate methods for data mining and analysis. In this project, students will deal with datasets of increasing complexity and develop a set of analyses to extract meaningful information from the data.

Type of data

Data that will be shared by the teaching staff, under the BIDS standard data organization which is currently being extended to electrophysiology data by the members of the INT and the CONECT team. The data we will use consists of:

• publicly available recordings from the dorsomedial frontal cortex of NHPs performing a time-interval reproduction task (https://github.com/jazlab/Meirhaeghe2021)
• publicly available recordings from the motor cortex (M1/PMd) during an instructed reach-to-grasp task (https://www.nature.com/articles/sdata201855, available at the following URL in BIDS: https://gin.g-node.org/sprenger/multielectrode_grasp/src/bep_animalephys)
• https://www.biorxiv.org/content/10.1101/2021.03.30.437692v4 : V1, gratings-like, natural stimulations : extracellular electrophy recordings in cat V1

Methods

Data visualisation, neural decoding, principal component analysis, kinematic and geometric analyses of neural trajectories in high-dimensional space, hypothesis-testing, null distributions and statistics.