Summer Research Program - SRP brochure 2023

Summer Research Program (SRP)

EPFL School of Life Sciences Impact Report 2023

Table of contents Summer Research Program...................................................................................................................4 List of participants / Activities...............................................................................................................6

Participating students..............................................................................................................................8 Diana Laura Aguilera Lozano ............................................................................................................................. 10 Boshi An..................................................................................................................................................................... 12 Karina Araslanova................................................................................................................................................... 14 Alefiya Taher Dohadwala...................................................................................................................................... 16 Krishna Gajjar........................................................................................................................................................... 18 Alireza Gargoori Motlagh..................................................................................................................................... 20 Mahsa Geramimanesh......................................................................................................................................... 22 Katarzyna Glinka..................................................................................................................................................... 24 Dessislava Ilieva ..................................................................................................................................................... 26 Christian Kenneth .................................................................................................................................................. 28 Justine Lam ............................................................................................................................................................ 30 Rosemarie Faustina Le ....................................................................................................................................... 32 Addison Liu ............................................................................................................................................................. 34 Alessandro Lucatelli ............................................................................................................................................ 36 Mariia Minaeva ....................................................................................................................................................... 38 Francesca Montellato .......................................................................................................................................... 40 Anar Nasanjargal .................................................................................................................................................. 42 Cailyn Mae Ong ..................................................................................................................................................... 44 Aleksandr Refeld .................................................................................................................................................. 46 Khondamir Rustamov ......................................................................................................................................... 48 Anushree Sabnis .................................................................................................................................................. 50 Ruam Salaroli ........................................................................................................................................................ 52 Nora Wittmann ...................................................................................................................................................... 54 Nora Xiao ................................................................................................................................................................ 56

Donors..........................................................................................................................................................58 Thank you................................................................................................................................................... 62

A word from the program directors This past summer, the Summer Research Program (SRP) welcomed 24 talented international students into our life sciences labs where they experienced working on cutting-edge interdisciplinary biomedical research projects. Every year, a common theme emerges in their feedback: SRP was an unforgettable life-changing experience both scientifically and personally. In addition to expanding their scientific know-how and thinking, their eyes are opened to the complexities of a new culture and the challenges of an ever-changing world.

Participating SRP students are rigorously selected from over 700 applications from everywhere in the world. We strive for diverse students representing the global south and north who show strong potential as future scientific leaders. All this is done, while showcasing our outstanding labs and EPFL as an institution of excellence to the world. Thus, SRP is an exciting opportunity to educate and make lasting connections with young international students.

All of this is made possible by the investment of time and energy by our staff and the continual financial support by the School of Life Sciences and our external donors.

SRP is an adventure well worth participating in! Read more to find out why!

Bruno Correia

EPFL | Summer Research Program 2023

Johannes Gräff

3

Summer Research Program This summer, our laboratories welcomed 24 outstanding

future researchers representing 19 different nationalities.

The SRP strives to discover and nurture talent and potential worldwide, from all backgrounds.

Hands-on research experience

Weekly Workshops

Each student is matched with a specific laboratory based on the student’s interest and background. The student works as a member of the research group for a period of eight weeks under the supervision of the lab head or a member of the research group on a specific project related to the laboratory’s research. The participating labs come from our five institutes which study a variety of interdisciplinary research questions in neuroscience, global health, bioengineering and cancer.

The participants attend a series of weekly workshops given by faculty and researchers from the EPFL and outside experts. The workshops cover a variety of topics ranging from scientific integrity, climate change and learning about a professor’s life and professional experience. The goal of these workshops is to develop a strong sense of community in the group while preparing the students for a future career in life science as leaders.

Poster presentation Each participant presents a poster of their results obtained during the summer at the closing student symposium. This provides an opportunity for participants to discuss their data and results with their peers and the scientific community at the EPFL and UNIL.

Facts

Benefits to the Students

Over the last 17 years, SRP has grown to be a truly international program.

The participants:

• • • • •

16% of the participants return to the EPFL to continue their studies in some capacity.

381 students were selected from over 9150 applicants.

A living stipend is provided and the majority of travel expenses are covered by program sponsors and the School of Life Sciences.

Housing in Lausanne for the duration of the stay is provided.

The working language is English.

Selection criteria

• • • • • •

Gain hands-on interdisciplinary research experience.

Put classroom learning into action to solve current research problems.

Improve critical thinking skills by evaluating scientific information, designing experiments and testing hypotheses.

Experience the excitement and challenges of scientific research gaining insight into what a research career entails.

Present their research to the local scientific community during lab meetings and at the closing symposium.

Prepare for future independent research projects and advanced research in graduate school.

Fellowships are awarded on a competitive basis, applicants are required to have completed two years of undergraduate study in a life sciences program, be in the top 5% of their class, demonstrate a strong potential and have a keen interest in a life sciences career.

EPFL | Summer Research Program 2023

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List of participants / Activities

Name

Country

Lab

Sponsor

Diana Aguilera Lozano

Mexico

Radenovic

Hirzel Stiftung

Boshi An

China

Mathis A.

School of Life Sciences

Karina Araslanova

Russia

Aztekin

Fondation Recherche Cancer ISREC

Alefiya Taher Dohadwala

Singapor

Maerkl

School of Life Sciences

Krishna Gajjar

Kenya

McCabe

McCall MacBain Foundation

Alireza Gargoori Motlagh

Iran

La Manno

Fondation Recherche Cancer ISREC

Mahsa Geramimanesh

Iran

Rahi

Debiopharm

Katarzyna Glinka

Poland

Manley

School of Life Sciences

Dessislava Ilieva

Bulgaria

Gönczy

Fondation Recherche Cancer ISREC

Christian Kenneth

Indonesia

Dal Peraro

McCall MacBain

Justine Lam

Canada

Courtine

McCall MacBain

Rosemarie Faustina Le

USA

Blanke

ThinkSwiss

Addison Liu

USA

Sakar

ThinkSwiss

Alessandro Lucatelli

Italy

Van De Ville

Fondation Mia et Mile Pinkas

Mariia Minaeva

Russia

Barth

School of Life Sciences

Francesca Montellato

Italy

Persat

UCB Suisse

Anar Nasanjargal

Mongolia

Schuhmacher

McCall MacBain Foundation

Cailyn Mae Ong

Philippines

Fellay

Fondation Recherche Cancer ISREC

Aleksandr Refeld

Russia

Tang

School of Life Sciences

Khondamir Rustamov

Uzbekistan

Correia

Fondation Protechno

Anushree Sabnis

India

Ijspeert

McCall MacBain Foundation

Ruam Salaroli

Brazil

Gräff

McCall MacBain Foundation

Nora Wittmann

Hungary

Blokesch

School of Life Sciences

Nora Xiao

USA

Gerstner

ThinkSwiss

Research : 8 weeks of hands-on research experience in EPFL labs Visits : Campus Biotech Geneva, Agora Cancer Research Center Lausanne Talks and workshops : Alumni testimonials, principal investigator testimonials, climate fresk, scientific integrity Social activities : BBQs, hike in Swiss mountains, cultural awareness events Highlight : Joint Symposium with UNIL Summer undergraduate research program (SUR)

EPFL | Summer Research Program 2023

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Participating students

EPFL | Summer Research Program 2023

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Diana Laura Aguilera Lozano

National Autonomous University of Mexico Radenovic Lab of Nanoscale Biology Supervisor : Khalid Ibrahim

Abstract Assessing the Morphological and Material Properties of Dhh1 Condensates Using Machine Learning and Differential Dynamic Microscopy

The Dhh1 protein functions as a regulator of liquidliquid phase separation in biocondensates, actively contributing to RNA metabolism. In this study, we quantified the changes in morphological parameters of Dhh1 droplets over time under various conditions, including pH, presence of ATP, Poly(U) length, and temperature. Additionally, we employed differential dynamic microscopy to characterize the diffusion properties of these condensates.

EPFL | Summer Research Program 2023

11

Boshi An

Peking University A. Mathis Lab of Computational Neuroscience & AI Supervisor : Alberto Chiappa

Transformer The core of recent Large Language Models

FeedForward Network

Reinforcement Learning The machine learning paradigm inspired from animal learning in the trial-and-error manner

Output

Output

Feed Forward

X

V

Simulators

V

X

Powers the efficient training of RL agents by simulating the physical world

Softmax Transformer

Attention QKT

Action Positional Encoding

QKT

Q

K

Pos

+ Embedding

V

Q

K

Pos

+

V

Myosuite: a collection of musculoskeletal environments and tasks powered by MuJoCo physics engine.

Embedding

Observation

DexterousHands: a collection of robotic hand environments powered by IsaacGym physics engine, can run fully on GPU, providing incredible efficiency.

Reward

Our Solution A transformer-based network which takes observations as tokens, solving the muscle control problem in a sequence-to-sequence manner

Our Problem Human have a great control of its muscles, we can learn to do different tasks with a single intrinsic control policy, which is our central nervous system.

Task Input

V.S.

Proprioception Input

Machine learning algorithms suffer from catastrophic interference, which is the tendency of an artificial neural network to abruptly and drastically forget previously learned information upon learning new information.

How Human Control Muscles

Transformer Encoder

Muscle Index

Transformer Decoder

Muscle Activation

Project Arnold Multi-task Muscle Transformer Boshi An*, Alberto Chiappa* Alexander Mathis

Is transformer better than MLP in multi-task setting?

YES!

Is transformer the best tool for single task setting?

Reference:

NO!

Todorov, Dmitrii I., et al. "The interplay between cerebellum and basal ganglia in motor adaptation: A modeling study." PLoS One 14.4 (2019): e0214926.

Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems 30 (2017).

Caggiano, Vittorio, et al. "MyoSuite--A contact-rich simulation suite for musc…

Chen, Yuanpei, et al. "Towards human-level bimanual dexterous manipulation with reinforcement learning." Advances in Neural Information Processing Systems 35 (2022): 5150-5163.

JACK MITCHELL//GETTY IMAGES

EPFL | Summer Research Program 2023

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Karina Araslanova

Lomonosov Moscow State University Aztekin Lab of Structural Regeneration Supervisor : Georgios Tsissios

Regeneration in a dish: Investigating limb regeneration competency using frog and mouse models

Karina Araslanova, Georgios Tsissios, Can Aztekin; Laboratory of Structural Regeneration Regeneration-incompetent model mouse embryo

Regeneration-competent model Xenopus tadpole Cross-species comparisons provide insights into the basis of limb regeneration-competency and pave new ways to overcome regeneration-incompetency

development regeneration

9 dpa

ex vivo post-amputation

in vivo regeneration

14 dpa

9 dpa

3 dpa

intact

Limb explants - robust ex vivo approach for studying limb development and regeneration stainless steel

limb explant

membrane

media

Fig.1 Time-series of Xenopus laevis tadpol limb regeneration in vivo and ex vivo Unstained whole-mount limbs (upper) and cryosections of limb explants (lower; N =1; n=6) stained with alcian blue; arrows indicate epithelial wound closure; scale bar = 200nm

development

3 dpa

regeneration

ex vivo post-amputation

intact

development

0 hpa ex vivo

1 dpa ex vivo

3 dpa ex vivo

0 hpa ex vivo

6 hpa ex vivo

3 dpa ex vivo

Fig.3 Time-series of mouse embryonic limb development and “regeneration” Whole-mount limbs (upper; N=1; n=3) and cryosections of limb explants (lower; N=1; n=6) stained with alcian blue; arrows indicate no epithelial wound closure; scale bar = 200nm

Effects of experimental conditions on explant growth can be studied directly 500000

condition A

condition B

condition C

condition D

ex vivo approach benefit regeneration studies allowing to:

explant area, μm

400000

300000

200000

100000

0 0dpa

2dpa

4dpa

0dpa

2dpa

4dpa

0dpa

2dpa

4dpa

0dpa

2dpa

4dpa

Work is ongoing on studying molecular differences of regeneration process in two directions

• • •

clearly observe regeneration process directly treat regenerating limbs with drugs easely manipulate environmetal conditions

Fig.2 Growth kinetics of tadpole limb explants regeneration in four different experimental conditions (red lines represent means; N=1, n=10)

regeneration-competent and regeneration-incompetent models Mouse ex vivo Xenopus ex vivo

in vivo and ex vivo regeneration Xenopus ex vivo Morphogenetic signals are cruical for proper patterning in development and regeneration

Noggin ia a morphogen which regulates bone formation in development and regeneration preventing bone overgrowth

DAPI COL2a1 NOGGIN DAPI COL2a1 NOGGIN

intact

3 dpa

1 dpa

DAPI COL2a1 NOGGIN DAPI COL2a1 NOGGIN

intact

Xenopus in vivo

Time

1dpa

(Bretaud et al., 2018; Fowler & Larsson, 2020)

3dpa

9 dpa

9 dpa

Different types of collagen were shown to be expressed during the limb regeneration.

Col2a1 expression pattern corresponds to distal cartilage elements during development

3 dpa

Extracellular matrix is a pivotal part of microenvironment and can massively influence regeneration outcome

6 dpa

6 dpa

6 dpa

Time

(Brunet et al., 1998; Beck et al., 2006)

Acknowledgements

Fig.4 Time-series of Xenopus laevis tadpole limb regeneration in vivo and ex vivo Immuno-fluorescent staining of cryosections of Xenopus limbs post-amputation Scale bar = 200nm; N = 1; n = 3

EPFL | Summer Research Program 2023

Great thanks to all members of Aztekin Lab, BIOP and PTH for helping me. And thanks to EPFL SRP for making this experience possible. This work is supported by EPFL School of Life Sciences ELISIR Scholarship, the Foundation Gabriella GiorgiCavaglieri, Branco Weiss Fellowship, SNSF NRP79 (4079-40-206349) and ISREC Fondation

Fig.5 Time-series of Xenopus laevis tadpole and mouse limb regeneration ex vivo Immuno-fluorescent staining of cryosections of limb explants post-amputation Scale bar = 200nm; N = 1; n = 3

15

Alefiya Taher Dohadwala National University of Singapore

Maerkl Lab of Biological Network Characterization Supervisor : Pao-Wan Lee

Enhancement of Protein Expression in PURE Cell-Free Protein Expression System with Addition of and Incubation with Liposomes Dohadwala Alefiya Taher, Supervisor: Pao-Wan Lee Laboratory of Biological Network Characterisation, School of Engineering, EPFL

Introduction

Proteins Energy and Resources DNA

Liposomes could be made with Ni-NTA, allowing His-tagged proteins to be in closer

Previous works have used 2 3 nanoaggregates and macromolecules to increase proximity Limitations: Limited reaction rates due to longer diffusion time & resource use not optimised 1

Can we improve protein expression by using liposomes to bring PURE proteins closer?

Methodology

Fluorescence

Adding liposomes to the cell-free mixture

Liposomes without Ni-NTA and Liposomes with Ni-NTA

Incubating liposomes with ribosomes and RNA polymerase, then adding mixture to reaction

Time

Fluorescence visualisation

Kinetics reading

Results Does addition of liposomes improve protein expression?

1A

Does incubation of liposomes with RNA polymerase and ribosomes increase protein expression?

2A25000

50000

45000

20000

40000

Relative Fluorescence

Relative Fluorescence

35000

30000

25000

15000

10000

20000

15000 5000 10000

5000

Liposomes with NiNTA

Ni-NTA Liposomes, Incubated

Liposomes without NiNTA

1C12000

No Ni-NTA Liposomes No Liposomes

10000 Arbituary Units

Ni-NTA Liposome

2B

Ni-NTA Liposomes, Not Incubated

9:52:24

9:44:24

9:36:24

9:28:24

9:20:24

9:12:24

10:00:24

9:04:24

8:56:24

8:48:24

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8:24:24

8:16:24

8:08:24

8:00:24

7:52:24

7:44:24

7:36:24

7:28:24

7:20:24

7:12:24

7:04:24

6:56:24

6:48:24

6:40:24

6:32:24

6:24:24

6:16:24

6:08:24

6:00:24

5:52:24

5:44:24

5:36:24

5:28:24

5:20:24

5:12:24

5:04:24

4:56:24

4:48:24

No Ni-NTA Liposomes, Incubated

No Ni-NTA Liposomes, Not Incubated

No Liposomes

9000

1 hour incubation

No incubation

8000 6000 4000

2C

8000 Arbituary Units

No Liposomes

4:40:24

Time

TIme

1B

4:32:24

4:24:24

4:16:24

4:08:24

4:00:24

3:52:24

3:44:24

3:36:24

3:28:24

3:20:24

3:12:24

3:04:24

2:56:24

2:48:24

2:40:24

2:32:24

2:24:24

2:16:24

2:08:24

2:00:24

1:52:24

1:44:24

1:36:24

1:28:24

1:20:24

1:12:24

1:04:24

0:56:24

0:48:24

0:40:24

0:32:24

0:24:24

0:16:24

0:00:24

0:08:24

0

0

7000 6000 5000 4000 3000 2000 1000

2000

0

0

NiNTA Liposome

No NiNTA liposome

No Liposome

Figure 1A. Relative fluorescence over time of setups with Ni-NTA liposomes, No liposomes and liposomes without Ni-NTA. 1B. Native protein gel images of the reaction mixture of the setups from figure 1A. GFP was visualised. Each row of images is from a duplicate experiment. 1C. Fluorescence from one protein band from each set up in 1B was quantified via ImageJ.

Ni-NTA Liposome, Incubated

Ni-NTA Liposome, Not Incubated

No Ni-NTA Liposome, Incubated

No Ni-NTA No Liposome Liposome, Not Incubated

Figure 2A. Relative fluorescence over time of setups with liposomes incubated for one hour, no incubation and no liposomes added. 2B. Native protein gel image of the reaction mixture of the 1 hour incubation set-up and no incubations set-up with Ni-NTA liposomes. GFP was visualised. Each row of images is from a duplicated experiment. 2C. Fluorescence from one protein band from each set up in 2B was quantified via ImageJ. Graph includes bands from setups with No Ni-NTA liposomes and no liposomes.

Discussion

Conclusion and Future Work

Addition of liposomes with NiNTA groups results in increased protein expression in the PURE system. Incubation of these liposomes with ribosomes and T7 RNA polymerase further increases protein expression.

Addition of and incubation with liposomes increases protein expression in the PURE system. This can be further improved by optimizing:

Incubation leading to greater protein expression alludes to NiNTA-6His interactions being a major driving force in increasing proximity and protein expression.

Incubation appears to be specific.

Incubation with other proteins leads to lower expression levels (data not shown). This could be further optimised to enhance expression

Incubation Concentration Temperature Time

Ragunathan Ganesh SRP Program SRP Directors Maerkl Lab Dr. Fanjun Li A big thank you Dr. Sebastian Maerkl Laura Grasemann Pao-Wan Lee Alice Emery- and Committee Amogh Baranwal Dr. Maria Lopez Laura Roset to: All at EPFL! Goodman

EPFL | Summer Research Program 2023

Citations:

1. Garenne, D. et al. Cell-free gene expression. Nat Rev Methods Primers 1, 1–18 (2021).

2. Thakur, M. et al. Self-assembled nanoparticle-enzyme aggregates enhance functional protein production in pure transcriptiontranslation systems. PLOS ONE 17, e0265274 (2022).

3. Ge, X., Luo, D. & Xu, J. Cell-Free Protein Expression under Macromolecular Crowding Conditions. PLOS ONE 6, e28707 (2011).

17

Krishna Gajjar Clark University

McCabe Lab of Neural Genetics and Disease Supervisor : Rebecca Smith

SPARC as a tool to visualize and manipulate single motor neurons in Drosophila Krishna J. Gajjar1, Rebecca C. Smith1, Brian D. McCabe1

1Brain Mind Institute, École Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland

(Shutterstock)

Introduction

Results

Single-cell labelling techniques provide powerful insights into neural circuit development and processing2.

Motor Neuron Line: R81A07 R81A07-Gal4; UAS-PhiC31

SPARC (Sparse Predictive Activity through Recombinase Competition) is a tool that enables the manipulation of precise portions of post-mitotic cells in Drosophila (Fig. 1-2)3.

UAS-SPARC2-D-CD8::GFP

GFP

UAS-SPARC2-I-CD8::GFP

Aims: (1) Test whether SPARC can label non-overlapping motor neurons in the Ventral Nerve Cord (VNC) of Drosophila, and (2) Assess the stochasticity and reliability of the approach.

Recombinasedependent genetic competition

UAS-SPARC2-S-CD8::GFP

Sparse and stochastic labelling

Figure 1: A schematic of the SPARC/SPARC2 toolkit. In each of the targeted subset of cells, either reaction 1 or reaction 2 occurs; this enables stochastic labelling3.

SPARC2 variant

attP truncation (bp)

D

60

I

38

S

34

B

Sparse Motor Neuron Line: R48F12 R81A07-Gal4; UAS-PhiC31

R48F12-Gal4; UAS-PhiC31 UAS-SPARC2-D-CD8::GFP

GFP UAS-SPARC2-D-CD8::GFP

A

Scale bar 50.0 µm

Figure 4: All SPARC2 variants (D, I, and S) stochastically label different subsets of R81A07 neurons in each fly.

Figure 2: (A) The level of attP truncation associated with each SPARC variant3; (B) Percentage of cells labelled by each SPARC combination3.

Methods GFP

UAS-SPARC2-I-CD8::GFP

Strategy: Establish various genetic crosses, pairing each of the two motor neuron (MN) lines (R81A07 and sparse R48F12) with different SPARC variants (D/I/S).

Experimental genotype (grown at 29ºC):

*X = D, I, or S

Figure 3: Experimental setup, from progeny collection and VNC dissection to imaging.

References 2. Luo, L., Callaway, E. M., & Svoboda, K. (2018). Genetic Dissection of Neural Circuits: A Decade of Progress. Neuron, 98(2), 256–281. https://doi.org/10.1016/j.neuron.2018.03.040 3. Isaacman-Beck, J., Paik, K. C., Wienecke, C. F. R., Yang, H. H., Fisher, Y. E., Wang, I. E., Ishida, I. G., Maimon, G., Wilson, R. I., & Clandinin, T. R. (2020). SPARC enables genetic manipulation of precise proportions of cells. Nature neuroscience, 23(9), 1168–1175. https://doi.org/10.1038/s41593-020-0668-9

EPFL | Summer Research Program 2023

Scale bar 50.0 µm

Figure 5: SPARC2-D stochastically labels different subsets of R48F12 neurons in each fly.

Conclusions R81A07: SPARC2-I and SPARC2-S were the most effective in visualizing individual, non-overlapping MNs. R48F12: No single MNs were labelled using SPARC2-D. This likely due to the use of a sparse MN line.

Scale bar = 50.0 µm

Future Directions: • Quantify the number of MNs labelled by each SPARC variant.

• Test multiple MN lines, such as OK6, HB9, and OK371.

• To better visualize single MNs, repeat with confocal microscopy.

Acknowledgements A thank you to my wonderful supervisor, Rebecca Smith and the rest of the McCabe Lab for supporting me throughout the summer. I would also like to extend my gratitude to the SRP and the McCall MacBain foundation for making this experience possible.

19

Alireza Gargoori Motlagh Sharif University of Technology

La Manno Lab of Brain Development and Biological Data Science

Supervisor : Halima Schede

Fast and Scalable MALDI-MSI Normalization Method Using Variational Inference Alireza Gargoori Motlagh Laboratory of Brain Development and Biological Data Science – Gioele La Manno

Abstract

Method MAIA normalizes images by fitting a Bayesian model that accounts for different sources of noise in a regularized way and allows a conser vative nonlinear rescaling of intensity values such that qualifications can be properly compared across sections.

MALDI mass spectrometry (MALDI-MSI) is a technique that enables the label-free spatial quantification of small molecules, producing up to several thousands of distribution maps in parallel.

Increasing evidence and studies have focused on the significance of lipids as primary controllers in various processes. Yet, presently, there are no methods available to combine information from diverse lipid and metabolite datasets.

One of the limitations of MALDI imaging is the experimental variability due to differences in matrix deposition and instrument sensitivity.

Challenges

Intensities’ Regularization Modeling We can model the MALDI-MSI data intensities’ histogram as a gaussian mixture model and approximate the true foreground distribution, where the non-biological batch effects have been taken into account.

Here we present MAIA (MAss Imaging Analyzer) that permits analysis of MALDI-MSI data which make it possible to integrate and analyze multiple acquisitions at once.

3. intensity normalization and batch correction of multiple slides (𝜆𝜆) (𝛾𝛾)

To achieve our goal, we implement a stochastic variational inference method in NumPyro library, which is powered by JAX, a machine learning framework for accelerated linear algebra and autograd.

This project’s goal was focused on developing a fast and scalable method for the normalization part to deal with the technical effects.

Results Normalization

Prior Estimation

Histogram of Intensities Raw Data

Any downstream analysis requires the data to be normalized in order not to capture any experimental variations as a source of biological significance.

MAIA normalizes images by fitting a regularized GMM using SVI, which allows us to approximate the parameters of interest in a scalable way compared to MCMC method.

Normalized

A proper prior could enhance the speed of convergence in SVI, as well as helping the initialization of the method.

Further Applications

Clusters

Sagittal medial sections

MAIA is able to integrate multiple MALDI-MSI acquisitions together to create 3D and 4D objects that can be studied using common multivariate analysis approaches

8hpf

24hpf

48hpf

72hpf

Speed-up Method

CPU

GPU

Old

6h:21min

~49min

Convergence Steps 25000

New

~10min

~1min

5000

Special thanks to my supervisor, Halima Hannah Schede

EPFL | Summer Research Program 2023

21

Mahsa Geramimanesh

Azad University, Central Tehran Branch Rahi Lab of the Physics of Biological Systems Supervisor : Vojislav Gligorovski

Efficient Plasmid Design for Markerless Genome Editing Mahsa Geramimanesh, Vojislav Gligorovski, Sahand Jamal Rahi

Introduction:

Laboratory of the Physics of Biological Systems, École Polytechnique Fédérale de Lausanne (EPFL)

- Yeast is a single-cell eukaryotic microorganism with a fast doubling time, and its genome can be easily edited.

- The "pop-in pop-out" strategy allows for marker-free modification of the yeast genome. It involves inserting a plasmid into the yeast genome using a matching sequence, followed by recombination between plasmid segments that leaves only the essential part of the plasmid in the genome.

- To optimize plasmid design for efficient pop-out, several plasmids were created to tag histones (HTB2 gene) with fluorescence by varying the ratio between the lengths of the homology parts for pop-out vs. pop-in (K).

- During the cell cycle, the amount of histone varies. Thus, marking HTB2 allows us to observe the different phases of the cell cycle.

Keywords: Yeast, Genetic Modifications, Tagging Histones, Cell Cycle, Fluorescent Imaging, Plasmid Design

Experimental Methods:

Fig 2. Segmentation using Neural Network to measure the fluorescent level

Fig 3.

Fluorescent imaging for Fig 1. Pop-in and pop-out method

HTB2 with 60x and 40x

Results: t = 0 min

t = 2h 30 min

t = 5h

t = 7h 30 min

Fig 4. Movie of the

Scan here to watch the full video:

modified strain with HTB2 fused to a red fluorescent protein

Fig 6.

Fig 5. Level of the

Higher K

total fluorescence

values lead

intensity during a

to a higher

cell cycle

efficiency of pop-out

Conclusion: - A larger ratio of the homology lengths for pop-out to pop-in, leads to a more efficient pop-out.

- As a result, scientists can now create plasmids that have high pop-out efficiency.

- Different phases of the cell cycle can be observed by only using marked histones. During the S phase of the yeast cell cycle, there is an increase in the level of histones. However, in anaphase, when cells split nuclei, the level of histones drops again, and this phenomenon can be seen through the decreasing fluorescence of HTB2 under a microscope.

References - Targeting, disruption, replacement, and allele rescue: Integrative DNA transformation in yeast, Methods in Enzymology, V 194, 1991, P 281-301

EPFL | Summer Research Program 2023

23

Katarzyna Glinka University of Glasgow

Manley Lab of Experimental Biophysics Supervisors : Kyle Douglass, Christian Zimmerli

> ĂƌƌĂLJ ŵƵůƚŝͲŵŽĚĂů ŵŝĐƌŽƐĐŽƉĞ ĨŽƌ Ă ƉůƵŶŐĞ ĨƌĞĞnjĞƌ <ĂƚĂƌnjLJŶĂ 'ůŝŶŬĂ͕ <LJůĞ ŽƵŐůĂƐƐ͕ ŚƌŝƐƚŝĂŶ ŝŵŵĞƌůŝ

>ĂďŽƌĂƚŽƌLJ ŽĨ džƉĞƌŝŵĞŶƚĂů ŝŽƉŚLJƐŝĐƐ͕ École WŽůLJƚĞĐŚŶŝƋƵĞ &édérale de Lausanne ϭ͘ ĂĐŬŐƌŽƵŶĚ͗ WůƵŶŐĞ ĨƌĞĞnjŝŶŐ ŝƐ Ă ƚĞĐŚŶŝƋƵĞ ĨŽƌ ĐƌLJŽͲĨŝdžĂƚŝŽŶ͘ /ƚ ŝƐ ďĞŶĞĨŝĐŝĂů ƚŽ ĐŽŶƐƚƌƵĐƚ Ă ƐLJƐƚĞŵ ĐŽŶƐŝƐƚŝŶŐ ŽĨ ĂŶ ŽƉƚŝĐĂů ŵŝĐƌŽƐĐŽƉĞ ĂŶĚ Ă ƌŽďŽƚŝĐ Ăƌŵ ƚŚĂƚ ƌĞŵŽǀĞƐ ƚŚĞ ƐĂŵƉůĞ ĨƌŽŵ ƚŚĞ ŵŝĐƌŽƐĐŽƉĞ ĂŶĚ ƚƌĂŶƐƉŽƌƚƐ ŝƚ ƚŽ ƚŚĞ ƉůƵŶŐĞ ĨƌĞĞnjĞƌ͘ /ƚ ůŝŵŝƚƐ ƚŚĞ ĐŚĂŶŐĞƐ ƚŚĞ ƐĂŵƉůĞ ƵŶĚĞƌŐŽĞƐ ďĞƚǁĞĞŶ ŝŵĂŐŝŶŐ ĂŶĚ ĐƌLJŽͲĨŝdžĂƚŝŽŶ͕ ǁŚŝĐŚ ŚĞůƉƐ ǁŝƚŚ ĐŽƌƌĞůĂƚŝŶŐ ƚŚĞ ĚĂƚĂ ƚĂŬĞŶ ƉƌŝŽƌ ƚŽ ĂŶĚ ĂĨƚĞƌ ĨŝdžĂƚŝŽŶ͕ ĞƐƉĞĐŝĂůůLJ ǁŚĞŶ ŝŵĂŐŝŶŐ ƌĂƌĞ ĞǀĞŶƚƐ͘ /Ŷ ĐŽŶǀĞŶƚŝŽŶĂů ŵŝĐƌŽƐĐŽƉLJ͕ ƚŚĞƌĞ ŝƐ Ă ƚƌĂĚĞͲŽĨĨ ďĞƚǁĞĞŶ ƚŚĞ ƌĞƐŽůƵƚŝŽŶ ĂŶĚ ĨŝĞůĚ ŽĨ ǀŝĞǁ͘ &ŽƵƌŝĞƌ WƚLJĐŚŽŐƌĂƉŚLJ ĂůůŽǁƐ ƵƐ ƚŽ Ɛƚ ƚŚĞ ƌĞƐŽůƵƚŝŽŶ ǁŚŝůĞ ŬĞĞƉŝŶŐ Ă ǁŝĚĞ ĨŝĞůĚ ŽĨ ǀŝĞǁ ΀ϭ΁͘ /ƚ ŝƐ Ă ƉƌŽŵŝƐŝŶŐ ŵŽĚĂůŝƚLJ ĨŽƌ ƚŚŝƐ ƐLJƐƚĞŵ ďĞĐĂƵƐĞ ŝƚ ĐŽƵůĚ ĂůůŽǁ ƵƐ ƚŽ ŽďƐĞƌǀĞ ŽƌŐĂŶĞůůĞƐ ǁŚŝůĞ ŝŵĂŐŝŶŐ ƚŚĞ ǁŚŽůĞ ƐĂŵƉůĞ ŽĨ ŵĂŶLJ ĐĞůůƐ Ăƚ ŽŶĐĞ ǁŝƚŚŽƵƚ ƚŚĞ ŶĞĞĚ ĨŽƌ ƐĐĂŶŶŝŶŐ͘ dŚŝƐ ǁŽƵůĚ ĂůůŽǁ ƵƐ ƚŽ ĐŽƌƌĞůĂƚĞ ŶŽƚ ŽŶůLJ ƚŚĞ ƉŽƐŝƚŝŽŶƐ ďƵƚ ĂůƐŽ ƚŚĞ ƐƚĂƚĞ ŽĨ ŽďƐĞƌǀĞĚ ĐĞůůƐ͘ ^ƵĐŚ Ă ƐLJƐƚĞŵ ǁŽƌŬŝŶŐ ĨĂƐƚ ĞŶŽƵŐŚ ǁŽƵůĚ ĂůƐŽ ĂůůŽǁ ĨŽƌ ĨƌĞĞnjŝŶŐ ƚŚĞ ƐĂŵƉůĞ ĚƵƌŝŶŐ Ă ƐƉĞĐŝĨŝĐ ĞǀĞŶƚ ŽĨ ŝŶƚĞƌĞƐƚ͘ Ϯ͘ ŝŵ͗ /ŵĂŐĞ ƐƵďͲĐĞůůƵůĂƌ ĨĞĂƚƵƌĞƐ ůŝǀĞ͕ ũƵƐƚ ďĞĨŽƌĞ ƉůƵŶŐĞ ĨƌĞĞnjŝŶŐ ƚŚĞ ƐĂŵƉůĞ͘ dŚŝƐ ƉƌŽũĞĐƚ ĞdžƉůŽƌĞĚ ĂǀĂŝůĂďůĞ ŵŽĚĂůŝƚŝĞƐ͗ ŝŵƉůĞŵĞŶƚĞĚ ďƌŝŐŚƚĨŝĞůĚ͕ ĚĂƌŬĨŝĞůĚ ĂŶĚ ƉŚĂƐĞ ĐŽŶƚƌĂƐƚ ŝŵĂŐŝŶŐ͕ ĂŶĚ ŵĂĚĞ ĂĚǀĂŶĐĞƐ ŝŶ ŝŵƉůĞŵĞŶƚŝŶŐ &ŽƵƌŝĞƌ WƚLJĐŚŽŐƌĂƉŚLJ͘

> ĂƌƌĂLJ

^ĂŵƉůĞ ŝŶ Ă ƚŚĞƌŵĂů ďĂƚŚ

>t KďũĞĐƚŝǀĞ

dƵďĞ ůĞŶƐ

ĂŵĞƌĂ

&ŝŐƵƌĞ ϭ͗ dŚĞ ĚŝĂŐƌĂŵ ŽĨ ƚŚĞ ĞdžƉĞƌŝŵĞŶƚĂů ƐĞƚƵƉ

;ĂͿ

;ďͿ

ϯ͘ DĞƚŚŽĚŽůŽŐLJ͗ ŵĞĐŚĂŶŝĐĂů Ăƌŵ ǁŝůů ƌĞŵŽǀĞ ƚŚĞ ƐĂŵƉůĞ ĨƌŽŵ ƚŚĞ ŵŝĐƌŽƐĐŽƉĞ ĂŶĚ ƚƌĂŶƐƉŽƌƚ ŝƚ͘ tĞ ƚŚĞƌĞĨŽƌĞ ŶĞĞĚ Ă ůŽƚ ŽĨ ƐƉĂĐĞ ĂƌŽƵŶĚ ƚŚĞ ƐĂŵƉůĞ ĐŚĂŵďĞƌ ƚŽ ĂǀŽŝĚ ĚĂŵĂŐŝŶŐ ƚŚĞ ŽƉƚŝĐĂů ĐŽŵƉŽŶĞŶƚƐ ;&ŝŐ͘ ϭͿ͘ ^ŽůƵƚŝŽŶ͗ hƐĞ Ă ůŽŶŐ ǁŽƌŬŝŶŐ ĚŝƐƚĂŶĐĞ ;>t Ϳ ŽďũĞĐƚŝǀĞ͕ ĂŶĚ > ĂƌƌĂLJ ĂƐ ƚŚĞ ŝůůƵŵŝŶĂƚŝŽŶ ;ŝƚ ĐĂŶ ďĞ ƉŽƐŝƚŝŽŶĞĚ ĨƵƌƚŚĞƌ ĂǁĂLJ͕ ŝƐ ĐŚĞĂƉĞƌ ĂŶĚ ĂůůŽǁƐ ƚŽ ƐǁĂƉ ŵŽĚĂůŝƚŝĞƐ ĞĂƐŝůLJͿ͘ ϯ͘ϭ DƵůƚŝͲŵŽĚĂů ŵŝĐƌŽƐĐŽƉLJ͗ ĂĐŚ > ŝůůƵŵŝŶĂƚĞƐ ƚŚĞ ƐĂŵƉůĞ Ăƚ Ă ƵŶŝƋƵĞ ĂŶŐůĞ͘ tĞ ĐĂŶ ĂĐŚŝĞǀĞ ĚŝĨĨĞƌĞŶƚ ŵŽĚĂůŝƚŝĞƐ ďLJ ĚŝƐƉůĂLJŝŶŐ ĚŝĨĨĞƌĞŶƚ ŝůůƵŵŝŶĂƚŝŽŶ ƉĂƚƚĞƌŶƐ ŽŶ ƚŚĞ > ĂƌƌĂLJ ΀Ϯ΁͘

;ĐͿ

&ŝŐƵƌĞ Ϯ͗ /ŵĂŐĞƐ ƚĂŬĞŶ ǁŝƚŚ ŽƵƌ ŵŝĐƌŽƐĐŽƉĞ ĂŶĚ ĚŝĂŐƌĂŵƐ ŽĨ ŝůůƵŵŝŶĂƚŝŽŶ ƉĂƚƚĞƌŶƐ ƵƐĞĚ ĂͿ ƌŝŐŚƚĨŝĞůĚ ďͿ ĂƌŬĨŝĞůĚ ĐͿ WŚĂƐĞ ĐŽŶƚƌĂƐƚ

ƌŝŐŚƚĨŝĞůĚ͗ /ůůƵŵŝŶĂƚĞ ǁŝƚŚ Ă ĐŝƌĐůĞ ŽĨ ĐĞŶƚƌĂů > Ɛ ;&ŝŐ͘ ϮĂͿ͘ ĂƌŬĨŝĞůĚ͗ /ůůƵŵŝŶĂƚĞ ǁŝƚŚ ƚŚĞ > Ɛ ŽƵƚƐŝĚĞ ƚŚĞ ĂĐĐĞƉƚĂŶĐĞ ĂŶŐůĞ ƐĞƚ ďLJ ƚŚĞ ŶƵŵĞƌŝĐĂů ĂƉĞƌƚƵƌĞ ;E Ϳ ŽĨ ƚŚĞ ŽďũĞĐƚŝǀĞ ;&ŝŐ͘ ϮďͿ ΀Ϯ΁͘ WŚĂƐĞ ĐŽŶƚƌĂƐƚ͗ tĞ ƵƐĞ ĂƐLJŵŵĞƚƌŝĐ ŝůůƵŵŝŶĂƚŝŽŶ ;ĐŽŵƉůĞŵĞŶƚĂƌLJ ŚĂůĨͲĐŝƌĐůĞƐͿ ƚŽ ƉƌŽĚƵĐĞ ĚŝĨĨĞƌĞŶƚŝĂů ƉŚĂƐĞ ĐŽŶƚƌĂƐƚ ; W Ϳ ŝŵĂŐĞƐ ;&ŝŐ ϯͿ͕ ĂĐĐŽƌĚŝŶŐ ƚŽ ƚŚĞ ĨŽƌŵƵůĂ ΀Ϯ΁͗ 𝐼𝐼𝑡𝑡𝑡𝑡𝑡𝑡/𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 − 𝐼𝐼𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏/𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝐼𝐼 = 𝐼𝐼𝑡𝑡𝑡𝑡𝑡𝑡/𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 + 𝐼𝐼𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏/𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟

ϯ͘Ϯ &ŽƵƌŝĞƌ WƚLJĐŚŽŐƌĂƉŚLJ ;&WͿ͗ dŚĞ ǁŽƌŬĨůŽǁ ĨŽƌ &W ŝƐ ƐƵŵŵĂƌŝƐĞĚ ŝŶ &ŝŐ͘ ϰ ΀ϭ΁͘ &W ŝŵĂŐĞ ƌĞĐŽŶƐƚƌƵĐƚŝŽŶ ŝƐ ƉƌĞƐĞŶƚĞĚ ŝŶ &ŝŐ͘ϴ

ŽŶƐƚƌƵĐƚ ĂŶĚ ĂůŝŐŶ ƚŚĞ ƐLJƐƚĞŵ

,ŝŐŚ LJŶĂŵŝĐ ZĂŶŐĞ ;, ZͿ ŝŵĂŐĞƐ͗ Ŷ ĞdžƉĞƌŝŵĞŶƚĂů ĐŚĂůůĞŶŐĞ ĂƌŝƐĞƐ ǁŚĞŶ ŝŵĂŐŝŶŐ ǁŝƚŚ ƐŝŶŐůĞ > Ɛ͗ ƚŚĞ ďƌŝŐŚƚŶĞƐƐ ĐŚĂŶŐĞƐ ĚĞƉĞŶĚŝŶŐ ŽŶ ƚŚĞ ƉŽƐŝƚŝŽŶ ŽĨ ƚŚĞ > ŝŶ ƚŚĞ ĂƌƌĂLJ ;ŝ͘Ğ͕͘ ƚŚĞ ĂŶŐůĞ ŽĨ ŝůůƵŵŝŶĂƚŝŽŶͿ ;&ŝŐ͘ ϱͿ͘ dŽ ĂĐĐŽƵŶƚ ĨŽƌ ƚŚŝƐ͕ ǁĞ ĐŽůůĞĐƚ ƚŚƌĞĞ ĚĂƚĂƐĞƚƐ ƵŶĚĞƌ ĚŝĨĨĞƌĞŶƚ ĞdžƉŽƐƵƌĞ ƚŝŵĞƐ ĂŶĚ ƌĞĐŽŶƐƚƌƵĐƚ ĂŶ , Z ŝŵĂŐĞ ďLJ ĞůŝŵŝŶĂƚŝŶŐ ŽǀĞƌͲ ĂŶĚ ƵŶĚĞƌĞdžƉŽƐĞĚ ƉŝdžĞůƐ ΀ϭ΁͘

Ͳ WŽƐŝƚŝŽŶ ŽĨ ĞĂĐŚ > ĂŶĚ ƚŚĞ ĐŽƌƌĞƐƉŽŶĚŝŶŐ ǁĂǀĞǀĞĐƚŽƌ

WƵƉŝů ƌĞĐŽǀĞƌLJ ǁŝƚŚ 'ƌĂĚŝĞŶƚ ĞƐĐĞŶƚ ;' Ϳ͗ dŽ ŝŵĂŐĞ ǁŝƚŚ &W͕ ǁĞ ŶĞĞĚ ƚŽ ƌĞĐŽŶƐƚƌƵĐƚ ƚŚĞ ƉƵƉŝů ĨƵŶĐƚŝŽŶ ŽĨ ƚŚĞ ƐLJƐƚĞŵ͘ dŚŝƐ ŝƐ ĚŽŶĞ ǁŝƚŚ ƚŚĞ ' ĂůŐŽƌŝƚŚŵ͕ ǁŚŝĐŚ ƌĞĐŽǀĞƌƐ ƚŚĞ ĂďĞƌƌĂƚŝŽŶƐ ĚĞƐĐƌŝďĞĚ ďLJ ĞƌŶŝŬĞ ƉŽůLJŶŽŵŝĂůƐ ΀ϭ΁͘ dŚĞ ĂůŐŽƌŝƚŚŵ ŝƚĞƌĂƚŝǀĞůLJ ĨŝŶĚƐ ƚŚĞ ŵŝŶŝŵƵŵ ŽĨ ƚŚĞ ůŽƐƐ ĨƵŶĐƚŝŽŶ ďLJ ĐĂůĐƵůĂƚŝŶŐ ŝƚƐ ŐƌĂĚŝĞŶƚ͕ ĂƐ ŝůůƵƐƚƌĂƚĞĚ ŝŶ &ŝŐ͘ ϲ͘ dŚĞ ĂůŐŽƌŝƚŚŵ ǁŽƌŬƐ ǁĞůů ŽŶ ƐŝŵƵůĂƚĞĚ ĚĂƚĂ ;&ŝŐ͘ ϳͿ͕ ďƵƚ ĨƵƌƚŚĞƌ ǁŽƌŬ ŝƐ ƌĞƋƵŝƌĞĚ ƚŽ ƐƵĐĐĞƐƐĨƵůůLJ ƌĞĐŽŶƐƚƌƵĐƚ ƚŚĞ ƉƵƉŝů ĨƌŽŵ Ă ƌĞĂů ĚĂƚĂƐĞƚ͘ &ŝŐƵƌĞ ϱ͗ ŽŵƉĂƌŝƐŽŶ ďĞƚǁĞĞŶ ƚŚĞ ĐĞŶƚƌĂů ĂŶĚ ŽĨĨͲĂdžŝƐ > ŝůůƵŵŝŶĂƚŝŽŶ͘ /ŵĂŐĞƐ ƚĂŬĞŶ ƵŶĚĞƌ ǀĂƌLJŝŶŐ ĞdžƉŽƐƵƌĞ ƚŝŵĞƐ ĂŶĚ ŐĂŝŶ

𝑥𝑥,𝑦𝑦

𝐼𝐼(𝑥𝑥, 𝑦𝑦)

2

'ƌĂĚŝĞŶƚ

WƵƉŝů ĨƵŶĐƚŝŽŶ ƉĂƌĂŵĞƚĞƌƐ

&ŝŐƵƌĞ ϲ͗ ' ŽƉĞƌĂƚŝŽŶ ƉƌŝŶĐŝƉůĞ Ͳ ƐĐŚĞŵĂƚŝĐ

tĞ ĐĂŶ ĂůƐŽ ƵƐĞ ŵƵůƚŝͲĂdžŝƐ W ĚĂƚĂ ;Ğ͘Ő͕͘ ϰ ŚĂůĨͲ ĐŝƌĐůĞƐͿ ƚŽ ƌĞĐŽŶƐƚƌƵĐƚ ƚŚĞ ƉŚĂƐĞ ǁŝƚŚ ĂŶ ĂůŐŽƌŝƚŚŵ ďĂƐĞĚ ŽŶ dŝŬŚŽŶŽǀ ĚĞĐŽŶǀŽůƵƚŝŽŶ ;&ŝŐ͘ ϮĐͿ ΀ϯ͕ ϰ͕ ϱ΁͘

ŽůůĞĐƚ ƚŚĞ ĚĂƚĂ͗ Ͳ ^ĞƌŝĞƐ ŽĨ ŝŵĂŐĞƐ ǁŝƚŚ ƐŝŶŐůĞ > ŝůůƵŵŝŶĂƚŝŽŶ Ͳ ŽůůĞĐƚ ϯ ƐƚĂĐŬƐ ŽĨ ŝŵĂŐĞƐ ƵŶĚĞƌ ĚŝĨĨĞƌĞŶƚ ĞdžƉŽƐƵƌĞ ƚŝŵĞƐ ŽŵďŝŶĞ ƚŚĞ ϯ ƐƚĂĐŬƐ ŝŶƚŽ Ă ƐŝŶŐůĞ ,ŝŐŚ LJŶĂŵŝĐ ZĂŶŐĞ ;, ZͿ ĚĂƚĂƐĞƚ ZĞĐŽŶƐƚƌƵĐƚ Ă ĐŽŵƉůĞdž ŽďũĞĐƚ ĨƌŽŵ ƚŚĞ ĚĂƚĂƐĞƚ Ͳ hƐĞ ƚŚĞ ƌW/ ĂůŐŽƌŝƚŚŵ ƚŽ ƌĞĐŽǀĞƌ ŝŶƚĞŶƐŝƚLJ ĂŶĚ ƉŚĂƐĞ

&ŝŐƵƌĞ ϰ͗ &W ǁŽƌŬĨůŽǁ͘ dŚŝƐ ƉƌŽũĞĐƚ ĨŽĐƵƐĞĚ ŽŶ ƚŚĞ ŚŝŐŚůŝŐŚƚĞĚ ƚĂƐŬƐ

ϱ͘ EĞdžƚ ƐƚĞƉƐ͗ /ŵŵĞĚŝĂƚĞ ĨƵƚƵƌĞ ǁŽƌŬ ǁŝůů ĨŽĐƵƐ ŽŶ ŝŵƉůĞŵĞŶƚŝŶŐ &W͕ ŝŶ ƉĂƌƚŝĐƵůĂƌ͕ ƌĞĐŽǀĞƌŝŶŐ ƚŚĞ ƉƵƉŝů ǁŝƚŚ ' ͘ &ƵƌƚŚĞƌ ǁŽƌŬ ŝŶĐůƵĚĞƐ͗ • /ŶĐŽƌƉŽƌĂƚŝŶŐ ƚŚĞ ƐĂŵƉůĞ ĐŚĂŵďĞƌ ŝŶƚŽ ƚŚĞ microscope’s ĚĞƐŝŐŶ • KƉƚŝŵŝƐŝŶŐ ŝŵĂŐĞ ĂĐƋƵŝƐŝƚŝŽŶ ĂŶĚ ƌĞĐŽŶƐƚƌƵĐƚŝŽŶ͕ ƚŽ ĞŶĂďůĞ ůŝǀĞ ŝŵĂŐŝŶŐ͘

&ŝŐƵƌĞ ϯ͗ W ŝŵĂŐĞƐ ƌĞĐŽŶƐƚƌƵĐƚĞĚ ĨƌŽŵ ƉĂŝƌƐ ŽĨ ĚĂƚĂƐĞƚƐ ĐŽůůĞĐƚĞĚ ƵŶĚĞƌ ĐŽŵƉůĞŵĞŶƚĂƌLJ ŚĂůĨͲĐŝƌĐůĞ ŝůůƵŵŝŶĂƚŝŽŶ͘ dŚĞ ŵĞƚŚŽĚ ŚŝŐŚůŝŐŚƚƐ ĨĞĂƚƵƌĞƐ ĂŶĚ ƐŚĂƉĞƐ ĂůŽŶŐ ƚŚĞ ƐLJŵŵĞƚƌLJ ĂdžŝƐ ŽĨ ŝůůƵŵŝŶĂƚŝŽŶ ;ŚŽƌŝnjŽŶƚĂů ĂŶĚ ǀĞƌƚŝĐĂů͕ ƌĞƐƉĞĐƚŝǀĞůLJͿ͘

ZĞĐŽǀĞƌ ƚŚĞ ƉƵƉŝů ĨƵŶĐƚŝŽŶ͗ Ͳ hƐĞ ƚŚĞ 'ƌĂĚŝĞŶƚ ĞƐĐĞŶƚ ;' Ϳ ĂůŐŽƌŝƚŚŵ

ϰ͘ KƵƚĐŽŵĞƐ͗ • ƌŝŐŚƚĨŝĞůĚ͕ ĚĂƌŬĨŝĞůĚ ĂŶĚ ƉŚĂƐĞ ĐŽŶƚƌĂƐƚ ŵŝĐƌŽƐĐŽƉLJ ĐĂŶ ƐƵĐĐĞƐƐĨƵůůLJ ďĞ ŝŵƉůĞŵĞŶƚĞĚ ŝŶ ŽƵƌ ƐLJƐƚĞŵ • WƌŽŐƌĞƐƐ ǁĂƐ ŵĂĚĞ ŽŶ ƚŚĞ ŝŵƉůĞŵĞŶƚĂƚŝŽŶ ŽĨ &ŽƵƌŝĞƌ WƚLJĐŚŽŐƌĂƉŚLJ͕ ďƵƚ ŝƚ ƌĞƋƵŝƌĞƐ ŵŽƌĞ ǁŽƌŬ͘ dŚĞ ŵĞƚŚŽĚ ŚĂƐ ŶŽƚ ďĞĞŶ ǀĂůŝĚĂƚĞĚ LJĞƚ͕ ďƵƚ ŝƚ ƌĞŵĂŝŶƐ Ă ƉƌŽŵŝƐŝŶŐ ŵŽĚĂůŝƚLJ ĨŽƌ ŝŵĂŐŝŶŐ ŽƌŐĂŶĞůůĞƐ ŽĨ ŵĂŶLJ ĐĞůůƐ Ăƚ ŽŶĐĞ͘

>ŽƐƐ ĨƵŶĐƚŝŽŶ 𝐿𝐿 = ෍ 𝜓𝜓 𝑥𝑥, 𝑦𝑦 −

ĂůŝďƌĂƚĞ ƚŚĞ ƐLJƐƚĞŵ͗

&ŝŐƵƌĞ ϴ͗ &W ŝŵĂŐĞ ƌĞĐŽŶƐƚƌƵĐƚŝŽŶ – ĐƵƌƌĞŶƚ ƐƚĂƚĞ͘ ĂͿ ZĂǁ ĚĂƚĂ͕ ŝŵĂŐĞ ƚĂŬĞŶ ǁŝƚŚ ƚŚĞ ĐĞŶƚƌĂů > ŽŶ ďͿ ZĞĐŽŶƐƚƌƵĐƚĞĚ ŝŵĂŐĞ͗ ĂŵƉůŝƚƵĚĞ – ĐĂŶ ŽďƐĞƌǀĞ ŝŵƉƌŽǀĞŵĞŶƚ ŝŶ ƌĞƐŽůƵƚŝŽŶ ĐͿ ZĞĐŽŶƐƚƌƵĐƚĞĚ ŝŵĂŐĞ͗ ƉŚĂƐĞ – ŶŽƚ ƌĞĐŽŶƐƚƌƵĐƚĞĚ ĐŽƌƌĞĐƚůLJ

&ŝŐƵƌĞ ϳ͗ ' ƌĞĐŽŶƐƚƌƵĐƚŝŽŶ ŽŶ ƐŝŵƵůĂƚĞĚ ĚĂƚĂ ;ƚŽƉͿ ĂŶĚ ƚŚĞ ŐƌŽƵŶĚ ƚƌƵƚŚ ;ďŽƚƚŽŵͿ

;ĂͿ

;ďͿ

;ĐͿ

ZĞĨĞƌĞŶĐĞƐ͗ ϭͿ :ŝĂŶŐ ^͕ ^ŽŶŐ W͕ tĂŶŐ d͕ Ğƚ Ăů͘ ^ƉĂƚŝĂůͲ ĂŶĚ &ŽƵƌŝĞƌͲĚŽŵĂŝŶ ƉƚLJĐŚŽŐƌĂƉŚLJ ĨŽƌ ŚŝŐŚͲƚŚƌŽƵŐŚƉƵƚ ďŝŽͲŝŵĂŐŝŶŐ͘ EĂƚƵƌĞ WƌŽƚŽĐŽůƐ͘

ϮϬϮϯ DĂLJ͗ϭͲϯϯ͘ ϮͿ >ŝƵ ͕ dŝĂŶ >͕ >ŝƵ ^͕ tĂůůĞƌ >͘ ZĞĂůͲƚŝŵĞ ďƌŝŐŚƚĨŝĞůĚ͕ ĚĂƌŬĨŝĞůĚ͕ ĂŶĚ ƉŚĂƐĞ ĐŽŶƚƌĂƐƚ ŝŵĂŐŝŶŐ ŝŶ Ă ůŝŐŚƚͲĞŵŝƚƚŝŶŐ ĚŝŽĚĞ ĂƌƌĂLJ ŵŝĐƌŽƐĐŽƉĞ͘ :ŽƵƌŶĂů ŽĨ ŝŽŵĞĚŝĐĂů KƉƚŝĐƐ͘ ϮϬϭϰ KĐƚ͖ϭϵ;ϭϬͿ͗ϭϬϲϬϬϮ͘ ϯͿ dŝĂŶ >͕ tĂůůĞƌ >͘ YƵĂŶƚŝƚĂƚŝǀĞ ĚŝĨĨĞƌĞŶƚŝĂů ƉŚĂƐĞ ĐŽŶƚƌĂƐƚ ŝŵĂŐŝŶŐ ŝŶ ĂŶ > ĂƌƌĂLJ ŵŝĐƌŽƐĐŽƉĞ͘ KƉƚŝĐƐ džƉƌĞƐƐ͘ ϮϬϭϱ DĂLJ͖Ϯϯ;ϵͿ͗ϭϭϯϵϰͲϰϬϯ͘ ϰͿ WŚŝůůŝƉƐ &͕ ŚĞŶ D͕ tĂůůĞƌ >͘ ^ŝŶŐůĞͲƐŚŽƚ ƋƵĂŶƚŝƚĂƚŝǀĞ ƉŚĂƐĞ ŵŝĐƌŽƐĐŽƉLJ ǁŝƚŚ ĐŽůŽƌͲŵƵůƚŝƉůĞdžĞĚ ĚŝĨĨĞƌĞŶƚŝĂů ƉŚĂƐĞ ĐŽŶƚƌĂƐƚ ;Đ W Ϳ͘ W>Ž^ KE ͘ ϮϬϭϳ &Ğď͖ϭϮ;ϮͿ͗ĞϬϭϳϭϮϮϴ͘ ϱͿ ŚĞŶ D͕ WŚŝůůŝƉƐ &͕ tĂůůĞƌ >͘ YƵĂŶƚŝƚĂƚŝǀĞ ĚŝĨĨĞƌĞŶƚŝĂů ƉŚĂƐĞ ĐŽŶƚƌĂƐƚ ; W Ϳ ŵŝĐƌŽƐĐŽƉLJ ǁŝƚŚ ĐŽŵƉƵƚĂƚŝŽŶĂů ĂďĞƌƌĂƚŝŽŶ ĐŽƌƌĞĐƚŝŽŶ͘ KƉƚŝĐƐ džƉƌĞƐƐ͘ ϮϬϭϴ ĞĐ͖Ϯϲ;ϮϱͿ͗ϯϮϴϴϴ͘

EPFL | Summer Research Program 2023

ĐŬŶŽǁůĞĚŐĞŵĞŶƚƐ͗ dŚŝƐ ǁŽƌŬ ǁĂƐ ƐƵƉƉŽƌƚĞĚ ďLJ W&> ^ĐŚŽŽů ŽĨ >ŝĨĞ ^ĐŝĞŶĐĞƐ ^ƵŵŵĞƌ ZĞƐĞĂƌĐŚ WƌŽŐƌĂŵ͘ dŚĂŶŬ LJŽƵ ƚŽ WƌŽĨ͘ ^ƵůŝĂŶĂ DĂŶůĞLJ ĨŽƌ ƚŚĞ ŽƉƉŽƌƚƵŶŝƚLJ ƚŽ ũŽŝŶ > ĨŽƌ ƚŚĞ ƐƵŵŵĞƌ͕ ĂŶĚ ƚŽ Ăůů ƚŚĞ > ŵĞŵďĞƌƐ ĨŽƌ ƚŚĞŝƌ ǁĂƌŵ ǁĞůĐŽŵĞ͘ ^ƉĞĐŝĂů ƚŚĂŶŬƐ ƚŽ <LJůĞ ŽƵŐůĂƐƐ ĨŽƌ ŚŝƐ ƐƵƉƉŽƌƚ ĂŶĚ ƉĂƚŝĞŶĐĞ͘

25

Dessislava Ilieva

University of Manchester Gönczy Lab of Cell and Developmental Biology Supervisor : Ella Müller

Generating an optogenetic C. elegans strain for deciphering mechanisms of symmetry breaking Dessislava Ilieva1,2, Ella Müller2, Pierre Gönczy2 1University of Manchester

2École Polytechnique Fédérale de Lausanne

Is cortical relaxation sufficient for symmetry breaking in C. elegans?

What is the spatiotemporal control of symmetry breaking?

Rose & Gönczy, WormBook (2014)

General approach: Optogenetically recruit MEL-11, RGA-3, OCRL-1 to induce local relaxation at a specific site on the cortex in a temporally regulated manner.

Plan:

1) Generate the DNA sequence containing the desired constructs to be expressed – POI::ePDZ::mCherry.

2) Inject the DNA sequence into worms.

3) Screen for worms with the correct insertion and expression 4) Cross these worms to the PH::eGFP::LOV optogenetic partner strain and photoactivate.

1) Generate the DNA sequence containing the desired constructs to be expressed – POI::ePDZ::mCherry.

CRISPR repair template:

MosSCI constructs: Mel-11 has a good PAM sequence around the stop codon making it suitable for the CRISPR strategy.

For each construct: RGA-3, MEL-11, OCRL-1: MosSCI backbone, catalytic domains of the 3 POIs, ePDZ::mCherry. The plasmid is then generated by Gibson assembly.

Insert ePDZ::mCherry from plasmid

2) Inject the DNA sequence into worms and 3) Screen for worms with the correct insertion and expression of the desired product.

C.elegans work for MosSCI strains: Injection of the 3 Gibson assembly MosSCI constructs – then screen for injection by selecting normally moving worms (rescued unc-

119 mutants) – and for insertion of the extrachromosomal array: no green pharynx and histamine sensitivity.

Figure adapted from Frøkjær-Jensen, C. et al, 2008.

Selection against green pharynx marked by pMyo2::GFP when the injected DNA stays as an extrachromosomal array DIC

Worm expressing the desired construct

GFP

+ selection for worms expressing mCherry in the gonad and embryos

Conclusion and perspectives: In this study, we generated a MosSCI C. elegans strain in which expression of OCRL-1::ePDZ::mCherry was observed. By contrast, the MEL-11 and RGA-3 constructs seemed to be silenced despite their genomic integration potentially due to toxicity when overexpressed as an additional copy of the catalytic domains. To determine the silencing mechanism, now the RGA-3 and MEL-11 males that have the insertion are crossed back to the unc-119 mutant. However, CRISPR is potentially a better strategy, as it modifies the endogenous copy of the three genes and thus cannot be silenced. Therefore, the CRISPR strategy will be explored further as well. The successful strains will then be crossed to the PH::eGFP::LOV optogenetic partner and one-cell stage embryos of the progeny will be photoactivated in order to determine whether local cortical relaxation is sufficient to induce symmetry breaking.

References:

Rose, L. & Gönczy, P. Polarity establishment, asymmetric division and segregation of fate determinants in early C. elegans embryos. WormBook 1-43 (2014).

Frøkjær-Jensen, C., Wayne Davis, M., Hopkins, C. et al. Single-copy insertion of transgenes in Caenorhabditis elegans. Nat Genet 40, 1375–1383 (2008). https://doi.org/10.1038/ng.248

EPFL | Summer Research Program 2023

27

Christian Kenneth

Atma Jaya Catholic University of Indonesia Dal Peraro Lab for Biomolecular Modeling Supervisors : Lucien Krapp, Fernando ATP Meireles, Simon Crouzet

Fragment-based approach contrastive learning for robust prediction of protein-small molecule binding interfaces Christian Kenneth1, Lucien F. Krapp1, Fernando ATP Meireles1, Simon Crouzet1, Matteo Dal Peraro1 Institute of Bioengineering, School of Life Sciences, Ecole Fédérale de Lausanne (EPFL) and Swiss Institute of Bioinformatics (SIB), Lausanne 1015, Switzerland.

1

Introduction Objective

Preliminary Study

► We aim to train a robust model to predict protein binding interfaces with small molecule fragments using contrastive learning. Such a model would open up new possibilities in drug discovery.

G10

G5

► We trained PeSTo1 to predict the ligand binding interface within protein by grouping the ligand based on its chemical property and scaffold. ► Chemically similar ligands are more difficult to distinguish; therefore the model is confused.

Methods

Results

Datasets ▶ The non-redundant protein-ligand complex structure dataset retrieved from BioLiP2 using 90% sequence identity as subunits cluster threshold.

Residues that interact with each fragment of the ligand are grouped using OS pymol package and used as labels. ▶ Due to memory limitation, complexes are filtered with maximum subunits sequence length of 512 residues. Data are split into approx. 80% training set �14182 complexes), 10% validation set �1773 complexes), and 10% testing set �1772 complexes).

▼ Distribution of fragment-residue unique pairs �8069 unique fragments).

Model Architecture

▶ Flattened validation loss indicating model convergence after 40 epochs. Training process was halted to prevent overfitting. ▶ We aim for the minimum validation loss, which indicates the model capability to generalize new data.

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Smiles Encoder

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▼ Fragmentation of the ligand is performed using BRICS algorithm provided by RDKit package. ▶ Multi-label supervised asymmetriccontrastive loss function is used to evaluate the model. ▶ Multi-label: one subunit-many ligands. ▶ Supervised: compare predicted with actual interacting residues.

▼ Asymmetric-contrastive: able to learn from both projections; fragment→residue and residue→ fragment, while maximizing the correct fragment-residue pair and minimizing the incorrect ones. ▶ To encode Smiles representation of fragments and subunits structure, we utilize mol2vec3 and PeSTo1 respectively.

Training ▶ The model was trained for 3 days on a single NVIDIA RTX 2070S GPU. Model is optimized with Adam optimizer and the learning rate of 10�3. We also use gradient accumulation with accumulation step of 32 to simulate batch size with lower memory consumption.

EPFL | Summer Research Program 2023

Analyze model performance on test dataset (e.g. accuracy, cosine similarity matrix, etc).

Not Good!

F18 A19 G20 L21 G22

Good!

Use for zero-shot prediction of protein-small molecule binding interfaces.

References 1. Krapp, L. F., Abriata, L. A., Rodriguez, F. C., Dal Peraro, M. PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces. Nat. Commun. 14, 2175 �2023�.

2. Yang, J., Roy, A., Zhang, Y. BioLiP� a semi-manually curated database for biologically relevant ligand-protein interactions. Nucleic Acids Res. 41, D1096� D1103 �2013�.

3. Jaeger, S., Fulle, S., Turk, S. Mol2vec: unsupervised machine learning approach with chemical intuition. J Chem Inf Model. 58, 27�35 �2018�.

29

Justine Lam

University of Rochester Courtine Lab Supervisor : Quentin Barraud

UNRAVELLING THE ROLE OF CELIAC GANGLIA IN THE RESTORATION OF HEMODYNAMIC STABILITY PERTAINING TO NEUROLOGICAL DISORDERS Justine Lam1,2,13, Suje Amir1,2, Remi Hudelle1,2, Elaine Soriano1,2,8,9,10,11,12, Lois Mahe1,2, Leonie Asboth1,2, Robin Demesmaeker1,2, Viviana Aureli1, Eduardo Martin-Moraud1, Julien Bally5, Quentin Barraud1,2, Bernard Schneider1,6, Erwan Bezard6, Stéphanie Lacour1, Jordan Squair1,2,4,5, Aaron A. Phillips8,9,10,11,12, Jocelyne Bloch1,2,4,5 and Gregoire Courtine1,2,4,5 1. Neuro-X, EPFL, Switzerland; 2. Defitech Centre for Interventional Therapies (.NeuroRestore), CHUV/EPFL/UNIL, Switzerland; 3. Institut des Maladies Neurodégénératives, Université de Bordeaux; 4. Neurosurgery Department, CHUV, Switzerland; 5. Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; 6. Institut des Maladies Neurodégénératives, Université de Bordeaux,France; 7. Bertarelli Platform for Gene Therapy, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; 8. Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; 9. Department of Clinical Neurosciences, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; 10. Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; 11. International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada; 12.

RestoreNetwork, Hotchkiss Brain Institute, Libin Cardiovascular Institute, McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; 13. University of Rochester, Rochester, New York, United States.

HIGHLIGHTS

1

The neuroprosthetic baroreflex restores hemodynamic function thereby enabling greater mobility in a patient with Multiple System Atrophy (MSA)

Hemodynamic instability in MSA 70° T8

Preclinical models with targeted neuronal ablation or overexpression of alpha synuclein (αSyn) leading to neuronal degeneration aid in testing the requirements to restore hemodynamic stability in neurodegenerative diseases

T9 T11

Healthy

2

C2

Celiac ganglia

Arterial constriction

150

125

100

75 0

C3

C3

Syncope

Systolic Blood Pressure (mmHg)

C2

Largely

2.5

5

Time (min)

7.5

EES RESTORES HEMODYNAMIC STABILITY IN ANIMAL MODELS OF NEURODEGENERATION Neuroprosthetic

Implanted wireless system

T11 T12 T13

3

Lower body negative pressure chamber

RVLM AAV5 CAG Flex DTR

∆ Systolic blood pressure (mmHg)

RVLM

Control

−20

10

0

Tilt aborted due to symptom severity

60

Orthostatic Challenge

20

16 12 8 4 0

Time (s) 250

200

250

300

With neuroprosthesis:

200

Before therapy 1 month

150

3 months

100 50 0

C3 Neurons

AAV5 CAG Flex DTR

C3 Neurons

C1 ablation

Control

0

C2 ablation

30

60

90

0

Time (s)

30

60

Dapi TH

After targeted C3 ablation +

0

40

−20

Olig001 MBP WT syn WPRE

EES ON

RVLM

20

−40

RVLM

Lorem ipsum

Orthostatic Challenge

0

−60 0

30

60

30

0

90

60

CONCLUSION

Targeting different areas of the hemodynamic circuit, such as C3, allow us to determine the efficacy and limitations of neuroprosthetic baroreflex as a therapy for hemodynamic instability.

0 40

EES ON

This knowledge will help determine which patients may benefit from neuroprosthetic baroreflex therapy. Our ultimate goal is to unravel the requirements to restore hemodynamic stability across neurological disorders.

−20 20

−40 Orthostatic Hypotension

Lorem ipsum

0

−60 60

90

Time (s)

0

30

60

90

Support and funding informations: Financial support was provided by SNSF Sino Suisse, the MSA Coalition, SNSF Sinergia.

EPFL | Summer Research Program 2023

Through anatomical analysis, we expect to see αSyn aggregates and a significant number of neurons degenerated in the region of viral injection.

90

After targeted αSyn overexpression +

After targeted overexpression of αSyn

30

We expect that targeted overexpression of αSyn will lead to a gradual degeneration when injected in C1, C2 or C3.

Time (s)

Targeted overexpression of αSyn*

0

Dapi TH

90

Targeted overexpression of αSyn*

After targeted C3 ablation

∆ Systolic blood pressure (mmHg)

150

100

50

C2 ablationNucleus Ambiguus

−40

∆ Systolic blood pressure (mmHg)

0

EES ON

C1 ablation

Cell type ablation of C3 Neurons

Olig001 MBP WT syn WPRE

60

40

0

Celiac Ganglia

40

Time (s)

Cell type specific ablation of C3 Neurons

−60

AAV5 CAG Flex DTR

20

40

Control

0

Syncope

0

ANATOMICAL CORRELATES IN RELATION TO RESTORATION OF HEMODYNAMIC STABILITY WITH EES

Respective ablation of C1 and C2 Neurons AAV5 CAG Flex DTR

Return to sitting position

100

Thoracic Stimulation No Stimulation with abdominal binder No Stimulation

Without stimulation With stimulation

Number of syncopes per week

(C3) Celiac ganglia

150

50

10min tilt table test

Large diameter

Up to 50 % depletion

T10 T11 T12

70° Tilt

200

Hemodynamic stability restored with the neuroprosthetic baroreflex

C1

(C2) Sympathetic preganglionic neurons

Supine

250

ARCIM electrode array

Neuroprosthetic C1

White matter

Finometer

MSA Pronounced depletion

T12

Laminectomy

Walking distance (m)

(C1 ) Rostroventrolateralmedulla (RVLM)

Roots

T10

neurons

3

Systolic blood pressure (mmHg)

The neuroprosthetic baroreflex has been shown to restore hemodynamic stability across different preclinical models reflecting varying degrees of neuronal degeneration in Rostroventrolateral Sympathe c medulla (RVLM C1) and Sympathetic preganglionic neurons (C2), but not 0° preganglionic the Celiac Ganglia (C3)

2

NEUROPROSTHETIC BAROREFLEX RESTORES HEMODYNAMIC STABILITY IN A PATIENT WITH MSA

Baseline Corrected Blood Pressure (mmHg)

1

*Expected Results

31

Rosemarie Faustina Le University of Houston

Blanke Lab of Cognitive Neuroscience Supervisors : Nathalie Meyer, Mariana Babo-Rebelo

Case of the Missing Hippocampus

Rosemarie Le1, Nathalie Heidi Meyer2, Mariana Babo-Rebelo2, Olaf Blanke2,3 1. Department of Biology and Biochemistry, University of Houston, Houston, Texas 77204, United States; 2. Laboratory of Cognitive Neuroscience, Neuro-X Institute & Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), 1202 Geneva, Switzerland; 3. Department of Clinical Neurosciences, Geneva University Hospital, 1205 Geneva, Switzerland

Tracing the Tracts

The Magic Patient

Yellow: “Normal” Hippocampus; Red: Patient’s Hippocampus1 • Only patient’s episodic autobiographical memory (EAM) affected1 • Manipulation of bodily self-consciousness (BSC) impacts EAM1 • Recollection improved when BSC unaltered for healthy participants but the opposite for the patient1 • Patient had significant differences in resting-state functional connectivity than healthy participants1 • Higher connectivity between precuneus and medial prefrontal cortex1 • Lower connectivity between sides of hippocampus and parahippocampus1

Objective: analyze structural connectivity in regions critical to EAM and BSC

Mystery Clues

1.

Streamlines Around Hippocampal Lesions

What’s Next ?

Compare patient with controls through fixel-based analysis

vs.

2. Perform voxel-based analysis since many studies still look at fractional anisotropy, mean diffusion, etc.

Individual Connectome of Patient

The red rectangles highlight the structural connectivity between regions whose functional connectivity were previously analyzed3.

13 = Left medial orbitofrontal cortex 15 = Left parahippocampus 24 = Left precuneus 40 = Left hippocampus 47 = Right hippocampus 64 = Right parahippocampus 73 = Right precuneus

3. Find new database or generate our own controls for group connectomics

References

1. Meyer NH. Bodily Self-Consciousness as a Framework to Link Sensory Information and Self-Related Components of Episodic Memory: Behavioral, Neuroimaging, and Clinical Evidence. 2023.

2. M. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith. FSL. NeuroImage, 62:782-90, 2012 3. J.-D. Tournier, R. E. Smith, D. Raffelt, R. Tabbara, T. Dhollander, M. Pietsch, D. Christiaens, B. Jeurissen, C.-H. Yeh, and A. Connelly. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 202 (2019), pp. 116–37 ‘image: Flaticon.com’. This poster has been designed using images from Flaticon.com

EPFL | Summer Research Program 2023

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Addison Liu

Harvard University Sakar Lab of Micro BioRobotic Systems Supervisor : Lorenzo Noseda

Abstract With increasing interest in minimally invasive surgery and diagnostic procedures in the human brain, there arises a demand for innovative robotic platforms capable of precise and safe navigation through the brain’s intricate structures. This study introduces the design and characterization of a novel small-scale tendon-based robot for intracranial applications.

By leveraging thin-film technology, our robot offers a combination of dexterity, miniaturization, and soft interaction, which are essential for operations within the delicate cerebral environment.

EPFL | Summer Research Program 2023

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Alessandro Lucatelli Politecnico di Milano

Van De Ville Lab of Medical Image Processing Supervisor : Enrico Amico

&ƵŶĐƚŝŽŶĂů ŽŶŶĞĐƚŽŵĞ &ŝŶŐĞƌƉƌŝŶƚŝŶŐ ŽĨ ^ƚƌŽŬĞ WĂƚŝĞŶƚƐ ůĞƐƐĂŶĚƌŽ >ƵĐĂƚĞůůŝ 1 ͕ ŶĚƌĞĂ ^ĂŶƚŽƌŽ 1 ͕ &ůĂǀŝĂ WĞƚƌƵƐŽ 1 ͕ &ĂďŝĞŶŶĞ tŝŶĚĞů 1 ͕ >ŝƐĂ &ůĞƵƌLJ 1 ͕ ůĞŶĂ ĞŶĞĂƚŽ 1 , &ƌŝĞĚŚĞůŵ ͘ ,ƵŵŵĞů 1 ͕ 1,2 ϭ ŝŵŝƚƌŝ sĂŶ Ğ sŝůůĞ ͕ ŶƌŝĐŽ ŵŝĐŽ 1,2

1 Neuro-X Institute, École polytechnique fédérale de Lausanne, 1202 Geneva, Switzerland; 2 Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.

ď

Ă

& &

ŽŵƉƵƚĂƚŝŽŶĂů &ƌĂŵĞǁŽƌŬ Đ 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖 = 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖, 𝑖𝑖 ∗ 100

ZK/Ɛ

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖 =

σ𝑗𝑗≠𝑖𝑖 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖, 𝑗𝑗 2 𝑛𝑛 − 1

∗ 100

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖 = 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖 − 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑖𝑖

WĞĂƌƐŽŶ ŽƌƌĞůĂƚŝŽŶ

ZK/Ɛ

&ŝŐ ϭ͗ ,Žǁ ƚŽ ĐŽŵƉƵƚĞ Ă &ƵŶĐƚŝŽŶĂů ŽŶŶĞĐƚŽŵĞ ĨƌŽŵ ĨDZ/ ĚĂƚĂ

&ŝŐ Ϯ͗ ,Žǁ ƚŽ ĐŽŵƉƵƚĞ /ŶĚŝǀŝĚƵĂů /ĚĞŶƚŝĨŝĂďŝůŝƚLJ ^ĐŽƌĞƐ ĨƌŽŵ &ƵŶĐƚŝŽŶĂů ŽŶŶĞĐƚŽŵĞƐ

/ х Ϭ͘ϱ͗ ŐŽŽĚͲ/ Ɛ FC(i,j)

/ ф ͲϬ͘Ϯ͗ ďĂĚͲ/ Ɛ

Subjects &ŝŐ ϯ͗ ĚŐĞͲǁŝƐĞ &ŝŶŐĞƌƉƌŝŶƚŝŶŐ ǁŝƚŚ ĞdžĂŵƉůĞƐ ŽĨ ŐŽŽĚ ĂŶĚ ďĂĚ / Ɛ ĞĚŐĞƐ

dŚĞ dŝDĞ^ ĂƚĂƐĞƚ

&ƵŶĐƚŝŽŶĂů ŽŶŶĞĐƚŽŵĞ ;& Ϳ͗

/ĚĞŶƚŝĨŝĂďŝůŝƚLJ DĂƚƌŝdž ;/ŵĂƚͿ͗

/ŶĚŝǀŝĚƵĂů /ĚĞŶƚŝĨŝĂďŝůŝƚLJ ƐĐŽƌĞƐ͗

dŚĞ ŵĂƚƌŝdž ;&ŝŐ͘ ϭͿ ƚŚĂƚ ĞŶĐŽĚĞƐ ƚŚĞ ƐƚĂƚŝƐƚŝĐĂů ĚĞƉĞŶĚĞŶĐŝĞƐ͕ ĐŽŵƉƵƚĞĚ ĂƐ WĞĂƌƐŽŶ ŽƌƌĞůĂƚŝŽŶ͕ ďĞƚǁĞĞŶ ƚŚĞ ĨDZ/ ƐŝŐŶĂů ĐŽŵŝŶŐ ĨƌŽŵ ƉĂŝƌƐ ŽĨ ďƌĂŝŶ ƌĞŐŝŽŶƐ͘

'ŝǀĞŶ Ă ƐĞƚ ŽĨ Ŷ & Ɛ ͕ ŽŶĞ ĨŽƌ ƐƵďũĞĐƚ͕ ŽŶ Ă ƚĞƐƚ ƐĞƐƐŝŽŶ ĂŶĚ Ŷ & Ɛ ŽŶ Ă ƌĞƚĞƐƚ ƐĞƐƐŝŽŶ͕ /ŵĂƚ ;&ŝŐ͘ ϮďͿ ƐƚŽƌĞƐ ƚŚĞ WĞĂƌƐŽŶ ŽƌƌĞůĂƚŝŽŶ ďĞƚǁĞĞŶ Ăůů ƚŚĞ ƉĂŝƌƐ ŽĨ & Ɛ ;&ŝŐ͘ ϮĂͿ͘

DĞĂƐƵƌĞƐ ŽĨ ŚŽǁ ŵƵĐŚ Ă ƐƵďũĞĐƚ ŝƐ ƐŝŵŝůĂƌ ƚŽ ŚŝŵͬŚĞƌƐĞůĨ ďĞƚǁĞĞŶ ƚŚĞ ƚǁŽ ƐĞƐƐŝŽŶƐ ;/ƐĞůĨͿ͕ ŚŽǁ ƐŝŵŝůĂƌ ŚĞͬƐŚĞ ŝƐ ƚŽ ŽƚŚĞƌƐ ;/ŽƚŚĞƌƐͿ͕ ĂŶĚ ŚŽǁ ǁĞůů ǁĞ ĐĂŶ ĚŝĨĨĞƌĞŶƚŝĂƚĞ ŚŝŵͬŚĞƌ ĨƌŽŵ Ăůů ƚŚĞ ŽƚŚĞƌƐ ;/ĚŝĨĨͿ͘

&ƵŶĐƚŝŽŶĂů EĞƚǁŽƌŬ &ŝŶŐĞƌƉƌŝŶƚŝŶŐ͗ ƵƐŝŶŐ ŽŶůLJ Ă ƉŽƌƚŝŽŶ ŽĨ ƚŚĞ & ĂƐƐŽĐŝĂƚĞĚ ƚŽ Ă ŐŝǀĞŶ &ƵŶĐƚŝŽŶĂů EĞƚǁŽƌŬ ƚŽ ŝĚĞŶƚŝĨŝĨLJ ŝŶĚŝǀŝĚƵĂůƐ͘ ĚŐĞͲǁŝƐĞ &ŝŶŐĞƌƉƌŝŶƚŝŶŐ͗ ĞǀĂůƵĂƚŝŶŐ ƚŚĞ ĐŽŶƚƌŝďƵƚŝŽŶ ŽĨ ƐŝŶŐůĞ ĞĚŐĞƐ ŽŶ ƐƵďũĞĐƚƐ ŝĚĞŶƚŝĨŝĂďŝůŝƚLJ ;&ŝŐ͘ ϯͿ͘

dŚĞ ƐƚĂďůĞ ĂŶĚ ƉůĂƐƚŝĐ ĐŽŶŶĞĐƚŝŽŶƐ ŽǀĞƌ ƚŝŵĞ &ŝŐ ϳ͗ ƌĂŝŶ ŵĂƉƐ ŽĨ ŐŽŽĚͲ/ Ɛ ;ƵƉƉĞƌ ƚƌŝĂŶŐƵůĂƌͿ ĂŶĚ ďĂĚͲ/ Ɛ ;ůŽǁĞƌ ƚƌŝĂŶŐƵůĂƌͿ ŶŽĚĂů ƐƚƌĞŶŐƚŚ͘ dŚĞ ŵŽƌĞ ƌĞĚͬďůƵĞ ƚŚĞ ĂƌĞĂ ŝƐ͕ ƚŚĞ ŚŝŐŚĞƌ ƚŚĞ ŶŽĚĂů ƐƚƌĞŶŐƚŚ͘

• dŚĞ ƚŝŵĞ ǁŝŶĚŽǁ dϭͲdϮ ƚŚĞƌĞ ŝƐ ĂŶ ĞdžƉůŽƌĂƚŽƌLJ ƉŚĂƐĞ ŝŶ ǁŚŝĐŚ ǁŚŽůĞ ďƌĂŝŶ ƉůĂƐƚŝĐŝƚLJ ŽĐĐƵƌƐƐ͘

dŚĞ ĚĂƚĂƐĞƚ͕ ƉƌŽǀŝĚĞĚ ďLJ ,ƵŵŵĞů >Ăď ; W&>Ϳ͕ ĐŽŶƚĂŝŶƐ ŵƵůƚŝƉůĞ ĨDZ/ ĂĐƋƵŝƐŝƚŝŽŶ ŽĨ ƐƚƌŽŬĞ ƉĂƚŝĞŶƚƐ ĂĨƚĞƌ ƚŚĞ ƐƚƌŽŬĞ͕ ĂƐ ǁĞůů ĂƐ ďĞŚĂǀŝŽƌĂů ƐĐŽƌĞ ŝŶ ϲ ĚŝĨĨĞƌĞŶƚ ĚŽŵĂŝŶƐ Ăƚ ĞĂĐŚ ƚŝŵĞ ƉŽŝŶƚ͘

dŚĞ ŝĚĞŶƚŝĨŝĂďŝůŝƚLJ ĂĨƚĞƌ Ă ƐƚƌŽŬĞ Differential Identifiability (Idiff)

Self Similarity (Iself)

• KŶůLJ ƚŚĞ ĐŚĂŶŐĞƐ ƚŚĂƚ ůĞĂĚ ƚŽ ďĞƚƚĞƌ ƉĞƌĨŽƌŵĂŶĐĞƐ ĂƌĞ ŬĞƉƚ͕ ǁŚŝůĞ ƚŚĞ ŽƚŚĞƌ ĐŽŶŶĞĐƚŝŽŶƐ ĂƌĞ ƌĞƐƚŽƌĞĚ ƚŽ ƚŚĞ ŽƌŝŐŝŶĂů ĐŽŶĨŝŐƵƌĂƚŝŽŶ ĚƵƌŝŶŐ dϮͲdϯ͘

Others Similarity (Iothers)

tŚĂƚ ĚŽĞƐ ƐĞůĨ ƐŝŵŝůĂƌŝƚLJ ƚĞůů ƵƐ ĂďŽƵƚ ƌĞĐŽǀĞƌLJ ͍ Ă

ď

&ŝŐ ϱ͗ /ŶĚŝǀŝĚƵĂů ŝĚĞŶƚŝĨŝĂďŝůŝƚLJ ƐĐŽƌĞƐ ĚĞƌŝǀĞĚ ĨƌŽŵ dϭͲďĂƐĞĚ &ŝŶŐĞƌƉƌŝŶƚŝŶŐ ŽǀĞƌ ƚŝŵĞ Differential Identifiability (Idiff)

Self Similarity (Iself)

Others Similarity (Iothers)

Đ

&ŝŐ ϲ͗ /ŶĚŝǀŝĚƵĂů ŝĚĞŶƚŝĨŝĂďŝůŝƚLJ ƐĐŽƌĞƐ ĨŽƌ ĞĂĐŚ &ƵŶĐƚŝŽŶĂů EĞƚǁŽƌŬ͕ ĚĞƌŝǀĞĚ ĨƌŽŵ dϭͲďĂƐĞĚ &ƵŶĐƚŝŽŶĂů EĞƚǁŽƌŬ &ŝŶŐĞƌƉƌŝŶƚŝŶŐ ŽǀĞƌ ƚŝŵĞ͘

• ƵƌŝŶŐ ƚŚĞ ƉŚĂƐĞƐ ŽĨ ŚŝŐŚ ƉůĂƐƚŝĐŝƚLJ ƚŚĂƚ ŽĐĐƵƌ ĂĨƚĞƌ Ă ƐƚƌŽŬĞ͕ ƚŚĞ ĨĞĂƚƵƌĞ ŽĨ ƵŶŝƋƵĞŶĞƐƐ ƚŚĂƚ ŶŽƌŵĂůůLJ ĂůůŽǁ ƵƐ ƚŽ ĨŝŶŐĞƌƉƌŝŶƚ ƚŚĞ ƉŽƉƵůĂƚŝŽŶ ĂƌĞ ůŽƐƚ͕ ŚĞŶĐĞ ŝƚ ŝƐ ŶŽƚ ƉŽƐƐŝďůĞ ƚŽ ƌĞĐŽŐŶŝnjĞ ŝŶĚŝǀŝĚƵĂůƐ͘ • ůŽŶŐ ǁŝƚŚ ƚŚĞ ƌĞĐŽǀĞƌLJ ŽĨ ďƌĂŝŶ ĨƵŶĐƚŝŽŶ ƚŚĞƌĞ ŝƐ ĂůƐŽ Ă ƉĂƌƚŝĂů ƌĞĐŽǀĞƌLJ ŽĨ ƚŚĞƐĞ ĨĞĂƚƵƌĞ ŽĨ ƵŶŝƋƵĞŶĞƐƐ͕ ŚĞŶĐĞ ƚŚĞ ŝĚĞŶƚŝĨŝĂďŝůŝƚLJ ŽĨ ƚŚĞ ƉŽƉƵůĂƚŝŽŶ͘ • dŚĞ ďƌĂŝŶ ƌĞŽƌŐĂŶŝnjĂƚŝŽŶ ĂĨƚĞƌ Ă ƐƚƌŽŬĞ ŝƐ Ă ǁŚŽůĞ ďƌĂŝŶ ƉŚĞŶŽŵĞŶĂ͕ ƐŝŶĐĞ ƚŚĞƌĞ ĂƌĞ ŶŽ ƐŝŐŶŝĨŝĐĂŶƚ ĚŝĨĨĞƌĞŶƚƐ ĂŵŽŶŐ ƚŚĞ ϳ zĞŽ EĞƚǁŽƌŬ ǁĞ ĐŽŶƐŝĚĞƌĞĚ ŝŶ ƚĞƌŵƐ ŽĨ ŝĚĞŶƚŝĨŝĂďŝůŝƚLJ ŽǀĞƌ ƚŝŵĞ͘

&ŝŐ ϴ͗ WĂƌƚŝĂů >ĞĂƐƚ ^ƋƵĂƌĞ ŽƌƌĞůĂƚŝŽŶ ;W>^ Ϳ ƚŽ ĂƐƐĞƐƐ ďƌĂŝŶͲďĞŚĂǀŝŽƌ ĂƐƐŽĐŝĂƚŝŽŶ ŝŶ ƐƚƌŽŬĞ͘ tĞ ƌĞƉŽƌƚ ƚŚĞ ĞdžƉůĂŝŶĞĚ ĐŽǀĂƌŝĂŶĐĞ ŽĨ ƚŚĞ W>^ ƐŝŐŶŝĨŝĐĂƚŝǀĞ ĐŽŵƉŽŶĞŶƚƐ ǁŚĞŶ ĐŽŶƐŝĚĞƌŝŶŐ ƚŚĞ ƌĞůĂƚŝŽŶƐŚŝƉ ďĞƚǁĞĞŶ ;ĂͿ ƉĂƚŝĞŶƚΖƐ ƐĞůĨ ƐŝŵŝůĂƌŝƚLJ ΀/ƐĞůĨ΁ ǁŝƚŚ ďĞŚĂǀŝŽƌĂů ƐĐŽƌĞƐ͕ ĂŶĚ ;ďͿ & ĐŽŶŶĞĐƚŝŽŶƐ ǁŝƚŚ ďĞŚĂǀŝŽƌĂů ƐĐŽƌĞƐ͘ ;ĐͿ ĞŚĂǀŝŽƌĂů ƐĂůŝĞŶĐĞƐ Ăƚ ƚŝŵĞ dϯ ĨŽƌ /ƐĞůĨͲW>^ ͘ ^ŝŐŶŝĨŝĐĂŶƚ ďĞŚĂǀŝŽƌĂů ƐĂůŝĞŶĐĞƐ ĐŽƌƌĞƐƉŽŶĚ ƚŽ ďĂƌƐ ǁŝƚŚ ŵĞĂŶƐ ŚŝŐŚĞƌ ƚŚĂŶ ƐƚĂŶĚĂƌĚ ĚĞǀŝĂƚŝŽŶ͘

ZĞĨĞƌĞŶĐĞƐ ΀ϭ΁ &ŝŶŶ͕ ͕͘ ^ŚĞŶ͕ y͕͘ ^ĐŚĞŝŶŽƐƚ͕ ͘ Ğƚ Ăů͘ &ƵŶĐƚŝŽŶĂů ĐŽŶŶĞĐƚŽŵĞ ĨŝŶŐĞƌƉƌŝŶƚŝŶŐ͗ ŝĚĞŶƚŝĨLJŝŶŐ ŝŶĚŝǀŝĚƵĂůƐ ƵƐŝŶŐ ƉĂƚƚĞƌŶƐ ŽĨ ďƌĂŝŶ ĐŽŶŶĞĐƚŝǀŝƚLJ͘ EĂƚ EĞƵƌŽƐĐŝ͕ ϭϲϲϰ–ϭϲϳϭ ;ϮϬϭϱͿ͘ ŚƚƚƉƐ͗ͬ​ͬĚŽŝ͘ŽƌŐͬϭϬ͘ϭϬϯϴͬŶŶ͘ϰϭϯϱ ΀Ϯ΁ ŵŝĐŽ͕ ͕͘ 'ŽŹŝ͕ :͘ dŚĞ ƋƵĞƐƚ ĨŽƌ ŝĚĞŶƚŝĨŝĂďŝůŝƚLJ ŝŶ ŚƵŵĂŶ ĨƵŶĐƚŝŽŶĂů ĐŽŶŶĞĐƚŽŵĞƐ͘ ^Đŝ ZĞƉ ϴ͕ ϴϮϱϰ ;ϮϬϭϴͿ͘ ŚƚƚƉƐ͗ͬ​ͬĚŽŝ͘ŽƌŐͬϭϬ͘ϭϬϯϴͬƐϰϭϱϵϴͲϬϭϴͲϮϱϬϴϵͲϭ ΀ϯ΁ &ůĞƵƌLJ >͕ <ŽĐŚ W:͕ tĞƐƐĞů D:͕ ŽŶǀŝŶ ͕ ^ĂŶ DŝůůĂŶ ͕ ŽŶƐƚĂŶƚŝŶ ͕ sƵĂĚĞŶƐ W͕ ĚŽůƉŚƐĞŶ :͕ ĂĚŝĐ DĞůĐŚŝŽƌ ͕ ƌƺŐŐĞƌ :͕ ĞĂŶĂƚŽ ͕ ĞƌŽŶŝ D͕ DĞŶŽƵĚ W͕ Ğ >ĞŽŶ ZŽĚƌŝŐƵĞnj ͕ ƵĨĨĞƌĞLJ s͕ DĞLJĞƌ E,͕ ŐŐĞƌ W͕ ,ĂƌƋƵĞů ^͕ WŽƉĂ d͕ ZĂĨĨŝŶ ͕ 'ŝƌĂƌĚ '͕ dŚŝƌĂŶ :W͕ sĂŶĞLJ ͕ ůǀĂƌĞnj s͕ dƵƌůĂŶ :>͕ DƺŚů ͕ >ĠŐĞƌ ͕ DŽƌŝƐŚŝƚĂ d͕ DŝĐĞƌĂ ^͕ ůĂŶŬĞ K͕ sĂŶ Ğ sŝůůĞ ͕ ,ƵŵŵĞů & ͘ dŽǁĂƌĚ ŝŶĚŝǀŝĚƵĂůŝnjĞĚ ŵĞĚŝĐŝŶĞ ŝŶ ƐƚƌŽŬĞͲdŚĞ dŝDĞ^ ƉƌŽũĞĐƚ͗ WƌŽƚŽĐŽů ŽĨ ůŽŶŐŝƚƵĚŝŶĂů͕ ŵƵůƚŝͲŵŽĚĂů͕ ŵƵůƚŝͲ ĚŽŵĂŝŶ ƐƚƵĚLJ ŝŶ ƐƚƌŽŬĞ͘ &ƌŽŶƚ EĞƵƌŽů͘ ϮϬϮϮ ^ĞƉ Ϯϲ͖ϭϯ͗ϵϯϵϲϰϬ͘ ĚŽŝ͗ ϭϬ͘ϯϯϴϵͬĨŶĞƵƌ͘ϮϬϮϮ͘ϵϯϵϲϰϬ

EPFL | Summer Research Program 2023

• tŚĞŶ ŝƚ ŝƐ ƉŽƐƐŝďůĞ ƚŽ ĨŝŶŐĞƉƌŝŶƚ͕ ƚŚĞ ƐĞůĨ ƐŝŵŝůĂƌŝƚLJ ŐŝǀĞƐ ƵƐ ŵŽƌĞ ŝŶĨŽƌŵĂƚŝŽŶ ĂďŽƵƚ ƚŚĞ ďĞŚĂǀŝŽƌĂů ƐĐŽƌĞ ƚŚĂŶ ĐůĂƐƐŝĐ ŵĞƚŚŽĚƐ ƵƐŝŶŐ ƚŚĞ ĨƵŶĐƚŝŽŶĂů ĐŽŶŶĞĐƚŽŵĞ͘ • dŚĞ ƐĞůĨ ƐŝŵŝůĂƌŝƚLJ ŽĨ ďĞƚǁĞĞŶ ŶĞƚǁŽƌŬƐ ĐŽŶŶĞĐƚŝŽŶƐ ĞdžƉůĂŝŶƐ ƚŚĞ ŵŽƚŽƌ ƐĐŽƌĞƐ ŽĨ ƚŚĞ ƐƵďũĞĐƚ͕ ǁŚŝůĞ ƚŚĞ ƐĞůĨ ƐŝŵŝůĂŝƌƚLJ ŽĨ ǁŝƚŚŝŶ ŶĞƚǁŽƌŬƐ ĐŽŶŶĞĐƚŝŽŶƐ ĞdžƉůĂŝŶƐ ƚŚĞ ŵĞŵŽƌLJ͕ ĂƚƚĞŶƚŝŽŶ͕ ĞdžĞĐƵƚŝǀĞ ŽŶĞƐ͘

37

Mariia Minaeva

Karolinska Institutet, KTH Royal Institute of Technology, Stockholm University

Barth Lab of Protein and Cell Engineering Supervisor : Mahdi Hijazi

Exploring robustness of class A GPCR signaling through the integration of genetic variation and allostery Email: mariia.minaeva@epfl.ch Twitter: @mariia_minaeva

Mariia Minaeva, Mahdi Hijazi and Patrick Barth Laboratory of Protein and Cell Engineering, EPFL

Background Given their amenability to mutagenesis-driven modulation, GPCRs are vital drug targets. Thus, robust protein design approaches are crucial, with diverse strategies like molecular dynamics-based allosteric scoring and conservation-based scoring. Our study investigates the effectiveness of these scores in explaining activity outcomes for deep mutagenesis screening of 𝛽𝛽2AR. We reveal the synergistic potential of conservation-based and allosteric scoring, hinting at their combined value for enhanced protein design.

GEMME: conservation scores [1]

EC50: activity estimates [2]

AlloDy: allosteric scoring [3]

We utilized GEMME, a predictive tool that employs explicit evolutionary modeling. The outcome, represented by multiple sequence alignment based conservation scores ΔΔE, quantifies the evolutionary distance between a mutant and the wild-type sequence. A ΔΔE value close to 0 signifies highly conservative substitutions with minimal effects on protein structure. Conversely, lower ΔΔE values indicate an increased likelihood of mutations causing structural disruptions to the protein.

Using a comprehensive mutant library of 7800 out of 7828 possible variants, activity levels were assessed based on empirically determined half-maximal activity measurements.

After normalization, a semi supervised clustering approach segregated residues into six distinct clusters. The clusters represent residues with varying mutation tolerance levels, ranging from highly intolerant to tolerant.

Using Molecular Dynamics the AlloDy identifies residual interaction residual patterns.

By calculating Mutual Information (MI) between dihedrals, it quantifies the correlated motions between residues followed by pathway construction among MI pairs beyond a predetermined distance cutoff. These networks are clustered into coherent allosteric pipelines and the resulting allosteric score represents residual hubscores in the allosteric networks.

WT

WT

Mutant Mutant Ligand concentration

Ligand concentration

Clustering based on activity estimates

Conservation correlates with mutation intolerance based on activity estimations

Allosteric scores neither correlate with conservation nor overlap with SNVs Globally Intolerant Globally Intolerant Hydrophilic Sensitive (-) Charge Sensitive Proline Sensitive Tolerant

Conclusions

References

● Conservation explain mutational effects in intolerant residues ● Allosteric scores provide complementary with conservation ● Residues with high allosteric potential are underrepresented among single nucleotide variants

1.

2.

3.

EPFL | Summer Research Program 2023

Laine, Elodie et al., Mol. biol. and evolution 36.11, 2019 Jones, Eric M., et al. Elife 9, 2020) Jefferson, Robert E., et al. Nature Comm. 14.1, 2023

39

Francesca Montellato

Scuola Normale Superiore of Pisa Persat Lab of Microbial Mechanics Supervisor : Tania Distler

Investigating the interaction between P. aeruginosa and macrophages in the lung Francesca Montellato1,2, Tania Distler1, Alexandre Persat1 1Global Health Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland 2Scuola Normale Superiore di Pisa (SNS), Pisa, Italy

RESULTS

BACKGROUND Pseudomonas aeruginosa is a multi-drug resistant pathogen that causes acute and chronic infections in immunocompromised subjects and patients with a disrupted epithelial barrier. Moreover, it is one of the principal causes of hospital-acquired infections linked to medical devices. Therefore, P. aeruginosa has been listed by the WHO among the five pathogens that most urgently require new clinical treatment strategies.

1. Which specific cytokines do macrophages express during infection?

1. Measurement of cytokine expression levels We selected some of the major cytokines secreted by macrophages in different activation states. CXCL2 is a chemokine involved in neutrophil recruitment. IL-1β and TNF are pro-inflammatory cytokines. IL-1RN and IL-10 are anti-inflammatory cytokines while IL-6 plays both pro- and antiinflammatory roles.

During airway infections, alveolar macrophages represent the first line of defence of the immune system. They recognize pathogens and release cytokines orchestrating an inflammatory response. Importantly, they also mediate pathogen clearance by phagocytosis.

In order to characterize the response of macrophages to P. aeruginosa, we will quantify cytokine expression during infection by qPCR.

1. E

2. Does bacterial chemotaxis play a role in macrophage killing by P. aeruginosa?

During acute infection, P. aeruginosa targets host cells through injection of cytotoxins. By microscopy, we observe that P. aeruginosa accumulates around macrophages followed by the induction of cell death. This directed movement of bacteria towards macrophages suggests that chemosensory processes may be involved.

Chemotaxis allows bacterial movement along gradients of chemoeffectors (e.g. nutrients, different chemicals) in the environment. P. aeruginosa has a chemotaxis system composed of 26 different receptors and over 20 effectors that can control the movement of the flagellum.

We sought to identify the factors involved in bacterial sensing of macrophages by constructing knockouts of several components of the P. aeruginosa chemosensory system.

1. qPCR assay

For every cytokine, the expression level peaks at 4 hours of infection. The highest expression is observed for the cytokines IL-1β, IL-6 and TNF. This indicates that macrophages acquire a pro-inflammatory phenotype in response to P. aeruginosa.

We isolated RNA from macrophages infected with P. aeruginosa. RNA was reverse transcribed into cDNA. From the pool of cDNA, we quantified cytokine expression by qPCR.

1. Kgukhgjhgj

METHODS

2. Knocked out genes CheY CheZ

2. Generation of chemotaxis mutants We amplified the flanking regions of the gene of interest (GOI) by PCR. The primers for the homology arms were designed with overhangs allowing for Gibson assembly into a digested vector. Eventually, the vector will be transferred to the P. aeruginosa strain, which will lead to the recombination of the plasmid with the genome under several selection steps, resulting in the deletion of the GOI.

McpN TlpQ PctABC

Response regulator that modulates swimming direction by controlling the flagellar rotational switch Phosphorylates CheY and controls CheY diffusion in the cell Receptor for nitrate Receptor for histamine Receptors for aminoacids

OUTLOOK 1. The results presented here need to be validated by the addition of more biological replicates. To better characterize the macrophage response, it will be useful to add more time points and compare the macrophage response between different mutants of P. aeruginosa.

2. The generated chemotaxis mutants can be used in microscopy experiments in order to observe their behavior towards macrophages.

Images created with BioRender.com

EPFL | Summer Research Program 2023

41

Anar Nasanjargal

New Mongol College of Technology Schuhmacher Lab of Chemical and Membrane Biology Supervisor : Linda Wedemann

Mapping the interactome of signaling lipids Anar Nasanjargal Supervisor: Linda Wedemann Schuhmacher laboratory: Chemical and Membrane biology laboratory

1 Introduction Studying lipids in cell signaling presents challenges mainly due to methodological issues. The wide variety of lipid species produced by cells further complicates their investigation1. In order to study aspects such as lipid metabolism, lipid-protein interactions, and intracellular lipid localization, we employ a chemical biology approach by synthesizing trifunctional lipids.

In this project, we focus on the lipid class of diacylglycerols. The synthetically modified lipids are equipped with a photocage enabling temporal control of lipid release. Additionally a diazirine group is incorporated into the lipid, making it photo-crosslinkable to proteins to form protein-lipid complexes, and a alkyne group allowing for attachment of biotin for enrichment of the lipid-protein complex2. Using such an interdisciplinary approach will allow us to study signaling lipids in their cellular environment in unprecedented detail.

Trifunctional lipid probes The trifunctional Diacylglycerol includes 3 modifications: a) A photocleavable coumarin cage for temporal control over lipid release b) A diazirine group for photo-crosslinking to form lipid-protein complexes c) An alkyne group for click-chemistry (fluorophore / biotin attachment)

SDS page To be evaluate successful cell lysis and thereby protein release, we performed SDS PAGE. kDa

Precision Plus unstained ladder

Input

Eluate

Flow through Wash 1 Wash 2

Wash 3

250 150 100 75 -

A Structure of a trifunctional lipid

Figure 4:

SDS PAGE result of pulled-down samples - Colloidal Coomassie dye.

Input – whole cellular protein before pull-down, eluate- the output of pull down process, flow through- unbound supernatant after pull-down incubation, wash 1,2,3- washes after binding

50 37 25 20 15 10 -

B General workflow with the trifunctional lipid

Western blotting Our goal with western blotting is to assess the efficiency of the pull-down process in isolating biotin-clicked lipid-protein complexes, which can be subsequently analyzed using mass spectrometry.

Input 10 μl

Eluate 10 μl

Flow through W1 10 μl 10 μl

Input 20 μl

Flow Eluate through W1 20 μl 20 μl 20 μl

Flow Input Eluate through W1 No UV No UV No UV No UV

Input

Eluate

Flow through

W1

Input No UV

Eluate No UV

Flow through No UV

W1 No UV

Figure 1: Trifunctional lipids. A) Chemical structure with annotation of a trifunctional Diacylglycerol (DAG). B) Workflow of Photo-uncaging, Diazirine crosslinking and alkyne click, in the project either Alexa Fluoro 594 fluorophore azide or biotin-azide were used.

2 Method Overview

Figure 5:

Western blot membrane – Streptavidin Dylight 680.

Samples loaded with different concentration of lipids.

Controls are not illuminated by UV (no uncaging or crosslinking, no protein-lipid complexes induced).

a) Establishment of Biotin Click procedure • Cell culture • Lipid loading, uncaging, crosslinking • Cell lysis • Biotin-click to the lipid (copper-click) b) Establishment of Biotin – Pulldown • Protein precipitation • Biotin-Streptavidin pulldown • SDS-PAGE • Western Blotting

4 Conclusion • The imaging result shows, that the biotin click can be successfully performed, and made us more confident to continue with next steps of the workflow.

• From the SDS page result we can conclude that the cells were lysed properly, and intracellular proteins thereby released to use for further analysis.

• Successful establishment of the sample preparation pipeline for the signaling lipid interactome analysis, some more optimization required for the pull-down.

• In the future, if we can efficiently isolate biotin-clicked lipid-protein complexes, we can continue the experiment with mass spectroscopy to detect what specific proteins are attached to the lipid.

Nikon spinning disk – for imaging

3 Results Biotin click To verify the binding of biotin to DAGs as intended, we conducted imaging.

Control: No lipid, all click components except biotin

Acknowledgement I really appreciate to my supervisor, Linda Wedemann, as well as to my PI, Milena Schuhmacher, and my lab colleagues for hosting me and teaching me a lot of things. I also extend my thanks to the SRP organizing team, including Alice Goodman and McCall McBain sponsors, for providing me with the opportunity to experience this incredible journey. Special thanks for my friends from SRP.

Control: No lipid, no biotin

AF 594

AF 647

AF 594 dye clicked

Western blot membrane – Streptavidin Dylight 680.

Samples prepared under same condition as SDS page. Controls are not illuminated by UV.

The first Western blot (Figure 5) did not give much information regarding the workflow because there were lower amount of cells per samples. So we used more cells in subsequent experiments and saw better result (Figure 6). The lack of protection of sunlight might be the reason why there are bands in the no UV controls also. The strong bands at 13.5 kDa in the eluate is streptavidin originating from the pull-down processing.

Figure 2: Work flow of the project

Biotin clicked Strep-AF 647

Figure 6:

Figure 3:

The AF647 signal is observed in the biotin-clicked samples due to the specific binding of fluorescently labelled streptavidin antibodies with biotin molecules.

For AF594 dye-clicked samples, an AF594 signal is visible, while the other samples do not display such a signal. The slight signals seen in AF647 might be due to the close proximity of the detection wavelengths of AF647 and AF594.

EPFL | Summer Research Program 2023

References

1.

2.

Höglinger, Doris, et al. "Trifunctional lipid probes for comprehensive studies of single lipid species in living cells." Proceedings of the National Academy of Sciences 114.7 (2017): 1566-1571.

Schuhmacher, Milena, et al. "Live-cell lipid biochemistry reveals a role of diacylglycerol side-chain composition for cellular lipid dynamics and protein affinities." Proceedings of the National Academy of Sciences 117.14 (2020): 7729-7738.

43

Cailyn Mae Ong

University of the Philippines Diliman Fellay Lab of Human Genomics of Infection and Immunity Supervisor : Valeriia Timonina

Exploring the link between clonal hematopoiesis of indeterminate potential (CHIP), chronic infections, and inflammation 1

1

1,2

Ong, C., Timonina, V., Fellay, J.

1 - Fellay Laboratory, Global Health Institute, Swiss Federal Institute of Technology in Lausanne, Lausanne, Switzerland 2 - Swiss Institute of Bioinformatics

Introduction

Results

Clonal hematopoiesis occurs when a hematopoietic stem cell (HSC) mutates and begins to produce mutated blood cells, forming a large clone (group of blood cells) with a cancer-associated mutation [1].

Exploratory data analysis

clonal expansion

clonal hematopoiesis

mutated HSC

mutated blood cells

clone with mutation

While some individuals with clonal hematopoiesis develop blood-related cancers or malignancies, a significant portion of individuals with clonal hematopoiesis do not, despite having these mutations. This phenomenon is known as clonal hematopoiesis of indeterminate potential (CHIP) [1].

Figure 1. Counts and percentage of individuals in Cohort 1 (Multiplex Serology data) with CHIP.

Figure 2. Proportion of individuals who have CHIP in each age range.

Figure 3. Percentage of prevalence of infection for each pathogen in the Multiplex Serology data.

Figure 4. Frequency of CHIP-associated mutations in different genes.

CHIP is known to be more prevalent in individuals with HIV [2], but we are not sure why. One hypothesis for how CHIP occurs is that mutated HSCs not only induce an inflammatory environment but also are more resistant to the deleterious effects of inflammation, allowing them to expand in a proinflammatory environment [3]. If this is the case, chronic infections that cause an increase in baseline inflammation levels would be associated with CHIP, and this association would also be measurable through levels of inflammatory biomarkers such as C-reactive protein (CRP).

CHIP

inflammation

?

This project aims to investigate the prevalence of CHIP in people in the UK Biobank and clarify the association between CHIP, chronic infections, and inflammation levels.

chronic infections

Methodology whole exome sequences (.cram)

Cohort 1 9,050 individuals with Multiplex Serology data in UK Biobank Cohort 2 417, 640 individuals

call somatic mutations with Mutect2

annotate variants with Annovar (.vcf)

call and filter out samples with CHIP variants

statistical analysis to determine association

Associating CHIP with pathogens, CRP CHIP does not have significant association with pathogen seropositivity CHIP also does not significantly associate with pathogen burden Pathogen burden = # of pathogens the individual is infected by CHIP does not have a significant association with CRP In persons of European ancestry (EU), the p-value is borderline (between 0.05 and 0.1) Potentially a larger dataset may make the association (if any) clearer (use all of UKB, ~400,000 individuals)

Associating CRP with pathogens

UK Biobank

Cohort 1 9,050 individuals Multiplex Serology data CHIP data (called) CHIP ~ CRP CHIP ~ pathogens CRP ~ pathogens

Cohort 2 417,640 individuals CRP levels and blood count data CHIP data (called) CHIP ~ CRP

References [1] Jaiswal, S., & Ebert, B. L. (2019). Clonal hematopoiesis in human aging and disease. Science, 366(6465), eaan4673.

[2] Bick, A. G., Popadin, K., Thorball, C. W., Uddin, M. M., Zanni, M. V., Yu, B., ... & Fellay, J. (2022). Increased prevalence of clonal hematopoiesis of indeterminate potential amongst people living with HIV. Scientific reports, 12(1), 577.

[3] Avagyan, S., Henninger, J. E., Mannherz, W. P., Mistry, M., Yoon, J., Yang, S., ... & Zon, L. I. (2021). Resistance to inflammation underlies enhanced fitness in clonal hematopoiesis. Science, 374(6568), 768-772.

[4] Hodel, F., Naret, O., Bonnet, C., Brenner, N., Bender, N., Waterboer, T., ... & Fellay, J. (2022). The combined impact of persistent infections and human genetic variation on C-reactive protein levels. BMC medicine, 20(1), 1-9.

EPFL | Summer Research Program 2023

Table 1. Contingency table showing for each pathogen the prevalence, number of individuals with/without CHIP, and the p-value of the association of CRP with each pathogen.

CRP was found to have significant association with several pathogens We tested CRP association with pathogens to establish clearer link between infection with pathogens and inflammation Association of CRP with C. trachomatis and H. pylori was previously established in CoLaus|PsyCoLaus + UKB cohort [4] The following pathogens have significant association (in addition to those with previously established association): HSV-1, CMV, HHV6A, T. gondii, HPV-16 Doing multivariate analysis leaves HSV-1, HHV-6A, T. gondii, and H. pylori as pathogens with significant association with CRP

Conclusion and Next Steps CRP is associated with HSV-1, HHV-6A, T. gondii, and H. pylori in a cohort of 9,050 individuals The association between CHIP and CRP remains unclear Using Cohort 2, check for an association between CHIP and CRP If there is one, Mendelian Randomization will be performed to check the causality of the association

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Aleksandr Refeld ITMO University

Tang Lab of Biomaterials for Immunoengineering Supervisor : Lucia Bonati

Abstract Incorporation of cytoskeleton-binding modules into chimeric antigen receptors for enhanced immunotherapy

CAR-T technology has been a major breakthrough in tumor immunotherapy in the last decade. However, it comes with significant limitations. Interestingly, the immune synapse formed by CAR-T cells is less organized than the one formed by unmodified T-cells. Thus, the idea of designing CARs that would lead to a more organized immune synapse is highly interesting.

In this project we are aiming at constructing a CAR containing a cytoskeleton-binding modules in different positions, thus varying their proximity to the membrane, which can affect the performance of CAR-T cells.

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Khondamir Rustamov

National University of Uzbekistan Correia Lab of Protein Design and Immunoengineering Supervisor : Casper Goverde

Computational design of soluble analogues of integral membrane protein structures Khondamir Rustamov1,2, Casper Goverde1,2, Bruno E. Correia1,2

1Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL),

Lausanne, Switzerland.

2Swiss Institute of Bioinformatics (SIB), Lausanne, CH.

Results

Introduction

• Membrane proteins are important drug targets because they play crucial roles in cellular function and are involved in various diseases.

• Screening membrane proteins remains challenging due to low expression levels and insolubility.

• Recently, Goverde et al. demonstrated the design of a soluble GPCR topology using AF2seq and ProteinMPNN [1,2].

• We wish to expand on this approach by keeping as much natural function as possible.

• We evaluated the efficiency of two different approaches (pLDDT and RMSD based) for assessing the designed structures when the masked template mode was employed in AlphaFold predictions.

• Our findings indicate that the pLDDT-based approach correlates similarly in both the masked template and single sequence modes of AlphaFold.

Me th o d s

TM Hydrophobic Surface only

• • •

TM Surface only

Selection strategies: • Only hydrophobic residues from the transmembrane surface (33% of all residues).

• All transmembrane surface residues (43%).

• All residues located more than 5 Å away from the TM except Functional site functional site (58%).

Soluble proteins design We designed 75 AF2seq trajectories (25 for each strategy) based on a given crystal structure template [3].

Amino acids in selected positions across all AF2seq designs and crystal structures were subsequently predicted using the ProteinMPNN and Soluble MPNN networks.

Next, we utilized AlphaFold to predict the structures of all sequences. We calculated the confidence (pLDDT), RMSD, sequence similarity, and the fraction of apolar surface for all resulting structures [1].

• •

ProteinMPNN-predicted sequences demonstrate high confidence; however, these structures have a high number of apolar residues on the surface, making them insoluble.

The structures of soluble MPNN-predicted sequences illustrate a high confidence rate (pLDDT > 80, pTM > 0.8) and contain high amount of polar residues on the transmembrane surfaces of the designed structures, making them soluble.

Conclusion • AlphaFold predictions using sequence only resulted in misprediction and low confidence structures. To improve the prediction quality, a slight structural bias towards the xtal structure was added (termed masked template) [4].

Conclusion • Our research demonstrates that soluble MPNN can effectively increase the solubility of membrane proteins whilst leaving many of the natural sequence untouched.

• Structures predicted from Soluble MPNN sequences exhibit structural similarity and confidence.

Future work • The solubilized membrane proteins are validated in vitro.

• Binding of natural ligands and drugs with designed proteins will be investigated.

• High throughput drug screening against these drugs will become more feasible and cheaper.

Perspective • This approach offers the capability to generate soluble analogs of membrane proteins while preserving ligand binding recognition and conformational signal transduction functions. This advancement not only facilitates the study of their function in more biochemically accessible soluble formats but also enhances the development of novel drugs and therapies targeting this challenging class of proteins.

 Laboratory of protein design and

References [1]

immunoengineering (LPDI) x.rustamov @cat-science.uz

[3] [2] [4]

@KhRRustamov

EPFL | Summer Research Program 2023

Goverde, C. A., Pacesa, M., Dornfeld, L. J., Georgeon, S., Rosset, S., Dauparas, J., Shellhaas, C., Kozlov, S., Baker, D., Ovchinnikov, S., & Correia, B. Computational design of soluble analogues of integral membrane protein structures. BioRxiv, 2023-05. (2023).

Goverde, C., Wolf, B., Khakzad, H., Rosset, S. & Correia, B. E. De novo protein design by inversion of the AlphaFold structure prediction network. Biorxiv 2022.12.13.520346 (2022).

Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., & Baker, D. Robust deep learning–based protein sequence design using ProteinMPNN. Science, 378(6615), 49-56. (2022).

Roney, J. P., & Ovchinnikov, S. State-of-the-art estimation of protein model accuracy using AlphaFold. Physical Review Letters, 129(23) (2022).

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Anushree Sabnis

Veermata Jijabai Technological Institute Ijspeert Lab of Biorobotics Supervisor : Raphael Zufferey

Anushree Sabnis Veermata Jijabai Technological Institute, Mumbai

Experimental Validation

Model Formulation

Flapping wing simulation and optimisation with Aeroelasticity

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Ruam Salaroli

Federal University of Espírito Santo Gräff Lab of Neuroepigenetics Supervisor : Lisa Watt

Molecular tools for the regulation of Kdm6b expression Ruam Salaroli, Lisa Watt, Johannes Gräff Laboratory of Neuroepigenetics, Brain Mind Institute, School of Life Sciences. École Polytechnique Fédérale de Lausanne, Switzerland

Introduction Remote memories are long-lasting and resistant to change. Despite their stability, these memories can be attenuated using remote extinction paradigms [1]. In a previous study on this topic by Gräff and colleagues, it was

found that after a remote fear extinction paradigm (Fig. 1), Kdm6b is upregulated in neurons located in the dentate gyrus of the hippocampus, but not for recent memory extinction (unpublished data).

Results 1 shRNA targeting Kdm6b reduces mRNA expression Using a construct expressing our shRNA targeting Kdm6b under a U6 promoter, we show a significant reduction (~80%; F(3,14) = 4.3, p=0.024) of mRNA expression of Kdm6b only via co-transfection of two shRNA targeting different regions (Fig. 4A). On the other hand, switching the construct to a CMV promoter produces a significant knockdown of ~50% with shRNA target 1 alone (F(3,27) = 3.8, p=0.021) (Fig. 4B).

A

B

Figure 1. Experimental paradigm for fear extinction in mice after contextual fear conditioning (CFC). Mouse illustration adapted from scidraw.io.

KDM6B is a relevant enzyme for regulation of gene expression (Fig. 2), via its action as a histone H3 lysine 27-specific (H3K27) demethylase [2].

Additionally, it has been implicated as a risk gene for autism spectrum disorder (ASD) [3], as well as involved in learning and memory [2] and cocaine reward memory [4].

Figure 4. Knockdown of Kdm6b using two distinct shRNA systems. A, simplified schematics of the U6 promoter driving the expression of shRNA target (top), and the relative mRNA expression of Kdm6b in N2A cell after plasmid transfection with either scramble sequence, one of two targets against Kdm6b or co-transfection of both targets (bottom). B, simplified schematics of the CMV promoter driving the expression of the shRNA and eGFP tag (top). Relative mRNA expression were measured in samples that were either non-transfected, targeting the firefly luciferase (FF3) used as a control, and two of the target 1 with 1 bp difference in the Kdm6b targeting sequence. All the date are expressed as average of normalized expression to housekeeping gene (GAPDH) ± SEM. One-way ANOVA was performed to compare the effect the different constructs, followed by Tukey’s HSD Test for multiple comparisons.

2 CRISPR-Cas9-mediated Kdm6b knockout

Figure 2. Simplified schematic of the removal of the repressive epigenetic mark H3K27 trimethylation by KDM6B. Adapted from Jones, Nature (2007).

CRISPR-Cas9 with Kdm6b targeting guides were transfected into N2A cells.

Although the ANOVA showed no significance (F(8,41) = 1.92, p=0.082), there appears to be a trend towards reducing the level of Kdm6b. According to Tukey's HSD Test for multiple comparisons, both sgC and sgD had a marginal difference (p=0.06 and p=0.07, respectively) compared to the non-transfected group.

However, it remains unclear what role Kdm6b plays in the extinction of remote fear memories and whether it is essential for the modulation of this persistent type of memory. Therefore, we aimed to develop molecular tools to control Kdm6b expression for conditional in vivo experiments.

Methods

Figure 5. Knockout of Kdm6b using CRISPR-Cas9 system. Simplified scheme of the Cas9 construct (top). Relative mRNA expression of Kdm6b in N2A cell after plasmid transfection with different constructs – non-targeting (NT) or guide sequences A to G (bottom). Statistics performed as in Figure 4.

Conclusion o Our results show that shRNA targeting Kdm6b promotes an efficient strategy for reducing mRNA expression, while the CRISPR-Cas9-mediated system reveals a trend;

Figure 3. Workflow for the experimental methodology. Created with BioRender.com

Future direction -

Fluorescence-activated cell sorting (FACS) for eGFP-tagged plasmids; Establish a configuration for inducible shRNA expression for in vitro and in vivo experiments;

o It is possible that the lack of a more robust knockdown/-out in both eGFPtagged constructs is due to the absence of cell sorting. Therefore, the results presented here underestimate the true performance of the strategies used.

References: [1] Silva & Gräff. Face your fears: attenuating remote fear memories by reconsolidation-updating. Trends in Cognitive Sciences, 27, 404-416, 2023 [2] Gao et al. Kdm6B Haploinsufficiency causes ASD/ADHD-like behavioral deficits in mice. Frontiers in Behavioral Neuroscience, 16, 905783. 2022.

[3] Wang et al. KDM6B cooperates with Tau and regulates synaptic plasticity and cognition via inducing VGLUT1/2. Molecular psychiatry, 27, 5213-5226. 2022.

[4] Zhang et al. The histone demethylase KDM6B in the medial prefrontal cortex epigenetically regulates cocaine reward memory. Neuropharmacology, 141, 113-125. 2018.

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Nora Wittmann University of Vienna

Blokesch Lab of Molecular Microbiology Supervisor : Alexis Proutière

Abstract Effect of surface attachment on type six secretion in pandemic Vibrio cholerae

Vibrio cholerae, the causative agent of the disease cholera, originates from seawater where it is often found associated with zoo- or phytoplankton. In this aquatic environment as well as in the human gut, V. cholerae has to face competition from other bacteria and eukaryotic predators. To resist against such predators, V. cholerae produces a type six secretion system (T6SS). The T6SS machinery injects toxic effectors into neighboring cells leading to their death. In current pandemic V. cholerae strains, production of the T6SS is tightly controlled by a complex regulatory network. In these strains, the production of T6SS is low under liquid culture conditions but increases when the bacteria face solid surfaces. During this summer project, we tried to decipher the regulatory networks responsible for this enhanced T6SS production on a solid surface.

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Nora Xiao

University of North Texas Gerstner Lab of Computational Neuroscience Supervisor : Christos Sourmpis

Single-trial reconstruction of electrophysiological recordings using LFADS Nora Xiao1, Christos Sourmpis2, Wulfram Gerstner2

1California Institute of Technology, Pasadena, USA; 2École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Background

Results: Low-dimensional trial representation

• Multi-area simultaneous electrophysiological recordings call for novel data-driven approaches that allow us to reveal underlying factors governing neural activity.

• Our aim is to apply the latent factor analysis via dynamical systems (LFADS) [2] approach to recordings from a mouse performing a tactile detection task [1].

• We applied the method to data from 2 recording sessions with 2 brain areas per session.

• We calculated a two-dimensional UMAP (Uniform Manifold Approximation and Projection) representation of recordings from the testing set and simulated trials from the model.

• LFADS is a deep learning approach to infer latent dynamics from neural spiking data.

• The UMAP was fitted on the training set.

Hit

Miss

CR

FA

Session 1 (ntrials=30)

Hit Miss

False alarm (FA) Correct rejection (CR) Experimental paradigm. Left: Tactile detection task setup. Right: The stimuli and the licking action of the mouse organize the trials into four types (hit, miss, false alarm, and correct rejection). Adapted from [1].

Session 2 (ntrials=81)

LFADS model LFADS (Latent Factor Analysis via Dynamical Systems) • A recurrent neural network (RNN)-based architecture that can discover low-dimensional dynamics.

• LFADS uses a variational auto-encoder (VAE) architecture, a model that learns efficient representations of input data by encoding it into a compressed form and then decoding it back to reconstruct the original data.

• Steps • The encoder compresses spiking data into a single vector which serves as the initial state g0 to the generator. The output from the generator is multiplied by matrix Wfac to give the vector of dynamic factors ft.

• ft is multiplied by the readout matrix Wrate and then exponentiated to give the inferred firing rates. Finally, these rates are compared with the observed spikes.

The model was implemented from github.com/snel-repo/autolfads-tf2.

LFADS architecture. Adapted from [2].

Results: Neural activity • Activity was discerned by area (wS1, tjM1, and wM2) and trial type (Hit, Miss, CR, and FA), and simulated trials from the model were plotted against the averaged recordings from the testing set.

• Neural activity was averaged over the trials and the population of neurons.

Hit

Miss

Session 1 (ntrials=30)

Model Data

Conclusion As was found in Pandarinath et al. (2018), the LFADS methods successfully captured single-trial dynamics and discerned between different trial-type conditions.

Next steps • Applying our methods to recordings from more trials will provide a more robust encoding, particularly for trial-type conditions.

• Tuning the hyperparameters of our model may allow a better fit to the data; however, the default parameters used were sufficient for our data.

• Modeling more sessions with crucial areas for the sensorimotor transformation pathway could clarify the essential architecture required for achieving such a process.

Session 2 (ntrials=81)

Acknowledgments Special thanks to • The members of the Laboratory of Computational Neuroscience at EPFL. • The program committee of the Life Sciences Summer Research Program: Alice Emery-Goodman, Johannes Gräff, and Bruno Correia.

• This work was supported by the ThinkSwiss Summer School Scholarship.

References

[1] Esmaeili, V. et al. (2021). Rapid suppression and sustained activation of distinct cortical regions for a

delayed sensory-triggered motor response. Neuron, 109(13), 2183-2201.e9. https://doi.org/10.1016/j.neuron.2021.05.005.

[2] Pandarinath, C. et al. (2018). Inferring single-trial neural population dynamics using sequential autoencoders. Nature Methods, 15(10), 805-815. https://doi.org/10.1038/s41592-018-0109-9.

100 ms

CR

FA

100 ms

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Donors The generosity of our community of committed donors continues to make a significant difference to our students’ lives. We are truly thankful to the following donors and partners for their trailblazing support.

DebioPharm EPFL School of Life Sciences Hirzel Foundation ISREC Foundation McCall MacBain Foundation Pinkas Fondation ProTechno Foundation ThinkSwiss UCB

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‘‘

Thank you

Becoming friends with students from all over the world and different cultures and being exposed to the brightest minds from all over the world felt empowering!

‘‘

The lessons I learnt from failed experiments will stay with me across my career. Interacting with lab mates and having intellectually stimulating discussions is something I will never forget!

‘‘

Being a part of something larger and contributing to the scientific community of a renowned institution.

This experience has broadened my skills and perspective through hands-on projects, mentorship, and collaborations.

‘‘

Experiencing real research was an amazing opportunity that will help me for a more informed decision about my future academic life.

‘‘

Supervising students is always an enriching experience for the lab and supervisors. Having a student from an external institution allows us to create new ties between labs and share perspectives that lead to better research.

‘‘

SRP students add new perspectives to the lab, and add a lot of energy over the summer when sometimes the lab can feel empty when people go on holiday.

‘‘

The experience of supervising an SRP student was enriching for me.

The students’ questions allowed me to refine my explanations of the project and I also learned patience with the pace of students.

‘‘

Working with SRP students helped me to improve my project development skills, and management skills.

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A special thanks for the kind support All participating labs, their principals investigators and student supervisors Direction : Bruno Correia, Johannes Gräff Program commitee : Aleksandar Antanasijevic, Michele de De Palma, Patrick Barth, Alex Persat, Pavan Ramdya Coordination : Alice Emery-Goodman Workshops : Quentin Barraud, Laurra Batti, Maria Brbic, Cristina Colangelo, Michele De Palma, Nadine Fournier, Susan Gasser, Julien Jabelot, Máté Kiss, Alice Klein, Elizaveta Kozlova, Danny Labes, Achilleas Laskaratos, Roeltje Maas, Christoph Merten, Denis Migliorini, Gaspard Pardon, Laura Plassmann, Héloise Sandoz, Bernard Schneider, Amanda Skarda, Dace Stiebrina, Joan Suris, Jeremy Wong On-line Application : Michel Naguib Human Resources : Valérie Bise, Cathy Freire, Mathieu Helbling Financial Services : Harald Hirling, Viviane Maire, Patricia Rivas UNIL : Laurence Flückiger, John Pannell, Thierry Roger, Communication : Laurence Mauro, Eva Schier, Titouan Veuillet Donors & External Relations : Line Déglise, Sacha Sidjanski Accreditation : Edson Bally, Anaïs Perrone Photography : Adrian Alberola, Felix Imhof, Titouan Veuillet

Project & Texts : Alice Emery-Goodman & SV Deanship

Design: SV Communications

Printing: EPFL Repro

Copyrights: EPFL School of Life Sciences 2023

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SRP website

EPFL School of Life Sciences Station 19 CH-1015 Lausanne epfl.ch/schools/sv/education/summer-research-program/