EMBL Seminars

Abstract
[Traditional flow cytometry has relied heavily on fluorescent molecular markers and investigator-driven hypotheses to detect and sort cells of interest. However, suitable molecular markers are often unavailable or insufficient to distinguish biologically critical cell subpopulations effectively. To address this, we have developed a series of data-centric AI-based instruments, termed Learning Cytometer, to find and sort critical cells by leveraging high-content, label-free optical data. 

I will first introduce Ghost Cytometry (Science, 2018; eLife, 2021), which enabled the first and fast “imaging" cell sorter. By combining label-free and fluorescence image data analyzed through supervised and unsupervised machine learning approaches, Ghost Cytometry identifies cells based on complex morphological signatures, particularly beneficial when molecular markers are unavailable or undesirable. Using several case studies, I will show the capabilities and applications of Ghost Cytometry in classifying diverse cell phenotypes and responses, highlighting its potential as a tool for unbiased, high-content cell analysis and sorting in biomedical research. 

Finally, as time permits, I will briefly introduce two recent advancements: Deep Nanometry (Nature Communications, 2025), a technology enabling ultrasensitive nanoparticle characterization, and another for time-lapse single-cell spectrometry (in submission).].

About the speaker
[Networked biophotonics and microfluidics
RCAST at Univ of Tokyo
ThinkCyte inc.].

Connection details
Zoom*: [https://embl-org.zoom.us/j/93633159006?pwd=AQjra4V7PEoZl5aEqp7bchjceOOSCZ.1] (Meeting ID: [93633159006]

We live in an era of unprecedented scientific breakthroughs – gene editing, stem cell research, AI-driven discoveries, and rapid vaccine development are revolutionizing our world. Yet, alongside these advancements, misinformation, ethical dilemmas, and scientific scepticism have become more prominent, raising crucial questions about trust in science.

How do we navigate a "post-truth" society where scientific credibility is constantly challenged? How can researchers, policymakers, and science communicators strengthen the bond between science and society?

The 2025 Science & Society Conference "In Science We Trust?" is your opportunity to tackle these pressing issues head-on. Over two thought-provoking days, we’ll examine how public perception of science has evolved, explore the societal impact of trust in research, and discuss the role of scientists in fostering transparency and engagement. Through inspiring keynote speeches, lively panel discussions, and poster sessions, you’ll gain insights from leading experts and discuss together about the future of trust in science.

The conference welcomes not only life science researchers and students, but also professionals from different fields who are dedicated to fostering a trustworthy relationship between science and society. Whether you are a science communicator, educator, policy-maker, ombudsman or ethicist, this event is sure to be of interest to you!

Abstract
In situ cryoET data collection requires experienced users and appreciable microscope time to carefully select targets for tilt series acquisition. To facilitate full automation, I developed smart parallel automated cryo-electron tomography (SPACEtomo), a workflow using machine learning approaches for lamella detection and biological feature segmentation followed by automated target setup and parallel acquisition. This degree of automation will be essential for obtaining statistically relevant datasets and high-resolution structures of macromolecules in their native context. Such large datasets subsequently require careful data management and vast processing resources to obtain scientific insights. I will show how DECTRIS CLOUD can be leveraged to gain easy access to virtually unlimited cloud-based computing power for cryoEM and cryoET data processing.

Abstract
My lab has developed lightweight, robust and interpretable deep learning models that can predict diverse biochemical profiles spanning transcription factor binding, chromatin accessibility, nascent transcription, steady state transcription and reporter assays. Our models (1) detect, learn and correct cryptic experimental biases, (2) reveal underlying causal sequence syntax and its pleiotropy across biochemical and cellular contexts, (3) encode biophysical parameters and (4) predict effects of regulatory genetic variants. Our models match or surpass the performance of massive, multi-task supervised models and self-supervised DNA language models across a battery of carefully curated benchmarks, including molecular QTLs from diverse cell contexts and ancestries, reporter assays, CRISPR-based genome editing experiments, and fine mapped GWAS variants. By systematically interpreting a foundational resource of ~5000 regulatory DNA sequence models trained on bulk and single cell datasets from diverse adult and fetal cellular contexts, we can begin unraveling the incredible complexity and context-specificity of regulatory sequence lexicons, syntax and genetic variation encoded in the human genome.

Abstract
Drastic epigenetic reprogramming occurs during mammalian early embryogenesis. Deciphering the molecular events underlying these processes is crucial for understanding how epigenetic information is transmitted between generations and how life really begins. Probing these questions was previously hindered by the scarce experimental materials that are available in early development. By developing a set of ultra-sensitive chromatin analysis technologies, we investigated chromatin reprogramming during early mouse development for chromatin accessibility, histone modifications, and 3D architecture. These studies unveiled highly dynamic and non-canonical chromatin regulation during maternal-to-zygotic transition and zygotic genome activation (ZGA). However, how ZGA is kickstarted and how the early development program is progressively driven by transcription factors (TFs) remain enigmatic. Recently, we identified key TFs that act at the onset of ZGA, and those that connect ZGA to the first cell fate commitment. In this talk, I will discuss how TFs and epigenetic factors cooperatively establish embryonic program and restore the embryonic epigenomes when the life begins. 

Abstract
Large Language Models (LLMs) like ChatGPT are transforming the way we do science and very prominently how we analyse scientific data. In this talk, I will introduce the fundamentals of LLMs and their emerging multimodal extensions, such as Vision-Language Models (VLMs). Computer scientists seek to enable analysis of biological images using VLMs, turning images into numbers and insights ideally directly. I will discuss current capabilities and limitations of these models for bio-image analysis and highlight the unique potential of LLMs for automating data analysis workflows through code-generation. Emphasizing practical examples, I will demonstrate how LLM-driven code-generation accelerates data exploration, preprocessing, and quantitative analysis in bio-image data science.

Abstract
Memory formation relies on a bidirectional interplay between synaptic plasticity and nucleus-templated transcriptional programs, but how precisely this interplay is orchestrated by epigenetic mechanisms remains to a large extent unknown. In this talk, I will showcase our recent efforts to better understand this aspect from two angles. First, we have found that chromatin plasticity in the mouse brain is a key determinant for memory allocation, the process by which neurons become recruited into the memory trace: When we increased chromatin plasticity by enzymatic overexpression of histone acetyl transferases (HATs), neurons with elevated histone acetylation were preferentially recruited into the encoding ensemble and memory retention was enhanced, while optogenetic silencing of the epigenetically altered neurons prevented memory expression. Second, we have found that after learning, the epigenetic make-up of a single locus in the encoding ensemble is necessary and sufficient to bidirectionally alter memory performance across different phases of memory consolidation. Together, these findings stipulate that before and after memory encoding, epigenetic mechanisms play a pivotal role as molecular memory aids. 

Abstract
[Text for abstract].

About the speaker
[Biographical information about the speaker].

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Attachments
[Link to a file (for example a pdf of the seminar’s programme) - the file can be uploaded on the intranet]

Connection details
Zoom*: [https://embl-org.zoom.us/j/96374261689?pwd=TnNxRWtQY2lyc2pSa2JpY3NGcDlhZz09] (Meeting ID: [963 7426 1689], Password: [DBU])

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