Christoph Bock Lab @ CeMM & MedUni Vienna

Medical Epigenomics | Bock Lab at CeMM & MedUni Vienna Led by Christoph Bock, the Medical Epigenomics Lab pursues technology-driven biomedical research for cancer, immunology, and precision medicine.

Additional information about our research: cemm.at/research/gro... | bocklab.org | med-ai.org
Full job posting: cemm.at/fileadmin/im...
Further details & application portal: cemm.at/postdoc-prog... & cemm.onlyfy.jobs/job/7k2624z5

Maybe get some inspiration from our recent papers and consider proposing a project idea or research direction (as part of your application's cover letter) that connects your and our research expertise. Be ambitious & creative!

What excites you? What expertise do you bring from your PhD? What do you envision your future independent research group to focus on (or the start-up company you will found)? What could be a truly exciting & impactful postdoc project?

Multimodal learning enables chat-based exploration of single-cell data - Nature Biotechnology CellWhisperer uses multimodal learning of transcriptomes and text to answer questions about single-cell RNA-sequencing data.

3. Cancer: Epigenetic tumor heterogeneity (nature.com/articles/s41...)
4. Technology: High-content CRISPR screening for causality at scale (nature.com/articles/s43...)
5. Biomedical ML/AI: Chat-based single-cell data analysis with natural language (nature.com/articles/s41...)

Structural cells are key regulators of organ-specific immune responses - Nature Structural cells implement a broad range of immune-regulatory functions beyond their roles as barriers and connective tissues, and they utilize an epigenetically encoded potential for immune gene acti...

Examples of our recent research:
1. Immunology: Immune regulation by non-immune cells (nature.com/articles/s41...)
2. Cell engineering: CRISPR-optimized CAR T cell immunotherapy (www.nature.com/articles/s41...)

We are open to purely wet-lab candidates (including organoids & mouse models), purely computational candidates (including ML/AI), and those who want to pursue a mix of wet-lab & computational work. We are a highly interdisciplinary & collaborative research group with ambitions in molecular medicine.

PostDoc Program CeMM Pre-ERC Postdoc Program

HIRING: We recruit postdocs via the CeMM Pre-ERC Postdoc Program (cemm.at/postdoc-prog...). Seeking 3 types of candidates: (1) experimental systems biology in immunity or cancer; (2) bioinformatics & biomedical ML/AI; (3) tech-dev & bioengineering (CRISPR, single-cell, spatial, cell programming).

Medical Epigenomics | Bock Lab at CeMM & MedUni Vienna Led by Christoph Bock, the Medical Epigenomics Lab pursues technology-driven biomedical research for cancer, immunology, and precision medicine.

Further details & application portal: apply.cemm.at

Additional information about our research: cemm.at/research/gro... | bocklab.org | med-ai.org

What excites you? What could be a great PhD project? Please have a look at our recent publications and consider proposing an idea or research direction (as part of your application's cover letter) that you might want to pursue in our PhD. (4/5)

Multimodal learning enables chat-based exploration of single-cell data - Nature Biotechnology CellWhisperer uses multimodal learning of transcriptomes and text to answer questions about single-cell RNA-sequencing data.

4. Technology: High-content CRISPR screening for causality at scale (nature.com/articles/s43...)
5. Biomedical ML/AI: Chat-based single-cell data analysis with natural language (nature.com/articles/s41...)

Systematic discovery of CRISPR-boosted CAR T cell immunotherapies - Nature CELLFIE, a CRISPR platform for optimizing cell-based immunotherapies, identifies gene knockouts that enhance CAR T cell efficacy using in vitro and in vivo screens.

Examples of our recent research:
1. Immunology: Immune regulation by non-immune cells (nature.com/articles/s41...)
2. Cell engineering: CRISPR-optimized CAR T cell immunotherapy (www.nature.com/articles/s41...)
3. Cancer: Epigenetic tumor heterogeneity (nature.com/articles/s41...)

CeMM PhD Program – Molecular Medicine Research in Vienna Join the CeMM PhD Program in Vienna and pursue cutting-edge research in cancer, immunology, aging, and precision medicine. Applications open in 2025!

HIRING: We recruit PhD students via the AITHYRA-CeMM PhD Program (cemm.at/phd-program). Seeking 3 types of candidates: (1) experimental systems biology in immunity or cancer; (2) bioinformatics & biomedical ML/AI; (3) tech-dev & bioengineering (CRISPR, single-cell, spatial, cell programming) (1/5)

Nice example of "garbage-in, garbage out" behavior of CellWhisperer. We could probably add checks to mark meaningless queries. But for now we rely on cooperative users, and CellWhisperer has a clear warning: "Please keep in mind that CellWhisperer is an AI system and may produce misleading results."

This is figure 1, which gives an overview of the CellWhisperer multimodal AI for natural-language analysis of transcriptome data.

A paper in Nature Biotechnology presents CellWhisperer, which uses multimodal learning of transcriptomes and text to answer questions about single-cell RNA-sequencing data. go.nature.com/3XqzItR 🧬 🧪

Multimodal learning enables chat-based exploration of single-cell data - Nature Biotechnology CellWhisperer uses multimodal learning of transcriptomes and text to answer questions about single-cell RNA-sequencing data.

Multimodal learning enables chat-based exploration of single-cell data - @bocklab.bsky.social @cemm.oeaw.ac.at @meduniwien.ac.at go.nature.com/3WPGJnW

🤝 Huge thanks to the team! Moritz Schaefer & Peter Peneder with Daniel Malzl, Salvo Lombardo, Mihaela Peycheva, Jake Burton, Anna Hakobyan, Varun Sharma, Thomas Krausgruber, Celine Sin, Jörg Menche, Eleni Tomazou, Christoph Bock. @cemm.oeaw.ac.at, @meduniwien.ac.at, @stanna-ccri.bsky.social (11/11)

🧬 CellWhisperer introduces a chat-based way to explore scRNA-seq data. By enabling natural language analysis, it bridges biologists and bioinformaticians—paving the way for AI-driven bioinformatics assistants. (10/11)

Multimodal learning enables chat-based exploration of single-cell data - Nature Biotechnology CellWhisperer uses multimodal learning of transcriptomes and text to answer questions about single-cell RNA-sequencing data.

Ready to talk to cells?
📖 Read the paper: doi.org/10.1038/s415...
🧬 Try the web app with public datasets: cellwhisperer.bocklab.org
🖥️ Analyze your own datasets: github.com/epigen/cellw...
(9/11)

📚 We trained on >1 million bulk & pseudo-bulk transcriptomes with textual annotations that we AI-curated from GEO & @CELLxGENE Census. Our training data is open source and useful for developing multimodal biomedical AI models and future bioinformatics research assistants. (8/11)

🪄 How does CellWhisperer work behind the scenes? We trained a multimodal AI that links transcriptomes and text, enabling free-text search and annotation of RNA profiles. And we connected this model to an LLM that we fine-tuned into a chat assistant for transcriptome data (7/11)

🚀 We also validated CellWhisperer’s chat-based analysis with conventional bioinformatics. CellWhisperer was >4x faster (and 10x cooler 😊). Our recommendation: Use CellWhisperer for dataset exploration – but statistics is still important to ensure rigor & reproducibility (6/11)

🆕 The CellWhisperer paper (doi.org/10.1038/s415...) includes several new analyses beyond our 2024 bioRxiv preprint (biorxiv.org/content/10.1...). For example, we used CellWhisperer for an AI-guided analysis of human organ development (5/11)

🔬 You can easily query large transcriptome datasets for your favorite biological process using CellWhisperer. Just open Tabula Sapiens (cellwhisperer.cemm.at/tabulasapiens/) or GEO (cellwhisperer.cemm.at/geo/) in CellWhisperer & type your query into the chat box – for example “infection” (4/11)

🔍 We investigate one of the identified cell clusters by selecting the cells & prompting CellWhisperer with ‘Describe these cells in detail’. This interactive workflow is enabled by seamless integration of the CellWhisperer AI chat box into a version of CELLxGENE Explorer (3/11)

⚙️ To get started, let’s find cells by typing into the CellWhisperer chat box. For example ‘Show me structural cells with immune functions’. CellWhisperer scores each transcriptome by how well it matches this textual query and colors by query match (red: high, blue: low) (2/11)

🗨️ Just published in Nature Biotechnology: Our CellWhisperer AI enables chat-based analysis of single-cell sequencing data. You can talk to your cells & figure out the biology without writing any computer code. Paper here: www.nature.com/articles/s41.... Annotated walkthrough in a thread below (1/11)

Multimodal learning enables chat-based exploration of single-cell data - Nature Biotechnology CellWhisperer uses multimodal learning of transcriptomes and text to answer questions about single-cell RNA-sequencing data.

Ready to talk to cells?
📖 Read the paper: www.nature.com/articles/s41...
🧬 Try the web app with public datasets: cellwhisperer.bocklab.org
🖥️ Analyze your own datasets: github.com/epigen/cellw...
(9/11)

📚 We trained on >1 million bulk & pseudo-bulk transcriptomes with textual annotations that we AI-curated from GEO & CELLxGENE Census. Our training data is open source and useful for developing multimodal biomedical AI models and future bioinformatics research assistants. (8/11)

🪄 How does CellWhisperer work behind the scenes? We trained a multimodal AI that links transcriptomes and text, enabling free-text search and annotation of RNA profiles. And we connected this model to an LLM that we fine-tuned into a chat assistant for transcriptome data (7/11)

🚀 We also validated CellWhisperer’s chat-based analysis with conventional bioinformatics. CellWhisperer was >4x faster (and 10x cooler 😊). Our recommendation: Use CellWhisperer for dataset exploration – but statistics is still important to ensure rigor & reproducibility (6/11)