𝗬𝗼𝘂𝗿 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗴𝗼𝘁 𝘆𝗼𝘂 𝘁𝗵𝗲 𝗼𝗳𝗳𝗲𝗿. 𝗬𝗼𝘂𝗿 𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗼𝗻 𝘀𝗸𝗶𝗹𝗹𝘀 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝘄𝗶𝘁𝗵 𝗶𝘁.

What would be your negotiation advice to the younger generation?

And to senior PIs and department heads reading this: 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂𝗿 𝘁𝗿𝗮𝗶𝗻𝗲𝗲𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗶𝘀. Share what you know. 𝐸𝑠𝑝𝑒𝑐𝑖𝑎𝑙𝑙𝑦 support the young women in your groups. Every person who negotiates well makes it easier for the next one.

Negotiation isn't a one-time thing. 𝗜𝘁 𝘪𝘴 𝘁𝗵𝗲 𝗷𝗼𝗯. As a PI, we negotiate for grants, contracts, space, and people. How you negotiate your own position signals how you'll fight for your team.

Talk to people. Salary transparency in academia is still mostly informal. Build your network and use it.

And importantly: 𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗲 𝘣𝘦𝘧𝘰𝘳𝘦 𝘆𝗼𝘂 𝗮𝗰𝗰𝗲𝗽𝘁 𝘁𝗵𝗲 𝗼𝗳𝗳𝗲𝗿. Once you accept, there is usually little to no chance to negotiate later, because your biggest leverage is gone. Take that seriously.

Your leverage is built 𝑙𝑜𝑛𝑔 𝑏𝑒𝑓𝑜𝑟𝑒 the offer arrives.
Be great at what you do.
Be visible. Generate alternatives.
Do your homework on what's possible in terms of salary and start-up packages.

And the data is clear: 𝘄𝗼𝗺𝗲𝗻 𝗱𝗶𝘀𝗽𝗿𝗼𝗽𝗼𝗿𝘁𝗶𝗼𝗻𝗮𝘁𝗲𝗹𝘆 𝗿𝗲𝗰𝗲𝗶𝘃𝗲 𝗹𝗼𝘄𝗲𝗿 𝘀𝗮𝗹𝗮𝗿𝗶𝗲𝘀 and are often expected to stay on the "don't ask" side. That needs to change.

A few things I'd tell any junior scientist about to start their first PI or industry position:

Because we don't want to seem "difficult."

Here's what I've learned: 𝘁𝗵𝗲𝗿𝗲 𝗶𝘀 𝘢𝘭𝘮𝘰𝘴𝘵 𝘢𝘭𝘸𝘢𝘺𝘴 𝗿𝗼𝗼𝗺 𝘁𝗼 𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗲. Even in systems like Germany, where salaries look rigid on paper, top-ups and start-up packages are negotiated all the time.

The difference? Some people ask. Others don't.

One thing nobody teaches you in academia: 𝗵𝗼𝘄 𝘁𝗼 𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗲.

We spend 10+ years learning how to do science, write grants, and present data. But when the job offer finally comes, most people just… accept it.

Because we're relieved.
Because we think the salary is fixed.

Using vDISCO tissue clearing, and a new ultrabright chemical labeling strategy. This work introduces LINCS for rapid, scalable 3D connectivity mapping from single neurons to whole brains, advances for high-throughput circuit mapping.

Great work by Rui Lin lab.
www.sciencedirect.com/science/arti...

Be my colleague! :)

Come work with us!

Job available at the amazing Erturk lab in Munich

Hiring in Munich: Staff Scientist / Postdoc in Tissue Clearing and 3D Image Analysis. We’re looking for someone with strong hands-on expertise in tissue clearing, light-sheet microscopy, and quantitative 3D image analysis. Shares and recommendations are very welcome.
www.linkedin.com/jobs/view/43...

In this recent Nature Methods Perspective, I outline how tissue clearing, spatial-omics, and AI converge to create deep 3D histology, enabling unbiased, organism-scale biology and next-generation digital pathology. www.nature.com/articles/s41...

A new preprint introduces aDISCO, a DISCO-based clearing approach that makes whole archival FFPE human tissues transparent and antibody-compatible, enabling true 3D light-sheet histology across brain & multiple organs at cellular resolution. Aguzzi & Helmchen teams.
www.biorxiv.org/content/10.1...

We’re hiring – Computer Science PhD at iBIO (Helmholtz Munich / LMU).

Build AI-driven, whole-body multi-omics atlases by combining scRNA-seq, spatial transcriptomics, proteomics & 3D imaging.

Looking for strong omics data analysis skills with ML/DL.
Apply or share:
www.linkedin.com/jobs/view/43...

This is going to be fun! I promise!

My team at Deep Piction (Munich) is hiring a Senior Director, Drug Discovery (Long Covid) to build and lead our early discovery engine. If you have 10+ years in early-stage biotech/pharma, let’s talk.
www.linkedin.com/jobs/view/43...

Most transcriptomics studies sample ~0.001% of a mouse. Which region do we choose? Often: we guess.
Meet DISCO-seq 🤗 flipping the order: RNA-preserving clearing → whole-organ/whole-body 3D imaging → pick ROIs in 3D → unbiased scRNA-seq.
Preprint: www.biorxiv.org/content/10.6...

Single cell transcriptomics from cleared tissue? Impossible you say? Check out our latest preprint!

Nanocarrier imaging at single-cell resolution across entire mouse bodies with deep learning - Nature Biotechnology An integrated experimental and deep learning pipeline reveals cell-level targeting of nanocarriers in whole bodies.

Very cool paper in NBT from the Dietz and Erturk labs @erturklab.bsky.social @markuselsner.bsky.social go.nature.com/4gNLjeC

✨ DISCO-seq bridges 3D anatomy and single-cell genomics.
Unbiased, imaging-guided, whole-organ transcriptomics—from brain tumors to infection biology.

Take-home: most spatial biology still starts from 2D slices + predefined ROIs/genes.
DISCO-seq changes the paradigm: whole-organ/whole-body 3D first → unbiased single-cell transcriptomics second.

Spike S1 induces region- and condition-specific gene programs, highlighting spatial immune specialization along the gut.

Small intestine scRNA-seq preserves cell-type diversity across conditions/regions (PBS vs S1; duodenum vs ileum) with expected epithelial, immune, and stromal populations.

Whole-body application: 3D DISCO imaging maps organ-specific SARS-CoV-2 Spike S1 tropism.
In the gut, we see distinct enrichment patterns (duodenum vs ileum), enabling unbiased ROI selection.

DISCO-seq resolves region- and cell-type-specific programs in GBM: neurons and microglia show distinct states across core vs remote niches.

Imaging-guided ROI selection enables spatially resolved transcriptomics: tumor core vs remote rostral/caudal ROIs within the intact brain.

3D quantification maps tumor burden across annotated brain regions, distinguishing small blobs, clusters, and large lesions—revealing region-specific heterogeneity

Glioblastoma: whole-brain 3D imaging reveals dissemination beyond the primary lesion.
Satellite tumors + contralateral spread become visible in 3D—often missed in 2D or standard clinical imaging.