Researchers in Nikolaus Rajewsky’s lab at Max Delbrück Centre combined high-resolution, single-cell spatial technologies to map a tumour’s cellular neighbourhoods in 3D and identify potential targets for personalised cancer therapy.

They describe their findings in two separate papers in Cell Systems.

Understanding not just what cells are present in a tumour, but where they are located and how they interact with other cells around them – their cellular neighbourhoods – can provide detailed insights that help doctors determine which treatments or therapies might be most effective for a specific patient.

An international research team led by the Berlin Institute for Medical Systems Biology at the Max Delbrück Centre (MDC-BIMSB) combined spatial transcriptomics in 3D and extracellular matrix imagining to gain unprecedented detail about the inner workings of an early-stage lung tumour.

The proof-of-concept study was published in “Cell Systems”.

“Tumours are complex ecosystems where tumour cells live in close contact with the surrounding extracellular matrix. They interact with many other cell types,” says Professor Nikolaus Rajewsky, director of the MDC-BIMSB, head of the Systems Biology of Regulatory Elements lab and senior author on both papers.

“The data we can obtain now in tumour tissues from a patient are becoming so precise and comprehensive that we can computationally predict the molecular mechanisms which are driving phenotypes. This is new and fundamentally important for making personalised medicine a reality.”

From 2D to 3D

Transcriptomics documents what RNA is being actively expressed in cells, which indicates the activities the cell is engaged in and reveals the cell types present in a sample.

Spatial transcriptomics does this but for individual cells to build a 2D map.

The team got early access to the CosMx instrument from the company NanoString, which does this at extremely high resolution – 1,000 different RNA molecules can be detected at one time, compared to traditional methods that identify just a handful of molecule types at once.

The team analysed 340,000 individual cells from the lung tumour, identifying 18 cell types.

The 3D analysis was powered by a new computational algorithm, STIM, which aligns datasets to reconstruct 3D virtual tissue blocks.

“We realised that spatial transcriptomics datasets can be modelled as images,” says Dr. Nikos Karaiskos, a postdoctoral researcher in the Rajewsky lab and co-corresponding author of the second “Cell Systems” paper describing STIM in detail.

Leveraging imaging techniques, STIM marries the fields of computer vision and spatial transcriptomics.

The team worked closely with Dr. Stephan Preibisch, a former principal investigator at MDC-BIMSB who is now at Howard Hughes Medical Institute’s Janelia Research Campus in the U.S., to bring this collaborative effort to fruition.

They then worked with the Systems Biology Imaging Platform in Mitte to apply a separate imaging technique, called second harmonic generation, to map elastin and collagen in cellular neighbourhoods, which in the lung are the main extracellular matrix constituents.

Areas with more elastin were healthier, while those with more collagen surrounded the tumour cells, which indicates harmful tissue remodelling.

“So not only do we know what cell types are present, we know how they are grouped with their neighbours, and we could begin to understand how tumour cells rewire non-malignant cells at the tumour surface to support tumour growth,” explains Tancredi Massimo Pentimalli, MD, the first paper author who is pursuing a PhD in the Rajewsky Lab and the Berlin School of Integrative Oncology at Charité – Universitätsmedizin Berlin.

Cells talk

But the analysis did not stop there.

The team was able to understand precise phenotypes – for example, if fibroblasts, which form connective tissue, were activated and remodelling the tissue or not.

They were also able to listen in on cell-to-cell communication and determine how tumour cells were blocking immune cells from entering the tumour.

“This immune suppression mechanism is well-known and suggests immunotherapy would help,” Pentimalli says.

“Immune checkpoint inhibitors would reverse the suppression and then you have this army of immune cells that are already in position ready to attack. It was exciting to see how our approach identified this dynamic and could help direct a personalised immunotherapy plan.”

Notably, these key insights were only possible with data in 3D – in 2D it was impossible to distinguish between the tumour and other immune cells embedded in the tumour surface.

Pathology 2.0

The beauty of this approach is that, while very high-tech, it starts with a routine tissue sample found in any pathology lab.

For this study, the group used a tissue sample of a lung tumour that was several years old, preserved with formalin and embedded in paraffin wax – the standard method pathologists use to preserve archival tissues.

“We were able to extract all this wealth of molecular information from one very thin section of a sample that has been sitting around at room temperature for years,” Pentimalli says.

“This is pathology 2.0 – not just looking at the cells under a microscope to make a diagnosis, but bringing molecular insight to the clinic.”

Next steps

Now that the proof-of-concept has been established, the team plans to apply the approach to larger datasets.

They are currently working on 700 samples from 200 patients and collaborating with Dr. Fabian Coscia, who leads the Spatial Proteomics Lab at Max Delbrück Centre, to integrate protein activity into the analysis.

Max Delbrück Center 

The Max Delbrück Centre for Molecular Medicine in the Helmholtz Association aims to transform tomorrow’s medicine through our discoveries of today.

At locations in Berlin-Buch, Berlin-Mitte, Heidelberg and Mannheim, our researchers harness interdisciplinary collaboration to decipher the complexities of disease at the systems level – from molecules and cells to organs and the entire organism.

Through academic, clinical, and industry partnerships, as well as global networks, we strive to translate biological discoveries into applications that enable the early detection of deviations from health, personalise treatment, and ultimately prevent disease.

First founded in 1992, the Max Delbrück Centre today inspires and nurtures a diverse talent pool of 1,800 people from over 70 countries.

We are 90 percent funded by the German federal government and 10 percent by the state of Berlin.

Source: Max Delbrück Center for Molecular Medicine in the Helmholtz Association