Multiomics immune profiling of a patient-relevant orthotopic lung cancer model using SEPARATE-Seq

By: Pauline M. R. Bardet, Lize Allonsius, Eva Hadadi, Daliya Kancheva, Morgane Paque, Sarka Vosahlikova, Aarushi A. Caro, Anouk Van Audenhove, Ayla Debraekeleer, Ria Roelandt, Kevin Verstaen, Michal Hensler, Delphine Hoton, Gisèle Mateu Cabrera, Louis Boon, Arnaud Blomme, Sofie Deschoemaeker, Geert Raes, Elly Marcq, Ahmed E. I. Hamouda, Jan P. Böttcher, Jitka Fucikova, Pierre Close, Frank Aboubakar Nana & Damya Laoui

Researchers at VUB map the immune landscape of lung cancer using a patient-relevant model, revealing how immune cells behave across different tumour environments and providing valuable insights for future cancer immunotherapies.

Why Better Lung Cancer Models Matter

Relevant preclinical models are essential for developing better cancer therapies. Yet, many experimental cancer models do not fully capture the complexity of human tumours. As such, a lot of preclinical studies rely on subcutaneous tumour models, where cancer cells are implanted under the skin, but while practical, these models fail to capture the unique organ-specific immune landscape.

In this study, researchers developed a patient-relevant orthotopic lung adenocarcinoma model where tumours grow directly in the lung and closely reproducing key immune features observed in human lung cancer. One of the key advantages is that the model can be dissected into tumour tissue and adjacent non-tumour tissue, making it possible to study the tumour microenvironment in a way that better resembles patient samples.

"The orthotopic model closely reproduces key immune features observed in human lung cancer."

Introducing SEPARATE-Seq

The publication also provides a proof of concept for SEPARATE-Seq (Streptavidin Enabled PARtitioning And Tag Evaluation for RNA-Sequencing), a broadly applicable method that combines single-cell RNA sequencing with streptavidin-enabled compartmentalisation. Here, it was leveraged to make the distinction between vascular and intratissue immune cells. By integrating SEPARATE-Seq with spatial transcriptomics, the researchers generated a detailed multiomics map of the immune landscape in lung cancer. This showed, among other findings, T-cell exhaustion, dysfunctional NK cells and distinct neutrophil states, all dependent on their location. Additionally, spatial transcriptomics revealed specialised immune niches within the tumour, including lipid-associated macrophages at the tumour edge and local interferon-stimulated cell hubs. Importantly, many of these findings were linked back to what is seen in patients, highlighting the translational relevance of the model.

Figure 1: Graphical abstract. SEPARATE-Seq combined with spatial transcriptomics highlights the differences in the immune cell compartment of the classically used subcutaneous model (top right) and a novel orthotopic model for lung cancer (top left). The orthotopic model reflects the patient situation (bottom left) better in terms of immune cell diversity and changes upon tumour presence. Figure Created using BioRender, Laoui D., 2026

What Did the Researchers Discover?

Together, these results show that immune cell identity and function are strongly shaped by their spatial context: whether cells are located in the blood vessels, inside the tissue, within the tumour, or in adjacent lung tissue, they respond to the different cues within. The resulting resource, made available through an interactive webtool, provides a comprehensive reference for studying immune cell diversity in a reproducible preclinical lung cancer model.

Figure 2: Spatial transcriptomics section of a mouse tumour-bearing lung, illustrating the detected transcripts, the annotated cells and identified cellular niches (left to right). Spatial transcriptomics was performed using Vizgen, Merscope.

Societal impact

Lung cancer remains one of the most challenging cancers to treat, and improving therapy requires experimental models that better reflect the biology of patients. This work provides such a model and a rich immune reference map that can help researchers understand why some immune responses fail, why certain therapies are less effective, and which immune cell states may be promising therapeutic targets.

By identifying spatially restricted immune niches and patient-like immune dysfunctions, the study creates a strong foundation for testing new immunotherapies and combination strategies before moving toward clinical studies. In the long term, this can contribute to more rational therapy development and better translation from laboratory models to patient benefit.

"By making computation no longer the bottleneck, the VSC helped streamline the analysis process and allowed us to focus on the biological interpretation."

The Role of VSC

The VSC infrastructure was crucial for the computational analysis of the large single-cell and spatial transcriptomics datasets in this study in an efficient way. Especially reanalysing the human dataset required substantial computing power and memory; an issue that was solved thanks to the VSC making these resources available on a broad scale and in a user-friendly way. By making computation no longer the bottleneck, the VSC helped streamline the analysis process and allowed us to focus on the biological interpretation.

Original Publication

Read the original research article, "Multiomics immune profiling of a patient-relevant orthotopic lung cancer model using SEPARATE-Seq," published in Nature Communications, here.

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