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The Landmark Project is a large-scale public-private partnership dedicated to the discovery of disease-modifying drug targets and predictive biomarkers for human brain disease, with a primary focus on Parkinson's disease (PD).

Using the latest single-cell and multiomic technologies, Landmark will profile a minimum of 50 million single brain cells across ultiple brain regions from 400 post-mortem brain donors. Each brain tissue sample and patient-matched cerebrospinal fluid (CSF) will be profiled using genome-wide proteomics and metabolomics (including lipidomics and fatty-acid omics, alongside quantitative and digitised neuropathological examination- establishing a globally unique dataset for molecular causal inference.

The scientific foundation for the programme was published in . In recognition of the programme's exceptional scientific potential, Landmark has since attracted extensive funding from multiple global pharmaceutical companies;  UCB, Roche, GSK, Eisai and Novartis, as well as Parkinson's UK and The Gatsby Foundation.

Led by Professor Michael Johnson, the programme brings together world-leading expertise in neuropathology, genomics, biostatistics and bioinformatics- united by a shared mission to understand and ultimately halt the progression of Parkinson's disease.

Why it is important

Parkinson's disease affects millions worldwide, yet the biological mechanisms that drive its progression remain poorly understood. Existing research has largely been unable to distinguish the molecular causes of PD from its consequences- a critical distinction for developing effective therapies. The Landmark Programme addresses this gap through an unprecedented integration of cutting-edge technologies and analytical methods.

What Makes Landmark Unique?

The programme's defining feature is the integration of multiomic data with quantitative neuropathology for causal inference- combining multiple layers of biological data (genes, proteins, metabolites, and single-cell gene expression) with precise measurements of brain pathology to determine what is actually causing disease, not merely what is associated with it.

A second unique feature is the use of novel Proximity Ligation Assays (PLA) to detect and quantify oligomeric (small aggregate) forms of alpha-synuclein and tau in brain tissue. These protein species are invisible to standard neuropathological methods, meaning that without them, critical disease signals would be missed entirely.

Multiomic Data Generation

The programme generates four complementary layers of biological data from brain tissue and CSF:

This multiomics approach allows the programme to identify molecular quantitative trait loci (molQTLs)- genetic variants that influence gene, protein, or metabolite levels-  which serve as the foundation for causal inference.

The Four Research Themes

The scientific work is organised into four interlacing Themes, each addressing a distinct but related question:

Theme 1 — Drivers of Parkinson's Pathology

A cross-sectional study examining what drives the accumulation of PD pathology across five brain regions and four stages of disease severity (Braak stages 1–6). All 400 samples will undergo snRNA-seq to identify cell-type-specific molecular drivers of disease.

Theme 2 — Causes of Parkinson's Dementia

Up to 50% of PD patients develop dementia- but why some do and others don't remains unclear. This theme compares brain tissue from controls, PD patients without dementia, and PD patients with dementia across five brain regions, with a focus on the role of amyloid and Lewy body pathology in driving cognitive decline.

Theme 3 — Neuronal Vulnerability in Parkinson's

Not all dopamine-producing neurons are equally vulnerable to PD. This theme investigates why neurons in the Substantia Nigra (SNc) degenerate in PD while those in the adjacent Ventral Tegmental Area (VTA) are relatively spared — a fundamental and unresolved question in PD biology.

Theme 4 — Target Discovery & Validation

The largest theme, analysing snRNA-seq and proteomic data from >200 PD cases across three brain regions (~600 samples), linked to patient-level genetics and quantitative neuropathology. This is the primary engine for drug target and causal biomarker discovery, using causal inference to identify molecules that are not merely associated with PD, but causally implicated in it.

CSF Analysis

Proteomic analysis of ~200 CSF samples from PD  and control donors, linked to genetic, clinical and neuropathological data. This offers a unique opportunity to identify measurable causal biomarkers that could serve as outcome measures in early-stage clinical trials of disease-modifying therapies.

Analytical Approach: From Data to Drug Targets

The programme employs a sophisticated analytical framework built on four pillars:

1. Cross-Sectional Analysis

Rigorous quality-controlled workflows for cell-type annotation, differential gene expression, trajectory inference, and functional annotation will be applied consistently across all themes.

2. Genetically Anchored Causal Inference

Mendelian Randomisation (MR) uses naturally occurring genetic variation as a "natural experiment" to test whether a molecular factor (e.g., a protein) causally influences disease risk.

This approach also reveals the directionality of causal relationships critically informing whether a therapeutic strategy should activate or inhibit a given target.

3. Mediation Analysis

Explores the causal pathways through which risk factors drive disease progression-distinguishing factors that initiate PD from those that drive its progression once established.

4. Foundation Models (AI)

The programme will integrate generative AI models trained on single-cell data to predict cell-type-specific transcriptional responses to hypothetical gene perturbations - enabling in silico target validation at scale. The unprecedented size and consistency of Landmark's dataset make it ideally suited for training such models.

Why Landmark Matters

The Landmark Programme represents a step-change in our ability to identify the causal molecular drivers of Parkinson's disease, and to translate that knowledge into validated drug targets and biomarkers that can accelerate the development of disease modifying therapies.

By combining the world's largest PD brain bank, state-of-the-art multiomic technologies, and cutting-edge causal inference methodology, all within a rigorously governed public-private partnership, Landmark is uniquely positioned to deliver discovery and breakthroughs that could transform the lives of the 10 million people worldwide living with Parkinson's disease.

PhD students:

• Wiemann, Liv A
• Rahbar, Parisa
• Gregorovicsova, Katarina