2 Datasets

Through the course of this spatialPlaybook, we will take advantage of several different publicly available spatial datasets that are listed below. We will demonstrate several questions that could be answered or explored for each of these datasets using the available information.

Disease	Technology	Title	Segmentation	Alignment	Clustering	Localisation	Microenvironments	Patient Classification
Head and neck squamous cutaneous cell carcinoma	IMC	Ferguson 2022	X		X	X	X
Breast cancer	MIBI-TOF	Risom 2022	X	X	X	X	X	X
Mouse embryogenesis	Slide-seq	Stickels 2021	X	X	X	X
Breast cancer	MIBI-TOF	Keren 2018	X		X	X	X	X
Type 1 diabetes	IMC	Damond 2019	X			X

2.1 Spatial Proteomics

2.1.1 IMC

Imaging Mass Cytometry (IMC) is a high-resolution, multiplexed imaging technique that combines laser ablation with mass cytometry to visualize metal-tagged antibodies in tissue sections or cell samples. Using a pulsed laser, IMC systematically ablates the sample, releasing metal isotopes that are then analysed by time-of-flight mass spectrometry. This allows for the simultaneous detection of 40+ biomarkers at subcellular resolution, typically around 1 µm, without the spectral overlap issues found in fluorescence-based imaging.

2.1.1.1 Head and neck cutaneous squamous cell carcinoma (Ferguson 2022)

Squamous cell carcinoma (SCC) is the second most common skin cancer, with high-risk head and neck SCC (HNcSCC) being aggressive and prone to recurrence or metastasis, particularly in immunosuppressed patients. This study used IMC to profile the tumour microenvironment of 31 patients to identify cellular interactions that were associated with tumour progression. A panel of 36 markers was used, and patients were classified into one of two categories: non-progressors (NP) for those that were negative for metastases and progressors (P) that were positive for metastases. The study identified early immune responses that were crucial in controlling tumour progression and improving patient prognosis.

Ferguson et al. (2022). High-Dimensional and Spatial Analysis Reveals Immune Landscape–Dependent Progression in Cutaneous Squamous Cell Carcinoma. Clinical Cancer Research, 28(21), 4677-4688. (DOI)

2.1.1.2 Type 1 diabetes progression (Damond 2019)

Type 1 diabetes (T1D) results from the autoimmune destruction of insulin-producing β cells. This study analysed pancreatic tissue obtained from 12 patients at 3 different stages of diabetes: non-diabetic, early onset, and long-term using a 35-plex antibody panel. Analysis revealed key cellular movements that preceded the destruction of insulin-producing β cells, highlighting potential targets for future therapies and treatments.

Damond et al. (2019). A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry. Cell Metabolism, 29(3), 755-768.e5. (DOI)

2.1.2 MIBI-TOF

MIBI-TOF (multiplexed ion beam imaging by time-of-flight) is an imaging technique that uses bright ion sources and orthogonal time-of-flight mass spectrometry to image metal-tagged antibodies at subcellular resolution in clinical tissue sections. It is capable of imaging approximately 40 labelled antibodies, providing a highly detailed and multiplexed view of tissue architecture and protein expression. MIBI-TOF can capture image fields of around 1 mm² with exceptional spatial resolution, reaching down to 260 nm.

2.1.2.1 Ductal carcinoma in situ (Risom 2022)

Ductal carcinoma in situ (DCIS) is a pre-invasive lesion considered a precursor to invasive breast cancer (IBC). This study utilized MIBI-TOF with a 37-plex antibody panel to analyze spatial relationships within the Washington University Resource Archival Human Breast Tissue (RAHBT) cohort. The findings identified key drivers of IBC relapse and emphasized the critical role of the tumour microenvironment in influencing disease progression.

Risom et al. (2022). Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma. Cell, 185(2), 299-310.e18 (DOI)

2.1.2.2 Triple Negative Breast Cancer (Keren 2018)

This study profiles 36 proteins in tissue samples from 41 patients with triple-negative breast cancer (a particularly aggressive form of cancer) using MIBI-TOF. The dataset captures high-resolution, spatially resolved data on 17 distinct cell populations, immune composition, checkpoint protein expression, and tumor-immune interactions. Patients were classified into three categories based on the type of tumour: cold (no immune cell infiltration), compartmentalised (immune cells spatially separated from tumor cells), and mixed (immune cells mixed with tumor cells).

Keren et al. (2018). A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging. Cell, 174(6), 1373-1387.e1319. (DOI)

In the following section, we provide a quick guide to help you get started with performing spatial analysis.