Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis

The current landscape of spatial biomarkers for prediction of response to immune checkpoint inhibition

Enabling the examination of cell-cell relationships in tissue, spatially resolved omics technologies have revolutionised our perspectives on cancer biology. Clinically, the development of immune checkpoint inhibitors (ICI) has advanced cancer therapeutics. However, a major challenge of effective implementation is the identification of predictive biomarkers of response. In this review we examine the potential added predictive value of spatial biomarkers of response to ICI beyond current clinical benchmarks.

Spatial effects of infiltrating T cells on neighbouring cancer cells and prognosis in stage III CRC patients

Colorectal cancer (CRC) is one of the most frequently occurring cancers, but prognostic biomarkers identifying patients at risk of recurrence are still lacking. In this study, we aimed to investigate in more detail the spatial relationship between intratumoural T cells, cancer cells, and cancer cell hallmarks as prognostic biomarkers in stage III colorectal cancer patients. We conducted multiplexed imaging of 56 protein markers at single-cell resolution on resected fixed tissue from stage III CRC patients who received adjuvant 5-fluorouracil (5FU)-based chemotherapy. Images underwent segmentation for tumour, stroma, and immune cells, and cancer cell 'state' protein marker expression was quantified at a cellular level. We developed a Python package for estimation of spatial proximity, nearest neighbour analysis focusing on cancer cell-T-cell interactions at single-cell level. In our discovery cohort (Memorial Sloan Kettering samples), we processed 462 core samples (total number of cells: 1,669,228) from 221 adjuvant 5FU-treated stage III patients. The validation cohort (Huntsville Clearview Cancer Center samples) consisted of 272 samples (total number of cells: 853,398) from 98 stage III CRC patients. While there were trends for an association between the percentage of cytotoxic T cells (across the whole cancer core), it did not reach significance (discovery cohort: p = 0.07; validation cohort: p = 0.19). We next utilised our region-based nearest neighbour approach to determine the spatial relationships between cytotoxic T cells, helper T cells, and cancer cell clusters. In both cohorts, we found that shorter distance between cytotoxic T cells, T helper cells, and cancer cells was significantly associated with increased disease-free survival. An unsupervised trained model that clustered patients based on the median distance between immune cells and cancer cells, as well as protein expression profiles, successfully classified patients into low-risk and high-risk groups (discovery cohort: p = 0.01; validation cohort: p = 0.003). © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives

Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.

In the Murine and Bovine Maternal Mammary Gland Signal Transducer and Activator of Transcription 3 is Activated in Clusters of Epithelial Cells around the Day of Birth

Signal transducers and activators of transcription (STAT) proteins regulate mammary development. Here we investigate the expression of phosphorylated STAT3 (pSTAT3) in the mouse and cow around the day of birth. We present localised colocation analysis, applicable to other mammary studies requiring identification of spatially congregated events. We demonstrate that pSTAT3-positive events are multifocally clustered in a non-random and statistically significant fashion. Arginase-1 expressing cells, consistent with macrophages, exhibit distinct clustering within the periparturient mammary gland. These findings represent a new facet of mammary STAT3 biology, and point to the presence of mammary sub-microenvironments.

Spatial Effects of Infiltrating T cells on Neighbouring Cancer Cells and Prognosis in Stage III CRC patients

Colorectal cancer (CRC) is one of the most frequently occurring cancers, but prognostic biomarkers identifying patients at risk of recurrence are still lacking. In this study, we aimed to investigate in more detail the spatial relationship between intratumoural T cells, cancer cells, and cancer cell hallmarks, as prognostic biomarkers in stage III colorectal cancer patients. We conducted multiplexed imaging of 56 protein markers at single cell resolution on resected fixed tissue from stage III CRC patients who received adjuvant 5-fluorouracil-based chemotherapy. Images underwent segmentation for tumour, stroma and immune cells, and cancer cell 'state' protein marker expression was quantified at a cellular level. We developed a Python package for estimation of spatial proximity, nearest neighbour analysis focusing on cancer cell - T cell interactions at single-cell level. In our discovery cohort (MSK), we processed 462 core samples (total number of cells: 1,669,228) from 221 adjuvant 5FU-treated stage III patients. The validation cohort (HV) consisted of 272 samples (total number of cells: 853,398) from 98 stage III CRC patients. While there were trends for an association between percentage of cytotoxic T cells (across the whole cancer core), it did not reach significance (Discovery cohort: p = 0.07, Validation cohort: p = 0.19). We next utilized our region-based nearest neighbourhood approach to determine the spatial relationships between cytotoxic T cells, helper T cells and cancer cell clusters. In the both cohorts, we found that lower distance between cytotoxic T cells, T helper cells and cancer cells was significantly associated with increased disease-free survival. An unsupervised trained model that clustered patients based on the median distance between immune cells and cancer cells, as well as protein expression profiles, successfully classified patients into low-risk and high-risk groups (Discovery cohort: p = 0.01, Validation cohort: p = 0.003).

Enabling neighbour labelling: using synthetic biology to explore how cells influence their neighbours

Cell-cell interactions are central to development, but exploring how a change in any given cell relates to changes in the neighbour of that cell can be technically challenging. Here, we review recent developments in synthetic biology and image analysis that are helping overcome this problem. We highlight the opportunities presented by these advances and discuss opportunities and limitations in applying them to developmental model systems.

Co-cultures of cerebellar slices from mice with different reelin genetic backgrounds as a model to study cortical lamination

Background: Reelin has fundamental functions in the developing and mature brain. Its absence gives rise to the Reeler phenotype in mice, the first described cerebellar mutation. In homozygous mutants missing the Reelin gene ( reln -/-), neurons are incapable of correctly positioning themselves in layered brain areas such as the cerebral and cerebellar cortices. We here demonstrate that by employing ex vivo cultured cerebellar slices one can reduce the number of animals and use a non-recovery procedure to analyze the effects of Reelin on the migration of Purkinje neurons (PNs). Methods: We generated mouse hybrids (L7-GFP relnF1/) with green fluorescent protein (GFP)-tagged PNs, directly visible under fluorescence microscopy. We then cultured the slices obtained from mice with different reln genotypes and demonstrated that when the slices from reln -/- mutants were co-cultured with those from reln +/- mice, the Reelin produced by the latter induced migration of the PNs to partially rescue the normal layered cortical histology. We have confirmed this observation with Voronoi tessellation to analyze PN dispersion. Results: In images of the co-cultured slices from reln -/- mice, Voronoi polygons were larger than in single-cultured slices of the same genetic background but smaller than those generated from slices of reln +/- animals. The mean roundness factor, area disorder, and roundness factor homogeneity were different when slices from reln -/- mice were cultivated singularly or co-cultivated, supporting mathematically the transition from the clustered organization of the PNs in the absence of Reelin to a layered structure when the protein is supplied ex vivo. Conclusions: Neurobiologists are the primary target users of this 3Rs approach. They should adopt it for the possibility to study and manipulate ex vivo the activity of a brain-secreted or genetically engineered protein (scientific perspective), the potential reduction (up to 20%) of the animals used, and the total avoidance of severe surgery (3Rs perspective).

Co-cultures of cerebellar slices from mice with different reelin genetic backgrounds as a model to study cortical lamination

Background: Reelin has fundamental functions in the developing and mature brain. Its absence gives rise to the Reeler phenotype in mice, the first described cerebellar mutation. In homozygous mutants missing the Reelin gene ( reln -/-), neurons are incapable of correctly positioning themselves in layered brain areas such as the cerebral and cerebellar cortices. We here demonstrate that by employing ex vivo cultured cerebellar slices one can reduce the number of animals and use a non-recovery procedure to analyze the effects of Reelin on the migration of Purkinje neurons (PNs). Methods: We generated mouse hybrids (L7-GFP relnF1/) with green fluorescent protein (GFP)-tagged PNs, directly visible under fluorescence microscopy. We then cultured the slices obtained from mice with different reln genotypes and demonstrated that when the slices from reln -/- mutants were co-cultured with those from reln +/- mice, the Reelin produced by the latter induced migration of the PNs to partially rescue the normal layered cortical histology. We have confirmed this observation with Voronoi tessellation to analyze PN dispersion. Results: In images of the co-cultured slices from reln -/- mice, Voronoi polygons were larger than in single-cultured slices of the same genetic background but smaller than those generated from slices of reln +/- animals. The mean roundness factor, area disorder, and roundness factor homogeneity were different when slices from reln -/- mice were cultivated singularly or co-cultivated, supporting mathematically the transition from the clustered organization of the PNs in the absence of Reelin to a layered structure when the protein is supplied ex vivo. Conclusions: Neurobiologists are the primary target users of this 3Rs approach. They should adopt it for the possibility to study and manipulate ex vivo the activity of a brain-secreted or genetically engineered protein (scientific perspective), the potential reduction (up to 20%) of the animals used, and the total avoidance of severe surgery (3Rs perspective).

Multiplex imaging in immuno-oncology

Multiplex imaging has emerged as an invaluable tool for immune-oncologists and translational researchers, enabling them to examine intricate interactions among immune cells, stroma, matrix, and malignant cells within the tumor microenvironment (TME). It holds significant promise in the quest to discover improved biomarkers for treatment stratification and identify novel therapeutic targets. Nonetheless, several challenges exist in the realms of study design, experiment optimization, and data analysis. In this review, our aim is to present an overview of the utilization of multiplex imaging in immuno-oncology studies and inform novice researchers about the fundamental principles at each stage of the imaging and analysis process.