Akoya Biosciences (United States)

Spatially mapping the transcriptome and proteome in the same tissue section can profoundly advance our understanding of cellular heterogeneity and function. Here we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multimodal spatial omics approach combining sequencing-based spatial transcriptomics and multiplexed protein imaging on the same section, enabling both single-cell-resolution cell typing and transcriptome-wide interrogation of biological pathways. DBiTplus utilizes spatial barcoding and RNase H-mediated cDNA retrieval, preserving tissue architecture for multiplexed protein imaging. We developed computational pipelines to integrate these modalities, allowing imaging-guided deconvolution to generate single-cell-resolved spatial transcriptome atlases. We demonstrate DBiTplus across diverse samples including frozen mouse embryos, and formalin-fixed paraffin-embedded human lymph nodes and lymphoma tissues, highlighting its compatibility with challenging clinical specimens. DBiTplus uncovered mechanisms of lymphomagenesis, progression and transformation in human lymphomas. Thus, DBiTplus is a unified workflow for spatially resolved single-cell atlasing and unbiased exploration of biological mechanisms in a cell-by-cell manner at transcriptome scale.

Figure 1 Distinct CD8 patterns distinguish the IM from the CT on whole-slide specimens.A) A histogram of whole-slide CD8 density (normalized to the intratumoral mean) shows the 200-mm IM peak beyond the tumor border.B) A histogram of core placement relative to the tumor border shows cores that miss the IM peak due to geographic variationAbstracts

ABSTRACT Although multiplex imaging allows simultaneous mapping of complex tissue architectures and cellular phenotypes, the dearth of user-friendly robust image analysis workflows remains a significant limitation to its widespread use, restricting multiplex imaging to laboratories with image analysis expertise. Here, we introduce a no-code model-based customisable suite, CellMAPS, which allows both image processing and spatial analyses by non-specialists. We also developed new tools for tissue de-arraying, image normalisation and cell segmentation to increase usability and reduce analysis subjectivity. We have also introduce new spatial analysis tools for the partition of tissue architectures and cellular microenvironments. We demonstrate the capabilities of CellMAPS in various diseases captured using different multiplex imaging platforms. Overall, CellMAPS provides a platform for inexperienced laboratories to implement multiplex imaging and complex spatial analyses, which ultimately should lead to the broader adoption of these technologies in the biomedical field.

Abstract BackgroundSingle‐cell technologies have revealed significant microglial cell heterogeneity across the human brain in both health and disease. However, the integration of high‐plex protein and spatial information in single‐cell approaches constitutes a challenge essential for advancing our cell biology comprehension in the neuroscience field.MethodIn the present study, we employed co‐detection by indexing (CODEX), a protein multiplexed imaging technology, for the first time to unravel the association between different microglial populations and pathological features of Alzheimer’s disease (AD) in the human brain. We used a 32‐plex panel of DNA‐barcoded antibodies able to detect neurons, oligodendrocytes, astrocytes, myeloid cells, vascular components, and pathological markers, in the same brain tissue section. In addition, we implemented algorithms to segment morphologically complex cells and advanced data analysis pipelines.ResultOur results provide a comprehensive mapping of the human brain cytoarchitecture, showing different cell phenotypes based on their protein expression and morphology, cell interaction dynamics, and cell spatial organizations in healthy and AD individuals. Through the identification of different microglial phenotypes, we found a specific subpopulation associated to amyloid‐ß plaques in AD brains. Interestingly, this subpopulation exhibited shared properties with border‐associated macrophages.ConclusionThis study presents a novel approach to explore spatial brain cell heterogeneity in the context of neurological diseases and brings new insights into the microglial diversity in AD.

Abstract Background Mucosal head and neck squamous cell carcinoma (HNSCC) is often diagnosed at an advanced stage, where the prognosis is poor due to the high rates of recurrence and metastasis [1]. With approximately one million new cases projected in 2024, worldwide mortality of HNSCC is estimated to reach 50% of detected cases the same year [2]. Patients with early-stage tumours showed a 50–60% five-year survival rate in the US. Immune checkpoint inhibitors (ICIs) have shown promising results in prolonging survival in a subset of patients with recurrent or metastatic disease. However, challenges remain, particularly the limited efficacy of PD-1/PD-L1 blockade therapies. PD-L1 protein expression has been shown to be limited in its predictive power for ICI therapies. Emerging evidence shows that intricate characterisation of the tumour microenvironment (TME) is fundamental to understand interacting cells. Methods In this study, we sought to apply a more nuanced approach to understanding cellular interactions by mapping PD-1/PD-L1 interactions across whole-slide HNSCC tissue samples collected prior to ICI therapy. We used a combination of spatial proteomics (Akoya Biosciences) and an in situ proximity ligation assay (isPLA, Navinci Diagnostics) to visualise PD-1/PD-L1 interactions across cell types and cellular neighbourhoods within the tumour TME. Results Our findings indicate the existence of isPLA+ PD-1/PD-L1 interactions between macrophages/CD3 T cell-enriched neighbourhoods and tumour cells at the tumour-stroma boundaries in ICI-resistant tumours. The presence of these dense macrophage-tumour layers, which are either absent or dispersed in responders, indicates a barrier that may restrict immune cell infiltration and promote immune escape mechanisms. In contrast, responders had abundant B and T cell aggregates, predominantly around the tumour edges linked to enhanced immune responses to ICI therapy and better clinical outcomes. Conclusion This study highlights the utility of isPLA in detecting distinct tumour-immune interactions within the TME, offering new cellular interaction metrics for stratifying and optimising immunotherapy strategies.

BackgroundRecurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) generally has a poor prognosis for patients with limited treatment options. While incorporating immune checkpoint inhibitors (ICIs) has now become the standard of care, the efficacy is variable, with only a subset of patients responding. The complexity of the tumor microenvironment (TME) and the role of tertiary lymphoid structures (TLS) have emerged as critical determinants for immunotherapeutic response.MethodsIn this study, we analyzed two independently collected R/M HNSCC patient tissue cohorts to better understand the role of TLS in response to ICIs. Utilizing a multi-omics approach, we first performed targeted proteomic profiling using the Nanostring GeoMx Digital Spatial Profiler to quantify immune-related protein expression with spatial resolution. This was further characterized by spatially resolved whole transcriptome profiling of TLSs and germinal centers (GCs). Deeper single-cell resolved proteomic profiling of the TLSs was performed using the Akoya Biosciences Phenocycler Fusion platform.ResultsOur proteomic analysis revealed the presence of T lymphocyte markers, including CD3, CD45, and CD8, expressing cells and upregulation of immune checkpoint marker PD-L1 within tumor compartments of patients responsive to ICIs, indicative of ‘hot tumor’ phenotypes. We also observed the presence of antigen-presenting cells marked by expression of CD40, CD68, CD11c, and CD163 with upregulation of antigen-presentation marker HLA-DR, in patients responding to ICIs. Transcriptome analysis of TLS and GCs uncovered a marked elevation in the expression of genes related to immune modulation, diverse immune cell recruitment, and a potent interferon response within the TLS structure. Notably, the distribution of TLS-tumor distance was found to be significantly different across response groups (H = 9.28, p = 0.026). The proximity of TLSs to tumor cells was found to be a critical indicator of ICI response, implying that patients with TLSs located further from tumor cells have worse outcomes.ConclusionThe study underscores the multifaceted role of TLSs in modulating the immunogenic landscape of the TME in R/M HNSCC, likely influencing the efficacy of ICIs. Spatially resolved multi-omics approaches offer valuable insights into potential biomarkers for ICI response and highlight the importance of profiling the TME complexity when developing therapeutic strategies and patient stratification.

Single-cell spatial analysis of proteins is rapidly becoming increasingly important in revealing biological insights. Here, we present a protocol for automated high-plex multi-slide immunofluorescence staining and imaging of human head and neck cancer formalin-fixed paraffin-embedded (FFPE) sections using PhenoCycler-Fusion 2.0 technology. We describe steps for preparing human head and neck cancer FFPE tissues, staining with a panel of immunophenotyping markers, and Flow Cell assembly. We then detail procedures for setting up for a PhenoCycler-Fusion run, post-run Flow Cell removal, and downstream analyses. For complete details on the use and execution of this protocol, please refer to Jhaveri etal.1.

Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis using existing methods. For the first time, we demonstrate the application of a ML tool across five emerging spatial transcriptomic and proteomic technologies to identify oncogenic stem-like cell types in the tumor microenvironment.