IL-1β+ macrophages fuel pathogenic inflammation in pancreatic cancer

Decoding the tumor microenvironment with spatial technologies

Clear cell renal cell carcinoma (ccRCC) is a complex disease with remarkable immune and metabolic heterogeneity. Here we perform genomic, transcriptomic, proteomic, metabolomic and spatial transcriptomic and metabolomic analyses on 100 patients with ccRCC from the Tongji Hospital RCC (TJ-RCC) cohort. Our analysis identifies four ccRCC subtypes including De-clear cell differentiated (DCCD)-ccRCC, a subtype with distinctive metabolic features. DCCD cancer cells are characterized by fewer lipid droplets, reduced metabolic activity, enhanced nutrient uptake capability and a high proliferation rate, leading to poor prognosis. Using single-cell and spatial trajectory analysis, we demonstrate that DCCD is a common mode of ccRCC progression. Even among stage I patients, DCCD is associated with worse outcomes and higher recurrence rate, suggesting that it cannot be cured by nephrectomy alone. Our study also suggests a treatment strategy based on subtype-specific immune cell infiltration that could guide the clinical management of ccRCC. 

Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progression.

Abstract The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST. 

Spatial transcriptomics in development and disease

Immune checkpoint inhibitors (ICI) represent the cornerstone for treatment of patients with metastatic clear-cell renal cell carcinoma (ccRCC). Despite a favorable response for a subset of patients, others experience primary progressive disease highlighting the need to precisely understand plasticity of cancer cells and their crosstalk with the microenvironment to better predict therapeutic response and personalize treatment. Single-cell RNA sequencing of ccRCC at different disease stages and normal adjacent tissue (NAT) from patients identified 46 cell populations, including 5 tumor subpopulations, characterized by distinct transcriptional signatures representing an epithelial to mesenchymal transition gradient and a novel inflamed state. Deconvolution of the tumor and microenvironment signatures in public datasets and in data from the BIONIKK clinical trial (NCT02960906) revealed a strong correlation between mesenchymal-like ccRCC cells and myofibroblastic cancer-associated fibroblasts (myCAFs), which are both enriched in metastases and correlate with poor patient survival. Spatial transcriptomics and multiplex immune staining uncovered spatial proximity of mesenchymal-like ccRCC cells and myCAFs at the tumor-NAT interface. Moreover, enrichment in myCAFs was associated with primary resistance to ICI therapy in the BIONIKK clinical trial. This data highlights the epithelial-mesenchymal plasticity of ccRCC cancer cells and their relationship with myCAFs, a critical component of the microenvironment associated with poor outcome and ICI resistance. 

Mesenchymal-like tumor cells and myofibroblastic cancer-associated fibroblasts are associated with progression and immunotherapy response of clear-cell renal cell carcinoma

http://www.cell.com/article/S1535610822005487/pdf

Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer

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Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer