A review of spatial profiling technologies for characterizing the tumor microenvironment in immuno-oncology

Abstract

Interpreting the mechanisms and principles that govern gene activity and how these genes work according to -their cellular distribution in organisms has profound implications for cancer research. The latest technological advancements, such as imaging-based approaches and next-generation single-cell sequencing technologies, have established a platform for spatial transcriptomics to systematically quantify the expression of all or most genes in the entire tumor microenvironment and explore an array of disease milieus, particularly in tumors. Spatial profiling technologies permit the study of transcriptional activity at the spatial or single-cell level. This multidimensional classification of the transcriptomic and proteomic signatures of tumors, especially the associated immune and stromal cells, facilitates evaluation of tumor heterogeneity, details of the evolutionary trajectory of each tumor, and multifaceted interactions between each tumor cell and its microenvironment. Therefore, spatial profiling technologies may provide abundant and high-resolution information required for the description of clinical-related features in immuno-oncology. From this perspective, the present review will highlight the importance of spatial transcriptomic and spatial proteomics analysis along with the joint use of other sequencing technologies and their implications in cancers and immune-oncology. In the near future, advances in spatial profiling technologies will undoubtedly expand our understanding of tumor biology and highlight possible precision therapeutic targets for cancer patients.

Contribution of Proteomics in Transplantation: Identification of Injury and Rejection Markers

“…Spatial technologies are currently among the most innovative techniques currently available. 24 Interest in spatial technology has increased over the years because of our better understanding of the diseased microenvironment, cellular heterogeneity, and altered cell-cell interactions. These initially developed as multiplexed immunohistochemistry assays in which 2 stains could identify 2 epitopes of interest.…”

Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns in Breast Multiplexed Digital Pathology

“…Increasing attention has been given to studying the TME at varying scales [10,11], including the analysis of cell type compositions in various cancers [12][13][14], the quantification of spatial distances between pairs of cell phenotypes [15], and the characterization of cellular neighborhoods involving more than two cell types [16][17][18]. While these works have unveiled numerous unique patterns across a spectrum of cancers, associating them with clinical implications such as treatment response and patient prognoses remains an important and difficult challenge.…”

SPADE: spatial deconvolution for domain specific cell-type estimation

“…For example, spatial transcriptomics has demonstrated great promise in elucidating the cellular basis of brain function 3 , and in enabling precision treatments for heart disease 4 or cancer 5 . Moreover, spatial transcriptomics has shown immense potential for studying the immune system 6 . Profiling the transcriptome of immune cells in various tissues has yielded insights into how the immune system responds to infection and disease 7 .…”