Investigating Inter- and Intrasample Diversity of Single-Cell RNA Sequencing Datasets
Tumor heterogeneity can arise from a variety of extrinsic and intrinsic sources and drives unfavorable outcomes. With recent technological advances, single-cell RNA sequencing has become a way for researchers to easily assay tumor heterogeneity at the tra
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Introduction Intratumor heterogeneity (ITH) is a major determinant of tumor progression, the evolution of resistance to therapy, and can fuel tumor evolution and the development of metastasis. ITH is present on multiple different levels, ranging from genetic [1] to epigenetic/cell phenotypic [2, 3] and metabolic [4] to microenvironmental heterogeneity [5]. Single-cell DNA and RNA sequencing have made it possible to identify ITH in a way that cannot be captured by bulk sample profiling [6, 7], because they can, in principle, characterize important differences or common features on the level of the individual cell. Estimating cellular heterogeneity by way of diversity and uncertainty about the identity of an individual in the context of others in a sample is thus an important task. One important quantitative method to assess heterogeneity it by calculating the degree of variation between individual entities, which can be achieved using the concept of a diversity index [8]. Here, we present a method to use single-cell RNA sequencing data and clustering algorithms to calculate a general diversity index
Joseph Markowitz (ed.), Translational Bioinformatics for Therapeutic Development, Methods in Molecular Biology, vol. 2194, https://doi.org/10.1007/978-1-0716-0849-4_10, © Springer Science+Business Media, LLC, part of Springer Nature 2021
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Meghan C. Ferrall-Fairbanks and Philipp M. Altrock
in order to estimate intratumor heterogeneity, and use it as a starting point for clinical correlations [9], or mathematical modeling [10, 11]. ITH is of clinical interest because it serves as a reservoir for therapeutic resistance and is likely a driver of clinical progression with single and combination therapies, when targeted therapies. The clinical implications of ITH have not been explored in all types of cancer, on all scales of heterogeneity. Further, it is unknown whether certain therapeutics could directly decrease ITH and thus serve to mitigate this critical resistance mechanism. The primary objective of this manuscript is to introduce a multiscale approach to measure ITH using single-cell RNA sequencing. This method can be applied downstream of a number of computational and statistical approaches that integrates scRNA-seq data, and will become an important step in the quest to generate foundational evidence that ITH as a relevant clinical factor in those cancer types that have been lacking behind in terms of describing and clinically assessing tumor heterogeneity. Eventually, it would be the goal to describe ITH such that it can be modified by, for example, epigenetic therapeutics that either increase or reduce it to avert rapid resistance evolution. Single-cell RNA sequencing (scRNA-seq) can be used to estimate cellular diversity, especially in the context of intratumor heterogeneity. Novel scRNA-seq technologies have become a costeffective method to identify transcriptomic changes at high resolution. Intratumor heterogeneity can be identified for many disease at various stages [12], and have the potential t
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