Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data.

Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing or single-cell DNA sequencing and are based on calling copy number alterations and single nucleotide variants (SNVs). Single-cell RNA sequencing (scRNA-seq) is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this article, we demonstrate for the first time that scRNA-seq data contain sufficient evolutionary signal and can also be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Both standardized expression values and SNVs appear to be equally capable of reconstructing a similar pattern of phylogenetic relationship. This pattern is stable even when phylogenetic uncertainty is taken in account. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer.

Accounting for Errors in Data Improves Divergence Time Estimates in Single-cell Cancer Evolution.

Single-cell sequencing provides a new way to explore the evolutionary history of cells. Compared to traditional bulk sequencing, where a population of heterogeneous cells is pooled to form a single observation, single-cell sequencing isolates and amplifies genetic material from individual cells, thereby preserving the information about the origin of the sequences. However, single-cell data are more error-prone than bulk sequencing data due to the limited genomic material available per cell. Here, we present error and mutation models for evolutionary inference of single-cell data within a mature and extensible Bayesian framework, BEAST2. Our framework enables integration with biologically informative models such as relaxed molecular clocks and population dynamic models. Our simulations show that modeling errors increase the accuracy of relative divergence times and substitution parameters. We reconstruct the phylogenetic history of a colorectal cancer patient and a healthy patient from single-cell DNA sequencing data. We find that the estimated times of terminal splitting events are shifted forward in time compared to models which ignore errors. We observed that not accounting for errors can overestimate the phylogenetic diversity in single-cell DNA sequencing data. We estimate that 30-50% of the apparent diversity can be attributed to error. Our work enables a full Bayesian approach capable of accounting for errors in the data within the integrative Bayesian software framework BEAST2.

Tuberous sclerosis complex: a complex case.

Tuberous sclerosis complex (TSC) is an inheritable disorder characterized by the formation of benign yet disorganized tumors in multiple organ systems. Germline mutations in the TSC1 (hamartin) or more frequently TSC2 (tuberin) genes are causative for TSC. The malignant manifestations of TSC, pulmonary lymphangioleiomyomatosis (LAM) and renal angiomyolipoma (AML), may also occur as independent sporadic perivascular epithelial cell tumor (PEComa) characterized by somatic TSC2 mutations. Thus, discerning TSC from the copresentation of sporadic LAM and sporadic AML may be obscured in TSC patients lacking additional features. In this report, we present a case study on a single patient initially reported to have sporadic LAM and a mucinous duodenal adenocarcinoma deficient in DNA mismatch repair proteins. Moreover, the patient had a history of Wilms' tumor, which was reclassified as AML following the LAM diagnosis. Therefore, we investigated the origins and relatedness of these tumors. Using germline whole-genome sequencing, we identified a premature truncation in one of the patient's TSC2 alleles. Using immunohistochemistry, loss of tuberin expression was observed in AML and LAM tissue. However, no evidence of a somatic loss of heterozygosity or DNA methylation epimutations was observed at the TSC2 locus, suggesting alternate mechanisms may contribute to loss of the tumor suppressor protein. In the mucinous duodenal adenocarcinoma, no causative mutations were found in the DNA mismatch repair genes MLH1, MSH2, MSH6, or PMS2 Rather, clonal deconvolution analyses were used to identify mutations contributing to pathogenesis. This report highlights both the utility of using multiple sequencing techniques and the complexity of interpreting the data in a clinical context.

A Skull Might Lie: Modeling Ancestral Ranges and Diet from Genes and Shape of Tree Squirrels.

Tropical forests of Central and South America represent hotspots of biological diversity. Tree squirrels of the tribe Sciurini are an excellent model system for the study of tropical biodiversity as these squirrels disperse exceptional distances, and after colonizing the tropics of the Central and South America, they have diversified rapidly. Here, we compare signals from DNA sequences with morphological signals using pictures of skulls and computational simulations. Phylogenetic analyses reveal step-wise geographic divergence across the Northern Hemisphere. In Central and South America, tree squirrels form two separate clades, which split from a common ancestor. Simulations of ancestral distributions show western Amazonia as the epicenter of speciation in South America. This finding suggests that wet tropical forests on the foothills of Andes possibly served as refugia of squirrel diversification during Pleistocene climatic oscillations. Comparison of phylogeny and morphology reveals one major discrepancy: Microsciurus species are a single clade morphologically but are polyphyletic genetically. Modeling of morphology-diet relationships shows that the only group of species with a direct link between skull shape and diet are the bark-gleaning insectivorous species of Microsciurus. This finding suggests that the current designation of Microsciurus as a genus is based on convergent ecologically driven changes in morphology.