Biology subjects

Yanai, I.

Publications and source records attributed to Yanai, I..

5 recordsLinked to original sources

bioRxiv2018

Cancer archetypes co-opt and adapt the transcriptional programs of existing cellular states

Transcriptional profiling has revealed a diverse range of cancer cell states, however an understanding of their function has remained elusive. Using a combination of zebrafish melanoma modeling and human validation, we have identified a conserved stress-like state that confers intrinsic drug resistance. The stress-like state expresses genes such as fos, hsp70 and ubb, all required for adaptation to diverse cellular stresses, and we confirmed its existence using immunofluorescence and spatial transcriptomics. We provide evidence that this state has a higher tumor seeding capabilities compared to non-stressed cells, and confers intrinsic resistance to MEK inhibitors, a commonly used melanoma therapeutic. Furthermore, the stress-like program can be induced by extrinsic processes such as heat shock, and confers resistance to both MEK and BRAF inhibitors in both zebrafish and human melanomas. Collectively, our study suggests that the transcriptional states associated with therapeutic failure are established during the earliest steps of tumorigenesis.

cancer biology↗

bioRxiv2018

Widespread transcriptional scanning in testes modulates gene evolution rates

A long-standing question in molecular biology relates to why the testes express the largest number of genes relative to all other organs. Here, we report a detailed gene expression map of human spermatogenesis using single-cell RNA-Seq. Surprisingly, we found that spermatogenesis-expressed genes contain significantly fewer germline mutations than unexpressed genes, with the lowest mutation rates on the transcribed DNA strands. These results suggest a model of transcriptional scanning to reduce germline mutations by correcting DNA damage. This model also explains the rapid evolution in sensory- and immune-defense related genes, as well as in male reproduction genes. Collectively, our results indicate that widespread expression in the testes achieves a dual mechanism for maintaining the DNA integrity of most genes, while selectively promoting variation of other genes.

evolutionary biology↗

bioRxiv2018

Building a tumor atlas: integrating single-cell RNA-Seq data with spatial transcriptomics in pancreatic ductal adenocarcinoma

To understand tissue architecture it is necessary to understand both which cell types are present and their physical relationships to one another. Single-cell RNA-Seq (scRNA-Seq) has made significant progress towards the unbiased and systematic characterization of cell populations within a tissue by studying hundreds and thousands of cells in a single experiment. However, the characterization of the spatial organization of individual cells within a tissue has been more elusive. The recently introduced spatial transcriptomics method (ST) reveals the spatial pattern of gene expression within a tissue section at a resolution of a thousand 100 m spots across the tissue, each capturing the transcriptomes of [~]10-20 cells. Here, we present an approach for the integration of scRNA-Seq and ST data generated from the same sample of pancreatic cancer tissue. Using markers for cell types identified by scRNA-Seq, we robustly deconvolved the cell type composition of each ST spot to generate a spatial atlas of cell proportions across the tissue. Studying this atlas, we found that distinct spatial localizations accompany each of the cell populations that we identified. Our results provide a framework for creating a tumor atlas by mapping single-cell populations to their spatial region, as well as the inference of cell architecture in any tissue.

cancer biology↗

bioRxiv2017

K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data

High-throughput single-cell RNA-Seq (scRNA-Seq) is a powerful approach for studying heterogeneous tissues and dynamic cellular processes. However, compared to bulk RNA-Seq, single-cell expression profiles are extremely noisy, as they only capture a fraction of the transcripts present in the cell. Here, we propose the k-nearest neighbor smoothing (kNN-smoothing) algorithm, designed to reduce noise by aggregating information from similar cells (neighbors) in a computationally efficient and statistically tractable manner. The algorithm is based on the observation that across protocols, the technical noise exhibited by UMI-filtered scRNA-Seq data closely follows Poisson statistics. Smoothing is performed by first identifying the nearest neighbors of each cell in a step-wise fashion, based on partially smoothed and variance-stabilized expression profiles, and then aggregating their transcript counts. We show that kNN-smoothing greatly improves the detection of clusters of cells and co-expressed genes, and clearly outperforms other smoothing methods on simulated data. To accurately perform smoothing for datasets containing highly similar cell populations, we propose the kNN-smoothing 2 algorithm, in which neighbors are determined after projecting the partially smoothed data onto the first few principal components. We show that unlike its predecessor, kNN-smoothing 2 can accurately distinguish between cells from different T cell subsets, and enables their identification in peripheral blood using unsupervised methods. Our work facilitates the analysis of scRNA-Seq data across a broad range of applications, including the identification of cell populations in heterogeneous tissues and the characterization of dynamic processes such as cellular differentiation. Reference implementations of our algorithms can be found at https://github.com/yanailab/knn-smoothing.

bioinformatics↗

bioRxiv2017

Human to yeast pathway transplantation: cross-species dissection of the adenine de novo pathway regulatory node

Pathway transplantation from one organism to another represents a means to a more complete understanding of a biochemical or regulatory process. The purine biosynthesis pathway, a core metabolic function, was transplanted from human to yeast. We replaced the entire Saccharomyces cerevisiae adenine de novo pathway with the cognate human pathway components. A yeast strain was \"humanized\" for the full pathway by deleting all relevant yeast genes completely and then providing the human pathway in trans using a neochromosome expressing the human protein coding regions under the transcriptional control of their cognate yeast promoters and terminators. The \"humanized\" yeast strain grows in the absence of adenine, indicating complementation of the yeast pathway by the full set of human proteins. While the strain with the neochromosome is indeed prototrophic, it grows slowly in the absence of adenine. Dissection of the phenotype revealed that the human ortholog of ADE4, PPAT, shows only partial complementation. We have used several strategies to understand this phenotype, that point to PPAT/ADE4 as the central regulatory node. Pathway metabolites are responsible for regulating PPATs protein abundance through transcription and proteolysis as well as its enzymatic activity by allosteric regulation in these yeast cells. Extensive phylogenetic analysis of PPATs from diverse organisms hints at adaptations of the enzyme-level regulation to the metabolite levels in the organism. Finally, we isolated specific mutations in PPAT as well as in other genes involved in the purine metabolic network that alleviate incomplete complementation by PPAT and provide further insight into the complex regulation of this critical metabolic pathway.

synthetic biology↗