Itai Yanai

Genes linked by fusion events are generally of the same functional category: A systematic analysis of 30 microbial genomes

Recent work in computational genomics has shown that a functional association between two genes can be derived from the existence of a fusion of the two as one continuous sequence in another genome. For each of 30 completely sequenced microbial genomes, we established all such fusion links among its genes and determined the distribution of links within and among 15 broad functional categories. We found that 72% of all fusion links related genes of the same functional category. A comparison of the distribution of links to simulations on the basis of a random model further confirmed the significance of intracategory fusion links. Where a gene of annotated function is linked to an unclassified gene, the fusion link suggests that the two genes belong to the same functional category. The predictions based on fusion links are shown here for Methanobacterium thermoautotrophicum, and another 661 predictions are available at http://fusion.bu.edu.

A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells

Reversing the dysfunctional Txa0cell state that arises inxa0cancer and chronic viral infections is the focus of therapeutic interventions; however, current therapies are effective in only some patients and some tumor types. To gain a deeper molecular understanding of the dysfunctional Txa0cell state, we analyzed population and single-cell RNA profiles of CD8(+) tumor-infiltrating lymphocytes (TILs) and used genetic perturbations to identify a distinct gene module for Txa0cell dysfunction that can be uncoupled from Txa0cell activation. This distinct dysfunction module is downstream of intracellular metallothioneins that regulate zinc metabolism and can be identified at single-cell resolution. We further identify Gata-3, a zinc-finger transcription factor in the dysfunctional module, as a regulator of dysfunction, and we use CRISPR-Cas9 genome editing to show that it drives a dysfunctional phenotype in CD8(+) TILs. Our results open novel avenues for targeting dysfunctional Txa0cell states while leaving activation programs intact.

scDual-Seq: mapping the gene regulatory program of Salmonella infection by host and pathogen single-cell RNA-sequencing

The interaction between a pathogen and a host is a highly dynamic process in which both agents activate complex programs. Here, we introduce a single-cell RNA-sequencing method, scDual-Seq, that simultaneously captures both host and pathogen transcriptomes. We use it to study the process of infection of individual mouse macrophages with the intracellular pathogen Salmonella typhimurium. Among the infected macrophages, we find three subpopulations and we show evidence for a linear progression through these subpopulations, supporting a model in which these three states correspond to consecutive stages of infection.

Computational biologists: moving to the driver's seat

The recent shift of computational biologists from bioinformatics service providers to leaders of cutting-edge programs highlights the accompanying cultural and conceptual changes that should be implemented by funding bodies and academic institutions.

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.

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.

New skin for the old RNA-Seq ceremony: the age of single-cell multi-omics

New methods for simultaneously quantifying protein and gene expression at the single-cell level have the power to identify cell types and to classify cell populations.

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.ABSTRACT Tumors evolve as independent systems comprising complex survival-ensuring functions, however the nature of these distinct processes and their recurrence across cancers is not clear. Here we propose that melanoma cancer-cells can be classified to three ‘archetypes’ that co-opt the neural crest, mature melanocytes, and stress gene expression programs, respectively, have a unique subclonal structure, and are conserved between zebrafish and human melanomas. Studying the natural history of a zebrafish melanoma tumor at the single-cell level, we found that one archetype exclusively exhibits the signature of the Warburg effect, suggesting that a shifting balance in energy production occurs differentially in the tumor. Deconvolving bulk human melanomas, we found that patients with a dominant fraction of the neural crest archetype show worse survival rates, indicating a clinical relevance for the composition of archetypes. Finally, we provide evidence that extending our approach to other cancer types can reveal universal and cancer-specific archetypes.

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 the physical relationships among them. Single-cell RNA-Seq (scRNA-Seq) has made significant progress towards the unbiased and systematic identification of cell populations within a tissue, however, the characterization of their spatial organization within it 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 µm spots across the tissue, each capturing the transcriptomes of multiple cells. Here, we present an approach for the integration of scRNA-Seq and ST data generated from the same sample, and deploy it on primary pancreatic tumors from two patients. Applying our multimodal intersection analysis (MIA), we annotated the distinct micro-environment of each cell type identified by scRNA-Seq. We further found that subpopulations of ductal cells, macrophages, dendritic cells, and cancer cells have spatially restricted localizations across the tissue, as well as distinct co-enrichments with other cell types. Our mapping approach provides an efficient framework for the integration of the scRNA-Seq-defined subpopulation structure and the ST-defined tissue architecture in any tissue.To understand the architecture of a tissue it is necessary to know both the cell populations and their physical relationships to one another. Single-cell RNA-Seq (scRNA-Seq) has made significant progress towards the unbiased and systematic characterization of the cell populations within a tissue, as well as their cellular states, 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 one thousand 100 µm spots, 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 three cancer cell populations that we identified. Strikingly, we find that subpopulations defined in the scRNA-Seq data also exhibit spatial segregation in the atlas, suggesting such an atlas may be used to study the functional attributes of subpopulations. 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.

Development and Evolution through the Lens of Global Gene Regulation

Evolution and development are two inherently intertwined processes. As the embryo develops it does so in ways that both reflect past constraints and bias the future evolution of the species. While research exploiting this insight typically studies individual genes, transcriptomic analyses have sparked a new wave of discoveries. In this opinion piece, I review the evidence arising from transcriptomics on the topics of the evolution of germ layers, the phylotypic stage, and developmental constraints. The spatiotemporal pattern of gene expression across germ layers provides evidence that the endoderm was the first germ layer to evolve. Comparing transcriptome dynamics throughout developmental time across distant species reveals a mid-developmental transition under strong developmental constraints. These studies highlight the efficiency of exploratory data analysis using computational tools and comparative approaches for discovery.