Single cell Hi-C identifies plastic chromosome conformations underlying the gastrulation enhancer landscape.

Embryonic development involves massive proliferation and differentiation of cell lineages. This must be supported by chromosome replication and epigenetic reprogramming, but how proliferation and cell fate acquisition are balanced in this process is not well understood. Here we use single cell Hi-C to map chromosomal conformations in post-gastrulation mouse embryo cells and study their distributions and correlations with matching embryonic transcriptional atlases.

Dissecting cellular crosstalk by sequencing physically interacting cells.

Crosstalk between neighboring cells underlies many biological processes, including cell signaling, proliferation and differentiation. Current single-cell genomic technologies profile each cell separately after tissue dissociation, losing information on cell-cell interactions. In the present study, we present an approach for sequencing physically interacting cells (PIC-seq), which combines cell sorting of physically interacting cells (PICs) with single-cell RNA-sequencing.

MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions.

scRNA-seq profiles each represent a highly partial sample of mRNA molecules from a unique cell that can never be resampled, and robust analysis must separate the sampling effect from biological variance. We describe a methodology for partitioning scRNA-seq datasets into metacells: disjoint and homogenous groups of profiles that could have been resampled from the same cell. Unlike clustering analysis, our algorithm specializes at obtaining granular as opposed to maximal groups.

Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma.

Tumor immune cell compositions play a major role in response to immunotherapy, but the heterogeneity and dynamics of immune infiltrates in human cancer lesions remain poorly characterized. Here, we identify conserved intratumoral CD4 and CD8 T cell behaviors in scRNA-seq data from 25 melanoma patients. We discover a large population of CD8 T cells showing continuous progression from an early effector "transitional" into a dysfunctional T cell state.

The European Collaborative Project on Inflammation and Vascular Wall Remodeling in Atherosclerosis - Intravascular Ultrasound (ATHEROREMO-IVUS) study.

Aims: The European Collaborative Project on Inflammation and Vascular Wall Remodeling in Atherosclerosis - Intravascular Ultrasound (ATHEROREMO-IVUS) study was designed as an exploratory clinical study in order to investigate the associations between genetic variation, coronary atherosclerosis phenotypes, and plaque vulnerability as determined by IVUS.

Methods And Results: The ATHEROREMO-IVUS study was a prospective, observational study of 581 patients with stable angina pectoris or acute coronary syndrome (ACS) who were referred for coronary angiography to the Thoraxcenter, Rotterdam, enriched with 265 IBIS-2 participants (total population, n=846). Prior to catheterisation, blood samples were drawn for genetic analyses.

Cell-cycle dynamics of chromosomal organization at single-cell resolution.

Chromosomes in proliferating metazoan cells undergo marked structural metamorphoses every cell cycle, alternating between highly condensed mitotic structures that facilitate chromosome segregation, and decondensed interphase structures that accommodate transcription, gene silencing and DNA replication. Here we use single-cell Hi-C (high-resolution chromosome conformation capture) analysis to study chromosome conformations in thousands of individual cells, and discover a continuum of cis-interaction profiles that finely position individual cells along the cell cycle. We show that chromosomal compartments, topological-associated domains (TADs), contact insulation and long-range loops, all defined by bulk Hi-C maps, are governed by distinct cell-cycle dynamics.

Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies.

In epigenome-wide association studies (EWAS), different methylation profiles of distinct cell types may lead to false discoveries. We introduce ReFACTor, a method based on principal component analysis (PCA) and designed for the correction of cell type heterogeneity in EWAS. ReFACTor does not require knowledge of cell counts, and it provides improved estimates of cell type composition, resulting in improved power and control for false positives in EWAS.

Characterization of whole-genome autosomal differences of DNA methylation between men and women.

Background: Disease risk and incidence between males and females reveal differences, and sex is an important component of any investigation of the determinants of phenotypes or disease etiology. Further striking differences between men and women are known, for instance, at the metabolic level. The extent to which men and women vary at the level of the epigenome, however, is not well documented.

A Note on the Relations Between Spatio-Genetic Models.

Modeling human genetic variation along the continuous geographic space is a new research direction that has been stirring interest in the community during the past few years. Multiple recent works suggested different probabilistic models for the relation between geography and genetic sequence, and applied them to geographic localization, detection of selection, and correction of confounding in Genome-Wide Association Studies (GWAS). Prior to these developments, continuous representations of genetic structure were produced almost exclusively using dimensionality reduction techniques, mostly principal component analysis (PCA).

PIGS: improved estimates of identity-by-descent probabilities by probabilistic IBD graph sampling.

Identifying segments in the genome of different individuals that are identical-by-descent (IBD) is a fundamental element of genetics. IBD data is used for numerous applications including demographic inference, heritability estimation, and mapping disease loci. Simultaneous detection of IBD over multiple haplotypes has proven to be computationally difficult.

No evidence from genome-wide data of a Khazar origin for the Ashkenazi Jews.

The origin and history of the Ashkenazi Jewish population have long been of great interest, and advances in high-throughput genetic analysis have recently provided a new approach for investigating these topics. We and others have argued on the basis of genome-wide data that the Ashkenazi Jewish population derives its ancestry from a combination of sources tracing to both Europe and the Middle East. It has been claimed, however, through a reanalysis of some of our data, that a large part of the ancestry of the Ashkenazi population originates with the Khazars, a Turkic-speaking group that lived to the north of the Caucasus region ~1,000 years ago.

Enhanced localization of genetic samples through linkage-disequilibrium correction.

Characterizing the spatial patterns of genetic diversity in human populations has a wide range of applications, from detecting genetic mutations associated with disease to inferring human history. Current approaches, including the widely used principal-component analysis, are not suited for the analysis of linked markers, and local and long-range linkage disequilibrium (LD) can dramatically reduce the accuracy of spatial localization when unaccounted for. To overcome this, we have introduced an approach that performs spatial localization of individuals on the basis of their genetic data and explicitly models LD among markers by using a multivariate normal distribution.

Fast and accurate inference of local ancestry in Latino populations.

Motivation: It is becoming increasingly evident that the analysis of genotype data from recently admixed populations is providing important insights into medical genetics and population history. Such analyses have been used to identify novel disease loci, to understand recombination rate variation and to detect recent selection events. The utility of such studies crucially depends on accurate and unbiased estimation of the ancestry at every genomic locus in recently admixed populations.

Joint analysis of multiple metagenomic samples.

The availability of metagenomic sequencing data, generated by sequencing DNA pooled from multiple microbes living jointly, has increased sharply in the last few years with developments in sequencing technology. Characterizing the contents of metagenomic samples is a challenging task, which has been extensively attempted by both supervised and unsupervised techniques, each with its own limitations. Common to practically all the methods is the processing of single samples only; when multiple samples are sequenced, each is analyzed separately and the results are combined.