Metabolic alterations mediated by STAT3 promotes drug persistence in CML.

Leukemic stem cells (LSCs) can acquire non-mutational resistance following drug treatment leading to therapeutic failure and relapse. However, oncogene-independent mechanisms of drug persistence in LSCs are incompletely understood, which is the primary focus of this study. We integrated proteomics, transcriptomics, and metabolomics to determine the contribution of STAT3 in promoting metabolic changes in tyrosine kinase inhibitor (TKI) persistent chronic myeloid leukemia (CML) cells.

Mapping Distinct Bone Marrow Niche Populations and Their Differentiation Paths.

The bone marrow microenvironment is composed of heterogeneous cell populations of non-hematopoietic cells with complex phenotypes and undefined trajectories of maturation. Among them, mesenchymal cells maintain the production of stromal, bone, fat, and cartilage cells. Resolving these unique cellular subsets within the bone marrow remains challenging.

A comprehensive single cell transcriptional landscape of human hematopoietic progenitors.

Hematopoietic Stem/Progenitor cells (HSPCs) are endowed with the role of maintaining a diverse pool of blood cells throughout the human life. Despite recent efforts, the nature of the early cell fate decisions remains contentious. Using single-cell RNA-Seq, we show that existing approaches to stratify bone marrow CD34+ cells reveal a hierarchically-structured transcriptional landscape of hematopoietic differentiation.

Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data.

Single-cell RNA-sequencing has become a widely used, powerful approach for studying cell populations. However, these methods often generate multiplet artifacts, where two or more cells receive the same barcode, resulting in a hybrid transcriptome. In most experiments, multiplets account for several percent of transcriptomes and can confound downstream data analysis.

Population snapshots predict early haematopoietic and erythroid hierarchies.

The formation of red blood cells begins with the differentiation of multipotent haematopoietic progenitors. Reconstructing the steps of this differentiation represents a general challenge in stem-cell biology. Here we used single-cell transcriptomics, fate assays and a theory that allows the prediction of cell fates from population snapshots to demonstrate that mouse haematopoietic progenitors differentiate through a continuous, hierarchical structure into seven blood lineages.

Clonal analysis of lineage fate in native haematopoiesis.

Haematopoiesis, the process of mature blood and immune cell production, is functionally organized as a hierarchy, with self-renewing haematopoietic stem cells and multipotent progenitor cells sitting at the very top. Multiple models have been proposed as to what the earliest lineage choices are in these primitive haematopoietic compartments, the cellular intermediates, and the resulting lineage trees that emerge from them. Given that the bulk of studies addressing lineage outcomes have been performed in the context of haematopoietic transplantation, current models of lineage branching are more likely to represent roadmaps of lineage potential than native fate.

A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.

Although the function of the mammalian pancreas hinges on complex interactions of distinct cell types, gene expression profiles have primarily been described with bulk mixtures. Here we implemented a droplet-based, single-cell RNA-seq method to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two mouse strains. Cells could be divided into 15 clusters that matched previously characterized cell types: all endocrine cell types, including rare epsilon-cells; exocrine cell types; vascular cells; Schwann cells; quiescent and activated stellate cells; and four types of immune cells.

Molecular Genetic Evidence for Shared Etiology of Autism and Prodigy.

Child prodigies are rare individuals with an exceptional working memory and unique attentional skills that may facilitate the attainment of professional skill levels at an age well before what is observed in the general population. Some characteristics of prodigy have been observed to be quantitatively similar to those observed in autism spectrum disorder (ASD), suggesting possible shared etiology, though objectively validated prodigies are so rare that evidence has been sparse. We performed a family-based genome-wide linkage analysis on 5 nuclear and extended families to search for genetic loci that influence the presence of both prodigy and ASD, assuming that the two traits have the same genetic etiology in the analysis model in order to find shared loci.

StickWRLD as an Interactive Visual Pre-Filter for Canceromics-Centric Expression Quantitative Trait Locus Data.

As datasets increase in complexity, the time required for analysis (both computational and human domain-expert) increases. One of the significant impediments introduced by such burgeoning data is the difficulty in knowing what features to include or exclude from statistical models. Simple tables of summary statistics rarely provide an adequate picture of the patterns and details of the dataset to enable researchers to make well-informed decisions about the adequacy of the models they are constructing.

Addressing the unmet need for visualizing conditional random fields in biological data.

Background: The biological world is replete with phenomena that appear to be ideally modeled and analyzed by one archetypal statistical framework - the Graphical Probabilistic Model (GPM). The structure of GPMs is a uniquely good match for biological problems that range from aligning sequences to modeling the genome-to-phenome relationship. The fundamental questions that GPMs address involve making decisions based on a complex web of interacting factors.

Gene × smoking interactions on human brain gene expression: finding common mechanisms in adolescents and adults.

Background: Numerous studies have examined gene × environment interactions (G × E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here, we assessed G × E interactions using two publically available datasets to assess if DNA variation is associated with post-mortem brain gene expression changes based on smoking behavior, a biobehavioral construct that is part of a complex system of genetic and environmental influences.