Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors; scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.

BackgroundThe differentiation and maturation trajectories of fetal liver stem/progenitor cells (LSPCs) are not fully understood at single-cell resolution, and a priori knowledge of limited biomarkers could restrict trajectory tracking.ResultsWe employed marker-free single-cell RNA-Seq to characterize comprehensive transcriptional profiles of 507 cells randomly selected from seven stages between embryonic day 11.5 and postnatal day 2.5 during mouse liver development, and also 52 Epcam-positive cholangiocytes from postnatal day 3.25 mouse livers. LSPCs in developing mouse livers were identified via marker-free transcriptomic profiling. Single-cell resolution dynamic developmental trajectories of LSPCs exhibited contiguous but discrete genetic control through transcription factors and signaling pathways. The gene expression profiles of cholangiocytes were more close to that of embryonic day 11.5 rather than other later staged LSPCs, cuing the fate decision stage of LSPCs. Our marker-free approach also allows systematic assessment and prediction of isolation biomarkers for LSPCs.ConclusionsOur data provide not only a valuable resource but also novel insights into the fate decision and transcriptional control of self-renewal, differentiation and maturation of LSPCs.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4342-x) contains supplementary material, which is available to authorized users.

DNMT3A is frequently mutated in acute myeloid leukemia (AML). To explore the features of human AML with the hotspot DNMT3A R882H mutation, we generated Dnmt3a R878H conditional knockin mice, which developed AML with enlarged Lin − Sca1 + cKit + cell compartments. The transcriptome and DNA methylation profiling of bulk leukemic cells and the single-cell RNA sequencing of leukemic stem/progenitor cells revealed significant changes in gene expression and epigenetic regulatory patterns that cause differentiation arrest and growth advantage. Consistent with leukemic cell accumulation in G 2 /M phase, CDK1 was up-regulated due to mTOR activation associated with DNA hypomethylation. Overexpressed CDK1-mediated EZH2 phosphorylation resulted in an abnormal trimethylation of H3K27 profile. The mTOR inhibitor rapamycin elicited a significant therapeutic response in Dnmt3a R878H/WT mice.have been identified in a subset of acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and acute lymphoblastic leukemia (ALL), with DNMT3A R882 being the hotspot (1-4). Clinical features of most AML cases with DNMT3A mutations include preferential involvement of a monocytic lineage (AML-M4 and -M5 subtypes), thrombocytosis, onset at a relatively old age, and poor prognosis (2, 5, 6). Careful genotypephenotype correlations suggest that patients manifest DNMT3A mutations in preleukemic hematopoietic stem cells (HSCs)/multipotent progenitors (MPPs), which exhibit a competitive advantage over normal HSCs. Because these mutations occur at a very early stage among genetic abnormalities, they are likely involved in the development of leukemia (7,8).Functionally, the DNMT3A R882 mutation might disrupt epigenetic regulation. This kind of DNMT3A mutation confers reduced methyltransferase activity and promotes the possibility of dominant-negative consequences compared with the wild-type (WT) allele (2, 9, 10). Moreover, the DNMT3A mutation causes aberrant DNA hypomethylation and up-regulates a series of target genes involved in AML pathogenesis (11-13).In vivo animal tests have shown that the Dnmt3a gene plays an essential role in hematopoiesis regulation. Dnmt3a −/− HSCs expand remarkably, but their differentiation is inhibited when Dnmt3a is conditionally inactivated in the murine hematopoietic system; this phenomenon is consistent with a preleukemic state (7). Moreover, all lethally irradiated mice transplanted with Dnmt3a-deleted HSCs died within 1 y and were diagnosed with a spectrum of malignancies similar to those observed in patients carrying DNMT3A mutations, including MDS, AML, primary myelofibrosis, and ALL, suggesting that Dnmt3a functions as a tumor suppressor (7). With a second hit of mutations in various genes such as N-RAS, C-KIT, or FLT3 in Dnmt3a −/− mice, Dnmt3a deletion induces leukemic transformation (14, 15). Although these results indicate a major role of Dnmt3a deletion in facilitating the development of leukemia, the in vivo roles of DNMT3A mutants in leukemogenesis still need to be addressed. In our previous work, b...

Genetic heterogeneity of tumor is closely related to its clonal evolution, phenotypic diversity and treatment resistance, and such heterogeneity has only been characterized at single-cell sub-chromosomal scale in liver cancer. Here we reconstructed the single-variant resolution clonal evolution in human liver cancer based on single-cell mutational profiles. The results indicated that key genetic events occurred early during tumorigenesis, and an early metastasis followed by independent evolution was observed in primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. By parallel single-cell RNA-Seq, the transcriptomic phenotype of HCC was found to be related with genetic heterogeneity. For the first time we reconstructed the single-cell and single-variant clonal evolution in human liver cancer, and dissection of both genetic and phenotypic heterogeneity will facilitate better understanding of their relationship.

BackgroundWith the developments of DNA sequencing technology, large amounts of sequencing data have been produced that provides unprecedented opportunities for advanced association studies between somatic mutations and cancer types/subtypes which further contributes to more accurate somatic mutation based cancer typing (SMCT). In existing SMCT methods however, the absence of high-level feature extraction is a major obstacle in improving the classification performance.ResultsWe propose DeepCNA, an advanced convolutional neural network (CNN) based classifier, which utilizes copy number aberrations (CNAs) and HiC data, to address this issue. DeepCNA first pre-process the CNA data by clipping, zero padding and reshaping. Then, the processed data is fed into a CNN classifier, which extracts high-level features for accurate classification. Experimental results on the COSMIC CNA dataset indicate that 2D CNN with both cell lines of HiC data lead to the best performance. We further compare DeepCNA with three widely adopted classifiers, and demonstrate that DeepCNA has at least 78% improvement of performance.ConclusionsThis paper demonstrates the advantages and potential of the proposed DeepCNA model for processing of somatic point mutation based gene data, and proposes that its usage may be extended to other complex genotype-phenotype association studies.

Single-cell genomics has transformed our ability to examine cell fate choice. Examining cells along a computationally ordered “pseudotime” offers the potential to unpick subtle changes in variability and covariation among key genes. We describe a novel approach, scHOT – single cell Higher Order Testing - which provides a flexible and statistically robust framework for identifying changes in higher order interactions among genes. scHOT can be applied for cells along a continuous trajectory or across space and accommodates any higher order measurement including variability or correlation. We demonstrate the utility of scHOT by studying coordinated changes in higher order interactions during embryonic development of the mouse liver. Additionally, scHOT identifies subtle changes in gene-gene correlations across space using spatially-resolved transcriptomics data from the mouse olfactory bulb. scHOT meaningfully adds to first order differential expression testing and provides a framework for interrogating higher order interactions using single cell data.

BH3 peptide analogues are generally believed to exhibit great potency as cancer therapeutics via targeting antiapoptotic Bcl-2 proteins. Here, we describe the synthesis and identification of a new class of palmitoylated peptide BH3 analogues derived from the core region (h1–h4) of BH3 domains of proapoptotic Bcl-2 proteins and as alternative PTP1B inhibitors with antidiabetic potency in vitro and in vivo. PTP1B inhibitors are attractive for treatment of type 2 diabetes. We design the analogues using a simple lipidation approach and discovered novel lead analogues with promising antidiabetic potency in vitro and in vivo. The results presented here expanded the alternative target and function for the BH3 peptide analogues from one member Bim to other members of the proapoptotic Bcl-2 proteins and emphasize their therapeutic potential in T2DM. Furthermore, our findings may provide new proof of the regulatory function of Bcl-2 family proteins in mitochondrial nutrient and energy metabolism.

Somatic mutations of many cancer genes tend to co-occur (termed co-mutations) in certain patterns during tumor initiation and progression. However, the genetic and epigenetic mechanisms that contribute to the co-mutations of these cancer genes have yet to be explored. Here, we systematically investigated the association between the somatic co-mutations of cancer genes and high-order chromatin conformation. Significantly, somatic point co-mutations in protein-coding genes were closely associated with high-order spatial chromatin folding. We propose that these regions be termed Spatial Co-mutation Hotspots (SCHs) and report their occurrence in different cancer types. The conserved mutational signatures and DNA sequences flanking these point co-mutations, as well as CTCF-binding sites, are also enriched within the SCH regions. The genetic alterations that are harboured in the same SCHs tend to disrupt cancer driver genes involved in multiple signalling pathways. The present work demonstrates that high-order spatial chromatin organisation may contribute to the somatic co-mutations of certain cancer genes during tumor development.