Abstract ChIP-seq is a powerful method for obtaining genome-wide maps of protein-DNA interactions and epigenetic modifications. CHANCE (CHip-seq ANalytics and Confidence Estimation) is a standalone package for ChIP-seq quality control and protocol optimization. Our user-friendly graphical software quickly estimates the strength and quality of immunoprecipitations, identifies biases, compares the user's data with ENCODE's large collection of published datasets, performs multi-sample normalization, checks against quantitative PCR-validated control regions, and produces informative graphical reports. CHANCE is available at https://github.com/songlab/chance.

Single-cell transcriptomic data have rapidly become very popular in genomic science. Genomic science also has a long history of using network models to understand the way in which genes work together to carry out specific biological functions. However, working with single-cell data presents major challenges, such as zero inflation and technical noise. These challenges require methods to be specifically adapted to the context of single-cell data. Recently, much effort has been made to develop the theory behind statistical network models. This has lead to many new models being proposed, and has provided a thorough understanding of the properties of existing models. However, a large amount of this work assumes binary-valued relationships between network nodes, whereas genomic network analysis is traditionally based on continuous-valued correlations between genes. In this paper, we assess several established methods for genomic network analysis, we compare ways that these methods can be adapted to the single-cell context, and we use mixture-models to infer binary-valued relationships based on gene-gene correlations. Based on these binary relationships, we find that excellent results can be achieved by using subnetwork analysis methodology popular amongst network statisticians. This methodology thereby allows detection of functional subnetwork modules within these single-cell genomic networks.

Abstract: Comparative single-cell studies of group 3/4 medulloblastomas (MBs) and fetal brain have identified a common hierarchy of glutamatergic-lineage cell types and an apparent cell-of-origin in the rhombic lip. An increased understanding of the genetic regulators of stem-cell maintenance and differentiation in MB would clearly be of therapeutic benefit. There is a significant need for lineage-tracing systems to model MB malignant transformation, progression, and response to therapy. We developed in vivo and in organoid genetic lineage-tracing systems with a single-cell readout. A high-complexity lentiviral library expressing heritable polyadenylated molecular barcodes was transduced into D283 and D425 patient-derived cell lines. Barcoded cells were injected into the brains of immunocompromised mice. Both preimplantation barcoded cultures and the resulting mature tumors were profiled by single-cell RNA-sequencing (scRNA-seq). This captured both endogenous RNA and barcode transcripts. While only minimal barcode clash was detected in the preimplantation cultures, we observed a clonal expansion of barcodes in mature tumors which aligned with phylogenetics analysis of expressed mutations in endogenous RNA. Methods for assessing lineage coupling between transcriptional clusters and inferring their lineage relationship were developed. Ongoing efforts to recover barcodes from tumor sections via spatial transcriptomics will be presented. These studies fill a gap in status quo models of MB which are needed to leverage recent findings derived from single-cell analysis of human tumors.

© The Author(s) 2014.Genetic screens of an unprecedented scale have recently been made possible by the availability of high-complexity libraries of synthetic oligonucleotides designed to mediate either gene knockdown or gene knockout, coupled with next-gen

Summary

Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms.

Availability and implementation

https://github.com/diazlab/ELSA.

Contact

aaron.diaz@ucsf.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Unlabelled

Analysis of the composition of heterogeneous tissue has been greatly enabled by recent developments in single-cell transcriptomics. We present SCell, an integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface. Scripts and protocols for the high-throughput pre-processing of large ensembles of single-cell, RNA-seq datasets are provided as an additional resource.

Availability and implementation

Binary executables for Windows, MacOS and Linux are available at http://sourceforge.net/projects/scell, source code and pre-processing scripts are available from https://github.com/diazlab/SCellSupplementary information: Supplementary data are available at Bioinformatics online.

Contact

aaron.diaz@ucsf.edu.

Motivation

Single-cell RNA-sequencing (scRNA-seq) has enabled studies of tissue composition at unprecedented resolution. However, the application of scRNA-seq to clinical cancer samples has been limited, partly due to a lack of scRNA-seq algorithms that integrate genomic mutation data.

Results

To address this, we present.

Conics

COpy-Number analysis In single-Cell RNA-Sequencing. CONICS is a software tool for mapping gene expression from scRNA-seq to tumor clones and phylogenies, with routines enabling: the quantitation of copy-number alterations in scRNA-seq, robust separation of neoplastic cells from tumor-infiltrating stroma, inter-clone differential-expression analysis and intra-clone co-expression analysis.

Availability and implementation

CONICS is written in Python and R, and is available from https://github.com/diazlab/CONICS.

Supplementary information

Supplementary data are available at Bioinformatics online.

Background

Alternative splicing is a rich source of tumor-specific neoantigen targets for immunotherapy. This holds promise for glioblastomas (GBMs), the most common primary tumors of the adult brain, which are resistant to standard-of-care therapy. Although most clinical trials enroll patients at recurrence, most preclinical studies have been done with specimens from primary disease. There are limited expression data from GBMs at recurrence and surprisingly little is known about the evolution of splicing patterns under therapy.

Result

We profile 37 primary-recurrent paired human GBM specimens via RNA sequencing. We describe the landscape of alternative splicing in GBM at recurrence and contrast that to primary and non-malignant brain-tissue specimens. By screening single-cell atlases, we identify cell-type-specific splicing patterns and novel splicing events in cell-surface proteins that are suitable targets for engineered T cell therapies. We identify recurrent-specific isoforms of mitogen-activated kinase pathway genes that enhance invasiveness and are preferentially expressed by stem-like cells.

Conclusion

These studies shed light on gene expression in recurrent GBM and identify novel targets for therapeutic development.

Long noncoding RNAs (lncRNAs) have been described in cell lines and various whole tissues, but lncRNA analysis of development in vivo is limited. Here, we comprehensively analyze lncRNA expression for the adult mouse subventricular zone neural stem cell lineage. We utilize complementary genome-wide techniques including RNA-seq, RNA CaptureSeq, and ChIP-seq to associate specific lncRNAs with neural cell types, developmental processes, and human disease states. By integrating data from chromatin state maps, custom microarrays, and FACS purification of the subventricular zone lineage, we stringently identify lncRNAs with potential roles in adult neurogenesis. shRNA-mediated knockdown of two such lncRNAs, Six3os and Dlx1as, indicate roles for lncRNAs in the glial-neuronal lineage specification of multipotent adult stem cells. Our data and workflow thus provide a uniquely coherent in vivo lncRNA analysis and form the foundation of a user-friendly online resource for the study of lncRNAs in development and disease.