Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison

Background: Single-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used. Results: We show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required. Conclusion: Our comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.</p

Genome-wide DNA methylation profiling of non-small cell lung carcinomas

Background: Non-small cell lung carcinoma (NSCLC) is a complex malignancy that owing to its heterogeneity and poor prognosis poses many challenges to diagnosis, prognosis and patient treatment. DNA methylation is an important mechanism of epigenetic regulation involved in normal development and cancer. It is a very stable and specific modification and therefore in principle a very suitable marker for epigenetic phenotyping of tumors. Here we present a genome-wide DNA methylation analysis of NSCLC samples and paired lung tissues, where we combine MethylCap and next generation sequencing (MethylCap-seq) to provide comprehensive DNA methylation maps of the tumor and paired lung samples. The MethylCap-seq data were validated by bisulfite sequencing and methyl-specific polymerase chain reaction of selected regions. Results: Analysis of the MethylCap-seq data revealed a strong positive correlation between replicate experiments and between paired tumor/lung samples. We identified 57 differentially methylated regions (DMRs) present in all NSCLC tumors analyzed by MethylCap-seq. While hypomethylated DMRs did not correlate to any particular functional category of genes, the hypermethylated DMRs were strongly associated with genes encoding transcriptional regulators. Furthermore, subtelomeric regions and satelli Conclusions: Collectively, we provide a resource containing genome-wide DNA methylation maps of NSCLC and their paired lung tissues, and comprehensive lists of known and novel DMRs and associated genes in NSCLC

Targeted Genomic Sequencing of TSC1 and TSC2 Reveals Causal Variants in Individuals for Whom Previous Genetic Testing for Tuberous Sclerosis Complex Was Normal

Tuberous sclerosis complex (TSC) is caused by inactivating variants in TSC1 and TSC2. Somatic mosaicism, as well as the size and complexity of the TSC1 and TSC2 loci, makes variant identification challenging. Indeed, in some individuals with a clinical diagnosis of TSC, diagnostic testing fails to identify an inactivating variant. To improve TSC1 and TSC2 variant detection, we screened the TSC1 and TSC2 genomic regions using targeted HaloPlex custom capture and next-generation sequencing (NGS) in genomic DNA isolated from peripheral blood of individuals with definite, possible or suspected TSC in whom no disease-associated variant had been identified by previous diagnostic genetic testing. We obtained &gt;95% target region coverage at a read depth of 20 and &gt;50% coverage at a read depth of 300 and identified inactivating TSC1 or TSC2 variants in 83/155 individuals (54%); 65/113 (58%) with clinically definite TSC and 18/42 (43%) with possible or suspected TSC. These included 19 individuals with deep intronic variants and 54 likely cases of mosaicism (variant allele frequency 1-28%; median 7%). In 13 cases (8%), we identified a variant of uncertain significance (VUS). Targeted genomic NGS of TSC1 and TSC2 increases the yield of inactivating variants found in individuals with suspected TSC.</p

Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison

Background: Single-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used. Results: We show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required. Conclusion: Our comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.</p

Global quantitative proteomics reveals novel factors in the ecdysone signaling pathway in Drosophila melanogaster

The ecdysone signaling pathway plays a major role in various developmental transitions in insects. Recent advances in the understanding of ecdysone action have relied to a large extent on the application of molecular genetic tools in Drosophila. Here, we used a comprehensive quantitative SILAC MS-based approach to study the global, dynamic proteome of a Drosophila cell line to investigate how hormonal signals are transduced into specific cellular responses. Global proteome data after ecdysone treatment after various time points were then integrated with transcriptome data. We observed a substantial overlap in terms of affected targets between the dynamic proteome and transcriptome, although there were some clear differences in timing effects. Also, downregulation of several specific mRNAs did not always correlate with downregulation of their corresponding protein counterparts, and in some cases there was no correlation between transcriptome and proteome dynamics whatsoever. In addition, we performed a comprehensive interactome analysis of EcR, the major target of ecdysone. Proteins copurified with EcR include factors involved in transcription, chromatin remodeling, ecdysone signaling, ecdysone biosynthesis, and other signaling pathways. Novel ecdysone-responsive proteins identified in this study might link previously unknown proteins to the ecdysone signaling pathway and might be novel targets for developmental studies. To our knowledge, this is the first time that ecdysone signaling is studied by global quantitative proteomics. All MS data have been deposited in the ProteomeXchange with identifier PXD001455 (http://proteomecentral.proteomexchange.org/dataset/PXD001455)

Genome-wide DNA methylation profiling of non-small cell lung carcinomas

Background: Non-small cell lung carcinoma (NSCLC) is a complex malignancy that owing to its heterogeneity and poor prognosis poses many challenges to diagnosis, prognosis and patient treatment. DNA methylation is an important mechanism of epigenetic regulation involved in normal development and cancer. It is a very stable and specific modification and therefore in principle a very suitable marker for epigenetic phenotyping of tumors. Here we present a genome-wide DNA methylation analysis of NSCLC samples and paired lung tissues, where we combine MethylCap and next generation sequencing (MethylCap-seq) to provide comprehensive DNA methylation maps of the tumor and paired lung samples. The MethylCap-seq data were validated by bisulfite sequencing and methyl-specific polymerase chain reaction of selected regions. Results: Analysis of the MethylCap-seq data revealed a strong positive correlation between replicate experiments and between paired tumor/lung samples. We identified 57 differentially methylated regions (DMRs) present in all NSCLC tumors analyzed by MethylCap-seq. While hypomethylated DMRs did not correlate to any particular functional category of genes, the hypermethylated DMRs were strongly associated with genes encoding transcriptional regulators. Furthermore, subtelomeric regions and satellite repeats were hypomethylated in the NSCLC samples. We also identified DMRs that were specific to two of the major subtypes of NSCLC, adenocarcinomas and squamous cell carcinomas. Conclusions: Collectively, we provide a resource containing genome-wide DNA methylation maps of NSCLC and their paired lung tissues, and comprehensive lists of known and novel DMRs and associated genes in NSCLC

Sensitive detection of mitochondrial DNA variants for analysis of mitochondrial DNA-enriched extracts from frozen tumor tissue

Abstract Large variation exists in mitochondrial DNA (mtDNA) not only between but also within individuals. Also in human cancer, tumor-specific mtDNA variation exists. In this work, we describe the comparison of four methods to extract mtDNA as pure as possible from frozen tumor tissue. Also, three state-of-the-art methods for sensitive detection of mtDNA variants were evaluated. The main aim was to develop a procedure to detect low-frequent single-nucleotide mtDNA-specific variants in frozen tumor tissue. We show that of the methods evaluated, DNA extracted from cytosol fractions following exonuclease treatment results in highest mtDNA yield and purity from frozen tumor tissue (270-fold mtDNA enrichment). Next, we demonstrate the sensitivity of detection of low-frequent single-nucleotide mtDNA variants (≤1% allele frequency) in breast cancer cell lines MDA-MB-231 and MCF-7 by single-molecule real-time (SMRT) sequencing, UltraSEEK chemistry based mass spectrometry, and digital PCR. We also show de novo detection and allelic phasing of variants by SMRT sequencing. We conclude that our sensitive procedure to detect low-frequent single-nucleotide mtDNA variants from frozen tumor tissue is based on extraction of DNA from cytosol fractions followed by exonuclease treatment to obtain high mtDNA purity, and subsequent SMRT sequencing for (de novo) detection and allelic phasing of variants

Allogeneic Mature Human Dendritic Cells Generate Superior Alloreactive Regulatory T Cells in the Presence of IL-15

Expansion of Ag-specific naturally occurring regulatory T cells (nTregs) is required to obtain sufficient numbers of cells for cellular immunotherapy. In this study, different allogeneic stimuli were studied for their capacity to generate functional alloantigen-specific nTregs. A highly enriched nTreg fraction (CD4(+)CD25(bright)CD127(-) T cells) was alloantigen-specific expanded using HLA-mismatched immature, mature monocyte-derived dendritic cells (moDCs), or PBMCs. The allogeneic mature moDC-expanded nTregs were fully characterized by analysis of the demethylation status within the Treg-specific demethylation region of the FOXP3 gene and the expression of both protein and mRNA of FOXP3, HELIOS, CTLA4, and cytokines. In addition, the Ag-specific suppressive capacity of these expanded nTregs was tested. Allogeneic mature moDCs and skin-derived DCs were superior in inducing nTreg expansion compared with immature moDCs or PBMCs in an HLA-DR- and CD80/CD86-dependent way. Remarkably, the presence of exogenous IL-15 without IL-2 could facilitate optimal mature moDC-induced nTreg expansion. Allogeneic mature moDC-expanded nTregs were at low ratios (<1:320), potent suppressors of alloantigen-induced proliferation without significant suppression of completely HLA-mismatched, Ag-induced proliferation. Mature moDC-expanded nTregs were highly demethylated at the Treg-specific demethylation region within the FOXP3 gene and highly expressed of FOXP3, HELIOS, and CTLA4. A minority of the expanded nTregs produced IL-10, IL-2, IFN-gamma, and TNF-alpha, but few IL-17-producing nTregs were found. Next-generation sequencing of mRNA of moDC-expanded nTregs revealed a strong induction of Treg-associated mRNAs. Human allogeneic mature moDCs are highly efficient stimulator cells, in the presence of exogenous IL-15, for expansion of stable alloantigen-specific nTregs with superior suppressive function