Pratap Venepally

Nuclear RNA-seq of single neurons reveals molecular signatures of activation

Single-cell sequencing methods have emerged as powerful tools for identification of heterogeneous cell types within defined brain regions. Application of single-cell techniques to study the transcriptome of activated neurons can offer insight into molecular dynamics associated with differential neuronal responses to a given experience. Through evaluation of common whole-cell and single-nuclei RNA-sequencing (snRNA-seq) methods, here we show that snRNA-seq faithfully recapitulates transcriptional patterns associated with experience-driven induction of activity, including immediate early genes (IEGs) such as Fos, Arc and Egr1. SnRNA-seq of mouse dentate granule cells reveals large-scale changes in the activated neuronal transcriptome after brief novel environment exposure, including induction of MAPK pathway genes. In addition, we observe a continuum of activation states, revealing a pseudotemporal pattern of activation from gene expression alone. In summary, snRNA-seq of activated neurons enables the examination of gene expression beyond IEGs, allowing for novel insights into neuronal activation patterns in vivo.

RNA-sequencing from single nuclei.

Significance One of the central goals of developmental biology and medicine is to ascertain the relationships between the genotype and phenotype of cells. Single-cell transcriptome analysis represents a powerful strategy to reach this goal. We advance these strategies to single nuclei from neural progenitor cells and dentate gyrus tissue, from which it is very difficult to recover intact cells. This provides a unique means to carry out RNA sequencing from individual neurons that avoids requiring isolation of single-cell suspensions, eliminating potential changes in gene expression due to enzymatic-cell dissociation methods. This method will be useful for analysis of processes occurring in the nucleus and for gene-expression studies of highly interconnected cells such as neurons. It has recently been established that synthesis of double-stranded cDNA can be done from a single cell for use in DNA sequencing. Global gene expression can be quantified from the number of reads mapping to each gene, and mutations and mRNA splicing variants determined from the sequence reads. Here we demonstrate that this method of transcriptomic analysis can be done using the extremely low levels of mRNA in a single nucleus, isolated from a mouse neural progenitor cell line and from dissected hippocampal tissue. This method is characterized by excellent coverage and technical reproducibility. On average, more than 16,000 of the 24,057 mouse protein-coding genes were detected from single nuclei, and the amount of gene-expression variation was similar when measured between single nuclei and single cells. Several major advantages of the method exist: first, nuclei, compared with whole cells, have the advantage of being easily isolated from complex tissues and organs, such as those in the CNS. Second, the method can be widely applied to eukaryotic species, including those of different kingdoms. The method also provides insight into regulatory mechanisms specific to the nucleus. Finally, the method enables dissection of regulatory events at the single-cell level; pooling of 10 nuclei or 10 cells obscures some of the variability measured in transcript levels, implying that single nuclei and cells will be extremely useful in revealing the physiological state and interconnectedness of gene regulation in a manner that avoids the masking inherent to conventional transcriptomics using bulk cells or tissues.

Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons

A protocol is described for sequencing the transcriptome of a cell nucleus. Nuclei are isolated from specimens and sorted by FACS, cDNA libraries are constructed and RNA-seq is performed, followed by data analysis. Some steps follow published methods (Smart-seq2 for cDNA synthesis and Nextera XT barcoded library preparation) and are not described in detail here. Previous single-cell approaches for RNA-seq from tissues include cell dissociation using protease treatment at 30 °C, which is known to alter the transcriptome. We isolate nuclei at 4 °C from tissue homogenates, which cause minimal damage. Nuclear transcriptomes can be obtained from postmortem human brain tissue stored at -80 °C, making brain archives accessible for RNA-seq from individual neurons. The method also allows investigation of biological features unique to nuclei, such as enrichment of certain transcripts and precursors of some noncoding RNAs. By following this procedure, it takes about 4 d to construct cDNA libraries that are ready for sequencing.

Sequence analysis of a complete 1.66 Mb Prochlorococcus marinus MED4 genome cloned in yeast

Marine cyanobacteria of the genus Prochlorococcus represent numerically dominant photoautotrophs residing throughout the euphotic zones in the open oceans and are major contributors to the global carbon cycle. Prochlorococcus has remained a genetically intractable bacterium due to slow growth rates and low transformation efficiencies using standard techniques. Our recent successes in cloning and genetically engineering the AT-rich, 1.1 Mb Mycoplasma mycoides genome in yeast encouraged us to explore similar methods with Prochlorococcus. Prochlorococcus MED4 has an AT-rich genome, with a GC content of 30.8%, similar to that of Saccharomyces cerevisiae (38%), and contains abundant yeast replication origin consensus sites (ACS) evenly distributed around its 1.66 Mb genome. Unlike Mycoplasma cells, which use the UGA codon for tryptophane, Prochlorococcus uses the standard genetic code. Despite this, we observed no toxic effects of several partial and 15 whole Prochlorococcus MED4 genome clones in S. cerevisiae. Sequencing of a Prochlorococcus genome purified from yeast identified 14 single base pair missense mutations, one frameshift, one single base substitution to a stop codon and one dinucleotide transversion compared to the donor genomic DNA. We thus provide evidence of transformation, replication and maintenance of this 1.66 Mb intact bacterial genome in S. cerevisiae.

Genetic Analysis Using an Isogenic Mating Pair of Aspergillus fumigatus Identifies Azole Resistance Genes and Lack of MAT Locus’s Role in Virulence

Invasive aspergillosis (IA) due to Aspergillus fumigatus is a major cause of mortality in immunocompromised patients. The discovery of highly fertile strains of A. fumigatus opened the possibility to merge classical and contemporary genetics to address key questions about this pathogen. The merger involves sexual recombination, selection of desired traits, and genomics to identify any associated loci. We constructed a highly fertile isogenic pair of A. fumigatus strains with opposite mating types and used them to investigate whether mating type is associated with virulence and to find the genetic loci involved in azole resistance. The pair was made isogenic by 9 successive backcross cycles of the foundational strain AFB62 (MAT1-1) with a highly fertile (MAT1-2) progeny. Genome sequencing showed that the F9 MAT1-2 progeny was essentially identical to the AFB62. The survival curves of animals infected with either strain in three different animal models showed no significant difference, suggesting that virulence in A. fumigatus was not associated with mating type. We then employed a relatively inexpensive, yet highly powerful strategy to identify genomic loci associated with azole resistance. We used traditional in vitro drug selection accompanied by classical sexual crosses of azole-sensitive with resistant isogenic strains. The offspring were plated under varying drug concentrations and pools of resulting colonies were analyzed by whole genome sequencing. We found that variants in 5 genes contributed to azole resistance, including mutations in erg11A (cyp51A), as well as multi-drug transporters, erg25, and in HMG-CoA reductase. The results demonstrated that with minimal investment into the sequencing of three pools from a cross of interest, the variation(s) that contribute any phenotype can be identified with nucleotide resolution. This approach can be applied to multiple areas of interest in A. fumigatus or other heterothallic pathogens, especially for virulence associated traits.

A bioinformatic filter for improved base-call accuracy and polymorphism detection using the Affymetrix GeneChip® whole-genome resequencing platform

DNA resequencing arrays enable rapid acquisition of high-quality sequence data. This technology represents a promising platform for rapid high-resolution genotyping of microorganisms. Traditional array-based resequencing methods have relied on the use of specific PCR-amplified fragments from the query samples as hybridization targets. While this specificity in the target DNA population reduces the potential for artifacts caused by cross-hybridization, the subsampling of the query genome limits the sequence coverage that can be obtained and therefore reduces the techniques resolution as a genotyping method. We have developed and validated an Affymetrix Inc. GeneChip® array-based, whole-genome resequencing platform for Francisella tularensis, the causative agent of tularemia. A set of bioinformatic filters that targeted systematic base-calling errors caused by cross-hybridization between the whole-genome sample and the array probes and by deletions in the sample DNA relative to the chip reference sequence were developed. Our approach eliminated 91% of the false-positive single-nucleotide polymorphism calls identified in the SCHU S4 query sample, at the cost of 10.7% of the true positives, yielding a total base-calling accuracy of 99.992%.

Transcriptomic evidence for modulation of host inflammatory responses during febrile Plasmodium falciparum malaria.

Identifying molecular predictors and mechanisms of malaria disease is important for understanding how Plasmodium falciparum malaria is controlled. Transcriptomic studies in humans have so far been limited to retrospective analysis of blood samples from clinical cases. In this prospective, proof-of-principle study, we compared whole-blood RNA-seq profiles at pre-and post-infection time points from Malian adults who were either asymptomatic (n = 5) or febrile (n = 3) during their first seasonal PCR-positive P. falciparum infection with those from malaria-naïve Dutch adults after a single controlled human malaria infection (n = 5). Our data show a graded activation of pathways downstream of pro-inflammatory cytokines, with the highest activation in malaria-naïve Dutch individuals and significantly reduced activation in malaria-experienced Malians. Newly febrile and asymptomatic infections in Malians were statistically indistinguishable except for genes activated by pro-inflammatory cytokines. The combined data provide a molecular basis for the development of a pyrogenic threshold as individuals acquire immunity to clinical malaria.

NeatFreq: reference-free data reduction and coverage normalization for De Novo sequence assembly

BackgroundDeep shotgun sequencing on next generation sequencing (NGS) platforms has contributed significant amounts of data to enrich our understanding of genomes, transcriptomes, amplified single-cell genomes, and metagenomes. However, deep coverage variations in short-read data sets and high sequencing error rates of modern sequencers present new computational challenges in data interpretation, including mapping and de novo assembly. New lab techniques such as multiple displacement amplification (MDA) of single cells and sequence independent single primer amplification (SISPA) allow for sequencing of organisms that cannot be cultured, but generate highly variable coverage due to amplification biases.ResultsHere we introduce NeatFreq, a software tool that reduces a data set to more uniform coverage by clustering and selecting from reads binned by their median kmer frequency (RMKF) and uniqueness. Previous algorithms normalize read coverage based on RMKF, but do not include methods for the preferred selection of (1) extremely low coverage regions produced by extremely variable sequencing of random-primed products and (2) 2-sided paired-end sequences. The algorithm increases the incorporation of the most unique, lowest coverage, segments of a genome using an error-corrected data set. NeatFreq was applied to bacterial, viral plaque, and single-cell sequencing data. The algorithm showed an increase in the rate at which the most unique reads in a genome were included in the assembled consensus while also reducing the count of duplicative and erroneous contigs (strings of high confidence overlaps) in the deliverable consensus. The results obtained from conventional Overlap-Layout-Consensus (OLC) were compared to simulated multi-de Bruijn graph assembly alternatives trained for variable coverage input using sequence before and after normalization of coverage. Coverage reduction was shown to increase processing speed and reduce memory requirements when using conventional bacterial assembly algorithms.ConclusionsThe normalization of deep coverage spikes, which would otherwise inhibit consensus resolution, enables High Throughput Sequencing (HTS) assembly projects to consistently run to completion with existing assembly software. The NeatFreq software package is free, open source and available at https://github.com/bioh4x/NeatFreq.

Monitoring the Long-Term Molecular Epidemiology of the Pneumococcus and Detection of Potential ‘Vaccine Escape’ Strains

Background While the pneumococcal protein conjugate vaccines reduce the incidence in invasive pneumococcal disease (IPD), serotype replacement remains a major concern. Thus, serotype-independent protection with vaccines targeting virulence genes, such as PspA, have been pursued. PspA is comprised of diverse clades that arose through recombination. Therefore, multi-locus sequence typing (MLST)-defined clones could conceivably include strains from multiple PspA clades. As a result, a method is needed which can both monitor the long-term epidemiology of the pneumococcus among a large number of isolates, and analyze vaccine-candidate genes, such as pspA, for mutations and recombination events that could result in ‘vaccine escape’ strains. Methodology We developed a resequencing array consisting of five conserved and six variable genes to characterize 72 pneumococcal strains. The phylogenetic analysis of the 11 concatenated genes was performed with the MrBayes program, the single nucleotide polymorphism (SNP) analysis with the DNA Sequence Polymorphism program (DnaSP), and the recombination event analysis with the recombination detection package (RDP). Results The phylogenetic analysis correlated with MLST, and identified clonal strains with unique PspA clades. The DnaSP analysis correlated with the serotype-specific diversity detected using MLST. Serotypes associated with more than one ST complex had a larger degree of sequence polymorphism than a serotype associated with one ST complex. The RDP analysis confirmed the high frequency of recombination events in the pspA gene. Conclusions The phylogenetic tree correlated with MLST, and detected multiple PspA clades among clonal strains. The genetic diversity of the strains and the frequency of recombination events in the mosaic gene, pspA were accurately assessed using the DnaSP and RDP programs, respectively. These data provide proof-of-concept that resequencing arrays could play an important role within research and clinical laboratories in both monitoring the molecular epidemiology of the pneumococcus and detecting ‘vaccine escape’ strains among vaccine-candidate genes.

LOCUST: a custom sequence locus typer for classifying microbial isolates

Summary LOCUST is a custom sequence locus typer tool for classifying microbial genomes. It provides a fully automated opportunity to customize the classification of genome-wide nucleotide variant data most relevant to biological research. Availability and Implementation Source code, demo data, and detailed documentation are freely available at http://sourceforge.net/projects/locustyper . Contact lbrinkac@jcvi.org. Supplementary information Supplementary data are available at Bioinformatics online.