Abstract Telomeres are the protective nucleoprotein structures at the end of linear eukaryotic chromosomes. Telomeres’ repetitive nature and length have traditionally challenged the precise assessment of the composition and length of individual human telomeres. Here, we present Telo-seq to resolve bulk, chromosome arm-specific and allele-specific human telomere lengths using Oxford Nanopore Technologies’ native long-read sequencing. Telo-seq resolves telomere shortening in five population doubling increments and reveals intrasample, chromosome arm-specific, allele-specific telomere length heterogeneity. Telo-seq can reliably discriminate between telomerase- and ALT-positive cancer cell lines. Thus, Telo-seq is a tool to study telomere biology during development, aging, and cancer at unprecedented resolution.
Conventional analysis of enzymatic activity, often carried out on pools of cells, is blind to heterogeneity in the population. Here, we combine microfluidics with a previously developed isothermal rolling circle amplification-based assay to investigate multiple enzymatic activities in down to single cells. This microfluidics-meditated assay performs at very high sensitivity in picoliter incubators with small quantities of biological materials. Furthermore, we demonstrate the assay's capability of multiplexed detection of at least three enzyme activities at the single molecule level.
Abstract Complex chromosomal rearrangements, including translocations, play a critical role in oncogenesis and are often identified as recurrent genetic aberrations in hematologic malignancies and solid tumors[1]. Translocation detection by karyotyping of an individual’s metaphase chromosomes remains challenging for detection of balanced rearrangements, which have no gain or loss of genetic material. Rearrangement detection using whole-genome sequencing is a viable alternative, but it relies on breakpoint-spanning reads and requires high sequencing depth (30-60x) with long reads to achieve high sensitivity, especially in repetitive regions of the genome[2]. Here, we describe a workflow for translocation detection at low sequencing depth with Oxford Nanopore’s long read chromatin conformation capture technique, Pore-C[3]. Importantly, for translocation calling, Pore-C does not rely on breakpoint-spanning reads but rather the high intrachromosomal interaction frequency of genomic regions around the breakpoint, thus largely lowering sequencing depth requirements and reducing mapping issues in low complexity regions. We prepared, barcoded, and sequenced Pore-C libraries of 3 cancer cell lines including lung cancer (A549), Acute Monocytic Leukemia (THP-1), melanoma (COLO 829) and its matching normal (COLO 829 BL) on a single Q20+ MinION flow cell (FLO-MIN114). Genome-wide contact maps of low-pass data (<1.5 Gbps per sample) were compared against higher depth (>30 Gbps per sample) Hi-C maps [4], revealing that low-pass Pore-C successfully captured large-scale genomic rearrangement. For example, chromosomal pairs chr8-chr11 and chr15-chr19 in A549, chr9-chr11 and chr1-chr20 in THP1, and chr7-chr15 in Colo829 were detected at <0.5X depth of sequencing coverage. These translocation events were further validated by breakpoint analysis using adaptive sampling, where targeted regions were previously confirmed by independent studies[4],[5]. Low-pass Pore-C detects translocations in an unbiased manner and does not require prior knowledge of the translocation structure. Combined with its simple sample-preparation workflow and the capability to provide genome-wide copy-number information in a single experiment, it can serve as a cost-effective and comprehensive tool for cancer genomic studies. [1] Chromosomal translocations in human cancer, Nature, 372, 143 (1994) [2] Hi-C as a tool for precise detection and characterisation of chromosomal rearrangements and copy number variation in human tumors, Genome Biology, 18, 125 (2017) [3] Identifying synergistic high-order 3D chromatin conformations from genome-scale nanopore concatemer sequencing, Nature Biotechnology, 40, 1488(2022) [4] Chromosomal translocations detection in cancer cells using chromosomal conformation capture data, Genes (Basel), 13, 7 (2022) [5] A multi-platform reference for somatic structural variation detection, Cell Genomics, 2, 6 (2022) Citation Format: Scott Hickey, Xiaoguang Dai, Sergey Aganezov, John Beaulaurier, Eoghan Harrington, Sissel Juul. Translocation detection in cancer using low-pass pore-c sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 405.
We present a Rolling-Circle-Enhance-Enzyme-Activity-Detection (REEAD) system with potential use for future point-of-care diagnosis of malaria. In the developed setup, specific detection of malaria parasites in crude blood samples is facilitated by the conversion of single Plasmodium falciparum topoisomerase I (pfTopI) mediated cleavage-ligation events, happening within nanometer dimensions, to micrometer-sized products readily detectable at the single molecule level in a fluorescence microscope. In principle, REEAD requires no special equipment and the readout is adaptable to simple colorimetric detection systems. Moreover, with regard to detection limit the presented setup is likely to outcompete standard gold immuno-based diagnostics. Hence, we believe the presented assay forms the basis for a new generation of easy-to-use diagnostic tools suitable for the malaria epidemic areas in developing countries.
Astronaut Kate Rubins sequenced DNA on the International Space Station (ISS) for the first time in August 2016 (Figure 1A). A 2D sequencing library containing an equal mixture of lambda bacteriophage, Escherichia coli, and Mus musculus was prepared on the ground with a SQK_MAP006 kit and sent to the ISS frozen and loaded into R7.3 flow cells. After a total of 9 on-orbit sequencing runs over 6 months, it was determined that there was no decrease in sequencing performance on-orbit compared to ground controls (1). A total of ~280,000 and ~130,000 reads generated on-orbit and on the ground, respectively, identified 90% of reads that were attributed to 30% lambda bacteriophage, 30% Escherichia coli, and 30% M. musculus (Figure 1B). Extensive bioinformatics analysis determined comparable 2D and 1D read accuracies between flight and ground runs (Figure 1C), and data collected from the ISS were able to construct directed assemblies of E.coli and lambda genomes at 100% and M. musculus mitochondrial genome at 96.7%. These findings validate sequencing as a viable option for potential on-orbit applications such as environmental microbial monitoring and disease diagnosis. Current microbial monitoring of the ISS applies culture-based techniques that provide colony forming unit (CFU) data for air, water, and surface samples. The identity of the cultured microorganisms in unknown until sample return and ground-based analysis, a process that can take up to 60 days. For sequencing to benefit ISS applications, spaceflight-compatible sample preparation techniques are required. Subsequent to the testing of the MinION on-orbit, a sample-to-sequence method was developed using miniPCR™ and basic pipetting, which was only recently proven to be effective in microgravity. The work presented here details the in- flight sample preparation process and the first application of DNA sequencing on the ISS to identify unknown ISS-derived microorganisms.
Description of uploaded files: Basmati334.basmati.not_scaffolded.fa - Polished genome assembly for Basmati 334 but not scaffolded. Basmati334.basmati.not_scaffolded.sorted.gff - Gene annotation for the assembly Basmati334.basmati.not_scaffolded.fa Basmati334.basmati.ragoo_scaffold.fa - Polished genome assembly for Basmati 334 and scaffolding with RaGOO using the Nipponbare RAPDB1.0 as reference genome. Basmati334.basmati.ragoo_scaffold.sorted.gff - Gene annotation for the assembly Basmati334.basmati.ragoo_scaffold.fa Basmati334.basmati.ragoo_scaffold.repeatmasker.bed - Repetitive DNA coordinates for the assembly Basmati334.basmati.ragoo_scaffold.fa CONSEL.tar.gz - Files used for CONSEL analysis - Folder CONSEL/PHYLOGENY_TEST/ contains the input files for CONSEL - Folder CONSEL/CONSEL_RESULT/ contains the CONSEL test results DADI_ANALYSIS.tar.gz - Input file for dadi analysis and scripts used for dadi modeling DomSufid.sadri.not_scaffolded.fa - Polished genome assembly for Dom Sufid but not scaffolded. DomSufid.sadri.not_scaffolded.sorted.gff - Gene annotation for the assembly DomSufid.sadri.not_scaffolded.fa DomSufid.sadri.ragoo_scaffold.fa - Polished genome assembly for Dom Sufid and scaffolding with RaGOO using the Nipponbare RAPDB1.0 as reference genome. DomSufid.sadri.ragoo_scaffold.sorted.gff - Gene annotation for the assembly DomSufid.sadri.ragoo_scaffold.fa DomSufid.sadri.ragoo_scaffold.repeatmasker.bed - Repetitive DNA coordinates for the assembly DomSufid.sadri.ragoo_scaffold.fa Four_rice_population.vcf.gz - Filtered SNP VCF file used in the basmati population relationship with japonica and aus. MULTIZ_ALIGNMENT.tar.gz - Reference genome alignment using Nipponbare RAPDB1.0 as reference and aligning various Oryza de novo genome assemblies Multi_Oryza_gene_FASTAs.tar.gz - Using the alignments from MULTIZ_ALIGNMENT/ pulled out coding DNA sequences of each Nipponbare RAPDB1.0 gene Obarthii_outgroup_AlignedToBasmatiScaffolded_genome.fa - O. barthii reference genome sequence was aligned to scaffolded Basmati 334 reference genome. For every Basmati 334 genome coordinate was converted into a O. barthii sequence resulting in a basmati-ized O. barthii genome sequence. Not alignable regions were indicated as 'N'. Only_basmati_rice_population.vcf.gz - Filtered SNP VCF file used in the basmati population analysis. Oryza_LTR_DivergenceTime.txt - LTR retrotransposon annotated in various Oryza reference genomes and their estimated insertion time (based on the divergence between the LTRs). TWISST.tar.gz - TWISST input and results file. - Four_rice_population.geno.gz, genotype file generated from the genomic_general from S. Martin and used as input for TWISST analysis. - *.trees.gz phylogenetic trees generated from sliding windows - *.data.tsv sliding window coordinates - *.weights.csv.gz topology weights
Additional file 2: Table S1. Inversion detect by sniffles in the Nipponbare reference genome. Table S2. The 78 circum-basmati samples with Illumina sequencing result used in this study. Table S3. Names of the Basmati 334 and Dom Sufid genome gene models that had a deletion frequency of zero across the population. Table S4. Names of the Basmati 334 and Dom Sufid genome gene models that had a deletion frequency of above 0.3 and omitted from down stream analysis. Table S5. Orthogroup status for the Basmati 334, Dom Sufid, R498, Nipponbare, and N22 genome gene models. Table S6. Count and repeat types of the presence-absence variation (PAV) in the Basmati 334 or Dom Sufid genome in comparison to the Nipponbare genome. Table S7. Gene ontology results for orthogroups where gene members from the circum-basmati are missing. Table S8. Gene ontology results for orthogroups where gene members from circum-aus, indica, and japonica are missing. Table S9. Population frequency across the 78 circum-basmati samples for orthogroups that were specifically missing a gene in the Basmati 334 and Dom Sufid genome gene models. Table S10. Genome coordinates of the LTR retrotransposons of the Basmati 334 genomes. Table S11. Genome coordinates of the LTR retrotransposons of the Dom Sufid genomes. Table S12. Genome coordinates of the Gypsy elements indicated with a single star in Fig. 3. Table S13. Genome coordinates of the Copia elements indicated with a single star in Fig. 3. Table S14. Genome coordinates of the Gypsy elements indicated with a double star in Fig. 3. Table S15. Genome coordinates of the Copia elements indicated with a triple star in Fig. 3. Table S16. The 82 Oryza population samples with Illumina sequencing result used in this study. Table S17. a i parameter estimates for the 13 different demographic models. See Additional file 1: Figure S9 for visualization of the estimating parameters.
As a newly developed assay for the detection of endogenous enzyme activity at the single-catalytic-event level, Rolling Circle Enhanced Enzyme Activity Detection (REEAD) has been used to measure enzyme activity in both single human cells and malaria-causing parasites, Plasmodium sp. Current REEAD assays rely on organic dye-tagged linear DNA probes to report the rolling circle amplification products (RCPs), the cost of which may hinder the widespread use of REEAD. Here we show that a new class of activatable probes, NanoCluster Beacons (NCBs), can simplify the REEAD assays. Easily prepared without any need for purification and capable of large fluorescence enhancement upon hybridization, NCBs are cost-effective and sensitive. Compared to conventional fluorescent probes, NCBs are also more photostable. As demonstrated in reporting the human topoisomerases I (hTopI) cleavage-ligation reaction, the proposed NCBs suggest a read-out format attractive for future REEAD-based diagnostics.
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