Classifications

• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6844—Nucleic acid amplification reactions
  • C12Q1/6858—Allele-specific amplification
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6804—Nucleic acid analysis using immunogens
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
  • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
  • C12N15/09—Recombinant DNA-technology
  • C12N15/10—Processes for the isolation, preparation or purification of DNA or RNA
  • C12N15/1003—Extracting or separating nucleic acids from biological samples, e.g. pure separation or isolation methods; Conditions, buffers or apparatuses therefor
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
  • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
  • C12N15/09—Recombinant DNA-technology
  • C12N15/10—Processes for the isolation, preparation or purification of DNA or RNA
  • C12N15/1034—Isolating an individual clone by screening libraries
  • C12N15/1082—Preparation or screening gene libraries by chromosomal integration of polynucleotide sequences, HR-, site-specific-recombination, transposons, viral vectors
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6806—Preparing nucleic acids for analysis, e.g. for polymerase chain reaction [PCR] assay
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6869—Methods for sequencing
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6869—Methods for sequencing
  • C12Q1/6874—Methods for sequencing involving nucleic acid arrays, e.g. sequencing by hybridisation
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q2522/00—Reaction characterised by the use of non-enzymatic proteins
  • C12Q2522/10—Nucleic acid binding proteins
  • C12Q2522/101—Single or double stranded nucleic acid binding proteins
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q2535/00—Reactions characterised by the assay type for determining the identity of a nucleotide base or a sequence of oligonucleotides
  • C12Q2535/122—Massive parallel sequencing
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q2537/00—Reactions characterised by the reaction format or use of a specific feature
  • C12Q2537/10—Reactions characterised by the reaction format or use of a specific feature the purpose or use of
  • C12Q2537/164—Methylation detection other then bisulfite or methylation sensitive restriction endonucleases
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q2545/00—Reactions characterised by their quantitative nature
  • C12Q2545/10—Reactions characterised by their quantitative nature the purpose being quantitative analysis
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q2545/00—Reactions characterised by their quantitative nature
  • C12Q2545/10—Reactions characterised by their quantitative nature the purpose being quantitative analysis
  • C12Q2545/101—Reactions characterised by their quantitative nature the purpose being quantitative analysis with an internal standard/control
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/154—Methylation markers
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/166—Oligonucleotides used as internal standards, controls or normalisation probes

Definitions

• the invention relates to the field of methylated DNA immunoprecipitation-sequencing and, more specifically, to methods of absolute quantification of cell-free methylated DNA.
• Methylated DNA immunoprecipitation-sequencing is becoming popular to measure DNA methylation. While cell-free methylated DNA immunoprecipitation- sequencing (cfMeDIP-seq) is robust for measuring DNA methylation at hypermethylated regions, there can be biological and technical variation that may influence results. Additionally, MeDIP-seq experiments traditionally quantifies read counts relative to experiment. This can contribute to lack of reproducibility and makes it difficult to compare results between different studies.
• a method of capturing and analyzing cell-free methylated DNA in a sample comprising the steps of: a) subjecting the sample to library preparation to permit subsequent sequencing of the cell-free methylated DNA; b) adding a predetermined amount of a set of control synthetic DNA fragments, wherein the control synthetic DNA fragments each have a known nucleic acid sequence that does not substantially align to a target genome sequence, and wherein at least some of the control synthetic DNA fragments in the set are methylated; c) denaturing the sample; d) capturing cell-free methylated DNA and the control synthetic DNA fragments using a binder selective for methylated polynucleotides; and e) amplifying and sequencing the captured cell-free methylated DNA and the control synthetic DNA fragments.
• a method of identifying a sequence for a control synthetic DNA fragment for use in capturing and analysis of cell-free methylated DNA, the method comprising the steps of: a) generating nucleic acid sequences based on a plurality of target fragment lengths, a target combined guanine and cytosine (G+C) content, and a target number of CpG dinucleotides for each fragment; and b) eliminating generated sequences that align to a human genome; wherein the plurality of target fragment lengths comprises 3 to 7 target fragment lengths that are between 50 to 500 base pairs (bp); wherein the target G+C content is between 0% to 100%; and wherein the target number of CpG dinucleotides for each fragment is between 0 and 1 ⁇ 2 of the length of the fragment in base pairs.
• Figure 1 shows an experimental design for pilot testing of a set of synthetic spike-in control fragments.
• Figure 2 shows an experimental design for determining an amount of spike-in synthetic controls by spiking into HCT116 cell line.
• Figure 3 shows data transformation of fragment length.
• A. Fragment length before transformation.
• B. Fragment length after z-score normalization.
• Figure 4 shows data transformation of number of CpGs within a fragment.
• A. CpG distribution before cube root transformation.
• B. CpG distribution after cube root transformation.
• Figure 5 shows DNA methylation specificity of the cfMeDIP-seq method.
• Figure 6 shows results from sequencing synthetic DNA fragments only.
• Graphs show distributions of read counts with fragment length, G+C content, and number of CpGs within fragment.
• A. Fragment length distribution of unique methylated input samples.
• B. Fragment length distribution of unique methylated output samples.
• C. G+C content distribution of unique methylated input samples.
• D. G+C content distribution of unique methylated output samples.
• E. Number of CpGs in fragment distribution of unique methylated input samples, faceted by G+C content.
• F. Number of CpGs in fragment distribution of unique methylated output samples, faceted by G+C content.
• Figure 7 shows results from sequencing synthetic DNA fragments only.
• Graphs show distributions of read counts with fragment length, G+C content, and number of CpGs within fragment.
• A. Fragment length distribution of unique unmethylated input samples.
• B. Fragment length distribution of unique unmethylated output samples.
• C. G+C content distribution of unique unmethylated input samples.
• D. G+C content distribution of unique unmethylated output samples.
• E. Number of CpGs in fragment distribution of unique unmethylated input samples, faceted by G+C content.
• F. Number of CpGs in fragment distribution of unique unmethylated output samples, faceted by G+C content.
• Figure 8 shows a comparison of number of reads used for spike-in controls (black bars) compared to number of reads used for biological samples, HCT116 (white bars).
• Figure 9 shows DNA methylation specificity of cfMeDIP-seq with spike-in to HCT116 on the MiSeq, 1 million reads.
• Figure 10 shows DNA methylation specificity of cfMeDIP-seq with spike-in to HCT116 on the NovaSeq, 60 million reads per sample.
• Figure 11 shows a comparison of total reads used for synthetic spike in controls (black bars) compared to biological sample, HCT 116 (white bars).
• Figure 12 shows Bland-Altman plot depicting performance of a Gaussian generalized mixed model compared to known molality.
• X-axis mean values between the calculated and known molality values.
• Y-axis variance between the calculated and known concentration values.
• Bold dotted lines 95% confidence intervals.
• Light dotted lines 95% confidence interval margins.
• Figure 13 shows experimental design using synthetic spike-in control DNA to (A) assess technical bias and (B) optimize the synthetic DNA amount.
• Figure 14 shows assessing biases in fragment length, G+C content, and CpG fraction in input, output and 0.01 ng spike-in of synthetic DNA.
• Figure 15 shows Correlation between (A) picomoles and standard deviation and (B) picomoles and mappability score.
• Figure 16 shows correlation between calculated picomoles and M-values and between read counts and M-values.
• Figure 17 shows association between known variables and principal components. Left) Proportion of variance explained by each principal component. Right) Association between known technical and clinical variables to each principal component. *** p ⁇ 0.001.
• a cell-free methylated DNA immunoprecipitation-sequencing (cfMeDIP-seq) method was developed to work with low input DNA and with circulating cell-free DNA (cfDNA).
• the cfMeDIP-seq method measures DNA methylation using low input cfDNA, making it ideal for liquid biopsy applications.
• the DNA methylation profiles obtained from cfMeDIP-seq helps to provide tissue of origin information, important in circulating tumour DNA studies.
• 1-6 Similar to classical enrichment protocols that are immunoprecipitation based and sequencing protocols such as RNA-seq, interpretation requires a reference or control for comparison. Reference controls have consisted of spike-in reference DNA fragments of known sequence. 7-11
• Spike-in controls overcome the assumption that DNA or RNA yields are equal in different experimental conditions and across all genomic regions. 8 As a result, spike-in controls also adjust for biological and technical bias. The addition of spike-in controls drastically changes the interpretation of RNA-seq, ChIP-seq and genomic sequencing results. 7-11 It has been suggested that all genome-wide analyses would benefit from the addition of spike-in controls. 8 Normalizing data by total number of reads per sample often masks differences in the variable of interest. Normalizing data to assume reference control DNA is the same between samples, allows for more accurate detection of differences and adjustment of biological variables that can influence results. 89 While DNA and RNA sequencing experiments have utilized spike-in controls, enrichment methods of measuring genome-wide DNA methylation have not.
• the present inventors have introduced new synthetic spike-in DNA controls to be used for cfMeDIP-seq.
• the spike-in controls correct for fragment length, G+C content and CpG fraction, and can be used to assess non-specific binding, an integral part of cfMeDIP-seq analysis.
• spike-in controls with unique molecular index (UMI) were designed to adjust for polymerase chain reaction (PCR) bias, fragment length, combined guanine and cytosine (G+C) content, and number of CpG dinucleotides (CpG) per fragment. These modifications generate a quantitative measure of methylated DNA, rather than relative read counts and help mitigate batch effects.
• the spike-in controls are used in sequencing methods such as cfMeDIP-seq (cell-free methylated DNA immunoprecipitation and high-throughput sequencing).
• CfMeDIP-seq is used to perform genome-wide DNA methylation mapping using cell-free DNA.
• methylated DNA refers to DNA having methyl groups added as well as derivatives thereof.
• oxidized derivatives of methylated cytosine are derived through the 5mC oxidation pathway, and include 5- hydroxymethylcytosine, 5-formylcytosine and 5-carboxylcytosine (see Song, et al. Trends Biochem Sci. 2013 Oct; 38(10): 480-484, the entire content of which is incorporated hereby in reference).
• a method of capturing and analyzing cell-free methylated DNA in a sample comprises: a. subjecting the sample to library preparation to permit subsequent sequencing of the cell-free methylated DNA; b. adding a predetermined amount of a set of control synthetic DNA fragments, wherein the control synthetic DNA fragments each have a known nucleic acid sequence that does not substantially align to a target genome sequence, and wherein at least some of the control synthetic DNA fragments in the set are methylated; c. denaturing the sample; d. capturing cell-free methylated DNA and the control synthetic DNA fragments using a binder selective for methylated polynucleotides; and e.
• this method further comprises the step of calculating an amount, a concentration, or a molality of the cell-free methylated DNA in the sample based on the sequenced control synthetic DNA fragments.
• Cell-free methylated DNA is DNA that is circulating freely in the bloodstream and are methylated at various known regions of the DNA. Samples, for example, plasma samples, can be taken to analyze cell-free methylated DNA.
• library preparation includes end-repair, A-tailing, adapter ligation, or any other preparation performed on the cell-free DNA to permit subsequent sequencing of DNA.
• a “target genome sequence” refers to a genome to which the cell-free methylated DNA in the sample will be sequenced against.
• the target genome is a human genome.
• the target genome is a nonhuman genome.
• a “nucleic acid sequence that does not substantially align to a target genome sequence” refers to sequences having less than 30%, less than 20%, less than 10%, less than 5%, less than 3%, or less than 1% identity in an alignment to a target genome sequence.
• a nucleic acid sequence that does not substantially align to a target genome sequence has no more than 2, no more than 3, no more than 4, no more than 5, no more than 6, no more than 7, no more than 8, no more than 9, or no more than 10 aligned nucleotides identical to a target genome sequence.
• NGS next-generation sequencing
• PCR polymerase chain reaction
• Sanger sequencing also available are next-generation sequencing (NGS) techniques, also known as high-throughput sequencing, which includes various sequencing technologies including: lllumina (Solexa) sequencing, Roche 454 sequencing, Ion torrent: Proton / PGM sequencing, SOLiD sequencing.
• lllumina Solexa
• Roche 454 sequencing Ion torrent: Proton / PGM sequencing
• SOLiD sequencing SOLiD sequencing.
• NGS allows for the sequencing of DNA and RNA much more quickly and cheaply than the previously used Sanger sequencing.
• said sequencing is optimized for short read sequencing.
• DNA samples may be denatured, for example, using sufficient heat.
• the set of control synthetic DNA fragments comprises a plurality of fragments having different predetermined lengths. In some embodiments, the set of control synthetic DNA fragments comprises between 3 to 7 predetermined fragment lengths, between 3 to 6 predetermined fragment lengths, or between 3 to 5 predetermined fragment lengths. In one embodiment, the set of control synthetic DNA fragments comprises 3 predetermined fragment lengths.
• control synthetic DNA fragments are 50 to 500 base pairs (bp) in length, preferably 80 to 320 bp in length.
• a set of synthetic DNA fragments has fragments of increasing lengths.
• a set has three predetermined lengths of 100 bp, 150bp, and 300 bp.
• a set of synthetic DNA fragments has fragments that are multiples of a shortest fragment length.
• a set has three predetermined lengths of 80 bp, 160 bp, and 320 bp.
• G+C content refers to the percentage of nucleotides in a DNA fragment that are guanine or cytosine.
• the control synthetic DNA fragments have a G+C content of between 0% to 100%, preferably between 25% to 75%.
• the three predetermined fragment lengths have a G+C content of 35%, 50%, and 65%, respectively.
• a CpG dinucleotide is a region of DNA where a cytosine nucleotide is followed by a guanine nucleotide in the linear sequence of bases along its 5' ⁇ 3' direction.
• each of the control synthetic DNA fragments comprises a number of CpG dinucleotides ranging between 0 and 1 ⁇ 2 of the length of the fragment in base pairs.
• each of the control synthetic DNA fragments comprises 1 to 25 CpG dinucleotides, preferably 1 to 16 CpG dinucleotides.
• the control synthetic DNA fragments have 1 , 2, or 4 CpG sites per shortest fragment length.
• the control synthetic DNA fragments have one CpG site per 20 bp, per 40 bp or per 80 bp.
• control synthetic DNA fragments have a nucleic acid sequence as set forth in on or more of SEQ ID NO: 1-59.
• the method further comprises: i. sequencing the captured cell-free methylated DNA and the control synthetic DNA fragments; ii. comparing the sequenced cell-free methylated DNA against the known nucleic acid sequences of the control synthetic DNA fragments; and iii. comparing any unmatched DNA from (ii) against the target genome sequence.
• some of the control synthetic DNA fragments in the set are methylated, while some of the control synthetic DNA fragments in the set are not methylated. In one embodiment, half of the control synthetic DNA fragments in the set are methylated, and the other half are unmethylated. In one embodiment, all of the control synthetic DNA fragments are methylated.
• the set of control synthetic DNA fragments comprise a first sequence that is methylated, and a second sequence that is unmethylated.
• the method further comprises estimating the amount of captured cell-free methylated DNA before amplification using unique molecular identifier (UMI) adapters.
• UMI unique molecular identifier
• the binder is a protein comprising a methyl-CpG-binding domain.
• MBD2 protein methyl-CpG-binding domain
• MBD methyl-CpG-binding domain
• MBD refers to certain domains of proteins and enzymes that is approximately 70 residues long and binds to DNA that contains one or more symmetrically methylated CpGs.
• MBD of MeCP2, MBD1 , MBD2, MBD4 and BAZ2 mediates binding to DNA, and in cases of MeCP2, MBD1 and MBD2, preferentially to methylated CpG.
• Human proteins MECP2, MBD1 , MBD2, MBD3, and MBD4 comprise a family of nuclear proteins related by the presence in each of a methyl-CpG-binding domain (MBD). Each of these proteins, with the exception of MBD3, is capable of binding specifically to methylated DNA.
• the binder is an antibody and capturing cell-free methylated DNA comprises immunoprecipitating the cell-free methylated DNA using the antibody.
• immunoprecipitation refers a technique of precipitating an antigen (such as polypeptides and nucleotides) out of solution using an antibody that specifically binds to that particular antigen. This process can be used to isolate and concentrate a particular protein or DNA from a sample and requires that the antibody be coupled to a solid substrate at some point in the procedure.
• the solid substrate includes for examples beads, such as magnetic beads. Other types of beads and solid substrates are known in the art.
• One exemplary antibody is 5-methylcytosine antibody.
• the method described herein further comprises the step of adding a second amount of control DNA to the sample after step (b).
• exemplary antibodies are 5-hydroxymethylcytosine antibody, 5-formylcytosine antibody, and 5-carboxylcytosine antibody.
• the sample has less than 100 ng of cell-free DNA
• the method further comprises adding a first amount of filler DNA to the sample, wherein at least a portion of the filler DNA is methylated.
• the filler DNA consisted of amplicons similar in size to an adapter-ligated cfDNA library and is composed of unmethylated and in vitro methylated DNA at different methylation levels. The addition of this filler DNA serves a practical use, allowing for the normalization of input DNA amount to 100 ng. This ensures that the downstream protocol remains the same for all samples regardless of the amount of available cfDNA.
• fill DNA can be noncoding DNA or it can consist of amplicons.
• the first amount of filler DNA comprises about 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% methylated filler DNA with remainder being unmethylated filler DNA. In preferred embodiments, the first amount of filler DNA comprises about 50% methylated filler DNA. In some embodiments, between 5% and 50%, between 10%-40%, or between 15%-30% are methylated filler DNA
• the first amount of filler DNA is from 20 ng to 100 ng. In preferred embodiments, 30 ng to 100 ng of filler DNA. In more preferred embodiments 50 ng to 100 ng of filler DNA.
• the filler DNA is 50 bp to 800 bp long. In preferred embodiments, 100 bp to 600 bp long; and in more preferred embodiments 200 bp to 600 bp long.
• the filler DNA is double stranded.
• the filler DNA may also be endogenous or exogenous DNA.
• the filler DNA is non-human DNA, and in preferred embodiments, l DNA.
• l DNA refers to Enterobacteria phage l DNA.
• the filler DNA has no alignment to human DNA.
• methods of identifying a sequence for a control synthetic DNA fragment is provided.
• the control synthetic DNA fragment is then used in capturing and analysis of cell-free methylated DNA.
• the method involves: a. generating nucleic acid sequences based on a plurality of target fragment lengths, a target combined guanine and cytosine (G+C) content, and a target number of CpG dinucleotides for each fragment; and b. eliminating generated sequences that align to a human genome;
• the plurality of target fragment lengths comprises 3 to 7 different target fragment lengths that are multiples of a unit length that is also the shortest fragment.
• a fragment length is between 50 to 500 base pairs (bp), preferably 80 to 320 bp.
• the target G+C content is between 0% to 100%, preferably between 25% to 75%.
• the target number of CpG sites for each fragment is between 0 and 1 ⁇ 2 of the length of the fragment in base pairs, and 1 , 2, or 4 CpG sites per shortest fragment length.
• the target number of CpG dinucleotides is 1-25, preferably 1-16 CpG dinucleotides per fragment.
• the control synthetic DNA fragments have one CpG site per 20 bp, per 40 bp or per 80 bp.
• the method generates nucleic acid sequences that has three target fragment lengths that are 80 bp, 160 bp, or 320 bp, and the target G+C content is 35%, 50%, or 65%, respectively.
• spike-in controls were designed with integrated use of unique molecular indexes (UMIs) to adjust for polymerase chain reaction (PCR) bias, and immunoprecipitation bias caused by the fragment length, G+C content, and CpG density of the DNA fragments.
• UMIs unique molecular indexes
• PCR polymerase chain reaction
• immunoprecipitation bias caused by the fragment length, G+C content, and CpG density of the DNA fragments.
• DNA fragments were designed with combinations of methylation status (methylated and unmethylated), fragment length in base pair (bp) (80 bp, 160 bp, 320 bp), G+C content (35%, 50%, 65%), and fraction of CpGs within a fragment (1/80 bp, 1/40 bp, 1/20 bp). Spike-in control DNA sequence were checked to ensure they had no cross alignment to the human genome and minimized formation of secondary structures to avoid issues with amplification.
• the cfMeDIP-seq was carried out on either solely spike-in DNA fragments, spike-in DNA added to sheared HCT116 genomic DNA or spike-in DNA added to cfDNA from the stored plasma of acute myeloid leukemia (AML) patient samples to assess technical and biological biases, determine optimal amount of spike-in DNA required for an experiment and to assess batch effects, respectively.
• AML acute myeloid leukemia
• Spike-in controls were designed with unique molecular index (UMI) to adjust for polymerase chain reaction (PCR) bias, fragment length, G+C content, and number of CpGs per fragment to allow for absolute quantification rather than relative read counts.
• UMI unique molecular index
• Paired-end sequencing data previously generated by the cfMeDIP-seq protocol 6 was used to assess the different properties of cfDNA to aid in the design of synthetic controls 12 .
• the number of CpGs was assessed as an integer of fragment length in base pairs (i.e. 1 CpG in 80 bp fragment is comparable to 2 CpGs in a 160 bp fragment).
• the following spike-in fragment parameters were set as shown in Table 1. Number of CpGs within a fragment were set as an integer of fragment length [1/80, 1/40, 1/20]
• GenRGenS v.2.0 was used to generate the sequences.
• 13 BLASTn was used to ensure no alignment to the human reference genome (GRCh38/hg38), choosing sequences with the highest E-values possible.
• Integrated DNA Technologies (IDT) UNAFoldTM software (IDT, Coralville, USA) was used to check for secondary DNA structure for 80 bp and 160 bp fragments. 4 UNAFold does not support sequences over 280 bp, therefore, RNAstructureTM software 8 was used to check for secondary DNA structures of the 320 bp fragments.
• the methylation reaction was incubated at 37 °C for 30 min, then 65 °C for 20 min.
• the methylated product was purified using the MinElute PCR Purification KitTM (Qiagen, Hilden, Germany, Cat: 28004).
• MinElute PCR Purification KitTM Qiagen, Hilden, Germany, Cat: 28004.
• the original PCR amplicon and the methylated PCR amplicon were digested with either HpyCH4IV, Hpall or Afel restriction enzyme dependent on the fragment (Table 2). Methylation was considered verified when the PCR amplicon had a single band when run on a 2% agarose gel. Once the methylated fragments were verified, molar amount of synthetic fragments were measured using Qubit.
• CfMeDIP-seq was performed on only the spike-in controls, not spiked in to a biological sample. Within each group of fragments lengths (80 bp, 160 bp, 320 bp), the total number of synthetic fragments was added together in equimolar amounts. The samples were then pooled together in equal amounts to make up 10 ng of input DNA (3:33 ng per fragment length). CfMeDIP-seq was as per Shen et al. [7], UMI adapters were used to account for PCR amplification bias, which required the adapter ligation to be an overnight incubation at 4 °C with final adapter concentration adjusted to 0.09 ⁇ mol.
• HCT116 Spike-in controls to HCT116.
• the sheared HCT116 cfDNA mimic was kept at constant 10 ng, while varying the different concentrations of synthetic fragment pools, from 0.1 ng, 0.3 ng, and 1.0 ng of DNA.
• the samples were prepared the same way as in the pilot testing and sequenced on the MiSeq Nano, 1 million reads per flow cell, paired end 150 bp (lllumina, San Diego, USA). High resolution sequencing was then performed spiking in 0.1 ng, 0.05 ng and 0.01 ng of our control into 10 ng of HCT116 on the NovaSeq, 60 million reads per sample, paired end 2x100 bp (see Figure 2).
• cfMeDIP-seq was performed using the spike-in control DNA pools as the input.
• the input pool consisted of 9.99 ng of synthetic spike-in DNA, with equimolar amounts of each fragment size and within each fragment size pool, equimolar amounts of each methylation status (Table 2).
• the cfMeDIP-seq was performed as per Shen et al (2016) 2 with slight modifications.
• the xGen Stubby Adapter and unique dual indexing (UDI) primer pairs (Integrated DNA technologies, Coralville, IA, USA, Cat# 10005921) were used to account for PCR amplification bias.
• Adapter ligation was performed overnight at 4 °C with final adapter concentration adjusted to 0.09 pmol by dilution. For each sample, 1 ng of the DNA denaturation product was saved as input. For each sample, we amplified both input and outputs followed by purification and dual size selected using AMPure XP beads (Beckman Coulter, Brea, CA, USA) for 150 bp-200 bp. Samples underwent sequencing (Princess Margaret Genomics Centre, Toronto, ON, CA) on a MiSeq Nano flowcell (lllumina, San Diego, CA, USA), paired-end 2x150 bp, 1 million reads per flow cell (Figure 13).
• HCT116 genomic DNA ATCC, Manassas, VA, USA, RRID: CVCL_0291.
• the HCT 116 genomic DNA was sheared was sheared using a LE220 ultrasonicator (Covaris, Woburn, MA, USA) and size selected using AMPure XP beads (Beckman Coulter, Brea, CA, USA) to mimic cfDNA input.
• Bioinformatic preprocessing The adapters were trimmed using fastp version 0.11.5 2 (see Formula 1) and removed reads with a phred score of less than 20.
• the reads were aligned to the sequences of the designed fragments using BowTie2 version 0.11 ,5 3 (see Formula 2). Subsequently, sequences that did not align to the present synthetic DNA were aligned to the human reference genome (GRCh38/hg38). 15 Over 98% of reads aligned to either spike-in control sequences or the human genome in every sample. Read pairs were removed when at least one read in the pair did not align or had low quality. Low quality was defined as a Phred score ⁇ 20. Reads that contained the same UMI were collapsed as counted as one read by matching the UMI sequences for each read per sample.
• Absolute quantification from spike-in control data A generalized linear model was created to predict molar amount from deduplicated spike-in control read counts based on UMI consensus sequence, G+C content, CpG fraction, fragment length. To do this, the stats package in R version 3.4.1. was used. To reduce its left skew, a cube root transformation of CpG fraction was used. A Gaussian generalized linear model (Equation 6) was used to calculate molar amount ( ⁇ ) for each DNA fragment present in the original sample using regression coefficients ( ⁇ ) learned for each experiment.
• This model includes read counts (xreads) , number of fragments (xfragments) , length of fragments (xien), G+C content of fragments (XGC) , and CpG fraction of fragments Regression coefficients (b) for each experiment and model can be found in Table 3. Table 3. Regression coefficients (b). A Gaussian generalized linear model was used for all experiments.
• Standard deviation was calculated between the two replicate samples for which 0.01 ng of synthetic DNAwas spiked into 10 ng of HCT116 genomic DNA.
• the relationship between molar amount and mappability scores was assessed, and molar amount and standard deviation excluding simple repeat regions, 24 regions listed in ENCODE blacklist, 25 regions with mappability score ⁇ 0.5 and regions with standard deviation > 0.25.
• HOMER version 4.10.4 was used to investigate whether specific transcription factor binding motifs associated with our outliers. Window size was set to 300 bp and outliers were compared to the HOMER-generated randomized genomic background.
• HCT116 genomic DNA was run in triplicate on the lllumina EPIC array (lllumina, San Diego, CA, USA).
• the HCT116 genomic DNA samples run on the EPIC array are technical replicates of the HCT116 genomic DNA spiked with 0.01 ng spike-in control.
• EPIC array data was normalized and preprocessed using sesame.
• 27 CpGs was annotated on the EPIC array to 300 bp genomic windows. When >1 CpG probe annotated to a window, probe M-values were averaged across the window.
• Windows were removed that mapped to UCSC simple repeats, 24 ENCODE blacklist, 25 regions of low mappability ⁇ 0.50, and regions where standard deviation between replicates > 0.25. Correlation was assessed between EPIC array M-values and picomoles and EPIC array M-values and read counts at windows that contained > 3 CpG probes, > 5 CpG probes, > 7 CpG probes and > 10 CpG probes.
• the unmethylated fragments showed the same enrichment to 160 bp fragments and higher G+C content. This suggested that the unspecific binding of the cfMeDIP method was partial to fragments with higher G+C content. There was no association between the number of CpGs present within a fragment and the number of reads (Figure 7).
• HCT116 The total number of reads that were used towards the synthetic spike-in controls compared to the total number of reads used to our biological sample, HCT116 was assessed. This allowed optimization of the amount of spike-in controls that would be used in subsequent experiments, to maximize reads of a biological sample of interest while still getting enough information on the control fragments to correct biological and technical bias. Spiking in 0.01 ng of synthetic controls allowed use of ⁇ 0.01% of the reads to the controls, while leaving the rest to the biological sample. There was still >650,000 reads of control sequence to use for analysis ( Figure 11). Therefore, it was decided to use 0.01 ng of spike-in controls fragments in subsequent experiments.
• the synthetic spike-in controls output as well as the spike-in controls in 10 ng of HCT116 show an enrichment of 160 bp fragments which we expect due to our size selection step for fragments between 150 bp-200 bp.
• the cfMeDIP method we maintained enrichment for the 160 bp fragments and observe an enrichment towards fragments with higher G+C content and high CpG fraction (Figure 14).
• the total number of reads used towards spike-in controls was assessed compared to the total number of reads used towards the biological sample, HCT 116 genomic DNA. This allowed optimization of the amount of spike-in controls to be used in subsequent experiments, maximizing reads to biological sample of interest while obtaining sufficient reads from the spike-in controls to correct for biological and technical bias.
• Spiking in 0.01 ng of synthetic spike-in control DNA into present cfMeDIP-seq experiments allowed use of ⁇ 1% of the reads to the controls, while leaving the rest to biological sample. There were still >650,000 reads of control sequence to use for analysis. Therefore, it was decided to use 0.01 ng of spike-in control fragments in subsequent experiments.
• Filtering problematic regions removes potential sources of biological and technical artifacts.
• ENCODE blacklist regions regions with mappability scores ⁇ 0.5 and regions where standard deviation between the replicates > 0.25, we observed no relationship between molar amount and standard deviation, and no relationship between molar amount and mappability scores. This suggests that removing these regions is beneficial to reduce biological and technical artifacts.
• AluSp a Genomic position defined by hg38, 1-start, fully closed. b All elements, families, and names that overlap our 300 bp genomic windows. Element, family, and name as defined in the UCSC Genome Browser RepeatMasker track, from
• the linear model was used to calculate molar amount for each 300 bp genomic window. Significant correlation was observed between molar amount and M-values across the genome in our HCT116 genomic DNA samples. A higher correlation was observed when the analyses was restricted to high CpG dense regions, defined as 300 bp windows with > 5 CpG probes representing DNA methylation on the EPIC array, within the 300 bp window. This is not surprising as the cfMeDIP-seq technique preferentially measures DNA methylation at high CpG dense regions. To compare with the current standard, read counts were correlated to M-values ( Figure 16). It was shown that molar amount performs similarly to read counts, but has the advantage of allowing for absolute quantification.
• spike-in controls helps to mitigate differences between batches due to a number of technical factors including: technician, adapters, sequencing machine, and adapter ligation incubation.
• technician, adapters, sequencing machine, and adapter ligation incubation it was observed that principal components significantly associated with batch made up ⁇ 5% of the variance within the data.
• data normalized using QSEA had principal components significantly associated with processing batch contributing to >85% of the variance within the data.

Landscapes

• Chemical & Material Sciences (AREA)
• Life Sciences & Earth Sciences (AREA)
• Health & Medical Sciences (AREA)
• Organic Chemistry (AREA)
• Engineering & Computer Science (AREA)
• Zoology (AREA)
• Wood Science & Technology (AREA)
• Genetics & Genomics (AREA)
• Proteomics, Peptides & Aminoacids (AREA)
• Analytical Chemistry (AREA)
• Bioinformatics & Cheminformatics (AREA)
• Biotechnology (AREA)
• General Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• General Health & Medical Sciences (AREA)
• Molecular Biology (AREA)
• Microbiology (AREA)
• Biophysics (AREA)
• Biochemistry (AREA)
• Immunology (AREA)
• Biomedical Technology (AREA)
• Chemical Kinetics & Catalysis (AREA)
• Plant Pathology (AREA)
• Crystallography & Structural Chemistry (AREA)
• Pathology (AREA)
• Virology (AREA)
• Bioinformatics & Computational Biology (AREA)
• Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Priority Applications (9)

Applications Claiming Priority (2)

Related Child Applications (1)

Publications (1)

Family

ID=75849048

Family Applications (1)

Country Status (9)

Cited By (3)

Families Citing this family (1)

Citations (3)

Family Cites Families (6)

• 2020
  • 2020-11-06 CN CN202080092232.8A patent/CN115087744A/zh active Pending
  • 2020-11-06 KR KR1020227018998A patent/KR20220098183A/ko active Pending
  • 2020-11-06 JP JP2022526702A patent/JP2023502018A/ja active Pending
  • 2020-11-06 GB GB2207732.5A patent/GB2609715B/en active Active
  • 2020-11-06 EP EP24177800.0A patent/EP4435118A3/en active Pending
  • 2020-11-06 EP EP20884272.4A patent/EP4055183A4/en not_active Withdrawn
  • 2020-11-06 WO PCT/CA2020/051507 patent/WO2021087615A1/en not_active Ceased
  • 2020-11-06 CA CA3157323A patent/CA3157323A1/en active Pending
  • 2020-11-06 BR BR112022008714A patent/BR112022008714A2/pt unknown
• 2022
  • 2022-05-04 US US17/736,570 patent/US20230024827A1/en active Pending

Patent Citations (3)

Non-Patent Citations (6)

Also Published As

Similar Documents

Legal Events