KAPA EpiVault EvoPrep Kit

• Reduce methylation bias (mBias) seen in traditional end-repair modules in standard library prep
• Lock down accurate biological signal from precious, low-input cfDNA samples

Figure 1. mBias plots showing the percentage of methylated CpG dinucleotides across library reads.

In this figure we can see mBias plots showing the percentage of methylated CpG dinucleotides across library reads prepared from a well-characterized hypermethylated control sample (mHCT116, from Zymo Research), enzymatically sheared to mimic size and damage in plasma cfDNA. Also included here are three healthy plasma cfDNA samples. 

From this graph you can see the methylation signal lost across the sequencing read in all samples in the no enhancer control (in green) when compared to the KAPA EpiVault EvoPrep chemistry (in blue).This difference is pronounced in the hypermethylated cell line data at the top of the graph (mHCT116), where the KAPA EpiVault EvoPrep chemistry clearly preserves methylation signal lost in standard workflows (“no enhancer control” in green). Healthy cfDNA inputs show a more accurate view of what users will experience. Here we still clearly see that the KAPA EpiVault EvoPrep chemistry (samples in blue colors) shows higher overall percentage of methylated CpGs than the same samples prepared with standard workflows (“no enhancer control” in green shades), allowing for the preservation of methylation signal.

Figure 2. The sliprate in methylation control and healthy cfDNA samples showing the impact on methylation signal conservation. 

Sliprate measures how much methylation signal is lost in a sequencing read. It is calculated as the slope of the mBias plot across the first 100 base pairs, expressed as a negative number, where a value of 0 (or close to 0) means that little to no methylation loss occurred. The more negative the value, the greater the methylation signal loss. Included in this data is a hypermethylated control (mHCT, from Zymo Research) as well as three healthy cfDNA samples. From this graph, you can see that the sliprate for all samples is close to zero for the KAPA EpiVault EvoPrep chemistry (in blue) when compared to the no enhancer control (in green). This difference is particularly pronounced in the data for mHCT116 where the KAPA EpiVault EvoPrep chemistry (in blue) clearly preserves methylation signal lost in standard workflows (“no enhancer control” in green) in hypermethylated DNA.

Figure 3. Comparison of KAPA EpiVault EvoPrep Kit versus multiple vendors for mBias (A) and sliprate (B) in a cfDNA-like (hypermethylated cell line sheared to cfDNA size) sample and in healthy cfDNA (sliprate only).

Here a ssDNA library preparation chemistry was included as an additional control since they typically have no mBias. These results show that the KAPA EpiVault EvoPrep Kit has similar performance to gold standard ssDNA library prep kits with near zero mBias but with a much shorter workflow time (refer to Figure 4B).

• Only two additional tubes integrated into the existing end repair and A-tailing step in the KAPA EvoPrep Boost workflow
• Zero added incubation steps
• Libraries (post-ligation) that are ready for your preferred methylation conversion in ~70 minutes¹

Figure 4. The new KAPA EpiVault EvoPrep workflow as compared to the existing KAPA EvoPrep Boost workflow and other suppliers. 

(A) The KAPA EpiVault EvoPrep Kit seamlessly integrates in the existing KAPA EvoPrep Boost workflow with two new tubes and no new incubation steps. Achieve [post-ligation] libraries that are ready for your preferred methylation conversion in ~70 minutes.¹ Now with a shorter A-tailing time. (B) Comparison table of KAPA EpiVault EvoPrep Kit, KAPA EvoPrep Boost Kit and various suppliers for a number of metrics (turnaround time; hands on time; steps and tubes required; etc.). The KAPA EpiVault EvoPrep Kit workflow has the shortest turnaround time, low hands on time, and lower use of plastics than most other workflows.

• Investigating tissue-specific epigenetics and cell-of-origin patterns—nucleosome occupancy and positioning reflect underlying chromatin state across different cell types
• Maintaining multi-nucleosomal DNA integrity for fragment size profiling
• Enabling studies on chromatin architecture and linker region footprints
• Facilitating epigenetic and histone modification workflows

Figure 5. Nucleosome conservation in 1 ng cfDNA input with the KAPA EpiVault EvoPrep workflow (A) when compared to the KAPA EvoPrep Boost workflow (B).

No impact is observed to di- and tri-nucleosome (shown with the blue arrow in the zoomed in portion) recovery through the introduction of the new enhancers as part of the KAPA EpiVault Kit.