WATCHMAKER RNA LIBRARY PREP KITS

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Sensitivity meets speed

Watchmaker RNA Library Prep Kits deliver stranded, high-complexity RNA-seq libraries in under 3.5 hours with inputs ranging from 0.25 to 100 ng total RNA.

The easily automated workflow saves time and reduces consumable use by incorporating fewer cleanup and handling steps. Performance with low-input and degraded samples is improved by the incorporation of a novel, engineered reverse transcriptase.

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Key Features and Benefits

  • A novel treatment step improves robustness with challenging FFPE samples
  • The rapid, streamlined workflow generates stranded, high-quality libraries in under 3.5 hours
  • Reduced handling and purification steps enhance automatability
  • A reverse transcriptase engineered specifically for RNA sequencing improves conversion of RNA to cDNA

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Watchmaker mRNA Library Prep Kit

A rapid and sensitive solution for your gene expression studies.

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Applications

  • RNA sequencing with degraded samples, such as FFPE
  • Targeted sequencing protocols employing hybridization capture
  • Gene fusion detection

Streamlined, automatable workflow

Figure 1. A simple, rapid workflow for scalable sample processing.

Figure 1. A simple, rapid workflow for scalable sample processing. The Watchmaker RNA Library Prep Kit combines and shortens reaction steps and has fewer cleanups than other commercially available kits.

Key Performance Data

A solution designed with FFPE samples in mind

FFPE samples are inherently challenging to process due to template damage and residual cross-linking. Watchmaker’s novel FFPE treatment step, paired with a reverse transcriptase engineered for RNA sequencing, provides highly sensitivity performance and makes more clinically relevant samples addressable.

Data shown were generated with ribosomal and globin depletion upstream of library prep. Learn more about the Watchmaker RNA Library Prep Kit with Polaris Depletion.

Figure 2A. Detect more genes from degraded samples. Libraries were prepared in duplicate from five independent FFPE samples using 100 ng. Data were randomly downsampled to 16M paired reads per library. Unique genes identified using featureCounts with a cutoff of 5 deduplicated raw reads.
Figure 2B. Increased gene detection sensitivity with degraded samples. Libraries were prepared in duplicate from five independent FFPE samples using 100 ng with the Polaris Depletion Kit employed upstream of library prep. Data were randomly downsampled to 16M paired reads per library. Overlap analysis of genes identified between workflows, stratified by FFPE block. The Watchmaker solution detects more unique genes, indicating improved sensitivity.
A solution designed with FFPE samples in mind
Figure 2A. Detect more genes from degraded samples. Libraries were prepared in duplicate from five independent FFPE samples using 100 ng. Data were randomly downsampled to 16M paired reads per library. Unique genes identified using featureCounts with a cutoff of 5 deduplicated raw reads.
Figure 2A. Detect more genes from degraded samples. Libraries were prepared in duplicate from five independent FFPE samples using 100 ng. Data were randomly downsampled to 16M paired reads per library. Unique genes identified using featureCounts with a cutoff of 5 deduplicated raw reads.
Figure 2B. Increased gene detection sensitivity with degraded samples. Libraries were prepared in duplicate from five independent FFPE samples using 100 ng with the Polaris Depletion Kit employed upstream of library prep. Data were randomly downsampled to 16M paired reads per library. Overlap analysis of genes identified between workflows, stratified by FFPE block. The Watchmaker solution detects more unique genes, indicating improved sensitivity.
Figure 2B. Increased gene detection sensitivity with degraded samples. Libraries were prepared in duplicate from five independent FFPE samples using 100 ng with the Polaris Depletion Kit employed upstream of library prep. Data were randomly downsampled to 16M paired reads per library. Overlap analysis of genes identified between workflows, stratified by FFPE block. The Watchmaker solution detects more unique genes, indicating improved sensitivity.
Improve sensitivity with low-input samples

Template-limited sample types, such as fine needle biopsies, can be difficult to process robustly and reproducibly—limiting the sensitivity of associated assays. The Watchmaker RNA Library Prep Kit generates high-complexity libraries with as little as 0.25 ng of RNA.

Data shown were generated with ribosomal and globin depletion upstream of library prep. Learn more about the Watchmaker RNA Library Prep Kit with Polaris Depletion.

Figure 3A. Maintain complexity with low inputs. Libraries were prepared in duplicate from five independent FFPE samples using 100 ng and rRNA and globin depletion upstream of library prep. Data were randomly downsampled to 16M paired reads per library. Unique genes identified using featureCounts with a cutoff of 5 unique raw reads.
Figure 3B. Maintain complexity with low inputs. Libraries were prepared in duplicate from five independent FFPE samples using 100 ng and rRNA and globin depletion upstream of library prep. Data were randomly downsampled to 16M paired reads per library. Overlap analysis of genes identified between workflows, stratified by input amount. The Watchmaker solution detects more unique genes, indicating improved sensitivity.
Improve sensitivity with low-input samples
Figure 3A. Maintain complexity with low inputs. Libraries were prepared in duplicate from five independent FFPE samples using 100 ng and rRNA and globin depletion upstream of library prep. Data were randomly downsampled to 16M paired reads per library. Unique genes identified using featureCounts with a cutoff of 5 unique raw reads.
Figure 3A. Maintain complexity with low inputs. Libraries were prepared in duplicate from five independent FFPE samples using 100 ng and rRNA and globin depletion upstream of library prep. Data were randomly downsampled to 16M paired reads per library. Unique genes identified using featureCounts with a cutoff of 5 unique raw reads.
Figure 3B. Maintain complexity with low inputs. Libraries were prepared in duplicate from five independent FFPE samples using 100 ng and rRNA and globin depletion upstream of library prep. Data were randomly downsampled to 16M paired reads per library. Overlap analysis of genes identified between workflows, stratified by input amount. The Watchmaker solution detects more unique genes, indicating improved sensitivity.
Figure 3B. Maintain complexity with low inputs. Libraries were prepared in duplicate from five independent FFPE samples using 100 ng and rRNA and globin depletion upstream of library prep. Data were randomly downsampled to 16M paired reads per library. Overlap analysis of genes identified between workflows, stratified by input amount. The Watchmaker solution detects more unique genes, indicating improved sensitivity.
Accurately quantify gene expression across input amounts

Agreement between high- and low-input libraries with respect to unique genes identified and their abundance is indicative of how well sample complexity is maintained as RNA input amounts decrease. Watchmaker better preserves the gene expression profile as RNA input amount decreases from 500 ng to 10 ng, whereas other commercial solutions result in data distortion with low inputs.

Data shown were generated with ribosomal and globin depletion upstream of library prep. Learn more about the Watchmaker RNA Library Prep Kit with Polaris Depletion.

Figure 4. Prevent data distortion with low inputs. Libraries were prepared from a whole blood sample in triplicate using 500 ng and 10 ng of RNA. Data were randomly downsampled to 16M paired reads per library. Differential expression analysis between averaged 500 ng (control) and 10 ng whole blood samples using DESeq2. Results indicate that other vendors lose representation of low abundance genes at 10 ng, while the Watchmaker solution does not.
Accurately quantify gene expression across input amounts
Figure 4. Prevent data distortion with low inputs. Libraries were prepared from a whole blood sample in triplicate using 500 ng and 10 ng of RNA. Data were randomly downsampled to 16M paired reads per library. Differential expression analysis between averaged 500 ng (control) and 10 ng whole blood samples using DESeq2. Results indicate that other vendors lose representation of low abundance genes at 10 ng, while the Watchmaker solution does not.
Figure 4. Prevent data distortion with low inputs. Libraries were prepared from a whole blood sample in triplicate using 500 ng and 10 ng of RNA. Data were randomly downsampled to 16M paired reads per library. Differential expression analysis between averaged 500 ng (control) and 10 ng whole blood samples using DESeq2. Results indicate that other vendors lose representation of low abundance genes at 10 ng, while the Watchmaker solution does not.
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