WATCHMAKER RNA LIBRARY PREP KITS with Polaris Depletion

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Sensitivity meets speed

Rapidly prepare stranded, whole transcriptome sequencing libraries using Watchmaker RNA Library Prep Kits with Polaris™ Depletion. The highly streamlined Polaris Depletion module depletes highly abundant rRNA and globin transcripts in human, mouse, and rat samples, providing improved coverage of biologically interesting transcripts, including long non-coding RNAs.

The automation-friendly workflow reduces total turnaround time, hands-on time, and consumable use through a reduction in bead purification and reaction steps. A novel, engineered reverse transcriptase improves the conversion of RNA to cDNA, enabling high-quality performance with 1 ng to 1000 ng of total RNA, as well as FFPE-derived RNA.

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Key Features and Benefits

  • Increase sensitivity with clinically relevant samples, including FFPE and inputs as low as 1 ng
  • Generate high-complexity libraries in under 4.5 hours, including rRNA and globin depletion
  • Improve automatability with fewer purifications and reagent additions
  • Consolidate disparate/niche workflows into one process with broad sample type and input compatibility
  • Realize the performance benefits of a reverse transcriptase specifically tailored for RNA-seq applications

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Watchmaker mRNA Library Prep Kit

A rapid and sensitive solution for your gene expression studies.

Applications

  • RNA sequencing with degraded samples, such as FFPE
  • Detection of long non-coding RNAs (lncRNAs)
  • Gene fusion, splice variant and isoform detection
  • Gene expression analysis
  • Novel transcript discovery
  • Single nucleotide variant (SNV) detection
  • Targeted sequencing protocols employing hybridization capture

Process more samples easily with a simple, rapid workflow

Improve automatability and reduce turnaround time

Figure 1. Improve automatability and reduce turnaround time. The Watchmaker solution combines and shortens enzymatic steps and has fewer bead purifications in comparison to commercially-available kits, resulting in a highly automatable workflow with significantly reduced hands-on time (up to one hour per plate) and consumable use (up to 1,000 pipette tips per plate).

Key Performance Data

Access more information from FFPE samples

Processing FFPE samples is inherently challenging due to the template damage incurred during fixation and the presence of residual cross-links. Watchmaker’s novel FFPE treatment step, paired with a reverse transcriptase specifically engineered for RNA-seq applications, delivers excellent sensitivity and enables researchers to address a broader range of clinically relevant samples effectively.

Figure 2A. Access more information from FFPE. Libraries were prepared in duplicate from 10 ng of a matched fresh frozen and FFPE sample set using Watchmaker RNA Library Prep with Polaris Depletion, KAPA RNA HyperPrep with RiboErase (HMR) Globin, NEBNext Ultra II Directional RNA Library Prep with Globin & rRNA Depletion, and Illumina Stranded Total RNA Prep, Ligation with Ribo-Zero Plus. Data were downsampled to 11M paired reads per library. Unique genes were identified using featureCounts with a cutoff of 5 deduplicated raw reads. Only genes identified in both technical replicates were counted. In a gene detection overlap analysis between the fresh frozen and FFPE data sets for each chemistry, Watchmaker FFPE libraries detect a significantly higher percentage of the unique genes identified in the fresh frozen control.
Figure 2B. Access more information from FFPE. Watchmaker detects more unique genes for both the fresh frozen and FFPE samples.
Improved gene detection sensitivity with FFPE
Figure 2A. Access more information from FFPE. Libraries were prepared in duplicate from 10 ng of a matched fresh frozen and FFPE sample set using Watchmaker RNA Library Prep with Polaris Depletion, KAPA RNA HyperPrep with RiboErase (HMR) Globin, NEBNext Ultra II Directional RNA Library Prep with Globin & rRNA Depletion, and Illumina Stranded Total RNA Prep, Ligation with Ribo-Zero Plus. Data were downsampled to 11M paired reads per library. Unique genes were identified using featureCounts with a cutoff of 5 deduplicated raw reads. Only genes identified in both technical replicates were counted. In a gene detection overlap analysis between the fresh frozen and FFPE data sets for each chemistry, Watchmaker FFPE libraries detect a significantly higher percentage of the unique genes identified in the fresh frozen control.
Figure 2A. Access more information from FFPE. Libraries were prepared in duplicate from 10 ng of a matched fresh frozen and FFPE sample set using Watchmaker RNA Library Prep with Polaris Depletion, KAPA RNA HyperPrep with RiboErase (HMR) Globin, NEBNext Ultra II Directional RNA Library Prep with Globin & rRNA Depletion, and Illumina Stranded Total RNA Prep, Ligation with Ribo-Zero Plus. Data were downsampled to 11M paired reads per library. Unique genes were identified using featureCounts with a cutoff of 5 deduplicated raw reads. Only genes identified in both technical replicates were counted. In a gene detection overlap analysis between the fresh frozen and FFPE data sets for each chemistry, Watchmaker FFPE libraries detect a significantly higher percentage of the unique genes identified in the fresh frozen control.
Figure 2B. Access more information from FFPE. Watchmaker detects more unique genes for both the fresh frozen and FFPE samples.
Figure 2B. Access more information from FFPE. Watchmaker detects more unique genes for both the fresh frozen and FFPE samples.
Increase sensitivity with ultra-low-input samples

Robust and reproducible sample processing can be difficult for applications and samples where RNA quantities are restrictive, such as fine needle biopsies, limiting the sensitivity of associated assays. Watchmaker RNA Library Prep Kits with Polaris Depletion enable the generation of high quality libraries with as little as 1 ng of RNA.

Figure 3A. Improve sequencing economy and gene detection sensitivity. Libraries were prepared from a whole blood sample in triplicate using the RNA inputs indicated. Data were randomly downsampled to 16M paired reads per library. Unique genes identified using featureCounts with a cutoff of 5 deduplicated raw reads.
Figure 3B. Improve sequencing economy and gene detection sensitivity. Libraries were prepared from a whole blood sample in triplicate using the RNA inputs indicated. Data were randomly downsampled to 16M paired reads per library. Inter-workflow overlap analysis of genes identified, stratified by input amount. Results indicate improved sensitivity with the Watchmaker solution as evidenced by fewer wasted bases and more unique genes detected.
Figure 3C. Improve sequencing economy and gene detection sensitivity. Libraries were prepared from a whole blood sample in triplicate using the RNA inputs indicated. Data were randomly downsampled to 16M paired reads per library. Analysis of the percentage of bases wasted due to either failure to align to the reference or aligning to rRNA and globin mRNA regions.
Increase sensitivity with ultra-low-input samples
Figure 3A. Improve sequencing economy and gene detection sensitivity. Libraries were prepared from a whole blood sample in triplicate using the RNA inputs indicated. Data were randomly downsampled to 16M paired reads per library. Unique genes identified using featureCounts with a cutoff of 5 deduplicated raw reads.
Figure 3A. Improve sequencing economy and gene detection sensitivity. Libraries were prepared from a whole blood sample in triplicate using the RNA inputs indicated. Data were randomly downsampled to 16M paired reads per library. Unique genes identified using featureCounts with a cutoff of 5 deduplicated raw reads.
Figure 3B. Improve sequencing economy and gene detection sensitivity. Libraries were prepared from a whole blood sample in triplicate using the RNA inputs indicated. Data were randomly downsampled to 16M paired reads per library. Inter-workflow overlap analysis of genes identified, stratified by input amount. Results indicate improved sensitivity with the Watchmaker solution as evidenced by fewer wasted bases and more unique genes detected.
Figure 3B. Improve sequencing economy and gene detection sensitivity. Libraries were prepared from a whole blood sample in triplicate using the RNA inputs indicated. Data were randomly downsampled to 16M paired reads per library. Inter-workflow overlap analysis of genes identified, stratified by input amount. Results indicate improved sensitivity with the Watchmaker solution as evidenced by fewer wasted bases and more unique genes detected.
Figure 3C. Improve sequencing economy and gene detection sensitivity. Libraries were prepared from a whole blood sample in triplicate using the RNA inputs indicated. Data were randomly downsampled to 16M paired reads per library. Analysis of the percentage of bases wasted due to either failure to align to the reference or aligning to rRNA and globin mRNA regions.
Figure 3C. Improve sequencing economy and gene detection sensitivity. Libraries were prepared from a whole blood sample in triplicate using the RNA inputs indicated. Data were randomly downsampled to 16M paired reads per library. Analysis of the percentage of bases wasted due to either failure to align to the reference or aligning to rRNA and globin mRNA regions.
Retain quantitative gene expression information 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. Ideally, the relative gene expression profile should remain consistent across input amounts for a given sample. 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.

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 and rRNA and globin depletion upstream of library prep. 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.
Retain sample complexity with low-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 and rRNA and globin depletion upstream of library prep. 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 and rRNA and globin depletion upstream of library prep. 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.
Efficiently sequence blood samples while covering lncRNAs

Overabundant globin mRNAs in blood samples pose a challenge for efficient RNA sequencing, as ≥ 30% of bases map to these transcripts if traditional mRNA capture is used. Additionally, mRNA capture omits a significant subset of long non-coding RNAs (lncRNAs) that have demonstrated regulatory functions.¹ Polaris Depletion removes both rRNA and globin mRNA while also providing excellent coverage of lncRNAs, such as MALAT1 which has been implicated in at least 17 cancer types, including lung, breast, and pancreatic.²

  1. Statello, L., Guo, CJ., Chen, LL. et al. Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol 22, 96–118 (2021).
  2. Amodio, N., Raimondi, L., Juli, G. et al. MALAT1: a druggable long non-coding RNA for targeted anti-cancer approaches. J Hematol Oncol 11, 63 (2018).
Figure 5A. Improve efficiency and characterize lncRNAs. Polaris Depletion improves both library prep and sequencing efficiency through the combined removal of rRNA and globin mRNA. This focuses sequencing reads on what matters: informative exonic and lncRNAs.
Figure 5B. Improve efficiency and characterize lncRNAs. The Watchmaker solution also provides improved coverage across MALAT1, especially the GC-rich region outlined in red. Libraries were prepared with 10 ng of intact whole blood-derived RNA.
Efficiently sequence blood samples while covering lncRNAs
Figure 5A. Improve efficiency and characterize lncRNAs. Polaris Depletion improves both library prep and sequencing efficiency through the combined removal of rRNA and globin mRNA. This focuses sequencing reads on what matters: informative exonic and lncRNAs.
Figure 5A. Improve efficiency and characterize lncRNAs. Polaris Depletion improves both library prep and sequencing efficiency through the combined removal of rRNA and globin mRNA. This focuses sequencing reads on what matters: informative exonic and lncRNAs.
Figure 5B. Improve efficiency and characterize lncRNAs. The Watchmaker solution also provides improved coverage across MALAT1, especially the GC-rich region outlined in red. Libraries were prepared with 10 ng of intact whole blood-derived RNA.
Figure 5B. Improve efficiency and characterize lncRNAs. The Watchmaker solution also provides improved coverage across MALAT1, especially the GC-rich region outlined in red. Libraries were prepared with 10 ng of intact whole blood-derived RNA.
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