SMART-Seq mRNA Long Read

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Library preparation workflow for the SMART-Seq mRNA Long Read kit.

Library preparation workflow for the SMART-Seq mRNA Long Read kit. Starting with high-integrity RNA (RIN≥8) or intact cells, first-strand cDNA synthesis is primed by the SMART-Seq LR Oligo-dT and performed by an MMLV-derived reverse transcriptase (RT). Upon reaching the 5' end of each mRNA molecule, the RT adds nontemplated nucleotides to the first-strand cDNA. The SMART-Seq LR TSO contains a sequence that is complementary to the nontemplated nucleotides added by the RT. This primer hybridizes to the first-strand cDNA. In the template switching step, the RT uses the remainder of the SMART-Seq LR TSO as a template for the incorporation of an additional sequence on the end of the first-strand cDNA. The first-strand cDNA is then barcoded by forward and reverse primers from the SMART-Seq Long Read Index Kit (96 rxns) and amplified by the first PCR; a second PCR enriches for barcoded fragments. Samples are pooled and end-prepped, and sequencing adapters are ligated using the Ligation Sequencing Kit V14 (ONT). After sequencing, samples are basecalled and demultiplexed using ONT's Guppy. Downstream data analysis was performed using Cutadapt, Minimap2, SAMtools, Bedtools, and Salmon.

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SMART-Seq mRNA LR generates barcoded cDNA with a wide range of fragment lengths.

SMART-Seq mRNA LR generates barcoded cDNA with a wide range of fragment lengths. SMART-Seq mRNA LR workflow was used to create cDNA from 10 pg or 10 ng total mouse brain RNA (n = 8). cDNA size distribution was measured on a 2100 Bioanalyzer (Agilent Technologies) using an Agilent High Sensitivity DNA Kit. Panel A. Barcoded cDNA was pooled per input, and libraries were generated using the Ligation Sequencing Kit V14. Libraries were sequenced on a MinION Flow Cell (ONT) for 72 hr. Samples were basecalled and demultiplexed using ONTs' Guppy and read-length distribution was plotted using MS-Excel. Panels B & C. Read-length distributions are shown for a representative sample of 10 pg (Panel B) and 10 ng (Panel C) total mouse brain RNA.

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SMART-Seq mRNA Long Read demonstrates high sensitivity across a broad range of inputs.

SMART-Seq mRNA Long Read demonstrates high sensitivity across a broad range of inputs. To evaluate the performance, cDNA and sequencing libraries were generated from 10 pg and 10 ng total mouse brain RNA (MBR) using the SMART-Seq mRNA LR workflow. After sequencing, data was basecalled and demultiplexed using ONT's Guppy, and reads per barcode were downsampled to 400,000 reads. The read distribution for 10 pg and 10 ng MBR input. Panel A. is similar, showing consistent performance across the input range (n = 8 for 10 ng; n = 7 for 10 pg). Panel B. Reads of the 10 pg MBR dataset downsampled to assess gene and transcript sensitivity. Panel C. 10 ng MBR dataset downsampled to assess gene and transcript sensitivity. While neither of the two conditions (10 pg and 10 ng) were sequenced until saturation, both highlight the high gene and transcript sensitivity across read depths.

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SMART-Seq mRNA LR is highly reproducible across replicates for a broad range of inputs.

SMART-Seq mRNA LR is highly reproducible across replicates for a broad range of inputs. The SMART-Seq mRNA LR workflow was used to create cDNA from 10 ng MBR of 96 replicates. Barcoded cDNA of all 96 samples were pooled, and libraries were generated and sequenced according to the workflow.

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SMART-Seq mRNA LR yields even gene-body coverage for ultra-low inputs.

SMART-Seq mRNA LR yields even gene-body coverage for ultra-low inputs. 10 pg and 10 ng MBR samples underwent the SMART-Seq mRNA LR workflow. Gene-body coverage was assessed for the average of eight replicates of 10 pg and 10 ng MBR samples.

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SMART-Seq mRNA LR is compatible with direct cell inputs.

SMART-Seq mRNA LR is compatible with direct cell inputs. cDNA was generated from single K562 cells or 1,000 K562 cells using the SMART-Seq mRNA LR workflow. After sequencing, data was basecalled and demultiplexed using Guppy, and reads per barcode were downsampled to 300,000 reads. Panel A. The average gene count for each condition is shown above the corresponding bar (n = 8 for single cell and n = 2 for 1,000 cells). Panel B. Pearson correlation was calculated from the gene matrix across eight single K562 cells. Panel C. Downsampling analysis of single K562 cells shows gene and transcript count per read depth.

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SMART-Seq mRNA LR detects full-length isoforms and gene fusions.

SMART-Seq mRNA LR detects full-length isoforms and gene fusions. cDNA was generated using the SMART-Seq mRNA LR workflow. Basecalling and demultiplexing were performed using ONT's G uppy, and reads were aligned using Minimap2. Panels A & B. Isoforms of Snap25 (Panel A) and Nbr1 (Panel B) detected from 10 pg MBR input are visualized in IGV. Panel C. NUP214::XKR3 gene fusions detected from single-cell input are visualized in IGV.

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SMART-Seq mRNA LR is compatible with automation and miniaturization.

SMART-Seq mRNA LR is compatible with automation and miniaturization. The SMART-Seq mRNA LR workflow was used to create cDNA from 10 ng MBR of 96 replicates manually on the benchtop with the full reaction volumes or automated at 1/8-volume on a mosquito HV (SPT Labtech). Barcoded cDNA of all 96 samples per experiment was pooled, and libraries were generated and sequenced for 72 hr according to the workflow. Samples were basecalled and demultiplexed using ONT Guppy and aligned with Minimap2. Panel A. Render of the mosquito HV liquid handler. Panel B. Gene counts were assessed with Salmon. Panel C. Mapping statistics of manually prepared cDNA compared to cDNA prepared using the mosquito HV.

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SMART-Seq mRNA LR performance with mRNA reference standards

SMART-Seq mRNA LR performance with mRNA reference standards. To evaluate the performance of the SMART-Seq mRNA LR workflow, cDNA was generated from SIRV-Set 4 (Lexogen) mRNA reference standards which contains both ERCC quantification controls and Long SIRV mRNA standards. 10 ng of mouse brain total RNA was prepared—with SIRV spike-ins added to account for approximately 5% of reads—using the workflow described in Figure 1. Libraries were sequenced by ONT MinION, FASTQ data was read-strand corrected using the Restrander tool, and data were aligned with minimap2. ERCC standard abundance for measured vs. theoretical concentration was plotted.

Back

SMART-Seq mRNA LR performance with mRNA reference standards

SMART-Seq mRNA LR performance with mRNA reference standards. To evaluate the performance of the SMART-Seq mRNA LR workflow, cDNA was generated from SIRV-Set 4 (Lexogen) mRNA reference standards which contains both ERCC quantification controls and Long SIRV mRNA standards. 10 ng of mouse brain total RNA was prepared—with SIRV spike-ins added to account for approximately 5% of reads—using the workflow described in Figure 1. Libraries were sequenced by ONT MinION, FASTQ data was read-strand corrected using the Restrander tool, and data were aligned with minimap2. IGV plots of data from 1 kb, 4 kb, 6 kb, and 8 kb long transcripts from the ERCC and long SIRV isoform set. Red colored reads indicate positive strand reads, blue indicates very rare negative-strand reads, and small purple or red marks indicate small indels/variations common in ONT-sequencing. Full-length is defined as reads that cover at least 90% of expected mRNA length.

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Visualization showing completeness of fragment lengths and strand orientation accuracy of the SSmRNA LR kit

Visualization showing completeness of fragment lengths and strand orientation accuracy of the SSmRNA LR kit. This experiment used Spike-In RNA Variant (SIRV) controls (600–2,492 bp), showcasing detection of full-length fragments with a high percentage of complete transcripts, and accurate identification of strand orientation across all isoforms. Panel A. Coverage analysis of complete, full-length fragments. Panel B . Strand identification across different SIRV-spike-in species visualized via SIRVsuite.

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Automation- and miniaturization-friendly SSmRNA LR workflow comparison

SMART-Seq mRNA LR performance with mRNA reference standards. To evaluate the performance of the SMART-Seq mRNA LR workflow, cDNA was generated from SIRV-Set 4 (Lexogen) mRNA reference standards which contains both ERCC quantification controls and Long SIRV mRNA standards. 10 ng of mouse brain total RNA was prepared—with SIRV spike-ins added to account for approximately 5% of reads—using the workflow described in Figure 1. Libraries were sequenced by ONT MinION, FASTQ data was read-strand corrected using the Restrander tool, and data were aligned with minimap2. ERCC standard abundance for measured vs. theoretical concentration was plotted.

Back

Library preparation workflow for the SMART-Seq mRNA Long Read kit.

Library preparation workflow for the SMART-Seq mRNA Long Read kit. Starting with high-integrity RNA (RIN≥8) or intact cells, first-strand cDNA synthesis is primed by the SMART-Seq LR Oligo-dT and performed by an MMLV-derived reverse transcriptase (RT). Upon reaching the 5' end of each mRNA molecule, the RT adds nontemplated nucleotides to the first-strand cDNA. The SMART-Seq LR TSO contains a sequence that is complementary to the nontemplated nucleotides added by the RT. This primer hybridizes to the first-strand cDNA. In the template switching step, the RT uses the remainder of the SMART-Seq LR TSO as a template for the incorporation of an additional sequence on the end of the first-strand cDNA. The first-strand cDNA is then barcoded by forward and reverse primers from the SMART-Seq Long Read Index Kit (96 rxns) and amplified by the first PCR; a second PCR enriches for barcoded fragments. Samples are pooled and end-prepped, and sequencing adapters are ligated using the Ligation Sequencing Kit V14 (ONT). After sequencing, samples are basecalled and demultiplexed using ONT's Guppy. Downstream data analysis was performed using Cutadapt, Minimap2, SAMtools, Bedtools, and Salmon.

Back

SMART-Seq mRNA LR generates barcoded cDNA with a wide range of fragment lengths.

SMART-Seq mRNA LR generates barcoded cDNA with a wide range of fragment lengths. SMART-Seq mRNA LR workflow was used to create cDNA from 10 pg or 10 ng total mouse brain RNA (n = 8). cDNA size distribution was measured on a 2100 Bioanalyzer (Agilent Technologies) using an Agilent High Sensitivity DNA Kit. Panel A. Barcoded cDNA was pooled per input, and libraries were generated using the Ligation Sequencing Kit V14. Libraries were sequenced on a MinION Flow Cell (ONT) for 72 hr. Samples were basecalled and demultiplexed using ONTs' Guppy and read-length distribution was plotted using MS-Excel. Panels B & C. Read-length distributions are shown for a representative sample of 10 pg (Panel B) and 10 ng (Panel C) total mouse brain RNA.

Back

SMART-Seq mRNA Long Read demonstrates high sensitivity across a broad range of inputs.

SMART-Seq mRNA Long Read demonstrates high sensitivity across a broad range of inputs. To evaluate the performance, cDNA and sequencing libraries were generated from 10 pg and 10 ng total mouse brain RNA (MBR) using the SMART-Seq mRNA LR workflow. After sequencing, data was basecalled and demultiplexed using ONT's Guppy, and reads per barcode were downsampled to 400,000 reads. The read distribution for 10 pg and 10 ng MBR input. Panel A. is similar, showing consistent performance across the input range (n = 8 for 10 ng; n = 7 for 10 pg). Panel B. Reads of the 10 pg MBR dataset downsampled to assess gene and transcript sensitivity. Panel C. 10 ng MBR dataset downsampled to assess gene and transcript sensitivity. While neither of the two conditions (10 pg and 10 ng) were sequenced until saturation, both highlight the high gene and transcript sensitivity across read depths.

Back

SMART-Seq mRNA LR is highly reproducible across replicates for a broad range of inputs.

SMART-Seq mRNA LR is highly reproducible across replicates for a broad range of inputs. The SMART-Seq mRNA LR workflow was used to create cDNA from 10 ng MBR of 96 replicates. Barcoded cDNA of all 96 samples were pooled, and libraries were generated and sequenced according to the workflow.

Back

SMART-Seq mRNA LR yields even gene-body coverage for ultra-low inputs.

SMART-Seq mRNA LR yields even gene-body coverage for ultra-low inputs. 10 pg and 10 ng MBR samples underwent the SMART-Seq mRNA LR workflow. Gene-body coverage was assessed for the average of eight replicates of 10 pg and 10 ng MBR samples.

Back

SMART-Seq mRNA LR is compatible with direct cell inputs.

SMART-Seq mRNA LR is compatible with direct cell inputs. cDNA was generated from single K562 cells or 1,000 K562 cells using the SMART-Seq mRNA LR workflow. After sequencing, data was basecalled and demultiplexed using Guppy, and reads per barcode were downsampled to 300,000 reads. Panel A. The average gene count for each condition is shown above the corresponding bar (n = 8 for single cell and n = 2 for 1,000 cells). Panel B. Pearson correlation was calculated from the gene matrix across eight single K562 cells. Panel C. Downsampling analysis of single K562 cells shows gene and transcript count per read depth.

Back

SMART-Seq mRNA LR detects full-length isoforms and gene fusions.

SMART-Seq mRNA LR detects full-length isoforms and gene fusions. cDNA was generated using the SMART-Seq mRNA LR workflow. Basecalling and demultiplexing were performed using ONT's G uppy, and reads were aligned using Minimap2. Panels A & B. Isoforms of Snap25 (Panel A) and Nbr1 (Panel B) detected from 10 pg MBR input are visualized in IGV. Panel C. NUP214::XKR3 gene fusions detected from single-cell input are visualized in IGV.

Back

SMART-Seq mRNA LR is compatible with automation and miniaturization.

SMART-Seq mRNA LR is compatible with automation and miniaturization. The SMART-Seq mRNA LR workflow was used to create cDNA from 10 ng MBR of 96 replicates manually on the benchtop with the full reaction volumes or automated at 1/8-volume on a mosquito HV (SPT Labtech). Barcoded cDNA of all 96 samples per experiment was pooled, and libraries were generated and sequenced for 72 hr according to the workflow. Samples were basecalled and demultiplexed using ONT Guppy and aligned with Minimap2. Panel A. Render of the mosquito HV liquid handler. Panel B. Gene counts were assessed with Salmon. Panel C. Mapping statistics of manually prepared cDNA compared to cDNA prepared using the mosquito HV.

Back

SMART-Seq mRNA LR performance with mRNA reference standards

SMART-Seq mRNA LR performance with mRNA reference standards. To evaluate the performance of the SMART-Seq mRNA LR workflow, cDNA was generated from SIRV-Set 4 (Lexogen) mRNA reference standards which contains both ERCC quantification controls and Long SIRV mRNA standards. 10 ng of mouse brain total RNA was prepared—with SIRV spike-ins added to account for approximately 5% of reads—using the workflow described in Figure 1. Libraries were sequenced by ONT MinION, FASTQ data was read-strand corrected using the Restrander tool, and data were aligned with minimap2. ERCC standard abundance for measured vs. theoretical concentration was plotted.

Back

SMART-Seq mRNA LR performance with mRNA reference standards

SMART-Seq mRNA LR performance with mRNA reference standards. To evaluate the performance of the SMART-Seq mRNA LR workflow, cDNA was generated from SIRV-Set 4 (Lexogen) mRNA reference standards which contains both ERCC quantification controls and Long SIRV mRNA standards. 10 ng of mouse brain total RNA was prepared—with SIRV spike-ins added to account for approximately 5% of reads—using the workflow described in Figure 1. Libraries were sequenced by ONT MinION, FASTQ data was read-strand corrected using the Restrander tool, and data were aligned with minimap2. IGV plots of data from 1 kb, 4 kb, 6 kb, and 8 kb long transcripts from the ERCC and long SIRV isoform set. Red colored reads indicate positive strand reads, blue indicates very rare negative-strand reads, and small purple or red marks indicate small indels/variations common in ONT-sequencing. Full-length is defined as reads that cover at least 90% of expected mRNA length.

Back

Visualization showing completeness of fragment lengths and strand orientation accuracy of the SSmRNA LR kit

Visualization showing completeness of fragment lengths and strand orientation accuracy of the SSmRNA LR kit. This experiment used Spike-In RNA Variant (SIRV) controls (600–2,492 bp), showcasing detection of full-length fragments with a high percentage of complete transcripts, and accurate identification of strand orientation across all isoforms. Panel A. Coverage analysis of complete, full-length fragments. Panel B . Strand identification across different SIRV-spike-in species visualized via SIRVsuite.

Back

Automation- and miniaturization-friendly SSmRNA LR workflow comparison

SMART-Seq mRNA LR performance with mRNA reference standards. To evaluate the performance of the SMART-Seq mRNA LR workflow, cDNA was generated from SIRV-Set 4 (Lexogen) mRNA reference standards which contains both ERCC quantification controls and Long SIRV mRNA standards. 10 ng of mouse brain total RNA was prepared—with SIRV spike-ins added to account for approximately 5% of reads—using the workflow described in Figure 1. Libraries were sequenced by ONT MinION, FASTQ data was read-strand corrected using the Restrander tool, and data were aligned with minimap2. ERCC standard abundance for measured vs. theoretical concentration was plotted.