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Biostate’s Solution for RNAseq

Gene Expression Data – Simplified, Accelerated, Affordable

Obtaining accurate gene expression data from RNAseq workflows is both costly and complex. Traditional depletion or enrichment steps used to target RNAs of interest rely on expensive enzymes, introduce a variety of biases, or exclude lncRNAs (long noncoding RNAs).

To address these challenges, Biostate has developed an innovative solution that integrates two proprietary technologies:

BIRT (Barcode-Integrated Reverse Transcription) – for efficient sample multiplexing PERD (Probes for Excess RNA Depletion) – for nonenzymatic depletion of ribosomal RNA (rRNA) Together, BIRT+PERD delivers key advantages:

1. 16x increase in sample throughput

2. Up to 90% reduction in cost

3. Ability to measure lncRNAs

4. Compatibility with low-yield, degraded samples or budget-constrained projects

By adopting BIRT+PERD, NGS service providers can expand their client base, increase operational efficiency, and deliver superior value across research, clinical, and pharmaceutical sectors.

Patent-pending BIRT technology (Patent App. #63/570082) integrates sample-specific barcodes directly during the reverse transcription (RT) step using proprietary hairpin primers. Instead of traditional oligo(dT) priming, BIRT uses random polyN priming, which helps generate cDNAs from highly degraded samples, avoids 3’-end bias, and captures RNAs with short or no polyadenylation such as lncRNAs.

In conventional RNAseq workflows, samples are processed individually until late stages of library preparation. BIRT changes this by enabling the pooling of barcoded cDNAs from up to 16 samples immediately after the RT step. This pooling reduces hands-on time, reagent use, and costs, as downstream processes (e.g., adapter ligation and purification) can be performed collectively.

BIRT: Barcode-Integrated Reverse TranscriptionBIRT: Barcode-Integrated Reverse Transcription

Patent-pending BIRT technology (Patent App. #63/570082) integrates sample-specific barcodes directly during the reverse transcription (RT) step using proprietary hairpin primers. Instead of traditional oligo(dT) priming, BIRT uses random polyN priming, which helps generate cDNAs from highly degraded samples, avoids 3’-end bias, and captures RNAs with short or no polyadenylation such as lncRNAs.

In conventional RNAseq workflows, samples are processed individually until late stages of library preparation. BIRT changes this by enabling the pooling of barcoded cDNAs from up to 16 samples immediately after the RT step. This pooling reduces hands-on time, reagent use, and costs, as downstream processes (e.g., adapter ligation and purification) can be performed collectively.

Key Benefits of BIRT:

01
Maintains high reproducibility across barcoded samples (Fig. 1A, R² = 0.85)
02
Each barcode represents ~4.7% of total reads (Fig. 1B)
03
Requires as little as 10 ng of input RNA (vs. 1 µg in typical methods) (Fig. 1C, left)
04
Effective for degraded RNA, including FFPE (Formalin-Fixed Paraffin-Embedded) tissue on histopathology slides
05
Achieves strong results from just one 4-micron FFPE section, reducing tissue consumption
06
Enables practical RNA measurement from low-yield samples like plasma and extracellular evRNA (Fig. 1C, right)

BIRT + PERD PerformanceSuperior Gene Expression Fidelity Without Compromise

ChatGPT Image May 16, 2025, 12_35_26 PM

Conventional mRNA enrichment and rRNA depletion techniques aim to deliver expression data on more unique genes than total RNA. However, BIRT+PERD outperforms both methods (Fig. 1E).

In delivering more unique genes, it’s critical that a method preserves the true differential expression information in the sample. When comparing measured differences in gene expression (DEG), BIRT+PERD correlates closely with “undistorted” total RNA, just like conventional RNase H-based depletion methods.

Moreover, the two depletion methods (BIRT+PERD and RNase H) produce highly similar DEG results, particularly for low-abundance transcripts (Fig. 1F), suggesting that BIRT+PERD maintains both depth and accuracy in gene expression profiling.

Fig.1