Sample Success

Get Results With Challenging Samples

Serial testing of archived samples can lead to sample exhaustion and Quantity Not Sufficient (QNS) results, limiting the amount of information that can be gained from rare clinical specimens. The HTG workflow requires very small amount of input material, due to the preservation of RNAs in the extraction-free chemistry, allowing you to measure expression levels of the entire human transcriptome, while still preserving the sample for additional follow-up studies.

Extraction-Free Chemistry Eliminates Bias

Maximize Use of Every Clinical Sample

Wide Range of Sample Types Supported

Extraction-Free Chemistry Eliminates Bias

Our extraction-free chemistry does not require nucleic acid extraction and therefore reduces sample input requirements, especially from FFPE samples. This is due to the preservation and utility of small and or partly fragmented RNAs. This also eliminates biases associated with nucleic acid extraction, size selection, PCR amplification, cDNA synthesis and adapter ligation.

Maximize Use of Every Clinical Sample

Assays that require large sample amount in the form of multiple slides can be a difficult barrier for many clinical studies. Our proprietary technology is unique in allowing samples with low quality or limited quantity to be processed in a simple workflow. Studies typically require only a single slide (minimum 8 mm2) to achieve improved results for both archival FFPE samples and low sample input.

Wide Range of Sample Types Supported

Clinical testing often requires analysis of diverse sample types and generating actionable data can be challenging. HTG panels support a variety of samples including formalin-fixed paraffin-embedded (FFPE) tissue, fine-needle aspirate (FNA) and core-needle biopsies (CNBs), PAXgene, extracted RNA, blood/serum and cells. HTG’s extraction-free chemistry allows for multiplexed, quantitative gene expression profiling using a single 8 mm2 FFPE tissue section or 15μl of most liquid biopsy types.
Normalization and principal component analysis tools: