https://www.seqanswers.com/ This is the top level page that shows all categories that hold the articles on this site en Sat, 17 Oct 2026 23:42:26 GMT vBulletin 60 https://www.seqanswers.com/images/misc/rss.png https://www.seqanswers.com/ https://www.seqanswers.com/articles/327714-multiomic-liquid-biopsy-what-dna-rna-panels-reveal-that-dna-alone-can’t Wed, 14 Oct 2026 17:55:00 GMT Tumors contain complex mixtures of cell populations that evolve in response to treatment and host-specific factors. Tissue biopsies provide only...


Tumors contain complex mixtures of cell populations that evolve in response to treatment and host-specific factors. Tissue biopsies provide only static snapshots of a tumor’s state. But what if we could follow that evolution over time using a minimally invasive technique?

Liquid biopsies can detect tumor-derived material circulating in the bloodstream, providing a way to study treatment response, detect emerging resistance, and discover new biomarkers. Circulating tumor DNA (ctDNA), a fraction of cell-free DNA (cfDNA), is among the most widely studied analytes. But researchers are increasingly interested in adding other omic layers. Cell-free RNA (cfRNA), which includes RNA carried in extracellular vesicles, is one of them, and it helps researchers connect genotype with gene expression and activity within the tumor microenvironment.

However, cfDNA and cfRNA are often present at low concentrations, exhibit distinct stability profiles, and differ in their biological and clinical interpretation. The advantages of combining cfDNA and cfRNA in liquid biopsy can be hampered by the added complexity of the workflow.

In this article, we will cover:

• The complementary value of analyzing DNA, RNA, and proteins in liquid biopsy samples
• The technical challenges of combining multiple liquid biopsy analytes
• How to balance analyte-specific requirements with streamlined workflows
• What the future of multiomic liquid biopsy could look like

What RNA adds to DNA-based liquid biopsy

Analyzing ctDNA within the cfDNA pool provides useful information about “tumor-specific genomic alterations, such as mutations, copy-number changes, and emerging resistance-associated variants,” said Dr. Khatereh Motamedchaboki, Sr. Global Manager in Business Development, Life Science Mass Spectrometry, Omics at Thermo Fisher Scientific. This makes ctDNA a powerful tool to monitor tumor evolution and clonal selection during therapy.

But detecting a genomic alteration does not mean it is actively expressed. Adding cfRNA to liquid biopsy panels helps connect these alterations to gene expression and provides a more dynamic view of the tumor functional state, as Dr. Jonathan Shaffer, Head of NGS R&D at QIAGEN, explained.

Gene expression changes also provide information about cellular identity that driver mutations cannot. Dr. Emily Leproust, CEO and Co-founder of Twist Bioscience, highlighted lineage plasticity, in which tumor cells alter their gene expression programs to escape treatment, as seen in neuroendocrine transformation in EGFR-driven lung cancer or prostate cancer.

Combining cfDNA and cfRNA can be very useful to detect cancer-driving gene fusions and abnormal splicing events that produce oncogenic proteins, as Dr. Leproust and Dr. Shaffer highlighted. Fusion breakpoints can occur within large introns and vary significantly between individuals, making them difficult to predict based on DNA sequence alone. In contrast, as Dr. Leproust explained, the skipped transcript provides one clear, consistent signal, as seen with MET exon 14 skipping.

By bypassing these large, repetitive introns, cfRNA “compresses the target space and reduces the sequencing reads needed to detect complex structural variants,” said Dr. Shaffer. He added that each mutated DNA copy can generate 20–2,000 mRNA copies, and thus adding cfRNA could increase assay sensitivity.

However, to enjoy the benefits of working with cfDNA and cfRNA for liquid biopsy, labs need to manage a more complex workflow that preserves both analytes and delivers results within a sufficiently short turnaround time.

Combining cfDNA and cfRNA: key workflow challenges

The integration of cfDNA and cfRNA in liquid biopsy means working with analytes that show very different stability profiles. On top of that, sample material is often limited, and tumor-derived signals can be scarce, occurring at variant allele frequency (VAFs) as low as 0.1%. Labs need to take the specific characteristics of each analyte into account while combining them in a workflow that remains efficient.

Sample collection and handling to preserve both cfDNA and cfRNA

One of the main workflow challenges starts right after sample collection. As Dr. Markus Sprenger-Haussels, Vice President R&D, PreAnalytiX – A QIAGEN and BD Joint Venture, explained, stabilizing samples and processing them as soon as possible are key to preserving the analytes being studied. He stressed that delayed processing of unstabilized blood samples leads to leukocyte lysis and release of genomic DNA into the plasma, increasing background DNA, reducing the relative fraction of ctDNA, and thus affecting assay sensitivity and interpretation.

For cfRNA, the challenge is even more complex, as beyond degradation, “ongoing cellular and biological processes after blood collection can alter the expression profile of the sample,” according to Dr. Sprenger-Haussels.

Both Dr. Leproust and Dr. Sprenger-Haussels also emphasized the importance of standardized handling from collection through plasma storage, including:

• Using collection tubes and stabilization procedures validated for the intended analytes.
• Controlling collection-to-processing times and transport temperatures.
• Minimizing residual cells and platelets during plasma separation.
• Aliquoting plasma before freezing and avoiding repeated freeze–thaw cycles.

When prompt processing is not possible, validated stabilization options can extend the handling window. PAXgene Blood ccfDNA Tubes (RUO*) allow whole blood to be stored for up to 10 days at temperatures up to 25°C before plasma separation for cfDNA analysis. Emerging data also suggests that plasma from these tubes can support cfRNA extraction and downstream analysis with appropriate extraction and analytical workflows.

cfDNA and cfRNA extraction: together or not?

After plasma preparation, the main decision comes down to extracting cfDNA and cfRNA together or separately. Dr. Sprenger-Haussels explained that co-extraction saves sample volume and simplifies the workflow, making it advantageous when working with limited material.

However, extraction conditions optimized for one analyte can reduce the recovery of the other or bias fragment sizes, as cfDNA and cfRNA show distinct fragment and structural profiles. The workflow should also balance recovery with inhibitor elution.

QIAGEN approaches co-extraction with the QIAamp ccfDNA/RNA Kit, a two-stage silica-column workflow that can co-isolate cfDNA and cfRNA from 1–4 mL of plasma or serum, helping conserve limited sample. Protein precipitation and successive silica-column wash steps reduce contaminant carryover, and the final eluate is concentrated with a MinElute cleanup.

Still, Dr. Sprenger-Haussels recommends monitoring DNA and RNA separately. Key controls include:

• Recovery: DNA and RNA spike-ins added before extraction, spanning relevant fragment sizes.
• Contamination: a full-process negative control and a characterized positive sample.
• cfDNA: quantify with a sensitive method such as qPCR or dPCR, check fragment sizes and high-molecular-weight DNA contamination by capillary electrophoresis, and assess amplifiability.
• cfRNA: check reverse-transcription performance and detection of both short and longer targets. Include a no-RT control to identify DNA-derived signal.
• Downstream suitability: monitor sequencing performance; include low-frequency variant, fusion or expression controls where relevant.

When sample volume is less limited, or when cfDNA and cfRNA concentrations need to be adjusted independently for downstream analysis, Dr. Sprenger-Haussels recommends sequential recovery using dedicated purification technologies, such as the QIAamp MinElute ccfDNA Kit and the exoRNeasy Kit for extracellular-vesicle-associated RNA.

The decision ultimately comes down to the biological question, the available sample volume, the specific DNA and RNA populations to study, and the downstream assay requirements.

Library preparation: balancing sensitivity and workflow efficiency

At the library preparation stage, labs again face a choice: prepare cfDNA and cfRNA libraries together or separately.

Dr. Leproust explained that separate library preparation offers better control over each analyte’s particularities, which can be especially useful when working with very low-input samples or when end repair and adapter ligation are needed to capture short cfDNA fragments. Some applications that benefit from independent library preparation are those where sensitivity is key, such as minimal residual disease (MRD) or low-VAF detection, or when adding cfRNA to an already optimized cfDNA workflow.

The trade-off is extra processing and sample requirements. To limit this, Dr. Leproust recommends workflows optimized for each analyte, so laboratories can meet DNA/RNA sensitivity needs with less input and preserve material for other omic analysis. “Twist’s cfDNA Library Preparation Kit with TrueAmp™ Polymerase Mix maximizes molecular conversion efficiency and high-fidelity amplification, supporting reliable variant detection even in sample inputs down to 1 ng,” she explained.

cfDNA library prep can be run in parallel with Twist’s RNA Library Prep Kit. According to Dr. Leproust, they take about two and five hours, respectively, meaning both libraries can be ready within a day.

Combined library preparation to simplify workflows

An alternative is to combine DNA and RNA library preparation in a single workflow. Dr. Shaffer reported that the QIAseq Multimodal DNA/RNA Library Kit can be used with cfDNA and cfRNA, as long as cfRNA is adequately preserved during preanalytical processing. Combining both analytes in the same workflow reduces sample splitting, transfers, and handling steps and can produce sequencing-ready libraries in about six hours.

The QIAGEN kit also adds Unique Molecular Indices (UMIs) to both DNA and RNA, helping distinguish biological signals from technical noise. This approach can help conserve limited sample material and simplify the workflow, but as Dr. Shaffer highlighted, labs still need to validate DNA and RNA recovery and sensitivity for their specific application.

The future of multiomic liquid biopsy beyond DNA and RNA

Combining DNA and RNA in liquid biopsies helps connect tumor genetics with gene expression, but as Dr. Motamedchaboki noted, “genomic and transcriptomic alterations do not always correlate with functional cellular states or therapeutic response.”

Thus, researchers are increasingly exploring additional omic layers, including proteomics, protein post-translational modifications like glycoproteomics and phosphoproteomics, as well as metabolomics and lipidomics, to characterize dysregulated signaling pathways, adaptive resistance mechanisms, and clinically relevant patient subtypes. Extracellular vesicles are attracting attention in this field as “they carry molecular information that reflects active biological processes within tumor cells and their microenvironment,” added Dr. Motamedchaboki.

But detecting circulating proteins and lipids of interest in plasma can be difficult, as they show low abundance against a much larger background. Advances in mass spectrometry are improving sensitivity and throughput. This is achieved through advances like the Thermo Scientific Orbitrap Astral Zoom Mass Spectrometer, which uses ion pre-accumulation and a dedicated low-input detector mode. Also, the targeted affinity-based methods like Olink Assays can be combined with mass spectrometry to obtain broader protein coverage when working with very low-abundance targets.

Dr. Motamedchaboki shared with us her visions on the future of omics in liquid biopsies: “In the future, as these technologies mature and become more broadly accessible, clinicians may be able to leverage an integrated clinical multiomics workflow that combines genomic, transcriptomic, proteomic, phosphoproteomic, lipidomic, and metabolomic data.” This more comprehensive view will help improve patient stratification, more accurately predict therapeutic response, and support more dynamic treatment decisions in precision oncology.


* Unless otherwise indicated, QIAGEN and PreAnalytix products mentioned are for research use only. Not for use in diagnostic procedures. Products and product claims may differ from country to country based on regulations, clearances and approvals and may not be available in certain country. Please visit www.preanalytix.com, www.qiagen.com or contact your local supplier or country representative for further details.

About the author: Nuria M. Wentzien got her Ph.D. from the University of Granada in 2023. After finishing her Ph.D., she discovered that science communication brings together her two favorite worlds: science and writing. Her career reflects her passion for learning and exploring new topics, from a Bachelor's degree in Biochemistry (with a strong focus on human sciences) to a Ph.D. in sustainable agriculture. She brings this same curiosity to every project, making readers feel as inspired by science as she does. Now dedicated to helping life-science companies communicate rigorous science in an engaging way, she is the co-founder of Helixa Communications.]]> Articles SEQadmin2 https://www.seqanswers.com/articles/327714-multiomic-liquid-biopsy-what-dna-rna-panels-reveal-that-dna-alone-can’t https://www.seqanswers.com/articles/327696-new-genomics-technologies-take-aim-at-long-standing-limits Mon, 28 Sep 2026 18:27:00 GMT Researchers using sequencing and genomics tools often have to make trade-offs. They can choose between speed or scale, short reads or long-range...

Researchers using sequencing and genomics tools often have to make trade-offs. They can choose between speed or scale, short reads or long-range information, or targeted panels or a view of the whole transcriptome. New technologies that have been released this year are built to address those tough choices.

We asked six companies the same four questions to learn about their latest products. The new technologies bring a lot to the table, including rethinking sequencing throughput and flexibility, bringing long-range insights to short-read data, extending whole-transcriptome analysis to spatial and archived samples, and mapping base-editing outcomes directly in cells.

Read on to learn more from the experts.

Atera
10x Genomics
Kamila Belhocine, Ph.D., Senior Director, Spatial Biology, 10x Genomics

1. What is the new innovation?

Atera is a new in situ spatial biology platform engineered to deliver whole-transcriptome spatial analysis without compromising on throughput, plex, or performance.



A single Atera instrument can process over 800 1 cm² whole transcriptome samples in both fresh-frozen and FFPE tissue. That’s nearly a billion cells and trillions of transcripts in a single year. Atera instruments also have a 212 nm per-pixel resolution with analytical software that can provide sub-30 nm localization, compatibility with standard microscopy slides, support custom panels, and are powered by onboard GPUs and optimized algorithms that can analyze petabytes of data in real time.

2. What challenge is it solving?

Historically, scientists have used spatial biology tools to analyze molecules, cells, and tissues in their native context, but it came with a tradeoff. Legacy technologies didn’t allow for the kind of throughput to make population-scale insights possible in a realistic period of time.

Because of this technological jump, Atera removes constraints that have previously sequestered spatial tools into niche discovery and enables analysis of full clinical trial cohorts as opposed to subsets of sample populations. For example, an Atera instrument can analyze up to 3,000 core biopsy samples in a year. That kind of throughput is critical for scientists, as generating meaningful insights at scale requires large, high-quality datasets—especially to build complex, foundational AI models.

3. How do you see scientists using this technology?

Ultimately, Atera is designed for researchers who need spatial insights at population-level scale.

We’ve seen spatial tools utilized in fields such as oncology, immunology, neurodegenerative disease, and developmental biology. Spatial insights, especially at scale, can help scientists uncover important biomarkers, accelerate drug target identification and validation, explore core mechanisms of both healthy and diseased tissue, and even inform treatment strategy, patient stratification, and potentially predict treatment response. Atera’s compatibility with formalin-fixed paraffin-embedding (FFPE) is critical, as many archival samples are preserved in that format but are difficult to work with.

Academic and medical institutions continue to utilize our technology to drive foundational discovery, such as Brigham and Women’s Hospital, which aims to use Atera to accelerate target discovery in inflammatory and fibrotic diseases, or The Broad Institute, which will be using the platform to search for predictive biomarkers and train AI models.

Another one of our partners, The German Cancer Research Center (DKFZ), used Atera to study a colorectal tumor sample and found something exceptional: tumors that were previously thought to have low immune infiltration were found to harbor active and diverse immune cell populations.

For biotech and pharmaceutical partners, a core goal is to uncover biology to help accelerate the drug pipeline, whether for target identification and validation, biomarker discovery, mechanism of action studies, or toxicology.

Some of the other organizations and research consortiums we’ve partnered with have a specific goal: to power AI models with massive amounts of high-quality data, such as Human Cell Atlas, PharosAI, and Bioptimus.

4. What has been the response from scientists?

Initial responses from researchers reflect genuine excitement for the breadth of new insights this approach makes possible. We’re already getting feedback that access to this kind of throughput is rapidly expanding the scope of new projects. It’s something we’re incredibly proud of. Our partners who have used Atera in early access include Allen Institute and DKFZ.

Another exciting aspect to Atera is that we have a full roadmap planned to incorporate even more features to simplify its workflow, incorporate protein multiomics and variant calling, and more. Plus, we’re investing heavily into a software ecosystem that allows any scientist to effectively interpret output without needing a background in bioinformatics.


BaseMap™
Broken String Biosciences
Felix Dobbs, Ph.D., Co-founder and Chief Operations Officer, Broken String Biosciences

1. What is the new innovation?

BaseMap™ is a new cellular approach for characterizing adenine base editing activity, designed to show exactly where deamination events are occurring. It can identify intended on-target editing, bystander deamination, and off-target activity directly in cells, providing a more complete picture of editing outcomes within their cellular context.



The important distinction is that BaseMap does not rely on a proxy system to predict where editing might occur. It measures editing-associated events directly in the cells being studied, providing nucleotide-level information about the location and nature of those events.

2. What challenge is it solving?

Base editing has created a new characterization challenge. Unlike conventional nuclease-based genome editing, base editors are designed to make targeted nucleotide changes without creating a programmed double-strand break. This means that understanding their activity requires looking beyond conventional DNA break mapping alone.

Researchers need to understand whether the intended base has been edited, whether neighboring bases have also been modified through bystander deamination, and whether unintended activity is occurring elsewhere in the genome.

Existing approaches can provide important pieces of this picture, but there is a gap in the ability to directly characterize these different outcomes in a cellular system. This is increasingly important as base editing moves toward therapeutic development, with recent FDA draft guidance highlighting the importance of assessing off-target editing and genome integrity in cells representative of the intended target tissue.

3. How do you see scientists using this technology?

BaseMap™ is provided as a kit, enabling researchers to run the workflow in-house using their own cells rather than sending samples to an external service. This is particularly important for base editing, where cellular context can influence editing outcomes and where access to relevant cell models may be critical.

There is currently no comparable commercially available approach that enables researchers to directly characterize on-target deamination, bystander products, and off-target activity within a cellular system. BaseMap is designed to fill that gap.

We see immediate applications in the development and optimization of base editing systems, where researchers can compare editors, guide RNAs, and experimental conditions and understand the wider editing profile associated with each. For therapeutic developers, this can support candidate selection and genome integrity assessment as programs progress toward the clinic. More broadly, it provides researchers with a new way to investigate how base editors behave across different genomic and cellular contexts.

4. What has been the response from scientists?

The response we have seen reflects a growing need for more complete characterization of base editing outcomes, particularly as the field moves from demonstrating editing efficiency toward understanding specificity and safety.

One of the practical advantages is that researchers can investigate on-target activity, bystander products, and off-target activity within a cellular context rather than building their understanding from separate proxy approaches. This gives them a more connected view of what is happening following editing and can help inform decisions around editor, guide, and condition selection.

Importantly, the aim is not simply to generate more data. It is to provide information that helps researchers distinguish between candidates and make better-informed decisions earlier in development, before significant time and resources have been committed to a particular editing strategy.


TruPath™ Genome
Illumina
James Han, Vice President of Bioinformatics, Illumina

1. What is the new innovation?

TruPath™ Genome’s innovative proximity read mapping provides long-range insights on short-read sequencing. TruPath Genome is Illumina’s simplest genomic workflow to date, featuring the ease of on-flow cell library preparation. Patterned flow cell technology and advanced informatics seamlessly add long-distance insights for comprehensive variant detection. DRAGEN™ algorithms incorporate proximity information from TruPath Genome flow cells to improve read alignment and variant calling.

2. What challenge is it solving?

Traditional short-read sequencing methods struggle to capture difficult-to-map or “dark” regions of the genome. TruPath Genome innovates with flow cell configuration and informatics to accurately characterize these challenging regions. It extends Illumina’s high-quality reads to difficult-to-map regions, including variant phasing, and improved detection of structural variants and short tandem repeats.

3. How do you see scientists using this technology?

We believe genetic disease researchers will find TruPath Genome to be an unmatched workflow for variant detection and accuracy. It maximizes discovery potential and research dollars by giving comprehensive results.

4. What has been the response from scientists?

Early customers like University Medical Center Utrecht’s Marcel Nelen and the University of Exeter’s Emma Baple are already demonstrating TruPath Genome’s potential applications in rare disease and NIPD research.

Overall, customers are finding the technology detects even the hardest-to-call variants.

They appreciate that TruPath Genome reduces the need for additional or reflex assays. Unlike other methods that require off-instrument library preparation with trained laboratory personnel and ancillary equipment, the TruPath Genome workflow is on flow cell and only takes about 10 minutes of hands-on time. They also like that it operates on the sequencers they already use, so it delivers powerful new capabilities from the instruments they already have.


T7+
MGI Tech
Erin Zhang, Ph.D., Product Director, MGI Tech

1. What is the new innovation?

T7+ is our next-generation powerhouse sequencer, which addresses the trade‑off in large‑scale genomic sequencing. Users traditionally have to choose between speed and scale, which is a constraint that has been slowing down research for years. T7+ makes it possible for users to process many samples really quickly.



Several technological highlights help achieve this superior performance. The density of DNA Nanoballs (DNB) on the flow cell is ultra-high, allowing us to significantly boost the throughput without compromising the imaging speed. We have also developed a TDI camera with high-resolution and high-speed. Our proprietary RiCT (RCA in coiled tubing) technology allows for high-speed DNB preparation and can adjust reagent flow rates to achieve peak efficiency with smart sensors. In addition, an optimized base-calling algorithm makes the data generation four times faster.

2. What challenge is it solving?

To ensure that T7+ addresses real customer challenges, we worked closely with hundreds of labs to learn about their pain points. T7+ has addressed three major issues. First is the trade-off between speed and throughput. T7+ is able to generate more than 14 Tb data within 24h.

Next is the complexity of operating sequencers. We built a 7-in-1 highly integrated compact architecture: DNB making, DNB loading, sequencing, pure water bucket, waste bucket, data analysis, and data compression are all consolidated inside around a one‑meter‑footprint instrument. For the reagent cartridge, the modular design and RFID auto-identification largely minimize the manual handling operation and ensure error-free setup. Also, we designed the smart control center to be as easy as a smartphone.

Another challenge is cost. We placed heavy emphasis on cost optimization and made our technology more cost effective. With lower costs, researchers can generate more data with the same funding.

3. How do you see scientists using this technology?

T7+ has been used by both academic and industrial customers for a wide range of applications, spanning from fundamental biological research to translational clinical studies. Scientists have deployed it in diverse research projects, including population cohort study, spatial transcriptomics of brain injury repair, agricultural strain identification, and deciphering the evolution of the code of life through ancient human DNA samples.

Researchers at Fudan University used T7+ for PCR‑free whole‑genome sequencing in population cohorts. The lab leveraged the platform’s uniform read coverage to resolve challenging genomic regions such as homopolymers and tandem repeats, demonstrating how consistent, high-accuracy sequencing delivers high-quality variant calling of SNPs, Indels and SVs.

Moving from population genetics to clinical translational research, the platform has also proven valuable in oncology research. One customer in the U.S. has applied DNBSEQ™ sequencing technology to oncology applications using circulating tumor DNA (ctDNA). Their work highlighted how ctDNA is transitioning from a research tool to a vital clinical marker for minimal residual disease (MRD) and early cancer detection.

Outside academic labs, commercial service providers particularly value T7+’s ability to support pooling highly diverse library types in a single flow cell. Many service providers have pooled multiple library types spanning WGS, metagenomics, RNA, single-cell, methylation, and others, while still maintaining high sequencing data output with high Q30 data quality. This demonstrates T7+’s operational flexibility and stability for commercial service labs that routinely handle heterogeneous sample mixes.

Looking ahead, T7+’s outstanding data quality, throughput, and cost efficiency position it to support an even broader spectrum of applications. We expect it to play a key role in large-scale population genomics initiatives and multi-omics studies, while also expanding into more clinical directions as the platform develops.

4. What has been the response from scientists?

Customers from both academia and industry have responded enthusiastically to the T7+, citing its accuracy, higher throughput, simplified workflow, and cost efficiency. The feedback spans different user segments, each highlighting distinct benefits that collectively demonstrate the platform’s broad impact on research and clinical workflows.

A professor from Tongji University studying microbial ecology noted that the collaboration with MGI has allowed him to explore lake microbial diversity, and T7+ delivered excellent sequencing results. This reflects how the platform empowers fundamental research with higher data quality.

Customers from clinical research have the same feeling. A customer from the U.S. emphasized the platform’s dual strengths: methylation data achieved exceptional Q40 scores with 14B reads, while whole-genome data were delivered at a remarkably low cost, at around $100 per genome. This combination of high accuracy and affordability makes the T7+ particularly attractive for large-volume oncology sequencing and clinical testing workflows.

What’s more, commercial service providers also voiced strong approval. The service provider JMDNA (Shanghai) Bio-Medical Technology Co., Ltd., specializing in multiomics research and clinical applications, commented that the T7+ features improved sequencing quality, enabling faster run times, and lowering sequencing costs. It makes large-scale sequencing services feasible for genomics research, helping end-user researchers reduce costs and boost efficiency.

Taken together, these responses reveal that T7+ has consistently proven itself as a transformative tool that not only enhances data quality and operational efficiency but also substantially reduces sequencing costs. This strong positive reception underscores the platform’s potential to accelerate scientific discovery and clinical translation worldwide.


Evercode™ WT FFPE
Parse Biosciences
Aisling Sinclair, Senior Product Marketing Manager, Parse Biosciences

1. What is the new innovation?

Parse Biosciences’ Evercode™ WT FFPE brings scalable, true whole-transcriptome single-cell sequencing to formalin-fixed, paraffin-embedded (FFPE) tissue. It combines reverse transcription-based chemistry optimized for degraded RNA with Parse’s instrument-free combinatorial barcoding approach.



Researchers can generate high-quality gene expression data from archived samples while capturing RNA directly across the whole transcriptome. This provides a direct, sequence-based view of gene expression and access to biology that targeted methods may miss, including unexpected transcripts, isoforms, long noncoding RNAs, and expressed variants.

2. What challenge is it solving?

FFPE is the standard preservation method for many clinical tissue samples. Biobanks therefore contain enormous collections of archived tissue paired with clinical information that can help researchers study disease progression and treatment response. However, fixation fragments and crosslinks RNA, making it difficult to generate high-quality single-cell data from these samples.

Probe-based methods have made FFPE tissue more accessible, but they measure predefined sequences selected during assay design. As a result, researchers are limited to the targets represented in the assay, and differences in probe binding and performance can influence observed gene counts.

Evercode WT FFPE makes true whole-transcriptome discovery possible from archived tissue. Researchers can follow the biology wherever it leads, from established disease pathways to unexpected cell states, transcripts, isoforms, and regulatory programs. The result is a more complete view of each sample and a stronger foundation for understanding disease biology.

3. How do you see scientists using this technology?

One of the clearest near-term applications is translational research, particularly oncology, where large FFPE collections span disease stages, treatment histories, and clinical outcomes. Researchers can use these samples to characterize the tumor microenvironment, compare treatment responders with nonresponders, examine changes before and after therapy, or follow how tumors evolve between primary disease and recurrence. Whole-transcriptome capture also supports the study of isoforms, long noncoding RNAs, expressed variants, and molecular programs that may contribute to disease progression or treatment resistance.

The opportunity extends beyond oncology to any field with valuable archived tissue. These samples are often precious and finite, and researchers may have only one opportunity to profile them at single-cell resolution. Whole-transcriptome sequencing helps them make the most of that opportunity by capturing everything present, which preserves the possibility of finding what they do not yet know to look for. As new questions emerge and our understanding of the transcriptome grows, the value of those datasets can grow with it.

4. What has been the response from scientists?

The response has been very encouraging. Researchers are especially excited by the combination of whole-transcriptome capture without predefined probes and the ability to scale that discovery across substantial FFPE archives. In our conversations to date, scientists have discussed moving from initial pilots to cohorts of hundreds and, in some cases, thousands of archived samples. For researchers who have traditionally had to work within targeted panels, Evercode WT FFPE offers something genuinely new, the freedom to ask broader questions of valuable tissue collections at meaningful scale.


AXELIOS 1
Roche
Bellal Moghis, Director of Marketing, Sequencing and Life Sciences, Roche Diagnostics

1. What is the new innovation?

The core innovation is sequencing by expansion (SBX), a new class of next-generation sequencing (NGS) that sequences a surrogate molecule called an Xpandomer. These Xpandomers are approximately 50 times longer than the original DNA, creating a much stronger signal and higher signal-to-noise ratio in nanopores. This enables real-time data generation, allowing sequencing to occur in as little as four hours without compromising data quality.



2. What challenge is it solving?

AXELIOS 1 addresses the high costs and rigid requirements that have historically created barriers to NGS adoption and scale. Many existing high-throughput systems require large sample batches, which is impractical for many laboratories. AXELIOS 1 offers a flexible, economically viable solution that allows labs to maintain cost efficiency with smaller batches while scaling as demand increases. It also provides complete workflow solutions, helping researchers integrate new technology more seamlessly.

3. How do you see scientists using this technology?

Whole-genome sequencing (WGS) is a primary application, especially where rapid turnaround is critical for research. We also see significant interest in quantitative applications, such as single-cell sequencing, where the simplex workflow reduces costs per million reads. Researchers can leverage the platform’s distinct run modes—duplex for high-accuracy sequencing and simplex for quantitative counts—to tailor performance to their application. Key research fields include oncology, neurodegenerative disease, and rare genetic disease research.

4. What has been the response from scientists?

Scientists appreciate the flexibility to match sequencing capacity to the work at hand and switch between different workflows—such as routine, high-efficiency sequencing and rapid-response applications like SBX-Fast—on the same instrument. This capability effectively changes the economics for many service laboratories, allowing them to increase capacity and offer applications that were previously not financially feasible, all without the need to adopt multiple platforms.

For Research Use Only. Not for use in Diagnostic Procedures. AXELIOS is a trademark of Roche.


About the author: Niki Spahich, Ph.D., earned her Ph.D. in genetics and genomics from Duke University, where she studied Haemophilus influenzae membrane proteins that contribute to respiratory infections. She later explored Staphylococcus aureus metabolism during her postdoctoral fellowship in the Department of Microbiology and Immunology at the University of North Carolina - Chapel Hill. In 2016, Niki co-founded Science Riot, a nonprofit dedicated to providing entertaining and engaging science outreach events, and in 2020, she launched The Scientist Speaks, a popular podcast series for researchers at the bench. Niki has led custom content creation for marketers seeking to reach life scientists since 2019.]]> Articles SEQadmin2 https://www.seqanswers.com/articles/327696-new-genomics-technologies-take-aim-at-long-standing-limits