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As next-generation sequencing (NGS) moves deeper into clinical research, population-scale programs, and AI-driven discovery, a new bottleneck has become impossible to ignore: sample prep. For Pranav Patel, PhD, co-founder and CEO of n6 Tec, the urgency has been building for a decade. “What led to n6 is just sheer frustration,” he says.* “We’re taking genomics to scale, pushing immense volumes of data through machine learning—and how can we afford compromised data?”

Patel’s team studied operators using the same kits and protocols across multiple labs and found a stubborn pattern. “The largest variability comes from operators, not the kits anymore,” he explains. Even something as basic as choosing whether a low-input sample needs 10 or 12 PCR cycles can become a judgment call. Each decision introduces drift. Drift becomes variability. And variability undermines the very datasets fueling modern genomics.

Reinventing PCR from the inside out

Patel didn’t intend to build a PCR machine. But to eliminate cycle-count guesswork, he needed a system that could decide for itself. That idea became the icon96 PCR platform—previously known as iconPCR—launched in 2024 and now shipping worldwide.

Traditional 96-well thermocyclers use a single heating-and-cooling element under the block, making it impossible to independently stop individual wells as they reach the desired amplification level. A camera can detect synthesis in real time, but inaccurate DNA amplification between wells is unavoidable.

For low-input samples—such as cell-free DNA from liquid biopsies—under amplification is catastrophic. “You just don’t have enough material to sequence,” explains Yann Jouvenot, PhD, senior director of product at n6 Tec.

Overamplification is subtler but equally damaging. “The more you push PCR, the more duplicates you create. Those are useless reads,” Jouvenot notes. “And you get artifacts—chimeras, bubble products—genomic garbage.” Avoiding that garbage at the source, rather than cleaning around it, is icon96’s core proposition.

Scientists at one company attempted to micro-manage amplification manually. They spiked SYBR green into each tube, watched the fluorescence rise, paused their thermocycler mid-run, pulled out individual tubes the instant they reached the desired point, and then resumed cycling.

icon96 addresses these problems head-on. The system features 96 independently controlled thermocyclers, each effectively its own miniature instrument. As Patel puts it: “Instead of you determining the number of cycles for an entire plate, you tell it how much DNA you want, and it will adjust the cycle numbers for you. It’s doing exactly the same PCR you’ve been doing for 30 years—but it thinks for you.”

Its signature feature, AutoNorm, performs real-time quantification and automatic normalization during amplification. That means no manual quantification, no individual cleanup of libraries, and no post-hoc correction after damage has already been done. Every sample is driven to the same point on the amplification curve—never over, never under.

This is more than convenience, Patel stresses. “Not only is it normalizing, it’s preserving the best data quality that’s possible from that sample. You just don’t get that with any other method right now.”

A path toward ubiquity

The technical challenge—coordinating millisecond-level temperature control across 96 independent wells—required engineers from outside biotech, particularly the semiconductor world. The resulting icon96 platform is plug-and-play.

Jouvenot expects the impact to grow as sequencing costs fall. “More automation drives costs down, and removing decision points increases reproducibility,” he says. “Ultimately, that’s better for diagnostics

and healthcare.”

Patel is even more direct: “PCR is in every genomics lab. We want icon96 to be just as ubiquitous—a gold standard for how NGS data gets generated.”

With libraries prepared faster, cheaper, and more consistently—and with data quality preserved at the earliest possible step—the icon96 may be the kind of deceptively simple tool that reshapes an entire workflow. In an era defined by scaling genomics, that might be exactly what the field needs.

Learn more and start preserving data quality at the earliest possible step: hubs.ly/Q03XH1Ry0.

* Pranav Patel’s quotations come from an interview on a Mendelspod podcast.