In high-throughput labs, consistency is critical, yet DNA extraction, the bedrock of molecular diagnostics, remains a common source of variability and contamination. This guide outlines the most common pitfalls in genomic DNA extraction for high-volume labs and what solutions can help you eliminate them. 

India has 1.3 billion human genomes. This represents around 20% of the world's population. Their DNA sequences, however,  just make up around 0.2% of global genetic databases. 

This is beginning to change.

The genomics revolution in India is gaining remarkable momentum. With ambitious initiatives to sequence tens of thousands (eventually millions) of human genomes under initiatives like the GenomeIndia project, the need for high-throughput, reliable, and scalable laboratory infrastructure has never been greater. 

This surge in genomic activity is placing unprecedented demands on laboratories.

To keep up, labs need infrastructure that can handle large sample volumes without compromising on quality.

But even high-throughput labs face unique challenges that can disrupt downstream results, especially at the very first step: DNA extraction.

1. Inconsistent Yield and Purity: 

In high-volume labs, even subtle inconsistencies in DNA yield and purity can derail downstream applications such as PCR, library prep, or sequencing. These inconsistencies often originate from:

• Improper lysis: When cells aren’t completely lysed, a portion of the genomic DNA remains trapped within cell membranes, leading to under-representation.

• Inadequate reagent mixing or pipetting errors: Reagent distribution must be homogeneous at micro-volumes. Manual pipetting introduces air bubbles, uneven volumes, and shear forces—each of which can result in variable recovery or sheared DNA.

• Degraded input samples: High-throughput labs often receive samples of uneven quality. Repeated freeze-thaw cycles or prolonged transport times can cause fragmentation or chemical degradation, compounding yield and purity issues.

Such variability not only reduces the reproducibility of experiments but also leads to wasted sequencing runs and incorrect interpretation of copy number or structural variants.

2. Compromised DNA Quality Due to High Sample Volume:

 Processing large volumes of samples increases the likelihood of encountering degraded or suboptimal specimens. Factors such as delayed processing, improper storage, or repeated freeze-thaw cycles can lead to DNA degradation. Degraded DNA may result in low yields and poor-quality sequencing data, affecting downstream applications.

3. Increased Risk of Cross-Contamination: 

Handling numerous samples simultaneously elevates the risk of cross-contamination. Common sources include:

• Aerosol generation during pipetting, particularly while opening/closing tubes or using multi-channel pipettes.

• Reagent reuse without aliquoting, which becomes a cross-contamination risk when dipping into shared buffers or enzymes.

• Unclean workspaces and overloaded benches, where tube caps, gloves, or shared pipettes carry over trace DNA.

These risks are amplified when personnel are under time pressure, working across multiple stations, or when protocols require frequent sample transfers. A study published in PubMed, which examined cross-contamination in a high-volume clinical lab, suggests an extremely wide range of laboratory cross-contamination rates between 0.1 and 65%. 

4. Sample Tracking and Data Management: 

When sample identity is lost, everything downstream is compromised, regardless of how well the extraction itself went. Unfortunately, high-volume labs often rely on:

• Manual labelling with handwritten or printed labels that are prone to smudging, duplication, or transcription errors.

• Tube swaps, especially in 96-well or 384-well formats, where even a single row/column shift during loading leads to irrecoverable sample misassignment.

• Excel-based or non-integrated systems make it difficult to trace errors or rerun specific cases.
  
  

5. Bottlenecks in Throughput and Cost Reduction: 

High throughput does not automatically mean high efficiency. Labs often face: 

• Manual reagent preparation, which varies between technicians and consumes critical hours each day.

• Centrifugation and incubation steps, which are batch-based, fixed-time procedures that halt progress while instruments sit idle.

• Human dependency, where even a single technician’s absence or inattention can cause cascading delays across workflows.

• Poorly designed protocols lead to idle steps where staff wait for incubations to finish or rush to process batches before the day ends.

These inefficiencies ultimately raise the cost per sample and slow down turnaround time, two metrics labs must minimise to remain clinically and commercially viable.

6. Infrastructure and Computational Challenges: 

The vast amount of data generated in high-throughput genomic studies demands substantial storage and computational capabilities. Efficient data management systems are required to store, process, and analyse sequencing data without delays. Inadequate computational infrastructure can hinder data analysis and slow down research progress. 

Moore’s Law describes a trend coined by the Intel co-founder Gordon Moore, who stated that:

“The number of transistors on an integrated circuit will double every two years with minimal rise in cost.”

According to a paper published in ScienceDirect, until recent years, Moore’s law managed to keep ahead of the genomic curve, slightly outpacing the generation of biological sequence data by its growth in storage and processing capacity. However, since 2008, genomics data has been outpacing Moore’s Law by a factor of 4.

The solution to all these?  Automated workflows.

Automated workflows address many challenges faced by high-throughput genomic DNA extraction labs by combining robotics, standardized protocols, and digital tracking to enhance accuracy, consistency, and efficiency. Precise control of incubation times, reagent volumes, and mixing minimizes variability and preserves DNA quality, while closed-system liquid handling reduces contamination risks by limiting sample exposure and aerosol generation.

Integration with Laboratory Information Management Systems (LIMS) and barcode scanning ensures real-time digital tracking of every sample, significantly reducing mislabeling and sample mix-ups. These systems create an audit trail and enforce protocol adherence, helping maintain process integrity at scale.

Automation enables continuous, parallel processing of hundreds or thousands of samples, which increases throughput and shortens turnaround times while lowering reagent waste and labor costs. Built-in software also preprocesses data and flags anomalies early, easing the computational load and producing cleaner datasets for downstream analysis.

Furthermore, automated platforms support data security and regulatory compliance through encryption, role-based access control, and audit logging, critical for protecting sensitive genetic information in large-scale operations.

At Cambrian, we’ve dedicated significant effort to perfecting automated systems designed specifically for high-throughput labs focused on gDNA extraction.

You can explore these solutions here.

Recently, we hosted a webinar featuring Strand Life Sciences, one of India’s leading diagnostic labs, where they shared insights on how they scaled their workflow and how Manta played a key role in that success.

Watch it here