Cell2Read: an automated workflow to generate sequencing-ready DNA libraries from human cell suspensions

Kathryn Whitehead ¹, Sarah Planchak ¹, Trinity Williams ¹

⁰Center for Biomedical Engineering, School of Engineering, Brown University, Providence, RI 02912, United States.

Biology Methods & Protocols +

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December 30, 2025

View abstract on PubMed

Summary

Cell2Read automates cell lysis and sample preparation for next-generation sequencing (NGS), enabling high-quality DNA analysis from as few as 1500 cells. This novel method ensures reproducible results for diverse cell types, crucial for genomic research and diagnostics.

Area Of Science

Genomics
Molecular Biology
Biotechnology

Background

Next-generation sequencing (NGS) requires efficient sample preparation, especially from limited cell inputs.
Current methods can be labor-intensive and may struggle with low cell numbers or diverse sample types.
Automated solutions are needed to improve reproducibility and accessibility in genomic analysis.

Purpose Of The Study

To introduce and validate Cell2Read, a novel automated workflow for integrated cell lysis and sample preparation for NGS.
To assess the performance of Cell2Read with low cell inputs and various cell types.
To evaluate the method's suitability for analyzing heterogeneous cell populations, including those relevant to oncology.

Main Methods

Development of an automated platform integrating cell lysis, DNA extraction, and library preparation.
Optimization of diffusion kinetics and thermal geometries for low-input cell processing.
Testing across multiple human cell lines (HepG2, Caov3, HEY A8, OVCAR 8, MDA-MB-231) and primary cells.
Evaluation of sequencing integrity, DNA yield, quality scores, GC bias, and alignment rates.
Assessment of performance with heterogeneous cell suspensions, including spiked cancer cells.

Main Results

Cell2Read successfully processed cell suspensions as low as 1500 cells without compromising sequencing integrity.
Consistent DNA yield (≥10 ng) and high sequencing quality (alignment rates >95% for ≥3125 cells) were achieved.
The method demonstrated reproducibility across diverse cell types and comparable performance to manual protocols.
Non-biased sequencing of heterogeneous cell suspensions was achieved, with DNA representation proportional to cancer cell concentration.
The system yielded high-quality genomic data from low-input samples, suitable for clinical diagnostics and research.

Conclusions

Cell2Read provides a technically validated, scalable, and automated solution for NGS sample preparation.
The workflow significantly expands accessibility to genomic analysis from low-input human samples.
Its robustness and reproducibility make it ideal for oncology research and clinical diagnostics.
Cell2Read ensures high-quality, unbiased sequencing data, supporting accurate genomic profiling.

Keywords:

DNA extraction automation automated DNA library preparation cancer cell detection cell lysis high-throughput genomics low-input sequencing oncology diagnostics