Pushing the Limits of Sensitivity: Can We Sequence Individual Molecules in Cells?

The ability to sequence individual molecules inside cells represents one of the most exciting frontiers in biotechnology, molecular biology, and genomics. Single-molecule sequencing has the potential to revolutionize cellular analysis, offering unprecedented insights into gene expression, RNA processing, and protein synthesis at the most fundamental level.

Traditional sequencing technologies rely on bulk RNA or DNA extraction, averaging signals from thousands to millions of molecules. However, with advances in high-resolution sequencing methods, researchers can now push the limits of sensitivity to detect and analyze single molecules inside individual cells.

Single-molecule sequencing refers to techniques that detect and analyze individual DNA or RNA molecules without the need for amplification or bulk extraction. This approach enables researchers to:

• Identify rare genetic variants and mutations.
• Study RNA modifications and alternative splicing at an individual molecule level.
• Observe real-time molecular interactions inside live cells.
• Detect low-abundance transcripts that would be missed in bulk sequencing.

Applications of Single-Molecule Sequencing

1. Single-Cell Transcriptomics – Measuring gene expression heterogeneity between individual cells.
2. Epigenetic Modifications – Detecting methylation patterns and RNA modifications at a molecular level.
3. Structural Variation Analysis – Identifying genomic rearrangements, fusions, and long-read sequencing insights.
4. Live-Cell RNA Imaging – Observing RNA dynamics in real-time inside living cells.
5. Synthetic Biology & Biomolecular Engineering – Designing and optimizing molecules for biotechnological applications.

Several cutting-edge sequencing platforms are making ultra-sensitive molecular analysis possible. These include:

1. Nanopore Sequencing

Nanopore-based sequencing, pioneered by Oxford Nanopore Technologies, enables real-time, single-molecule sequencing without amplification. It works by passing DNA or RNA molecules through a nanopore, detecting changes in ion flow to infer sequences.

Advantages:

• Long-read sequencing capability.
• Ability to detect base modifications (e.g., methylation, pseudouridylation).
• Portable sequencing devices for real-time molecular analysis.

Single-molecule fluorescence-based sequencing detects individual molecules by tagging nucleotides with fluorescent dyes and imaging them under high-resolution microscopy.

Methods Include:

• smFISH (Single-Molecule Fluorescence In Situ Hybridization) – Detects specific RNA molecules inside cells.
• RNA-Seq with Molecular Barcoding – Increases sensitivity and reduces noise in sequencing data.
• Real-Time Imaging of RNA Processing – Tracks RNA splicing, transport, and translation.

PacBio’s SMRT sequencing uses a zero-mode waveguide (ZMW) nanostructure to observe DNA polymerase activity in real time, sequencing individual molecules with high accuracy.

Advantages:

• Long-read sequencing (~100,000+ bp reads).
• Direct detection of epigenetic modifications.
• Highly accurate base calling with HiFi read

Emerging spatial omics technologies enable sequencing of individual molecules while preserving their spatial context inside cells.

Key Approaches:

• Slide-Seq – Maps RNA expression patterns onto a 2D spatial grid.
• MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization) – Simultaneously detects thousands of RNAs inside single cells.
• SeqFISH+ – Allows highly multiplexed RNA imaging in intact tissue samples.

These methods provide valuable insights into cellular organization, tissue architecture, and single-cell gene expression.

Despite the promise of single-molecule sequencing, several challenges must be addressed:

1. Signal-to-Noise Ratio & Detection Sensitivity

• Low-abundance molecules can be difficult to distinguish from background noise.
• Single-molecule signals require high-precision optics, amplification strategies, and computational correction methods.

2. High Computational Demand & Data Processing

• Massive data volumes require advanced bioinformatics pipelines.
• Machine learning (ML) algorithms are essential for reducing noise, aligning reads, and improving accuracy.

3. Cost & Scalability

• Current single-molecule platforms are more expensive than traditional bulk sequencing methods.
• Scaling up for large-scale studies requires improvements in efficiency and affordability.

1. AI-Based Base Calling & Error Correction

• Deep learning models improve sequencing accuracy by reducing base-calling errors.
• Example: DeepNano and Bonito AI for nanopore sequencing.

2. Pattern Recognition for RNA Modifications

• Neural networks can identify m6A methylation, pseudouridylation, and other RNA modifications.
• DeepSignal and Nanopolish are AI-driven tools that enhance molecular sequencing precision.

3. Spatial Transcriptomics AI Analysis

• AI models reconstruct gene expression maps from spatially resolved sequencing data.
• This helps in understanding cell heterogeneity and tissue organization.

4. Data Compression & Feature Extraction

• AI reduces storage and computational costs by extracting key features from large datasets.
• Autoencoders and clustering algorithms help detect rare molecular signatures.

The future of single-molecule sequencing will focus on:

1. Improving Real-Time, Live-Cell Sequencing

• Developing minimally invasive sequencing technologies.
• Enabling continuous molecular tracking in living cells.

1. Integrating AI for Ultra-Sensitive Molecular Analysis

• Enhancing accuracy with deep learning-based base calling.
• AI-driven identification of genetic mutations and modifications.

1. Scaling Up for High-Throughput Applications

• Reducing sequencing costs for large-scale biomedical studies.
• Increasing accessibility to ultra-sensitive sequencing platforms

Single-molecule sequencing represents the next frontier in molecular biology and genomics, enabling ultra-sensitive detection of individual molecules inside cells. By integrating cutting-edge sequencing technologies, AI-driven bioinformatics, and spatial transcriptomics, researchers are pushing the limits of sensitivity to explore the molecular world at unprecedented resolution.

As advancements continue, single-molecule sequencing will play a crucial role in molecular diagnostics, synthetic biology, and fundamental biological research, transforming our ability to understand and manipulate the building blocks of life