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Results for “single-molecule measurements”

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13 recordsLinked to original sources
PubMed2025

Emerging protein sequencing technologies: proteomics without mass spectrometry?

INTRODUCTION: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been a leading method for proteomics for 30 years. Advantages provided by LC-MS/MS are offset by significant disadvantages, including cost. Recently, several non-mass spectrometric methods have emerged, but little information is available about their capacity to analyze the complex mixtures routine for mass spectrometry. AREAS COVERED: We review recent non-mass-spectrometric methods for sequencing proteins and peptides, including those using nanopores, sequencing by degradation, reverse translation, and short-epitope mapping, with comments on bioinformatics challenges, fundamental limitations, and areas where new technologies will be more or less competitive with LC-MS/MS. In addition to conventional literature searches, instrument vendor websites, patents, webinars, and preprints were also consulted to give a more up-to-date picture. EXPERT OPINION: Many new technologies are promising. However, demonstrations that they outperform mass spectrometry in terms of peptides and proteins identified have not yet been published, and astute observers note important disadvantages, especially relating to the dynamic range of single-molecule measurements of complex mixtures. Still, even if the performance of emerging methods proves inferior to LC-MS/MS, their low cost could create a different kind of revolution: a dramatic increase in the number of biology laboratories engaging in new forms of proteomics research.

Proteomics↗
PubMed2025

Free energy spectroscopy reveals the mechanistic landscape of chromatin compaction.

Eukaryotic genomic DNA is repeatedly wrapped into nucleosome spools: the basic building block of chromatin. This organization regulates the physical accessibility of the genome to gene transcription, replication, and repair regulatory factors. Chromatin compaction is controlled by multivalent weak interactions, resulting in a complicated conformational landscape that remains challenging to characterize. This work reports a method for characterizing chromatin compaction, Free Energy Spectroscopy (FES), which is based on DNA nanotechnology and transmission electron microscopy. This method experimentally determines the chromatin compaction free energy landscape in terms of end-to-end distance and nucleosome stacking interactions. By deconvolving the free energy landscapes of partially and fully compact tetranucleosomes, FES revealed three separate mechanisms by which linker histones reshape the compaction energetics to condense chromatin. This study establishes FES as a method with the potential to help answer a broad range of mechanistic questions about genome and epigenome function.

DNA nanotechnology↗
PubMed2026

SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomics dataset.

SUMMARY: Image-based spatial transcriptomics (iST) deliver gene expression measurements of RNA transcripts in tissue slices with single-molecule resolution and spatial context preserved. Modern Graph Neural Network (GNN) models are promising methods for capturing the complex molecular and cellular phenotypes in tissues at single-transcript and single-cell levels. A key application of GNNs is the detection of spatial domains or niches, that is, groups of molecules and/or cells that collaboratively work together to produce complex phenotypes. Due to the vast number of detected transcripts in (iST) dataset, applying GNNs on RNA molecule graphs is not trivial. We present a Python package, SpatialRNA, for easy (sub)graph generation from tissue samples and provide comprehensive tutorials for convenient and efficient application of Graph Neural Network models under the PyG framework. This highly scalable tool comprehensively segments tissue into spatial domains, aiding in biological interpretation of iST data and its underlying molecular microenvironments. AVAILABILITY AND IMPLEMENTATION: The SpatialRNA package is freely accessible from online repository https://github.com/ruqianl/spatialrna and can be installed via pip. Comprehensive tutorials, guidance on parameter selection, and complete workflows of case studies are available from the documentation website https://ruqianl.github.io/spatialrna_docs/, and uploaded on Zenodo with a DOI 10.5281/zenodo.17339575.

Neural Networks, Computer↗
PubMed2026

Recovering membrane interaction kinetics of single molecules from 3D tracking data.

Interactions between cytosolic biomolecules and the bacterial inner membrane are fundamental to many cellular processes, yet directly measuring their binding kinetics in living cells remains challenging. Conventional 2D single-molecule tracking analyses can be insufficient, particularly when membrane association does not markedly alter the diffusion rate. Here, we present a method to recover membrane interaction kinetics from 3D single-molecule trajectories in rod-shaped bacteria. Using simulated 3D tracking data, we identify membrane-associated motion by quantifying how well short trajectory segments follow the circular curvature of the cell membrane. The resulting measure is further analyzed using a hidden Markov modeling framework, enabling robust discrimination between cytosolic and membrane-bound states and capturing the dynamics of state transitions without requiring diffusion-rate changes or direct colocalization with membrane markers. This work establishes a general framework for extracting membrane interaction kinetics from 3D single-molecule tracking data in live bacteria and highlights the value of realistic microscopy simulations for quantitative interpretation and systematic bias assessment.

Kinetics↗
PubMed2026

Advances in Single-Molecule Immunoassay: From Counting Strategies to CRISPR-Enhanced Biosensing.

Single-molecule immunoassays (SMIs) overcome the sensitivity limitations of conventional bulk measurements by enabling a paradigm shift from analog to digital signal readouts, thereby facilitating highly sensitive quantification of ultra-low-abundance biomarkers for precision diagnostics. This review provides a systematic overview of recent advances in SMI technologies and the conceptual framework underlying their evolution. First, discretization strategies for single-molecule counting are classified into hard discretization, based on physical confinement, and soft discretization, based on spatiotemporal isolation, within heterogeneous and homogeneous assay systems, respectively. The fundamental mechanisms by which these strategies mitigate diffusion limitations and enhance signal-to-noise ratios are discussed. Second, the integration of SMIs with CRISPR-based diagnostic systems (CRISPR-dx) is examined, with particular emphasis on their complementary roles in target recognition and signal amplification. Finally, recent applications of SMIs in the diagnosis of oncological, neurological, infectious, and cardiovascular diseases are summarized, along with a critical discussion of current engineering challenges and future directions toward clinical translation.

Immunoassay↗
PubMed2025

Nanopore-based sequencing of active DNA replication reveals key principles of metazoan replication fork progression, origin and termination sites.

Balancing replication fork progression and origin usage is essential to maintain genome stability, but measuring replication fork progression rates and origin usage throughout the genome has been challenging. Here, we use nanopore sequencing combined with DNAscent to measure replication fork progression together with origin and termination site usage with single-molecule precision throughout the Drosophila genome with nearly full genome coverage. We find that replication fork progression rates are not uniform throughout the genome. Rather, fork progression is slowest in euchromatin, and this is not correlated with active transcription. Replication origins are also influenced by chromatin, but the exact position of initiation is highly variable and are often several kilobases away from ORC binding sites. Termination sites lack any chromatin or sequence motifs and appear nearly random throughout the genome. By measuring DNA replication dynamics at near full genome coverage, our work reveals key principles of metazoan replication dynamics.

Journal Article↗
PubMed2027

Nanopore Sequencing for Chikungunya Virus: Principles and Application.

Nanopore sequencing is transforming viral genomics through real-time, portable, long-read analysis of RNA and DNA. Unlike traditional short-read platforms, it detects nucleotide sequences by measuring ionic current changes as nucleic acids pass through nanoscale pores, enabling direct single-molecule sequencing and base modification detection. Its simplicity, flexibility, and capacity for ultra-long reads make it ideal for resolving complex genomic regions, structural variants, and full viral genomes. These advantages have accelerated its use in pathogen surveillance and outbreak response, especially in resource-limited settings. For chikungunya virus (CHIKV), nanopore sequencing allows rapid, culture-independent recovery of complete genomes from clinical and vector samples, enabling real-time tracking of viral diversity, evolution, and spread. Experiences from Ebola, Zika, and COVID-19 have demonstrated the power of portable sequencing, now applied to CHIKV monitoring. Advances in tools such as Guppy, Dorado, Minimap2, and Medaka enhance read quality, consensus accuracy, and downstream analyses. Despite challenges in basecalling and error correction, robust quality control pipelines ensure reliable results. Ongoing improvements in chemistry, flow cell design, and machine learning will further enhance fidelity and throughput, establishing nanopore sequencing as a cornerstone of CHIKV genomic surveillance and epidemic preparedness.

Chikungunya virus↗
PubMed2025

Enhancing the sensitivity of non-invasive cervical cancer detection using CpG methylation haplotype profiling.

DNA methylation is a critical epigenetic modification that regulates gene expression and plays a significant role in cancer development. This methylation signature can be detected in cancer-derived DNA from non-invasive samples, such as plasma, urine or Pap smears. However, in early-stage cancers-when detection is most critical-the concentration of cancer DNA is often low, limiting the sensitivity of current detection methods. Traditional DNA methylation detection techniques, which rely on methylation ratio-based measurements, may obscure subtle variations in methylation patterns, further reducing detection sensitivity. In this study, we analyzed cervical scraping specimens and examined whether detecting cancer-specific methylation patterns in cervical cancer could be enhanced using a Highly Methylated Haplotype (HMH) approach. This novel approach captures highly methylated haplotypes at single-molecule resolution using next-generation sequencing, providing greater detail than conventional methods. HMHs in specific DNA regions are a hallmark of cancer and stand out in contrast to sporadic methylation commonly observed in non-cancerous tissues. We applied HMH profiling to a gene panel of four biomarkers (CA10, DPP10, FMN2, and HAS1) previously validated in cervical cancer studies. At pre-specified cutoffs (99th percentile of normals), haplotype-based scoring achieved 89.9% sensitivity for invasive cancer at high specificity (~ 94-98%), outperforming median (78.0%) and single-CpG (71.6%) methods. For clinically relevant endpoints, the combined panel detected 51-52% of CIN2 + and 66-67% of CIN3 + cases, again exceeding the performance of median- and single-CpG-based scoring methods.These findings demonstrate the potential of HMH to substantially enhance sensitivity in cervical cancer detection, offering a promising approach for non-invasive diagnostics.

Humans↗
PubMed2026

NanoSSL: attention mechanism-based self-supervised learning method for protein identification using nanopores.

MOTIVATION: Nanopores are cutting-edge interdisciplinary tools that can analyze biomolecules at the single-molecule level for many applications, e.g. DNA sequencing. Efforts are underway to extend nanopores to proteomics, including the development of machine learning algorithms for protein sequencing and identification. However, single-molecule data are intrinsically noisy and hard to process. Moreover, the development and performance of machine learning for nanopore is jeopardized by data scarcity. Self-supervised learning is an emerging method that may yield advantages in nanopore scenarios. RESULTS: We propose and experimentally validate Nanopore analysis using Self-Supervised Learning (NanoSSL), a generative self-supervised learning framework based on attention mechanisms for the identification of protein signals from nanopores. Leveraging a two-step approach consisting of self-supervised pre-training and supervised fine-tuning, NanoSSL learns useful feature representations from empirical data to facilitate downstream classification tasks. Inspired by the concept of fragmentation in conventional protein sequencing technologies, during pretraining each translocation event is split into multiple non-overlapping fragments of equal size, some of which are randomly masked and reconstructed using a masked autoencoder. Learning the feature representations of the reconstructed nanopore events facilitates molecular identification in fine-tuning. In this study, we retested a publicly available nanopore multiplexed protein sensing dataset for model iteration, and subsequently measured Alzheimer's disease biomarker Aβ1-42 using homemade solid-state nanopores. Empirical results indicated NanoSSL achieved an unprecedented performance across four metrics: accuracy, precision, recall, and F1 score, when classifying two mutated Aβ1-42, E22G and G37R. The self-supervised learning and attention mechanism were verified as the source of performance gains. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://doi.org/10.5281/zenodo.17172822.

Nanopores↗
PubMed2026

HoT auto-blinking probes enable real-time, super-resolution chromatin imaging in live cells and tissues.

Single-molecule localization microscopy (SMLM) enables visualization of chromatin architecture at nanoscale resolution. However, high-performance DNA probes suitable for SMLM in both live cells and tissues remain limited. We developed Hoechst-6-Carboxytetramethylrhodamine (6-TAMRA) derivative (HoT) probes-rhodamine-based derivatives conjugated to a Hoechst moiety-through structural fine-tuning of rhodamine spirocyclization. HoTs are self-assembling, auto-blinking probes with excellent photostability and high temporal resolution. They permeate live cells, enabling long-term, real-time nanoscopic chromatin imaging in live and fixed cells and in tissue sections. In live cells, we identified nanoscale features in the 3D organization of chromatin and quantified DNA fiber kinetics at high resolution. We quantified DNA compaction in single cells within retinal and colon cancer sections. OligoSTORM (stochastic optical reconstruction microscopy)-labeled gene loci can be visualized and measured within their HoT-labeled chromatin footprints. Our work provides powerful tools for investigating chromatin structure and functions in living cells and tissues, with applications ranging from cancer diagnosis to retinal regeneration.

Chromatin↗
PubMed2025

Single-Molecule Nanopore Detection of Non-Canonical Thymine-Melamine Hydrogen Bonding Base Pair in DNA Abasic Site.

The binding of small molecules to DNA may represent a mutagenic process capable of inducing genomic structural alterations and functional impairment. Melamine (MA), a toxic small molecule, exhibits a hydrogen-bonding interface structurally analogous to adenine, enabling to form non-canonical thymine-melamine (T-MA) base pairs like Watson-Crick pairing. This property allows MA to program DNA nanostructure formation. Given MA's documented biological consequences, such as kidney disease, reproductive toxicity, and central nervous system dysfunction, sensitive detection of MA-DNA interactions has become critically important. However, such subtle structural changes remain challenging to identify because of the paucity of effective detection approaches in a high-resolution manner. To overcome this limitation, nanopore measurement is employed to identify T-MA hydrogen bonding base pairing in DNA. Results demonstrate that nanopore enables unambiguous identification of T-MA hydrogen bonding via mechanically unzipping thymine-melamine-thymine (T-MA-T) triplets in DNA structures. The approach achieves single-base-pair resolution, as evidenced by nucleotide substitutions flanking the abasic site in complex DNA structures. In addition, nanopore-based kinetic analysis reveals an enhanced intramolecular stability in MA-binding DNA compared to those consisting of complete canonical DNA pairs. This research establishes a powerful platform for high-resolution interrogation of DNA-small molecule interactions and quantitative biophysical characterization of mutagenic modifications at the nanoscale.

Single Molecule Imaging↗
PubMed2026

A practical guide to studying genome function using single-molecule genomics.

Single-molecule genomics (SMG) has transformed our ability to study the mechanisms that regulate the genome by enabling profiling of the activity of regulatory factors on individual DNA molecules genome-wide. SMG is able to quantify molecular heterogeneity and the co-occurrence of regulatory events, including epigenetic modifications, transcription factor binding and chromatin organization on single DNA molecules. SMG reveals dynamics of chromatin interactions that cannot be measured by conventional genomics assays. Therefore, SMG offers a unique platform to study how regulatory events combine to control genome activity. In this Expert Recommendation article, we provide a practical guide for adopting SMG and outline best practices.

Journal Article↗
PubMed2025

Single-cell multi-omic detection of DNA methylation and histone modifications reconstructs the dynamics of epigenomic maintenance.

DNA methylation and histone modifications encode epigenetic information. Recently, major progress was made to measure either mark at a single-cell resolution; however, a method for simultaneous detection is lacking, preventing study of their interactions. Here, to bridge this gap, we developed scEpi2-seq. Our technique provides a readout of histone modifications and DNA methylation at the single-cell and single-molecule level. Application in a cell line with the FUCCI cell cycle reporter system reveals how DNA methylation maintenance is influenced by the local chromatin context. In addition, profiling of H3K27me3 and DNA methylation in the mouse intestine yields insights into epigenetic interactions during cell type specification. Differentially methylated regions also demonstrated independent cell-type regulation in addition to H3K27me3 regulation, which reinforces that CpG methylation acts as an additional layer of control in facultative heterochromatin.

DNA Methylation↗