Biology subjects

Nieves, D. J.

Publications and source records attributed to Nieves, D. J..

9 recordsLinked to original sources

bioRxiv2026

SynthMLM: A framework for interpretable analysis and synthetic localisation data generation for SMLM

Quantitative analysis of single-molecule localisation microscopy (SMLM) data remains challenging because biologically diverse, well-annotated datasets are limited, whilst nanoscale protein organisation is heterogeneous and difficult to describe with hand-tuned metrics. We present SynthMLM, a framework that infers interpretable structural descriptors from experimental SMLM data and uses these descriptors to generate synthetic localisation datasets. We demonstrate SynthMLM by generating descriptor-matched synthetic datasets corresponding to diverse experimental SMLM datasets and evaluating their agreement with real data using descriptor-level and embedding-based measures. By enabling controlled generation of synthetic localisation data, SynthMLM provides a practical resource for benchmarking SMLM analysis methods, testing algorithm failure modes, and developing machine-learning workflows where large, labelled datasets are required.

biophysics↗

bioRxiv2025

Nanoscale spatial-omics via contrastive embedding of single-molecule localisation data

Omics approaches have revolutionised biology, and cells can now be routinely characterised on the genomic, transcriptomic and proteomic levels. However, there is an additional pillar; the (nanoscale) spatial organisation of molecules in the cell - information now accessible through super-resolution microscopy. We present a contrastive learning framework for nanoscale spatial-omics that embeds single-molecule localisation microscopy data into a latent space representing protein architecture directly to enabling comparative analysis. Using simulated and experimental data, we demonstrate its ability to enable new bioanalysis capabilities including assessing changes to cellular nanoscale architecture arising from pharmacological treatments, cell type, fluorophore selection or data-processing workflows. The approach supports downstream tasks such as clustering proteins by nanoscale organisation, mapping dose-response trajectories and identifying batch effects in replicate datasets, establishing contrastive learning as a scalable foundation for nanoscale spatial-omics and providing a platform for comparative phenotyping, quality control, and hypothesis generation.

bioinformatics↗

bioRxiv2024

Nano-org, a functional resource for single-molecule localisation microscopy data

We present a publicly accessible, curated, and functional resource, termed "nano-org", containing single-molecule localisation microscopy (SMLM) data representing the nanoscale distributions of proteins in cells. Nano-org is searchable by comparing the statistical similarity of the datasets it contains. This unique functionality allows the resource to be used to understand the relationships of nanoscale architectures between proteins, cell types or conditions, enabling a new field of spatial nano-omics.

biophysics↗

bioRxiv2023

Regulation of Toxic RNA Foci and Repeat Expansion DMPK Transcripts: Role of MBNL Proteins and RNA Decay Pathways

Myotonic dystrophy type 1 (DM1) is a progressive, multisystemic disorder caused by an expansion of CTG repeats in the 3 untranslated region of the DMPK gene. When transcribed the mutant RNAs accumulate in affected tissues appearing as distinct foci when visualised by in situ hybridisation. The RNA foci are aggregates of CUG repeat-containing RNAs that sequester RNA-binding proteins, particularly muscleblind-like (MBNL) proteins, leading to their dysfunction and causing downstream molecular and cellular defects. Here we show the double knock-out of MBNL1 and 2 prevents RNA foci formation and nuclear retention of mutant DMPK mRNA in DM1 cells as well as promoting their degradation and nuclear export. Using stochastic optical reconstruction microscopy (STORM), we find the presence of both large foci and micro foci in DM1 cells. Large foci consist of multiple DMPK transcripts, while many micro foci are (CUG)n fragments. The absence of MBNL proteins not only prevents the aggregation of multiple DMPK transcripts into large foci, but also promotes their degradation and nuclear processing. However, although a substantial amount of MBNL1 proteins are bound to the mutant transcripts, the pools of free MBNL1 proteins are similar in DM1 nuclei to those in controls. Furthermore, we have identified several factors that are involved in the control of mutant DMPK mRNA turnover, including XRN2, EXOSC10, UPF1 and STAU1. Our study indicates that these factors are implicated in the RNA foci accumulation and the degradation of mutant DMPK mRNA. UPF1 and STAU1 may have additional roles beyond degradation, impacting the nuclear processing of mutant DMPK mRNA. Our study also highlights the critical role of MBNL proteins in regulating mutant DMPK mRNA metabolism: the absence of MBNLs in DM1 appears to expedite the processing of mutant DMPK mRNA mediated by these RNA decay factors. Significance statementOur investigations uncovered valuable data on the RNA foci dynamics in DM1, revealing the intricate mechanisms that underlie their formation, stability, and turnover. Our findings also contributed to delineate the complex pathways involved in the transportation and degradation of the mutant mRNA and provided insights into the critical role played by MBNL proteins in these processes. Studying the degradation mechanism of mutant DMPK mRNA in myotonic dystrophy may provide a foundation for comprehending the mechanisms of RNA degradation in other diseases caused by short tandem repeat (STR) mutations, such as Huntingtons disease, Fragile X syndrome, and several types of ataxia. Additionally, the use of cutting-edge STORM technology can provide a valuable tool for investigating RNA foci in other STR expansion disorders.

genetics↗

bioRxiv2021

A framework for evaluating the performance of SMLM cluster analysis algorithms

Single molecule localisation microscopy (SMLM) generates data in the form of Cartesian coordinates of localised fluorophores. Cluster analysis is an attractive route for extracting biologically meaningful information from such data and has been widely applied. Despite the range of developed cluster analysis algorithms, there exists no consensus framework for the evaluation of their performance. Here, we use a systematic approach based on two metrics, the Adjusted Rand Index (ARI) and Intersection over Union (IoU), to score the success of clustering algorithms in diverse simulated clustering scenarios mimicking experimental data. We demonstrate the framework using three analysis algorithms: DBSCAN, ToMATo and KDE, show how to deduce optimal analysis parameters and how they are affected by fluorophore multiple blinking. We propose that these standard conditions and metrics become the basis for future analysis algorithm development and evaluation.

biophysics↗

bioRxiv2020

Quantitative mapping of membrane nanoenvironments through single-molecule imaging of solvatochromic probes

Environmentally-sensitive fluorophores report on their local biochemical or biophysical environments through changes in their emission. We combine the solvatochromic probe di-4-ANEPPDHQ with multi-channel SMLM and quantitative analysis of the resulting marked point patterns to map biophysical environments in the mammalian cell membrane. We show that plasma membrane properties can be mapped with nanoscale resolution, and that partitioning between ordered and disordered regions is observed on length scales below 300 nm.

biophysics↗

bioRxiv2019

tagPAINT: Stoichiometric and covalent labelling of protein tags for multiplexed and quantitative DNA-PAINT imaging

Recently, DNA-PAINT single molecule localisation microscopy (SMLM) has shown great promise for quantitative imaging. However, labelling strategies so far have relied on approaches that are multivalent or affinity-based. Here, we demonstrate tagPAINT - the covalent labelling of expressed protein tags (SNAP tag and Halo tag) with single DNA docking strands for single molecule localisation microscopy via DNA-PAINT. We utilised tagPAINT for T-cell receptor signalling proteins at the immune synapse as a proof of principle.

biophysics↗

bioRxiv2019

Stoichiometric quantification of spatially dense assemblies with qPAINT

Quantitative PAINT (qPAINT) is a useful method for counting well-separated molecules within nanoscale assemblies. But whether cross-reactivity in densely-packed arrangements perturbs measurements is unknown. Here we establish that qPAINT measurements are robust even when target molecules are separated by as little as 3 nm, sufficiently close that single-stranded DNA binding sites can interact.

biophysics↗

bioRxiv2018

Conformational states control Lck switching between free and confined diffusion modes in T cells

T cell receptor (TCR) phosphorylation by Lck is an essential step in T cell activation. It is known the conformational states of Lck control enzymatic activity; however, the underlying principles of how Lck finds its substrate in the plasma membrane remain elusive. Here, single-particle tracking is paired with photoactivatable localization microscopy (sptPALM) to observe the diffusive modes of Lck in the plasma membrane. Individual Lck molecules switched between free and confined diffusion in resting and stimulated T cells. Conformational state, but not partitioning into membrane domains, caused Lck confinement as open conformation Lck was more confined than closed. Further confinement of kinase-dead versions of Lck suggests that Lck interacts with open active Lck to cause confinement, irrespectively of kinase activity. Our data supports a model that confined diffusion of open Lck results in high local phosphorylation rates and closed Lck diffuses freely to enable wide-range scanning of the plasma membrane.

immunology↗