Biology subjects

Nightingale, L.

Publications and source records attributed to Nightingale, L..

6 recordsLinked to original sources

bioRxiv2026

Determinants of cysteine coverage by chemoproteomics

Covalent chemoproteomics has become a powerful route to expanding the druggable proteome, enabling proteome-wide screening of compounds that covalently modify nucleophilic residues, most commonly cysteine. Yet typical experiments detect only 5-18% of the ~250,000 cysteines encoded by the human proteome, and the determinants of this limited coverage remain poorlydefined. Here we combine cysteine featurisation, explainable machine learning (ML) and robust statistical filtering to establish what governs cysteine detectability in iodoacetamide- desthiobiotin (IA-DTB) chemoproteomics. Using a scalable feature-generation pipeline, we computed physicochemical and structural properties for cysteines and the corresponding tryptic peptides and trained models that predict detectability with high accuracy. Shapley analysis identifies protein abundance and intrinsic peptide properties governing LC-MS behaviour as the dominant determinants, whereas cysteine structural descriptors contribute comparatively little, indicating that detection is constrained downstream of probe reactivity. We further introduce a distance-based statistical filter that improves reproducibility across replicates, and show that cell line selection, subcellular fractionation and alternative electrophiles together expand coverage by >30%. More broadly, this work moves optimisation of chemoproteomics workflows from empirical trial-and-error towards rational, data-driven design.

bioinformatics↗

bioRxiv2024

Robust proteome profiling of cysteine-reactive fragments using label-free chemoproteomics

Identifying pharmacological probes for human proteins represents a key opportunity to accelerate the discovery of new therapeutics. High-content screening approaches to expand the ligandable proteome offer the potential to expedite the discovery of novel chemical probes to study protein function. Screening libraries of reactive fragments by chemoproteomics offers a compelling approach to ligand discovery, however, optimising sample throughput, proteomic depth, and data reproducibility remains a key challenge. We report a versatile, label-free quantification proteomics platform for competitive profiling of cysteine-reactive fragments against the native proteome. This high-throughput platform combines SP4 plate-based sample preparation with rapid chromatographic gradients. Data-independent acquisition performed on a Bruker timsTOF Pro 2 consistently identified [~]23,000 cysteine sites per run, with a total of [~]32,000 cysteine sites profiled in HEK293T and Jurkat lysate. Crucially, this depth in cysteinome coverage is met with high data completeness, enabling robust identification of liganded proteins. In this study, 80 reactive fragments were screened in two cell lines identifying >400 ligand-protein interactions. Hits were validated through concentration-response experiments and the platform was utilised for hit expansion and live cell experiments. This label-free platform represents a significant step forward in high-throughput proteomics to evaluate ligandability of cysteines across the human proteome.

systems biology↗

bioRxiv2024

Spherical Phenotype Clustering

Phenotypic screening experiments produce many microscope images of cells under diverse perturbations, with biologically significant responses often subtle or difficult to identify visually. A central challenge is to extract image representations that distinguish activity from controls and group phenotypically similar perturbations. In this work we propose new adaptations of contrastive loss functions that incorporate experimental metadata as learned class vectors, and a geometrically inspired variant, called SPC, where class vectors are confined to the unit sphere and updated only by attractive terms (allowing more overlap of phenotypically similar classes). The approach is tested on two popular benchmarking datasets, BBBC021 and RxRx3-core; and we also evaluate performance on uncurated screens of HaCaT cells to gauge effectiveness in a realistic use-case scenario. We find we outperform prior methods across the three datasets and on a wide array of metrics measuring phenotype grouping, biological recall, drug-target interaction and mechanism-of-action inference. We also show we maintain this improved performance compared to models over 10x larger in parameter count, and that SPC can be used as an effective fine-tuning technique. The method is easy to implement and is well suited to settings with limited data or compute resources.

bioinformatics↗

bioRxiv2022

OptoPi: An open source flexible platform for the analysis of small animal behaviour

Behaviour is the ultimate output of neural circuit computations, and therefore its analysis is a cornerstone of neuroscience research. However, every animal and experimental paradigm requires different illumination conditions to capture and, in some cases, manipulate specific behavioural features. This means that researchers often develop, from scratch, their own solutions and experimental set-ups. Here, we present OptoPi, an open source, affordable ([~] {pound}600), behavioural arena with accompanying multi-animal tracking software. The system features highly customisable and reproducible visible and infrared illumination and allows for temporally precise optogenetic stimulation. OptoPi acquires images using a Raspberry Pi camera, features motorised LED-based illumination, Arduino control, as well as spectrum and irradiance monitoring to fine-tune illumination conditions with real time feedback. Our open-source software (BIO) can be used to simultaneously track multiple animals while accurately keeping individual animals identity both in on-line and off-line modes. We demonstrate the functionality of OptoPi by recording and tracking under different illumination conditions the spontaneous behaviour of larval zebrafish as well as adult Drosophila flies and their first instar larvae, an experimental animal that due to its small size and transparency has classically been hard to track. Further, we showcase OptoPis optogenetic capabilities through a series of experiments using transgenic Drosophila larvae.

neuroscience↗

bioRxiv2021

Automatic instance segmentation of mitochondria in electron microscopy data

We present a new method for rapid, automated, large-scale 3D mitochondria instance segmentation, developed in response to the ISBI 2021 MitoEM Challenge. In brief, we trained separate machine learning algorithms to predict (1) mitochondria areas and (2) mitochondria boundaries in image volumes acquired from both rat and human cortex with multi-beam scanning electron microscopy. The predictions from these algorithms were combined in a multi-step post-processing procedure, that resulted in high semantic and instance segmentation performance. All code is provided via a public repository.

bioinformatics↗

bioRxiv2020

Citizen science, cells and CNNs - deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realising the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope of HeLa cells imaged with Serial Blockface SEM. We present our approach for aggregating multiple volunteer annotations to generate a high quality consensus segmentation, and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the nuclear envelope, which we share here, in addition to our archived benchmark data.

cell biology↗