Biology subjects

Ulicna, K.

Publications and source records attributed to Ulicna, K..

3 recordsLinked to original sources

bioRxiv2023

Inflammation and autophagy dysfunction in metachromatic leukodystrophy: a central role for mTOR?

Metachromatic leukodystrophy (MLD) is a lysosomal storage disorder typically resulting from biallelic loss-of-function variants in the ARSA gene which encodes the lysosomal enzyme, arylsulphatase A, leading to the accumulation of its substrate, sulphatide, and widespread demyelination. Although gene therapy is available for MLD, it is limited by high cost and a narrow window for intervention, which means the development of therapies for MLD remains a key goal. The aim of the present study was to explore disease mechanisms in MLD with a view to identifying novel targets for therapeutic intervention for patients who cannot avail of gene therapy. Postmortem globus pallidus and dentate nucleus tissue was obtained from MLD cases (N=5; age 2-33 years old) and compared to age-, sex and ethnicity matched controls (N=5) and studied using discovery proteomics which demonstrated a marked inflammatory response, activation of the mTOR pathway, oxidative stress and metabolic remodelling in MLD cases. Histological analysis of inflammatory markers, including the terminal fragment of complement pathway activation, C3d, and the secreted glycoprotein YKL-40, a commonly used biomarker for inflammation, demonstrated their enrichment in MLD cases. Given that the mTOR pathway plays a key role in supressing autophagy, we next investigated autophagy and identified the accumulation of autophagosomes in MLD cases, consistent with deficient autophagy. Taken together, these findings suggest inflammation and autophagy dysfunction are key processes involved in MLD and that the mTOR pathway could be a novel therapeutic target for MLD.

neuroscience↗

bioRxiv2023

Learning dynamic image representations for self-supervised cell cycle annotation

Time-based comparisons of single-cell trajectories are challenging due to their intrinsic heterogeneity, autonomous decisions, dynamic transitions and unequal lengths. In this paper, we present a self-supervised framework combining an image autoencoder with dynamic time series analysis of latent feature space to represent, compare and annotate cell cycle phases across singlecell trajectories. In our fully data-driven approach, we map similarities between heterogeneous cell tracks and generate statistical representations of single-cell trajectory phase durations, onset and transitions. This work is a first effort to transform a sequence of learned image representations from cell cycle-specific reporters into an unsupervised sequence annotation.

cell biology↗

bioRxiv2020

Automated deep lineage tree analysis using a Bayesian single cell tracking approach

Single-cell methods are beginning to reveal the intrinsic heterogeneity in cell populations, which arises from the interplay or deterministic and stochastic processes. For example, the molecular mechanisms of cell cycle control are well characterised, yet the observed distribution of cell cycle durations in a population of cells is heterogenous. This variability may be governed either by stochastic processes, inherited in a deterministic fashion, or some combination of both. Previous studies have shown poor correlations within lineages when observing direct ancestral relationships but remain correlated with immediate relatives. However, assessing longer-range dependencies amid noisy data requires significantly more observations, and demands the development of automated procedures for lineage tree reconstruction. Here, we developed an open-source Python library, btrack, to facilitate retrieval of deep lineage information from live-cell imaging data. We acquired 3,500 hours of time-lapse microscopy data of epithelial cells in culture and used our software to extract 22,519 fully annotated single-cell trajectories. Benchmarking tests, including lineage tree reconstruction assessments, demonstrate that our approach yields high-fidelity results and achieves state-of-the-art performance without the requirement for manual curation of the tracker output data. To demonstrate the robustness of our supervision-free cell tracking pipeline, we retrieve cell cycle durations and their extended inter- and intra-generational family relationships, for up to eight generations, and up to fourth cousin relationships. The extracted lineage tree dataset represents approximately two orders of magnitude more data, and longer-range dependencies, than in previous studies of cell cycle heritability. Our results extend the range of observed correlations and suggest that strong heritable cell cycling is present. We envisage that our approach could be extended with additional live-cell reporters to provide a detailed quantitative characterisation of biochemical and mechanical origins to cycling heterogeneity in cell populations.

biophysics↗