Biology subjects

Lowe, A. R.

Publications and source records attributed to Lowe, A. R..

11 recordsLinked to original sources

bioRxiv2026

Transcriptional adaptation influences buffering among essential paralog gene families

Buffering among paralogous genes is a well-described phenomenon in which genetic redundancy provides genetic robustness. Shared function among paralogs allows for a compensation effect when one or more paralog family genes are inactivated. Considering the role of transcriptional adaptation in influencing gene expression among highly homologous genes, we sought to identify whether transcriptional adaptation has a role in paralog buffering. We performed Perturb-seq of 110 paralog gene knockouts and assessed compensatory gene expression effects among paralogs as well as at the whole transcriptome level. This analysis identified six paralog pairs that undergo a transcriptional adaptation following CRISPR-mediated loss of one paralog in the pair. Further, we found that the upregulation of TIA1 following paralog TIAL1 loss requires nonsense-mediated decay, and active nuclear import via the importin IPO8. These data provide a comprehensive evaluation of transcriptional adaptation and show that transcriptional adaptation is recurrent but not pervasive among paralogous human genes.

genetics↗

bioRxiv2026

A multimodal perturbation atlas defines the phenotypic resolution of cellular morphology.

Modeling cellular behavior requires measurements that capture how cells evolve across time, environments, and interventions. Microscopy is uniquely suited to this goal: it is non-destructive and can be applied to living cells in their native context. Yet its phenotypic resolving power remains incompletely characterized relative to molecular assays. Here, we present a multimodal perturbation atlas of 1,000 pooled CRISPR knockouts in A549 cells, profiled by fluorescence microscopy (42 live, 13 fixed markers), label-free quantitative phase imaging of the same live cells (at single timepoints), and single-cell RNA sequencing (scRNA-seq). We develop deep learning frameworks to interpret the rich cell-biological signatures in these ~65M single-cell profiles. At matched reagent cost, phase imaging exceeds the phenotypic resolution of both fluorescence imaging and scRNA-seq, and more reliably recovers higher-order pathway organization. These results establish intrinsic morphology as a high-precision readout of cellular state, and lay a foundation for live-cell profiling of phenotypic trajectories.

systems biology↗

bioRxiv2024

Spatial and temporal signatures of cell competition revealed by K-function analysis

It is generally hypothesised that in MDCK wild-type versus ScrKD co-cultures mechanical competition leads to the physical elimination of ScrKD cells, while in MDCK wild-type versus RasV12 co-cultures biochemical competition results in the non-apoptotic extrusion of RasV12 cells. However, the exact spatiotemporal signatures of competitive interactions in these systems remains largely unknown. Utilising advanced single-cell image analysis, this research quantifies the spatiotemporal dynamics of these competitive interactions. Time-lapse microscopy, combined with cellular segmentation, tracking, cell-state classification, and K-function clustering analysis provide detailed insights into how wild-type cell proliferation relates to the elimination of mutant cells. We find striking differences in the underlying processes of each competition type. In the ScrKD competition, elimination seems driven by diffuse proliferative forces unrelated to the immediate elimination site. In contrast, RasV12 cell elimination is closely linked to local increases in wild-type cell mitoses prior to the RasV12 extrusion. Both competitions trigger an increase in local wild-type cell proliferation post-elimination, although the timing of these responses vary. This study not only sheds light on the diverse mechanisms of cell competition but also underscores the complexity of cellular interactions in tissue dynamics, providing new perspectives on cellular quality control and a new quantitative approach to characterise these interactions.

biophysics↗

bioRxiv2023

Modulation of NK cell migration by IL-15 defined using a new tool for data-driven clustering of heterogeneous cell behaviour trajectories

Advances in imaging, cell segmentation, and cell tracking now routinely produce microscopy datasets of a size and complexity comparable to transcriptomics or proteomics. New tools are required to process this phenomics type data. Cell PLasticity Analysis TOol (cellPLATO) is a Python-based analysis software designed for measurement and classification of diverse cell behaviours based on clustering of parameters of cell morphology and motility. cellPLATO is used after segmentation and tracking of cells from live cell microscopy data. The tool extracts morphological and motility metrics from each cell per timepoint, before being using them to segregate cells into behavioural subtypes with dimensionality reduction. Resultant cell tracks have a behavioural ID for each cell per timepoint corresponding to their changing behaviour over time in a sequence. Similarity analysis allows the grouping of behavioural sequences into discrete trajectories with assigned IDs. Trajectories and underlying behaviours generate a phenotypic finger-print for each experimental condition, and representative cells are mathematically identified and graphically displayed for human understanding of each subtype. Here, we use cellPLATO to investigate the role of IL-15 in modulating NK cell migration on ICAM-1 or VCAM-1. We find 8 behavioural subsets of NK cells based on their shape and migration dynamics, and 4 trajectories of behaviour. Therefore, using cellPLATO we show that IL-15 increases plasticity between cell migration behaviours and that different integrin ligands induce different forms of NK cell migration.

cell biology↗

bioRxiv2023

Virtual perturbations to assess explainability of deep-learning based cell fate predictors

Explainable deep learning holds significant promise in extracting scientific insights from experimental observations. This is especially so in the field of bio-imaging, where the raw data is often voluminous, yet extremely variable and difficult to study. However, one persistent challenge in deep learning assisted scientific discovery is that the workings of artificial neural networks are often difficult to interpret. Here we present a simple technique for investigating the behaviour of trained neural networks: virtual perturbation. By making precise and systematic alterations to input data or internal representations thereof, we are able to discover causal relationships in the outputs of a deep learning model, and by extension, in the underlying phenomenon itself. As an exemplar, we use our recently described deep-learning based cell fate prediction model. We trained the network to predict the fate of less fit cells in an experimental model of mechanical cell competition. By applying virtual perturbation to the trained network, we discover causal relationships between a cells environment and eventual fate. We compare these with known properties of the biological system under investigation to demonstrate that the model faithfully captures insights previously established by experimental research.

biophysics↗

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↗

bioRxiv2023

Epithelioids: Self-sustaining 3D epithelial cultures to study long-term processes

Studying long-term biological processes such as the colonization of aging epithelia by somatic mutant clones has been slowed by the lack of suitable culture systems. Here we describe epithelioids, a facile, cost-effective method of culturing multiple mouse and human epithelia. Esophageal epithelioids self-maintain without passaging for at least a year, recapitulating the 3D structure, cell dynamics, transcriptome, and genomic stability of the esophagus. Live imaging over 5 months showed epithelioids replicate in vivo cell dynamics. Epithelioids enable the study of cell competition and mutant selection in 3D epithelia, and how anti-cancer treatments modulate the competition between transformed and wild type cells. Epithelioids are a novel method with a wide range of applications in epithelial tissues, particularly the study of long term processes, that cannot be accessed using other culture models.

cell biology↗

bioRxiv2021

Learning the rules of cell competition without prior scientific knowledge

AO_SCPLOWBSTRACTC_SCPLOWDeep learning is now a powerful tool in microscopy data analysis, and is routinely used for image processing applications such as segmentation and denoising. However, it has rarely been used to directly learn mechanistic models of a biological system, owing to the complexity of the internal representations. Here, we develop an end-to-end machine learning model capable of learning the rules of a complex biological phenomenon, cell competition, directly from a large corpus of time-lapse microscopy data. Cell competition is a quality control mechanism that eliminates unfit cells from a tissue and during which cell fate is thought to be determined by the local cellular neighborhood over time. To investigate this, we developed a new approach ({tau}-VAE) by coupling a probabilistic encoder to a temporal convolution network to predict the fate of each cell in an epithelium. Using the{tau} -VAEs latent representation of the local tissue organization and the flow of information in the network, we decode the physical parameters responsible for correct prediction of fate in cell competition. Remarkably, the model autonomously learns that cell density is the single most important factor in predicting cell fate - a conclusion that is in agreement with our current understanding from over a decade of scientific research. Finally, to test the learned internal representation, we challenge the network with experiments performed in the presence of drugs that block signalling pathways involved in competition. We present a novel discriminator network that, using the predictions of the{tau} -VAE, can identify conditions which deviate from the normal behaviour, paving the way for automated, mechanism-aware drug screening.

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↗

bioRxiv2019

Dual role of Miro protein clusters in mitochondrial cristae organisation and ER-Mitochondria Contact Sites

Mitochondrial Rho (Miro) GTPases localize to the outer mitochondrial membrane and are essential machinery for the regulated trafficking of mitochondria to defined subcellular locations. However, their sub-mitochondrial localization and relationship with other critical mitochondrial complexes remains poorly understood. Here, using super-resolution fluorescence microscopy, we report that Miro proteins form nanometer-sized clusters along the mitochondrial outer membrane in association with the Mitochondrial Contact Site and Cristae Organizing System (MICOS). Using knockout mouse embryonic fibroblasts (MEF) we show that Miro1 and Miro2 are required for normal mitochondrial cristae architecture and endoplasmic reticulum-mitochondria contacts sites (ERMCS). Further, we show that Miro couples MICOS to TRAK motor protein adaptors to ensure the concerted transport of the two mitochondrial membranes and the correct distribution of cristae on the mitochondrial membrane. The Miro nanoscale organization, association with MICOS complex and regulation of ERMCS reveal new levels of control of the Miro GTPases on mitochondrial functionality.

cell biology↗

bioRxiv2017

PyFolding: An open-source software package for graphing, analysis and simulation of thermodynamic and kinetic models of protein folding

Abstract]For many years, curve fitting software has been heavily utilized to fit simple models to various types of biophysical data. Although such software packages are easy to use for simple functions, they are often expensive and present substantial impediments to applying more complex models or for the analysis of large datasets. One field that is relient on such data analysis is the thermodynamics and kinetics of protein folding. Over the past decade, increasingly sophisticated analytical models have been generated, but without simple tools to enable routine analysis. Consequently, users have needed to generate their own tools or otherwise find willing collaborators. Here we present PyFolding, a free, open source, and extensible Python framework for graphing, analysis and simulation of the biophysical properties of proteins. To demonstrate the utility of PyFolding, we have used it to analyze and model experimental protein folding and thermodynamic data. Examples include: (i) multi-phase kinetic folding fitted to linked equations, (ii) global fitting of multiple datasets and (iii) analysis of repeat protein thermodynamics with Ising model variants. Moreover, we demonstrate how Pyfolding is easily extensible to novel functionality beyond applications in protein folding via the addition of new models. Example scripts to perform these and other operations are supplied with the software, and we encourage users to contribute notebooks and models to create a community resource. Finally, we show that PyFolding can be used in conjunction with Jupyter notebooks as an easy way to share methods and analysis for publication and amongst research teams.

biochemistry↗