Biology subjects

Stephenson, G.

Publications and source records attributed to Stephenson, G..

6 recordsLinked to original sources

bioRxiv2026

Interplay of mechanics, growth and viral infection dynamics in a spatially expanding bacterial colony

We investigate the dynamics of the spread of the infection due to a phage- during the range expansion of an E coli colony. We find a rich panoply of spatio-temporal dynamics at large colony-level length scales, arising due to the interplay of mechanical forces, growth and viral infection dynamics at smaller cellular length scales. Strikingly, the phages "surf" the front of the growing bacterial colony by hitchhiking on E coli that are advected due to the growth of bacteria in the colony, resulting in an anisotropic spread of the phages. We identify microscopic processes - of the phage release during lysis (occurring on short millisecond timescales at length scales close to that of a single bacterium) and local nematic alignment of the rod-like E coli bacteria (occurring on timescales comparable to the bacterial growth rate and the length scales of a few bacterial cells) - that enhance the advective effects driving the hitchhiking behavior. Combining our experiments with a mathematical model, we explain the multiple spatio-temporal dynamical regimes - from the coexistence of uninfected bacteria, resistant (lysogenic) cells and phages, to fixed points where the entire population turns resistant or remains uninfected. Our work broaches a new frontier in the investigation of eco-evolutionary processes arising from the mechanical and ecological forces due to the interactions between viruses and their hosts.

biophysics↗

bioRxiv2026

MISO: A Controlled Ablation of Masking, Initialization, Sampling, and Optimization for Segmentation in Volumetric Electron Microscopy

Multi-organelle segmentation in volumetric electron microscopy (vEM) faces several challenges, including severe class imbalance, the presence of small, rare classes, and inconsistent class coverage across crops. While recent work has focused primarily on architectural design, the impact of sampling, loss functions, and masking strategies on training effectiveness remains comparatively underexplored in vEM organelle segmentation. Here, we systematically evaluate sampling strategies, loss configurations, masking approaches, and model families (CNNs and vision transformers) on the CellMap benchmark. Using 289 annotated 3D FIB-SEM crops, we establish a 32-class segmentation benchmark with stratified train, validation, and test splits, and evaluate all the methods under the same training and inference settings. Across controlled ablations, the proposed combination of repeat-factor sampling, Tversky-BCE loss, and masking achieved the strongest rare-class performance, increasing rare-class mean Dice (mDice) from 0.3244 under uniform sampling to 0.3409. This corresponds to an absolute gain of +0.0165 mDice and a 5.1% relative improvement, while preserving comparable performance on common classes. Overall, we find that sampling, loss design, and masking contribute as much to performance variation as the choice of architecture, highlighting the importance of training-recipe design alongside model architecture in vEM organelle segmentation.

Cell Biology↗

bioRxiv2026

Temporal Transcriptomics Identifies Isoform-specific Trans-regulation by Multiple lncRNAs in Human iPSCs

Some long non-coding RNAs (lncRNAs) are known to regulate gene expression. However, the underlying temporal dynamics of lncRNAs influencing gene and epigenetic regulation and mechanisms of lncRNA regulation in trans are less understood. To investigate this, we genetically engineered 17 doxycycline-inducible lncRNA transgenes for ectopic expression at the H11 safe harbor locus in human pluripotent stem cells (hiPSCs), and we generated high-density temporal RNA-seq and ATAC-seq profiles. Most lncRNA transgenes were induced at 2 hours and maintained expression through the 96-hour time course. Surprisingly, when we sought to identify gene expression changes due to the lncRNAs, we found that the global transcriptional landscape was dominated by a strong systemic response triggered by doxycycline exposure. We rigorously defined this cohort of genes as a Doxycycline-Responsive Gene Signature (DRGS). The DRGS was also present in at least 28 public datasets from dox-inducible transgene studies involving diverse cell types. Next, we determined which lncRNAs exhibited trans-regulatory events. We identified DANCR, FENDRR, LINC00667, LINC00847, LNCPRESS1, and PNKY as lncRNAs that regulate specific transcript expression in trans. The downstream target genes encoded 53 mRNAs and 10 lncRNAs. None of the target lncRNAs altered gene expression proximal to their own loci (i.e., triggering secondary cis-effects). Surprisingly, the target genes of LINC00847 (transcribed from chromosome 22) were substantially enriched on chromosome 19, with a preponderance of target genes encoding RNA metabolism and RNA splicing factors. Collectively, our study provides a resource to discern artifacts in the doxycycline-inducible system and identifies temporally regulated targets of 6 lncRNAs for future mechanistic studies.

genomics↗

bioRxiv2024

In situ generation of Aβ42 oligomers via secondary nucleation triggers neurite degeneration and synaptic dysfunction in human iPSC-derived glutamatergic neurons

The aggregation of A{beta}42 into misfolded oligomers is a central event in the pathogenesis of Alzheimers disease. In this study, we aimed to develop a robust experimental system that recapitulates A{beta}42 oligomerization in living cells to gain insight into their neurotoxicity and to provide a platform to characterize the effects of inhibitors of this process. Our strategy is based on the in situ generation of A{beta}42 oligomers via secondary nucleation by repeatedly treating the cells with A{beta}42 monomers in the presence of pre-formed A{beta}42 fibrils. This approach enables an accurate control over the levels of on-pathway soluble A{beta}42 oligomers and cell-associated aggregates, as well as the study of their neurotoxic effects. By implementing this approach in human glutamatergic neurons derived from induced pluripotent stem cells (iPSCs), we were able to replicate key aspects of Alzheimers disease, including neurite degeneration and synaptic dysfunction. Using BRICHOS, a molecular chaperone that specifically inhibits secondary nucleation, we confirmed that aggregation in this system occurs through secondary nucleation, and that quantitative parameters for comparing potential A{beta}42 aggregation inhibitors can be obtained. Overall, our results demonstrate that by in situ generation of on-pathway A{beta}42 oligomers, one can obtain translational cellular models of AD to bridge the gap between basic research and clinical applications.

cell biology↗

bioRxiv2020

Segmentation and analysis of mother machine data: SAM

Time-lapse imaging of bacteria growing in micro-channels in a controlled environment has been instrumental in studying the single cell dynamics of bacterial growth. This kind of a microfluidic setup with growth chambers is popularly known as mother machine [1]. In a typical experiment with such a set-up, bacterial growth can be studied for numerous generations with high resolution and temporal precision using image processing. However, as in any other experiment involving imaging, the image data from a typical mother machine experiment has considerable intensity fluctuations, cell intrusion, cell overlapping, filamentation etc. The large amount of data produced in such experiments makes it hard for manual analysis and correction of such unwanted aberrations. We have developed a modular code for segmentation and analysis of mother machine data (SAM) for rod shaped bacteria where we can detect such aberrations and correctly treat them without manual supervision. We track cumulative cell size and use an adaptive segmentation method to avoid faulty detection of cell division. SAM is currently written and compiled using MATLAB. It is fast ([~] 15 min/GB of image) and can be efficiently coupled with shell scripting to process large amount of data with systematic creation of output file structures and graphical results. It has been tested for many different experimental data and is publicly available in Github.

systems biology↗

bioRxiv2020

THE IMPACT OF MISTRANSLATION ON PHENOTYPIC VARIABILITY AND FITNESS

Phenotypic variation is widespread in natural populations, and can significantly alter their ecology and evolution. Phenotypic variation often reflects underlying genetic variation, but also manifests via non-heritable mechanisms. For instance, translation errors result in about 10% of cellular proteins carrying altered sequences. Thus, proteome diversification arising from translation errors can potentially generate phenotypic variability, in turn increasing variability in the fate of cells or of populations. However, this link remains unverified. We manipulated mistranslation levels in Escherichia coli, and measured phenotypic variability between single cells (individual level variation), as well as replicate populations (population level variation). Monitoring growth and survival, we find that mistranslation indeed increases variation across E. coli cells, but does not consistently increase variability in growth parameters across replicate populations. Interestingly, although any deviation from the wild type (WT) level of mistranslation reduces fitness in an optimal environment, the increased variation is associated with a survival benefit under stress. Hence, we suggest that mistranslation-induced phenotypic variation can impact growth and survival and has the potential to alter evolutionary trajectories.

evolutionary biology↗