Biology subjects

May, L.

Publications and source records attributed to May, L..

9 recordsLinked to original sources

bioRxiv2026

Exploration of Structural Optic Nerve Changes in Mouse Models of Retinal and Neuronal Degeneration with Optical Coherence Tomography

PurposeThe optic nerve head (ONH) is a central feature of the retina, affected in many human ocular pathologies, yet it has remained underexplored in most mouse models of disease. We hypothesize that the analysis of the ONH can yield valuable insight into the phenotype of retinal diseases and that pathological changes can be detected using state-of-the-art optical coherence tomography (OCT). MethodsFour mouse models - the 5xFAD, PS19 and APP/PS1 models of Alzheimers disease (AD) as well as the SOD1 knockout mouse model - were imaged using a polarization-sensitive OCT system to investigate potential disease related changes of the ONH. 5xFAD and SOD1 animals were investigated longitudinally to study disease progression. Additionally, aging effects in wild type mice were studied. ResultsTwo different analysis methods for the segmentation of the ONH were implemented and evaluated. Longitudinal changes to the ONH in 5xFAD animals were observed, specifically an increase of ONH volume from 3 to 5 months of age followed by a strong decrease until 9 months of age. Significant differences between transgenic (tg) and non-transgenic (ntg) animals, as well as sex dependent distinctions were found. Also, for the APP/PS1 model disease related differences between ntg and tg APP/PS1 were significant. ConclusionsWe demonstrated a simple segmentation of the ONH structure based on OCT intensity images and show its potential as a preclinical biomarker in amyloid mouse models of AD.

neuroscience↗

bioRxiv2025

Longitudinal investigation of spatial memory and retinal parameters in a 5xFAD model of Alzheimers disease reveals differences dependent on genotype and sex

SignificanceThe retinal phenotype of Alzheimers disease (AD) is poorly understood. The connection between spatial memory and retinal phenotype is poorly investigated. Additionally, the influence of sex on the disease in mouse models is not sufficiently clear and requires further investigation. AimTo investigate the retina and spatial memory of 5xFAD mouse models of AD by measuring retinal and behavioral parameters. ApproachA custom-built optical coherence tomography (OCT) system is used to image the retina of both eyes of 32 transgenic 5xFAD mice and 32 non-transgenic littermates over the course of 6 months (3-9 months of age) to investigate retinal parameters. The Morris Water Maze (MWM) test was performed to examine correlations between the retinal and spatial memory phenotype of the mouse model. ResultsData were acquired in the form of OCT reflectivity images and OCT angiograms as well as video recordings of the MWM test. Layer thickness and vascular density were calculated from the resulting data. Behavioral data was extracted from the videos acquired from the MWM. Total retinal and inner retinal layer thickness increased slightly over the measurement period, while outer retinal layer and retinal nerve fiber layer thickness showed no significant change. The correlation analysis between MWM and layer thickness data revelated a positive correlation between inner nuclear layer thickness and MWM test day parameters. ConclusionsOCT and MWM data revealed sex-based differences in the retinal phenotype of the 5xFAD mouse model, with changes in retinal thickness in different stages of the study and dissimilar correlations between retinal and spatial memory phenotype.

neuroscience↗

bioRxiv2025

Low-Cost Screening of Algae for Extreme Tolerance to pH, Temperature, Salinity, and Light

Bioprospecting algae strains with tolerance to extreme conditions such as pH, temperature, salinity, and light is crucial for advancing biotechnology and environmental applications. However, traditional screening methods often involve significant costs and labor, restricting their accessibility and practical use. In this study, we developed and validated low-cost, high-throughput screening techniques, predominantly employing agar plates and liquid culture assays, to effectively differentiate tolerance levels among various algae strains. The methodologies were optimized using the model microalga Chlamydomonas reinhardtii and its closely related species Chlamydomonas incerta and the recently discovered extremophilic Chlamydomonas pacifica. We systematically evaluated the algae for tolerance to extremes by establishing precise gradients of pH (acidic to alkaline conditions), salinity (0 to 5 M NaCl), temperature (34-42{degrees}C), and light intensity (40 to 2977 E{middle dot}m-{superscript 2}{middle dot}s-{superscript 1}). Our results demonstrated that these cost-effective, agar plate-based methods effectively distinguished algae strains exhibiting superior tolerance to extreme environmental conditions. These screening techniques not only provided clear differentiation among the closely related strains but also delivered reproducible outcomes suitable for scaling up to larger bioprospecting efforts. Furthermore, the affordability and simplicity of these methods facilitate their implementation in resource-limited laboratories, thereby broadening participation in algae bioprospecting endeavors. This study highlights the potential of low-cost, accessible screening techniques to significantly enhance the discovery and characterization of algal strains with extreme traits. Ultimately, these methods support the development of robust algae-based resources, driving innovation in diverse industrial processes and environmental solutions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/653396v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@6b179borg.highwire.dtl.DTLVardef@dadbfforg.highwire.dtl.DTLVardef@a1750dorg.highwire.dtl.DTLVardef@baffc_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Establishing the green algae Chlamydomonas incerta as a platform for recombinant protein production

Chlamydomonas incerta, a genetically close relative of the model green alga Chlamydomonas reinhardtii, shows significant potential as a host for recombinant protein expression. Because of the close genetic relationship between C. incerta and C. reinhardtii, this species offers an additional reference point for advancing our understanding of photosynthetic organisms, and also provides a potential new candidate for biotechnological applications. This study investigates C. incertas capacity to express three recombinant proteins: the fluorescent protein mCherry, the hemicellulose-degrading enzyme xylanase, and the plastic-degrading enzyme PHL7. We have also examined the capacity to target protein expression to various cellular compartments in this alga, including the cytosol, secretory pathway, cytoplasmic membrane, and cell wall. When compared directly with C. reinhardtii, C. incerta exhibited a distinct but notable capacity for recombinant protein production. Cellular transformation with a vector encoding mCherry revealed that C. incerta produced approximately 3.5 times higher fluorescence levels and a 3.7-fold increase in immunoblot intensity compared to C. reinhardtii. For xylanase expression and secretion, both C. incerta and C. reinhardtii showed similar secretion capacities and enzymatic activities, with comparable xylan degradation rates, highlighting the industrial applicability of xylanase expression in microalgae. Finally, C. incerta showed comparable PHL7 activity levels to C. reinhardtii, as demonstrated by the in vitro degradation of a polyester polyurethane suspension, Impranil(R) DLN. Finally, we also explored the potential of cellular fusion for the generation of genetic hybrids between C. incerta and C. reinhardtii as a means to enhance phenotypic diversity and augment genetic variation. We were able to generate genetic fusion that could exchange both the recombinant protein genes, as well as associated selectable marker genes into recombinant offspring. These findings emphasize C. incertas potential as a robust platform for recombinant protein production, and as a powerful tool for gaining a better understanding of microalgal biology. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/618925v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@173d398org.highwire.dtl.DTLVardef@148ad36org.highwire.dtl.DTLVardef@63b5e9org.highwire.dtl.DTLVardef@3be081_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Pertpy: an end-to-end framework for perturbation analysis

Advances in single-cell technology have enabled the measurement of cell-resolved molecular states across a variety of cell lines and tissues under a plethora of genetic, chemical, environmental, or disease perturbations. Current methods focus on differential comparison or are specific to a particular task in a multi-condition setting with purely statistical perspectives. The quickly growing number, size, and complexity of such studies requires a scalable analysis framework that takes existing biological context into account. Here, we present pertpy, a Python-based modular framework for the analysis of large-scale perturbation single-cell experiments. Pertpy provides access to harmonized perturbation datasets and metadata databases along with numerous fast and user-friendly implementations of both established and novel methods such as automatic metadata annotation or perturbation distances to efficiently analyze perturbation data. As part of the scverse ecosystem, pertpy interoperates with existing libraries for the analysis of single-cell data and is designed to be easily extended.

bioinformatics↗

bioRxiv2024

Pre-training artificial neural networks with spontaneous retinal activity improves motion prediction in natural scenes

The ability to process visual stimuli rich with motion represents an essential skill for animal survival and is largely already present at the onset of vision. Although the exact mechanisms underlying its maturation remain elusive, spontaneous activity patterns in the retina, known as retinal waves, have been shown to contribute to this developmental process. Retinal waves exhibit complex spatio-temporal statistics and contribute to the establishment of circuit connectivity and function in the visual system, including the formation of retinotopic maps and the refinement of receptive fields in downstream areas such as the thalamus and visual cortex. Recent work in mice has shown that retinal waves have statistical features matching those of natural visual stimuli, such as optic flow, suggesting that they could prime the visual system for motion processing upon vision onset. Motivated by these findings, we examined whether artificial neural network (ANN) models trained on natural movies show improved performance if pre-trained with retinal waves. We employed the spatio-temporally complex task of next-frame prediction, in which the ANN was trained to predict the next frame based on preceding input frames of a movie. We found that pre-training ANNs with retinal waves enhances the processing of real-world visual stimuli and accelerates learning. Strikingly, when we merely replaced the initial training epochs on naturalistic stimuli with retinal waves, keeping the total training time the same, we still found that an ANN trained on retinal waves temporarily outperforms one trained solely on natural movies. Similar to observations made in biological systems, we also found that pre-training with spontaneous activity refines the receptive field of ANN neurons. Overall, our work sheds light on the functional role of spatio-temporally patterned spontaneous activity in the processing of motion in natural scenes, suggesting it acts as a training signal to prepare the developing visual system for adult visual processing. Author summaryBefore the onset of vision, the retina generates its own spontaneous activity, referred to as retinal waves. This activity is crucial for establishing neural connections and, hence, ensuring the proper functionality of the visual system. Recent research has shown that retinal waves exhibit statistical properties similar to those of natural visual stimuli, such as the optic flow of objects in the environment during forward motion. We investigate whether retinal waves can prepare the visual system for motion processing by pre-training artificial neural network (ANN) models with retinal waves. We tested the ANNs on next-frame prediction tasks, where the model predicts the next frame of a video based on previous frames. Our results showed that ANNs pre-trained with retinal waves exhibit faster learning on movies featuring naturalistic stimuli. Additionally, pre-training with retinal waves refined the receptive fields of ANN neurons, similar to processes seen in biological systems. Our work highlights the importance of spatio-temporally patterned spontaneous activity in preparing the visual system for motion processing in natural scenes.

neuroscience↗

bioRxiv2024

LarvaTagger: Manual and automatic tagging of Drosophila larval behaviour

MotivationAs more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data. We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth. ResultsWe reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data. AvailabilityAll the code is free and open source. Docker images are also available. See git-lab.pasteur.fr/nyx/LarvaTagger.jl.

bioinformatics↗

bioRxiv2023

Single-cell RNA sequencing reveals HIF1A as a severity-sensitive immunological scar in circulating monocytes of convalescent comorbidity-free COVID-19 patients

COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is characterized by a wide range of clinical symptoms and a poorly predictable disease course. Although in-depth transcriptomic investigations of peripheral blood samples from COVID-19 patients have been performed, the detailed molecular mechanisms underlying an asymptomatic, mild or severe disease course, particularly in patients without relevant comorbidities, remain poorly understood. While previous studies have mainly focused on the cellular and molecular dissection of ongoing COVID-19, we set out to characterize transcriptomic immune cell dysregulation at the single-cell level at different time points in patients without comorbidities after disease resolution to identify signatures of different disease severities in convalescence. With single-cell RNA sequencing we reveal a role for hypoxia-inducible factor 1-alpha (HIF1A) as a severity-sensitive long-term immunological scar in circulating monocytes of convalescent COVID-19 patients. Additionally, circulating complexes formed by monocytes with either T cells or NK cells represent a characteristic cellular marker in convalescent COVID-19 patients irrespective of their preceding symptom severity. Together, these results provide cellular and molecular correlates of recovery from COVID-19 and could help in immune monitoring and in the design of new treatment strategies.

immunology↗

bioRxiv2023

Early warning signals are hampered by a lack of critical transitions in empirical lake data

Quantifying the potential for abrupt non-linear changes in ecological communities is a key managerial goal, leading to a significant body of research aimed at identifying indicators of approaching regime shifts. Most of this work has built on the theory of bifurcations, with the assumption that critical transitions are a common feature of complex ecological systems. This has led to the development of a suite of often inaccurate early warning signals (EWSs), with more recent techniques seeking to overcome their limitations by analysing multivariate time series or applying machine learning. However, it remains unclear whether regime shifts and/or critical transitions are common occurrences in natural systems, and - if they are present - whether classic and second-generation EWS methods predict rapid community change. Here, using multitrophic data on nine lakes from around the world, we both identify the type of transition a lake is exhibiting, and the reliability of classic and second generation EWSs methods to predict whole ecosystem change. We find few instances of critical transitions in our lake dataset, with different trophic levels often expressing different forms of abrupt change. The ability to predict this change is highly technique dependant, with multivariate EWSs generally classifying correctly, classical rolling window univariate EWSs performing not better than chance, and recently developed machine learning techniques performing poorly. Our results suggest that predictive ecology should start to move away from the concept of critical transitions and develop methods suitable for predicting change in the absence of the strict bounds of bifurcation theory.

ecology↗