Biology subjects

Lambert, T.

Publications and source records attributed to Lambert, T..

16 recordsLinked to original sources

bioRxiv2026

Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry

Directed evolution and machine-learning-assisted evolution have transformed biocatalysis by enabling rapid optimisation of catalytic performance within compatible protein scaffolds. In parallel, advances in deep-learning-driven de novo protein design have expanded accessible protein architectural space, enabling the generation of functional catalysts for increasingly challenging transformations. We herein report a synergistic design-evolution strategy to encode new-to-nature regio- and enantioselective C-H amination reactions using engineered de novo haemproteins to synthesise chiral piperidines, a privileged pharmacophore in drug discovery. By integrating motif scaffolding, computational re-design, and directed evolution, we demonstrate de novo enzyme design can enable challenging, high-energy-barrier enzyme chemistry with programmable regio- and enantioselectivity, high catalytic efficiency, excellent enzyme stability, and scaffold diversity.

synthetic biology↗

bioRxiv2025

The NaaS Methodology applied to modeling chemotherapy-induced peripheral neuropathy with human hiPSC neurons

The Neuron-as-a-Sensor (NaaS) methodology is a human-relevant platform designed to detect compound-induced effects by capturing functional changes in neuronal activity. This is achieved by integrating hiPSC-derived neuronal cultures, compartmentalized MEA microfluidic devices, a detailed electrophysiology paradigm and a standardized analysis pipeline. Applied to chemotherapy-induced peripheral neuropathy (CIPN), NaaS leverages electrophysiological profiling to capture alterations in neuronal excitability beyond cytotoxicity. Using paclitaxel and oxaliplatin as reference compounds, we demonstrated drug-specific, time-dependent changes in spontaneous and thermally evoked activity that align with their distinct clinical neuropathic phenotypes. Dimensionality reduction of electrophysiological metrics enabled construction of a functional discrimination map, allowing robust separation of compound signatures from vehicle controls. These findings highlight the ability of NaaS to model clinically relevant neurotoxic effects in a scalable manner, supporting its application in both adverse effect prediction and therapeutic screening.

bioengineering↗

bioRxiv2025

OfUSA: OpenfUS Analyzer, a versatile open-source framework for the analysis and visualization of functional ultrasound imaging data across animal models

Functional ultrasound (fUS) imaging is rapidly gaining interest for its unprecedented ability to study large-scale brain dynamics, yet its adoption and broader dissemination have been hindered by the lack of standardized tools and methodologies to analyze and interpret its rich datasets. We present OpenfUS Analyzer (OfUSA), a companion software suite designed to help researchers quickly engage with fUS data and perform the full range of analyses needed to generate publication-ready results and figures without relying on additional software. OfUSA offers a versatile and modular architecture including preprocessing, recording quality assessment, signal dynamics exploration, statistical analysis and visualization. These functions are separated yet easily combined into analytic pipelines through a programming-free graphical interface. The framework can be applied across species and experimental contexts, either by registering data to anatomical atlases, as shown here for the mouse brain, or by analyzing data without atlas constraints, as illustrated in a primate dataset. This flexibility, together with its comprehensive functionality, makes OfUSA a practical solution for standardized and reproducible analysis of fUS data in both preclinical and translational research. Using OfUSA, we demonstrate the capacity to detect stimulus-evoked responses with high sensitivity, identify their spatial localization within brain networks, and quantify both their extent and temporal dynamics. These results highlight the softwares ability to capture robust activation patterns and provide detailed insights into brain function, thereby accelerating the use of fUS as a powerful tool for systems neuroscience. HighlightsWe present OpenfUS Analyzer (OfUSA), a novel software platform for the complete analysis of functional ultrasound (fUS) datasets. OfUSA combines a user-friendly graphical interface with a standardized, flexible workflow and powerful visualization tools, making it an ideal solution for fUS researchers at all experience levels. The softwares utility is first demonstrated through the analysis of rodent fUS data using a standardized atlas, while its versatility is further emphasized by the successful analysis of a primate fUS dataset without a template, thereby illustrating its adaptability to non-standard experimental conditions.

neuroscience↗

bioRxiv2025

Sequence-Based Generative AI-Guided Design of Versatile Tryptophan Synthases

Enzymes offer unparalleled selectivity and sustainability for chemical synthesis, yet their widespread industrial application is often hindered by the slow and uncertain process of discovering and optimizing suitable biocatalysts. While directed evolution remains the gold standard for enzyme optimization, its success hinges on the availability of a starting enzyme with measurable activity, a persistent bottleneck for many desired functions. Designing libraries likely to contain such functional starting points remains a major challenge. In this work, we use the GenSLM protein language model (PLM) along with a series of filters to generate novel sequences of the {beta}-subunit of tryptophan synthase (TrpB) that express in Escherichia coli, are stable, and are catalytically active in the absence of a TrpA partner. Many generated TrpBs also demonstrated significant substrate promiscuity, accepting non-canonical substrates typically inaccessible to natural TrpBs. Remarkably, several outperformed both natural and laboratory-optimized TrpBs on native and non-canonical substrates. Comparative analysis of the most active and promiscuous generated TrpB and its closest natural homolog confirmed that this enhanced functional versatility does not stem from the natural enzyme, highlighting the creative potential of generative models. Our results demonstrate that the model can generate enzymes which not only preserve natural structure and function but also acquire non-natural properties, establishing PLMs as powerful tools for biocatalyst discovery and engineering, with the potential in some cases to bypass further optimization.

biochemistry↗

bioRxiv2025

Smart Microscopy: Current Implementations and a Roadmap for Interoperability

Smart microscopy is transforming life sciences by automating experimental imaging workflows and enabling real-time adaptation based on feedback from images and other data streams. This shift increases throughput, improves reproducibility, and expands the functional capabilities of microscopes. However, the current landscape is highly fragmented. Academic researchers often develop custom solutions for specific scientific needs, while industry offerings are typically proprietary and tied to specific hardware. This diversity, while fostering innovation, also creates major challenges in interoperability, reproducibility, and standardization, which slows progress and adaption. This article presents a collaborative effort between academic and industry leaders to survey the current state of smart microscopy, highlight representative implementations, and identify common technical and organizational barriers. We propose a framework for greater interoperability based on shared standards, modular software design, and community-driven development. Our goal is to support collaboration across the field and lay the groundwork for a more connected, reusable, and accessible smart microscopy ecosystem. We conclude with a call to action for researchers, hardware developers, and institutions to join in building an open, interoperable foundation that will unlock the full potential of smart microscopy in life science research.

cell biology↗

bioRxiv2025

Real-time feedback control microscopy for automation of optogenetic targeting

Optogenetics has revolutionized our ability to study cellular signaling by enabling the precise control of cellular functions with light. Most classical implementations rely on fixed or manually updated illumination patterns, limiting their ability to accommodate living systems that move, change shape or rapidly adapt their signaling states. Here, we present an experimental platform for Feedback Adaptive Real-time Optogenetics (FARO) that combines automated image segmentation, tracking, feature extraction, and adaptive hardware control to dynamically adjust optogenetic stimulation based on live cell behavior. By continuously analyzing biosensor signals, FARO updates illumination patterns in real time across biological scales; from maintaining stimulation on specific subcellular regions, to selectively activating single cells in deforming tissue. This fully automated, Python-based framework is built on open standards for data management and microscope control and data handling, supporting large-scale experiments and long-term timelapse studies, compatible with different microscope hardware. Eliminating the need for human intervention to reposition light patterns or select target cells enables reproducible, systematic and high-throughput interrogation of spatiotemporal signaling. We show how automated and adaptive optogenetic perturbations are a powerful tool to study how local signaling events shape cellular behavior, from subcellular dynamics and single-cell migration up to emergent tissue-level processes.

cell biology↗

bioRxiv2024

Functional ultrasound imaging and neuronal activity: how accurate is the spatiotemporal match?

Over the last decade, functional ultrasound (fUS) has risen as a critical tool in functional neuroimaging, leveraging hemodynamic changes to infer neural activity indirectly. Recent studies have established a strong correlation between neural spike rates (SR) and functional ultrasound signals. However, understanding their spatial distribution and variability across different brain areas is required to thoroughly interpret fUS signals. In this regard, we conducted simultaneous fUS imaging and Neuropixels recordings during stimulus-evoked activity in awake mice within three regions the visual pathway. Our findings indicate that the temporal dynamics of fUS and SR signals are linearly correlated, though the correlation coefficients vary among visual regions. Conversely, the spatial correlation between the two signals remains consistent across all regions with a spread of approximately 300 micrometers. Finally, we introduce a model that integrates the spatial and temporal components of the fUS signal, allowing for a more accurate interpretation of fUS images.

neuroscience↗

bioRxiv2022

Resolution doubling in light-sheet microscopy via oblique plane structured illumination

Structured illumination microscopy (SIM) doubles the spatial resolution of a fluorescence microscope without requiring high laser powers or specialized fluorophores. However, the excitation of out-of-focus fluorescence can accelerate photobleaching and phototoxicity. In contrast, light-sheet fluorescence microscopy (LSFM) largely avoids exciting out-of-focus fluorescence, thereby enabling volumetric imaging with low photo-bleaching and intrinsic optical sectioning. Combining SIM with LSFM would enable gentle 3D imaging at doubled resolution. However, multiple orientations of the illumination pattern, which are needed for isotropic resolution doubling in SIM, are challenging to implement in a light-sheet format. Here we show that multidirectional structured illumination can be implemented in oblique plane microscopy, a LSFM technique that uses a single objective for excitation and detection, in a straightforward manner. We demonstrate isotropic lateral resolution below 150nm, combined with lower photo-toxicity compared to traditional SIM systems and volumetric acquisition speed exceeding 1Hz.

biophysics↗

bioRxiv2022

HIV integration in the human brain is linked to microglial activation and 3D genome remodeling

Exploration of genome organization and function in the HIV infected brain is critical to aid in the development of treatments for HIV-associated neurocognitive disorder (HAND) and HIV cure strategies. Here, we generated a resource comprised of single nuclei transcriptomics, complemented by cell-type-specific Hi-C chromosomal conformation ( 3D genome) and viral integration site sequencing (IS-seq) in frontal brain tissues from individuals with HIV encephalitis (HIVE), HIV-infected people without encephalitis (HIV+), and HIV uninfected (HIV-) controls. We observed profound 3D genomic reorganization of open/repressive (A/B) compartment structures encompassing 6.4% of the HIVE microglial genome that was associated with transcriptomic reprogramming, including down-regulation of homeostasis and synapse-related functions and robust activation of interferon signaling and cell migratory pathways. HIV RNA was detected in 0.003% of all nuclei in HIVE brain, predominantly in the most activated microglia where it ranked as the second most highly expressed transcript. Microglia from HIV+ brains showed, to a lesser extent, similar transcriptional alterations. IS-seq recovered 1,221 insertion events in glial nuclei that were enriched for chromosomal domains newly mobilized into a permissive chromatin environment in HIVE microglia. Brain and peripheral myeloid cell integration revealed a preference overall for transcription-permissive chromatin, but robust differences in the frequency of recurrent insertions, intergenic integration, and enrichment for pre-integration complex-associated factors at integration sites. Our resource highlights critical differences in the genomic patterns of HIV infection in brain versus blood and points to a dynamic interrelationship between inflammation-associated 3D genome remodeling and successful integration in brain.

neuroscience↗

bioRxiv2022

Large scale in vivo acquisition, segmentation and 3D reconstruction of cortical vasculature using functional ultrasound imaging.

The brain is composed of a dense and ramified vascular network comprising various sizes of arteries, veins, and capillaries. One way to assess the risk of cerebrovascular pathologies is to use computational models to predict the physiological effects of a reduction of blood supply and correlate these responses with observations of brain damage. Therefore, it is crucial to establish a detailed 3D organization of the brain vasculature, which could be used to develop more accurate in silico models. For this purpose, we have adapted our open-access functional ultrasound imaging platform previously designed for recording brain-wide activity that is now capable of fast and reproducible acquisition, segmentation, and reconstruction of the cortical vasculature. For the first time, it allows us to digitize the cortical vasculature in awake rodents with a [~]100 {micro}m3 spatial resolution. Contrary to most available strategies, our approach can be performed in vivo within minutes. Moreover, it is easy to implement since it neither requires exogenous contrast agents nor long post-processing time. Hence, we performed a cortex-wide reconstruction of the vasculature and its quantitative analysis, including i) classification of descending arteries versus ascending veins in more than 1500 vessels/animal, ii) quick estimation of their length. Importantly, we confirmed the relevance of our approach in a model of cortical stroke, which enables quick visualization of the ischemic lesion. This development contributes to extending the capabilities of ultrasound neuroimaging to understand better cerebrovascular pathologies such as stroke, vascular cognitive impairment, and brain tumors and is highly scalable for the clinic.

neuroscience↗

bioRxiv2022

A deep learning classification task for accurate brain navigation during functional ultrasound imaging

Positioning and navigation are essential components of neuroimaging as they improve the quality and reliability of data acquisition, leading to advances in diagnosis, treatment outcomes, and fundamental understanding of the brain. Functional ultrasound (fUS) imaging is an emerging technology providing high-resolution images of the brain vasculature, allowing for the monitoring of brain activity. However, as the technology is relatively new, there is no standardized tool for inferring the position in the brain from the vascular images. This study presents a deep learning-based framework designed to address this challenge. Our approach uses an image classification task coupled with a regression on the resulting probabilities to determine the position of a single image. We conducted experiments using a dataset of 51 rat brain scans to evaluate its performance. The training positions were extracted at intervals of 375 {micro}m, resulting in a positioning error of 176 {micro}m. Further GradCAM analysis revealed that the predictions were primarily driven by subcortical vascular structures. Finally, we assessed the robustness of our method in a cortical stroke where the brain vasculature is severely impaired. Remarkably, no specific increase in the number of misclassifications was observed, confirming the methods reliability in challenging conditions. Overall, our framework provides accurate and flexible positioning, not relying on a pre-registered reference but on conserved vascular patterns.

neuroscience↗

bioRxiv2021

Lowland plant migrations into alpine ecosystems amplify soil carbon loss under climate warming

Climate warming is releasing carbon from soils around the world1-3, constituting a positive climate feedback. Warming is also causing species to expand their ranges into new ecosystems4-9. Yet, in most ecosystems, whether range expanding species will amplify or buffer expected soil carbon loss is unknown10. Here we used two whole-community transplant experiments and a follow-up glasshouse experiment to determine whether the establishment of herbaceous lowland plants in alpine ecosystems influences soil carbon content under warming. We found that warming (transplantation to low elevation) led to a negligible decrease in alpine soil carbon content, but its effects became significant and 52% {+/-} 31% (mean {+/-} 95% CIs) larger after lowland plants were introduced at low density into the ecosystem. We present evidence that decreases in soil carbon content likely occurred via lowland plants increasing rates of root exudation, soil microbial respiration and CO2 release under warming. Our findings suggest that warming-induced range expansions of herbaceous plants have the potential to alter climate feedbacks from this system, and that plant range expansions among herbaceous communities may be an overlooked mediator of warming effects on carbon dynamics.

ecology↗

bioRxiv2021

Pore dynamics and asymmetric cargo loading in an encapsulin nanocompartment revealed by Cryo-EM and hydrogen/deuterium exchange mass spectrometry

Encapsulins are protein nanocompartments that house various cargo enzymes, including a family of decameric ferritin-like proteins. Here, we study a recombinant Haliangium ochraceum encapsulin:encapsulated ferritin complex using electron cryo-microscopy and hydrogen/deuterium exchange mass spectrometry to gain insight into the structural relationship between the encapsulin shell and its protein cargo. An asymmetric single particle reconstruction reveals four encapsulated ferritin decamers in a tetrahedral arrangement within the encapsulin nanocompartment. This leads to a symmetry mismatch between the protein cargo and the icosahedral encapsulin shell. The encapsulated ferritin decamers are offset from the interior face of the encapsulin shell. Using HDX-MS, we observed dynamic behavior of the major five-fold pore in the encapsulin shell and show the pore opening via the movement of the encapsulin A-domain. These data will accelerate efforts to engineer the encapsulation of heterologous cargo proteins and to alter the permeability of the encapsulin shell via pore modifications. TeaserCryo-EM and HDX-MS analysis of an encapsulin nanocompartment shows that the pores at the five-fold icosahedral vertex of the shell are flexible.

biochemistry↗

bioRxiv2021

Induction of Dopaminergic Neurons for Neuronal Subtype-Specific Modeling of Psychiatric Disease Risk

Dopaminergic neurons are critical to movement, mood, addiction, and stress. Current techniques for generating dopaminergic neurons from human induced pluripotent stem cells (hiPSCs) yield heterogenous cell populations with variable purity and inconsistent reproducibility between donors, hiPSC clones, and experiments. Here, we report the rapid (5 weeks) and efficient (~90%) induction of induced dopaminergic neurons (iDANs) through transient overexpression of lineage-promoting transcription factors combined with stringent selection across five donors. We observe maturation-dependent increase in dopamine synthesis, together with electrophysiological properties consistent with midbrain dopaminergic neuron identity, such as slow-rising after hyperpolarization potentials, an action potential duration of ~3ms, tonic sub-threshold oscillatory activity, and spontaneous burst firing at frequency of ~1.0-1.75 Hz. Transcriptome analysis reveals robust expression of genes involved in fetal midbrain dopaminergic neuron identity. Specifically expressed genes in iDANs, relative to their isogenic glutamatergic and GABAergic counterparts, were linked to the genetic risk architecture of a broad range of psychiatric traits, with iDANs showing particularly strong enrichment in loci conferring heritability for cannabis use disorder, schizophrenia, and bipolar disorder. Therefore, iDANs provide a critical tool for modeling midbrain dopaminergic neuron development and dysfunction in psychiatric disease.

neuroscience↗

bioRxiv2019

Aequorea victoria’s secrets

Using mRNA-Seq and de novo transcriptome assembly, we identified, cloned and characterized nine previously undiscovered fluorescent protein (FP) homologs from Aequorea victoria and a related Aequorea species, with most sequences highly divergent from avGFP. Among these FPs are the brightest GFP homolog yet characterized and a reversibly photochromic FP that responds to UV and blue light. Beyond green emitters, Aequorea species express purple- and blue-pigmented chromoproteins (CPs) with absorbances ranging from green to far-red, including two that are photoconvertible. X-ray crystallography revealed that Aequorea CPs contain a chemically novel chromophore with an unexpected crosslink to the main polypeptide chain. Because of the unique attributes of several of these newly discovered FPs, we expect that Aequorea will, once again, give rise to an entirely new generation of useful probes for bioimaging and biosensing.

biochemistry↗

bioRxiv2019

Accelerating iterative deconvolution and multiview fusion by orders of magnitude

We describe theoretical and practical advances in algorithm and software design, resulting in ten to several thousand-fold faster deconvolution and multiview fusion than previous methods. First, we adapt methods from medical imaging, showing that an unmatched back projector accelerates Richardson-Lucy deconvolution by at least 10-fold, in most cases requiring only a single iteration. Second, we show that improvements in 3D image-based registration with GPU processing result in speedups of 10-100-fold over CPU processing. Third, we show that deep learning can provide further accelerations, particularly for deconvolution with a spatially varying point spread function. We illustrate the power of our methods from the subcellular to millimeter spatial scale, on diverse samples including single cells, nematode and zebrafish embryos, and cleared mouse tissue. Finally, we show that our methods facilitate the use of new microscopes that improve spatial resolution, including dual-view cleared tissue light-sheet microscopy and reflective lattice light-sheet microscopy.

bioengineering↗