Biology subjects

Su, Y.

Publications and source records attributed to Su, Y..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

Nuclear blebs are associated with destabilized chromatin packing domains

Disrupted nuclear shape is associated with multiple pathological processes including premature aging disorders, cancer-relevant chromosomal rearrangements, and DNA damage. Nuclear blebs (i.e., herniations of the nuclear envelope) have been induced by (1) nuclear compression, (2) nuclear migration (e.g., cancer metastasis), (3) actin contraction, (4) lamin mutation or depletion, and (5) heterochromatin enzyme inhibition. Recent work has shown that chromatin transformation is a hallmark of bleb formation, but the transformation of higher-order structures in blebs is not well understood. As higher-order chromatin has been shown to assemble into nanoscopic packing domains, we investigated if (1) packing domain organization is altered within nuclear blebs and (2) if alteration in packing domain structure contributed to bleb formation. Using Dual-Partial Wave Spectroscopic microscopy, we show that chromatin packing domains within blebs are transformed both by B-type lamin depletion and the inhibition of heterochromatin enzymes compared to the nuclear body. Pairing these results with single-molecule localization microscopy of constitutive heterochromatin, we show fragmentation of nanoscopic heterochromatin domains within bleb domains. Overall, these findings indicate that translocation into blebs results in a fragmented higher-order chromatin structure. SUMMARY STATEMENTNuclear blebs are linked to various pathologies, including cancer and premature aging disorders. We investigate alterations in higher-order chromatin structure within blebs, revealing fragmentation of nanoscopic heterochromatin domains.

bioengineering↗

bioRxiv2024

Altruistic feeding and cell-cell signaling during bacterial differentiation actively enhance phenotypic heterogeneity

Starvation triggers bacterial spore formation, a committed differentiation program that transforms a vegetative cell into a dormant spore. Cells in a population enter sporulation non-uniformly to secure against the possibility that favorable growth conditions, which puts sporulation-committed cells at a disadvantage, may resume. This heterogeneous behavior is initiated by a passive mechanism: stochastic activation of a master transcriptional regulator. Here, we identify a cell-cell communication pathway that actively promotes phenotypic heterogeneity, wherein Bacillus subtilis cells that start sporulating early utilize a calcineurin-like phosphoesterase to release glycerol, which simultaneously acts as a signaling molecule and a nutrient to delay non-sporulating cells from entering sporulation. This produced a more diverse population that was better poised to exploit a sudden influx of nutrients compared to those generating heterogeneity via stochastic gene expression alone. Although conflict systems are prevalent among microbes, genetically encoded cooperative behavior in unicellular organisms can evidently also boost inclusive fitness.

microbiology↗

bioRxiv2024

Navigated range expansion promotes migratory culling

Motile organisms can expand into new territories and increase their fitness, while nonmotile viruses usually depend on host migration to spread across long distances. In general, faster host motility facilitates virus transmission. However, recent ecological studies have also shown that animal host migration can reduce viral prevalence by removing infected individuals from the migratory group. Here, we use a bacteria-bacteriophage co-propagation system to investigate how host motility affects viral spread during range expansion. We find that phage spread during chemotaxis-driven navigated range expansion decreases as bacterial migration speed increases. Theoretical and experimental analyses show that the navigated migration leads to a spatial sorting of infected and uninfected hosts in the co-propagating front of bacteria-bacteriophage, with implications for the number of cells left behind. The preferential loss of infected cells in the co-propagating front inhibits viral spread. Further increase in host migration speed leads to a phase transition that eliminates the phage completely. These results illustrate that navigated range expansion of the host can promote the migratory culling of infectious diseases in the migration group. Significance StatementHost migration is commonly believed to accelerate the spread of infectious diseases. However, recent ecological studies suggest that migration may impede this spread. In our study, we developed a synthetic host-virus co-propagation model to explore the impact of host range expansion on the interplay between host mobility and virus spatial distribution. Our experimental and theoretical analysis revealed the spatial sorting of uninfected and infected hosts in the navigated propagating front leads to faster back diffusion of infected hosts. This self-organized structure allowed the migrating host population to eradicate the infectious disease, independent of intricate host-virus dynamics.

systems biology↗

bioRxiv2024

Hemispheric multi-dimension features extraction analysis based on decoupled representation learning

The predominant approach in investigating brain structural asymmetry relies on predefined regions of interest, assessing variations between homologous brain regions through a single indicator, which is local, univariate, and relative. In response to this challenge, we employ decoupled representation learning from deep learning to extract hidden features containing hemisphere-specific information at a hemispheric systemic level. This novel approach enables a global and multivariate analysis of brain structural asymmetry. Our findings indicate a significant association between left-hemisphere-specific hidden features and language-related behavioral metrics, as well as a correlation between right-hemisphere-specific hidden features and social-related behavioral metrics. Tensor-based Morphometry results find the impact of left-hemisphere-specific features on the left inferior frontal sulcus within Brocas area, a crucial region for language processing. Additionally, right-hemisphere-specific features influenced the right rostral hippocampus, a region implicated in emotion regulation and spatial navigation. The findings from Neurosynth indicate that significant regions caused by left-hemisphere-specific features are correlated with language, while significant regions caused by right-hemisphere-specific features are associated with behaviors primarily governed by the right hemisphere. Furthermore, our study establishes a link between structural changes induced by hemisphere-specific features and several genes. Such findings demonstrate that the application of deep learning techniques allows for precise capture of hemisphere-specific information within individual hemispheres, offering a new perspective for future research on brain structural asymmetry.

neuroscience↗

bioRxiv2024

Three-dimensional spatio-angular fluorescence microscopy with a polarized dual-view inverted selective-plane illumination microscope (pol-diSPIM)

Polarized fluorescence microscopy is a valuable tool for measuring molecular orientations, but techniques for recovering three-dimensional orientations and positions of fluorescent ensembles are limited. We report a polarized dual-view light-sheet system for determining the three-dimensional orientations and diffraction-limited positions of ensembles of fluorescent dipoles that label biological structures, and we share a set of visualization, histogram, and profiling tools for interpreting these positions and orientations. We model our samples, their excitation, and their detection using coarse-grained representations we call orientation distribution functions (ODFs). We apply ODFs to create physics-informed models of image formation with spatio-angular point-spread and transfer functions. We use theory and experiment to conclude that light-sheet tilting is a necessary part of our design for recovering all three-dimensional orientations. We use our system to extend known two-dimensional results to three dimensions in FM1-43-labelled giant unilamellar vesicles, fast-scarlet-labelled cellulose in xylem cells, and phalloidin-labelled actin in U2OS cells. Additionally, we observe phalloidin-labelled actin in mouse fibroblasts grown on grids of labelled nanowires and identify correlations between local actin alignment and global cell-scale orientation, indicating cellular coordination across length scales.

biophysics↗

bioRxiv2024

Neuronal lysosome transfer to oligodendrocyte precursor cells: a novel mechanism of neuron-glia communication and its role in neurodegenerative disease

Oligodendrocyte precursor cells (OPCs) shape brain function through intricate regulatory mechanisms. Here, we observed that OPC processes establish connections with neuronal somata, with smaller lysosomes positioned near these contact sites. Tracking lysosomes demonstrated neuronal lysosomes were attracted to and released at these contact points, eventually becoming incorporated into OPC processes, suggesting a selective, OPC-evoked release of lysosomes from neuronal soma and their ingestion by OPCs, highlighting a unique lysosome-mediated communication between neurons and OPCs. Diminished branching of OPC processes resulted in fewer neuron-OPC contacts, fostering larger lysosome accumulation in neurons, altered neuronal activity and escalated prevalence of senescent neurons during aging. A similar reduction in OPC branching and neuronal lysosome accumulation was evident in an early-stage Alzheimers disease mouse model. Together, these findings underscore the pivotal role of OPC processes in modulating neuronal activity through direct somatic contact and lysosome ingestion, presenting a prospective therapeutic avenue for addressing neurodegenerative diseases.

neuroscience↗

bioRxiv2024

Neuropilin-1 controls vascular permeability through juxtacrine regulation of endothelial adherens junctions

Neuropilin-1 (NRP1) regulates endothelial cell (EC) biology through modulation of vascular endothelial growth factor receptor 2 (VEGFR2) signalling by presenting VEGFA to VEGFR2. How NRP1 impacts VEGFA-mediated vascular hyperpermeability has however remained unresolved, described as exerting either a positive or a passive function. Using EC-specific Nrp1 knock-out mice, we discover that EC-expressed NRP1 exerts an organotypic role. In the ear skin, VEGFA/VEGFR2-mediated vascular leakage was increased following loss of EC NRP1, implicating NRP1 in negative regulation of VEGFR2 signalling. In contrast, in the back skin and trachea, loss of EC NRP1 decreased vascular leakage. In accordance, phosphorylation of vascular endothelial (VE)-cadherin was increased in the ear skin but suppressed in the back skin of Nrp1 iECKO mice. NRP1 expressed on perivascular cells has been shown to impact VEGF-mediated VEGFR2 signalling. Importantly, expression of NRP1 on perivascular cells was more abundant in the ear skin than in the back skin. Global loss of NRP1 resulted in suppressed VEGFA-induced vascular leakage in the ear skin, implicating perivascular NRP1 as a juxtacrine co-receptor of VEGFA in this compartment. Altogether, we demonstrate that perivascular NRP1 is an active participant in EC VEGFA/VEGFR2 signalling and acts as an organotypic modifier of EC biology.

cell biology↗

bioRxiv2024

Mitochondrial ncRNA LDL-805 declines in alveolar epithelial type 2 cells of chronic obstructive pulmonary disease patients.

RationaleWe showed that levels of a murine mitochondrial noncoding RNA, mito-ncR-LDL805, increase in alveolar epithelial type 2 cells exposed to extracts from cigarette smoke. The transcripts translocate to the nucleus, upregulating nucleus-encoded mitochondrial genes and mitochondrial bioenergetics. This response is lost after chronic exposure to smoke in a mouse model of chronic obstructive pulmonary disease. ObjectivesTo determine if mito-ncR-LDL805 plays a role in human disease, this study aimed to (i) identify the human homologue, (ii) test if the smoke-induced response occurs in human cells, (ii) determine causality between the subcellular localization of the transcript and increased mitochondrial bioenergetics, and (iii) analyze mito-ncR-LDL805 transcript levels in samples from patients with chronic obstructive pulmonary disease. MethodsLevels and subcellular localization of the human homologue identified from an RNA transcript library were assessed in human alveolar epithelial type 2 cells exposed to smoke extract. Lipid nanoparticles were used for nucleus-targeted delivery of mito-ncR-LDL805 transcripts. Analyses included in situ hybridization, quantitative PCR, cell growth, and Seahorse mitochondrial bioenergetics assays. Measurements and Main ResultsThe levels of human homologue transiently increased and the transcripts translocated to the nuclei in human cells exposed to smoke extract. Targeted nuclear delivery of transcripts increased mitochondrial bioenergetics. Alveolar cells from humans with chronic obstructive pulmonary disease had reduced levels of the mito-ncR-LDL805. Conclusionsmito-ncR-LDL805 mediates mitochondrial bioenergetics in murine and human alveolar epithelial type 2 cells in response to cigarette smoke exposure, but this response is likely lost in diseases associated with chronic smoking, such as chronic obstructive pulmonary disease, due to its diminished levels. ImpactThis study describes a novel mechanism by which epithelial cells in the lungs adapt to the mitochondrial stress triggered by exposure to cigarette smoke. We show that a noncoding RNA in mitochondria is upregulated and translocated to the nuclei of alveolar epithelial type 2 cells to trigger expression of genes that restore mitochondrial bioenergetics. Mitochondria function and levels of the noncoding RNA decrease under conditions that lead to chronic obstructive pulmonary disease, suggesting that the mitochondrial noncoding RNA can serve as potential therapeutic target to restore function to halt disease progression.

cell biology↗

bioRxiv2023

Integrative genomics reveals the polygenic basis of seedlessness in grapevine

Seedlessness is a crucial quality trait in table grape (Vitis vinifera L.) breeding. However, the development of seeds involved intricate regulations, while the polygenic basis of seed abortion remains unclear. Here, we combine comparative genomics, population genetics, quantitative genetics, and integrative genomics to unravel the evolution and polygenic basis of seedlessness in grapes. We generated four haplotype-resolved telomere-to-telomere (T2T) genomes for two seedless grape cultivars, Thompson Seedless (TS, syn. Sultania) and Black Monukka (BM). Comparative genomics identified a [~]4.25 Mb hemizygous inversion on Chr10 specific in seedless cultivars, with seedless-associated genes VvTT16 and VvSUS2 located at breakpoints. Population genomic analyses of 548 grapevine accessions revealed two distinct clusters of seedless cultivars, tracing the origin of the seedlessness trait back to Sultania. Introgression, rather than convergent selection, shaped the evolutionary history of seedlessness in grape improvement. Genome-wide association study (GWAS) analysis identified 110 quantitative trait loci (QTLs) associated with 634 candidate genes, including novel candidate genes, such as three 11S GLOBULIN SEED STORAGE PROTEIN and two CYTOCHROME P450 genes, and well-known genes like VviAGL11. Integrative genomic analyses resulted in 339 core candidate genes categorized into 13 groups related to seed development. Machine learning based genomic selection achieved a remarkable 99% precision in predicting grapevine seedlessness. Our findings highlight the polygenic nature of seedless and provide novel candidate genes for molecular genetics and an effective prediction for seedlessness in grape genomic breeding.

genetics↗

bioRxiv2023

Interpretable deep learning framework towards understanding molecular changes in human brains with Alzheimer's disease: implication for microglia activation and sex differences in AD

INTRODUCTIONThe objective of this study is to characterize the dysregulation of gene expression in AD affected brain tissues through an interpretable deep learning framework. METHODSWe trained multi-layer perceptron models for the classification of neuropathologically confirmed AD vs. controls using transcriptomic data from three brain regions of ROSMAP study. The disease spectrum was then modeled as a progressive trajectory. SHAP value was derived to explain model predictions and identify significantly implicated genes for subsequent gene co-expression network analysis. RESULTSThe models achieved excellent performance in classification and prediction in two external datasets from Mayo RNA-seq cohort and Mount Sinai Brain Bank cohort. SHAP explainer revealed common and specific transcriptomic signatures from different brain regions. DISCUSSIONWe identified common gene signatures among different brain regions in microglia and sex specific modules in neurons implicated in AD. This work paves the way for utilizing artificial intelligence approaches in studying AD at the molecular level. Research-in-ContextO_LISystematic review: Postmortem brain transcriptomes have been analyzed to study the molecular changes associated with Alzheimers disease, usually by a direct contrast approach such as differential gene expression analysis. Nuanced gene regulatory networks thus cannot be easily pinpointed from convoluted data such as those from bulk-tissue profiling. We applied a novel interpretable deep learning approach to dissect the RNA-seq data collected from three different brain regions of a large clinical cohort and identified significant genes for network analysis implicated for AD. C_LIO_LIInterpretation: Our models successfully predicted neuropathological and clinical traits in both internal and external validations. We corroborated known microglial biology in addition to revealing novel sex chromosome-linked gene contributing to sex dimorphism in AD. C_LIO_LIFuture directions: The framework could have broad utility for interpreting multi-omic data such as those from single-cell profiling, to advance our understanding of molecular mechanisms of complex human disease such as AD. C_LI HighlightsO_LIWe applied novel interpretable deep learning methods to postmortem brain transcriptomes from three different brain regions C_LIO_LIWe interpreted the models to identify genes most strongly implicated in AD C_LIO_LINetwork analysis corroborated known microglial biology and revealed novel sex specific transcriptional factors associated with neuronal loss in AD C_LI

neuroscience↗

bioRxiv2023

Phase diversity-based wavefront sensing for fluorescence microscopy

Fluorescence microscopy is an invaluable tool in biology, yet its performance is compromised when the wavefront of light is distorted due to optical imperfections or the refractile nature of the sample. Such optical aberrations can dramatically lower the information content of images by degrading image contrast, resolution, and signal. Adaptive optics (AO) methods can sense and subsequently cancel the aberrated wavefront, but are too complex, inefficient, slow, or expensive for routine adoption by most labs. Here we introduce a rapid, sensitive, and robust wavefront sensing scheme based on phase diversity, a method successfully deployed in astronomy but underused in microscopy. Our method enables accurate wavefront sensing to less than {lambda}/35 root mean square (RMS) error with few measurements, and AO with no additional hardware besides a corrective element. After validating the method with simulations, we demonstrate calibration of a deformable mirror > 100-fold faster than comparable methods (corresponding to wavefront sensing on the ~100 ms scale), and sensing and subsequent correction of severe aberrations (RMS wavefront distortion exceeding {lambda}/2), restoring diffraction-limited imaging on extended biological samples.

bioengineering↗

bioRxiv2023

Energy Landscape near Glioma Decipher the Role of Glioma Stem Cell within Glioma Hierarchy

Glioma stem cells (GSCs) have been recognized as key players in glioma recurrence and therapeutic resistance, presenting a promising target for novel treatments. However, the limited understanding of the role GSCs play in the glioma hierarchy has drawn controversy and hindered research translation into therapies. Despite significant advances in our understanding of gene regulatory networks, the dynamics of these networks and their implications for glioma remain elusive. This study employs a systemic theoretical perspective to integrate empirical knowledge into a core endogenous network model for glioma, thereby elucidating its energy landscape through network dynamics computation. The model identifies two stable states corresponding to astrocytoma and oligodendroglioma, connected by a transitional state associated with GSCs, indicating the instability of GSCs in vivo and providing negative proof of their role as the cellular origin for glioma. We also obtained various stable states further indicative of gliomas multicellular origins and uncovered a group of transition states that could potentially induce tumor heterogeneity and therapeutic resistance. This research proposes that the transitional states linking both glioma stable states are central to glioma heterogeneity and therapy resistance, suggesting a combination of apoptosis-inducing and differentiation-promoting therapies as a potential strategy. Our approach holds promise for advancing glioma therapy by providing a new lens through which to view the complex landscape of glioma biology.

systems biology↗

bioRxiv2023

Multimodal single cell-resolved spatial proteomics reveals pancreatic tumor heterogeneity

Despite the advances in antibody-guided cell typing and mass spectrometry-based proteomics, their integration is hindered by challenges for processing rare cells in the heterogeneous tissue context. Here, we introduce Spatial and Cell-type Proteomics (SCPro), which combines multiplexed imaging and flow cytometry with ion exchange-based protein aggregation capture technology to characterize spatial proteome heterogeneity with single cell resolution. The SCPro was employed to explore the pancreatic tumor microenvironment and revealed the spatial alternations of over 5,000 proteins by automatically dissecting up to 100 single cells guided by multi-color imaging of centimeter-scale formalin-fixed, paraffin-embedded tissue slide. To enhance cell-type resolution, we characterized the proteome of 14 different cell types by sorting up to 1,000 cells from the same tumor, which allows us to deconvolute the spatial distribution of immune cell subtypes and leads to the discovery of a novel subtype of regulatory T cells. Together, the SCPro provides a multimodal spatial proteomics approach for profiling tissue proteome heterogeneity.

systems biology↗

bioRxiv2023

Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons With and Without Significant Amyloid Plaques

BackgroundAmyloid-{beta} (A{beta}) plaques play a pivotal role in Alzheimers disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing A{beta} plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of A{beta}-plaques. ObjectiveTo develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma A{beta}42/40 ratio. MethodsWe extracted hippocampal multivariate morphometry statistics (MMS) from MR images and together with plasma A{beta}42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimers Institute (BAI), including prediction accuracy, precision, recall rate, F1 score and AUC score. ResultsResults from ADNI (mean age 72.6, A{beta}+ rate 49.5%) and BAI (mean age 66.2, A{beta}+ rate 36.9%) datasets revealed the integrated multimodal (IMM) models superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma A{beta}42/40 (0.73 and 0.81) predictors. ConclusionOur IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimers disease diagnostics, leveraging diverse pathologic features related to A{beta} plaques and structural MRI.

neuroscience↗

bioRxiv2023

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained de-aberration networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

bioengineering↗

bioRxiv2023

ABTrans: A Transformer-based model for predicting interaction between anti-Abeta antibodies and peptides

Understanding the recognition of antibodies and A{beta} peptide is crucial for the development of more effective therapeutic agents. Here we studied the interaction between anti-A{beta} antibodies and different peptides by building a deep learning model, using the dodecapeptide sequences elucidated from phage display and known anti-A{beta} antibody sequences collected from public sources. Our multi-classification model, ABTrans was trained to determine the four levels of binding ability between anti-A{beta} antibody and dodecapeptide: not binding, weak binding, medium binding, and strong binding. The accuracy of our model reached 0.8278. Using the ABTrans, we examined the cross-reaction of anti-A{beta} antibodies with other human amyloidogenic proteins, and we found that Aducanumab and Donanemab have the least cross-reactions with other human amyloidogenic proteins. We also systematically screened all human proteins interaction with eleven selected anti-A{beta} antibodies to identify possible peptide fragments that could be an off-target candidate. Key PointsO_LIABTrans is a Transformer-based model that was developed for the first time to predict the interaction between anti-A{beta} antibodies and peptides. C_LIO_LIABTrans was trained using a dataset with 1.5 million peptides and 110 anti-A{beta} antibodies. C_LIO_LIABTrans achieved an accuracy of 0.8278 and is capable of determining the four levels of binding ability between antibody and A{beta}: not binding, weak binding, medium binding, and strong binding. C_LIO_LIABTrans has potential applications in predicting off-target and cross-reactivity effects of antibodies and in designing new anti-A{beta} antibodies. C_LI

bioinformatics↗

bioRxiv2023

Oligodendroglial precursors orchestrate immune network instigating early demyelination in experimental autoimmune encephalomyelitis

The immunomodulatory cellular network that triggers early inflammation and demyelination, the key steps in multiple sclerosis (MS) pathogenesis remains poorly characterized. Here, we demonstrate that overactivation of Wnt pathway promotes pathological transformation of oligodendrocyte precursor cells (OPCs) to replicate pathological OPCs in human MS. In mouse experimental autoimmune encephalomyelitis (EAE), pathological OPCs attract CD4+ T-helper 1 (Th1) cells into the spinal cord and brain through CC-chemokine ligand 4 (CCL4), whilst OPCs cooperate with Th1 cells inducing transformation of cytotoxic macrophages that execute early demyelination. Simultaneously, Th1 cells and cytotoxic macrophages upregulate Wnt signaling and CCL4 expression in OPCs, thus exerting positive feedback onto the OPC-immune cascade and establishing a vicious cycle propagating EAE pathogenesis. Breaking this cascade by targeting CCL4 reduces immune cell infiltration, alleviates demyelination, and attenuates EAE severity. Our findings demonstrate a closely coordinated network of OPCs and immune cells therefore providing an alternative insight into MS pathophysiology.

neuroscience↗

bioRxiv2023

LanTERN: a fluorescent sensor that specifically binds lanthanides

Lanthanides, a series of fifteen f-block elements, are crucial in modern technology, and their purification by conventional chemical means comes at a significant environmental cost. Synthetic biology offers promising solutions. However, the lack of biochemical tools to measure lanthanide binding is a bottleneck to progress. Here, we introduce LanTERN, a lanthanide-responsive fluorescent protein rationally engineered from the lanmodulin-binding protein, LanM. LanTERN was designed based on GCaMP, a genetically encoded calcium indicator that couples the ion binding of four EF hand motifs to increased GFP fluorescence. We engineered seven mutations across the parent constructs four EF hand motifs to switch specificity from calcium to lanthanides. The resulting protein, LanTERN, directly converts the binding of 10 measured lanthanides to 14-fold or greater increased fluorescence. LanTERN development opens new avenues for creating improved lanthanide-binding proteins and bio-sensing systems.

synthetic biology↗