Biology subjects

Ning, L.

Publications and source records attributed to Ning, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Joint reconstruction of multi-contrast relaxation-diffusion imaging

Diffusion MRI across multiple gradient directions, b-values and echo times (TEs) provides complementary tissue contrast but requires extensive sampling. Joint k-space and q-space undersampling requires reconstruction that preserves diffusion- and TE-dependent signal variation while exploiting shared anatomical information. Here we introduce DIFFUSE (Diffusion-relaxometry Implicit Field Fitting from Undersampled k-q Space across Echoes), a subject-specific joint k-q-TE image reconstruction framework. It fits a single implicit neural representation (INR) directly to complementary undersampled multi-coil k-space measurements across diffusion encodings and echo times. The INR (a self-supervised neural network) maps spatial location, b-value and TE to baseline b0 signals and spherical harmonic coefficients for directional diffusion-weighted image reconstruction. The measurement-specific forward model incorporates phase, coil sensitivities and motion and eddy current related transforms. We evaluated DIFFUSE using retrospective Human Connectome Project data, controlled motion simulations and in vivo multi-shell and multi-TE acquisitions. At sixfold in-plane acceleration, DIFFUSE reduced normalized mean squared error by 57-73% relative to SENSE across two diffusion shells in the retrospective experiment. In the controlled motion experiment, motion-aware DIFFUSE reduced normalized mean squared error by 66-72% relative to static DIFFUSE. The prospective study demonstrated multi-shell reconstruction at 1.3 mm isotropic resolution with significantly high signal-to-noise ratio (SNR). In a separate multi-TE experiment, a single representation jointly reconstructed all nine contrasts corresponding to 3 b-value/TE combinations. DIFFUSE thus provides a common reconstruction strategy for accelerated protocols spanning multiple diffusion encodings and echo times.

neuroscience↗

bioRxiv2026

High-SNR Whole-Brain Mesoscale Diffusion MRI Using Rotating-View Acquisitions (ROVER-dMRI)

Purpose: We aim to acquire and reconstruct high signal-to-noise ratio (SNR), whole-brain, mesoscale (isotropic submillimeter) diffusion MRI (dMRI) in vivo in a clinically feasible scan time. Methods: We present ROVER-dMRI, a unified rotating-view acquisition and super-resolution reconstruction framework. Thick slices (up to 10 mm)acquired across many rotated orientations improve the base SNR, and a shared multi-resolution hash-encoded implicit neural representation (INR) with per-direction MLP head and a physics-informed, Bloch-simulated slice-profile forward model reconstructs these views into a continuous isotropic volume (up to 13x super-resolution) in a self-supervised manner. ROVER-dMRI was evaluated in vivo on human brain (0.78 mm isotropic and 0.5 mm isotropic) and monkey brain (0.6 mm isotropic) across MRI platforms with varying gradient performance, and benchmarked against gSlider-SMS and least-squares super-resolution reconstruction (LS-SRR). Results: At 0.78 mm isotropic resolution, ROVER-dMRI diffusion- weighted images achieved higher SNR than the vendor product sequence, whose images were limited by the noise floor, while its directionally encoded color (DEC) maps showed lower noise than gSlider. When reducing the number of views from 20 to 13 (reducing the scan time by 65% ), ROVER-dMRI acquisition/reconstruction preserved all fine details in the fiber orientations. In the monkey brain, its DEC map matched a 60-direction gSlider acquisition at only 40% of the scan time. ROVER-dMRI preserved coherent fiber-orientation-distribution (FOD) structure, including crossing fibers, and recovered fine details in the cerebellum at 0.5 mm isotropic, that are not visible at 0.78 mm resolution. Compared to state-of- the-art 0.5 mm methods, ROVER-dMRI reduced scan time by 73% while acquiring more diffusion directions. Conclusion: ROVER-dMRI offers a practical route to high-SNR whole-brain mesoscale diffusion imaging in vivo, with the pulse sequence and reconstruction pipeline released as open source. KEYWORDS: diffusion MRI, super-resolution, implicit neural representation, hash encoding, physics informed learning

neuroscience↗

bioRxiv2026

Parental Genome Assemblies of Suyunuo1 Reveal Structural Variation Underlying Edible Waxy Maize Evolution, Superior Hybrid Performance and Yield - Flavor Balance

Edible waxy maize is a unique domesticated cereal valued for its superior sensory and nutritional properties, yet high quality gap free genomes remain lacking, hindering the exploration of structural variations (SVs) and domestication related divergence. Here, we assembled two chromosome level, gap free genomes of the elite waxy maize inbred lines Tongxi 5 and Hengbai 522, the parents of the widely cultivated hybrid Suyunuo 1. Comparative genomic analysis revealed substantial genome size variation, prominent megabase scale SVs, and extensive knob repeat expansion in Tongxi 5. We identified widespread gene presence absence variation and hyperdivergent regions enriched in transposable elements, which largely underpin genomic differentiation between waxy and field maize. Population genomic analyses further demonstrated asymmetric introgression from field maize into the two parents, whereas the conserved waxy haplotype supports a shared ancestral origin. Integrative GWAS based on SNPs, InDels and SVs revealed that SVs substantially contribute to phenotypic diversity and mediate the co-regulation of yield traits and flavor-related metabolites. Our results uncover key genomic events underlying post-domestication divergence and highlight the essential roles of SVs in heterosis and flavor-yield balance, providing valuable genomic resources for waxy maize improvement.

plant biology↗

bioRxiv2026

Field-Correcting GRAPPA (FCG): a technique to correctspatiotemporal-varying phase errors in Echo Planar Imaging

PurposeTo develop a Field-Correcting GRAPPA (FCG) technique to correct the spatiotemporal-varying phase errors in EPI caused by eddy currents. MethodsThe fast-changing gradient in EPI causes strong eddy current effects and associated spatiotemporal-varying phase errors, producing significant image artifacts. The use of higher gradient amplitude, slew rate, and ramp sampling factor for faster imaging exacerbates this problem. In this work, FCG was developed to address this challenge by using a multi-layer perceptron (MLP) to provide a compact representation of a family of GRAPPA-like kernels that correct the spatiotemporal-varying phase errors in the data. A dedicated calibration pipeline was designed to acquire high-quality source and target data for MLP training in both slice-by-slice and simultaneous multi-slice (SMS) acquisitions. To validate FCGs assumptions and performance, a field camera was used to provide ground-truth measurement of phase patterns. The performance of FCG was further validated on phantom and in vivo experiments using demanding EPI trajectories across multiple 3T and 7T systems. ResultsField camera measurements revealed strong spatiotemporal phase variations along the kx direction that repeat along ky during EPI readouts. The experiments on high-performance systems across 3T and 7T demonstrate that FCG can provide superior correction for the artifacts induced by spatiotemporal-varying phase errors compared with existing approaches. ConclusionFCG is an effective and robust method for correcting spatiotemporal phase errors in EPI, enabling improved image quality on high-performance systems.

bioengineering↗

bioRxiv2026

MaxEnt-DTD: Maximum-Entropy Estimation of Diffusion Tensor Distribution for Fiber Orientation and Microstructure Characterization

Diffusion MRI (dMRI) enables noninvasive characterization of white-matter fiber orientations and tissue microstructure, but widely used approaches, such as constrained spherical deconvolution (CSD) and parametric multicompartment models, typically address these features separately. The diffusion tensor distribution (DTD) framework jointly represents fiber orientation and microstructure, but estimating DTD from finite, noisy measurements is severely ill-posed. Existing inversion methods either rely on nonnegativity constrained basis representations, which are challenging to sale to high-dimensional and high-resolution distributions, or use sampling-based approaches with limited reliability. We propose MaxEnt-DTD, a maximum-entropy algorithm for DTD estimation from finite and noisy dMRI data. By deriving the Lagrange dual formulation, we reformulate a constrained infinite-dimensional optimization problem into a finite-dimensional unconstrained convex optimization problem, substantially reducing the parameter space and enabling tractable whole-brain DTD estimation. We evaluate MaxEnt-DTD using both synthetic and in vivo data from the Human Connectome Project protocol and a second dataset using advanced B-tensor diffusion encoding. We compare MaxEnt-DTD-derived fiber orientation distributions with results from CSD and Monte-Carlo inversion methods, and assess fiber-specific microstructure measures and rotation-invariant metrics based on the cumulants of DTD. The results demonstrate that MaxEnt-DTD provides a reliable and efficient framework for joint fiber-orientation and microstructure analysis in dMRI.

neuroscience↗

bioRxiv2025

A novel allele of Sh1 underlines the conversion of waxy corn to wx-sweet corn

AbstractWaxy corn and sweet corn are two major types of fresh-eating corn. Each has significant market value due to its unique sensory and nutritional profiles. Developing a variety that combines both traits would meet growing consumer demand and create new market opportunities. From a fast neutron-mutagenized population of the waxy corn inbred line HB522, we identified a mutant, wx-sweet, whose kernels exhibit both waxy and sweet properties at the filling stage. Using bulked segregant analysis (BSA) and fine mapping, we localized the causal locus to SHRUNKEN1 (Sh1) on chromosome 9, which was confirmed by an allelic test with a known Mu-insertion mutant. PCR and sequencing indicated a putative large structural variation in the second and third exons of Sh1 in wx-sweet, likely disrupting the reading frame. This novel sh1 allele significantly reduced sucrose synthase activity. The sh1 and waxy1 mutant genes act synergistically to remodel carbohydrate metabolism in wx-sweet endosperms. This remodeling, revealed by integrated transcriptomic and metabolomic analyses, drives transcriptional reprogramming and restructures metabolic flux. These changes thereby enhance sucrose and raffinose accumulation, which underlies the unique waxy-sweet texture in fresh-eating maize endosperm. In summary, our work not provides valuable genetic resources for fresh-eating corn breeding, but also elucidates, for the first time, the molecular mechanism underlying the synergistic formation of waxy and sweet texture. Key messageThe novel allele of Sh1 acts synergistically with waxy1 to regulate carbohydrate metabolism, which elucidates the molecular mechanism for the unique waxy-sweet texture in fresh-eating corn endosperm. Our work provides new strategies for sweet-waxy maize breeding.

genetics↗

bioRxiv2025

Deep learning models reading clinical data and liver omics strongly distinguish NASH from steatosis and suggest new genes involved in liver disease severity

Background & AimsMetabolic dysfunction-associated steatotic liver disease (MASLD, previously NAFLD) is a frequent co-morbidity of obesity and diabetes, with prevalence increasing worldwide in all age groups and both sexes. Only early stages of the disease are fully reversible. Recognising liver disease stages and elucidating the molecular underpinning of their progression are thus medically important. We developed a deep learning model to recognise simple steatosis from steatohepatitis combining liver transcriptomics, epigenetics, and clinical data. MethodsWe used clinical data, liver gene expression and liver DNA methylation gathered from 300 patients with obesity of the ABOS cohort (80 without NAFLD, 137 with simple steatosis, 83 with steatohepatitis). We selected non-redundant clinical variables, gene expressions and CpGs methylation levels most associated with severity using unsupervised approaches. We designed a multi-module, multi-layer perceptron to predict patients liver status. We trained five model instances on independent training/test sets and combined the predictions. ResultsWe used a score based on gene expression/DNA methylation and relevant principal component analysis (PCA) loadings to select 200 genes and 260 CpG methylations. Models trained on the three modalities reached an AUC of 0.945 overall on a validation set with accuracies above 81% for simple steatosis and 88% for NASH, outperforming any other machine learning model so far. We retrieved patient clusters previously found using clinical variables in the latent space of our clinical data module, but not in the gene expression and DNA methylation modules. While all three modules are needed to reach the best prediction accuracy in all classes, the gene expression module had the most impact on the decision. Independent models weighted gene expression inputs similarly, shining light on their importance. The most impactful genes were linked to immune responses and extracellular matrix. However, many of those genes were previously unassociated with steatotic liver disease onset or progression. ConclusionsA multi-omics deep-learning model can recognise steatohepatitis from simple liver steatosis with an AUC of 0.945 and identify new genes potentially involved in NAFLD progression. Gene expressions profiles predicting disease severity are largely different from those specific of clinical variable clusters. Impact and implicationsThis study suggests that clinical variables are not sufficient to recognise the severity of steatotic liver disease with high accuracy, but model efficiency increases when used together with liver epigenetics and transcriptomics.

molecular biology↗

bioRxiv2024

Time-division multiplexing (TDM) sequence removes bias in T2 estimation and relaxation-diffusion measurements

PurposeTo compare the performance of multi-echo (ME) and time-division multiplexing (TDM) sequences for accelerated relaxation-diffusion MRI (rdMRI) acquisition and to examine their reliability in estimating accurate rdMRI microstructure measures. MethodThe ME, TDM, and the reference single-echo (SE) sequences with six echo times (TE) were implemented using Pulseq with single-band (SB-) and multi-band 2 (MB2-) acceleration factors. On a diffusion phantom, the image intensities of the three sequences were compared, and the differences were quantified using the normalized root mean squared error (NRMSE). For the in-vivo brain scan, besides the image intensity comparison and T2-estimates, different methods were used to assess sequence-related effects on microstructure estimation, including the relaxation diffusion imaging moment (REDIM) and the maximum-entropy relaxation diffusion distribution (MaxEnt-RDD). ResultsTDM performance was similar to the gold standard SE acquisition, whereas ME showed greater biases (3-4x larger NRMSEs for phantom, 2x for in-vivo). T2 values obtained from TDM closely matched SE, whereas ME sequences underestimated the T2 relaxation time. TDM provided similar diffusion and relaxation parameters as SE using REDIM, whereas SB-ME exhibited a 60% larger bias in the map and on average 3.5x larger bias in the covariance between relaxation-diffusion coefficients. ConclusionOur analysis demonstrates that TDM provides a more accurate estimation of relaxation-diffusion measurements while accelerating the acquisitions by a factor of 2 to 3.

neuroscience↗

bioRxiv2024

Sterile kidney tissue injury induces neutrophil swarming in lung alveolar capillaries

Sterile acute kidney injury (AKI) is common in the clinic and frequently associated with hypoxemia that does not improve with dialysis and remains incompletely understood. AKI induces remote lung inflammation with neutrophil recruitment in mice and humans, but which cellular cues establish neutrophilic inflammation and how it contributes to hypoxemia is not known. Here we report that AKI induces rapid intravascular neutrophil retention in lung alveolar capillaries without any significant extravasation into tissue or alveoli, causing hypoxemia by reducing lung capillary blood flow in the absence of any significant lung interstitial or alveolar edema. In contrast to direct ischemic lung injury, lung neutrophil recruitment during remote lung inflammation did not require cues from intravascular non-classical monocytes or tissue-resident alveolar macrophages. Instead, lung neutrophil retention depended on neutrophil chemoattractant CXCL2 released by activated intravascular classical monocytes. Comparative single-cell RNA-sequencing analysis of direct and remote lung inflammation revealed that alveolar macrophages are highly activated and produce the neutrophil chemoattractant CXCL2 only in direct lung inflammation. Establishing a CXCL2 gradient into the alveolus by intratracheal administration of CXCL2 during AKI-induced remote lung inflammation enabled neutrophils to extravasate. We thus discovered important differences in lung neutrophil recruitment in direct versus remote lung inflammation and identified lung capillary neutrophil retention that negatively affects oxygenation by causing a ventilation-perfusion mismatch as a novel driver of AKI-induced hypoxemia.

physiology↗

bioRxiv2024

HortGenome Search Engine, a universal genomic search engine for horticultural crops

Horticultural crops comprising fruit, vegetable, ornamental, beverage, medicinal and aromatic plants play essential roles in food security and human health, as well as landscaping. With the advances of sequencing technologies, genomes for hundreds of horticultural crops have been deciphered in recent years, providing a basis for understanding gene functions and regulatory networks and for the improvement of horticultural crops. However, these valuable genomic data are scattered in warehouses with various complex searching and displaying strategies, which increases learning and usage costs and makes comparative and functional genomic analyses across different horticultural crops very challenging. To this end, we have developed a lightweight universal search engine, HortGenome Search Engine (HSE; http://hort.moilab.net), which allows querying genes, functional annotations, protein domains, homologs, and other gene-related functional information of more than 400 horticultural crops. In addition, four commonly used tools, including BLAST, Batch Query, Enrichment analysis, and Synteny Viewer, have been developed for efficient mining and analysis of these genomic data.

bioinformatics↗

bioRxiv2023

Adaptive immunity: from CRISPR to CRIHSP?

Clustered regularly interspaced short palindromic repeats (CRISPR) confer adaptive immunity in prokaryotes against viral infection and plasmid transformation. Here, we identified abundant clustered regularly interspaced homologous stem-loop pairs (CRIHSP) within human adaptive immunity-related genes, such as antigen receptor germline genes. By examining genomic stability under activation-induced cytidine deaminase (AID) overexpression, we found that CRIHSP structures are preferred targets for AID. Through collecting and analyzing cancer cell genomic mutation databases, we discovered that these mutation sites tend to cluster toward stem-loop structures. Importantly, the frequency of stem-loop sequences within immunoglobulin heavy chain variable (IGHV) gene segments was over 3-fold higher compared to that within other regions. We concurrently observed stem-loop structures frequently flanking RSS motifs. Conservative estimates indicate a 74.07% probability of immunoglobulin heavy chain diversity (IGHD) gene segment enclosure within stem-loop. These RSS-proximal stem-loops may assemble into homologous stem-loop pair (HSP)-like intermediates and subsequent CRIHSP arrays. Our results implicate CRIHSP architectures as critical enhancer of antigen receptor gene recombination and diversification. Further molecular dissection of these processes could potentially elucidate mechanisms underlying cancer cell genomic instability, chromatin folding and dynamic regulation, gene expression control, etc.

immunology↗

bioRxiv2023

Design of SARS-CoV-2 protease inhibitors with improved affinity and reduced sensitivity to mutations

Inhibitors of the SARS-CoV-2 main protease (Mpro) such as nirmatrelvir (NTV) and ensitrelvir (ETV) have proven effective in reducing the severity of COVID-19, but the presence of resistance-conferring mutations in sequenced viral genomes raises concerns about future drug resistance. Second-generation oral drugs that retain function on these mutants are thus urgently needed. We hypothesized that the covalent HCV protease inhibitor boceprevir (BPV) could serve as the basis for orally bioavailable drugs that inhibit SARS-CoV-2 Mpro more tightly than existing drugs. Performing structure-guided modifications of BPV, we developed a picomolar-affinity inhibitor, ML2006a4, with antiviral activity, oral pharmacokinetics, and therapeutic efficacy similar or superior to NTV. A crucial feature of ML2006a4 is a novel derivatization of the ketoamide reactive group that improves cell permeability and oral bioavailability. Finally, ML2006a4 is less sensitive to several mutations that cause resistance to NTV or ETV and occur in the natural SARS-CoV-2 population. Thus, anticipatory drug design can preemptively address potential resistance mechanisms.

microbiology↗

bioRxiv2023

Acute kidney injury-induced circulating TNFR1/2 elevations correlate with persistent kidney injury and progression to fibrosis

BackgroundElevated levels of circulating Tumor-Necrosis-Factor-Receptors 1 and 2 (cTNFR1/2) predict CKD progression. Whether acute kidney injury drives cTNFR1/2 elevations and whether they predict disease outcomes after AKI remains unknown. MethodsWe used AKI patient serum and urine samples, mouse models of kidney injury (ischemic, obstructive, toxic) and progression to fibrosis, nephrectomy, and related single cell RNA-sequencing datasets. ResultsWe show that TNFR1/2 serum and urine levels are highly elevated in all mouse models of kidney injury tested, beginning within one-hour post-injury, and correlate with its severity. Consistent with this, serum and urine TNFR1/2 levels are increased in AKI patients and correlate with severity of kidney failure. Interestingly, the extracellular vesicle (EV)-bound forms of cTNFR1/2 correlate with renal function better than their soluble forms. TNF neutralization does not affect early cTNFR1/2 elevations, suggesting that cTNFR1/2 levels do not reflect injury-induced TNF activity. Kidney tissue expression of TNFR1/2 after AKI is only mildly increased and bilateral nephrectomies lead to strong cTNFR1/2 elevations, suggesting release of these receptors by extrarenal sources. cTNFR1/2 remain elevated for weeks after severe kidney injury and at these later timepoints cTNFR1/2 correlate to remaining kidney injury. During AKI-to-CKD transition, kidney expression of TNFR1/2 and cTNFR2 levels, correlate with development of fibrosis. ConclusionsOur data demonstrate that AKI drives acute increases in cTNFR1/2 serum levels which negatively correlate with kidney function, in particular their EV-bound forms. Sustained TNFR1/2 elevations after kidney injury during AKI-to-CKD transition correlate with persistent tissue injury and progression to kidney fibrosis. Significance StatementCirculating TNF-Receptor-1 and -2 (cTNFR1/2) elevations predict chronic kidney disease progression. It remains unknown whether kidney injury can drive cTNFR1/2 elevations, whether cTNFR1/2 levels predict disease outcomes after AKI, and which are their circulating molecular forms. This study shows that kidney injury strongly increases cTNFR1/2 levels and that their extracellular vesicle-bound forms better correlate with kidney function loss, compared to their soluble forms. Sustained cTNFR1/2 elevations correlate to remaining kidney injury, and cTNFR1/2 levels post injury predict progression to kidney fibrosis. This work, thus, points to novel mechanisms for the observed cTNFR1/2 elevations in kidney disease and identifies unique predictive and diagnostic value in cTNFR1/2 elevations in AKI or AKI-to-CKD transition.

physiology↗

bioRxiv2023

A domain-agnostic MR reconstruction framework using a randomly weighted neural network

PurposeTo design a randomly-weighted neural network that performs domain-agnostic MR image reconstruction from undersampled k-space data without the need for ground truth or extensive in-vivo training datasets. The network performance must be similar to the current state-of-the-art algorithms that require large training datasets. MethodsWe propose a Weight Agnostic randomly weighted Network method for MRI reconstruction (termed WAN-MRI) which does not require updating the weights of the neural network but rather chooses the most appropriate connections of the network to reconstruct the data from undersampled k-space measurements. The network architecture has three components, i.e. (1) Dimensionality Reduction Layers comprising of 3d convolutions, ReLu, and batch norm; (2) Reshaping Layer is Fully Connected layer; and (3) Upsampling Layers that resembles the ConvDecoder architecture. The proposed methodology is validated on fastMRI knee and brain datasets. ResultsThe proposed method provides a significant boost in performance for structural similarity index measure (SSIM) and root mean squared error (RMSE) scores on fastMRI knee and brain datasets at an undersampling factor of R=4 and R=8 while trained on fractal and natural images, and fine-tuned with only 20 samples from the fastMRI training k-space dataset. Qualitatively, we see that classical methods such as GRAPPA and SENSE fail to capture the subtle details that are clinically relevant. We either outperform or show comparable performance with several existing deep learning techniques (that require extensive training) like GrappaNET, VariationNET, J-MoDL, and RAKI. ConclusionThe proposed algorithm (WAN-MRI) is agnostic to reconstructing images of different body organs or MRI modalities and provides excellent scores in terms of SSIM, PSNR, and RMSE metrics and generalizes better to out-of-distribution examples. The methodology does not require ground truth data and can be trained using very few undersampled multi-coil k-space training samples.

neuroscience↗

bioRxiv2022

Kinase-modulated bioluminescent indicators enable noninvasive imaging of drug activity in the brain

Aberrant kinase activity contributes to the pathogenesis of brain cancers, neurodegeneration, and neuropsychiatric diseases, but identifying kinase inhibitors that function in the brain is challenging. Drug levels in blood do not predict efficacy in the brain because the blood-brain barrier prevents entry of most compounds. Rather, assessing kinase inhibition in the brain requires tissue dissection and biochemical analysis, a time-consuming and resource-intensive process. Here, we report kinase-modulated bioluminescent indicators (KiMBIs) for non-invasive longitudinal imaging of drug activity in the brain based on a recently optimized luciferase-luciferin system. We develop an ERK KiMBI to report inhibitors of the Ras-Raf-MEK-ERK pathway, for which no bioluminescent indicators previously existed. ERK KiMBI discriminates between brain-penetrant and non-penetrant MEK inhibitors, reveals blood-tumor barrier leakiness in xenograft models, and reports MEK inhibitor pharmacodynamics in native brain tissues and intracranial xenografts. Finally, we use ERK KiMBI to screen ERK inhibitors for brain efficacy, identifying temuterkib as a promising brain-active ERK inhibitor, a result not predicted from chemical characteristics alone. Thus, KiMBIs enable the rapid identification and pharmacodynamic characterization of kinase inhibitors suitable for treating brain diseases.

pharmacology and toxicology↗

bioRxiv2022

Characterization of b-value dependent T2 relaxation rates for probing neurite microstructure

Brain tissue microstructure is characterized by heterogeneous diffusivity and transversal relaxation rates. Standard diffusion MRI (dMRI) is acquired using a single echo time (TE) and only provides information about heterogeneous diffusivity in the underlying tissue. Combined relaxation diffusion MRI (rdMR) integrates dMRI with multiple TEs to probe the coupling between relaxation rate and diffusivity. This work introduces a method to model rdMRI data signals by characterizing the apparent relaxation rate related to dMRI with different b-values. The proposed approach can extrapolate dMRI signals to ultra-long or ultra-short TEs to increase or reduce signals from intra-neurite water to improve the characterization of neurite microstructure without solving multi-compartment models. The performance of the proposed method was examined using an in vivo dataset acquired from a clinical scanner to estimate neurite sizes.

neuroscience↗

bioRxiv2022

Epigenetic changes associated with hyperglycaemia exposure in the longitudinal D.E.S.I.R. cohort.

AimUnderstanding DNA methylation dynamics associated with progressive hyperglycaemia exposure could provide early diagnostic biomarkers and an avenue for delaying type 2 diabetes (T2D) disease. We aimed to identify DNA methylation changes during a 6-year period associated with early hyperglycaemia exposure using the longitudinal D.E.S.I.R. cohort. MethodsWe selected individuals with progressive hyperglycaemia exposure based on T2D diagnostic criteria: 27 with long-term exposure, 34 with short-term exposure and 34 normoglycaemic controls. DNA from blood at inclusion and at the 6-years visit was subjected to methylation analysis using 850K methylation-EPIC arrays. A linear mixed model was used to perform an epigenome-wide association study (EWAS) and identify methylated changes associated with hyperglycaemia exposure during 6-year time-period. ResultsWe did not identify differentially methylated sites that reached FDR-significance in our cohort. Based on EWAS, we focused our analysis on methylation sites that had a constant effect during the 6-years across the hyperglycaemia groups compared to controls and found the most statistically significant site was the reported cg19693031 probe (TXNIP). We also performed an EWAS with HbA1c, using the inclusion and the 6-years methylation data and did not identify any FDR-significant CpGs. ConclusionsOur study reveals that DNA methylation changes are not robustly associated with hyperglycaemia exposure or HbA1c during a short-term period, however, our top loci indicate potential interest and should be replicated in larger cohorts.

genomics↗

bioRxiv2022

Methacrylate-Modified Gold Nanoparticles Enable Non-Invasive Monitoring of Photocrosslinked Hydrogel Scaffolds

Photocrosslinked hydrogels, such as methacrylate-modified gelatin (gelMA) and hyaluronic acid (HAMA), are widely utilized as tissue engineering scaffolds and/or drug delivery vehicles, but lack a suitable means for non-invasive, longitudinal monitoring of surgical placement, biodegradation, and drug release. Therefore, we developed a novel photopolymerizable X-ray contrast agent, methacrylate-modified gold nanoparticles (AuMA NPs), to enable covalent-linking to methacrylate-modified hydrogels (gelMA and HAMA) in one-step during photocrosslinking and non-invasive monitoring by X-ray micro-computed tomography (micro-CT). Hydrogels exhibited a linear increase in X-ray attenuation with increased Au NP concentration to enable quantitative imaging by contrast-enhanced micro-CT. The enzymatic and hydrolytic degradation kinetics of gelMA-Au NP hydrogels were longitudinally monitored by micro-CT for up to one month in vitro, yielding results that were consistent with concurrent measurements by optical spectroscopy and gravimetric analysis. Importantly, AuMA NPs did not disrupt the hydrogel network, rheology, mechanical properties, and hydrolytic stability compared with gelMA alone. GelMA-Au NP hydrogels were thus able to be bioprinted into well-defined three-dimensional architectures supporting endothelial cell viability and growth. Overall, AuMA NPs enabled the preparation of both conventional photopolymerized hydrogels and bioprinted scaffolds with tunable X-ray contrast for noninvasive, longitudinal monitoring of placement, degradation, and NP release by micro-CT.

bioengineering↗