Biology subjects

Mancini, M.

Publications and source records attributed to Mancini, M..

At least 19 recordsLinked to original sources

bioRxiv2026

From Brain Microstructure to Dynamics: Linking Grey and White Matter Architecture to Propagation Delays

Understanding the relationship between neural dynamics and underlying brain structure, and in particular the impact of the latter on conduction delays, remains a core question in neuroscience. In humans, we primarily access these phenomena at the macroscale through neurophysiological measures of propagation. At this scale, signal propagation is reflected in measurable delays arising from both white matter (WM) axonal conduction and grey matter (GM) synaptic and integrative processes. While specific WM features have been explicitly linked to single-axon conduction delays, no clear models link GM microstructural properties to large-scale propagation. To this aim, we combined advanced MRI microstructural modelling with resting-state MEG to link brain structure to signal propagation in a sample of 94 healthy controls. WM metrics included axonal diameter, myelination (g-ratio, myelin water fraction), and tract length, while GM was characterised using the SANDI model and cortical thickness. Propagation delays were quantified using the neuronal avalanches framework. Our findings indicated significant associations between all of WM and GM metrics and the propagation delays. When attempting to use our W/GM metrics we were able to predict up to ~26% of the observed delay using linear regression modelling. We also found associations between frequency-specific propagation dynamics and tract length. Finally, using a canonical correlation analysis we demonstrated multivariate coupling between our W/GM metrics and the MEG frequency-specific propagation delays. Our findings provide evidence for the link between tissue structure and large-scale neural dynamics, supporting the development of biologically grounded models of signal propagation.

neuroscience↗

bioRxiv2026

Increased Expression and Altered Functional Activities of Immune Receptors TREM1, PD-L1, and Others on Hematopoietic Progenitor Cells in a Mouse Model of Rheumatoid Arthritis

Hematopoietic stem and progenitor cells (HSPCs) sustain the production of hundreds of billions of new cells per day to maintain our blood and immune system. In this process, HSPCs regulate the hematopoietic output by sensing and integrating diverse physiological cues. Thus, HSPCs express many receptors traditionally studied for their functions in the immune system, and this allows HSPCs to directly detect microbial compounds, endogenous danger signals, cytokines, and other inflammatory mediators. However, how the expression levels of such receptors on HSPCs change under chronic inflammation and how such changes alter HSPC functions and immune cell production remains unexplored. Working in a murine model of rheumatoid arthritis, we demonstrate the induction of microbial sensors TLR2 and CD14, orphan inflammatory receptor TREM1, and checkpoint receptor PD-L1 on HSPCs and particularly the myeloid progenitor cells in the arthritis-afflicted mice. Furthermore, we demonstrate that the stimulation of HSPCs through these receptors in culture can significantly alter the dynamics of cell expansion and differentiation, with distinct responses from HSPCs of arthritis-afflicted versus healthy control mice. We hypothesize that the induction and stimulation of HSPCs through these immune receptors under chronic inflammation may impact the output and functional properties of their immune cell progeny, positing HSPCs as central players in the pathogenic inflammatory responses of rheumatoid arthritis and potentially other chronic inflammatory diseases. HIGHLIGHTSO_LIHematopoietic progenitor cells in murine models of rheumatoid arthritis show an upregulation of immune receptors TREM1, PD-L1, TLR2, and CD14. C_LIO_LIStimulation of murine hematopoietic stem and progenitor cells through these receptors in culture alters the dynamics of their expansion and differentiation. C_LIO_LIIn such cultures, hematopoietic stem and progenitor cells from mice afflicted with rheumatoid arthritis show altered responses to stimulation as compared to healthy controls. C_LI

immunology↗

bioRxiv2026

Characterization and therapeutic suppression of KEAP1-NRF2-driven resistance to KRAS inhibitors in pancreatic and lung cancer

The recent approval of KRAS inhibitors supports the therapeutic value of targeting mutant KRAS cancers. However, clinical efficacy is hindered by both primary and treatment-associated acquired resistance. We applied a CRISPR-Cas9 loss-of-function screen and identified loss of KEAP1 as a resistance mechanism to the KRASG12D-selective inhibitor MRTX1133 and the RAS(ON) multi-selective inhibitor RMC-7977 in pancreatic cancer models. RNA-sequencing analyses revealed a KEAP1KO transcriptome that is distinct from the ERK-, MYC-, and YAP/TAZ-TEAD-dependent transcriptional programs that drive KRAS inhibitor resistance, demonstrating a distinct mechanism of resistance. We then established a PDAC KEAP1-deficient (PKD) gene signature that was enriched in patients and preclinical models insensitive to KRAS inhibitor treatment. Finally, we observed that KEAP1-deficient cells exhibited elevated glutamine metabolism, and combination treatment with the glutamine antagonist DRP-104 (sirpiglenastat) enhanced KRAS inhibitor suppression of pancreatic and lung tumors. SIGNIFICANCEKEAP1 loss is associated with reduced response to KRAS inhibitor therapy. We demonstrate that KEAP1 loss-associated resistance can be overcome by pharmacologic inhibition of the KEAP1 loss-induced glutamine dependency, establishing a combination to enhance RAS inhibitor clinical efficacy.

cancer biology↗

bioRxiv2026

Pioneer factor IRF1 unlocks latent enhancers to rewire chromatin and immunometabolism in inflammatory macrophages

Macrophages undergo extensive chromatin and metabolic remodeling to mount effective inflammatory responses. Here, we identify Interferon Regulatory Factor 1 (IRF1) as a pioneer factor that orchestrates these processes during IFN{gamma}-driven macrophage activation. Integrative ATAC-seq, ChIP-seq, Hi-ChIP, and nascent RNA-seq demonstrated that IRF1 rapidly engages closed chromatin, initiates enhancer remodeling, and drives removal of repressive histone marks followed by deposition of activating modifications. IRF1-established enhancers form long-range chromatin interactions with target promoters, activating transcriptional programs that control immune effector functions, chromatin regulation, and cellular metabolism. Notably, IRF1 coordinates the IFN{gamma}-induced metabolic switch from oxidative phosphorylation to aerobic glycolysis by transcriptionally regulating key metabolic enzymes, with metabolite profiles consistent with increased glycolysis and pentose phosphate pathway engagement, while remodeling the tricarboxylic acid (TCA) cycle to support immunometabolic outputs. IRF1-deficient macrophages fail to execute this coordinated metabolic reprogramming. High-density IRF1 motif arrays promote enhanced chromatin occupancy and recruitment of the BRG1-containing chromatin remodeling SWI/SNF complex. Pharmacologic inhibition of SWI/SNF ATPase activity (SMARCA2/4) disrupts IRF1-dependent chromatin remodeling and gene induction. Moreover, IRF1-induced enhancer states persist after IFN{gamma} withdrawal, establishing durable epigenetic memory. Together, these findings establish IRF1 as a central integrator of chromatin remodeling, transcriptional control, and metabolic adaptation, linking pioneer-factor activity to macrophage plasticity and innate immune memory.

genomics↗

bioRxiv2025

Cerebrovascular response dynamics to hypercapnia in healthy aging

Cerebrovascular dysfunction is an early and underrecognized contributor to cognitive decline. Standard measures such as cerebrovascular reactivity (CVR) during hypercapnia capture only the amplitude of flow responses, providing limited insight into the timing of vascular adaptation. Temporal features, such as delay (onset latency) and time constant (rate of adjustment), together with gain (response amplitude) may serve as more sensitive indicators of vascular health, but cannot be directly obtained from conventional imaging. Here, we investigated cerebral blood flow (CBF), cerebral blood volume (CBV), and blood oxygenation level dependent (BOLD) signal dynamics during hypercapnic challenge in healthy aging. Using a physiologically validated computational model, we estimated delay, time constant, and gain by optimizing the mapping of end-tidal gases to their arterial counterparts in a region-of-interest framework. Once parametrized using CBF, the model successfully predicted CBV and BOLD responses in independent experimental sessions. Across subjects, aging was associated with widespread heterogeneous region-specific changes in delay and substantial reductions in gain and time constant, indicating that cerebrovascular responses become weaker and less adaptable with age. These results demonstrate that calibrated simulations have the ability to track vascular aging, allowing the extraction of parameters that may represent novel biomarkers of cerebrovascular dysfunction. Unlike conventional CVR, temporal hemodynamic parameters capture the dynamics of vascular adaptation, providing a complementary dimension for early detection and therapeutic monitoring in aging and disease.

neuroscience↗

bioRxiv2025

Non-neuronal signal fluctuations in Alzheimer's disease and in mild cognitive impairment

Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) permits the investigation neural activity thanks to the neurovascular coupling mechanism. However, neural activity accounts for only a portion of the observed BOLD signal fluctuations, as the vasculature integrates multiple physiological inputs that contribute to the response. Research focusing on isolating the vascular components of the BOLD signal revealed that markers of cerebrovascular health, such as cerebrovascular reactivity (CVR), serve as valuable biomarkers for neurodegenerative diseases. This study examines the relationship between vascular metrics and noise in a cohort comprising individuals with Alzheimers disease (AD), mild cognitive impairment (MCI), and healthy controls (HC). Vascular responses were assessed using three functional contrasts during a hypercapnic challenge: arterial spin labeling (ASL) to measure cerebral blood flow (CBF) reactivity, vascular space occupancy (VASO) to quantify cerebral blood volume (CBV) reactivity, and BOLD imaging. Noise metrics were derived from multi-echo BOLD resting-state data by isolating the TE-independent components of the signal. Mean correlation coefficients for noise vs ASL-CVR are: (-0.12 {+/-} 0.06) for HC, (-0.14 {+/-} 0.08) for MCI, (-0.11 {+/-} 0.05) for AD. Mean correlation coefficients for noise vs BOLD-CVR are: (0.25 {+/-} 0.11) for HC, (0.24 {+/-} 0.07) for MCI, (0.23 {+/-} 0.11) for AD. Mean correlation coefficients for noise vs VASO-CVR are: (0.13 {+/-} 0.10) for HC, (0.13 {+/-} 0.07) for MCI, (0.12 {+/-} 0.12) for AD. These results suggest that TE-independent noise relates to the three vascular contrasts to varying extents and directions, with no significant differences across groups. Further analysis within specific functional networks revealed group differences in specific networks. The observed cortical correlations between noise and vascular features provide important insights into brain function and the progression of neurodegenerative diseases, offering a potential avenue to disentangle vascular and neural contributions in brain network and connectivity studies.

neuroscience↗

bioRxiv2025

Fast segmentation with the NextBrain histological atlas

Structural brain analysis at the subregion level offers critical insights into healthy aging and neurodegenerative diseases. The NextBrain histological atlas was recently introduced to support such fine-grained investigations, but its existing Bayesian segmentation framework remains computationally prohibitive, particularly for large-scale studies. We present a new, open-source tool that dramatically accelerates segmentation using a hybrid approach combining: machine learning, contrast-adaptive segmentation; target-specific image synthesis; and fast diffeomorphic registration (all three with GPU support). Our method enables highly granular segmentation of brain MRI scans of any resolution and contrast (in vivo or ex vivo) at a fraction of the computational cost of the original method (<5 minutes on a GPU). We validate our tool on four different modalities (in vivo MRI, ex vivo MRI, HiP-CT, and photography) across a total of approximately 4,000 brain scans. Our results demonstrate that the accelerated approach achieves comparable accuracy to the original method in terms of Dice scores, while reducing runtime by over an order of magnitude. This work enables high-resolution anatomical analysis at unprecedented scale and flexibility, providing a practical solution for large neuroimaging studies. Our tool is publicly available in FreeSurfer (https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation).

neuroscience↗

bioRxiv2025

Targeting FSP1 triggers ferroptosis in lung cancer

Pre-clinical and clinical studies have demonstrated how dietary antioxidants or mutations activating antioxidant metabolism promote cancer, highlighting a central role oxidative stress in tumorigenesis. However, it is unclear if oxidative stress ultimately increases to a point of cell death. Emerging evidence indicates that cancer cells are susceptible to ferroptosis, a form of cell death triggered by uncontrolled lipid peroxidation1-3. Despite broad enthusiasm about harnessing ferroptosis as a novel anti-cancer strategy, whether ferroptosis is a barrier to tumorigenesis and if it can be leveraged therapeutically remains unknown4,5. Using genetically-engineered mouse models (GEMMs) of lung adenocarcinoma (LUAD), we performed tumor specific loss-of-function studies of the two key ferroptosis suppressors, glutathione peroxidase 4 (Gpx4)6,7 and ferroptosis suppressor protein 1 (Fsp1)8,9, and observed increased lipid peroxidation and robust suppression of tumorigenesis, suggesting that lung tumors are highly sensitive to ferroptosis. Furthermore, across multiple pre-clinical models, we found that FSP1 was required for ferroptosis protection in vivo, but not in vitro, underscoring a heightened need to buffer lipid peroxidation under physiological conditions. Lipidomic analyses revealed that Fsp1-knockout (Fsp1KO) tumors had an accumulation of lipid peroxides, and inhibition of ferroptosis with genetic, dietary, or pharmacological approaches effectively restored the growth of Fsp1KO tumors in vivo. Unlike GPX4, FSP1 expression was prognostic for disease progression and poorer survival in LUAD patients, highlighting its potential as a viable therapeutic target. To this end, we demonstrated that pharmacologic inhibition of FSP1 had significant therapeutic benefit in pre-clinical lung cancer models. Our studies highlight the importance of ferroptosis suppression in vivo and pave the way for FSP1 inhibition as a therapeutic strategy in lung cancer patients.

cancer biology↗

bioRxiv2025

FMO4 drives lung adenocarcinoma by stabilizing the MAT2A/MAT2B complex and hindering ferroptosis

Lung cancer is the leading cause of death by cancer in the world and finding new targets is a major medical need to tackle this disease. Here, upon proteomic analysis to identify common players in oncogenic EGFR- and KRAS-driven lung adenocarcinoma mouse models, we uncovered a largely unknown protein in cancer, flavin-containing monooxygenase 4 (FMO4), whose expression was increased in lung tumors compared with adjacent lung tissue. FMO4 expression was strongly increased also in lung cancer samples from patients compared with healthy lung, and its expression level was inversely correlated with overall survival. Remarkably, in vivo deletion of FMO4 greatly decreased tumor burden and increased survival in oncogenic KRAS-driven lung adenocarcinoma mice unveiling its crucial role in tumor biology. Mechanistically, we found that FMO4 loss of function promotes ferroptosis and cooperates with ferroptosis inducers in vitro and in vivo. Moreover, FMO4 facilitates the interaction between MAT2A and MAT2B, promoting the generation of cysteine from methionine, which in turn boosts the generation of glutathione, thus protecting lung adenocarcinoma against ferroptosis. In summary we identified a new target in lung adenocarcinoma with important implications in cancer biology.

cancer biology↗

bioRxiv2025

Trained Immunity Affecting Dendritic Cell Differentiation and Function in Rheumatoid Arthritis

Rheumatoid arthritis affects [~]0.5-1% of the adult population and results in joint inflammation, chronic pain, and many systemic comorbidities. Immune and inflammatory tissue damage is the main pathogenic mechanism in rheumatoid arthritis, and involves the hyperactivation of both innate and adaptive immune systems. Trained immunity has become well established as an important feature of the innate immune system that allows the host to mount functionally altered immune responses based on their previous history of immune exposures, independently of the classical adaptive immunological memory. However, the role of trained immunity in systemic autoimmune and inflammatory disorders remains poorly understood. In the current work, we demonstrate that emergency myelopoiesis is induced in chronic rheumatoid arthritis in murine models and acts not only to enhance innate immune cell numbers but also to produce functionally altered innate immune cells. Such effects are cell-intrinsic to hematopoietic stem and progenitor cells (HSPCs) and persist independently of the inflammatory disease milieu. Importantly, these trained immunity mechanisms impact not only macrophages but also dendritic cells, which are the major antigen presenting cells that bridge the innate and adaptive immune responses. Trained dendritic cells show changes in global gene expression profiles, and significantly altered responses to recall immune stimulation and capacity for T cell activation. This study therefore represents the first demonstration of dendritic cell trained immunity in rheumatoid arthritis.

immunology↗

bioRxiv2025

Pde5a Deficiency Prevents Diet-Induced Obesity via Adipose cAMP-PKA Activation Enhancing Fat Browning

Cyclic nucleotides are critical regulators of adaptive thermogenesis and adipogenesis, with their intracellular levels finely tuned by phosphodiesterases. Phosphodiesterase type 5 (PDE5A) modulates cyclic guanosine monophosphate levels in adipocytes. While PDE5A inhibition has shown promise in patients with diabetes, its role in metabolism remains unclear. Using Pde5a knockout mouse models, we demonstrated that mice lacking Pde5a exhibit enhanced browning of white adipose tissue and reduced hepatic fat content. Following high-fat diet, Pde5a-/- mice are resistant to obesity, displaying improved glucose metabolism and enhanced thermogenesis. These protective effects stem from an early developmental knockdown of Pde5a, leading to a metabolic reprogramming driven by cAMP-PKA pathway activation. The convergence of cGMP and cAMP signaling orchestrates thermogenic and systemic metabolic adaptations. Our findings establish PDE5A as a novel regulator of energy homeostasis, suggesting its inhibition as a valuable adjuvant therapy for metabolic disorders.

cell biology↗

bioRxiv2024

MDM2 stabilization of Notch intracellular domain upon DNA damage plays a major role in non-small cell lung carcinoma response to platinum chemotherapy

Despite major advances in lung cancer clinical management, majority of patients suffering non-small cell lung carcinoma (NSCLC) are treated in first line with platinum in combination with immune checkpoint inhibitors. Although platinum compounds normally display an initial therapeutic effect, relapse constitutes a major challenge in the clinical management of NSCLC patients. Therefore, it is fundamental to understand the relapse underlying mechanisms to find new therapeutic opportunities to improve patients survival. Here, we found that different DNA damage inducers increase the protein levels of Notch Intracellular Domain (NICD), i.e., the active form of NOTCH1. Mechanistically, we unveiled that upon platinum treatment, there was a concomitant increase of MDM2 together with NICD, and we also observed an MDM2-mediated ubiquitination and stabilization of NICD. Of note, using patient-derived xenografts displaying intrinsic carboplatin resistance, we demonstrated that the combination of carboplatin with MDM2 and NICD inhibitors increased survival and reduced tumor growth compared with carboplatin in monotherapy. Moreover, in patients with NSCLC who received platinum chemotherapy, MDM2 expression level in the tumor was correlated with poor progression-free survival, further validating MDM2 key role in the response to platinum compounds. Our findings open a therapeutic opportunity for NSCLC patients, the main lung cancer subtype of the leading cause of death by cancer worldwide.

cancer biology↗

bioRxiv2024

A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation

Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas [7], BigBrain [13], Julich [15]), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain, a next - generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct [~]10,000 histological sections into coherent 3D volumes with joint geometric constraints (no overlap or gaps between sections), as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build the first probabilistic histological atlas of the whole human brain. Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimers disease and healthy brain ageing based on [~]4,000 publicly available in vivo MRI scans. We publicly release: the raw and aligned data (including an online visualisation tool); the probabilistic atlas; the segmentation tool; and ground truth delineations for a 100 m isotropic ex vivo hemisphere (that we use for quantitative evaluation of our segmentation method in this paper). By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain holds promise to increase the specificity of MRI findings and ultimately accelerate our quest to understand the human brain in health and disease.

neuroscience↗

bioRxiv2023

Analysis and Modeling of Early Estradiol-induced GREB1 Single Allele Gene Transcription at the Population Level

Single molecule fluorescence in situ hybridization (smFISH) can be used to visualize transcriptional activation at the single allele level. We and others have applied this approach to better understand the mechanisms of activation by steroid nuclear receptors. However, there is limited understanding of the interconnection between the activation of target gene alleles inside the same nucleus and within large cell populations. Using the GREB1 gene as an early estrogen receptor (ER) response target, we applied smFISH to track E2-activated GREB1 allelic transcription over early time points to evaluate potential dependencies between alleles within the same nucleus. We compared two types of experiments where we altered the initial status of GREB1 basal transcription by treating cells with and without the elongation inhibitor flavopiridol (FV). E2 stimulation changed the frequencies of active GREB1 alleles in the cell population independently of FV pre-treatment. In FV treated cells, the response time to hormone was delayed, albeit still reaching at 90 minutes the same levels as in cells not treated by FV. We show that the joint frequencies of GREB1 activated alleles observed at the cell population level imply significant dependency between pairs of alleles within the same nucleus. We identify probabilistic models of joint alleles activations by applying a principle of maximum entropy. For pairs of alleles, we have then quantified statistical dependency by computing their mutual information. We have then introduced a stochastic model compatible with allelic statistical dependencies, and we have fitted this model to our data by intensive simulations. This provided estimates of the average lifetime for degradation of GREB1 introns and of the mean time between two successive transcription rounds. Our approach informs on how to extract information on single allele regulation by ER from within a large population of cells, and should be applicable to many other genes. AUTHOR SUMMARYAfter application of a gene transcription stimulus, in this case the hormone 17{beta}-estradiol, on large populations of cells over a short time period, we focused on quantifying and modeling the frequencies of GREB1 single allele activations. We have established an experimental and computational pipeline to analyze large numbers of high resolution smFISH images to detect and monitor active GREB1 alleles, that can be translatable to any target gene of interest. A key result is that, at the population level, activation of individual GREB1 alleles within the same nucleus do exhibit statistically significant dependencies which we quantify by the mutual information between activation states of pairs of alleles. After noticing that frequencies of joint alleles activations observed over our large cell populations evolve smoothly in time, we have defined a population level stochastic model which we fit to the observed time course of GREB1 activation frequencies. This provided coherent estimates of the mean time between rounds of GREB1 transcription and the mean lifetime of nascent mRNAs. Our algorithmic approach and experimental methods are applicable to many other genes.

bioinformatics↗

bioRxiv2021

The African swine fever modelling challenge: objectives, model description and synthetic data generation

African swine fever (ASF) is an emerging disease currently spreading at the interface between wild boar and pig farms in Europe and Asia. Current disease control regulations, which involve massive culling with significant economic and animal welfare costs, need to be improved. Modelling enables relevant control measures to be explored, but conducting the exercise during an epidemic is extremely difficult. Modelling challenges enhance modellers ability to timely advice policy makers, improve their readiness when facing emerging threats, and promote international collaborations. The ASF-Challenge, which ran between August 2020 and January 2021, was the first modelling challenge in animal health. In this paper, we describe the objectives and rules of the challenge. We then demonstrate the mechanistic multi-host model that was used to mimic as accurately as possible an ASF-like epidemic, provide a detailed explanation of the surveillance and intervention strategies that generated the synthetic data, and describe the different management strategies that were assessed by the competing modelling teams. We then outline the different technical steps of the challenge as well as its environment. Finally, we synthesize the lessons we learnt along the way to guide future modelling challenges in animal health. HighlightsO_LIThe 1st modelling challenge in animal health mimics ASF spread with synthetic data C_LIO_LIA mechanistic spatially-explicit stochastic model was developed to represent ASF spread and control C_LIO_LISynthetic data concerned date and location of detected cases in pigs and wild boar C_LIO_LIModelling ASF at the wildlife-livestock interface illustrates the reservoir role of wild fauna C_LI

ecology↗

bioRxiv2021

Insights from the IronTract challenge: optimal methods for mapping brain pathways from multi-shell diffusion MRI

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

neuroscience↗

bioRxiv2020

Identification of Celastrol as a novel HIV-1 Latency Reversal Agent by an Image-Based Screen

Although current antiretroviral therapies (ART) are successful in controlling HIV-1 infection, a stable viral reservoir reactivates when ART is discontinued. Consequently, there is a major research effort to develop approaches to disrupt the latent viral reservoir and enhance the immune systems ability to clear HIV-1. A number of small molecules, termed latency reversal agents (LRAs), have been identified which can reactivate latent HIV-1 in cell lines and patients cells ex vivo. However, clinical trials have suggested that combinations of LRAs will be required to efficiently reactivate HIV-1 in vivo, especially LRAs that act synergistically by functioning through distinct pathways. To identify novel LRAs, we used an image-based assay to screen a natural compound library for the ability to induce a low level of aggregation of resting primary CD4+ T cells from healthy donors. We identified celastrol as a novel LRA. Celastrol functions synergistically with other classes of LRA to reactivate latent HIV-1 in a Jurkat cell line, suggesting a novel mechanism in its LRA activity. Additionally, celastrol does not appear to activate resting CD4+ T cells at levels at which it can reactivate latent HIV-1. Celastrol appears to represent a novel class of LRAs and it therefore can serve as a lead compound for LRA development.

microbiology↗

bioRxiv2020

Tractography dissection variability: what happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

neuroscience↗