Biology subjects

Mohammadi, S.

Publications and source records attributed to Mohammadi, S..

35 records · Page 2Linked to original sources

bioRxiv2022

Deciphering the fibre-orientation independent component of R2* (R2,iso*) in the human brain with a single multi-echo gradient-recalled-echo measurement under varying microstructural conditions

The effective transverse relaxation rate (R2*) is sensitive to the microstructure of the human brain, e.g. the g-ratio characterising the relative myelination of axons. However, R2* depends on the orientation of the fibres relative to the main magnetic field degrading its reproducibility and that of any microstructural derivative measure. To decipher its orientation-independent part (R2,iso*), a second-order polynomial in time (M2) can be applied to single multi-echo gradient-recalled-echo (meGRE) measurements at arbitrary orientation. The linear-time dependent parameter, {beta}1, of M2 can be biophysically related to R2,iso* when neglecting the signal from the myelin water (MW) in the hollow cylinder fibre model (HCFM). Here, we examined the effectiveness of M2 using experimental and simulated data with variable g-ratio and fibre dispersion. We showed that the fitted {beta}1 effectively estimates R2,iso*when using meGRE with long maximum echo time (TEmax {approx} 54 ms) but its microscopic dependence on the g-ratio was not accurately captured. This error was reduced to less than 12% when accounting for the MW contribution in a newly introduced biophysical expression for {beta}1. We further used this new expression to estimate the MW fraction (0.14) and g-ratio (0.79) in a human optic chiasm. However, the proposed method failed to estimate R2,iso* for a typical in-vivo meGRE protocol (TEmax {approx} 18 ms). At this TEmax and around the magic angle, the HCFM-based simulations failed to explain the R2*-orientation-dependence. In conclusion, estimation of R2,iso* with M2 in vivo requires meGRE protocols with very long TEmax {approx} 54 ms.

neuroscience↗

bioRxiv2022

Axisymmetric diffusion kurtosis imaging with Rician bias correction: A simulation study

PurposeTo compare the estimation accuracy of axisymmetric diffusion kurtosis imaging (DKI) and standard DKI in combination with Rician bias correction (RBC) under the influence of noise. MethodsAxisymmetric DKI is more robust against noise-induced variation in the measured signal than standard DKI because of its reduced parameter space. However, its susceptibility to Rician noise bias at low signal-to-noise ratios (SNRs) is unknown. Here, we investigate two main questions: first, does Rician bias correction improve estimation accuracy of axisymmetric DKI?; second, is the estimation accuracy of axisymmetric DKI increased compared to standard DKI? Estimation accuracy was investigated on the five axisymmetric DKI tensor metrics (AxTM): the parallel and perpendicular diffusivity and kurtosis and the mean kurtosis, using a simulation study based on synthetic and in-vivo data. ResultsWe found that RBC was most effective for increasing accuracy of the parallel AxTM in highly to moderately aligned white matter. For the perpendicular AxTM, axisymmetric DKI without RBC performed slightly better than with RBC. However, the combination of axisymmetric DKI with RBC was the overall best performing algorithm across all five AxTM and the axisymmetric DKI framework itself substantially improved accuracy in tissues with low fiber alignment. ConclusionThe combination of axisymmetric DKI with RBC facilitates accurate DKI parameter estimation at unprecedented low SNRs ({approx} 15), possibly making it a valuable tool for neuroscience and clinical research studies where scan time is a limited resource. The tools used in this paper are publicly available in the open-source ACID toolbox for SPM.

neuroscience↗

bioRxiv2022

Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity

Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and [Formula], proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1 and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the methods sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.

neuroscience↗

bioRxiv2021

Epistasis is not a strong constraint on the recurrent evolution of toxin-resistant Na+,K+-ATPases among tetrapods

A growing body of theoretical and experimental evidence suggests that intramolecular epistasis is a major determinant of rates and patterns of protein evolution and imposes a substantial constraint on the evolution of novel protein functions. Here, we examine the role of intramolecular epistasis in the recurrent evolution of resistance to cardiotonic steroids (CTS) across tetrapods, which occurs via specific amino acid substitutions to the -subunit family of Na,K-ATPases (ATP1A). After identifying a series of recurrent substitutions at two key sites of ATP1A that are predicted to confer CTS resistance in diverse tetrapods, we then performed protein engineering experiments to test the functional consequences of introducing these substitutions onto divergent species backgrounds. In line with previous results, we find that substitutions at these sites can have substantial background-dependent effects on CTS resistance. Globally, however, these substitutions also have pleiotropic effects that are consistent with additive rather than background-dependent effects. Moreover, the magnitude of a substitutions effect on activity does not depend on the overall extent of ATP1A sequence divergence between species. Our results suggest that epistatic constraints on the evolution of CTS-resistant forms of Na,K-ATPase likely depend on a small number of sites, with little dependence on overall levels of protein divergence. We propose that dependence on a limited number sites may account for the observation of convergent CTS resistance substitutions observed among taxa with highly divergent Na,K-ATPases. Significance StatementIndividual amino acid residues within a protein work in concert to produce a functionally coherent structure that must be maintained even as orthologous proteins in different species diverge over time. Given this dependence, we expect identical mutations to have more similar effects on protein function in more closely related species. We tested this hypothesis by performing protein-engineering experiments on ATP1A, an enzyme mediating target-site insensitivity to cardiotonic steroids (CTS) in diverse animals. These experiments reveal that the phenotypic effects of substitutions can sometimes be background-dependent, but also that the magnitude of these phenotypic effects does not correlate with overall levels of ATP1A sequence divergence. Our results suggest that epistatic constraints are determined by states at a small number of sites, potentially explaining the frequent convergent CTS resistance substitutions among Na,K-ATPases of highly divergent taxa.

evolutionary biology↗

bioRxiv2021

Assessing donor-to-donor variability in human intestinal organoid cultures

Donor-to-donor variability in primary human organoid cultures has not been well characterized. As these cultures contain multiple cell types, there is greater concern that variability could lead to increased noise. In this work we investigated donor-to-donor variability in human gut adult stem cell (ASC) organoids. We examined intestinal developmental pathways during culture differentiation in ileum- and colon-derived cultures established from multiple donors, showing that differentiation patterns were consistent among cultures. This finding indicates that donor-to-donor variability in this system remains at a manageable level. Intestinal metabolic activity was evaluated by targeted analysis of central carbon metabolites and by analyzing hormone production patterns. Both experiments demonstrated similar metabolic functions among donors. Importantly, this activity reflected intestinal biology, indicating that these ASC organoid cultures are appropriate for studying metabolic processes. This work establishes a framework for generating high confidence data using human primary cultures through thorough characterization of variability.

developmental biology↗

bioRxiv2021

Single-cell profiling of the human primary motor cortex in ALS and FTLD

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two devastating and fatal neurodegenerative conditions. While distinct, they share many clinical, genetic, and pathological characteristics1, and both show selective vulnerability of layer 5b extratelencephalic-projecting cortical populations, including Betz cells in ALS2,3 and von Economo neurons (VENs) in FTLD4,5. Here, we report the first high resolution single-cell atlas of the human primary motor cortex (MCX) and its transcriptional alterations in ALS and FTLD across ~380,000 nuclei from 64 individuals, including 17 control samples and 47 sporadic and C9orf72-associated ALS and FTLD patient samples. We identify 46 transcriptionally distinct cellular subtypes including two Betz-cell subtypes, and we observe a previously unappreciated molecular similarity between Betz cells and VENs of the prefrontal cortex (PFC) and frontal insula. Many of the dysregulated genes and pathways are shared across excitatory neurons, including stress response, ribosome function, oxidative phosphorylation, synaptic vesicle cycle, endoplasmic reticulum protein processing, and autophagy. Betz cells and SCN4B+ long-range projecting L3/L5 cells are the most transcriptionally affected in both ALS and FTLD. Lastly, we find that the VEN/Betz cell-enriched transcription factor, POU3F1, has altered subcellular localization, co-localizes with TDP-43 aggregates, and may represent a cell type-specific vulnerability factor in the Betz cells of ALS and FTLD patient tissues.

neuroscience↗

bioRxiv2021

Single-cell anatomical analysis of human hippocampus and entorhinal cortex uncovers early-stage molecular pathology in Alzheimer's disease

The human hippocampal formation plays a central role in Alzheimers disease (AD) progression, cognitive traits, and the onset of dementia; yet its molecular states in AD remain uncharacterized. Here, we report a comprehensive single-cell transcriptomic dissection of the human hippocampus and entorhinal cortex across 489,558 cells from 65 individuals with varying stages of AD pathology. We transcriptionally characterize major brain cell types and neuronal classes, including 17 glutamatergic and 8 GABAergic neuron subpopulations. Combining evidence from human and mouse tissue-microdissection, neuronal cell isolation and spatial transcriptomics, we show that single-cell expression patterns capture fine-resolution neuronal anatomical topography. By stratifying subjects into early and late pathology groups, we uncover stage-dependent and cell-type specific transcriptional modules altered during AD progression. These include early-stage cell-type specific dysregulation of cellular and cholesterol metabolism, late-stage neuron-glia alterations in neurotransmission, and late-stage signatures of cellular stress, apoptosis, and DNA damage broadly shared across cell types. Late-stage signatures show signs of convergence in hippocampal and cortical cells, while early changes diverge; highlighting the relevance of characterizing molecular pathology across brain regions and AD progression. Finally, we characterize neuron subregion-specific responses to AD pathology and show that CA1 pyramidal neurons are the most transcriptionally altered while CA3 and dentate gyrus granule neurons the least. Our study provides a valuable resource to extend cell type-specific studies of AD to clinically relevant brain regions affected early by pathology in disease progression.

neuroscience↗

bioRxiv2021

Reducing susceptibility distortion related image blurring in diffusion MRI EPI data

Diffusion magnetic resonance imaging (MRI) is an increasingly popular technique in basic and clinical neuroscience. One promising application is to combine diffusion MRI with myelin maps from complementary MRI techniques such as multi-parameter mapping (MPM) to produce g-ratio maps that represent the relative myelination of axons and predict their conduction velocity. Statistical Parametric Mapping (SPM) can process both diffusion data and MPMs, making SPM the only widely accessible software that contains all the processing steps required to perform group analyses of g-ratio data in a common space. However, limitations have been identified in its method for reducing susceptibility-related distortion in diffusion data. More generally, susceptibility-related image distortion is often corrected by combining reverse phase-encoded images (blip-up and blip-down) using the arithmetic mean (AM), however, this can lead to blurred images. In this study we sought to (1) improve the susceptibility-related distortion correction for diffusion MRI data in SPM; (2) deploy an alternative approach to the AM to reduce image blurring in diffusion MRI data when combining blip-up and blip-down EPI data after susceptibility-related distortion correction; and (3) assess the benefits of these changes for g-ratio mapping. We found that the new processing pipeline, called consecutive Hyperelastic Susceptibility Artefact Correction (HySCO) improved distortion correction when compared to the standard approach in the ACID toolbox for SPM. Moreover, using a weighted average (WA) method to combine the distortion corrected data from each phase-encoding polarity achieved greater overlap of diffusion and more anatomically faithful structural white matter probability maps derived from minimally distorted multi-parameter maps as compared to the AM. Third, we showed that the consecutive HySCO WA performed better than the AM method when combined with multi-parameter maps to perform g-ratio mapping. These improvements mean that researchers can conveniently access a wide range of diffusion-related analysis methods within one framework because they are now available within the open-source ACID toolbox as part of SPM, which can be easily combined with other SPM toolboxes, such as the hMRI toolbox, to facilitate computation of myelin biomarkers that are necessary for g-ratio mapping.

neuroscience↗

bioRxiv2021

A representative reference for MRI-based human axon radius assessment using light microscopy

Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data on the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (reff). Because the current gold standard stems from neuroanatomical studies designed to estimate the frequency-weighted arithmetic mean radius (rarith) on small ensembles of axons, it is unsuited to estimate the tail-weighted reff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for reff. In a human corpus callosum, we assessed estimation accuracy and bias of rarith and reff. Furthermore, we investigated whether mapping anatomy-related variation of rarith and reff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the potential error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (normalized-root-mean-square-error of reff: 7.2 %; rarith: 21.5 %) and lower bias (normalized-mean-bias-error of reff: -1.7 %; rarith: 16 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.9 % and 3 % to reff. In conclusion, the proposed method accurately estimates reff at MRI voxel resolution across a human corpus callosum sample. Further investigations are required to assess generalization to brain areas with different axon radii ensembles.

neuroscience↗

bioRxiv2021

Raabin-WBC: a large free access dataset of white blood cells from normal peripheral blood

Accurate and early detection of peripheral white blood cell anomalies plays a crucial role in the evaluation of an individuals well-being. The emergence of new technologies such as artificial intelligence can be very effective in achieving this. In this regard, most of the state-of-the-art methods use deep neural networks. Data can significantly influence the performance and generalization power of machine learning approaches, especially deep neural networks. To that end, we collected a large free available dataset of white blood cells from normal peripheral blood samples called Raabin-WBC. Our dataset contains about 40000 white blood cells and artifacts (color spots). To reassure correct data, a significant number of cells were labeled by two experts, and the ground truth of nucleus and cytoplasm were extracted by experts for some cells (about 1145), as well. To provide the necessary diversity, various smears have been imaged. Hence, two different cameras and two different microscopes were used. The Raabin-WBC dataset can be used for different machine learning tasks such as classification, detection, segmentation, and localization. We also did some primary deep learning experiments on Raabin-WBC, and we showed how the generalization power of machine learning methods, especially deep neural networks, was affected by the mentioned diversity.

bioengineering↗

bioRxiv2021

Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders

Thousands of genetic variants acting in multiple cell types underlie complex disorders, yet most gene expression studies profile only bulk tissues, making it hard to resolve where genetic and non-genetic contributors act. This is particularly important for psychiatric and neurodegenerative disorders that impact multiple brain cell types with highly-distinct gene expression patterns and proportions. To address this challenge, we develop a new framework, SPLITR, that integrates single-nucleus and bulk RNA-seq data, enabling phenotype-aware deconvolution and correcting for systematic discrepancies between bulk and single-cell data. We deconvolved 3,387 post-mortem brain samples across 1,127 individuals and in multiple brain regions. We find that cell proportion varies across brain regions, individuals, disease status, and genotype, including genetic variants in TMEM106B that impact inhibitory neuron fraction and 4,757 cell-type-specific eQTLs. Our results demonstrate the power of jointly analyzing bulk and single-cell RNA-seq to provide insights into cell-type-specific mechanisms for complex brain disorders.

genomics↗

bioRxiv2020

Isolation of potential plant growth-promoting bacteria from nodules of legumes grown in arid Botswana soil

As the world population increases, improvements in crop growth and yield will be needed to meet rising food demands, especially in countries that have not developed agricultural practices optimized for their own soils and crops. In many African countries, farmers improve agricultural productivity by applying synthetic fertilizers and pesticides to crops, but their continued use over the years has had serious environmental consequences including air and water pollution as well as loss of soil fertility. To reduce the overuse of synthetic amendments, we are developing inocula for crops that are based on indigenous soil microbes, especially those that enhance plant growth and improve agricultural productivity in a sustainable manner. We first isolated environmental DNA from soil samples collected from an agricultural region to study the composition of the soil microbiomes and then used Vigna unguiculata (cowpea), an important legume crop in Botswana and other legumes as "trap" plants using the collected soil to induce nitrogen-fixing nodule formation. We have identified drought-tolerant bacteria from Botswana soils that stimulate plant growth; many are species of Bacillus and Paenibacillus. In contrast, the cowpea nodule microbiomes from plants grown in these soils house mainly rhizobia particularly Bradyrhizobium, but also Methylobacterium spp. Hence, the nodule microbiome is much more limited in non-rhizobial diversity compared to the soil microbiome, but also contains a number of potential pathogenic bacteria.

microbiology↗

bioRxiv2020

Concerted evolution reveals co-adapted amino acid substitutions in frogs that prey on toxic toads

Gene duplication is an important source of evolutionary innovation, but the adaptive division-of-labor between duplicates can be opposed by ongoing gene conversion between them. Here we document a tandem duplication of Na+,K+-ATPase subunit 1 (ATP1A1) shared by frogs in the genus Leptodactylus, a group of species that feeds on toxic toads. One ATP1A1 paralog evolved resistance to toad toxins while the other paralog retained ancestral susceptibility. We show that the two Leptodactylus paralogs are distinguished by 12 amino acid substitutions that were maintained by strong selection that counteracted the homogenizing effect of gene conversion. Protein-engineering experiments show that two major-effect substitutions confer toxin resistance, whereas the 10 additional substitutions mitigate deleterious pleiotropic effects on enzyme function. Our results highlight how trans-specific, neofunctionalized gene duplicates can provide unique insights into interactions between adaptive substitutions and the genetic backgrounds on which they arise. One Sentence SummarySelection counteracts gene conversion to maintain an adaptive division-of-labor between tandemly duplicated genes.

evolutionary biology↗

bioRxiv2019

Multi-resolution single-cell state characterization via joint archetypal/network analysis

Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although a large number of methods and approaches exist, robustly identifying underlying cell states and their associations is still a major challenge; given the nonexclusive and dynamic influence of multiple unknown sources of variability, the existence of state continuum at the time-scale of observation, and the inevitable snapshot nature of experiments. As a way to address some of these challenges, here we introduce ACTIONet, a comprehensive framework that combines archetypal analysis and network theory to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet uses multilevel matrix decomposition and network reconstruction to simultaneously learn cell state patterns, quantify single-cell states, and reconstruct a reproducible structural representation of the transcriptional state space that is geometrically mapped to a color space. A color-enhanced quantitative view of cell states enables novel visualization, prediction, and annotation approaches. Using data from multiple tissues, organisms, and developmental conditions, we illustrate how ACTIONet facilitates the reconstruction and exploration of single-cell state landscapes.

genomics↗

bioRxiv2019

Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders

The reference human interactome has been instrumental in the systems-level study of the molecular inner workings of the cell, providing a framework to analyze the network context of disease associated gene perturbations. However, reference organismal interactomes do not capture the tissue- and cell type-specific context in which proteins and modules preferentially act. Emerging single-cell profiling technologies, which survey the transcriptional cell-state distribution of complex tissues, could be used to infer the single-cell context of gene interactions. Here we introduce SCINET (Single-Cell Imputation and NETwork construction), a computational framework that reconstructs an ensemble of cell type-specific interactomes by integrating a global, context-independent reference interactome with a single-cell gene expression profile. SCINET addresses technical challenges of single-cell data by robustly imputing, transforming, and normalizing the initially noisy and sparse expression data. Subsequently, cell-level gene interaction probabilities and group-level gene interaction strengths are computed, resulting in cell type specific interactomes. We use SCINET to analyze the human cortex, reconstructing interactomes for the major cell types of the adult human brain. We identify network neighborhoods composed of topologically-specific genes that are central for cell-type influence but not for global interactome connectivity. We use the reconstructed interactomes to analyze the specificity and modularity of perturbations associated with neurodegenerative, neuropsychiatric, and neoplastic brain disorders; finding high variability across diseases, yet overall consistency in patterns of cell-type convergence for diseases of the same group. We infer for each disorder group disease gene networks with preferential cell-type specific activity that can aid the design and interpretation of cell-type resolution experiments. Finally, focusing on the pleiotropy of schizophrenia and bipolar disorder, we show how cell type specific interactomes enable the identification of disease genes with preferential influence on neuronal, glial, or glial-neuronal cells. The SCINET framework is applicable to any organism, cell-type/tissue, and reference network; it is freely available at https://github.com/shmohammadi86/SCINET.

systems biology↗

bioRxiv2018

DECODE-ing sparsity patterns in single-cell RNA-seq

An inherent challenge in interpreting single-cell transcriptomic data is the high frequency of zero values. This phenomenon has been attributed to both biological and technical sources, although the extent of the contribution of each remains unclear. Here, we show that the underlying gene presence/absence sparsity patterns are by themselves highly informative. We develop an algorithm, called DECODE, to assess the extent of joint presence/absence of genes across different cells, and to infer a gene dependency network. We show that this network captures biologically-meaningful pathways, cell-type specific modules, and connectivity patterns characteristic of complex networks. We develop a model that uses this network to discriminate biological vs. technical zeros, by exploiting each genes local network neighborhood. For inferred non-biological zeros, we build a predictive model that imputes the missing value of each gene based on activity patterns of its most informative neighbors. We show that our framework accurately infers gene-gene functional dependencies, pinpoints technical zeros, and predicts biologically-meaningful missing values in three diverse datasets.

bioinformatics↗

bioRxiv2016

Take ACTION to identify high-resolution cell types and associated transcriptional pathways

Single-cell transcriptomic data has the potential to radically redefine our view of cell type identity. Cells that were previously believed to be homogeneous are now clearly distinguishable in terms of their expression phenotype. Methods for automatically characterizing the functional identity of cells, and their associated properties, can be used to uncover processes involved in lineage differentiation as well as sub-typing cancer cells. They can also be used to suggest personalized therapies based on molecular signatures associated with pathology. We develop a new method, called ACTION, to infer the functional identity of cells from their transcriptional profile, classify them based on their dominant function, and reconstruct regulatory networks that are responsible for mediating their identity. Using ACTION, we identify novel Melanoma sub-types with differential survival rates and therapeutic responses, for which we provide biomarkers along with their underlying regulatory networks.

bioinformatics↗