Biology subjects

Meidanis, J.

Publications and source records attributed to Meidanis, J..

4 recordsLinked to original sources

bioRxiv2025

Interregional human assembloids recapitulate fetal brain morphologies and enhance neuronal complexity

Neuronal morphology governs how neurons connect, integrate, and process information, offering critical insights into the functional architecture of the brain. Characterizing the three-dimensional (3D) morphology of individual neurons is key not only for mapping circuit connectivity but also for understanding the cellular diversity that emerges during development. Neural organoids are valuable models of human brain development and disease, yet their morphological complexity remains poorly characterized despite advances in single-cell transcriptomics. Here, we use 3D confocal imaging and manual reconstruction of 735 neurons to analyze forebrain (dorsal and ventral) and thalamic (dorsal and ventral) organoids, as well as forebrain, thalamic, and corticothalamic assembloids. We find that organoids and assembloids exhibit distinct morphologies resembling fetal brain neurons, including immature pyramidal-like, double-bouquet, and bushy-like neurons. Interregional assembloids show greater neuronal morphological complexity than individual organoids, with more extensive dendritic branching, longer projections, and diverse soma shapes. Corticothalamic assembloids further display features of emerging connectivity. We observe dendritic spines with excitatory and inhibitory profiles and varicosities, indicative of maturing synaptic architecture. Together, our work makes an initial effort in describing the diversity of neuronal morphology in human neural organoids and assembloids. It further establishes structural phenotyping as a critical dimension for validating human neural models and underscores their value for modeling morphofunctional disorders.

neuroscience↗

bioRxiv2024

Mutation Reporter: fast identification of mutations in NGS files

SummaryNext-generation sequencing (NGS) has accelerated precision medicine by enabling simultaneous analysis of multiple genes and detection of low-frequency mutations. However, few open-source tools allow non-specialized users to transparently adjust quality parameters during mutation analysis. Mutation Reporter was developed to identify both single and compound amino acid alterations directly from raw fastq files of sequencing originated from RNA or exon sequences. The software provides full parameter control--including alignment e-value, minimum read length, minimum read depth, and minimum variant allele frequency (VAF). Availability and implementationMutation Reporter is available to users through a free GNU software license and can be accessed on GitHub (https://github.com/meidanis-lab/mutation-reporter) and as a Code Ocean capsule (https://codeocean.com/capsule/0121109/tree).

bioinformatics↗

bioRxiv2024

Deep Learning Outperforms Classical Machine Learning Methods in Pediatric Brain Tumor Classification through Mass Spectra

Pediatric brain tumors are the most common cause of death among all childhood cancers and surgical resection usually is the first step in disease management. During surgery, it is important to perform safe gross resection of tumors, retaining as much brain tissue as possible. Therefore, appropriate resection margin delineation is extremely relevant. Currently available methods for tissue analysis have limited precision, are time-consuming, and often require multiple invasive procedures. Our main goal is to test whether machine learning techniques are capable of classifying the pediatric brain tissue chemical profile generated by DESI-MSI, which is mainly lipidic, into normal or abnormal tissue and into low- and high-grade malignancy subareas within each sample. Our experiments show that deep learning methods outperform classical machine learning methods in the task of classifying brain tissue from DESI-MSI mass spectra, both in normal versus abnormal tissue, and, for malignant tissues, in low-grade versus high-grade malignancy. Our conclusion are based on the analysis of 34,870 annotated spectra, obtained from the neoplastic and non-neoplastic microanatomical stratification of individual samples from 116 pediatric patients who underwent brain tumor surgical resection at the Boldrini Childrens Center between 2000 and 2020. Support Vector Machines, Random, Forests, and Least Absolute Shrinkage and Selection Operator (LASSO) were among the classical machine learning techniques evaluated.

bioinformatics↗

bioRxiv2023

The genome and population genomics of allopolyploid Coffea arabica reveal the diversification history of modern coffee cultivars

Coffea arabica, an allotetraploid hybrid of C. eugenioides and C. canephora, is the source of approximately 60% of coffee products worldwide, and its cultivated accessions have undergone several population bottlenecks. We present chromosome-level assemblies of a di-haploid C. arabica accession and modern representatives of its diploid progenitors, C. eugenioides and C. canephora. The three species exhibit largely conserved genome structures between diploid parents and descendant subgenomes, with no obvious global subgenome dominance. We find evidence for a founding polyploidy event 350,000-610,000 years ago, followed by several pre-domestication bottlenecks, resulting in narrow genetic variation. A split between wild accessions and cultivar progenitors occurred [~]30.5 kya, followed by a period of migration between the two populations. Analysis of modern varieties, including lines historically introgressed with C. canephora, highlights their breeding histories and loci that may contribute to pathogen resistance, laying the groundwork for future genomics-based breeding of C. arabica.

genomics↗