Biomedical subjects

Mohammed A Mostajo-Radji

Publications and source records attributed to Mohammed A Mostajo-Radji.

4 recordsLinked to original sources

PubMed2026

Thalamic NRXN1-mediated input to human cortical progenitors drives excitatory neurogenesis.

The human cerebral cortex develops through coordinated signals from within the cortex and from other brain regions, including the thalamus. However, how thalamic neuronal projections influence early human cortical development remains less well-understood. In this study, we fused cortical and thalamic organoids to investigate how thalamic input shapes the maturation of human cortical cells. Using single-nuclei RNA-sequencing and cellular imaging, we found that thalamic input increases the production of cortical excitatory neurons. We identify neurexin-1 (NRXN1) as a mediator of physical contact between thalamic axons and cortical outer radial glia. Genetic knockout of thalamic NRXN1 reduced these contacts and attenuated the production of upper-layer excitatory neurons. These findings reveal a mechanism by which thalamic input regulates human cortical progenitors and shapes excitatory neuron production during development.

Animals↗

PubMed2025

Incubator-Free Organoid Culture in a Sealed Recirculatory System.

Organoids are powerful tools for studying development and disease, offering realistic organ-like human and animal tissues and facilitating experimental observation compared to live animal models. However, traditional organoid culture methods require a humidified incubator. This requirement complicates culture due to evaporative losses and restricted access to instrumentation, hindering the potential of organoids as physiologically accurate models easily subjected to detailed experimental observation. We introduce a compact, automated, sealed, incubator-free recirculatory organoid culture platform that replaces the air-liquid interface with a nonporous polymer gas exchanger and a liquid-phase gas buffer. This design prevents evaporation and stabilizes oxygen, pH, and osmolarity without feedback control. It enables single-actuator media exchange, simplifying automation. Dispensing with the incubator, we improve access for instruments such as live cell microscopes. We demonstrate compatibility with continuous multi-week live imaging of vascular organoids and show that brain organoids in this system maintain metabolic viability, structural fidelity, and electrophysiological activity comparable to traditional shaker-based cultures in an incubator.

Journal Article↗

PubMed2024

SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis.

Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (<3,500 cells) and across different samples. SIMS accurately predicts neuronal subtypes in the developing brain, shedding light on genetic changes during neuronal differentiation and postmitotic fate refinement. Finally, we apply SIMS to single-cell RNA datasets of cortical organoids to predict cell identities and uncover genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

Single-Cell Analysis↗

PubMed2023

Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis.

Large single-cell RNA datasets have contributed to unprecedented biological insight. Often, these take the form of cell atlases and serve as a reference for automating cell labeling of newly sequenced samples. Yet, classification algorithms have lacked the capacity to accurately annotate cells, particularly in complex datasets. Here we present SIMS (Scalable, Interpretable Machine Learning for Single-Cell), an end-to-end data-efficient machine learning pipeline for discrete classification of single-cell data that can be applied to new datasets with minimal coding. We benchmarked SIMS against common single-cell label transfer tools and demonstrated that it performs as well or better than state of the art algorithms. We then use SIMS to classify cells in one of the most complex tissues: the brain. We show that SIMS classifies cells of the adult cerebral cortex and hippocampus at a remarkably high accuracy. This accuracy is maintained in trans-sample label transfers of the adult human cerebral cortex. We then apply SIMS to classify cells in the developing brain and demonstrate a high level of accuracy at predicting neuronal subtypes, even in periods of fate refinement, shedding light on genetic changes affecting specific cell types across development. Finally, we apply SIMS to single cell datasets of cortical organoids to predict cell identities and unveil genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. When cell types are obscured by stress signals, label transfer from primary tissue improves the accuracy of cortical organoid annotations, serving as a reliable ground truth. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

Brain organoids↗