Biology subjects

Behboudi, R.

Publications and source records attributed to Behboudi, R..

3 recordsLinked to original sources

bioRxiv2026

Microchimerism in the human brain, quantitative assessment and single nuclei profiling establish cell types and diversity

Bi-directional maternal-fetal exchange during pregnancy creates a long-term microchimerism (Mc) legacy in both individuals, but its presence and cellular fate in human brain are largely unknown. We studied surgically resected epilepsy brain specimens with targetable maternal polymorphisms using polymorphism-specific quantitative PCR. Maternal Mc was prevalent, detectable in 70% of patients, and often at striking quantities spanning temporal, frontal, parietal, and hippocampal regions. Next, we employed single nucleus RNA profiling using cellector, a genetic demultiplexing tool designed to detect rare allogeneic cells. We identified Mc across major neural and glial populations. Finally, analysis of publicly available snRNA-seq datasets from neurotypical brains from gestation to late adulthood further revealed widespread Mc, persisting into advanced age, and preferentially adopting L2/3 intratelencephalic neuronal or microglial/macrophage-like fates. These data show that naturally acquired Mc is prevalent, diverse, and persistent in human brain, inviting reconsideration of what constitutes "self," with broad implications for health and disease.

neuroscience↗

bioRxiv2026

Cellector: A tool to detect foreign genotype cells in scRNAseq data with applications in leukemia and microchimerism.

The existence of rare, genetically distinct cells can occur in various samples such as transplant patients, naturally occurring microchimerism between maternal and fetal tissues, and cancer samples with sufficient mutational burden. Computational methods for detecting these foreign cells are vital to studying these biological conditions. An application that is of particular interest is that of leukemia patients post hematopoietic cell transplant (HCT). In many leukemias, a primary therapy is HCT, after which, the primary genotype of the bone marrow and blood cells should be of donor origin. If cells exist that are of the patients genotype and the cell type lineage of the particular leukemia, this is known as measurable residual disease (MRD). If the MRD is high enough, this may represent a relapse of the patients leukemia. Furthermore, accurately estimating the MRD is important for driving clinical decision making for these patients. Here we present Cellector, a computational method for identifying rare foreign genotype cells in single cell RNAseq (scRNAseq) datasets. We show cellector accurately detects microchimeric cells down to an exceedingly low percentage of these cells present (0.05% or lower).

bioinformatics↗

bioRxiv2025

Souporcell3: Robust Demultiplexing for High-Donor Single-Cell RNA-seq Datasets

MotivationAccurate demultiplexing of pooled single-cell RNA-seq (scRNAseq) data is critical for large-scale studies. However, existing methods like vireo, while effective up to [~]16 donors, often struggle with poor clustering due to local optima as donor numbers rise. In high-donor scenarios, overlapping genotypes, a dense genotype space, and increased doublet formation make demultiplexing challenging, requiring methods that are robust to sparse, high-dimensional data and maintain reliable accuracy even as sample complexity grows. ResultsWe present an enhanced version of souporcell capable of demultiplexing up to 64 donors. The method uses 10x merge for initialization, K-Harmonic Means for robust clustering, and iterative refinement with reinitialization of low-quality clusters and locking of high-quality ones. Compared to vireo, vireo with overclustering, and the original souporcell, our approach completely eliminates duplicate clusters and achieves consistently high Adjusted Rand Index (ARI) scores across various doublet rates, demonstrating improved accuracy and scalability. AvailabilitySouporcell3 source code and documentation are released on GitHub: https://github.com/wheaton5/souporcell

bioinformatics↗