Biology subjects

Rayan, N. A.

Publications and source records attributed to Rayan, N. A..

3 recordsLinked to original sources

bioRxiv2026

Inter-individual variation of cellular and gene-expression properties of the human striatum

The human brain varies from person to person in ways that shape behaviors and vulnerabilities, yet the cellular and molecular bases for inter-individual variation are largely unknown. Here we describe an analysis of cellular and gene-expression variation in four key structures of the striatum complex - the caudate, putamen, nucleus accumbens, and internal capsule - as well as the prefrontal cortex, from single-nucleus RNA-seq analysis of 3.9 million nuclei from 178 adult brain donors. We found that people with more astrocytes in any one brain region tended to have this property in all brain regions sampled; the same was true of striatal interneurons, microglia, and oligodendrocyte precursor cells (OPCs). OPCs showed attrition with age, declining in numbers by approximately 40% between age 30 and age 80 in both gray matter and white matter regions. We identified thousands of age-associated (but few sex-associated) variations in gene expression; the vast majority of these effects of age were cell-type-specific. Aging most strongly affected gene expression in projection neurons - especially striatal medium spiny neurons (MSNs/SPNs) - and had a much smaller effect on gene expression in interneurons. Individuals ages could be predicted to within about five years based on RNA-expression patterns from any of the striatal cell types. Common genetic variants detectably affected the expression levels of some ten thousand genes; the great majority of these effects were cell-type-specific. These data will provide a foundation for exploring natural inter-individual variation, aging, and tissue-based studies of human brain vulnerabilities.

neuroscience↗

bioRxiv2024

Single-cell analysis of human diversity in circulating immune cells

Lack of diversity and proportionate representation in genomics datasets and databases contributes to inequity in healthcare outcomes globally1,2. The relationships of human diversity with biological and biomedical phenotypes are pervasive3, yet remain understudied, particularly in a single-cell genomics context. Here we present the Asian Immune Diversity Atlas (AIDA), a multi-national single-cell RNA-sequencing (scRNA-seq) healthy reference atlas of human immune cells. AIDA comprises 1,265,624 circulating immune cells from 619 healthy donors and 6 controls, spanning 7 population groups across 5 countries. AIDA is one of the largest healthy blood datasets in terms of number of cells, and also the most diverse in terms of number of population groups. Though population groups are frequently compared at the continental level, we identified a pervasive impact of sub-continental diversity on cellular and molecular properties of immune cells. These included cell populations and genes implicated in disease risk and pathogenesis as well as those relevant for diagnostics. We detected single-cell signatures of human diversity not apparent at the level of cell types, as well as modulation of the effects of age and sex by self-reported ethnicity. We discovered functional genetic variants influencing cell type-specific gene expression, including context-dependent effects, which were under-represented in analyses of non-Asian population groups, and which helped contextualise disease-associated variants. We validated our findings using multiple independent datasets and cohorts. AIDA provides fundamental insights into the relationships of human diversity with immune cell phenotypes, enables analyses of multi-ancestry disease datasets, and facilitates the development of precision medicine efforts in Asia and beyond.

genomics↗

bioRxiv2021

Robust clustering and interpretation ofscRNA-seq data using reference componentanalysis

MotivationThe transcriptomic diversity of the hundreds of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Though clustering of cellular transcriptomes is the default technique for defining cell types and subtypes, single cell clustering can be strongly influenced by technical variation. In fact, the prevalent unsupervised clustering algorithms can cluster cells by technical, rather than biological, variation. ResultsCompared to de novo (unsupervised) clustering methods, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects. To leverage the advantages of supervised clustering, we present RCA2, a new, scalable, and broadly applicable version of our RCA algorithm. RCA2 provides a user-friendly framework for supervised clustering and downstream analysis of large scRNA-seq data sets. RCA2 can be seamlessly incorporated into existing algorithmic pipelines. It incorporates various new reference panels for human and mouse, supports generation of custom panels and uses efficient graph-based clustering and sparse data structures to ensure scalability. We demonstrate the applicability of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Importantly, RCA2 facilitates cell-type-specific QC, which we show is essential for accurate clustering of SC data from heterogeneous tissues. In the era of cohort-scale SC analysis, supervised clustering methods such as RCA2 will facilitate unified analysis of diverse SC datasets. AvailabilityRCA2 is implemented in R and is available at github.com/prabhakarlab/RCAv2

bioinformatics↗