Biology subjects

Bhaduri, A.

Publications and source records attributed to Bhaduri, A..

3 recordsLinked to original sources

bioRxiv2018

A draft single-cell atlas of human glioblastoma reveals a single axis of phenotype in tumor-propagating cells.

Tumor-propagating glioblastoma (GBM) stem-like cells (GSCs) of the proneural and mesenchymal molecular subtypes have been described. However, it is unknown if these two GSC populations are sufficient to generate the spectrum of cellular heterogeneity observed in GBM. The lineage relationships and niche interactions of GSCs have not been fully elucidated. We perform single-cell RNA-sequencing (scRNA-seq) and matched exome sequencing of human GBMs (12 patients; >37,000 cells) to identify recurrent hierarchies of GSCs and their progeny. We map sequenced cells to tumor-anatomical structures and identify microenvironment interactions using reference atlases and quantitative immunohistochemistry. We find that all GSCs can be described by a single axis of variation, ranging from proneural to mesenchymal. Increasing mesenchymal GSC (mGSC) content, but not proneural GSC (pGSC) content, correlates with significantly inferior survival. All clonal expressed mutations are found in the GSC populations, with a greater representation of mutations found in mGSCs. While pGSCs upregulate markers of cell-cycle progression, mGSCs are largely quiescent and overexpress cytokines mediating the chemotaxis of myeloid-derived suppressor cells. We find mGSCs enriched in hypoxic regions while pGSCs are enriched in the tumors invasive edge. We show that varying proportions of mGSCs, pGSCs, their progeny and stromal/immune cells are sufficient to explain the genetic and phenotypic heterogeneity observed in GBM. This study sheds light on a long-standing debate regarding the lineage relationships between GSCs and other glioma cell types.

cancer biology↗

bioRxiv2017

Human-specific NOTCH-like genes in a region linked to neurodevelopmental disorders affect cortical neurogenesis

Genetic changes causing dramatic brain size expansion in human evolution have remained elusive. Notch signaling is essential for radial glia stem cell proliferation and a determinant of neuronal number in the mammalian cortex. We find three paralogs of human-specific NOTCH2NL are highly expressed in radial glia cells. Functional analysis reveals different alleles of NOTCH2NL have varying potencies to enhance Notch signaling by interacting directly with NOTCH receptors. Consistent with a role in Notch signaling, NOTCH2NL ectopic expression delays differentiation of neuronal progenitors, while deletion accelerates differentiation. NOTCH2NL genes provide the breakpoints in typical cases of 1q21.1 distal deletion/duplication syndrome, where duplications are associated with macrocephaly and autism, and deletions with microcephaly and schizophrenia. Thus, the emergence of hominin-specific NOTCH2NL genes may have contributed to the rapid evolution of the larger hominin neocortex accompanied by loss of genomic stability at the 1q21. 1 locus and a resulting recurrent neurodevelopmental disorder.

neuroscience↗

bioRxiv2017

Saturating Single-Cell Datasets

High throughput methods for profiling the transcriptomes of single cells have recently emerged as transformative approaches for large-scale population surveys of cellular diversity in heterogeneous primary tissues. Efficient generation of such an atlas will depend on sufficient sampling of the diverse cell types while remaining cost-effective to enable a comprehensive examination of organs, developmental stages, and individuals. To examine the relationship between cell number and transcriptional heterogeneity in the context of unbiased cell type classification, we explicitly explored the population structure of a publically available 1.3 million cell dataset from the E18.5 mouse brain. We propose a computational framework for inferring the saturation point of cluster discovery in a single cell mRNA-seq experiment, centered around cluster preservation in downsampled datasets. In addition, we introduce a \"complexity index\", which characterizes the heterogeneity of cells in a given dataset. Using Cajal-Retzius cells as an example of a limited complexity dataset, we explored whether biological distinctions relate to technical clustering. Surprisingly, we found that clustering distinctions carrying biologically interpretable meaning are achieved with far fewer cells (20,000). Together, these findings suggest that most of the biologically interpretable insights from the 1.3 million cells can be recapitulated by analyzing 50,000 randomly selected cells, indicating that instead of profiling few individuals at high \"cellular coverage\", the much anticipated cell atlasing studies may instead benefit from profiling more individuals, or many time points at lower cellular coverage.\n\nRecent efforts seek to create a comprehensive cell atlas of the human body1,2 Current technology, however, makes it precipitously expensive to perform analysis of every cell. Therefore, designing effective sampling strategies be critical to generate a working atlas in an efficient, cost-effective, and streamlined manner. The advent of single cell and single nucleus mRNA sequencing (RNAseq) in droplet format3,4 now enables large scale sampling of cells from any tissue, and a recently released publicly available dataset of 1.3 million single cells from the E18.5 mouse brain generated with the 10X Chromium5 provides an opportunity to explore the relationship between population structure and the number of sampled cells necessary to reveal the underlying diversity of cell types. Here, we present a framework for how researchers can evaluate whether a dataset has reached saturation, and we estimate how many cells would be required to generate an atlas of the sample analyzed here. This framework can be applied to any organ or cell type specific atlas for any organism.

bioinformatics↗