Allison R. Bialas

Bio: Allison R. Bialas is an academic researcher from Boston Children's Hospital. The author has contributed to research in topics: Synapse & Systemic lupus erythematosus. The author has an hindex of 8, co-authored 13 publications receiving 9880 citations. Previous affiliations of Allison R. Bialas include Harvard University.

Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets

Abstract: Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.

Schizophrenia risk from complex variation of complement component 4

Abstract: Schizophrenia is a heritable brain illness with unknown pathogenic mechanisms. Schizophrenia's strongest genetic association at a population level involves variation in the major histocompatibility complex (MHC) locus, but the genes and molecular mechanisms accounting for this have been challenging to identify. Here we show that this association arises in part from many structurally diverse alleles of the complement component 4 (C4) genes. We found that these alleles generated widely varying levels of C4A and C4B expression in the brain, with each common C4 allele associating with schizophrenia in proportion to its tendency to generate greater expression of C4A. Human C4 protein localized to neuronal synapses, dendrites, axons, and cell bodies. In mice, C4 mediated synapse elimination during postnatal development. These results implicate excessive complement activity in the development of schizophrenia and may help explain the reduced numbers of synapses in the brains of individuals with schizophrenia.

TGF-β Signaling Regulates Neuronal C1q Expression and Developmental Synaptic Refinement

Abstract: Immune molecules, including complement proteins C1q and C3, have emerged as critical mediators of synaptic refinement and plasticity. Complement localizes to synapses and refines the developing visual system through C3-dependent microglial phagocytosis of synapses. Retinal ganglion cells (RGCs) express C1q, the initiating protein of the classical complement cascade, during retinogeniculate refinement; however, the signals controlling C1q expression and function remain elusive. Previous work implicated an astrocyte-derived factor in regulating neuronal C1q expression. Here we identify retinal transforming growth factor (TGF)-β as a key regulator of neuronal C1q expression and synaptic pruning in the developing visual system. Mice lacking TGF-β receptor II (TGFβRII) in retinal neurons had reduced C1q expression in RGCs and reduced synaptic localization of complement, and phenocopied refinement defects observed in complement-deficient mice, including reduced eye-specific segregation and microglial engulfment of RGC inputs. These data implicate TGF-β in regulating neuronal C1q expression to initiate complement- and microglia-mediated synaptic pruning.

Complement System in Neural Synapse Elimination in Development and Disease.

Abstract: Recent discoveries implicate the classical complement cascade in normal brain development and in disease. Complement proteins C1q, C3, and C4 participate in synapse elimination, tagging inappropriate synaptic connections between neurons for removal by phagocytic microglia that exist in a special, highly phagocytic state during the synaptic pruning period. Several neurodevelopmental disorders, such as schizophrenia and autism, are thought to be caused by an imbalance in synaptic pruning, and recent studies suggest that dysregulation of complement could promote this synaptic pruning imbalance. Moreover, in the mature brain, complement can be aberrantly activated in early stages of neurodegenerative diseases to stimulate synapse loss. Similar pathways can also be activated in response to inflammation, as in West Nile Virus infection or in lupus, where peripheral inflammation can promote microglia-mediated synapse loss. Whether synapse loss in disease is a true reactivation of developmental synaptic pruning programs remains unclear; nonetheless, complement proteins represent potential therapeutic targets for both neurodevelopmental and neurodegenerative diseases.

Integrating single-cell transcriptomic data across different conditions, technologies, and species.

Abstract: Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

Massively parallel digital transcriptional profiling of single cells

Abstract: Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system’s technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system’s ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients. Single-cell gene expression analysis is challenging. This work describes a new droplet-based single cell RNA-seq platform capable of processing tens of thousands of cells across 8 independent samples in minutes, and demonstrates cellular subtypes and host–donor chimerism in transplant patients.

SCANPY: large-scale single-cell gene expression data analysis

Abstract: Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells ( https://github.com/theislab/Scanpy ). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices ( https://github.com/theislab/anndata ).

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

Abstract: To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.