scholarly journals Integrative single-cell analysis by transcriptional and epigenetic states in human adult brain

2017 ◽  
Author(s):  
Blue B. Lake ◽  
Song Chen ◽  
Brandon C. Sos ◽  
Jean Fan ◽  
Yun Yung ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Neuronal Cell ◽  
Cell Types ◽  
Regulatory Elements ◽  
Molecular State ◽  
Adult Brain ◽  
Dna Accessibility ◽  
Human Adult

AbstractDetailed characterization of the cell types comprising the highly complex human brain is essential to understanding its function. Such tasks require highly scalable experimental approaches to examine different aspects of the molecular state of individual cells, as well as the computational integration to produce unified cell state annotations. Here we report the development of two highly scalable methods (snDrop-Seq and scTHS-Seq), that we have used to acquire nuclear transcriptome and DNA accessibility maps for thousands of single cells from the human adult visual and frontal cortex. This has led to the best-resolved human neuronal subtypes to date, identification of a majority of the non-neuronal cell types, as well as the cell-type specific nuclear transcriptome and DNA accessibility maps. Integrative analysis allowed us to identify transcription factors and regulatory elements shaping the state of different brain cell types, and to map genetic risk factors of human brain common diseases to specific pathogenic cell types and subtypes.

scholarly journals Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis

2021 ◽  
Author(s):  
Diogo M Ribeiro ◽  
Chaymae Ziyani ◽  
Olivier Delaneau
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Regulatory Element ◽  
Single Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Rna Seq ◽  
Regulatory Architecture ◽  
Gene Enhancer ◽  
Functional Relevance

Most human genes are co-expressed with a nearby gene. Yet, previous studies only reported this extensive local gene co-expression using bulk RNA-seq. Here, we leverage single cell datasets in >85 individuals to identify gene co-expression across cells, unbiased by cell type heterogeneity and benefiting from the co-occurrence of transcription events in single cells. We discover thousands of co-expressed genes in two cell types and (i) compare single cell to bulk RNA-seq in identifying local gene co-expression, (ii) show that many co-expressed genes – but not the majority – are composed of functionally-related genes and (iii) provide evidence that these genes are transcribed synchronously and their co-expression is maintained up to the protein level. Finally, we identify gene-enhancer associations using multimodal single cell data, which reveal that >95% of co-expressed gene pairs share regulatory elements. Our in-depth view of local gene co-expression and regulatory element co-activity advances our understanding of the shared regulatory architecture between genes.

scholarly journals Decoding gene regulation in the fly brain

2021 ◽  
Author(s):  
Jasper Janssens ◽  
Sara Aibar ◽  
Ibrahim Ihsan Taskiran ◽  
Joy N. Ismail ◽  
Katina I. Spanier ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Motif Discovery ◽  
Target Genes ◽  
Single Cells ◽  
Neuronal Cell ◽  
Cell Types ◽  
Regulatory Elements ◽  
Specific Cell ◽  
Drosophila Brain

The Drosophila brain is a work horse in neuroscience. Single-cell transcriptome analysis, 3D morphological classification, and detailed EM mapping of the connectome have revealed an immense diversity of neuronal and glial cell types that underlie the wide array of functional and behavioral traits in the fruit fly. The identities of these cell types are controlled by still unknown gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. To characterize the GRN for each cell type in the Drosophila brain, we profiled chromatin accessibility of 240,919 single cells spanning nine developmental timepoints, and integrated this data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which around 70,000 are linked to specific developmental trajectories, involving neurogenesis, reprogramming and maturation. For 40 cell types, their uniquely accessible regions could be associated with their expressed transcription factors and downstream target genes, through a combination of motif discovery, network inference techniques, and deep learning. We illustrate how these enhancer-GRNs can be used to reveal enhancer architectures leading to a better understanding of neuronal regulatory diversity. Finally, our atlas of regulatory elements can be used to design genetic driver lines for specific cell types at specific timepoints, facilitating the characterization of brain cell types and the manipulation of brain function.

scholarly journals The landscape of alternative polyadenylation in single cells of the developing mouse embryo

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Vikram Agarwal ◽  
Sereno Lopez-Darwin ◽  
David R. Kelley ◽  
Jay Shendure
Keyword(s):  
Rna Binding ◽  
Developmental Stages ◽  
Single Cells ◽  
Neuronal Cell ◽  
Alternative Polyadenylation ◽  
Cell Types ◽  
Untranslated Regions ◽  
Mammalian Development ◽  
Translation Rate ◽  
Dynamic Landscape

Abstract3′ untranslated regions (3′ UTRs) post-transcriptionally regulate mRNA stability, localization, and translation rate. While 3′-UTR isoforms have been globally quantified in limited cell types using bulk measurements, their differential usage among cell types during mammalian development remains poorly characterized. In this study, we examine a dataset comprising ~2 million nuclei spanning E9.5–E13.5 of mouse embryonic development to quantify transcriptome-wide changes in alternative polyadenylation (APA). We observe a global lengthening of 3′ UTRs across embryonic stages in all cell types, although we detect shorter 3′ UTRs in hematopoietic lineages and longer 3′ UTRs in neuronal cell types within each stage. An analysis of RNA-binding protein (RBP) dynamics identifies ELAV-like family members, which are concomitantly induced in neuronal lineages and developmental stages experiencing 3′-UTR lengthening, as putative regulators of APA. By measuring 3′-UTR isoforms in an expansive single cell dataset, our work provides a transcriptome-wide and organism-wide map of the dynamic landscape of alternative polyadenylation during mammalian organogenesis.

scholarly journals Methods and sensors for functional genomic studies of cell-cycle transitions in single cells

2020 ◽  
Vol 52 (10) ◽  
pp. 468-477
Author(s):  
Alexander C. Zambon ◽  
Tom Hsu ◽  
Seunghee Erin Kim ◽  
Miranda Klinck ◽  
Jennifer Stowe ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Imaging System ◽  
Single Cells ◽  
Cell Types ◽  
Cell Analysis ◽  
Mcf7 Cells ◽  
Genomic Studies ◽  
Cell Subpopulations

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1–5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.

scholarly journals CoRE-ATAC: A deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq data

2021 ◽  
Vol 17 (12) ◽  
pp. e1009670
Author(s):  
Asa Thibodeau ◽  
Shubham Khetan ◽  
Alper Eroglu ◽  
Ryan Tewhey ◽  
Michael L. Stitzel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Immune Cells ◽  
Single Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Enhancer Activity ◽  
Specific Variation ◽  
Single Nucleus ◽  
Model Training ◽  
Gain Loss

Cis-Regulatory elements (cis-REs) include promoters, enhancers, and insulators that regulate gene expression programs via binding of transcription factors. ATAC-seq technology effectively identifies active cis-REs in a given cell type (including from single cells) by mapping accessible chromatin at base-pair resolution. However, these maps are not immediately useful for inferring specific functions of cis-REs. For this purpose, we developed a deep learning framework (CoRE-ATAC) with novel data encoders that integrate DNA sequence (reference or personal genotypes) with ATAC-seq cut sites and read pileups. CoRE-ATAC was trained on 4 cell types (n = 6 samples/replicates) and accurately predicted known cis-RE functions from 7 cell types (n = 40 samples) that were not used in model training (mean average precision = 0.80, mean F1 score = 0.70). CoRE-ATAC enhancer predictions from 19 human islet samples coincided with genetically modulated gain/loss of enhancer activity, which was confirmed by massively parallel reporter assays (MPRAs). Finally, CoRE-ATAC effectively inferred cis-RE function from aggregate single nucleus ATAC-seq (snATAC) data from human blood-derived immune cells that overlapped with known functional annotations in sorted immune cells, which established the efficacy of these models to study cis-RE functions of rare cellswithout the need for cell sorting. ATAC-seq maps from primary human cells reveal individual- and cell-specific variation in cis-RE activity. CoRE-ATAC increases the functional resolution of these maps, a critical step for studying regulatory disruptions behind diseases.

scholarly journals Novel microfluidic platform accelerates processing of tissue into single cells for molecular analysis and primary culture models

2020 ◽  
Author(s):  
Jeremy Lombardo ◽  
Marzieh Aliaghaei ◽  
Quy Nguyen ◽  
Kai Kessenbrock ◽  
Jered Haun
Keyword(s):  
Single Cell ◽  
Stress Responses ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Types ◽  
Tissue Preparation ◽  
Cell Analysis ◽  
Processing Technologies ◽  
Microfluidic Platform ◽  
Wide Range

Abstract Tissues are composed of highly heterogeneous mixtures of cell subtypes, and this diversity is increasingly being characterized using high-throughput single cell analysis methods. However, these efforts are hindered by the fact that tissues must first be dissociated into single cell suspensions that are viable and still accurately represent phenotypes from the original tissue. Current methods for breaking down tissues are inefficient, labor-intensive, subject to high variability, and potentially biased towards cell subtypes that are easier to release. Here, we present a microfluidic platform consisting of three different tissue processing technologies that can perform the complete tissue to single cell workflow, including digestion, disaggregation, and filtration. First, we developed a new microfluidic digestion device that can be loaded with minced tissue specimens quickly and easily, and then use the combination of proteolytic enzyme activity and fluid shear forces to accelerate tissue breakdown. Next, we integrated dissociation and filter technologies into a single device, which enhanced single cell numbers and fully prepared the sample for single cell analysis. The final multi-device platform was then evaluated using a diverse array of tissue types that exhibited a wide range of properties. For murine kidney and mammary tumor, we found that microfluidic processing produced 2.5-fold more single, viable cells. Single cell RNA sequencing (scRNA-seq) further revealed that device processing enriched for endothelial cells, fibroblasts, and basal epithelium, and did not increase stress responses. For murine liver and heart, which are softer tissues containing fragile cell types, processing time could be reduced to 15 min, and even as short as 1 min. We also demonstrated that periodic recovery at defined time intervals produced substantially more hepatocytes and cardiomyocytes than continuous operation, most likely by preventing damage to fragile cell types. In future work, we will seek to integrate additional operations such as upstream tissue preparation and downstream microfluidic cell sorting and detection to create powerful point-of-care single cell diagnostic platforms.

scholarly journals HiCluster: A Robust Single-Cell Hi-C Clustering Method Based on Convolution and Random Walk

2018 ◽  
Author(s):  
Jingtian Zhou ◽  
Jianzhu Ma ◽  
Yusi Chen ◽  
Chuankai Cheng ◽  
Bokan Bao ◽  
...  
Keyword(s):  
Random Walk ◽  
Single Cell ◽  
Clustering Algorithm ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Genome Structure ◽  
Real Data ◽  
Cell Types ◽  
3D Genome ◽  
Cell Clustering

3D genome structure plays a pivotal role in gene regulation and cellular function. Single-cell analysis of genome architecture has been achieved using imaging and chromatin conformation capture methods such as Hi-C. To study variation in chromosome structure between different cell types, computational approaches are needed that can utilize sparse and heterogeneous single-cell Hi-C data. However, few methods exist that are able to accurately and efficiently cluster such data into constituent cell types. Here, we describe HiCluster, a single-cell clustering algorithm for Hi-C contact matrices that is based on imputations using linear convolution and random walk. Using both simulated and real data as benchmarks, HiCluster significantly improves clustering accuracy when applied to low coverage Hi-C datasets compared to existing methods. After imputation by HiCluster, structures similar to topologically associating domains (TADs) could be identified within single cells, and their consensus boundaries among cells were enriched at the TAD boundaries observed in bulk samples. In summary, HiCluster facilitates visualization and comparison of single-cell 3D genomes.

scholarly journals Histone modification dynamics as revealed by a multicolor immunofluorescence-based single-cell analysis

2020 ◽  
Author(s):  
Yoko Hayashi-Takanaka ◽  
Yuto Kina ◽  
Fumiaki Nakamura ◽  
Leontine E. Becking ◽  
Yoichi Nakao ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Histone Modifications ◽  
Histone Deacetylases ◽  
Single Cell Analysis ◽  
Marine Organism ◽  
Single Cells ◽  
Cell Types ◽  
Transient Overexpression ◽  
Overexpression System ◽  
External Stimuli

AbstractPost-translational modifications on histones can be stable epigenetic marks and transient signals that can occur in response to internal and external stimuli. Levels of histone modifications fluctuate during the cell cycle and vary among different cell types. Here we describe a simple system to monitor the levels of multiple histone modifications in single cells by multicolor immunofluorescence using directly labeled modification-specific antibodies. We first analyzed histone H3 and H4 modifications during the cell cycle. Levels of active marks, such as acetylation and H3K4 methylation, were increased during the S phase, in association with chromatin duplication. By contrast, levels of some repressive modifications gradually increased during the G2 and the next G1 phases. We applied this method to validate the target modifications of various histone demethylases in cells using a transient overexpression system. We also screened chemical compounds in marine organism extracts that affect histone modifications and identified psammaplin A, which was previously reported to inhibit histone deacetylases. Thus, the method presented here is a powerful and convenient tool for analyzing the changes in histone modifications.

scholarly journals A human cell atlas of fetal chromatin accessibility

Science ◽  
2020 ◽  
Vol 370 (6518) ◽  
pp. eaba7612 ◽  
Author(s):  
Silvia Domcke ◽  
Andrew J. Hill ◽  
Riza M. Daza ◽  
Junyue Cao ◽  
Diana R. O’Day ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Cell ◽  
Single Cells ◽  
Complex Trait ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Cell Type ◽  
Cell Type Specific

The chromatin landscape underlying the specification of human cell types is of fundamental interest. We generated human cell atlases of chromatin accessibility and gene expression in fetal tissues. For chromatin accessibility, we devised a three-level combinatorial indexing assay and applied it to 53 samples representing 15 organs, profiling ~800,000 single cells. We leveraged cell types defined by gene expression to annotate these data and cataloged hundreds of thousands of candidate regulatory elements that exhibit cell type–specific chromatin accessibility. We investigated the properties of lineage-specific transcription factors (such as POU2F1 in neurons), organ-specific specializations of broadly distributed cell types (such as blood and endothelial), and cell type–specific enrichments of complex trait heritability. These data represent a rich resource for the exploration of in vivo human gene regulation in diverse tissues and cell types.

scholarly journals Cell type-specific enhancer-promoter connectivity maps in the human brain and disease risk association

2019 ◽  
Author(s):  
Alexi Nott ◽  
Inge R. Holtman ◽  
Nicole G. Coufal ◽  
Johannes C.M. Schlachetzki ◽  
Miao Yu ◽  
...  
Keyword(s):  
Human Brain ◽  
Psychiatric Disorders ◽  
Complex Traits ◽  
Disease Risk ◽  
Cell Types ◽  
Regulatory Elements ◽  
Gene Promoters ◽  
Cell Type ◽  
Risk Variants ◽  
Cell Type Specific

AbstractUnique cell type-specific patterns of activated enhancers can be leveraged to interpret non-coding genetic variation associated with complex traits and diseases such as neurological and psychiatric disorders. Here, we have defined active promoters and enhancers for major cell types of the human brain. Whereas psychiatric disorders were primarily associated with regulatory regions in neurons, idiopathic Alzheimer’s disease (AD) variants were largely confined to microglia enhancers. Interactome maps connecting GWAS variants in cell type-specific enhancers to gene promoters revealed an extended microglia gene network in AD. Deletion of a microglia-specific enhancer harboring AD-risk variants ablated BIN1 expression in microglia but not in neurons or astrocytes. These findings revise and expand the genes likely to be influenced by non-coding variants in AD and suggest the probable brain cell types in which they function.One Sentence SummaryIdentification of cell type-specific regulatory elements in the human brain enables interpretation of non-coding GWAS risk variants.

Sign in / Sign up

Export Citation Format

Share Document