scholarly journals MIRA: Joint regulatory modeling of multimodal expression and chromatin accessibility in single cells

2021 ◽  
Author(s):  
Allen W. Lynch ◽  
Christina V. Theodoris ◽  
Henry Long ◽  
Myles Brown ◽  
X. Shirley Liu ◽  
...  
Keyword(s):  
Developmental Trajectories ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Branch Points ◽  
Individual Gene ◽  
Latent Space ◽  
Regulatory Analysis ◽  
Variable Influence ◽  
Lineage Trees ◽  
Early Developmental

Rigorously comparing gene expression and chromatin accessibility in the same single cells could illuminate the logic of how coupling or decoupling of these mechanisms regulates fate commitment. Here, we present MIRA: Probabilistic Multimodal Models for Integrated Regulatory Analysis, a comprehensive methodology that systematically contrasts transcription and accessibility to infer the regulatory circuitry driving cells along developmental trajectories. MIRA leverages joint topic modeling of cell states and regulatory potential modeling of individual gene loci. MIRA thereby represents cell states in an efficient and interpretable latent space, infers high fidelity lineage trees, determines key regulators of fate decisions at branch points, and exposes the variable influence of local accessibility on transcription at distinct loci. Applied to epidermal maintenance differentiation and embryonic brain development from two different multimodal platforms, MIRA revealed that early developmental genes were tightly regulated by local chromatin landscape whereas terminal fate genes were titrated without requiring extensive chromatin remodeling.

scholarly journals Unsupervised manifold alignment for single-cell multi-omics data

2020 ◽  
Author(s):  
Ritambhara Singh ◽  
Pinar Demetci ◽  
Giancarlo Bonora ◽  
Vijay Ramani ◽  
Choli Lee ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Practical Interest ◽  
Search Space ◽  
Chromatin Accessibility ◽  
Small Scale ◽  
Latent Space ◽  
Cell Experiment ◽  
Manifold Alignment ◽  
Unsupervised Algorithms

AbstractIntegrating single-cell measurements that capture different properties of the genome is vital to extending our understanding of genome biology. This task is challenging due to the lack of a shared axis across datasets obtained from different types of single-cell experiments. For most such datasets, we lack corresponding information among the cells (samples) and the measurements (features). In this scenario, unsupervised algorithms that are capable of aligning single-cell experiments are critical to learning an in silico co-assay that can help draw correspondences among the cells. Maximum mean discrepancy-based manifold alignment (MMD-MA) is such an unsupervised algorithm. Without requiring correspondence information, it can align single-cell datasets from different modalities in a common shared latent space, showing promising results on simulations and a small-scale single-cell experiment with 61 cells. However, it is essential to explore the applicability of this method to larger single-cell experiments with thousands of cells so that it can be of practical interest to the community. In this paper, we apply MMD-MA to two recent datasets that measure transcriptome and chromatin accessibility in ~2000 single cells. To scale the runtime of MMD-MA to a more substantial number of cells, we extend the original implementation to run on GPUs. We also introduce a method to automatically select one of the user-defined parameters, thus reducing the hyperparameter search space. We demonstrate that the proposed extensions allow MMD-MA to accurately align state-of-the-art single-cell experiments.

scholarly journals Learning latent embedding of multi-modal single cell data and cross-modality relationship simultaneously

2021 ◽  
Author(s):  
Ziqi Zhang ◽  
Chengkai Yang ◽  
Xiuwei Zhang
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Single Cells ◽  
Nonlinear Function ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Difficult Problem ◽  
Latent Space ◽  
Before And After ◽  
Low Dimensional

Single cell multi-omics studies allow researchers to understand cell differentiation and development mechanisms in a more comprehensive manner. Single cell ATAC-sequencing (scATAC-seq) measures the chromatin accessibility of cells, and computational methods have been proposed to integrate scATAC-seq with scRNA-seq data of cells from the same cell types. This computational task is particularly challenging when the two modalities are not profiled from the same cells. Some existing methods first transform the scATAC-seq data into scRNA-seq data and integrate two scRNA-seq datasets, but how to perform the transformation is still a difficult problem. In addition, most of the existing methods to integrate scRNA-seq and scATAC-seq data focus on preserving distinct cell clusters before and after the integration, and it is not clear whether these methods can preserve the continuous trajectories for cells from continuous development or differentiation processes. We propose scDART, a scalable deep learning framework that embeds the two data modalities of single cells, scRNA-seq and scATAC-seq data, into a shared low-dimensional latent space while preserving cell trajectory structures. Furthermore, scDART learns a nonlinear function represented by a neural network encoding the cross-modality relationship simultaneously when learning the latent space representations of the integrated dataset. We test scDART on both real and simulated datasets, and compare it with the state-of-the-art methods. We show that scDART is able to integrate scRNA-seq and scATAC-seq data well while preserving the continuous cell trajectories. scDART also predicts scRNA-seq data accurately from the scATAC-seq data using the neural network module that represents cross-modality relationships.

scholarly journals Single cell transcriptome atlas of mouse mammary epithelial cells across development

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Bhupinder Pal ◽  
Yunshun Chen ◽  
Michael J. G. Milevskiy ◽  
François Vaillant ◽  
Lexie Prokopuk ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Single Cell ◽  
Developmental Stages ◽  
Mammary Epithelial Cells ◽  
Expression Profiles ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Mammary Epithelial ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Background Heterogeneity within the mouse mammary epithelium and potential lineage relationships have been recently explored by single-cell RNA profiling. To further understand how cellular diversity changes during mammary ontogeny, we profiled single cells from nine different developmental stages spanning late embryogenesis, early postnatal, prepuberty, adult, mid-pregnancy, late-pregnancy, and post-involution, as well as the transcriptomes of micro-dissected terminal end buds (TEBs) and subtending ducts during puberty. Methods The single cell transcriptomes of 132,599 mammary epithelial cells from 9 different developmental stages were determined on the 10x Genomics Chromium platform, and integrative analyses were performed to compare specific time points. Results The mammary rudiment at E18.5 closely aligned with the basal lineage, while prepubertal epithelial cells exhibited lineage segregation but to a less differentiated state than their adult counterparts. Comparison of micro-dissected TEBs versus ducts showed that luminal cells within TEBs harbored intermediate expression profiles. Ductal basal cells exhibited increased chromatin accessibility of luminal genes compared to their TEB counterparts suggesting that lineage-specific chromatin is established within the subtending ducts during puberty. An integrative analysis of five stages spanning the pregnancy cycle revealed distinct stage-specific profiles and the presence of cycling basal, mixed-lineage, and 'late' alveolar intermediates in pregnancy. Moreover, a number of intermediates were uncovered along the basal-luminal progenitor cell axis, suggesting a continuum of alveolar-restricted progenitor states. Conclusions This extended single cell transcriptome atlas of mouse mammary epithelial cells provides the most complete coverage for mammary epithelial cells during morphogenesis to date. Together with chromatin accessibility analysis of TEB structures, it represents a valuable framework for understanding developmental decisions within the mouse mammary gland.

scholarly journals RA3 is a reference-guided approach for epigenetic characterization of single cells

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shengquan Chen ◽  
Guanao Yan ◽  
Wenyu Zhang ◽  
Jinzhao Li ◽  
Rui Jiang ◽  
...  
Keyword(s):  
Single Cell ◽  
Computational Analysis ◽  
Reference Data ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Biological Variation ◽  
Superior Performance ◽  
High Dimensionality ◽  
High Degree

AbstractThe recent advancements in single-cell technologies, including single-cell chromatin accessibility sequencing (scCAS), have enabled profiling the epigenetic landscapes for thousands of individual cells. However, the characteristics of scCAS data, including high dimensionality, high degree of sparsity and high technical variation, make the computational analysis challenging. Reference-guided approaches, which utilize the information in existing datasets, may facilitate the analysis of scCAS data. Here, we present RA3 (Reference-guided Approach for the Analysis of single-cell chromatin Accessibility data), which utilizes the information in massive existing bulk chromatin accessibility and annotated scCAS data. RA3 simultaneously models (1) the shared biological variation among scCAS data and the reference data, and (2) the unique biological variation in scCAS data that identifies distinct subpopulations. We show that RA3 achieves superior performance when used on several scCAS datasets, and on references constructed using various approaches. Altogether, these analyses demonstrate the wide applicability of RA3 in analyzing scCAS data.

scholarly journals Early developmental asymmetries in cell lineage trees in living individuals

2020 ◽  
Author(s):  
Liana Fasching ◽  
Yeongjun Jang ◽  
Simone Tomasi ◽  
Jeremy Schreiner ◽  
Livia Tomasini ◽  
...  
Keyword(s):  
Cell Fate ◽  
Cell Lineage ◽  
Postmortem Brain ◽  
Cell Lineages ◽  
Human Specimen ◽  
Balanced Contributions ◽  
Health And Disease ◽  
Induced Pluripotent ◽  
Lineage Trees ◽  
Early Developmental

AbstractPost-zygotic mosaic mutations can be used to track cell lineages in humans. By using cell cloning and induced pluripotent cell lines, we analyzed early cell lineages in two living individuals (a patient and a control), and a postmortem human specimen. Of ten reconstructed post-zygotic divisions, none resulted in balanced contributions of daughter lineages to tissues. In both living individuals one of two lineages from the first cleavage was dominant across tissues, with 90% frequency in blood. We propose that the efficiency of DNA repair contributes to lineage imbalance. Allocation of lineages in postmortem brain correlated with anterior-posterior axis, associating lineage history with cell fate choices in embryos. Recurrence of germline variants as mosaic suggested that certain loci may be particularly susceptible to mutagenesis. We establish a minimally invasive framework for defining cell lineages in any living individual, which paves the way for studying their relevance in health and disease.

scholarly journals Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Elliott Swanson ◽  
Cara Lord ◽  
Julian Reading ◽  
Alexander T Heubeck ◽  
Palak C Genge ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
Human Peripheral Blood ◽  
Single Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Specific Gene ◽  
Test Case ◽  
Cell Assays ◽  
Paired Measurement

Single-cell measurements of cellular characteristics have been instrumental in understanding the heterogeneous pathways that drive differentiation, cellular responses to signals, and human disease. Recent advances have allowed paired capture of protein abundance and transcriptomic state, but a lack of epigenetic information in these assays has left a missing link to gene regulation. Using the heterogeneous mixture of cells in human peripheral blood as a test case, we developed a novel scATAC-seq workflow that increases signal-to-noise and allows paired measurement of cell surface markers and chromatin accessibility: integrated cellular indexing of chromatin landscape and epitopes, called ICICLE-seq. We extended this approach using a droplet-based multiomics platform to develop a trimodal assay that simultaneously measures transcriptomics (scRNA-seq), epitopes, and chromatin accessibility (scATAC-seq) from thousands of single cells, which we term TEA-seq. Together, these multimodal single-cell assays provide a novel toolkit to identify type-specific gene regulation and expression grounded in phenotypically defined cell types.

scholarly journals A deep learning approach to capture the essence of Candida albicans morphologies

2021 ◽  
Author(s):  
Van Bettauer ◽  
Anna CBP Costa ◽  
Raha Parvizi Omran ◽  
Samira Massahi ◽  
Eftyhios Kirbizakis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Strategy ◽  
Developmental Trajectories ◽  
Generative Adversarial Networks ◽  
Learning Approach ◽  
Adversarial Networks ◽  
Latent Space ◽  
Contrast Microscopy ◽  
Opportunistic Human Pathogen ◽  
Community Meeting

We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen C. albicans. Our system entitled Candescence automatically detects C. albicans cells from Differential Image Contrast microscopy, and labels each detected cell with one of nine vegetative, mating-competent or filamentous morphologies. The software is based upon a fully convolutional one-stage object detector and exploits a novel cumulative curriculum-based learning strategy that stratifies our images by difficulty from simple vegetative forms to more complex filamentous architectures. Candescence achieves very good performance on this difficult learning set which has substantial intermixing between the predicted classes. To capture the essence of each C. albicans morphology, we develop models using generative adversarial networks and identify subcomponents of the latent space which control technical variables, developmental trajectories or morphological switches. We envision Candescence as a community meeting point for quantitative explorations of C. albicans morphology.

scholarly journals Early developmental trajectories of number knowledge and math achievement from 4 to 10 years: Low-persistent profile and early-life predictors

2018 ◽  
Vol 68 ◽  
pp. 84-98 ◽  
Author(s):  
Gabrielle Garon-Carrier ◽  
Michel Boivin ◽  
Jean-Pascal Lemelin ◽  
Yulia Kovas ◽  
Sophie Parent ◽  
...  
Keyword(s):  
Early Life ◽  
Math Achievement ◽  
Developmental Trajectories ◽  
Early Developmental ◽  
Number Knowledge

scholarly journals Developmental trajectory of prehematopoietic stem cell formation from endothelium

Blood ◽  
2020 ◽  
Vol 136 (7) ◽  
pp. 845-856 ◽  
Author(s):  
Qin Zhu ◽  
Peng Gao ◽  
Joanna Tober ◽  
Laura Bennett ◽  
Changya Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Fetal Liver ◽  
Developmental Trajectories ◽  
Cell Formation ◽  
Intermediate Stage ◽  
Chromatin Accessibility ◽  
Small Population ◽  
Developmental Trajectory ◽  
Mammalian Embryo ◽  
Hematopoietic Stem

Abstract Hematopoietic stem and progenitor cells (HSPCs) in the bone marrow are derived from a small population of hemogenic endothelial (HE) cells located in the major arteries of the mammalian embryo. HE cells undergo an endothelial to hematopoietic cell transition, giving rise to HSPCs that accumulate in intra-arterial clusters (IAC) before colonizing the fetal liver. To examine the cell and molecular transitions between endothelial (E), HE, and IAC cells, and the heterogeneity of HSPCs within IACs, we profiled ∼40 000 cells from the caudal arteries (dorsal aorta, umbilical, vitelline) of 9.5 days post coitus (dpc) to 11.5 dpc mouse embryos by single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin sequencing. We identified a continuous developmental trajectory from E to HE to IAC cells, with identifiable intermediate stages. The intermediate stage most proximal to HE, which we term pre-HE, is characterized by increased accessibility of chromatin enriched for SOX, FOX, GATA, and SMAD motifs. A developmental bottleneck separates pre-HE from HE, with RUNX1 dosage regulating the efficiency of the pre-HE to HE transition. A distal candidate Runx1 enhancer exhibits high chromatin accessibility specifically in pre-HE cells at the bottleneck, but loses accessibility thereafter. Distinct developmental trajectories within IAC cells result in 2 populations of CD45+ HSPCs; an initial wave of lymphomyeloid-biased progenitors, followed by precursors of hematopoietic stem cells (pre-HSCs). This multiomics single-cell atlas significantly expands our understanding of pre-HSC ontogeny.

scholarly journals Shaping development by stochasticity and dynamics in gene regulation

Open Biology ◽  
2017 ◽  
Vol 7 (5) ◽  
pp. 170030 ◽  
Author(s):  
Peng Dong ◽  
Zhe Liu
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Cell Fate ◽  
Single Molecule ◽  
Large Scale ◽  
Single Cells ◽  
Individual Gene ◽  
Functional Specification ◽  
Spatio Temporal

Animal development is orchestrated by spatio-temporal gene expression programmes that drive precise lineage commitment, proliferation and migration events at the single-cell level, collectively leading to large-scale morphological change and functional specification in the whole organism. Efforts over decades have uncovered two ‘seemingly contradictory’ mechanisms in gene regulation governing these intricate processes: (i) stochasticity at individual gene regulatory steps in single cells and (ii) highly coordinated gene expression dynamics in the embryo. Here we discuss how these two layers of regulation arise from the molecular and the systems level, and how they might interplay to determine cell fate and to control the complex body plan. We also review recent technological advancements that enable quantitative analysis of gene regulation dynamics at single-cell, single-molecule resolution. These approaches outline next-generation experiments to decipher general principles bridging gaps between molecular dynamics in single cells and robust gene regulations in the embryo.

Sign in / Sign up

Export Citation Format

Share Document