scholarly journals Calculating sample size requirements for temporal dynamics in single cell proteomics

2020 ◽  
Author(s):  
Hannah Boekweg ◽  
Amanda J. Guise ◽  
Edward D. Plowey ◽  
Ryan T. Kelly ◽  
Samuel H. Payne
Keyword(s):  
Single Cell ◽  
Time Course ◽  
Temporal Dynamics ◽  
False Positive Rate ◽  
Cell Types ◽  
State Transitions ◽  
Data Series ◽  
Cell Heterogeneity ◽  
Critical Question ◽  
Measurement Variability

AbstractSingle cell measurements are uniquely capable of characterizing cell-to-cell heterogeneity, and have been used to explore the large diversity of cell types and physiological functions present in tissues and other complex cell assemblies. An intriguing application of single cell proteomics is the characterization of proteome dynamics during biological transitions, like cellular differentiation or disease progression. Time course experiments, which regularly take measurements during state transitions, rely on the ability to detect dynamic trajectories in a data series. However, in a single cell proteomics experiment, cell-to-cell heterogeneity complicates the confident identification of proteome dynamics as measurement variability may be higher than expected. Therefore, a critical question for these experiments is how many data points need to be acquired during the time course to enable robust statistical analysis. We present here an analysis of the most important variables that affect statistical confidence in the detection of proteome dynamics: fold-change, measurement variability, and the number of cells measured during the time course. Importantly, we show that datasets with less than 16 measurements across the time domain suffer from low accuracy and also have a high false-positive rate. We also demonstrate how to balance competing demands in experimental design to achieve a desired result.

scholarly journals Emergence of neuronal diversity during vertebrate brain development

2019 ◽  
Author(s):  
Bushra Raj ◽  
Jeffrey A. Farrell ◽  
Aaron McKenna ◽  
Jessica L. Leslie ◽  
Alexander F. Schier
Keyword(s):  
Brain Development ◽  
Single Cell ◽  
Time Course ◽  
Temporal Dynamics ◽  
Cell Types ◽  
Developmental Time ◽  
Neural Progenitors ◽  
Neuronal Diversity ◽  
Gene Markers ◽  
Vertebrate Brain

ABSTRACTNeurogenesis in the vertebrate brain comprises many steps ranging from the proliferation of progenitors to the differentiation and maturation of neurons. Although these processes are highly regulated, the landscape of transcriptional changes and progenitor identities underlying brain development are poorly characterized. Here, we describe the first developmental single-cell RNA-seq catalog of more than 200,000 zebrafish brain cells encompassing 12 stages from 12 hours post-fertilization to 15 days post-fertilization. We characterize known and novel gene markers for more than 800 clusters across these timepoints. Our results capture the temporal dynamics of multiple neurogenic waves from embryo to larva that expand neuronal diversity from ∼20 cell types at 12 hpf to ∼100 cell types at 15 dpf. We find that most embryonic neural progenitor states are transient and transcriptionally distinct from long-lasting neural progenitors of post-embryonic stages. Furthermore, we reconstruct cell specification trajectories for the retina and hypothalamus, and identify gene expression cascades and novel markers. Our analysis reveal that late-stage retinal neural progenitors transcriptionally overlap cell states observed in the embryo, while hypothalamic neural progenitors become progressively distinct with developmental time. These data provide the first comprehensive single-cell transcriptomic time course for vertebrate brain development and suggest distinct neurogenic regulatory paradigms between different stages and tissues.

scholarly journals STAB: a spatio-temporal cell atlas of the human brain

2020 ◽  
Vol 49 (D1) ◽  
pp. D1029-D1037
Author(s):  
Liting Song ◽  
Shaojun Pan ◽  
Zichao Zhang ◽  
Longhao Jia ◽  
Wei-Hua Chen ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Temporal Dynamics ◽  
Cell Types ◽  
Brain Regions ◽  
Molecular Characteristics ◽  
Great Effort ◽  
Brain Functions ◽  
Spatio Temporal ◽  
The Brain

Abstract The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.

scholarly journals Interpretable Unsupervised Feature Extraction and Learning of Abnormal System State Transitions in Aircraft Sensor Data

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Rashmi Sundareswara ◽  
Franz David Betz ◽  
Tsai-Ching Lu
Keyword(s):  
Temporal Dynamics ◽  
False Positive Rate ◽  
Control Valve ◽  
True Positive Rate ◽  
Fault Analysis ◽  
Sensor Data ◽  
State Transitions ◽  
Flow Control Valve ◽  
System State ◽  
Positive Rate

Commercial aircrafts generate a huge amount of data during each flight by sampling hundreds of variables at different resolutions during all phases of flight. While having this enormous source of data is useful for learning of faulty system behavior, its huge dimensionality and size can be an impeding factor to such analysis. To address this problem, we have devised a data-driven process that automatically extracts persistent, underlying latent states that can succinctly describe the data and thereby reduce its dimensionality, while preserving the most salient aspects important for fault or potential fault analysis. By analyzing how these latent states transition in time by computing a transition matrix for every leg, which we use as features, we can classify certain precursors which are indicative of a potential fault. Specifically, this is achieved by supervised and unsupervised learning of hundreds of latent state transitions for a given subsystem. Analysis of temporal dynamics of state transitions allows us to pinpoint at what time the sensor variables were behaving atypically in a flight leg, thus allowing airline maintainers to fix the faulty component quicker and avoid flight delays due to unplanned maintenance. We demonstrate our method of supervised and unsupervised classification over temporal dynamics of system state transition on two subsystems, the Fan Air Modulating Valve (FAMV) and the Flow Control Valve (FCV), and have obtained 100% true positive rate (for both systems) and a false positive rate of 0.05-0.08%.

scholarly journals scAPAdb: a comprehensive database of alternative polyadenylation at single-cell resolution

2021 ◽  
Author(s):  
Sheng Zhu ◽  
Qiwei Lian ◽  
Wenbin Ye ◽  
Wei Qin ◽  
Zhe Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Alternative Polyadenylation ◽  
Cell Types ◽  
Single Cell Level ◽  
Cell Heterogeneity ◽  
Rna Seq ◽  
Cell Level ◽  
Eukaryotic Gene ◽  
User Friendly ◽  
Different Cell Types

Abstract Alternative polyadenylation (APA) is a widespread regulatory mechanism of transcript diversification in eukaryotes, which is increasingly recognized as an important layer for eukaryotic gene expression. Recent studies based on single-cell RNA-seq (scRNA-seq) have revealed cell-to-cell heterogeneity in APA usage and APA dynamics across different cell types in various tissues, biological processes and diseases. However, currently available APA databases were all collected from bulk 3′-seq and/or RNA-seq data, and no existing database has provided APA information at single-cell resolution. Here, we present a user-friendly database called scAPAdb (http://www.bmibig.cn/scAPAdb), which provides a comprehensive and manually curated atlas of poly(A) sites, APA events and poly(A) signals at the single-cell level. Currently, scAPAdb collects APA information from > 360 scRNA-seq experiments, covering six species including human, mouse and several other plant species. scAPAdb also provides batch download of data, and users can query the database through a variety of keywords such as gene identifier, gene function and accession number. scAPAdb would be a valuable and extendable resource for the study of cell-to-cell heterogeneity in APA isoform usages and APA-mediated gene regulation at the single-cell level under diverse cell types, tissues and species.

scholarly journals Single-Cell Genomics: Catalyst for Cell Fate Engineering

2021 ◽  
Vol 9 ◽  
Author(s):  
Boxun Li ◽  
Gary C. Hon
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
State Transitions ◽  
Small Scale ◽  
Specific Cell ◽  
Direct Reprogramming ◽  
Cell State

As we near a complete catalog of mammalian cell types, the capability to engineer specific cell types on demand would transform biomedical research and regenerative medicine. However, the current pace of discovering new cell types far outstrips our ability to engineer them. One attractive strategy for cellular engineering is direct reprogramming, where induction of specific transcription factor (TF) cocktails orchestrates cell state transitions. Here, we review the foundational studies of TF-mediated reprogramming in the context of a general framework for cell fate engineering, which consists of: discovering new reprogramming cocktails, assessing engineered cells, and revealing molecular mechanisms. Traditional bulk reprogramming methods established a strong foundation for TF-mediated reprogramming, but were limited by their small scale and difficulty resolving cellular heterogeneity. Recently, single-cell technologies have overcome these challenges to rapidly accelerate progress in cell fate engineering. In the next decade, we anticipate that these tools will enable unprecedented control of cell state.

scholarly journals Massively multiplex single-cell Hi-C

2016 ◽  
Author(s):  
Vijay Ramani ◽  
Xinxian Deng ◽  
Kevin L Gunderson ◽  
Frank J Steemers ◽  
Christine M Disteche ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Cell Heterogeneity ◽  
Proof Of Concept ◽  
Chromosome Conformation ◽  
Large Numbers ◽  
Conformational Properties ◽  
Novel Method ◽  
Different Cell Types

AbstractWe present combinatorial single cell Hi-C, a novel method that leverages combinatorial cellular indexing to measure chromosome conformation in large numbers of single cells. In this proof-of-concept, we generate and sequence combinatorial single cell Hi-C libraries for two mouse and four human cell types, comprising a total of 9,316 single cells across 5 experiments. We demonstrate the utility of single-cell Hi-C data in separating different cell types, identify previously uncharacterized cell-to-cell heterogeneity in the conformational properties of mammalian chromosomes, and demonstrate that combinatorial indexing is a generalizable molecular strategy for single-cell genomics.

scholarly journals Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET

2021 ◽  
Author(s):  
Kushagra Pandey ◽  
Hamim Zafar
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Markov Chain Model ◽  
Metric Learning ◽  
Transitional Cell ◽  
Cell Types ◽  
State Transitions ◽  
Chain Model ◽  
Rna Seq ◽  
Cell State

Despite recent advances in inferring cellular dynamics using single-cell RNA-seq data, existing trajectory inference (TI) methods face difficulty in accurately reconstructing cell-state manifold and inferring trajectory and cell fate plasticity for complex topologies. We present MARGARET, a novel TI method that utilizes a deep unsupervised metric learning-based approach for inferring the cellular embeddings and employs a novel measure of connectivity between cell clusters and a graph-partitioning approach to reconstruct complex trajectory topologies. MARGARET utilizes the inferred trajectory for determining terminal states and inferring cell-fate plasticity using a scalable absorbing Markov Chain model. On a diverse simulated benchmark, MARGARET outperformed state-of-the-art methods in recovering global topology and cell pseudotime ordering. When applied to experimental datasets from hematopoiesis, embryogenesis, and colon differentiation, MARGARET reconstructed major lineages and associated gene expression trends, better characterized key branching events and transitional cell types, and identified novel cell types, and branching events that were previously uncharacterized.

scholarly journals TedSim: temporal d​ynamics sim​ulation of single cell RNA-sequencing data and cell division history

2021 ◽  
Author(s):  
Xinhai Pan ◽  
Hechen Li ◽  
Xiuwei Zhang
Keyword(s):  
Cell Division ◽  
Single Cell ◽  
Genome Editing ◽  
Computational Methods ◽  
Temporal Dynamics ◽  
Cell Types ◽  
Sequencing Data ◽  
Gene Expressions ◽  
Cell Lineages ◽  
Multiple Cell

Recently, the combined scRNA-seq and CRISPR/Cas9 genome editing technologies have enabled simultaneous readouts of gene expressions and lineage barcodes, which allows for the reconstruction of the cell division tree, and makes it possible to trace the origin of each cell type. Computational methods are emerging to take advantage of the jointly profiled scRNA-seq and lineage barcode data to better reconstruct the cell division history or to infer the cell state trajectories. Here, we present TedSim (single cell Temporal dynamics Simulator), a simulator that simulates the cell division events from the root cell to present-day cells, simultaneously generating the CRISPR/Cas9 genome editing lineage barcodes and scRNA-seq data. In particular, TedSim generates cells from multiple cell types through cell division events. TedSim can be used to benchmark and investigate computational methods which use either or both of the two types of data, scRNA-seq and lineage barcodes, to study cell lineages or trajectories. TedSim is available at: https://github.com/Galaxeee/TedSim.

scholarly journals A lineage tree-based hidden Markov model to quantify cellular heterogeneity and plasticity

2021 ◽  
Author(s):  
Farnaz Mohammadi ◽  
Shakthi Visagan ◽  
Sean M Gross ◽  
Luka Karginov ◽  
JC Lagarde ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Single Cell ◽  
Cell Fate ◽  
Drug Response ◽  
Hidden Markov ◽  
Single Cells ◽  
State Transitions ◽  
Cycle Phase ◽  
Cell Heterogeneity

Cell plasticity, or the ability of cells within a population to reversibly alter their phenotype, is an important feature of tissue homeostasis during processes such as wound healing and cancer. Plasticity operates alongside other sources of cell-to-cell heterogeneity such as genetic mutations and variation in signaling. Ultimately these processes prevent most cancer therapies from being curative. The predominant methods of quantifying tumor-drug response operate on snapshot population-level measurements and therefore lack evolutionary dynamics, which are particularly critical for dynamic processes such as plasticity. Here we apply a tree-based adaptation of a hidden Markov model (tHMM) that employs single cell lineages as input to learn the characteristic patterns of single cell heterogeneity and state transitions in an unsupervised fashion. This model enables single cell classification based on the phenotype of individual cells and their relatives for improved specificity in pinpointing the structure and dynamics of variability in drug response. Integrating this model with a modular interface for defining observed phenotypes allows the model to easily be adapted to any phenotype measured in single cells. To benchmark our model, we paired cell fate with either cell lifetimes or individual cell cycle phase lengths (G1 and S/G2) as our observed phenotypes on synthetic data and demonstrated that the model successfully classifies cells within experimentally tractable dataset sizes. As an application, we analyzed experimental measurements of cell fate and phase duration in cancer cell populations treated with chemotherapies to determine the number of distinct subpopulations. In total, this tHMM framework allows for the flexible classification of single cell heterogeneity across lineages.

scholarly journals A dynamic, spatially periodic, micro-pattern of HES5 underlies neurogenesis in the mouse spinal cord.

2020 ◽  
Author(s):  
Veronica Biga ◽  
Joshua J Hawley ◽  
Ximena Soto ◽  
Emma Johns ◽  
Daniel Han ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Single Cell ◽  
Temporal Dynamics ◽  
Ex Vivo ◽  
Computational Modelling ◽  
Tissue Level ◽  
State Transitions ◽  
Cell Coupling ◽  
Cell Dynamics ◽  
Spatially Periodic

Ultradian oscillations of HES Transcription Factors (TFs) at the single cell level, enable cell state transitions. However, the tissue level organisation of HES5 dynamics in neurogenesis is unknown. Here, we analyse the expression of HES5 ex-vivo in the developing mouse ventral spinal cord and identify microclusters of 4-6 cells with positively correlated HES5 level and ultradian dynamics. These microclusters are spatially periodic along the dorsoventral axis and temporally dynamic, alternating between high and low expression with a supra-ultradian persistence time. We show that Notch signaling is required for temporal dynamics but not the spatial periodicity of HES5. Few Neurogenin-2 cells are observed per cluster, irrespective of high or low state, suggesting that the microcluster organization of HES5 enables the stable selection of differentiating cells. Computational modelling predicts that different cell coupling strengths underlie the HES5 spatial patterns and rate of differentiation, which is consistent with comparison between the motoneuron and interneuron progenitor domains. Our work shows a previously unrecognised spatiotemporal organisation of neurogenesis, emergent at the tissue level from the synthesis of single cell dynamics.

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