scholarly journals Dense reconstruction of elongated cell lineages: overcoming suboptimum lineage encoding and sparse cell sampling

2020 ◽  
Author(s):  
Ken Sugino ◽  
Rosa L. Miyares ◽  
Isabel Espinosa-Medina ◽  
Hui-Min Chen ◽  
Christopher J Potter ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Lineage Tracing ◽  
Simple Method ◽  
Cell Lineages ◽  
Stem Cell Divisions ◽  
Neuronal Lineage ◽  
Dense Reconstruction ◽  
Parameter Ranges ◽  
Neuronal Diversification

AbstractAcquiring both lineage and cell-type information during brain development could elucidate transcriptional programs underling neuronal diversification. This is now feasible with single-cell RNA-seq combined with CRISPR-based lineage tracing, which generates genetic barcodes with cumulative CRISPR edits. This technique has not yet been optimized to deliver high-resolution lineage reconstruction of protracted lineages. Drosophila neuronal lineages are an ideal model to consider, as multiple lineages have been morphologically mapped at single-cell resolution. Here we find the parameter ranges required to encode a representative neuronal lineage emanating from 100 stem cell divisions. We derive the optimum editing rate to be inversely proportional to lineage depth, enabling encoding to persist across lineage progression. Further, we experimentally determine the editing rates of a Cas9-deaminase in cycling neural stem cells, finding near ideal rates to map elongated Drosophila neuronal lineages. Moreover, we propose and evaluate strategies to separate recurring cell-types for lineage reconstruction. Finally, we present a simple method to combine multiple experiments, which permits dense reconstruction of protracted cell lineages despite suboptimum lineage encoding and sparse cell sampling.

Looking at the Developing Lung in Single-cell Resolution

2020 ◽  
Author(s):  
Ivana Mižíková ◽  
Bernard Thébaud
Keyword(s):  
Single Cell ◽  
Lung Development ◽  
Cell Types ◽  
Lineage Tracing ◽  
Cell Lineages ◽  
Heterogeneous Nature ◽  
Cell Transcriptome ◽  
Tracing Techniques ◽  
Postnatal Lung Development ◽  
Single Cell Transcriptome

Lung development is a complicated and delicate process, facilitated by spatially and temporarily coordinated crosstalk of up to 40 cell types. Developmental origin and heterogeneity of lung cell lineages in context of lung development have been a focus of research efforts for decades. Bulk RNA and protein measurements, RNA and protein labelling, and lineage tracing techniques have been traditionally employed. However, the complex and heterogeneous nature of lung tissue presents a particular challenge when identifying subtle changes in gene expression in individual cell types. Rapidly developing single-cell RNA sequencing (scRNA-seq) techniques allow for unbiased and robust assessment of complex cellular dynamics during biological processes in unprecedented ways. Discovered a decade ago, scRNA-seq has been applied in respiratory research to understand lung cellular composition and to identify novel cell types. Still, very few studies to date have addressed the single-cell transcriptome in healthy or aberrantly developing lung. In this mini-review, we discuss principal discoveries with scRNA-seq in the field of prenatal and postnatal lung development. In addition, we examine challenges and expectations, and propose future steps associated with the use of scRNA-seq to study developmental lung diseases.

scholarly journals Applications of Single-Cell Omics to Dissect Tumor Microenvironment

2020 ◽  
Vol 11 ◽  
Author(s):  
Tingting Guo ◽  
Weimin Li ◽  
Xuyu Cai
Keyword(s):  
Tumor Microenvironment ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Immune Checkpoint Blockade ◽  
Lineage Tracing ◽  
Great Success ◽  
Novel Technologies ◽  
Single Cell Sequencing

The recent technical and computational advances in single-cell sequencing technologies have significantly broaden our toolkit to study tumor microenvironment (TME) directly from human specimens. The TME is the complex and dynamic ecosystem composed of multiple cell types, including tumor cells, immune cells, stromal cells, endothelial cells, and other non-cellular components such as the extracellular matrix and secreted signaling molecules. The great success on immune checkpoint blockade therapy has highlighted the importance of TME on anti-tumor immunity and has made it a prime target for further immunotherapy strategies. Applications of single-cell transcriptomics on studying TME has yielded unprecedented resolution of the cellular and molecular complexity of the TME, accelerating our understanding of the heterogeneity, plasticity, and complex cross-interaction between different cell types within the TME. In this review, we discuss the recent advances by single-cell sequencing on understanding the diversity of TME and its functional impact on tumor progression and immunotherapy response driven by single-cell sequencing. We primarily focus on the major immune cell types infiltrated in the human TME, including T cells, dendritic cells, and macrophages. We further discuss the limitations of the existing methodologies and the prospects on future studies utilizing single-cell multi-omics technologies. Since immune cells undergo continuous activation and differentiation within the TME in response to various environmental cues, we highlight the importance of integrating multimodal datasets to enable retrospective lineage tracing and epigenetic profiling of the tumor infiltrating immune cells. These novel technologies enable better characterization of the developmental lineages and differentiation states that are critical for the understanding of the underlying mechanisms driving the functional diversity of immune cells within the TME. We envision that with the continued accumulation of single-cell omics datasets, single-cell sequencing will become an indispensable aspect of the immune-oncology experimental toolkit. It will continue to drive the scientific innovations in precision immunotherapy and will be ultimately adopted by routine clinical practice in the foreseeable future.

scholarly journals Dynamics of gene expression in single root cells ofA. thaliana

2018 ◽  
Author(s):  
Ken Jean-Baptiste ◽  
José L. McFaline-Figueroa ◽  
Cristina M. Alexandre ◽  
Michael W. Dorrity ◽  
Lauren Saunders ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Cell Type ◽  
Specific Expression ◽  
Root Cells ◽  
Cell Lineages ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTSingle-cell RNA-seq can yield high-resolution cell-type-specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach toA. thalianaroot cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single-cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single-cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution.

scholarly journals A Single-Cell Atlas and Lineage Analysis of the Adult Drosophila Ovary

2019 ◽  
Author(s):  
Katja Rust ◽  
Lauren Byrnes ◽  
Kevin Shengyang Yu ◽  
Jason S. Park ◽  
Julie B. Sneddon ◽  
...  
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Cell Types ◽  
Somatic Tissue ◽  
Lineage Tracing ◽  
Major Cell Type ◽  
Follicle Stem Cells ◽  
Drosophila Ovary ◽  
Adult Drosophila ◽  
Lineage Analysis

AbstractThe Drosophila ovary is a widely used model for germ cell and somatic tissue biology. We have used single-cell RNA-sequencing to build a comprehensive cell atlas of the adult Drosophila ovary containing unique transcriptional profiles for every major cell type in the ovary, including the germline and follicle stem cells. Using this atlas we identify novel tools for identification and manipulation of known and novel cell types and perform lineage tracing to test cellular relationships of previously unknown cell types. By this we discovered a new form of cellular plasticity in which inner germarial sheath cells convert to follicle stem cells in response to starvation.Graphical Abstract

scholarly journals Massively parallel single cell lineage tracing using CRISPR/Cas9 induced genetic scars

2017 ◽  
Author(s):  
Bastiaan Spanjaard ◽  
Bo Hu ◽  
Nina Mitic ◽  
Jan Philipp Junker
Keyword(s):  
Single Cell ◽  
Computational Analysis ◽  
Systematic Approach ◽  
Single Cells ◽  
Cell Lineage ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Lineage Tracing ◽  
Lineage Trees ◽  
Different Cell Types

A key goal of developmental biology is to understand how a single cell transforms into a full-grown organism consisting of many different cell types. Single-cell RNA-sequencing (scRNA-seq) has become a widely-used method due to its ability to identify all cell types in a tissue or organ in a systematic manner 1–3. However, a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here, we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes, we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses, LINNAEUS (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences) can be used as a systematic approach for identifying the lineage origin of novel cell types, or of known cell types under different conditions.

scholarly journals Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Nona Farbehi ◽  
Ralph Patrick ◽  
Aude Dorison ◽  
Munira Xaymardan ◽  
Vaibhao Janbandhu ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cells ◽  
Interstitial Cell ◽  
Single Cells ◽  
Cardiac Injury ◽  
Cell Types ◽  
Cell Lineages ◽  
Heart Repair ◽  
Cell Expression ◽  
Cell Community

Besides cardiomyocytes (CM), the heart contains numerous interstitial cell types which play key roles in heart repair, regeneration and disease, including fibroblast, vascular and immune cells. However, a comprehensive understanding of this interactive cell community is lacking. We performed single-cell RNA-sequencing of the total non-CM fraction and enriched (Pdgfra-GFP+) fibroblast lineage cells from murine hearts at days 3 and 7 post-sham or myocardial infarction (MI) surgery. Clustering of >30,000 single cells identified >30 populations representing nine cell lineages, including a previously undescribed fibroblast lineage trajectory present in both sham and MI hearts leading to a uniquely activated cell state defined in part by a strong anti-WNT transcriptome signature. We also uncovered novel myofibroblast subtypes expressing either pro-fibrotic or anti-fibrotic signatures. Our data highlight non-linear dynamics in myeloid and fibroblast lineages after cardiac injury, and provide an entry point for deeper analysis of cardiac homeostasis, inflammation, fibrosis, repair and regeneration.

scholarly journals High-throughput dense reconstruction of cell lineages

Open Biology ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 190229 ◽  
Author(s):  
Isabel Espinosa-Medina ◽  
Jorge Garcia-Marques ◽  
Connie Cepko ◽  
Tzumin Lee
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Howard Hughes Medical Institute ◽  
Complex Model ◽  
Model Organisms ◽  
Medical Institute ◽  
Cell Lineages ◽  
Dna Mutations ◽  
Dense Reconstruction ◽  
New Strategies

The first meeting exclusively dedicated to the ‘High-throughput dense reconstruction of cell lineages' took place at Janelia Research Campus (Howard Hughes Medical Institute) from 14 to 18 April 2019. Organized by Tzumin Lee, Connie Cepko, Jorge Garcia-Marques and Isabel Espinosa-Medina, this meeting echoed the recent eruption of new tools that allow the reconstruction of lineages based on the phylogenetic analysis of DNA mutations induced during development. Combined with single-cell RNA sequencing, these tools promise to solve the lineage of complex model organisms at single-cell resolution. Here, we compile the conference consensus on the technological and computational challenges emerging from the use of the new strategies, as well as potential solutions.

scholarly journals SCENITH: A flow cytometry based method for functional profiling energy metabolism with single cell resolution

2020 ◽  
Author(s):  
Rafael J. Argüello ◽  
Alexis J. Combes ◽  
Remy Char ◽  
Evens Bousiquot ◽  
Julien-Paul Gigan ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Culture Media ◽  
Ex Vivo ◽  
Cell Types ◽  
Energetic Metabolism ◽  
Simple Method ◽  
Functional Profiling ◽  
Cell Subpopulations ◽  
Rare Cells

AbstractEnergetic metabolism reprogramming is critical for cancer and immune responses. Current methods to functionally profile the global metabolic capacities and dependencies of cells are performed in bulk. We designed a simple method for complex metabolic profiling called SCENITH, for Single Cell ENergetIc metabolism by profilIng Translation inHibition. SCENITH allows for the study of metabolic responses in multiple cell types in parallel by flow cytometry. SCENITH is designed to perform metabolic studies ex vivo, particularly for rare cells in whole blood samples, avoiding metabolic biases introduced by culture media. We analyzed myeloid cells in solid tumors from patients and identified variable metabolic profiles, in ways that are not linked to their lineage nor their activation phenotype. SCENITH ability to reveal global metabolic functions and determine complex and linked immune-phenotypes in rare cell subpopulations will contribute to the information needed for evaluating therapeutic responses or patient stratification.

scholarly journals Lineage tracing on transcriptional landscapes links state to fate during differentiation

2018 ◽  
Author(s):  
Caleb Weinreb ◽  
Alejo Rodriguez-Fraticelli ◽  
Fernando Camargo ◽  
Allon M Klein
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Cell Types ◽  
Lineage Tracing ◽  
Cell Potential ◽  
Monocyte Differentiation ◽  
Cell Assays ◽  
Transcriptional Landscape ◽  
Developmental Hierarchy

AbstractA challenge in stem cell biology is to associate molecular differences among progenitor cells with their capacity to generate mature cell types. Though the development of single cell assays allows for the capture of progenitor cell states in great detail, these assays cannot definitively link cell states to their long-term fate. Here, we use expressed DNA barcodes to clonally trace single cell transcriptomes dynamically during differentiation and apply this approach to the study of hematopoiesis. Our analysis identifies functional boundaries of cell potential early in the hematopoietic hierarchy and locates them on a continuous transcriptional landscape. We reconstruct a developmental hierarchy showing separate ontogenies for granulocytic subtypes and two routes to monocyte differentiation that leave a persistent imprint on mature cells. Finally, we use our approach to benchmark methods of dynamic inference from single-cell snapshots, and provide evidence of strong early fate biases dependent on cellular properties hidden from single-cell RNA sequencing.

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.

Sign in / Sign up

Export Citation Format

Share Document