scholarly journals Single-cell Lineage Tracing by Integrating CRISPR-Cas9 Mutations with Transcriptomic Data

2019 ◽  
Author(s):  
Hamim Zafar ◽  
Chieh Lin ◽  
Ziv Bar-Joseph
Keyword(s):  
Single Cell ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
Expression Data ◽  
Random Mutation ◽  
Novel Technologies ◽  
Gene Sets ◽  
Lineage Tree ◽  
Novel Method ◽  
Differentiation Pathways

AbstractRecent studies combine two novel technologies, single-cell RNA-sequencing and CRISPR-Cas9 barcode editing for elucidating developmental lineages at the whole organism level. While these studies provided several insights, they face several computational challenges. First, lineages are reconstructed based on noisy and often saturated random mutation data. Additionally, due to the randomness of the mutations, lineages from multiple experiments cannot be combined to reconstruct a consensus lineage tree. To address these issues we developed a novel method, LinTIMaT, which reconstructs cell lineages using a maximum-likelihood framework by integrating mutation and expression data. Our analysis shows that expression data helps resolve the ambiguities arising in when lineages are inferred based on mutations alone, while also enabling the integration of different individual lineages for the reconstruction of a consensus lineage tree. LinTIMaT lineages have better cell type coherence, improve the functional significance of gene sets and provide new insights on progenitors and differentiation pathways.

scholarly journals Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Hamim Zafar ◽  
Chieh Lin ◽  
Ziv Bar-Joseph
Keyword(s):  
Single Cell ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
Expression Data ◽  
Random Mutation ◽  
Novel Technologies ◽  
Cell Lineages ◽  
Gene Sets ◽  
Lineage Tree ◽  
Differentiation Pathways

Abstract Recent studies combine two novel technologies, single-cell RNA-sequencing and CRISPR-Cas9 barcode editing for elucidating developmental lineages at the whole organism level. While these studies provided several insights, they face several computational challenges. First, lineages are reconstructed based on noisy and often saturated random mutation data. Additionally, due to the randomness of the mutations, lineages from multiple experiments cannot be combined to reconstruct a species-invariant lineage tree. To address these issues we developed a statistical method, LinTIMaT, which reconstructs cell lineages using a maximum-likelihood framework by integrating mutation and expression data. Our analysis shows that expression data helps resolve the ambiguities arising in when lineages are inferred based on mutations alone, while also enabling the integration of different individual lineages for the reconstruction of an invariant lineage tree. LinTIMaT lineages have better cell type coherence, improve the functional significance of gene sets and provide new insights on progenitors and differentiation pathways.

scholarly journals SCuPhr: A Probabilistic Framework for Cell Lineage Tree Reconstruction

2018 ◽  
Author(s):  
Hazal Koptagel ◽  
Seong-Hwan Jun ◽  
Jens Lagergren
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Copy Number ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
The Other ◽  
Sequencing Data ◽  
Data Set ◽  
Detailed Model ◽  
Lineage Tree

AbstractReconstruction of cell lineage trees from single-cell DNA sequencing data, has the potential to become a fundamental tool in study of development of disease, in particular cancer. For cells without copy number alterations that has not been exposed to specific marking techniques, that is normal cells, lineage tracing is naturally based on somatic point mutations. Current single cell sequencing techniques applicable to such cells require an amplification step, which introduces errors, and still often suffer from so-called allelic dropout. We present a detailed model of current technologies for the purpose of estimating the distance between cells without copy number changes, based on single-cell DNA sequencing data. The model is well suited for full Bayesian analysis by introducing prior probabilities for key parameters as well as maximum a posteriori estimation using expectation maximization algorithm. Our model outputs distance between two cells, simultaneously taking all the other cells into account. In particular, the model contains variables associated with pairs of loci, of which one is homozygous and the other heterozygous, and has the capacity to perform Bayesian probabilistic read phasing. By applying a fast distance based method, such as FNJ, to the estimated distance, a cell lineage tree can be obtained. In contrast to MCMC based methods, FNJ can easily handle data sets with tens of thousands of taxa. The high accuracy of the so obtained method, called SCuPhr, is shown in studies of several synthetic data set.

scholarly journals MEDALT: single-cell copy number lineage tracing enabling gene discovery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Fang Wang ◽  
Qihan Wang ◽  
Vakul Mohanty ◽  
Shaoheng Liang ◽  
Jinzhuang Dou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Copy Number ◽  
Speciation Analysis ◽  
Population Based ◽  
Lineage Tracing ◽  
Breast Cancer Patients ◽  
Lineage Tree ◽  
History Of ◽  
A Cell

AbstractWe present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer patients, our approaches effectively prioritize genes that are essential for breast cancer cell fitness and predict patient survival, including those implicating convergent evolution.The source code of our study is available at https://github.com/KChen-lab/MEDALT.

scholarly journals Stem cell dynamics in mouse hair follicles: A story from cell division counting and single cell lineage tracing

Cell Cycle ◽  
2010 ◽  
Vol 9 (8) ◽  
pp. 1504-1510 ◽  
Author(s):  
Ying V. Zhang ◽  
Brian S. White ◽  
David I. Shalloway ◽  
Tudorita Tumbar
Keyword(s):  
Stem Cell ◽  
Cell Division ◽  
Single Cell ◽  
Cell Lineage ◽  
Hair Follicles ◽  
Lineage Tracing ◽  
Cell Dynamics

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 Single-cell lineage tracing approaches in hematology research: technical considerations

2020 ◽  
Vol 89 ◽  
pp. 26-36 ◽  
Author(s):  
Joana Carrelha ◽  
Dawn S. Lin ◽  
Alejo E. Rodriguez-Fraticelli ◽  
Tiago C. Luis ◽  
Adam C. Wilkinson ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
Technical Considerations

scholarly journals Single cell lineage tracing reveals a role for TgfβR2 in intestinal stem cell dynamics and differentiation

2016 ◽  
Vol 113 (43) ◽  
pp. 12192-12197 ◽  
Author(s):  
Jared M. Fischer ◽  
Peter P. Calabrese ◽  
Ashleigh J. Miller ◽  
Nina M. Muñoz ◽  
William M. Grady ◽  
...  
Keyword(s):  
Single Cell ◽  
Transforming Growth Factor ◽  
Critical Role ◽  
Cell Lineage ◽  
Transforming Growth Factor Β ◽  
Lineage Tracing ◽  
Secretory Cells ◽  
Cancer Tissue ◽  
Tgfβ Signaling

Intestinal stem cells (ISCs) are maintained by a niche mechanism, in which multiple ISCs undergo differential fates where a single ISC clone ultimately occupies the niche. Importantly, mutations continually accumulate within ISCs creating a potential competitive niche environment. Here we use single cell lineage tracing following stochastic transforming growth factor β receptor 2 (TgfβR2) mutation to show cell autonomous effects of TgfβR2 loss on ISC clonal dynamics and differentiation. Specifically, TgfβR2 mutation in ISCs increased clone survival while lengthening times to monoclonality, suggesting that Tgfβ signaling controls both ISC clone extinction and expansion, independent of proliferation. In addition, TgfβR2 loss in vivo reduced crypt fission, irradiation-induced crypt regeneration, and differentiation toward Paneth cells. Finally, altered Tgfβ signaling in cultured mouse and human enteroids supports further the in vivo data and reveals a critical role for Tgfβ signaling in generating precursor secretory cells. Overall, our data reveal a key role for Tgfβ signaling in regulating ISCs clonal dynamics and differentiation, with implications for cancer, tissue regeneration, and inflammation.

scholarly journals A novel single-cell based method for breast cancer prognosis

2020 ◽  
Author(s):  
Xiaomei Li ◽  
Lin Liu ◽  
Greg Goodall ◽  
Andreas Schreiber ◽  
Taosheng Xu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Single Cell ◽  
Tumor Heterogeneity ◽  
Breast Cancer Prognosis ◽  
Cancer Prognosis ◽  
Biological Processes ◽  
Expression Data ◽  
Rna Seq ◽  
Novel Method

AbstractBreast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.Author summaryVarious computational methods have been developed for breast cancer prognosis. However, those methods mainly use the gene expression data generated by the bulk RNA sequencing techniques, which average the expression level of a gene across different cell types. As breast cancer is a heterogenous disease, the bulk gene expression may not be the ideal resource for cancer prognosis. In this study, we propose a novel method to improve breast cancer prognosis using scRNA-seq data. The proposed method has been applied to the EMT scRNA-seq dataset for identifying breast cancer signatures for prognosis. In comparison with existing bulk expression data based methods in breast cancer prognosis, our method shows a better performance. Our single-cell-based signatures provide clues to the relation between EMT and clinical outcomes of breast cancer. In addition, the proposed method can also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.

scholarly journals A lineage-tracing tool to map the fate of hypoxic tumour cells

2020 ◽  
Author(s):  
Jenny A.F. Vermeer ◽  
Jonathan Ient ◽  
Bostjan Markelc ◽  
Jakob Kaeppler ◽  
Lydie M.O. Barbeau ◽  
...  
Keyword(s):  
Single Cell ◽  
Intravital Microscopy ◽  
Fusion Gene ◽  
Hypoxia Inducible Factor ◽  
Cell Lineage ◽  
Tumour Cells ◽  
Lineage Tracing ◽  
Spatially Resolved ◽  
Summary Statement

AbstractIntratumoural hypoxia is a common characteristic of malignant treatment-resistant cancers. However, hypoxia-modification strategies for the clinic remain elusive. To date little is known on the behaviour of individual hypoxic tumour cells in their microenvironment. To explore this issue in a spatial and temporally-controlled manner we developed a genetically encoded sensor by fusing the O2-labile Hypoxia-Inducible Factor 1α to eGFP and a tamoxifen-regulated Cre recombinase. Under normoxic conditions HIF-1α is degraded but under hypoxia, the HIF-1α-GFP-Cre-ERT2 fusion protein is stabilised and in the presence of tamoxifen activates a tdTomato reporter gene that is constitutively expressed in hypoxic progeny. We visualise the random distribution of hypoxic tumour cells from hypoxic or necrotic regions and vascularised areas using immunofluorescence and intravital microscopy. Once tdTomato expression is induced, it is stable for at least 4 weeks. Using this system, we could show that the post-hypoxic cells were more proliferative in vivo than non-labelled cells. Our results demonstrate that single-cell lineage tracing of hypoxic tumour cells can allow visualisation of their behaviour in living tumours using intravital microscopy. This tool should prove valuable for the study of dissemination and treatment response of post-hypoxic tumour cells in vivo at single-cell resolution.Summary StatementHere we developed and characterised a novel HIF-1α-Cre fusion gene to trace the progeny of hypoxic tumour cells in a temporal and spatially resolved manner using intravital microscopy.

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