scholarly journals Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data

2021 ◽  
Author(s):  
Khalil Ouardini ◽  
Romain Lopez ◽  
Matthew G Jones ◽  
Sebastian Prillo ◽  
Richard Zhang ◽  
...  
Keyword(s):  
Single Cell ◽  
Goodness Of Fit ◽  
Genome Engineering ◽  
Cancer Metastasis ◽  
Single Cells ◽  
Cell Lineage ◽  
Real Data ◽  
Lineage Tracing ◽  
Lung Cancer Metastasis ◽  
Ancestral States

Novel experimental assays now simultaneously measure lineage relationships and transcriptomic states from single cells, thanks to CRISPR/Cas9-based genome engineering. These multimodal measurements allow researchers not only to build comprehensive phylogenetic models relating all cells but also infer transcriptomic determinants of consequential subclonal behavior. The gene expression data, however, is limited to cells that are currently present ("leaves" of the phylogeny). As a consequence, researchers cannot form hypotheses about unobserved, or "ancestral", states that gave rise to the observed population. To address this, we introduce TreeVAE: a probabilistic framework for estimating ancestral transcriptional states. TreeVAE uses a variational autoencoder (VAE) to model the observed transcriptomic data while accounting for the phylogenetic relationships between cells. Using simulations, we demonstrate that TreeVAE outperforms benchmarks in reconstructing ancestral states on several metrics. TreeVAE also provides a measure of uncertainty, which we demonstrate to correlate well with its prediction accuracy. This estimate therefore potentially provides a data-driven way to estimate how far back in the ancestor chain predictions could be made. Finally, using real data from lung cancer metastasis, we show that accounting for phylogenetic relationship between cells improves goodness of fit. Together, TreeVAE provides a principled framework for reconstructing unobserved cellular states from single cell lineage tracing data.

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 Lymphatic system is the mainstream for breast cancer dissemination and metastasis revealed by single-cell lineage tracing

2021 ◽  
Author(s):  
Kai Miao ◽  
Aiping Zhang ◽  
Fangyuan Shao ◽  
Lijian Wang ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Cancer Cells ◽  
Cancer Metastasis ◽  
Lymphatic System ◽  
Expression Patterns ◽  
Cell Lineage ◽  
Tumour Cells ◽  
Lineage Tracing

Abstract Cancer metastasis is the primary cause of cancer-related death, yet the forces that drive cancer cells through various steps and different routes to distinct target organs/tissues remain elusive. In this study, we applied a CellTag system-based single-cell lineage tracing approach to show the metastasis rate and route of breast cancer cells and their interactions with the tumour microenvironment (TME) during metastasis. The results indicate that only a small fraction of cells can intravasate from the primary site into the blood circulation, whereas more cells disseminate through the lymphatic system to different organs. Tumour cells derived from the same progenitor cell exhibit different gene expression patterns in different soils, and the cancer cell-TME communication paradigm varies significantly between primary and metastatic tumours. Furthermore, metastable cells require a prewired IL-2 expression ability to migrate in vivo. In summary, leveraging a single-cell lineage tracing system, we demonstrate that the crosstalk between tumour cells and the TME is the driving force controlling the preferential metastatic fate of cancer cells through the lymphatic system and that this metastasis can be suppressed by knockdown of IL-2.

scholarly journals HiCImpute: A Bayesian Hierarchical Model for Identifying Structural Zeros and Enhancing Single Cell Hi-C Data.

2021 ◽  
Author(s):  
Qing Xie ◽  
Chengong Han ◽  
Victor Jin ◽  
Shili Lin
Keyword(s):  
Quality Improvement ◽  
Data Quality ◽  
Single Cell ◽  
Single Cells ◽  
High Sensitivity ◽  
Real Data ◽  
Cell Types ◽  
Sequencing Depth ◽  
2D Data ◽  
Structural Zeros

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicate things further is the fact that not all zeros are created equal, as some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros), whereas others are indeed due to insufficient sequencing depth (sampling zeros), especially for loci that interact infrequently. Differentiating between structural zeros and sampling zeros is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchy model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values in sampling zeros. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data has led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.

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 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 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 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.

scholarly journals Chronic opioid treatment arrests neurodevelopment and alters synaptic activity in human midbrain organoids

2021 ◽  
Author(s):  
Hye Sung Kim ◽  
Yang Xiao ◽  
Xuejing Chen ◽  
Siyu He ◽  
Jongwon Im ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Cell Lineage ◽  
Induced Pluripotent Stem Cell ◽  
Synaptic Activity ◽  
Gene Expressions ◽  
Opioid Treatment ◽  
Cell Type Specific ◽  
Induced Pluripotent ◽  
The Impact

SummaryThe impact of long-term opioid exposure on the embryonic brain is crucial to healthcare due to the surging number of pregnant mothers with an opioid dependency. Current studies on the neuronal effects are limited due to human brain inaccessibility and cross-species differences among animal models. Here, we report a model to assess cell-type specific responses to acute and chronic fentanyl treatment, as well as fentanyl withdrawal, using human induced pluripotent stem cell (hiPSC)-derived midbrain organoids. Single cell mRNA sequencing (25,510 single cells in total) results suggest that chronic fentanyl treatment arrests neuronal subtype specification during early midbrain development and alters the pathways associated with synaptic activities and neuron projection. Acute fentanyl treatment, however, increases dopamine release but does not induce significant changes in gene expressions of cell lineage development. To date, our study is the first unbiased examination of midbrain transcriptomics with synthetic opioid treatment at the single cell level.

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