scholarly journals Capybara: A computational tool to measure cell identity and fate transitions

Author(s):  
Wenjun Kong ◽  
Yuheng C. Fu ◽  
Samantha A. Morris

SummaryTransitions in cell identity are fundamental to development, reprogramming, and disease. Single-cell technologies enable the dissection of tissue composition on a cell-by-cell basis in complex biological systems. However, highly-sparse single-cell RNA-seq data poses challenges for cell-type identification algorithms based on bulk RNA-seq. Single-cell analytical tools are also limited, where they require prior biological knowledge and typically classify cells in a discrete, categorical manner. Here, we present a computational tool, ‘Capybara,’ designed to measure cell identity as a continuum, at single-cell resolution. This approach enables the classification of discrete cell entities but also identifies cells harboring multiple identities, supporting a metric to quantify cell fate transition dynamics. We benchmark the performance of Capybara against other existing classifiers and demonstrate its efficacy to annotate cells and identify critical transitions within a well-characterized differentiation hierarchy, hematopoiesis. Our application of Capybara to a range of reprogramming strategies reveals previously uncharacterized regional patterning and identifies a putative in vivo correlate for an engineered cell type that has, to date, remained undefined. These findings prioritize interventions to increase the efficiency and fidelity of cell engineering strategies, showcasing the utility of Capybara to dissect cell identity and fate transitions. Capybara code and documentation are available at https://github.com/morris-lab/Capybara.

2018 ◽  
Author(s):  
Yuqi Tan ◽  
Patrick Cahan

Single cell RNA-Seq has emerged as a powerful tool in diverse applications, ranging from determining the cell-type composition of tissues to uncovering the regulators of developmental programs. A near-universal step in the analysis of single cell RNA-Seq data is to hypothesize the identity of each cell. Often, this is achieved by finding cells that express combinations of marker genes that had previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single cell RNA-Seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single cell RNA-Seq data in comparison to reference single cell RNA-Seq data. SingleCellNet compares favorably to other methods, and it is notably able to make sensitive and accurate classifications across platforms and species. We demonstrate how SingleCellNet can be used to classify previously undetermined cells, and how it can be used to assess the outcome of cell fate engineering experiments.


2021 ◽  
Author(s):  
Jing Liu ◽  
Shengyong Yu ◽  
Chunhua Zhou ◽  
Jiangping He ◽  
Xingnan Huang ◽  
...  

Abstract Single cell analysis provides clarity unattainable with bulk approaches. Here we apply single cell RNA-seq to a newly established BMP4 induced mouse primed to naive transition (Bi-PNT) system and show that the reset is not a direct reversal of cell fate but through developmental intermediates. We first show that mEpiSCs bifurcate into c-Kit+ naïve and c-Kit- placenta-like cells, among which, the naive branch undergoes further transition through a primordial germ cell-like cells (PGCLCs) intermediate capable of spermatogenesis in vivo. Indeed, deficiency of Prdm1/Blimp1, the key regulator for PGC specification, blocks the induction of PGCLCs and naïve cells. Instead, Gata2 knockout arrests placenta-like fate, but facilitates the generation of PGCLCs. Our results not only reveal a newly cell fate dynamics between primed and naive states at single-cell resolution, but also provide a model system to explore mechanisms involved in regaining germline competence from primed pluripotency.


2021 ◽  
Author(s):  
Chengxiang Qiu ◽  
Junyue Cao ◽  
Tony Li ◽  
Sanjay Srivatsan ◽  
Xingfan Huang ◽  
...  

Mammalian embryogenesis is characterized by rapid cellular proliferation and diversification. Within a few weeks, a single cell zygote gives rise to millions of cells expressing a panoply of molecular programs, including much of the diversity that will subsequently be present in adult tissues. Although intensively studied, a comprehensive delineation of the major cellular trajectories that comprise mammalian development in vivo remains elusive. Here we set out to integrate several single cell RNA-seq datasets (scRNA-seq) that collectively span mouse gastrulation and organogenesis. We define cell states at each of 19 successive stages spanning E3.5 to E13.5, heuristically connect them with their pseudo-ancestors and pseudo-descendants, and for a subset of stages, deconvolve their approximate spatial distributions. Despite being constructed through automated procedures, the resulting trajectories of mammalian embryogenesis (TOME) are largely consistent with our contemporary understanding of mammalian development. We leverage TOME to nominate transcription factors (TF) and TF motifs as key regulators of each branch point at which a new cell type emerges. Finally, to facilitate comparisons across vertebrates, we apply the same procedures to single cell datasets of zebrafish and frog embryogenesis, and nominate "cell type homologs" based on shared regulators and transcriptional states.


2019 ◽  
Author(s):  
Jacob C. Kimmel ◽  
Lolita Penland ◽  
Nimrod D. Rubinstein ◽  
David G. Hendrickson ◽  
David R. Kelley ◽  
...  

AbstractBackgroundAging is a pleiotropic process affecting many aspects of organismal and cellular physiology. Mammalian organisms are composed of a constellation of distinct cell type and state identities residing within different tissue environments. Due to technological limitations, the study of aging has traditionally focused on changes within individual cell types, or the aggregate changes across cell types within a tissue. The influence of cell identity and tissue environment on the trajectory of aging therefore remains unclear.ResultsHere, we perform single cell RNA-seq on >50,000 individual cells across three tissues in young and aged mice. These molecular profiles allow for comparison of aging phenotypes across cell types and tissue environments. We find transcriptional features of aging common across many cell types, as well as features of aging unique to each type. Leveraging matrix factorization and optimal transport methods, we compute a trajectory and magnitude of aging for each cell type. We find that cell type exerts a larger influence on these measures than tissue environment.ConclusionIn this study, we use single cell RNA-seq to dissect the influence of cell identity and tissue environment on the aging process. Single cell analysis reveals that cell identities age in unique ways, with some common features of aging shared across identities. We find that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating. These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A799-A799
Author(s):  
Dhiraj Kumar ◽  
Sreeharsha Gurrapu ◽  
Hyunho Han ◽  
Yan Wang ◽  
Seongyeon Bae ◽  
...  

BackgroundLong non-coding RNAs (lncRNAs) are involved in various biological processes and diseases. Malat1 (metastasis-associated lung adenocarcinoma transcript 1), also known as Neat2, is one of the most abundant and highly conserved nuclear lncRNAs. Several studies have shown that the expression of lncRNA Malat1 is associated with metastasis and serving as a predictive marker for various tumor progression. Metastatic relapse often develops years after primary tumor removal as a result of disseminated tumor cells undergoing a period of latency in the target organ.1–4 However, the correlation of tumor intrinsic lncRNA in regulation of tumor dormancy and immune evasion is largely unknown.MethodsUsing an in vivo screening platform for the isolation of genetic entities involved in either dormancy or reactivation of breast cancer tumor cells, we have identified Malat1 as a positive mediator of metastatic reactivation. To functionally uncover the role of Malat1 in metastatic reactivation, we have developed a knock out (KO) model by using paired gRNA CRISPR-Cas9 deletion approach in metastatic breast and other cancer types, including lung, colon and melanoma. As proof of concept we also used inducible knockdown system under in vivo models. To delineate the immune micro-environment, we have used 10X genomics single cell RNA-seq, ChIRP-seq, multi-color flowcytometry, RNA-FISH and immunofluorescence.ResultsOur results reveal that the deletion of Malat1 abrogates the tumorigenic and metastatic potential of these tumors and supports long-term survival without affecting their ploidy, proliferation, and nuclear speckles formation. In contrast, overexpression of Malat1 leads to metastatic reactivation of dormant breast cancer cells. Moreover, the loss of Malat1 in metastatic cells induces dormancy features and inhibits cancer stemness. Our RNA-seq and ChIRP-seq data indicate that Malat1 KO downregulates several immune evasion and stemness associated genes. Strikingly, Malat1 KO cells exhibit metastatic outgrowth when injected in T cells defective mice. Our single-cell RNA-seq cluster analysis and multi-color flow cytometry data show a greater proportion of T cells and reduce Neutrophils infiltration in KO mice which indicate that the immune microenvironment playing an important role in Malat1-dependent immune evasion. Mechanistically, loss of Malat1 is associated with reduced expression of Serpinb6b, which protects the tumor cells from cytotoxic killing by the T cells. Indeed, overexpression of Serpinb6b rescued the metastatic potential of Malat1 KO cells by protecting against cytotoxic T cells.ConclusionsCollectively, our data indicate that targeting this novel cancer-cell-initiated domino effect within the immune system represents a new strategy to inhibit tumor metastatic reactivation.Trial RegistrationN/AEthics ApprovalFor all the animal studies in the present study, the study protocols were approved by the Institutional Animal Care and Use Committee(IACUC) of UT MD Anderson Cancer Center.ConsentN/AReferencesArun G, Diermeier S, Akerman M, et al., Differentiation of mammary tumors and reduction in metastasis upon Malat1 lncRNA loss. Genes Dev 2016 Jan 1;30(1):34–51.Filippo G. Giancotti, mechanisms governing metastatic dormancy and reactivation. Cell 2013 Nov 7;155(4):750–764.Gao H, Chakraborty G, Lee-Lim AP, et al., The BMP inhibitor Coco reactivates breast cancer cells at lung metastatic sites. Cell 2012b;150:764–779.Gao H, Chakraborty G, Lee-Lim AP, et al., Forward genetic screens in mice uncover mediators and suppressors of metastatic reactivation. Proc Natl Acad Sci U S A 2014 Nov 18; 111(46): 16532–16537.


Author(s):  
Qianhui Huang ◽  
Yu Liu ◽  
Yuheng Du ◽  
Lana X. Garmire
Keyword(s):  
Rna Seq ◽  

Genomics ◽  
2021 ◽  
Vol 113 (6) ◽  
pp. 3582-3598
Author(s):  
Xiujun Sun ◽  
Li Li ◽  
Biao Wu ◽  
Jianlong Ge ◽  
Yanxin Zheng ◽  
...  

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.


Author(s):  
Lutz Frönicke ◽  
Denise N. Bronner ◽  
Mariana X. Byndloss ◽  
Bridget McLaughlin ◽  
Andreas J. Bäumler ◽  
...  
Keyword(s):  
Rna Seq ◽  

2018 ◽  
Author(s):  
Avi Z. Rosenberg ◽  
Carrie Wright ◽  
Karen Fox-Talbot ◽  
Anandita Rajpurohit ◽  
Courtney Williams ◽  
...  

AbstractAccurate, RNA-seq based, microRNA (miRNA) expression estimates from primary cells have recently been described. However, this in vitro data is mainly obtained from cell culture, which is known to alter cell maturity/differentiation status, significantly changing miRNA levels. What is needed is a robust method to obtain in vivo miRNA expression values directly from cells. We introduce expression microdissection miRNA small RNA sequencing (xMD-miRNA-seq), a method to isolate cells directly from formalin fixed paraffin-embedded (FFPE) tissues. xMD-miRNA-seq is a low-cost, high-throughput, immunohistochemistry-based method to capture any cell type of interest. As a proof-of-concept, we isolated colon epithelial cells from two specimens and performed low-input small RNA-seq. We generated up to 600,000 miRNA reads from the samples. Isolated epithelial cells, had abundant epithelial-enriched miRNA expression (miR-192; miR-194; miR-200b; miR-200c; miR-215; miR-375) and overall similar miRNA expression patterns to other epithelial cell populations (colonic enteroids and flow-isolated colon epithelium). xMD-derived epithelial cells were generally not contaminated by other adjacent cells of the colon as noted by t-SNE analysis. xMD-miRNA-seq allows for simple, economical, and efficient identification of cell-specific miRNA expression estimates. Further development will enhance rapid identification of cell-specific miRNA expression estimates in health and disease for nearly any cell type using archival FFPE material.


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