FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2792294474> ?p ?o ?g. }

• W2792294474 endingPage "548" @default.
• W2792294474 startingPage "539" @default.
• W2792294474 abstract "Function at the organ level manifests itself from a heterogeneous collection of cell types. Cellular heterogeneity emerges from developmental processes by which multipotent progenitor cells make fate decisions and transition to specific cell types through intermediate cell states. Although genetic experimental strategies such as lineage tracing have provided insights into cell lineages, recent developments in single-cell technologies have greatly increased our ability to interrogate distinct cell types, as well as transitional cell states in tissue systems. From single-cell data that describe these intermediate cell states, computational tools have been developed to reconstruct cell-state transition trajectories that model cell developmental processes. These algorithms, although powerful, are still in their infancy, and attention must be paid to their strengths and weaknesses when they are used. Here, we review some of these tools, also referred to as pseudotemporal ordering algorithms, and their associated assumptions and caveats. We hope to provide a rational and generalizable workflow for single-cell trajectory analysis that is intuitive for experimental biologists. Function at the organ level manifests itself from a heterogeneous collection of cell types. Cellular heterogeneity emerges from developmental processes by which multipotent progenitor cells make fate decisions and transition to specific cell types through intermediate cell states. Although genetic experimental strategies such as lineage tracing have provided insights into cell lineages, recent developments in single-cell technologies have greatly increased our ability to interrogate distinct cell types, as well as transitional cell states in tissue systems. From single-cell data that describe these intermediate cell states, computational tools have been developed to reconstruct cell-state transition trajectories that model cell developmental processes. These algorithms, although powerful, are still in their infancy, and attention must be paid to their strengths and weaknesses when they are used. Here, we review some of these tools, also referred to as pseudotemporal ordering algorithms, and their associated assumptions and caveats. We hope to provide a rational and generalizable workflow for single-cell trajectory analysis that is intuitive for experimental biologists. SummaryRecent developments in single-cell technologies have stimulated growth in analysis techniques, in particular, computational tools for ordering cell states as a function of pseudotemporal progression. We provide a review of current algorithms and a generalized single-cell workflow tailored for trajectory analysis, with a focus on underlying assumptions and caveats. Recent developments in single-cell technologies have stimulated growth in analysis techniques, in particular, computational tools for ordering cell states as a function of pseudotemporal progression. We provide a review of current algorithms and a generalized single-cell workflow tailored for trajectory analysis, with a focus on underlying assumptions and caveats. Cellular heterogeneity, defined by a diversity of co-occurring cell types in a tissue, is characteristic of practically every organ in the human body. The organs of the digestive system also comprise specialized cell populations that play important but diverse roles in absorption, secretion, and barrier function. For instance, distinct cell types of the pancreatic islet secrete different hormones, including insulin-secreting β cells, glucagon-secreting δ cells, and somatostatin-expressing δ cells.1Baron M. Veres A. Wolock S.L. Faust A.L. Gaujoux R. Vetere A. Ryu J.H. Wagner B.K. Shen-Orr S.S. Klein A.M. Melton D.A. Yanai I. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure.Cell Syst. 2016; 3: 346-360Abstract Full Text Full Text PDF PubMed Scopus (603) Google Scholar Likewise, the small and large intestines exist in a dynamic equilibrium of heterogeneous stem, transitional, and differentiated cell populations, with the latter responsible for nutrient absorption, antimicrobial peptide secretion, and formation and maintenance of the mucus layer in the gut.2Haber A.L. Biton M. Rogel N. Herbst R.H. Shekhar K. Smillie C. Burgin G. Delorey T.M. Howitt M.R. Katz Y. Tirosh I. Beyaz S. Dionne D. Zhang M. Raychowdhury R. Garrett W.S. Rozenblatt-Rosen O. Shi H.N. Yilmaz O. Xavier R.J. Regev A. A single-cell survey of the small intestinal epithelium.Nature. 2017; 551: 333-339Crossref PubMed Scopus (726) Google Scholar A fundamental question in developmental biology is the origin of cellular heterogeneity, which arises from a specification process initiated from multipotent cells. Recent developments in multiplex single-cell experimental tools have greatly facilitated the interrogation of individual cells; data on single cells then can be grouped into relevant cell populations. In digestive organ systems, populational analysis of single-cell data has been used for discovering previously unidentified β cell subpopulations in the pancreatic islet,1Baron M. Veres A. Wolock S.L. Faust A.L. Gaujoux R. Vetere A. Ryu J.H. Wagner B.K. Shen-Orr S.S. Klein A.M. Melton D.A. Yanai I. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure.Cell Syst. 2016; 3: 346-360Abstract Full Text Full Text PDF PubMed Scopus (603) Google Scholar novel markers of intestinal tuft cells,2Haber A.L. Biton M. Rogel N. Herbst R.H. Shekhar K. Smillie C. Burgin G. Delorey T.M. Howitt M.R. Katz Y. Tirosh I. Beyaz S. Dionne D. Zhang M. Raychowdhury R. Garrett W.S. Rozenblatt-Rosen O. Shi H.N. Yilmaz O. Xavier R.J. Regev A. A single-cell survey of the small intestinal epithelium.Nature. 2017; 551: 333-339Crossref PubMed Scopus (726) Google Scholar, 3McKinley E.T. Sui Y. Al-Kofahi Y. Millis B.A. Tyska M.J. Roland J.T. Santamaria-Pang A. Ohland C.L. Jobin C. Franklin J.L. Lau K.S. Gerdes M.J. Coffey J. Optimized multiplex immunofluorescence single-cell analysis reveals tuft cell heterogeneity.JCI Insight. 2017; 2: 11Crossref Scopus (57) Google Scholar endocrine progenitor cell heterogeneity,4Yan K.S. Gevaert O. Zheng G.X.Y. Anchang B. Probert C.S. Larkin K.A. Davies P.S. Cheng Z. Kaddis J.S. Han A. Roelf K. Calderon R.I. Cynn E. Hu X. Mandleywala K. Wilhelmy J. Grimes S.M. Corney D.C. Boutet S.C. Terry J.M. Belgrader P. Ziraldo S.B. Mikkelsen T.S. Wang F. von Furstenberg R.J. Smith N.R. Chandrakesan P. May R. Chrissy M.A.S. Jain R. Cartwright C.A. Niland J.C. Hong Y.-K. Carrington J. Breault D.T. Epstein J. Houchen C.W. Lynch J.P. Martin M.G. Plevritis S.K. Curtis C. Ji H.P. Li L. Henning S.J. Wong M.H. Kuo C.J. Intestinal enteroendocrine lineage cells possess homeostatic and injury-inducible stem cell activity.Cell Stem Cell. 2017; 21: 78-90Abstract Full Text Full Text PDF PubMed Scopus (207) Google Scholar and signaling mechanisms between neighboring intestinal epithelial cells,5Simmons A.J. Banerjee A. McKinley E.T. Scurrah C.R. Herring C.A. Gewin L.S. Masuzaki R. Karp S.J. Franklin J.L. Gerdes M.J. Irish J.M. Coffey R.J. Lau K.S. Cytometry-based single-cell analysis of intact epithelial signaling reveals MAPK activation divergent from TNF-α-induced apoptosis in vivo.Mol Syst Biol. 2015; 11: 835Crossref PubMed Scopus (30) Google Scholar among others. Populational analysis using single-cell tools is a powerful approach for dissecting tissue-level heterogeneity, and has been reviewed extensively elsewhere.6Mair F. Hartmann F.J. Mrdjen D. Tosevski V. Krieg C. Becher B. The end of gating? An introduction to automated analysis of high dimensional cytometry data.Eur J Immunol. 2016; 46: 34-43Crossref PubMed Scopus (150) Google Scholar, 7Haque A. Engel J. Teichmann S.A. Lönnberg T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications.Genome Med. 2017; 9: 75Crossref PubMed Scopus (374) Google Scholar Beyond defining cell populations, such as stem and differentiated cell types, single-cell experimental tools also can be used to characterize transitional intermediate cell states in various tissues and organoid systems.8Lyons J. Herring C.A. Banerjee A. Simmons A.J. Lau K.S. Multiscale analysis of the murine intestine for modeling human diseases.Integr Biol (Camb). 2015; 7: 740-757Crossref PubMed Google Scholar Thus, it theoretically should be possible, using single-cell data, to trace terminal cell types through intermediate cell states back to their roots of differentiation in a series of progenitor–progeny relationships. Here, we review current computational tools by which a “virtual lineage trace,” also known as a pseudotemporal order, can be extracted from multidimensional single-cell data. The theoretical basis of pseudotemporal ordering is that asynchronous sampling from multiple time points over development9Marco E. Karp R.L. Guo G. Robson P. Hart A.H. Trippa L. Yuan G.-C. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape.Proc Natl Acad Sci U S A. 2014; 111: E5643-E5650Crossref PubMed Scopus (177) Google Scholar or snap-shot sampling at a single time point of a continually renewing tissue (such as the intestine)10Trapnell C. Cacchiarelli D. Grimsby J. Pokharel P. Li S. Morse M. Lennon N.J. Livak K.J. Mikkelsen T.S. Rinn J.L. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.Nat Biotechnol. 2014; 32: 381-386Crossref PubMed Scopus (2470) Google Scholar can result in a dense sampling of transitional states that can be aligned to reflect a time course of state transitions (Figure 1). A cell state is represented by the position of a cell in a data space defined by multiple molecular markers that describe the identity and behavior of cells (Figure 1). Ordering is conducted on the basis of similarity in cell states; dense sampling of these states is required to obtain a continuum of data by which the relationship between cell states can be inferred. Because transitional cell states often are rare compared with differentiated cells in tissue, it is required for single-cell technologies to be able to query a large volume of data points, as well as simultaneously measure multiple markers, to fully depict a continuum of cell states. Here, we briefly review common single-cell tools that can evaluate many cells in a multiplex fashion in the context of their classification into either suspension approaches or in situ approaches. Suspension approaches involve cellular dissociation and then separate processing and analysis of individual cells, with the major caveat that the spatial context of the tissue is lost. Suspension approaches include protein-based techniques such as mass and multiparameter flow cytometry, and transcript-based techniques such as single-cell RNA-sequencing (scRNA-seq) and gene expression assays.11Kim T. Saadatpour A. Guo G. Saxena M. Cavazza A. Desai N. Jadhav U. Jiang L. Rivera M.N. Orkin S.H. Yuan G. Shivdasani R.A. Single-cell transcript profiles reveal multilineage priming in early progenitors derived from Lgr5(+) intestinal stem cells.Cell Rep. 2016; 16: 2053-2060Abstract Full Text Full Text PDF PubMed Scopus (53) Google Scholar The advantage of these approaches is in their high-throughput capacity to produce data. Flow and mass cytometry can analyze hundreds of thousands of cells in a multiplex fashion (20–40 protein analytes per cell) on the order of minutes,12Bendall S.C. Simonds E.F. Qiu P. Amir E.D. Krutzik P.O. Finck R. Bruggner R.V. Melamed R. Trejo A. Ornatsky O.I. Balderas R.S. Plevritis S.K. Sachs K. Pe’er D. Tanner S.D. Nolan G.P. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum.Science. 2011; 332: 687-696Crossref PubMed Scopus (1680) Google Scholar while scRNA-seq can quantify gene expression in an unbiased, genome-wide manner (thousands of gene analytes).13Grün D. Lyubimova A. Kester L. Wiebrands K. Basak O. Sasaki N. Clevers H. van Oudenaarden A. Single-cell messenger RNA sequencing reveals rare intestinal cell types.Nature. 2015; 525: 251-255Crossref PubMed Scopus (735) Google Scholar Multiple platforms of scRNA-seq exist, with variations in cell-containment strategies ranging from microwells14Treutlein B. Brownfield D.G. Wu A.R. Neff N.F. Mantalas G.L. Espinoza F.H. Desai T.J. Krasnow M. Quake S.R. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq.Nature. 2014; 509: 371-375Crossref PubMed Scopus (896) Google Scholar, 15Jaitin D.A. Kenigsberg E. Keren-Shaul H. Elefant N. Paul F. Zaretsky I. Mildner A. Cohen N. Jung S. Tanay A. Amit I. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types.Science. 2014; 343: 776-779Crossref PubMed Scopus (1101) Google Scholar, 16Ziegenhain C. Vieth B. Parekh S. Reinius B. Guillaumet-Adkins A. Smets M. Leonhardt H. Heyn H. Hellmann I. Enard W. Comparative analysis of single-cell RNA sequencing methods.Mol Cell. 2017; 65: 631-643Abstract Full Text Full Text PDF PubMed Scopus (735) Google Scholar to liquid-oil emulsion droplets17Klein A.M. Mazutis L. Akartuna I. Tallapragada N. Veres A. Li V. Peshkin L. Weitz D.A. Kirschner M.W. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.Cell. 2015; 161: 1187-1201Abstract Full Text Full Text PDF PubMed Scopus (1883) Google Scholar, 18Macosko E.Z. Basu A. Satija R. Nemesh J. Shekhar K. Goldman M. Tirosh I. Bialas A.R. Kamitaki N. Martersteck E.M. Trombetta J.J. Weitz D.A. Sanes J.R. Shalek A.K. Regev A. McCarroll S.A. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.Cell. 2015; 161: 1202-1214Abstract Full Text Full Text PDF PubMed Scopus (3690) Google Scholar, 19Zheng G.X.Y. Terry J.M. Belgrader P. Ryvkin P. Bent Z.W. Wilson R. Ziraldo S.B. Wheeler T.D. McDermott G.P. Zhu J. Gregory M.T. Shuga J. Montesclaros L. Underwood J.G. Masquelier D.A. Nishimura S.Y. Schnall-Levin M. Wyatt P.W. Hindson C.M. Bharadwaj R. Wong A. Ness K.D. Beppu L.W. Deeg H.J. McFarland C. Loeb K.R. Valente W.J. Ericson N.G. Stevens E.A. Radich J.P. Mikkelsen T.S. Hindson B.J. Bielas J.H. Massively parallel digital transcriptional profiling of single cells.Nat Commun. 2017; 8: 14049Crossref PubMed Scopus (2332) Google Scholar; many of the current iterations can query up to thousands of cells. A factor to consider when applying suspension approaches, especially on organs of the digestive system, is the perturbation imposed on cells when they are disaggregated from tissue. Cells of the hematopoietic system exist either as single-cell suspensions or in loosely connected tissues, which are readily amenable to single-cell analysis.12Bendall S.C. Simonds E.F. Qiu P. Amir E.D. Krutzik P.O. Finck R. Bruggner R.V. Melamed R. Trejo A. Ornatsky O.I. Balderas R.S. Plevritis S.K. Sachs K. Pe’er D. Tanner S.D. Nolan G.P. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum.Science. 2011; 332: 687-696Crossref PubMed Scopus (1680) Google Scholar For intestinal cells, specifically for those in the lamina propria, protocols have been developed such that the correct numbers and types of cells can be retrieved for single-cell analysis, providing critical insights into biological and disease processes.20Chuang L.-S. Villaverde N. Hui K.Y. Mortha A. Rahman A. Levine A.P. Haritunians T. Evelyn Ng S.M. Zhang W. Hsu N.-Y. Facey J.-A. Luong T. Fernandez-Hernandez H. Li D. Rivas M. Schiff E.R. Gusev A. Schumm L.P. Bowen B.M. Sharma Y. Ning K. Remark R. Gnjatic S. Legnani P. George J. Sands B.E. Stempak J.M. Datta L.W. Lipka S. Katz S. Cheifetz A.S. Barzilai N. Pontikos N. Abraham C. Dubinsky M.J. Targan S. Taylor K. Rotter J.I. Scherl E.J. Desnick R.J. Abreu M.T. Zhao H. Atzmon G. Pe’er I. Kugathasan S. Hakonarson H. McCauley J.L. Lencz T. Darvasi A. Plagnol V. Silverberg M.S. Muise A.M. Brant S.R. Daly M.J. Segal A.W. Duerr R.H. Merad M. McGovern D.P.B. Peter I. Cho J.H. A frameshift in CSF2RB predominant among Ashkenazi Jews increases risk for Crohn’s disease and reduces monocyte signaling via GM-CSF.Gastroenterology. 2016; 151: 710-723Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar For epithelial tissues that are tightly connected, additional factors must be considered so as to not introduce technical artifacts during the single-cell dissociation process.21Simmons A.J. Lau K.S. Deciphering tumor heterogeneity from FFPE tissues: its promise and challenges.Mol Cell Oncol. 2016; 4: e1260191Crossref PubMed Scopus (6) Google Scholar Disaggregation for Intracellular Signaling of Single Epithelial Cells from Tissue was developed as a fixation approach for preserving the intact state of epithelial cells for single-cell signaling analysis using mass and flow cytometry.5Simmons A.J. Banerjee A. McKinley E.T. Scurrah C.R. Herring C.A. Gewin L.S. Masuzaki R. Karp S.J. Franklin J.L. Gerdes M.J. Irish J.M. Coffey R.J. Lau K.S. Cytometry-based single-cell analysis of intact epithelial signaling reveals MAPK activation divergent from TNF-α-induced apoptosis in vivo.Mol Syst Biol. 2015; 11: 835Crossref PubMed Scopus (30) Google Scholar Disaggregation for Intracellular Signaling of Single Epithelial Cells from Tissue can be applied to formalin-fixed paraffin-embedded tissues, for instance, to observe signaling state alterations in human colorectal cancer specimens.22Simmons A.J. Scurrah C.R. McKinley E.T. Herring C.A. Irish J.M. Washington M.K. Coffey R.J. Lau K.S. Impaired coordination between signaling pathways is revealed in human colorectal cancer using single-cell mass cytometry of archival tissue blocks.Sci Signal. 2016; 9: rs11Crossref PubMed Scopus (18) Google Scholar On the scRNA-seq side, Adam et al23Adam M. Potter A.S. Potter S.S. Psychrophilic proteases dramatically reduce single-cell RNA-seq artifacts: a molecular atlas of kidney development.Development. 2017; 144: 3625-3632Crossref PubMed Scopus (164) Google Scholar adapted psychrophilic proteases for single-cell dissociation in the cold, which drastically reduces artifacts and maintains native cell states. Adaptation of a similar strategy to fixed24Alles J. Karaiskos N. Praktiknjo S.D. Grosswendt S. Wahle P. Ruffault P.-L. Ayoub S. Schreyer L. Boltengagen A. Birchmeier C. Zinzen R. Kocks C. Rajewsky N. Cell fixation and preservation for droplet-based single-cell transcriptomics.BMC Biol. 2017; 15: 44Crossref PubMed Scopus (122) Google Scholar, 25Thomsen E.R. Mich J.K. Yao Z. Hodge R.D. Doyle A.M. Jang S. Shehata S.I. Nelson A.M. Shapovalova N.V. Levi B.P. Ramanathan S. Fixed single-cell transcriptomic characterization of human radial glial diversity.Nat Methods. 2016; 13: 87-93Crossref PubMed Scopus (159) Google Scholar or frozen tissues26Guillaumet-Adkins A. Rodríguez-Esteban G. Mereu E. Mendez-Lago M. Jaitin D.A. Villanueva A. Vidal A. Martinez-Marti A. Felip E. Vivancos A. Keren-Shaul H. Heath S. Gut M. Amit I. Gut I. Heyn H. Single-cell transcriptome conservation in cryopreserved cells and tissues.Genome Biol. 2017; 18: 45Crossref PubMed Scopus (93) Google Scholar may enable scRNA-seq of preserved cell states. For cells that cannot be dissociated without compromising integrity, such as neurons with long and fragile axonal processes, single nucleus profiling of fresh and preserved tissues is a viable strategy to obtain a glimpse of cell state.27Hu P. Fabyanic E. Kwon D.Y. Tang S. Zhou Z. Wu H. Dissecting cell-type composition and activity-dependent transcriptional state in mammalian brains by massively parallel single-nucleus RNA-seq.Mol Cell. 2017; 68: 1006-1015Abstract Full Text Full Text PDF PubMed Scopus (78) Google Scholar, 28Habib N. Avraham-Davidi I. Basu A. Burks T. Shekhar K. Hofree M. Choudhury S.R. Aguet F. Gelfand E. Ardlie K. Weitz D.A. Rozenblatt-Rosen O. Zhang F. Regev A. Massively parallel single-nucleus RNA-seq with DroNc-seq.Nat Methods. 2017; 14: 955-958Crossref PubMed Scopus (473) Google Scholar, 29Habib N. Li Y. Heidenreich M. Swiech L. Avraham-Davidi I. Trombetta J.J. Hession C. Zhang F. Regev A. Div-seq: single-nucleus RNA-seq reveals dynamics of rare adult newborn neurons.Science. 2016; 353: 925-928Crossref PubMed Scopus (295) Google Scholar It should be noted, however, that transcriptomes obtained from the nucleus may be drastically different from those obtained from the entire cell. These efforts highlight recent developments into suspension approaches to enable high-throughput evaluation of native cell states for characterizing cellular heterogeneity and developmental events. Unlike suspension approaches, in situ imaging techniques allow cells and their niche components to be analyzed in their native spatial context. Because of the lack of tissue dissociation, communication mechanisms between niche cells and epithelial cells can be directly visualized and quantified. Recent advances have improved the multiplex capabilities of microscopy approaches, enabling detection and quantification of dozens of markers leading to accurate identification of cell types that reside within certain niches. Current multiplex imaging technologies for proteins can be classified either as mass-based or iterative. Mass-based imaging approaches, including imaging mass cytometry30Giesen C. Wang H.O. Schapiro D. Zivanovic N. Jacobs A. Hattendorf B. Schüffler P.J. Grolimund D. Buhmann J.M. Brandt S. Varga Z. Wild P.J. Günther D. Bodenmiller B. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry.Nat Methods. 2014; 11: 417-422Crossref PubMed Scopus (984) Google Scholar and multiplex ion beam imaging,31Angelo M. Bendall S.C. Finck R. Hale M.B. Hitzman C. Borowsky A.D. Levenson R.M. Lowe J.B. Liu S.D. Zhao S. Natkunam Y. Nolan G.P. Multiplexed ion beam imaging of human breast tumors.Nat Med. 2014; 20: 436-442Crossref PubMed Scopus (606) Google Scholar rely on metal-tagged antibodies coupled to mass spectrometry, while iterative approaches, including Multiplex Immunofluorescence,32Gerdes M.J. Sevinsky C.J. Sood A. Adak S. Bello M.O. Bordwell A. Can A. Corwin A. Dinn S. Filkins R.J. Hollman D. Kamath V. Kaanumalle S. Kenny K. Larsen M. Lazare M. Li Q. Lowes C. McCulloch C.C. McDonough E. Montalto M.C. Pang Z. Rittscher J. Santamaria-Pang A. Sarachan B.D. Seel M.L. Seppo A. Shaikh K. Sui Y. Zhang J. Ginty F. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue.Proc Natl Acad Sci U S A. 2013; 110: 11982-11987Crossref PubMed Scopus (424) Google Scholar Cyclic Immunofluorescence,33Lin J.-R. Fallahi-Sichani M. Sorger P.K. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method.Nat Commun. 2015; 6: 8390Crossref PubMed Scopus (281) Google Scholar and others,34Zrazhevskiy P. Gao X. Quantum dot imaging platform for single-cell molecular profiling.Nat Commun. 2013; 4: 1619Crossref PubMed Scopus (193) Google Scholar, 35Remark R. Merghoub T. Grabe N. Litjens G. Damotte D. Wolchok J.D. Merad M. Gnjatic S. In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide.Sci Immunol. 2016; 1: aaf6925Crossref PubMed Scopus (97) Google Scholar, 36Riordan D.P. Varma S. West R.B. Brown P.O. Automated analysis and classification of histological tissue features by multi-dimensional microscopic molecular profiling.PLoS One. 2015; 10: e0128975Crossref PubMed Scopus (19) Google Scholar rely on cycles of staining, imaging, and de-staining to enable multiplexity. Iterative approaches also are available for in situ gene expression analysis, including iterative RNA fluorescence in situ hybridization37Lubeck E. Coskun A.F. Zhiyentayev T. Ahmad M. Cai L. Single-cell in situ RNA profiling by sequential hybridization.Nat Methods. 2014; 11: 360-361Crossref PubMed Scopus (453) Google Scholar, 38Chen K.H. Boettiger A.N. Moffitt J.R. Wang S. Zhuang X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells.Science. 2015; 348: aaa6090Crossref PubMed Scopus (937) Google Scholar and in situ sequencing.39Lee J.H. Daugharthy E.R. Scheiman J. Kalhor R. Yang J.L. Ferrante T.C. Terry R. Jeanty S.S.F. Li C. Amamoto R. Peters D.T. Turczyk B.M. Marblestone A.H. Inverso S. Bernard A. Mali P. Rios X. Aach J. Church G.M. Highly multiplexed subcellular RNA sequencing in situ.Science. 2014; 343: 1360-1363Crossref PubMed Scopus (597) Google Scholar More recent work also has combined the barcoding capabilities of nucleic acids with protein antibody staining in an approach called nucleic acid exchange to achieve higher levels of efficiency and multiplexity.40Jungmann R. Avendaño M.S. Woehrstein J.B. Dai M. Shih W.M. Yin P. Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT.Nat Methods. 2014; 11: 313-318Crossref PubMed Scopus (639) Google Scholar Microscopy approaches also can query thousands of cells if whole tissues are imaged at the appropriate resolution, although the time for acquisition of such data sets can be large on a per-sample basis.3McKinley E.T. Sui Y. Al-Kofahi Y. Millis B.A. Tyska M.J. Roland J.T. Santamaria-Pang A. Ohland C.L. Jobin C. Franklin J.L. Lau K.S. Gerdes M.J. Coffey J. Optimized multiplex immunofluorescence single-cell analysis reveals tuft cell heterogeneity.JCI Insight. 2017; 2: 11Crossref Scopus (57) Google Scholar Currently, multiplex approaches are developed for 2-dimensional imaging, but future efforts may combine tissue clearing41Yang B. Treweek J.B. Kulkarni R.P. Deverman B.E. Chen C.K. Lubeck E. Shah S. Cai L. Gradinaru V. Single-cell phenotyping within transparent intact tissue through whole-body clearing.Cell. 2014; 158: 945-958Abstract Full Text Full Text PDF PubMed Scopus (648) Google Scholar, 42Sylwestrak E.L. Rajasethupathy P. Wright M.A. Jaffe A. Deisseroth K. Multiplexed intact-tissue transcriptional analysis at cellular resolution.Cell. 2016; 164: 792-804Abstract Full Text Full Text PDF PubMed Scopus (94) Google Scholar, 43Murray E. Cho J.H. Goodwin D. Ku T. Swaney J. Kim S.Y. Choi H. Park Y.G. Park J.Y. Hubbert A. McCue M. Vassallo S. Bakh N. Frosch M.P. Wedeen V.J. Seung H.S. Chung K. Simple, scalable proteomic imaging for high-dimensional profiling of intact systems.Cell. 2015; 163: 1500-1514Abstract Full Text Full Text PDF PubMed Scopus (278) Google Scholar along with intravital techniques44Ritsma L. Ellenbroek S.I.J. Zomer A. Snippert H.J. de Sauvage F.J. Simons B.D. Clevers H. van Rheenen J. Intestinal crypt homeostasis revealed at single-stem-cell level by in vivo live imaging.Nature. 2014; 507: 362-365Crossref PubMed Scopus (346) Google Scholar to enable 3-dimensional imaging of cells in real time. Although a variety of techniques can generate intricate multiplex images of intact tissue, challenges in the automatic identification of objects hinder quantitative analysis of spatial relationships among cells and niche components. Although these tools are in their infancy, in situ multiplex approaches hold the promise for understanding cell-to-environment interactions in the context of cell-state transitions. The choice of suspension or in situ techniques is highly dependent on the experimental question being sought and oftentimes can be complementary. Suspension approaches are much higher throughput in terms of the number of cells and analytes analyzed, whereas in situ techniques can afford spatial resolution. We have previously coupled the 2 classes of tools, using suspension-based signaling analysis and in situ microscopy to define neighbor cell signaling mechanisms.5Simmons A.J. Banerjee A. McKinley E.T. Scurrah C.R. Herring C.A. Gewin L.S. Masuzaki R. Karp S.J. Franklin J.L. Gerdes M.J. Irish J.M. Coffey R.J. Lau K.S. Cytometry-based single-cell analysis of intact epithelial signaling reveals MAPK activation divergent from TNF-α-induced apoptosis in vivo.Mol Syst Biol. 2015; 11: 835Crossref PubMed Scopus (30) Google Scholar An integrative strategy of using suspension-based analysis to deeply profile cell populations and in situ approaches to define spatial relationships between identified populations is one of many powerful strategies for delineating functionally meaningful relationships in tissue systems. Multiplex cytometry and scRNA-seq techniques both attempt to capture extremely complex cell states in the form of high-dimensional data, in proteomic or transcriptomic spaces, respectively. scRNA-seq is known to produce noisy data on a per-feature basis, especially for lowly expressed genes, owing to the processing and amplification of small amounts of nucleic acids16Ziegenhain C. Vieth B. Parekh S. Reinius B. Guillaumet-Adkins A. Smets M. Leonhardt H. Heyn H. Hellmann I. Enard W. Comparative analysis of single-cell RNA sequencing methods.Mol Cell. 2017; 65: 631-643Abstract Full Text Full Text PDF PubMed Scopus (735) Google Scholar and the biological phenomenon of bursting transcription.45Chubb J.R. Trcek T. Shenoy S.M. Singer R.H. Transcriptional pulsing of a developmental gene.Curr Biol. 2006; 16: 1018-1025Abstract Full Text Full Text PDF PubMed Scopus (511) Google Scholar The effects of noise are compounded in multidimensional space in a phenomenon known as the curse of dimensionality,46Bellman R.E. Adaptive control processes: a guided tour. Princeton University Press, Princeton, NJ1961Crossref Google Scholar which greatly affects downstream trajectory analysis when using the full ensemble of features. A way to mitigate this effect is to select and analyze only a subset of the most important features that maximally captures the phenomenon of interest, while ignoring uninformative or noisy features. The feature selection step is implicitly performed in candidate-based approaches, such as Cytometry Time-of-Flight and multiplex microscopy, because" @default.
• W2792294474 created "2018-03-29" @default.
• W2792294474 creator A5041520515 @default.
• W2792294474 creator A5053593549 @default.
• W2792294474 creator A5060368492 @default.
• W2792294474 creator A5081225372 @default.
• W2792294474 date "2018-01-01" @default.
• W2792294474 modified "2023-10-15" @default.
• W2792294474 title "Single-Cell Computational Strategies for Lineage Reconstruction in Tissue Systems" @default.
• W2792294474 cites W1566579818 @default.
• W2792294474 cites W1813068103 @default.
• W2792294474 cites W1874466506 @default.
• W2792294474 cites W1955913104 @default.
• W2792294474 cites W1978683461 @default.
• W2792294474 cites W1984883254 @default.
• W2792294474 cites W1995143570 @default.
• W2792294474 cites W1995424704 @default.
• W2792294474 cites W2001246746 @default.
• W2792294474 cites W2006554089 @default.
• W2792294474 cites W2012852001 @default.
• W2792294474 cites W2015255108 @default.
• W2792294474 cites W2024626836 @default.
• W2792294474 cites W2025828346 @default.
• W2792294474 cites W2033416556 @default.
• W2792294474 cites W2042423362 @default.
• W2792294474 cites W2042789810 @default.
• W2792294474 cites W2043565262 @default.
• W2792294474 cites W2047693963 @default.
• W2792294474 cites W2053129129 @default.
• W2792294474 cites W2068881679 @default.
• W2792294474 cites W2071010503 @default.
• W2792294474 cites W2073256527 @default.
• W2792294474 cites W2083048982 @default.
• W2792294474 cites W2095664313 @default.
• W2792294474 cites W2102212449 @default.
• W2792294474 cites W2103354991 @default.
• W2792294474 cites W2110699703 @default.
• W2792294474 cites W2126537932 @default.
• W2792294474 cites W2128120736 @default.
• W2792294474 cites W2135937351 @default.
• W2792294474 cites W2136453810 @default.
• W2792294474 cites W2151013106 @default.
• W2792294474 cites W2171172082 @default.
• W2792294474 cites W2177432730 @default.
• W2792294474 cites W2180807405 @default.
• W2792294474 cites W2189462190 @default.
• W2792294474 cites W2193067168 @default.
• W2792294474 cites W2223794222 @default.
• W2792294474 cites W2259229417 @default.
• W2792294474 cites W2344887288 @default.
• W2792294474 cites W2373831850 @default.
• W2792294474 cites W2406991867 @default.
• W2792294474 cites W2407916594 @default.
• W2792294474 cites W2410378998 @default.
• W2792294474 cites W2464146806 @default.
• W2792294474 cites W2473258837 @default.
• W2792294474 cites W2484573964 @default.
• W2792294474 cites W2486882743 @default.
• W2792294474 cites W2507650399 @default.
• W2792294474 cites W2523620612 @default.
• W2792294474 cites W2525469791 @default.
• W2792294474 cites W2531731128 @default.
• W2792294474 cites W2542768043 @default.
• W2792294474 cites W2557069473 @default.
• W2792294474 cites W2557172311 @default.
• W2792294474 cites W2561394844 @default.
• W2792294474 cites W2596707196 @default.
• W2792294474 cites W2610228741 @default.
• W2792294474 cites W2610774430 @default.
• W2792294474 cites W2611596972 @default.
• W2792294474 cites W2621070976 @default.
• W2792294474 cites W2626990934 @default.
• W2792294474 cites W2729843964 @default.
• W2792294474 cites W2746479894 @default.
• W2792294474 cites W2747841296 @default.
• W2792294474 cites W2747877289 @default.
• W2792294474 cites W2752378502 @default.
• W2792294474 cites W2767423581 @default.
• W2792294474 cites W2770880161 @default.
• W2792294474 cites W2775417722 @default.
• W2792294474 cites W2950030509 @default.
• W2792294474 cites W2950862527 @default.
• W2792294474 cites W2951506174 @default.
• W2792294474 cites W2951890476 @default.
• W2792294474 cites W2952221403 @default.
• W2792294474 cites W2953293240 @default.
• W2792294474 cites W2963899128 @default.
• W2792294474 cites W4251002338 @default.
• W2792294474 doi "https://doi.org/10.1016/j.jcmgh.2018.01.023" @default.
• W2792294474 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5924749" @default.
• W2792294474 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29713661" @default.
• W2792294474 hasPublicationYear "2018" @default.
• W2792294474 type Work @default.
• W2792294474 sameAs 2792294474 @default.
• W2792294474 citedByCount "34" @default.
• W2792294474 countsByYear W27922944742018 @default.
• W2792294474 countsByYear W27922944742019 @default.
• W2792294474 countsByYear W27922944742020 @default.

• next