Literature DB >> 22546911

Single-cell profiling of developing and mature retinal neurons.

Jillian J Goetz1, Jeffrey M Trimarchi.   

Abstract

Highly specialized, but exceedingly small populations of cells play important roles in many tissues. The identification of cell-type specific markers and gene expression programs for extremely rare cell subsets has been a challenge using standard whole-tissue approaches. Gene expression profiling of individual cells allows for unprecedented access to cell types that comprise only a small percentage of the total tissue(1-7). In addition, this technique can be used to examine the gene expression programs that are transiently expressed in small numbers of cells during dynamic developmental transitions(8). This issue of cellular diversity arises repeatedly in the central nervous system (CNS) where neuronal connections can occur between quite diverse cells(9). The exact number of distinct cell types is not precisely known, but it has been estimated that there may be as many as 1000 different types in the cortex itself(10). The function(s) of complex neural circuits may rely on some of the rare neuronal types and the genes they express. By identifying new markers and helping to molecularly classify different neurons, the single-cell approach is particularly useful in the analysis of cell types in the nervous system. It may also help to elucidate mechanisms of neural development by identifying differentially expressed genes and gene pathways during early stages of neuronal progenitor development. As a simple, easily accessed tissue with considerable neuronal diversity, the vertebrate retina is an excellent model system for studying the processes of cellular development, neuronal differentiation and neuronal diversification. However, as in other parts of the CNS, this cellular diversity can present a problem for determining the genetic pathways that drive retinal progenitors to adopt a specific cell fate, especially given that rod photoreceptors make up the majority of the total retinal cell population(11). Here we report a method for the identification of the transcripts expressed in single retinal cells (Figure 1). The single-cell profiling technique allows for the assessment of the amount of heterogeneity present within different cellular populations of the retina(2,4,5,12). In addition, this method has revealed a host of new candidate genes that may play role(s) in the cell fate decision-making processes that occur in subsets of retinal progenitor cells(8). With some simple adjustments to the protocol, this technique can be utilized for many different tissues and cell types.

Mesh:

Year:  2012        PMID: 22546911      PMCID: PMC3466632          DOI: 10.3791/3824

Source DB:  PubMed          Journal:  J Vis Exp        ISSN: 1940-087X            Impact factor:   1.355


  25 in total

Review 1.  Vertebrate neural cell-fate determination: lessons from the retina.

Authors:  F J Livesey; C L Cepko
Journal:  Nat Rev Neurosci       Date:  2001-02       Impact factor: 34.870

Review 2.  Confronting complexity: strategies for understanding the microcircuitry of the retina.

Authors:  R H Masland; E Raviola
Journal:  Annu Rev Neurosci       Date:  2000       Impact factor: 12.449

3.  Single-cell transcriptional analysis of neuronal progenitors.

Authors:  Ian Tietjen; Jason M Rihel; Yanxiang Cao; Georgy Koentges; Lisa Zakhary; Catherine Dulac
Journal:  Neuron       Date:  2003-04-24       Impact factor: 17.173

Review 4.  Gene expression and the myth of the average cell.

Authors:  Jeffrey M Levsky; Robert H Singer
Journal:  Trends Cell Biol       Date:  2003-01       Impact factor: 20.808

5.  Single-cell transcript analysis of pancreas development.

Authors:  Ming-Ko Chiang; Douglas A Melton
Journal:  Dev Cell       Date:  2003-03       Impact factor: 12.270

6.  An analysis of the gene expression program of mammalian neural progenitor cells.

Authors:  F J Livesey; T L Young; C L Cepko
Journal:  Proc Natl Acad Sci U S A       Date:  2004-01-20       Impact factor: 11.205

7.  Identification of novel genes preferentially expressed in the retina using a custom human retina cDNA microarray.

Authors:  Itay Chowers; Tushara L Gunatilaka; Ronald H Farkas; Jiang Qian; Abigail S Hackam; Elia Duh; Masaaki Kageyama; Chenwei Wang; Amit Vora; Peter A Campochiaro; Donald J Zack
Journal:  Invest Ophthalmol Vis Sci       Date:  2003-09       Impact factor: 4.799

8.  Representation is faithfully preserved in global cDNA amplified exponentially from sub-picogram quantities of mRNA.

Authors:  Norman N Iscove; Mary Barbara; Marie Gu; Meredith Gibson; Carolyn Modi; Neil Winegarden
Journal:  Nat Biotechnol       Date:  2002-08-12       Impact factor: 54.908

9.  A one-hit model of cell death in inherited neuronal degenerations.

Authors:  G Clarke; R A Collins; B R Leavitt; D F Andrews; M R Hayden; C J Lumsden; R R McInnes
Journal:  Nature       Date:  2000-07-13       Impact factor: 49.962

10.  Genomic analysis of mouse retinal development.

Authors:  Seth Blackshaw; Sanjiv Harpavat; Jeff Trimarchi; Li Cai; Haiyan Huang; Winston P Kuo; Griffin Weber; Kyungjoon Lee; Rebecca E Fraioli; Seo-Hee Cho; Rachel Yung; Elizabeth Asch; Lucila Ohno-Machado; Wing H Wong; Constance L Cepko
Journal:  PLoS Biol       Date:  2004-06-29       Impact factor: 8.029

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  7 in total

1.  Glycolytic reliance promotes anabolism in photoreceptors.

Authors:  Yashodhan Chinchore; Tedi Begaj; David Wu; Eugene Drokhlyansky; Constance L Cepko
Journal:  Elife       Date:  2017-06-09       Impact factor: 8.140

2.  Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells.

Authors:  Lauren A Laboissonniere; Takuma Sonoda; Seul Ki Lee; Jeffrey M Trimarchi; Tiffany M Schmidt
Journal:  J Vis Exp       Date:  2017-05-22       Impact factor: 1.355

3.  Transcriptomic landscape of the blastema niche in regenerating adult axolotl limbs at single-cell resolution.

Authors:  Nicholas D Leigh; Garrett S Dunlap; Kimberly Johnson; Rachelle Mariano; Rachel Oshiro; Alan Y Wong; Donald M Bryant; Bess M Miller; Alex Ratner; Andy Chen; William W Ye; Brian J Haas; Jessica L Whited
Journal:  Nat Commun       Date:  2018-12-04       Impact factor: 14.919

Review 4.  Overview of micro- and nano-technology tools for stem cell applications: micropatterned and microelectronic devices.

Authors:  Stefano Cagnin; Elisa Cimetta; Carlotta Guiducci; Paolo Martini; Gerolamo Lanfranchi
Journal:  Sensors (Basel)       Date:  2012-11-19       Impact factor: 3.576

5.  Loss of gq/11 genes does not abolish melanopsin phototransduction.

Authors:  Kylie S Chew; Tiffany M Schmidt; Alan C Rupp; Paulo Kofuji; Jeffrey M Trimarchi
Journal:  PLoS One       Date:  2014-05-28       Impact factor: 3.240

6.  A cascade model of information processing and encoding for retinal prosthesis.

Authors:  Zhi-Jun Pei; Guan-Xin Gao; Bo Hao; Qing-Li Qiao; Hui-Jian Ai
Journal:  Neural Regen Res       Date:  2016-04       Impact factor: 5.135

7.  Ipsilateral and Contralateral Retinal Ganglion Cells Express Distinct Genes during Decussation at the Optic Chiasm.

Authors:  Qing Wang; Florencia Marcucci; Isadora Cerullo; Carol Mason
Journal:  eNeuro       Date:  2016-12-02
  7 in total

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