Measuring Gene Expression Noise in Early Drosophila Embryos: The Highly Dynamic Compartmentalized Micro-environment of the Blastoderm Is One of the ... View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Alexander V. Spirov , Nina E. Golyandina , David M. Holloway , Theodore Alexandrov , Ekaterina N. Spirova , Francisco J. P. Lopes

ABSTRACT

Fluorescence imaging has become a widely used technique for quantitatively measuring mRNA or protein expression. The first measurements were on gene expression noise in bacteria and yeast. The relative biological and physicochemical simplicity of these single cells encouraged a number of groups to try similar approaches in multicellular organisms. Such work has been primarily on whole Drosophila embryos, where the genes forming the body plan are very well understood. The numerous sources of noise in complex embryonic tissues are a major challenge for characterizing gene expression noise. Here, we present our approach for first separating experimental from biological noise, followed by distinguishing sources of biological noise. We decompose raw signal into trend and residual noise using Singular Spectrum Analysis. We demonstrate our statistical techniques on the Drosophila Hunchback protein pattern. We show that the ‘texture noise’, arising from the pre-cellular compartmentalization of the embryo surface, which is highly dynamic in time, is a major component of total biological noise, and can exceed gene transcription/translation noise. More... »

PAGES

177-188

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-29066-4_16

DOI

http://dx.doi.org/10.1007/978-3-642-29066-4_16

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040307747


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0601", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biochemistry and Cell Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Genetics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "The I.M.Sechenov Institute of Evolutionary Physiology & Biochemistry, St.-Petersburg, Russia", 
          "id": "http://www.grid.ac/institutes/grid.419730.8", 
          "name": [
            "Computer Science and CEWIT, SUNY Stony Brook, Stony Brook, New York, USA", 
            "The I.M.Sechenov Institute of Evolutionary Physiology & Biochemistry, St.-Petersburg, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Spirov", 
        "givenName": "Alexander V.", 
        "id": "sg:person.0755512214.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0755512214.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Dept. Mathematics and Mechanics, St. Petersburg State University, St.-Petersburg, Russia", 
          "id": "http://www.grid.ac/institutes/grid.15447.33", 
          "name": [
            "Dept. Mathematics and Mechanics, St. Petersburg State University, St.-Petersburg, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Golyandina", 
        "givenName": "Nina E.", 
        "id": "sg:person.01303460345.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01303460345.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Mathematics Dept., British Columbia Institute of Technology, Burnaby, B.C., Canada", 
          "id": "http://www.grid.ac/institutes/grid.253312.4", 
          "name": [
            "Mathematics Dept., British Columbia Institute of Technology, Burnaby, B.C., Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Holloway", 
        "givenName": "David M.", 
        "id": "sg:person.01053003345.11", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01053003345.11"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Industrial Mathematics, University of Bremen, Germany", 
          "id": "http://www.grid.ac/institutes/grid.7704.4", 
          "name": [
            "Center for Industrial Mathematics, University of Bremen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Alexandrov", 
        "givenName": "Theodore", 
        "id": "sg:person.01204640152.10", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01204640152.10"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "AMS, SUNY Stony Brook, Stony Brook, New York, USA", 
          "id": "http://www.grid.ac/institutes/grid.36425.36", 
          "name": [
            "AMS, SUNY Stony Brook, Stony Brook, New York, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Spirova", 
        "givenName": "Ekaterina N.", 
        "id": "sg:person.01070562556.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01070562556.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "AMS, SUNY Stony Brook, Stony Brook, New York, USA", 
          "id": "http://www.grid.ac/institutes/grid.36425.36", 
          "name": [
            "AMS, SUNY Stony Brook, Stony Brook, New York, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lopes", 
        "givenName": "Francisco J. P.", 
        "id": "sg:person.01335501061.13", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01335501061.13"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2012", 
    "datePublishedReg": "2012-01-01", 
    "description": "Fluorescence imaging has become a widely used technique for quantitatively measuring mRNA or protein expression. The first measurements were on gene expression noise in bacteria and yeast. The relative biological and physicochemical simplicity of these single cells encouraged a number of groups to try similar approaches in multicellular organisms. Such work has been primarily on whole Drosophila embryos, where the genes forming the body plan are very well understood. The numerous sources of noise in complex embryonic tissues are a major challenge for characterizing gene expression noise. Here, we present our approach for first separating experimental from biological noise, followed by distinguishing sources of biological noise. We decompose raw signal into trend and residual noise using Singular Spectrum Analysis. We demonstrate our statistical techniques on the Drosophila Hunchback protein pattern. We show that the \u2018texture noise\u2019, arising from the pre-cellular compartmentalization of the embryo surface, which is highly dynamic in time, is a major component of total biological noise, and can exceed gene transcription/translation noise.", 
    "editor": [
      {
        "familyName": "Giacobini", 
        "givenName": "Mario", 
        "type": "Person"
      }, 
      {
        "familyName": "Vanneschi", 
        "givenName": "Leonardo", 
        "type": "Person"
      }, 
      {
        "familyName": "Bush", 
        "givenName": "William S.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-29066-4_16", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-642-29065-7", 
        "978-3-642-29066-4"
      ], 
      "name": "Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics", 
      "type": "Book"
    }, 
    "keywords": [
      "gene expression noise", 
      "expression noise", 
      "Drosophila embryos", 
      "biological noise", 
      "early Drosophila embryo", 
      "whole Drosophila embryos", 
      "multicellular organisms", 
      "body plan", 
      "embryonic tissues", 
      "embryo surface", 
      "protein patterns", 
      "single cells", 
      "protein expression", 
      "embryos", 
      "fluorescence imaging", 
      "major component", 
      "translation noise", 
      "yeast", 
      "blastoderm", 
      "genes", 
      "micro environment", 
      "organisms", 
      "compartmentalization", 
      "mRNA", 
      "bacteria", 
      "expression", 
      "number of groups", 
      "cells", 
      "tissue", 
      "major challenge", 
      "similar approach", 
      "patterns", 
      "numerous sources", 
      "source", 
      "signals", 
      "components", 
      "main source", 
      "analysis", 
      "number", 
      "approach", 
      "such work", 
      "group", 
      "surface", 
      "statistical techniques", 
      "challenges", 
      "time", 
      "work", 
      "technique", 
      "trends", 
      "spectrum analysis", 
      "imaging", 
      "texture noise", 
      "simplicity", 
      "plan", 
      "measurements", 
      "first measurement", 
      "noise", 
      "residual noise", 
      "singular spectrum analysis", 
      "raw signals"
    ], 
    "name": "Measuring Gene Expression Noise in Early Drosophila Embryos: The Highly Dynamic Compartmentalized Micro-environment of the Blastoderm Is One of the Main Sources of Noise", 
    "pagination": "177-188", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1040307747"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-29066-4_16"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-29066-4_16", 
      "https://app.dimensions.ai/details/publication/pub.1040307747"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-10-01T06:57", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/chapter/chapter_362.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-642-29066-4_16"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-29066-4_16'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-29066-4_16'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-29066-4_16'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-29066-4_16'


 

This table displays all metadata directly associated to this object as RDF triples.

181 TRIPLES      22 PREDICATES      86 URIs      78 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-29066-4_16 schema:about anzsrc-for:06
2 anzsrc-for:0601
3 anzsrc-for:0604
4 schema:author Nb87d9eb00d454177a97fe8055ec90e31
5 schema:datePublished 2012
6 schema:datePublishedReg 2012-01-01
7 schema:description Fluorescence imaging has become a widely used technique for quantitatively measuring mRNA or protein expression. The first measurements were on gene expression noise in bacteria and yeast. The relative biological and physicochemical simplicity of these single cells encouraged a number of groups to try similar approaches in multicellular organisms. Such work has been primarily on whole Drosophila embryos, where the genes forming the body plan are very well understood. The numerous sources of noise in complex embryonic tissues are a major challenge for characterizing gene expression noise. Here, we present our approach for first separating experimental from biological noise, followed by distinguishing sources of biological noise. We decompose raw signal into trend and residual noise using Singular Spectrum Analysis. We demonstrate our statistical techniques on the Drosophila Hunchback protein pattern. We show that the ‘texture noise’, arising from the pre-cellular compartmentalization of the embryo surface, which is highly dynamic in time, is a major component of total biological noise, and can exceed gene transcription/translation noise.
8 schema:editor N0b0301f5b9e24b248f844e565ef9bb8a
9 schema:genre chapter
10 schema:isAccessibleForFree false
11 schema:isPartOf N53d6a6f75af147058bc1b7dcb1fb2e60
12 schema:keywords Drosophila embryos
13 analysis
14 approach
15 bacteria
16 biological noise
17 blastoderm
18 body plan
19 cells
20 challenges
21 compartmentalization
22 components
23 early Drosophila embryo
24 embryo surface
25 embryonic tissues
26 embryos
27 expression
28 expression noise
29 first measurement
30 fluorescence imaging
31 gene expression noise
32 genes
33 group
34 imaging
35 mRNA
36 main source
37 major challenge
38 major component
39 measurements
40 micro environment
41 multicellular organisms
42 noise
43 number
44 number of groups
45 numerous sources
46 organisms
47 patterns
48 plan
49 protein expression
50 protein patterns
51 raw signals
52 residual noise
53 signals
54 similar approach
55 simplicity
56 single cells
57 singular spectrum analysis
58 source
59 spectrum analysis
60 statistical techniques
61 such work
62 surface
63 technique
64 texture noise
65 time
66 tissue
67 translation noise
68 trends
69 whole Drosophila embryos
70 work
71 yeast
72 schema:name Measuring Gene Expression Noise in Early Drosophila Embryos: The Highly Dynamic Compartmentalized Micro-environment of the Blastoderm Is One of the Main Sources of Noise
73 schema:pagination 177-188
74 schema:productId Nac9e6e6cfd3343ddba5090a83af5d2cb
75 Nc78cb3c9dbc24e5dae932182bc3bc196
76 schema:publisher N91c4f7b7681e4cf4bdbade11094ab6c8
77 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040307747
78 https://doi.org/10.1007/978-3-642-29066-4_16
79 schema:sdDatePublished 2022-10-01T06:57
80 schema:sdLicense https://scigraph.springernature.com/explorer/license/
81 schema:sdPublisher Nce9af893ea424009a5daa36648634522
82 schema:url https://doi.org/10.1007/978-3-642-29066-4_16
83 sgo:license sg:explorer/license/
84 sgo:sdDataset chapters
85 rdf:type schema:Chapter
86 N06013be3ff314a6692944754cd0d8d49 rdf:first N2a383b7c6bea4c68a6ed3b7d5a3cec58
87 rdf:rest Nbdcafd0b35474ddcb602e128cfddffd3
88 N0b0301f5b9e24b248f844e565ef9bb8a rdf:first Nf2a1743142f6403699b7231ae5d0c5e3
89 rdf:rest N06013be3ff314a6692944754cd0d8d49
90 N0c0f570e949c41718e37ccd9cc912b4a rdf:first sg:person.01204640152.10
91 rdf:rest N6e298961ed0f42de9e2993faf23cbe88
92 N14207f76aebe4ef79e85938a6ceefa99 rdf:first sg:person.01303460345.39
93 rdf:rest Nab9b8d00a4eb42108052ac791035a60d
94 N2a383b7c6bea4c68a6ed3b7d5a3cec58 schema:familyName Vanneschi
95 schema:givenName Leonardo
96 rdf:type schema:Person
97 N53d6a6f75af147058bc1b7dcb1fb2e60 schema:isbn 978-3-642-29065-7
98 978-3-642-29066-4
99 schema:name Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
100 rdf:type schema:Book
101 N6e298961ed0f42de9e2993faf23cbe88 rdf:first sg:person.01070562556.50
102 rdf:rest Nbcd2522e713b4506b7b822a9f2ee0f40
103 N91c4f7b7681e4cf4bdbade11094ab6c8 schema:name Springer Nature
104 rdf:type schema:Organisation
105 Nab9b8d00a4eb42108052ac791035a60d rdf:first sg:person.01053003345.11
106 rdf:rest N0c0f570e949c41718e37ccd9cc912b4a
107 Nac9e6e6cfd3343ddba5090a83af5d2cb schema:name dimensions_id
108 schema:value pub.1040307747
109 rdf:type schema:PropertyValue
110 Nb87d9eb00d454177a97fe8055ec90e31 rdf:first sg:person.0755512214.32
111 rdf:rest N14207f76aebe4ef79e85938a6ceefa99
112 Nbcd2522e713b4506b7b822a9f2ee0f40 rdf:first sg:person.01335501061.13
113 rdf:rest rdf:nil
114 Nbdcafd0b35474ddcb602e128cfddffd3 rdf:first Nf97b3357f92c420082a29ff8f88afcc1
115 rdf:rest rdf:nil
116 Nc78cb3c9dbc24e5dae932182bc3bc196 schema:name doi
117 schema:value 10.1007/978-3-642-29066-4_16
118 rdf:type schema:PropertyValue
119 Nce9af893ea424009a5daa36648634522 schema:name Springer Nature - SN SciGraph project
120 rdf:type schema:Organization
121 Nf2a1743142f6403699b7231ae5d0c5e3 schema:familyName Giacobini
122 schema:givenName Mario
123 rdf:type schema:Person
124 Nf97b3357f92c420082a29ff8f88afcc1 schema:familyName Bush
125 schema:givenName William S.
126 rdf:type schema:Person
127 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
128 schema:name Biological Sciences
129 rdf:type schema:DefinedTerm
130 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
131 schema:name Biochemistry and Cell Biology
132 rdf:type schema:DefinedTerm
133 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
134 schema:name Genetics
135 rdf:type schema:DefinedTerm
136 sg:person.01053003345.11 schema:affiliation grid-institutes:grid.253312.4
137 schema:familyName Holloway
138 schema:givenName David M.
139 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01053003345.11
140 rdf:type schema:Person
141 sg:person.01070562556.50 schema:affiliation grid-institutes:grid.36425.36
142 schema:familyName Spirova
143 schema:givenName Ekaterina N.
144 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01070562556.50
145 rdf:type schema:Person
146 sg:person.01204640152.10 schema:affiliation grid-institutes:grid.7704.4
147 schema:familyName Alexandrov
148 schema:givenName Theodore
149 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01204640152.10
150 rdf:type schema:Person
151 sg:person.01303460345.39 schema:affiliation grid-institutes:grid.15447.33
152 schema:familyName Golyandina
153 schema:givenName Nina E.
154 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01303460345.39
155 rdf:type schema:Person
156 sg:person.01335501061.13 schema:affiliation grid-institutes:grid.36425.36
157 schema:familyName Lopes
158 schema:givenName Francisco J. P.
159 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01335501061.13
160 rdf:type schema:Person
161 sg:person.0755512214.32 schema:affiliation grid-institutes:grid.419730.8
162 schema:familyName Spirov
163 schema:givenName Alexander V.
164 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0755512214.32
165 rdf:type schema:Person
166 grid-institutes:grid.15447.33 schema:alternateName Dept. Mathematics and Mechanics, St. Petersburg State University, St.-Petersburg, Russia
167 schema:name Dept. Mathematics and Mechanics, St. Petersburg State University, St.-Petersburg, Russia
168 rdf:type schema:Organization
169 grid-institutes:grid.253312.4 schema:alternateName Mathematics Dept., British Columbia Institute of Technology, Burnaby, B.C., Canada
170 schema:name Mathematics Dept., British Columbia Institute of Technology, Burnaby, B.C., Canada
171 rdf:type schema:Organization
172 grid-institutes:grid.36425.36 schema:alternateName AMS, SUNY Stony Brook, Stony Brook, New York, USA
173 schema:name AMS, SUNY Stony Brook, Stony Brook, New York, USA
174 rdf:type schema:Organization
175 grid-institutes:grid.419730.8 schema:alternateName The I.M.Sechenov Institute of Evolutionary Physiology & Biochemistry, St.-Petersburg, Russia
176 schema:name Computer Science and CEWIT, SUNY Stony Brook, Stony Brook, New York, USA
177 The I.M.Sechenov Institute of Evolutionary Physiology & Biochemistry, St.-Petersburg, Russia
178 rdf:type schema:Organization
179 grid-institutes:grid.7704.4 schema:alternateName Center for Industrial Mathematics, University of Bremen, Germany
180 schema:name Center for Industrial Mathematics, University of Bremen, Germany
181 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...