CG920 Genomics
Lesson 6
Gene Expression and Chemical Genetics
Jan Hejátko
Functional Genomics and Proteomics of Plants,
CEITEC - Central European Institute of Technology
And
National Centre for Bimolecular Research,
Faculty of Science,
Masaryk University, Brno
hejatko@sci.muni.cz, www.ceitec.eu
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 Literature resources for Lesson 06
 Brady, S. M. et al. A high-resolution root spatiotemporal map reveals dominant
expression patterns. Science. 318 (5851), 801-806 (2007).
 Karaiskos N, Wahle P, Alles J, Boltengagen A, Ayoub S, Kipar C, Kocks C, Rajewsky N,
Zinzen RP (2017) The Drosophila embryo at single-cell transcriptome resolution.
Science 358: 194-199
 Lecuyer, E., Yoshida, H., Parthasarathy, N., Alm, C., Babak, T., Cerovina, T., Hughes,
T.R., Tomancak, P., and Krause, H.M. (2007). Global analysis of mRNA localization
reveals a prominent role in organizing cellular architecture and function. Cell 131, 174-
187.
 Nevo-Dinur, K., Nussbaum-Shochat, A., Ben-Yehuda, S., and Amster-Choder, O. (2011).
Translation-independent localization of mRNA in E. coli. Science 331, 1081-1084
 Schonberger, J., Hammes, U.Z., and Dresselhaus, T. (2012). In vivo visualization of
RNA in plants cells using the lambdaN(22) system and a GATEWAY-compatible vector
series for candidate RNAs. The Plant Journal 71, 173-181.
 Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by
spatial transcriptomics. Science. 353 (6294), 78-82 (2016).
 Xia, K. et al. The single-cell stereo-seq reveals region-specific cell subtypes and
transcriptome profiling in arabidopsis leaves. Dev Cell. 57 (10), 1299-1310 e1294 (2022)
Literature
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3
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Spatial trascriptomics
 Quantitative analysis of gene expression
 DNA and protein chips
 Next generation transcriptional profiling
 Regulation of gene expression in the identification of
gene function by gain-of-function approaches
 T-DNA activation mutagenesis
 Ectopic expression and regulated gene expression systems
Outline
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4
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
Outline
5
 Identification and cloning of the promoter region of the gene
 Preparation of recombinant DNA carrying the promoter and
the reporter gene (uidA, GFP)
TATA box
Iniciation of transcription
promoter
5’ UTR
ATG…ORF of reporter gene
Transcriptional Fusion
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 Identification and cloning of the promoter region of the gene
 Preparation of recombinant DNA carrying the promoter and
the reporter gene (uidA, GFP)
 Preparation of transgenic organisms carrying this
recombinant DNA and their histological analysis
Transcriptional Fusion
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GUS Reporter in Mouse
Embryos
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8
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
Outline
9
 Identification and cloning of the promoter and coding region of
the analyzed gene
 Preparation of a recombinant DNA carrying the promoter and
the coding sequence of the studied gene in a fusion with the
reporter gene (uidA, GFP)
TATA box
promoter
5’ UTR
ATG…ORF of analysed gene…..….ATG…ORF of reporter gene….….....STOP
Translational Fusion
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 Preparation of transgenic organisms carrying the
recombinant DNA and their histological analysis
 Compared to transcriptional fusion, translation fusion allows
analysis of intercellular localization of gene product (protein)
or its dynamics
Histone 2A-GFP in Drosophila embryo by PAMPIN1-GFP in Arabidopsis
Translational Fusion
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Translational Fusion
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 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
Outline
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Databases
□ Analysis of expression using Genevestigator (AHP1 and
AHP2, Arabidopsis, Affymetrix ATH 22K Array)
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Databases
□ Analysis of expression using Genevestigator (AHP1 and
AHP2, Arabidopsis, Affymetrix ATH 22K Array)
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Databases
□ Analysis of expression using ePlant
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Databases
□ Analysis of expression using ePlant
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□ Analysis of expression using Genevestigator (AHP1 and
AHP2, Arabidopsis, Affymetrix ATH 22K Array)
Databases
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 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
Outline
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Fluorescence-Activated Cell Sorting (FACS)
□ High-Resolution Expression Map in Arabidopsis Root
Expression Maps - RNA
Brady et al., Science, 2007
Microarray expression profiles of 19 fluorescently sorted GFP-marked lines were
analyzed (3–9, 23, 24). The colors associated with each marker line reflect the
developmental stage and cell types sampled. Thirteen transverse sections were
sampled along the root's longitudinal axis (red lines) (10). CC, companion cells.
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□ High-Resolution Expression Map in Arabidopsis Root
Expression Maps - RNA
Brady et al., Science, 2007
(A) The majority of enriched GO terms (hierarchically clustered) are associated with
individual cell types (blue bar). A smaller number are present across multiple cell
types (green bar). (fig. S2) (B) GO category enrichment for hair cells confirms a
previous report (15). Enriched cis-elements and an enriched TF family were also
identified. (C) From the top 50% of varying probe sets, 51 dominant radial patterns
were identified. Pattern expression values were mean-normalized (rows) and log2
transformed to yield relative expression indices for each marker line (columns).
Marker line order is the same for all figures; see table S1 for marker line
abbreviations. (D) Pattern expression peaks were found across one to five cell
types. (E to G) Patterns where expression is enriched in single and multiple cell
types support transcriptional regulation of auxin flux and synthesis. In all heat maps
with probe sets, expression values were mean-normalized and log2 transformed.
Expression is false-colored in representations of a root transverse section, a cutaway
of a root tip, and in a lateral root primordium. (E) Auxin biosynthetic genes
(CYP79B2, CYP79B3, SUPERROOT1, and SUPERROOT2) are transcriptionally
enriched in the QC, lateral root primordia, pericycle, and phloem-pole pericycle (P =
1.99E–11, pattern 5). All AGI identifiers and TAIR descriptions are found in table S14.
(F) Auxin amido-synthases GH3.6 and GH3.17 that play a role in auxin homeostasis
show enriched expression in the columella, just below the predicted auxin
biosynthetic center of the QC (P = 8.82E–4, pattern 13). (G) The expression of the
auxin transporter, PIN-FORMED2, and auxin transport regulators (PINOID, WAG1)
are enriched in the columella, hair cells, and cortex (P = 1.03E–4, pattern 31).
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□ High-Resolution Expression Map in Drosophilla
Expression Maps - RNA
Nikos Karaiskos et al. Science 2017;science.aan3235
Deconstructing and reconstructing the embryo by single-cell transcriptomics
combined with spatial mapping.
(A) Single-cell sequencing of the Drosophila embryo: ~1000 handpicked stage 6 fly
embryos are dissociated per Drop-seq replicate, cells are fixed and counted, single
cells are combined with barcoded capture beads, and libraries are prepared and
sequenced. Finally, single-cell transcriptomes are deconvolved, resulting in a digital
gene expression matrix for further analysis.
(B) Mapping cells back to the embryo: Single-cell transcriptomes are correlated with
high-resolution gene expression patterns across 84 marker genes, cells are mapped
to positions within a virtual embryo, and expression patterns are computed by
combining the mapping probabilities with the expression levels (virtual in situ
hybridization).
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Expression Maps - Proteins
Ponten et al., J Int Med, 2011
□ Human Protein Atlas
Schematic flowchart of the Human Protein Atlas. For each gene, a signature
sequence (PrEST) is defined from the human genome sequence, and following RTPCR,
cloning and production of recombinant protein fragments, subsequent
immunization and affinity purification of antisera results inmunospecific antibodies.
The produced antibodies are tested and validated in various immunoassays.
Approved antibodies are used for protein profiling in cells (immunofluorescence) and
tissues (immunohistochemistry) to generate the images and protein expression data
that are presented in the Human Protein Atlas (Ponten et al., J Int Med, 2011).
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□ Human Protein Atlas
(http://www.proteinatlas.org/)
Expression Maps - Proteins
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□ Human Protein Atlas
(http://www.proteinatlas.org/)
Expression Maps - Proteins
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 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Spatial trascriptomics
Outline
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Spatial Transcriptomics
Ståhl,etal.,Science,2016
Spatially resolved gene expression. (A) Each array feature
contains uniqueDNA-barcodedprobes
containingacleavagesite,aT7amplificationand sequencing
handle, a spatial barcode, a unique molecular identifier
(UMI), and an oligo(dT) VN-capture region, where V is
anything but Tand where N is any nucleotide. cDNA (red) is
generated from captured mRNA by reverse transcription.
(B) Visualization of the expression of three genes by spatial
transcriptomics (top) and in situ hybridization (bottom).
Penk and Kctd12 in situ images are from the Allen Institute.
Cutoff normalized counts, Penk, 8; Doc2g,
13; and Kctd12, 19.
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Xia,etal.,Dev.Cell,2022
Spatial Transcriptomics
Cauline leaves were cryo-sectioned and positioned on top of
four separate chips (or sections) with seven leaf samples on
each chip. On the surface of the chip, a DNA nanoball (DNB)
is docked in a
grid-patterned array of spots. Each spot is 220 nm in
diameter and the center-to-center distance between
neighboring spots is 500 nm (Figure 1A). The DNB contains
random barcoded sequences, the coordinate identity (CID),
molecular identifiers (MIDs), and polyT sequence-containing
oligonucleotides designed to capture mRNAs. After cell wall
staining and imaging, the chips were used for Stereo-seq
library construction and data acquirement. In short, mRNA
was released from tissue cells through permeabilization and
was captured by polyT in the DNB. The released mRNA was
then reverse-transcribed and amplified into cDNA, which
was used for PCR amplification and library sequencing. The
sequencing data were visualized in the STOmics visualization
system (https://stereomap.cngb.org/) and processed using a
series of Stereo-seq exclusive tools, including SAW
(https://github.com/BGIResearch/SAW) and stereopy
(https://github.com/BGIResearch/stereopy). In total, we
selected data from 26 leaf samples with good morphology in
these four chips for further analyses.
(A) Schematic representation of the single-cell Stereo-seq
procedure. Arabidopsis thaliana cauline leaves are cryosectioned
and positioned on top of the chip surface with DNA
nanoball (DNB) docked in a grid-patterned array of spots, and
the capture probes contaiidentifiers), and PloyT oligos to
enable the recordation of the spatial coordinates, the
identification of unique transcripts per gene, and the capture
of mRNAs.
After cell wall staining and imaging, the same section is
sequenced with Stereo-seq. Through the combined highresolution
image and MIDs, a single-cell level of MID
distribution is achieved. A robust extraction method is built to
be used in extracting single cells and in the identification of
major cell types in cauline leaves. Using spatial single-cell
data, several cell subtypes are distinguished (i). Next, the leaf
is divided into four distinct parts, and spatial gene expression
pattern (ii) and spatial developmental trajectory (iii) are
determined.
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Spatial Transcriptomics
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 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Spatial trascriptomics
 Quantitative analysis of gene expression
 DNA and protein chips
Outline
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 Method, which provides quick comparison of a large
number of genes/proteins between the test sample and
control
 Oligo DNA chips are used the most
 There are commercialy available kits for the whole genome
 company Operon (Qiagen), 29.110 of 70-mer oligonucleotides
representing 26.173 genes coding proteins, 28.964 transcripts and 87
microRNA genes of Arabidopsis thaliana
 Possibility of use for the preparation of photolithography chips – facilitation
of oligonucletide synthesis e.g. for the whole human genome (about 3,1 x
109 bp) jit is possible to prepare 25-mers in only 100 steps, by this
technique
Affymetrix ATH1 Arabidopsis genome array
 Chips not only for the analysis of
gene expression, but also for e.g.
Genotyping (SNPs, sequencing
with chips, …)
DNA Chips
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DNA Chips
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Photolitography
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 For the correct interpretation of the results, good knowledge of
advanced statistical methods is required
 Control of accuracy of the measurement (repeated
measurements on several chips with the same
sample, comparing the same samples analysed on
different chips with each other)
 It is necessary to include a sufficient number of controls and
repeats
 Control of reproducibility of measurements
(repeated measurements with different samples
isolated under the same conditions on the same
chip – comparing with each other)
Che et al., 2002
 Identification of reliable measurement treshold
nespolehlivé
spolehlivé
 Finally comparing the experiment with the control
or comparing different conditions with each other >
the result
 Currently there‘s been a great number of results of various
experiments in publicly accessible databases
DNA Chips
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 Protein chips
 Chips with high density containing 104 proteins
 Analysis of protein-protein interactions, kinase substrates
and interactions with small molecules
 Possibility of using antibodies – more stable than proteins
Protein Chips
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 Identification of proteins interacting with integrin αIIbβ3
cytoplasmic domain of platelets
 Expression of cytoplasmic part as a
fusion peptide biotin-KVGFFKR
 Analysis of binding to the protein
chip containing 37.000 clones of E.
coli expressing human recombinant
proteins
 Confirmation of interaction by pulldown
analysis of peptides and by
coprecipitation of whole proteins as
well (e.g. chloride channel Icln)
 Other use: e.g. in the identification of
kinase substrates, when substrates
are bound to the chip and exposed
to kinases in the presense of
radiolabeled ATP (786 purified
proteins of barely, of which 21 were
identified as CK2α kinase
substrates; Kramer et al., 2004)
Lueking et al., 2005
Protein Chips
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36
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Spatial trascriptomics
 Quantitative analysis of gene expression
 DNA and protein chips
 Next generation transcriptional profiling
Outline
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WT hormonal mutant
Next Gen Transcriptional
Profiling
□ Transcriptional profiling via RNA sequencing
mRNA
Sequencing by Illumina and
number of transcripts determination
mRNA
cDNA cDNA
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Results of –omics Studies vs
Biologically Relevant Conclusions
□ Transcriptional profiling yielded more then 7K differentially
regulated genes…
gene locus sample_1 sample_2 status value_1 value_2 log2(fold_change) test_stat p_value q_value significant
AT1G07795 1:2414285-2414967 WT MT OK 0 1,1804 1.79769e+308
1.79769e+3
08 6.88885e-05
0,00039180
1 yes
HRS1 1:4556891-4558708 WT MT OK 0 0,696583 1.79769e+308
1.79769e+3
08 6.61994e-06
4.67708e-
05 yes
ATMLO14 1:9227472-9232296 WT MT OK 0 0,514609 1.79769e+308
1.79769e+3
08 9.74219e-05
0,00053505
5 yes
NRT1.6 1:9400663-9403789 WT MT OK 0 0,877865 1.79769e+308
1.79769e+3
08 3.2692e-08
3.50131e-
07 yes
AT1G27570 1:9575425-9582376 WT MT OK 0 2,0829 1.79769e+308
1.79769e+3
08 9.76039e-06 6.647e-05 yes
AT1G60095 1:22159735-22162419 WT MT OK 0 0,688588 1.79769e+308
1.79769e+3
08 9.95901e-08
9.84992e-
07 yes
AT1G03020 1:698206-698515 WT MT OK 0 1,78859 1.79769e+308
1.79769e+3
08 0,00913915 0,0277958 yes
AT1G13609 1:4662720-4663471 WT MT OK 0 3,55814 1.79769e+308
1.79769e+3
08 0,00021683 0,00108079 yes
AT1G21550 1:7553100-7553876 WT MT OK 0 0,562868 1.79769e+308
1.79769e+3
08 0,00115582 0,00471497 yes
AT1G22120 1:7806308-7809632 WT MT OK 0 0,617354 1.79769e+308
1.79769e+3
08 2.48392e-06
1.91089e-
05 yes
AT1G31370 1:11238297-11239363 WT MT OK 0 1,46254 1.79769e+308
1.79769e+3
08 4.83523e-05
0,00028514
3 yes
APUM10 1:13253397-13255570 WT MT OK 0 0,581031 1.79769e+308
1.79769e+3
08 7.87855e-06
5.46603e-
05 yes
AT1G48700 1:18010728-18012871 WT MT OK 0 0,556525 1.79769e+308
1.79769e+3
08 6.53917e-05
0,00037473
6 yes
AT1G59077 1:21746209-21833195 WT MT OK 0 138,886 1.79769e+308
1.79769e+3
08 0,00122789 0,00496816 yes
AT1G60050 1:22121549-22123702 WT MT OK 0 0,370087 1.79769e+308
1.79769e+3
08 0,00117953 0,0048001 yes
Ddii et al., unpublished
AT4G15242 4:8705786-8706997 WT MT OK 0,00930712 17,9056 10,9098 -4,40523 1.05673e-05 7.13983e-05 yes
AT5G33251 5:12499071-12500433 WT MT OK 0,0498375 52,2837 10,0349 -9,8119 0 0 yes
AT4G12520 4:7421055-7421738 WT MT OK 0,0195111 15,8516 9,66612 -3,90043 9.60217e-05 0,000528904 yes
AT1G60020 1:22100651-22105276 WT MT OK 0,0118377 7,18823 9,24611 -7,50382 6.19504e-14 1.4988e-12 yes
AT5G15360 5:4987235-4989182 WT MT OK 0,0988273 56,4834 9,1587 -10,4392 0 0 yes
Excample of an output of transcriptional profiling study using Illumina sequencing
performed in our lab. Shown is just a tiny fragment of the complete list, copmprising
about 7K genes revealing differential expression in the studied mutant.
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 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Spatial trascriptomics
 Quantitative analysis of gene expression
 DNA and protein chips
 Next generation transcriptional profiling
 Regulation of gene expression in the identification of
gene function by gain-of-function approaches
 T-DNA activation mutagenesis
Outline
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 Methods for identification of gene function using gain-of-function
approaches
 T-DNA activation mutagenesis
 Method enabling isolation of dominant mutants by random
insertion of constitutive promoter, resulting in
overexpression of the gene and therefore in corresponding
phenotypic changes
 First step: preparation of mutant library prepared by
tansformation of a strong constitutive promoter or enhancer
 Next step: search of interesting phenotypes
 Identification of the affected gene, e.g. by plasmid-rescue
Gain-of-Function Approaches
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TF TF
TF
40S
60S
TF TF TF TF
40S
60S
40S
60S
40S
60S
40S
60S
TF TF
TF
Activation Mutagenesis
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Isolation of CKI1 Gene
- Isolation of the gene using activation mutagenesis
- Mutant phenotype is a phenocopy of
exogenous application of cytokinins (CKI1,
CYTOKININ INDEPENDENT 1)
*- Tatsuo Kakimoto, Science 274 (1996), 982-985 *
*
no hormones
t-zeatin
K1 K2plasmid
rescue
35S::CK
1 cDNA
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 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Spatial trascriptomics
 Quantitative analysis of gene expression
 DNA and protein chips
 Next generation transcriptional profiling
 Regulation of gene expression in the identification of
gene function by gain-of-function approaches
 T-DNA activation mutagenesis
 Ectopic expression and regulated gene expression systems
Outline
44
35S LhG4
pOP
TATA
CKI1
activator
reporter
activator x reporter
x
Regulated Expression
Systems
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35S LhGR
pOP
TATA
CKI1
activator
reporter
activator x reporter
DEX DEX
+DEX DEX
DEX
DEX
x
Regulated Expression
Systems
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35S LhGR
pOP
TATA
CKI1
activator
reporter
activator x reporter
DEX DEX
+DEX DEX
DEX
DEX
x
pOP
TATA
GUS
DEX DEX
wt Col-
0
4C
Regulated Expression
Systems
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 UAS system
 Regulated transgene expression systems
 Allow time- or site-specific regulation of gene
expression, leading to a change in
phenotype and thereby identification of the
natural function of the gene
 pOP system
Regulated Expression
Systems
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UAS System
http://www.plantsci.cam.ac.uk/Haseloff/
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 Gene expression has spatiotemporal specificity
 Analysis of spatiotemporal specificity of gene
expression using
 Transcriptional fusion of the promoter of analyzed gene with
reporter gene
 Translational fusion of coding region of teh assayed gene with
reporter gene
 Publicly accessible databases frequently with s cellular resolution
 Quantitative analysis of gene expression
 DNA and proteinové chips
 Next gen transcriptional profiling
 Via regulating gene expression it is possible to
identify gene function – gain of function approaches
Key Concepts
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50
Discussion