it will give idea about MPSS.

1. Gene expressions analysis by massively
parallel signature sequencing (MPSS)
By: Dr. Ashish C Patel
Assistant Professor
Vet College, AAU, Anand

2.  DNA microarray, serial analyses of gene expression (SAGE),
cDNA sequencing and a variety of other technologies are
available for analysing the expression of hundreds to
thousands of genes simultaneously.
 Each of these existing technologies has limitations when it
comes to generating complete data sets for building relational
databases.
 Massively Parallel Signature Sequencing (MPSS) is a an
open-ended platform that analyses the level of gene expression
in a sample by counting the number of individual mRNA
molecules produced by each gene.

3.  Massively Parallel Signature Sequencing (MPSS)
a sequencing technique is developed by Sydney Brenner
which is bacteria-free bead-based library preparation,
“Megaclone” technology.
 In MPSS, mRNA transcripts did not need to be known and
could be discovered de novo.
 Genes with low level expression could be quantified by MPSS.
 All clones in a microbead library can be sequenced
simultaneously (so, called “massively parallel”).

4.  MPSS produces data in a digital format. MPSS Captures data
by counting virtually all mRNA molecules in a tissue or cell
sample.
 All genes are analysed simultaneously, and bioinformatics tools
are used to sort out the number of mRNAs from each gene
relative to the total number of molecules in the sample.
 Even genes that are expressed at low levels can be quantified
with high accuracy.
 Counting mRNAs with MPSS is based on the ability to identify
uniquely every mRNA in a sample by generating a 17-base
sequence for each mRNA at a specific site upstream from its
poly (A) tail.
 This 17 base sequence is used as mRNA identification
signature.
 To measure the level of expression of any given gene, the total
number of signatures for that gene’s mRNA is counted.

5. Principle of MPSS
 A sample’s mRNA are first converted to cDNA using reverse
transcriptase, which are fused to a small oligonucleotide "tag"
which allows the cDNA to be PCR amplified and then coupled to
microbeads. After several rounds of sequence determination,
using hybridization of fluorescent labeled probes, a sequence
signature of ~16-20 bp is determined from each bead.
 Fluorescent imaging captures the signal from all of the beads, so
DNA sequences are determined from all the beads in parallel,
approximately 1,000,000 sequence reads are obtained per
experiment.

6. Procedure of Cloning and Sequencing cDNA
Fragments on Beads
 MPSS signatures for mRNAs in a sample are generated by
sequencing ds cDNA fragments cloned onto microbeads using
the Lynx Megaclone technology

7.  Poly (A) mRNA molecules are
converted into double-stranded cDNA
molecules using biotynalated oligo dT
primer. Streptavidin is use to purify
biotynalated cDNA.
 cDNA digested with DpnII
 cDNA fragments cloned into a specially
designed plasmid vector containing a
unique barcode tag.
 Total 16.8 x 106 million different
32-base sequences available in the
reference tag library, and each cDNA
clone contains a different sequence.
 The library of cDNA inserts with
oligonucleotide tags are PCR- amplified.
The resulting linear molecules are
partially treated with an exonuclease to
make the 32-base tag single stranded.

8.  The 32-base tags at the end of
each of the cDNA molecules are
hybridised to 32-base
complementary tags of
microbeads.
 The end-product is a microbead
with approximately 100,000
identical cDNA molecules
covalently attached to the
surface.

9. Adaptor ligating
 Encoded adaptors are ligated
to the ends of the cDNA
molecules attached to the
microbeads.
Decoder hybridization
 Sixteen different fluorescent-
labelled decoder probes are
then sequentially hybridised to
the encoded adaptor ends in
order to assume the first four
nucleotides at the end of each
molecule.

10. Sequences of encoded adaptors
•Bead complementary
four-base overhangs
sequences in bold
•Decoder binding sites in
lowercase.

11. 17 bases Signature
determination
 The encoded adaptor from the
first round is then removed by
digestion with Bbv I, which
exposes the next four
nucleotides as a four-base
single- stranded overhang.
 The process is repeated
several times in order to
generate a total of 17 bases of
sequence

12.  Approximately one million
microbeads are loaded into a
specially designed flow-cell in a
way that allows them to stack
together along channels and form
a tightly packed monolayer in the
flow-cell.
 The flow-cell is connected to a
computer-controlled microfluidics
network that delivers different
reagents for the sequencing
reactions.
Sequencing

14.  A high-resolution CCD
camera is positioned directly
over the flow-cell in order to
capture fluorescent images
from the microbeads at
specific stages of the
sequencing reactions.
MPSS system

20. Dot plot showing the reproducibility
between MPSS runs
Each dot or x represents a total of 10,799 signatures that were generated
from two independent MPSS runs. The X and Y coordinates represent the
number of each signature. Each signature represented by a dot occurs
within a 0.99 confidence interval, while those represented with an x occur
outside this interval.

21. Accuracy of MPSS for gene expression
measurements
 Factors that contribute to the false signatures include errors in
the yeast genomic sequence, and errors introduced through
reverse transcription, PCR, and incorrect ligation of encoded
adaptors to non-complementary overhangs or single-stranded
tag complements on the microbeads.
 The accuracy of gene expression measurements was also
assessed by comparing expression levels of genes measured by
MPSS and other conventional methods.
 E.g. THP-1 genes measured by MPSS analysis. A database of
1,619,000 signatures from MPSS analysis was generated from
cDNAs derived from induced THP-1 cells. Separately, 1,839
clones were selected from the same cDNA library and
conventionally sequenced.

22.  The relative frequencies of the
most highly expressed genes were
similar and the error from MPSS
analysis was extremely low,
reflecting the advantage of large
samples of templates.
 A few of the expression
measurements are not in
agreement, such as apoferritin
heavy-chain transcript (HSAFH1)
and B94 protein mRNA
(HUMB94).
Percentage total for MPSS data is the average abundance.
Percentage total for the EST data is the number of sequences clustered out
of the 1,839 selected clones, with the 99% confidence interval.
(A 1% relative abundance corresponds to about 2,500 microbead
signatures for MPSS data and to about 18 sequences for the EST
data).

23. DATA HANDLING AND CALCULATION OF
RNA ABUNDANCE
 A typical MPSS experiment with about one million microbeads
will yield 250,000 to 400,000 high quality ~16-20 base signature
sequences.
 MPSS datasets are additive in nature, means that datasets from
multiple analyses with the same starting mRNA sample can be
combined.
 It involve in excess of one million mRNAs counted per sample.
Which increased sensitivity for all genes being analysed,
particularly those that are expressed at very low levels within the
sample.
 Each signature sequence in an MPSS data set is analysed,
compared with all other signatures and all identical signatures
are counted.

24.  The level of expression of any single gene is calculated by
The number of signatures from that gene
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The total number of signatures for all mRNAs in the dataset.
 The data for each gene are usually reported as the transcripts per
million (TPM).
 The numbers of genes that are expressed at varying levels within
the sample. For example, genes expressed at greater than 1,000,
100 -1,000, 10 to 100 and less than 10 TPM.

25. Troubleshoots in MPSS Data
 MPSS signature sequences can be connected to known genes
by comparison with data in the available genomic sequence
and expressed sequence tag (EST) databases.
 This method is not an efficient process sometime when a
signature for a gene is unknown in a particular sample only.
 When a gene does not contain a Dpn II site, or when there is a
sequence polymorphism in the Dpn II site.
 These problems can be easily overcome by digesting the
cDNA with an alternative enzyme.
 Incomplete sequence representation of a gene in the current
EST and cDNA clone databases can also complicate the
process of assigning a signature sequence to a gene.

26.  The sequence that corresponds to an MPSS signature for a
specific gene may not be present in an EST sequence database.
 For example, the signature sequence for the T-cell transcription
factor NFATc did not appear at any significant level in the
Human T-cell-related MPSS datasets using the RefSeq
database.
 Therefore, careful and thoughtful analysis of the available data
may be necessary during the process of assigning an MPSS
signature to a gene.

27. STATISTICAL ANALYSIS OF MPSS DATA
 MPSS data as categorical form in a statistical point of view.
 Which make possible that the large number of measurements of
a given signature in the dataset (typically ten to 1,000 or more)
as well as the size of the entire dataset (typically over one
million) to evaluate whether the particular gene signature is
differentially expressed in multiple different samples or not.
 To test whether a gene is differentially expressed between two
samples, the Z-test employed for analysis of SAGE data sets.
 If x1 and x2 represent the abundance of a specific signature in
samples 1 and 2, respectively, and n1 and n2 represent the total
number of signatures generated for all mRNAs in samples 1 and
2, the proportions p1= x1/n1 and p2= x2/n2 each follow a
binomial distribution.

28.  Since n1 and n2 are large in MPSS (around in 106), the
difference (p1 – p2) follows an approximate normal
distribution defined.
 where the unknown parameters p and q can be estimated as
^p = (x1 + x2) / (n1 + n2) and ^q = 1- ^p.
 The test statistic defined by equation
 Equation show an inverse relationship between the level of
expression and size of the difference that can be evaluated
between samples.
 For example, for p <0.001, it is possible to detect a two-fold
change for a gene that is expressed at a level of 30-40 copies
per million.
 For genes that are expressed at a higher abundance, it is
possible to detect a much smaller difference.

29.  A 40 percent difference can be determined for genes that are
expressed at about 200 copies per million.
 This compared with microarrays, where a significance test is
possible only if the experiment is replicated several times and
where differential expression can usually be detected only for
genes with relatively high levels of expression and with a large
difference between samples.

30. COMPARISON OF MPSS WITH cDNA SEQUENCING,
SAGE AND MICROARRAY TECHNOLOGIES
 cDNA sequencing, SAGE and other technologies are similar to
MPSS in that they are digital in nature and count mRNA
molecules in the sample.
 Direct sequencing of cDNAs was the first digital technology for
measuring gene expression.
 MPSS Vs. cDNA: Both MPSS and direct cDNA sequencing
involve the generation of a cDNA library as the first step of
analysis.
 Once the cDNA library is made, sequencing of cDNA clones
involves the purification and sequencing of DNA using standard
procedures that are both costly and time consuming.

31.  With Megaclone, at least one million cDNA molecules are
cloned onto beads and with MPSS, over one million clones are
sequenced simultaneously.
 MPSS Vs. SAGE: MPSS has two advantages over SAGE.
 First, SAGE is also a transcript counting technique that
generates a tag sequence for each mRNA. The length of the
SAGE tag is 14 nucleotides for SAGE procedure, which
compares with a 17-nucleotide signature with MPSS.
 Signature lengths of 14 nucleotides are 80 per cent unique,
while the 17-nucleotide signature lengths generated with MPSS
are approximately 95 per cent unique on the human genome
(Zhang, et.al, 1999).

32.  During Human genome project, much higher percentage of
MPSS signature sequences map to unique locations on the
genome compared with SAGE tags.
 Secondly, MPSS makes it possible to produce efficiently a very
large dataset of signature sequences.
 While Many SAGE tag sets are comprised of only 20,000 -
60,000 sequenced mRNAs. Knowing that a large percentage of
genes are expressed at a level of 0.01 per cent or less.
 But the dataset of 20,000 - 60,000 sequenced mRNAs has
enough depth ????? to allow the quantitation and analysis of all
genes within a sample, particularly for expressed at very low
levels in the cell.
 An MPSS dataset of one million or more signature sequences is
more likely to provide a depth of analysis that will allow low-
level expressed genes to be accurately quantify.

33.  MPSS Vs. Microarray: MPSS is most notable in that it is a
technology that has the potential to capture virtually all genes
present within the sample, and not just those that have been
placed on the microarray.
 No prior knowledge of a gene's sequence is required for MPSS.
 Microarrays have the limitation that homologous genes can
cross-hybridise, which makes it impossible to detect individual
members of highly homologous gene family members which are
not annotated earlier.
 But with MPSS, the signature sequence in the 3’ untranslated
region, can be different for individual family members.
Therefore, it is possible, in many cases, to differentiate highly
homologous genes from each other.

34.  The advantage of microarray is the high throughput analysis of
multiple samples.
 The microarray and MPSS technologies as being complementary
in nature different tools for different types of experiments.
 e.g. To generate in-depth and quantitative gene expression data for
building complex relational databases, MPSS may be the
technology of choice.
 After these databases are mined for interesting biological
information, it may be necessary to test whether sets of genes are
differentially expressed in a large number of samples (eg tumours
of a specific type). Here, the microarray platform be the
technology of choice.
 Both MPSS and at least one of the microarray technologies would
seem to be ideal for most investigators.

35.  MPSS has the advantage that it provides in-depth quantitation of
virtually all genes that are expressed in a sample.
 Since there is no requirement for prior knowledge of any gene
or genome, it is possible to generate quantitative gene
expression datasets from any organism.
 MPSS dataset involves one million or more signature
sequences, it has the sensitivity to quantitate accurately genes
that are expressed at very low levels within a cell.
 No other single technology has these performance
characteristics.

36.  To discover the molecular basis of hepatocyte function, they employed
Massively Parallel Signature Sequencing (MPSS) to determine the
transcriptomic profile of adult human hepatocytes.
 They found that about 10,279 UniGene clusters, representing 7,475
known genes, were detected in human hepatocytes.
 1,819 unique MPSS signatures matching the antisense strand of 1,605
non redundant UniGene clusters (such as APOC1, APOC2, APOB and
APOH) were highly expressed in hepatocytes.
 Some of the antisense transcripts expressed in hepatocytes could play
important roles in transcriptional interference via a cis-/trans regulation
mechanism.
Constitute the essential structural
proteins of certain lipoproteins
involved in lipid transport.

37.  In this study, two lineage-related prostate cancer cell lines, LNCaP and
C4-2, were used for transcriptome analysis with the aim of identifying
genes associated with prostate cancer progression.
 In LNCaP cell line, 3,180 genes were only detected by Affymetrix and
only 1,169 genes were detected by MPSS.
 Similarly, in C4-2 cell line, 4,121 genes were only detected by
Affymetrix and only 1,014 genes were detected by MPSS.
 A combination of transcription profiling technologies such as DNA
array and MPSS provides a more robust means to assess the expression
profile of an RNA sample.
 Finally, genes that were differentially expressed in cell lines were also
differentially expressed in primary prostate cancer and its metastases.

38.  They used MPSS to determine transcriptomes of 32 normal human
tissues and they found the patterns of expression of almost 20,000
genes with high sensitivity and specificity.
 The differences in gene expression between cell and tissue types
are largely determined by transcripts derived from a limited
number of tissue-specific genes.

39.  Using massively parallel signature sequencing (MPSS), they
identified a total of 4535 genes that are differentially expressed
between normal brain and GBM (Glio-blastoma multiforme) tissue.
 The expression changes of three up-regulated genes, CHI3L1,
CHI3L2, and FOXM1, and two down-regulated genes neurogranin
and L1CAM, were confirmed by quantitative PCR.
 The construction of an extended TGF- b signaling network with
overlaid gene expression changes between GBM and normal brain.