What was the main theme of the live session?

The main theme was integrating multiomics data, such as genomics, proteomics, and metabolomics, to answer biological questions.

What technologies are being advanced in the field of single-cell analysis?

Single-cell genomics, mainly transcriptomics, is established. New proteomics workflows and sample preparation advancements now allow for single-cell proteome analysis.

What challenges exist in proteomics regarding low-abundance proteins?

Challenges include high-abundance proteins overshadowing low-abundance ones, making sample preparation and fractionation crucial to enriching low-abundance proteins.

How is the field of proteomics shifting focus regarding drug target discovery?

Proteomics is focusing more on cell surface proteins, which are potential drug targets, instead of global proteomes.

What tools were demonstrated in the session?

Tools like mass spectrometers, software for mass spectrometry data analysis like MS Fragger, and NGS instruments were demonstrated.

What are some emerging fields within proteomics?

Emerging fields include single-cell proteomics, surface proteomics, and analysis of post-translational modifications (PTMs) like phosphorylation and glycosylation.

Why is big data analysis important in the context of multiomics?

Big data analysis is crucial for handling and integrating large datasets from genomics, proteomics, and metabolomics to build comprehensive models of biological systems.

What are the potential career opportunities in the field of multiomics?

There are many opportunities, especially in data analysis, given the significance and complexity of handling multiomics datasets.

What were some of the technological highlights presented?

Technologies such as high-resolution mass spectrometers, new workflows for protein and metabolite enrichment, and proteogenomics approaches were highlighted.

Live Session1: Integrating MultiomicsApproaches inProteomics Research&theChallenges of Data Analysis

01:04:00

https://www.youtube.com/watch?v=oYH2OSN7wSQ

Sintesi

TLDRThe session was a comprehensive exploration of the integration of multiomics data—genomics, proteomics, and metabolomics—targeted at understanding complex biological questions. It focused on new technological advancements in these fields, particularly in single-cell analysis. Technologies like single-cell proteomics and genomics, proteogenomics, and metabolomics were discussed, providing insights into their applications in drug discovery and clinical research. The session demonstrated various instruments and workflows, emphasizing the progression from traditional proteomics to more advanced techniques such as single-cell proteomics and analysis of surface proteins which are crucial in drug targeting. Additionally, cutting-edge tools like mass spectrometers and data analysis software such as MS Fragger were showcased to underline the importance of accurate data interpretation in the burgeoning field of multiomics. Overall, this live session highlighted the critical role of integrating multiple omics layers to improve our understanding and treatment of diseases, offering budding scientists a glimpse into career opportunities within this rapidly advancing field.

Punti di forza

🔬 Single-cell proteomics is now feasible with advanced workflows.
💡 Integrating multiomics data is vital for comprehensive biological insights.
🧬 Genomics, proteomics, and metabolomics together offer a holistic view of biological systems.
📊 Big data analytics is essential for handling and interpreting large multiomics datasets.
🧪 Surface proteomics focuses on druggable cell surface proteins.
🔗 Proteogenomics helps link genomic data with protein expression profiles.
🚀 Emerging fields include single-cell analysis and post-translational modification studies.
🔍 Data analysis tools like MS Fragger ease the handling of mass spectrometry data.
💼 Growing career opportunities in multiomics fields, particularly in data analysis.
📈 Advanced mass spectrometry techniques crucial for detailed proteomics analysis.

Linea temporale

00:00:00 - 00:05:00
The session starts with an introduction to the live demo, focusing on the integration of multi-omics data, specifically proteomics and genomics, to address biological questions. Single-cell proteomics is highlighted as an emerging field with advancements allowing a more comprehensive analysis at the single-cell level.
00:05:00 - 00:10:00
The speaker discusses cell surface proteomics and its significance for drug target discovery by focusing on cell surface proteins. This includes biological developments in understanding post-translational modifications like phosphorylation and glycosylation, emphasizing the importance of instruments like Orbitrap for high-resolution analysis.
00:10:00 - 00:15:00
A typical workflow for quantitative proteomics using biological samples is explained. The focus is on mass spectrometry for protein analysis, discussing sample preparation, ionization, and data acquisition. High-resolution mass spectrometry facilitates exploring proteomics in depth.
00:15:00 - 00:20:00
The session shifts to discussing Tandem Mass Tag (TMT) for proteomics, illustrating its use for sample multiplexing for quantitative analysis. This involves tagging peptides, running samples on mass spectrometry, and analyzing the data based on intensity for quantitative results.
00:20:00 - 00:25:00
The demonstration describes optimizing TMT methods to improve data accuracy. The process involves parameter settings in mass spectrometry to ensure reliable data acquisition and analysis, highlighting the importance of adequate experimental settings for effective proteomics.
00:25:00 - 00:30:00
MRM and PRM workflows for targeted proteomics are discussed, focusing on the validation of proteins using triple quadrupole instruments. These methods help confirm quantitative findings from earlier analyses, providing robust validation techniques.
00:30:00 - 00:35:00
Highlighting integration with metabolomics, the session discusses the analysis of metabolites using mass spectrometry, illustrating the potential of combining proteomic and metabolomic data from the same sample for a more comprehensive biological insight.
00:35:00 - 00:40:00
The use of next-generation sequencing in genomics is introduced for comprehensive genetic analysis using the Ion Torrent S5. The discussion includes steps for preparing libraries and amplification, enhancing genomic data integration with proteomic data for holistic insights.
00:40:00 - 00:45:00
A focus on various bioinformatics tools to analyze proteomics data is demonstrated, emphasizing the use of free and powerful tools like MS Fragger for analyzing complex mass spectrometry data efficiently, showcasing the analytical workflow from data acquisition to interpretation.
00:45:00 - 00:50:00
Discussing the integration of multiple data types (genomics, proteomics, and metabolomics), the speaker emphasizes the importance of multi-omics in understanding complex biological systems, with a drive towards precision medicine and system biology insights.
00:50:00 - 00:55:00
Demonstrations continue with insights into the statistical and computational challenges of handling large datasets in modern research environments, indicating cross-disciplinary efforts necessary for advancing biological research.
00:55:00 - 01:04:00
The session concludes by highlighting educational initiatives and career prospects in proteomics and multi-omics fields. The emphasis is on the growing demand for expertise in data analytics and the potential for integrating omics data to drive innovation and discovery in biomedicine.

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Domande frequenti

What was the main theme of the live session?
The main theme was integrating multiomics data, such as genomics, proteomics, and metabolomics, to answer biological questions.
What technologies are being advanced in the field of single-cell analysis?
Single-cell genomics, mainly transcriptomics, is established. New proteomics workflows and sample preparation advancements now allow for single-cell proteome analysis.
What challenges exist in proteomics regarding low-abundance proteins?
Challenges include high-abundance proteins overshadowing low-abundance ones, making sample preparation and fractionation crucial to enriching low-abundance proteins.
How is the field of proteomics shifting focus regarding drug target discovery?
Proteomics is focusing more on cell surface proteins, which are potential drug targets, instead of global proteomes.
What tools were demonstrated in the session?
Tools like mass spectrometers, software for mass spectrometry data analysis like MS Fragger, and NGS instruments were demonstrated.
What are some emerging fields within proteomics?
Emerging fields include single-cell proteomics, surface proteomics, and analysis of post-translational modifications (PTMs) like phosphorylation and glycosylation.
Why is big data analysis important in the context of multiomics?
Big data analysis is crucial for handling and integrating large datasets from genomics, proteomics, and metabolomics to build comprehensive models of biological systems.
What are the potential career opportunities in the field of multiomics?
There are many opportunities, especially in data analysis, given the significance and complexity of handling multiomics datasets.
What were some of the technological highlights presented?
Technologies such as high-resolution mass spectrometers, new workflows for protein and metabolite enrichment, and proteogenomics approaches were highlighted.

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Sottotitoli

Scorrimento automatico:

00:00:15
sir you're live now you can
00:00:23
start all right good morning everybody
00:00:25
uh welcome to today's live session uh
00:00:30
today we are going to plan to give you
00:00:32
some demonstrations from the lab uh
00:00:35
along with interacting with you life so
00:00:38
this could be your opportunity to talk
00:00:39
to us and ask questions but while you
00:00:43
are uh uh going to do that we are uh
00:00:46
going to show you some experiment in a
00:00:48
more thematic way uh today's theme is
00:00:51
going to be on integrating multix data
00:00:54
set to understand the uh different
00:00:56
biological questions and in this
00:00:59
particular session we are going to show
00:01:00
you some live experiments from different
00:01:03
type of proteomics and genomic
00:01:05
Technologies but then more Focus will be
00:01:07
how to do the data analysis and
00:01:09
integrate data from proteomics and
00:01:12
genomics so uh rather than making it
00:01:15
very theoretical uh I thought to keep it
00:01:18
very minimal in terms of talking to you
00:01:19
on the slides but we do more handson in
00:01:22
the lab uh but I'll give you very quick
00:01:25
flavor of you know where proix has
00:01:27
started making more impact understand
00:01:31
biology and addressing many clinical
00:01:33
questions right so something which is
00:01:36
coming up very new is about single cell
00:01:38
p and I thought to take this as the
00:01:40
first topic because one of the students
00:01:42
sh she asked a question about how the
00:01:45
field of single cell genomics and promic
00:01:47
is emerging are going to really make
00:01:50
impact so in this SL uh I'd like to say
00:01:53
that single cell uh genomics mainly
00:01:55
single cell transcriptomics is already
00:01:58
very established and successful approach
00:02:01
and people realize that you know they
00:02:02
can do sufficient RNA and do the transic
00:02:05
analysis with RNA seek but the challenge
00:02:07
was to look at from Singles at how to do
00:02:10
the entire proteum analysis but now with
00:02:13
the uh advancements of the new type of
00:02:16
mpic workflows and the new sample prep
00:02:19
it has really now started becoming a
00:02:21
reality that one could even look at the
00:02:23
proteum from single cell right so what I
00:02:25
showed you here on the slide think about
00:02:27
if we have to work on the cancer you
00:02:30
know to look for The Unique proteins
00:02:33
linked to given cancer uh what we do in
00:02:36
our own research which is a reality and
00:02:38
and others also do we take the tumor
00:02:41
tissue and that tumor tissue is a lump
00:02:43
of the mass of many type of cell right
00:02:46
we homogenize them Li them and in a in a
00:02:49
hope that we look at only cancer rated
00:02:52
protein but actually not no that's not
00:02:54
true because we are mixing a variety of
00:02:57
cell type in this example if you see
00:02:59
neutrophil macrofagos blood vessels t-
00:03:02
cell fibroblast all of them along with
00:03:04
the cancer cell now if you were to
00:03:06
disaggregate this tumor mass and look at
00:03:09
you know separate components of them
00:03:12
then only you know you can see you have
00:03:14
very few cancer cell but there are lot
00:03:16
of other type of abundant cells which
00:03:18
are present there so in which way we can
00:03:21
try to First Look at the cell sorting
00:03:24
and look at the Single Cell of our
00:03:26
interest which we are Keen to look at
00:03:28
the proteum or transcrip
00:03:30
to and based on that now can we do
00:03:33
sufficient ways of sample prep and
00:03:36
enrichment that we can get you know a
00:03:38
lot of peptides from that given cell and
00:03:40
one could run in the massp what you see
00:03:42
in the bottom panel to generate those
00:03:44
Mass Spectrum data and then look at the
00:03:46
data analysis and come up with lot of
00:03:48
you know different type of patterns so
00:03:50
this was definitely not possible a few
00:03:52
years back but now from last year and so
00:03:55
there's been a lot more progress being
00:03:56
made in this area that now we uh have
00:03:59
have good ability to look at the uh from
00:04:02
single cell even close to 5,000 proteins
00:04:05
3,500 to 5,000 protein is what people
00:04:08
are able to now achieve with the newer
00:04:10
uh advancements of the MPC like Tim sto
00:04:13
and estal orbit trap uh one could really
00:04:16
you know go to much deeper coverage with
00:04:18
the Single Cell as well but also like
00:04:20
sample becomes very crucial because you
00:04:22
need to make much more automated
00:04:24
processes so that you're you're not
00:04:26
losing the much peptide and also you are
00:04:28
looking at um how best to uh preserve
00:04:32
the peptides which you lost in the
00:04:33
multiple step so single po analysis
00:04:36
Scopes method there are many type of you
00:04:37
know new workflow which has emerged
00:04:39
which I'm not going to talk as a lecture
00:04:41
here but that just to give you flavor
00:04:43
that single cell genomics and proteomics
00:04:46
and soon single cell proteogenomics will
00:04:48
be a reality which is almost you know we
00:04:50
are making good advancement in this area
00:04:52
so now just another exciting thing is
00:04:54
happening in the proteum X right now to
00:04:56
look at the cell surface
00:04:58
proteum While most of the time we take
00:05:00
the again the the entire cell and try to
00:05:02
Li the cell and look at every protein
00:05:04
inside that which is the global proteum
00:05:06
but if you think about lot of drug
00:05:08
targets they work only on the surface
00:05:10
right so we should be focusing for the
00:05:13
drug Target Discovery more of the
00:05:14
membrane protein and the things are more
00:05:16
receptors and present outside the
00:05:18
membrane but when you combine everything
00:05:20
you are actually diluting with the
00:05:22
global abundant highly abundant protein
00:05:24
to these low abundant and more unique
00:05:26
targets so how to enrich only the
00:05:29
specific cell surface protein is another
00:05:32
emerging area in the proix which are
00:05:35
couple of you know good successful
00:05:36
examples shown here with also our
00:05:38
collaborator at UCSF who are developing
00:05:41
the new workflows to only enrich the
00:05:42
cell surface protein and again with the
00:05:45
these Advanced m is possible to
00:05:47
analyze thousands of protein from these
00:05:50
type of unique cell surface protein but
00:05:52
again biologically is are very important
00:05:55
development which are happening in the
00:05:56
field of proteomic right but if you
00:05:59
think about the understanding the
00:06:01
biology and looking at diseases It's
00:06:03
always important to look at the post
00:06:05
transation modification again it was
00:06:07
very difficult to look at various type
00:06:09
of PTM analysis because if you think
00:06:12
about the from the entire Global protein
00:06:15
what percentage gets phosphorated even
00:06:17
less than 1% so how to enrich those
00:06:20
phosphopeptide and then try to analyze
00:06:23
the proteum from that type of know less
00:06:26
abundant uh samples has been challenged
00:06:29
so example enrichment using different
00:06:31
type of iMac columns and different type
00:06:33
of tianium dioxide based Technologies to
00:06:35
enrich the phospho peptides followed by
00:06:39
running them in the mass spectometer
00:06:40
with very sensitive instruments and
00:06:43
acquiring all type of phosphor related
00:06:45
proteins or the phosphor map of the
00:06:47
proteum is another thing which is
00:06:49
emerging very heavily and not only like
00:06:51
you know phospho for that matter even
00:06:53
glyos are very important and very
00:06:55
complex and very
00:06:56
challenging and then think about biology
00:06:58
for UB and acation and variety of type
00:07:02
of degradation and many type of other
00:07:04
type of modifications are also equally
00:07:06
important to understand the biology and
00:07:07
disease so a lot of exciting things are
00:07:10
happening in this area of course in your
00:07:12
theoretical course of proteomics and
00:07:14
proteogenomics we did not have time to
00:07:16
touch upon all the latest advancements
00:07:18
but these are you know kind of some of
00:07:20
the quicker updates and again as I said
00:07:22
was not possible without the some of the
00:07:24
very latest high resolution mpect like
00:07:27
from the orbit trap Asal orbit trap
00:07:29
happen with the Tim stop mtic without
00:07:31
them it was not possible to go to that
00:07:33
level of depth of these very important
00:07:36
class of proteins of membrane proteins
00:07:38
and the ptms or looking at even the
00:07:40
Single Cell proteum but these are
00:07:42
started getting more and more reality
00:07:43
which actually adds lot of excitement
00:07:45
for all of you who are learning these
00:07:47
new areas and want to venture into this
00:07:49
new field by taking up the exciting
00:07:51
research problems in the future so uh
00:07:55
while this are some latest development
00:07:57
but what is the typical workflow of
00:07:59
doing protomet for all of these whatever
00:08:01
I spoke or in general if you want to do
00:08:03
qu quantity protomet a typical workflow
00:08:05
involves that you take some biological
00:08:07
sample it could be a cell line a
00:08:10
biological tissue sample or various type
00:08:12
of body fluid it could be saliva it
00:08:14
could be serum it could be C spinal
00:08:15
fluid many type of biological fluid
00:08:17
samples right all of them you stract the
00:08:19
protein you digest them with proteases
00:08:23
like a tripin choten ly make them
00:08:26
peptide form and then those peptides you
00:08:28
are ionizing them with the
00:08:31
electronization
00:08:32
ESI after ionization they are going to
00:08:35
go inside the mass spectrometer which
00:08:37
could be vary of MPS and then you are
00:08:39
acquiring their M byz and then you are
00:08:42
generating their MSS Spectra or msms
00:08:44
Spectra and going to analyze that data
00:08:47
typical pic workflow whatever type of
00:08:49
thing I'm talking they will fall in in
00:08:52
this kind of domain only thing which
00:08:54
becomes very critical is the sample
00:08:55
preparation part which we already spoke
00:08:58
in the last live session about different
00:08:59
type of sample prep how to make it more
00:09:01
unique and and and en that kind of
00:09:03
sample right but eventually as you go
00:09:06
along there are different type of
00:09:07
workflow being followed to look into the
00:09:11
quantitative type of workflows or
00:09:12
looking at a specific modification and
00:09:14
accordingly you to fine tune your
00:09:16
workflows for the data acquisition at
00:09:18
the mpect level and data analysis level
00:09:21
so uh we have this uh high resolution
00:09:23
Mass spectrometry facility at it Bombay
00:09:26
which we have established as additional
00:09:27
facility which is equipped with iy of
00:09:30
instruments uh and what we thought we
00:09:32
are going to show you some of these
00:09:33
instruments how they can be utilized for
00:09:36
different type of workflow so for
00:09:37
example here you
00:09:38
see orbit Fusion which is really
00:09:41
utilized for the D proteum type of
00:09:43
analysis or defo protom analysis utive
00:09:47
along with the HC configuration is
00:09:50
heavily utilized for metabolomics and
00:09:52
triple qurol instrument is used for the
00:09:54
targeted toomic or metabolomic based
00:09:57
analysis so we will you know take more
00:10:00
time in the lab and try to walk you
00:10:01
through with these type of workflows but
00:10:04
to begin with uh we will first talk to
00:10:07
you about one of the quantitive plumix
00:10:09
based approach which is still pretty
00:10:11
powerful which is known as the TMT or
00:10:14
tendem mass Char so idea is can you uh
00:10:18
you have three control sample and three
00:10:20
treatment samples you want to do
00:10:22
quantitative analysis quantity
00:10:24
proteom you digest the protein and after
00:10:27
that you are after digestion you have
00:10:29
these uh
00:10:30
peptides uh and those n terminal
00:10:33
peptides being labeled with these
00:10:35
isobaric tag which is having different
00:10:38
type of reporter ions for example tandom
00:10:40
Mass tag reporter 126 till TMT 131 these
00:10:44
are all reporter ions these are isobaric
00:10:47
although reporter are different but then
00:10:48
you add the carbonal group to make them
00:10:50
all same mass and then you are mixing
00:10:52
them with each of your six condition so
00:10:55
now every condition of three control and
00:10:57
three treatment they all got unique
00:10:59
barcoded right and now you can mix them
00:11:02
and after mixing them you run in the MPC
00:11:05
and then you can when you look at msms
00:11:07
level you can now segregate these
00:11:09
reporter I separately and use this
00:11:11
information of intensity to quantify the
00:11:14
signal this is one of the very powerful
00:11:17
way of doing the quantity protox other
00:11:20
way of doing quantity protx could be
00:11:22
label free quantification where every
00:11:24
sample you're not labeling but you are
00:11:26
going to run them individually in the MP
00:11:29
and then you are acquiring the data
00:11:31
which is more like you're building a
00:11:32
library of every patient is specific or
00:11:34
sample specific and then you are
00:11:36
counting the number of Spectra or the
00:11:39
relative intensity of all these abundant
00:11:42
peptide and trying to compare those
00:11:43
across your control and disease
00:11:45
condition to then come up with quantive
00:11:47
signal how much F change you can see
00:11:49
from control and treatment so two
00:11:51
different ways of doing analysis both of
00:11:54
these are coming as a part of data
00:11:56
dependent acquisition there's also new
00:11:58
emerging field known as Dia data
00:12:00
independent acquisition which I'm not
00:12:01
talking right now to keep it simple but
00:12:04
we will just you know walk you through
00:12:05
about TMT workflow and how to acquire
00:12:08
data uh in mpek uh in a live session
00:12:11
right now which will be done with uh Dr
00:12:14
ARA benergy he's going to show you the
00:12:16
workflow and talk to you about the data
00:12:19
how to acquire and analyze the data so
00:12:20
ARA please talk about TMT now so ad can
00:12:24
you help me to share the screen
00:12:48
okay so uh let me this okay so welcome
00:12:52
everyone uh welcome to the introduction
00:12:55
to the proteomic course and uh TMT based
00:12:58
proix so first of all like I will be
00:13:00
giving like what are the advantages of
00:13:03
sample multiplexing with the isic mass
00:13:05
tax though Professor has explained more
00:13:08
of it like how and where it is useful in
00:13:12
the F
00:13:13
of how and where it is useful in the
00:13:15
field of proteomics it will give you
00:13:17
increase through through uh when you are
00:13:20
using isobaric tags due to the reporters
00:13:23
and the I and
00:13:28
just yeah
00:13:29
uh when we are ISO using isobaric tax
00:13:32
where there is a very few chance like
00:13:34
the missing values will be very few as
00:13:36
compared to the level three
00:13:38
quantification we have a vast range of
00:13:40
sample flexibility whether it is from
00:13:42
human or bacteria or plant any species
00:13:46
of the samples can be originated and uh
00:13:49
like it can help us in multiplexing
00:13:51
means comparing more than two samples
00:13:53
means you can ideally now they have
00:13:56
taken out a range of 24 and 26 like one
00:13:59
go you can compare like more than two
00:14:02
samples at a time and multiple
00:14:03
comparison will help you to improve the
00:14:05
statistics now as the professor told
00:14:08
there is a mass reporter there is a mass
00:14:10
normalizer and there is a protein
00:14:12
reactive group your peptide will be your
00:14:14
peptide will be uh connected with the
00:14:17
balancer and balancer is connected with
00:14:20
the reporter this is the reporter what
00:14:21
you want to
00:14:23
see coming up to the workflow as you
00:14:26
have gone through our earlier session
00:14:28
where we we have shown you how to
00:14:30
prepare the samples we generally prepare
00:14:32
the samples we do a fraction we do a
00:14:34
labeling of the TMT TX after the
00:14:37
peptides have been uh generated and
00:14:39
followed by a cleanup followed by a
00:14:41
fractionation which can improve the
00:14:43
coverage followed by the concentration
00:14:46
data interpretation data analysis can be
00:14:48
done with the free software such as Max
00:14:50
and flf as also as the proteum discovery
00:14:54
which is one of the proprietary software
00:14:56
now when you are uh delet dealing with
00:14:59
the optimization of the TMT metods two
00:15:02
things you have to keep in mind if you
00:15:04
are using shorter gradients and shorter
00:15:06
columns that can increase a CO isolation
00:15:09
but uh if you are using 2 hours gradient
00:15:12
with a 50 cm column either you have to
00:15:14
increase the length of the column or the
00:15:16
length of the gradient a 15 cm column 1
00:15:18
hour to 1.5 hour of gradient is good
00:15:21
enough but if you are using 50 cm column
00:15:24
you can also load 500 nog to 1 microgram
00:15:27
of load which can be go to throughout
00:15:29
so this is how the gr establishment can
00:15:31
be done and as you know like this is a c
00:15:35
column so more of the hydrophobic
00:15:36
peptides will be there so this is the
00:15:38
interface how the method Edition is
00:15:40
being done so this is the method editor
00:15:42
which I am going to show you if the time
00:15:45
permits so here like in the peptide
00:15:49
quantification you can eily go to the
00:15:51
TMT ms3 and you can place it here you
00:15:54
can just drag this PMT and you can place
00:15:56
it here once that has been done you will
00:15:59
given an option like how you want to
00:16:00
analyze your ms1 data and MS2 data uh
00:16:04
the ms1 can be done in the OT mode and
00:16:08
because OT have a higher resolution and
00:16:10
MS2 mode as this orbitary Fusion what
00:16:13
Professor San showed in our facility is
00:16:15
of uh like trid Mass spectrometer is one
00:16:19
of the trid mass spectrometer it has
00:16:21
three kinds of mass analyzer qu IR graft
00:16:24
as well as the orbit the second mass
00:16:26
analyzis is a choice whether if you are
00:16:28
using a CD you can use OT mode like a
00:16:30
orbit mode and if you are using C
00:16:33
Collision induced dissociation you can
00:16:35
use the iron craft as the mass analyzer
00:16:38
and thirdly like in the DD MS2 like uh
00:16:41
which is the main for use for the
00:16:42
quantification you can use the OT so in
00:16:46
one like you can go to the 12 lakh
00:16:48
Delton like the 2 lakh Delton of the
00:16:50
Orit resolution but you can go in mass
00:16:54
in TMT Mass tax you can go to a range of
00:16:56
1 lakh 20,000
00:16:59
and severely like a multiple charge
00:17:01
State the charge State can be concluded
00:17:03
include from 2 to six because this is
00:17:06
one of the Rob which separate from the
00:17:08
mul because at a single go you can
00:17:11
monitor more than two chares monotropic
00:17:15
prepers keep it to simple pepti a full
00:17:18
msms resolution with Char Dynamic
00:17:20
exclusion and I res so these are the the
00:17:23
settings we are good to go and consider
00:17:26
selecting the most intense ion at a top
00:17:29
speed
00:17:31
mode so yeah so now this is done uh when
00:17:35
you come to the sensitivity and
00:17:36
specificity the isolation window is
00:17:38
generally from uh 7 to 2 m the choice
00:17:42
can be between 5 to 10 and for best
00:17:45
accuracy top 10 nches of the
00:17:47
prefractionated samples were taken so
00:17:50
this is how a ideal parameter or a
00:17:54
ideal Matrix of anmt workflow line so
00:17:57
this is a 2hour gradient
00:17:59
that is 120 Minutes you generally start
00:18:01
with less hydrophobic means more of the
00:18:03
Aquas to high hydrophobic which is an
00:18:06
organic solvent which is ACN and you can
00:18:09
easily obtain more than 2,000 proteins
00:18:11
in a single shot and 10,000 to 11,000
00:18:15
peptide zes in single and that can be
00:18:18
Quantified with the help of cium
00:18:21
discover okay so now let me uh let me
00:18:24
take you to the
00:18:26
original um mapping of the
00:18:30
instrument
00:18:45
just so this is the original interface
00:18:48
of the video so that 120 hour minutes
00:18:50
gradient you can see it here this is how
00:18:53
a chromatogram looks like and if I can
00:18:56
show you the total iron chromatogram
00:18:59
uh view iron map okay you can see the
00:19:03
Spectrum also this is how the Spectrum
00:19:05
looks like most of the illusion has
00:19:07
happened from 30 minutes to 110 minutes
00:19:09
so this is our gradient like the first
00:19:11
is the aqua space which I have shown you
00:19:14
5% of B then is the hydrophobic face and
00:19:17
after that again Aquas so if you zoom in
00:19:20
like
00:19:26
sorry okay this is hanging
00:19:30
is hanging a bit so if you zoom in each
00:19:33
paks represent so this is how the Crux
00:19:35
lights so you can see each Peak is a Ms
00:19:38
and the void between the two is the MS
00:19:40
Ms so this is how a peak is being seen
00:19:43
so if you click it here let this also be
00:19:46
there uh you say VI uh Spectrum okay
00:19:52
great so like you can see so this is the
00:19:55
MS so once you shift you can see if
00:19:58
between two Ms if between two Ms if the
00:20:01
fragmentation is of Ms Ms is more than
00:20:04
10 because we have selected top 10
00:20:06
abundant proteins if the transition is
00:20:08
coming more than 10 it is a very good
00:20:10
Spectrum at indeed it is a very good
00:20:13
Spectrum so this is how you can monitor
00:20:16
how the chromatogram looks
00:20:18
like once this is done you can see your
00:20:22
labeling efficiency uh TMT templex sence
00:20:26
can vary from 126 to uh 132 whatever the
00:20:31
maybe so you can simply go you can
00:20:33
simply go here you can uh go on the
00:20:40
ranges in the
00:20:43
ranges just a minute let it come in the
00:20:46
ranges you can select one more column
00:20:49
where you can select Ms sorry we have to
00:20:52
do it on the Spectrum so view
00:20:55
chromatogram once the chromatogram comes
00:20:58
you you can go on
00:21:03
the I have already done this
00:21:07
okay go to the
00:21:10
ranges once you go to the
00:21:12
ranges I will show you from
00:21:16
okay so so this is how it looks like so
00:21:20
this is a chromatogram view uh you go to
00:21:24
the ranges and suppose my labeling my
00:21:28
levels which are there are coming I will
00:21:31
click on submerge with another graph and
00:21:34
I will click on the mass range now it
00:21:38
will give you the mass of the MS
00:21:40
precursors which have been detected so
00:21:43
as we know our the labels which are
00:21:47
occurring in the uh DMT tags varies from
00:21:50
126 to 130 1 for four place or
00:21:55
132 whatever may be the ranges
00:21:59
we will just click on the same so this
00:22:02
is how you can take out the mass
00:22:04
range just click on okay it will take
00:22:07
some
00:22:15
time so it is going to tabulate and see
00:22:19
what all precursor which are present
00:22:21
here are labeled with that kind of
00:22:25
efficienc Tak a bit of time
00:22:32
by the time it comes like
00:22:35
uh uh we can show the
00:22:39
instrument comes we can show the
00:22:45
instrument all right so well these are
00:22:48
the live run which AR gu is about trying
00:22:50
to show you from
00:22:52
the uh orbit Fusion which we are
00:22:54
acquiring the data and again U to really
00:22:58
get you know proper fragmentation here
00:23:00
and looking at the uh reporter you need
00:23:04
to optimize the Collision energy and to
00:23:06
do lot optimization at the
00:23:08
chromatography level and msms
00:23:10
parameters um but eventually uh if your
00:23:14
labeling was highly efficient you have
00:23:17
done the proper labeling and if you are
00:23:19
able to now uh uh look at the data you
00:23:23
can acquire lot of good quantitative
00:23:25
accuracy uh what you see now also on the
00:23:29
um directly from the uh interface let's
00:23:34
um so AR if you can just unshare your
00:23:36
screen then we can have you on the full
00:23:47
screen see where the samples are
00:23:50
ined already loaded and it is running so
00:23:53
I cannot open the compartment box of the
00:23:55
uh Nano LC the sample is eled from here
00:23:59
and it comes to the or so this is the
00:24:03
main easy spray source of fusion and you
00:24:06
can see like this is the Trap column
00:24:08
what we are having two types of columns
00:24:10
are used before the sample is injected
00:24:12
into the instrument one is the tree
00:24:14
column or the Trap column if any debr
00:24:17
which is coming from the direct nanc it
00:24:19
will be trapped here and after that an
00:24:21
analytical column 15 cm analytical
00:24:24
column is used in which the samples is
00:24:26
in
00:24:28
sample injected so this is the ITC
00:24:31
isothermal calibration tube once the
00:24:33
sample goes inside it goes onto the uh
00:24:36
instrument and after that you can see
00:24:38
that kind of gradi so coming
00:24:43
on all right so thank you ARA this is
00:24:47
one of the workflow of doing the
00:24:49
quantitive protomet and once you have
00:24:53
identified let's say lot of protein
00:24:55
which shows you significant protein lead
00:24:58
uh which is a good hit for you so now
00:25:00
you need to validate them right
00:25:02
conventionally people used to validate
00:25:04
the targets using antibody based
00:25:06
approach like Eliza or Western broad but
00:25:09
for every protein uh we don't have uh so
00:25:12
nice antibodies available U for many it
00:25:16
is only polyon available so then how to
00:25:19
validate the target so since we are
00:25:21
acquiring data from masp which is more
00:25:24
about the peptide sequences and their
00:25:26
quantification idea came why not we only
00:25:29
validate the those peptides from
00:25:31
different type of mpect so we are let's
00:25:33
say discovering an orbit Fusion but can
00:25:35
we validate the targets of only specific
00:25:38
peptides of the proteins of Interest
00:25:40
using the triple quadrapole based
00:25:42
instrument or using another type of
00:25:43
orbit trap instrument so the two
00:25:45
different workflow which are currently
00:25:46
being heavily used one is multiple
00:25:49
reaction monitoring other is a par
00:25:51
reaction
00:25:52
monitoring multiple monitoring is one of
00:25:55
the uh triple Quadra pole based
00:25:57
configuration when you are having the
00:26:00
two set of quadr q1 and Q3 in between
00:26:03
you have Collision cell and you already
00:26:05
know that which of the specific protein
00:26:08
let's say five proteins of that 15
00:26:10
peptides of your interest you have told
00:26:13
the instrument with a hardware with a
00:26:15
spe specific software on the skyline
00:26:17
that you're only going to look at the
00:26:18
transitions of these 15 peptides and
00:26:22
only those the q1 is actually acquiring
00:26:24
data and doing fragmentation to generate
00:26:26
the relative iion uh spectrum of
00:26:29
PRM is much more powerful it can
00:26:31
actually look at all of
00:26:32
those precursors and fragment ties
00:26:35
simultaneously so then one could go much
00:26:38
higher number of transitions as compared
00:26:39
to the mrm based approach so in which
00:26:43
way these experiments can be done in the
00:26:44
lab let's have another live
00:26:47
demonstration from ainash who will walk
00:26:49
you through these workflows quickly
00:26:51
directly on the instrument so ainash
00:26:54
please show the mrm based workflow hello
00:26:57
Al so as you can see already discussed
00:27:00
about the targeted programing so this is
00:27:01
the T volage from the th scientific and
00:27:05
this has the basically the triple Quadra
00:27:07
so are the two marer and along with that
00:27:10
this is the L system connected so this
00:27:12
is the system and every LC system
00:27:15
basically multiple compartment this is
00:27:18
basically Auto sampler where we keep our
00:27:20
samples like this kind of files and so
00:27:23
these are the different kind of tray
00:27:25
where we keep our samples and from these
00:27:27
samples they go to the like like this is
00:27:30
basically the pump compartment and this
00:27:32
one is the and yeah so you can see these
00:27:35
are the pump compartment where all the
00:27:37
solvents like have been mixed and get
00:27:40
eluted during according to the our what
00:27:43
we said and this is the column
00:27:46
compartment where we keep our
00:27:47
compartment like keep our column and it
00:27:50
maintains some internal temperature for
00:27:52
the columns and these are the solventes
00:27:55
from this solvent the different gradient
00:27:57
has been prepared
00:27:58
so as you can see in the monitor so this
00:28:00
is
00:28:01
basically liquid system application so
00:28:06
this is the pump module where we can set
00:28:08
our gradient and this one is a sample
00:28:10
and we can set like set like where is
00:28:13
our sample is present and this is the
00:28:14
column compartment and we can set the
00:28:16
temperature and uh so all the method
00:28:19
file which has been used for the
00:28:21
targeted pics are set in the UN
00:28:23
application and uh and those method has
00:28:26
been like set in the X Co caliber so as
00:28:27
you can see this is a live run going on
00:28:29
so this are the chromatogram and
00:28:31
different kind of I and the
00:28:34
gradi here so as you can see like it
00:28:38
depend on the what chromat like what
00:28:40
gradiate we set according to that they
00:28:41
get uted and this kind of chatram come
00:28:44
so if I can also show so this is the one
00:28:47
of the sample R from the tumor sample so
00:28:50
this is not the Gan distribution as we
00:28:52
see the see in the global prot this is a
00:28:54
quite like like not that dense
00:28:58
because this is we are looking for the
00:29:00
only the targeted proteins and peptides
00:29:03
so that's why is not there so I can show
00:29:05
you some of the result from so after
00:29:07
that getting the raw files this kind of
00:29:09
raw file we can import those raw file in
00:29:12
the different another software which is
00:29:14
called as a Skyline and we can look for
00:29:16
the peak of the particular protein so
00:29:18
this is one of the study in the from the
00:29:20
co so as you can see so upper top top
00:29:22
panel is basically the co positive and
00:29:25
lower panel is basically the
00:29:28
so we have identified the multiple
00:29:30
protein which are basically shown the
00:29:32
differential regulation in the different
00:29:34
set of a group so as you can see the
00:29:36
also the intensity of in the like
00:29:38
negative sample and the positive sample
00:29:40
so basically this instrument has been
00:29:43
very good for the doing the targeted
00:29:44
proteomics and from the what after
00:29:47
analyzing theic global data set so yeah
00:29:51
over
00:29:52
to all right thank you Vash U so now
00:29:57
let's say say you already have looked at
00:30:00
you know your specific uh protein
00:30:02
targets and you are able to now
00:30:04
correlate that intensity what you have
00:30:06
observe the four changes they are same
00:30:08
whether you look for the discovery or
00:30:10
not in the validation stage additionally
00:30:13
from the since we're talking about mpek
00:30:16
mpek is also very powerful lcmsms
00:30:18
approach to look at the metabolites in a
00:30:21
field on as metabolomics right the way
00:30:23
we look at all the proteins and
00:30:24
proteomics likewise masp is very
00:30:26
powerful to look at all the metabolomics
00:30:29
for all the metabolites although there
00:30:31
other techniques as well like NMR can
00:30:33
also be used gcms can also be used they
00:30:35
give you very specialized proces of
00:30:37
metabolite but a global broad survey can
00:30:40
be done with the lcms based approach so
00:30:43
since let's say you think about you have
00:30:45
biological sample or tissue or cell
00:30:46
liate from which you want to do
00:30:48
proteomic you're going to extract the
00:30:50
protein out why not you know you also
00:30:53
extract the metabolite out on the same
00:30:54
sample for example you know if you're
00:30:56
doing methanol based PR reputation you
00:30:59
can even use the same sample for even
00:31:00
some part for the protein some part for
00:31:02
the metabolite analysis right how
00:31:04
powerful that could be if you are
00:31:06
integrating the data from the same
00:31:07
sample today's live session is also for
00:31:10
the broader audience talking about
00:31:11
proteogenomic and multiomic so now we
00:31:14
want to expand your view think about
00:31:16
from the same sample you are generating
00:31:18
next layer of information at the
00:31:20
metabolite level so metabolomics can be
00:31:22
done using various type of mass
00:31:24
spectometer one which we are going to
00:31:26
show you the another orbit trap Q
00:31:28
executive which is interfac with
00:31:30
uhplc and it is pretty straightforward
00:31:33
much simpler to do metabolomics as
00:31:35
compared to the proteomic of course you
00:31:37
need to optimize multiple parameter
00:31:40
which now an is going to quickly
00:31:41
demonstrate you metabolomics based
00:31:44
workflow directly from the
00:31:49
lab yeah hello everyone good morning so
00:31:52
now we will be sh you about the running
00:31:55
of the sample for the metabolite
00:31:57
identification
00:31:58
so as it was discussed that in the prot
00:32:01
there is the nanc or the Nan flow of
00:32:03
theow of the p and solution but here for
00:32:07
the metabolite what we generally use is
00:32:09
the HC here we uses the flate you can
00:32:12
see it is in the range of ml for ml per
00:32:15
minute so here is the same like in the
00:32:18
alage what a has showed there's the same
00:32:20
compartment of a and the column now
00:32:23
metabolize can be divided into three or
00:32:27
more classes on the basis of their
00:32:28
hydrophobicity so there can be
00:32:30
hydrophobic it can be in between
00:32:33
amphilic it can be hydrophilic so on
00:32:34
that basis of that the particular column
00:32:37
is used now if you are want to out only
00:32:40
the separate the identify hydrophobic so
00:32:43
then a C8 column as like it is being
00:32:46
used in the case of prot so then same
00:32:49
reverse B chromatography is being
00:32:53
principle is being followed where the in
00:32:55
the column compartment the column is
00:32:57
being added and in the solution you can
00:33:00
see that it is being made uh the
00:33:03
different layers different gradient of
00:33:05
hydrophobicity is being generated like
00:33:07
you can see here and on the basis of the
00:33:11
hydrophobicity compounds are eluted out
00:33:13
now after eluting out as it goes into
00:33:17
the mass spectrometer and this Mass
00:33:19
spectrometer is a high mass spectrometer
00:33:22
where it is consist of the quadruple and
00:33:24
the orbit trap so there in this two Mass
00:33:26
analyzers first
00:33:28
the precursor ion is getting selected at
00:33:31
ms1 level and then finally at the after
00:33:34
the fragmentation MS2 level is done so I
00:33:36
just quickly show you some runs that are
00:33:40
going on so you can see that these are
00:33:42
the Spectra at the msms level so the
00:33:46
metabolites now one thing that needs to
00:33:48
be understand that metabolites can be
00:33:50
identified at ms1 level with the
00:33:53
compound molecular weight but same
00:33:54
compound can have different molecular
00:33:56
weight so for this we need to check the
00:34:00
m to match with the known compound that
00:34:03
particular compound will have a unique
00:34:05
fragmentation picture so this is the Run
00:34:07
going on with the chromatogram on the
00:34:09
basis of the gradient the particular
00:34:11
compounds are getting analized and here
00:34:14
if you see the runs that has occurred so
00:34:17
here you can see this is the particular
00:34:19
compound that has eluted out now in the
00:34:20
case of metabolic we also need to add
00:34:23
some kind of internal standards and run
00:34:26
QC pools to check that our instrument is
00:34:29
not there's no technical variability in
00:34:32
between our run so for example this is
00:34:33
one of the standard which has the which
00:34:36
should be eluting out at any specific
00:34:38
time throughout your experiment with a
00:34:41
certain intensity and a good Pi so this
00:34:44
is in brief about the metabolomic
00:34:47
experiment Global met yeah thank
00:34:52
you all right thank you anit so again
00:34:56
some of these experiments what you see U
00:34:59
students who are live U it's not very
00:35:02
difficult to actually perform these
00:35:04
experiments right uh but what actually
00:35:06
becomes difficult to really analyze this
00:35:08
data because it takes a while to make
00:35:10
sense of these data while it actually is
00:35:12
very easy to generate these data but to
00:35:15
really get the maximum output from um
00:35:19
using these type of workflows of
00:35:21
metabolomic or proteomic you generate
00:35:24
huge amount of data and Analysis and
00:35:26
workflow that is is kind of very
00:35:29
challenging U I think know while we are
00:35:33
going to talk more about other
00:35:34
Technologies let me just take a question
00:35:36
from a student about
00:35:38
TMT uh how can we achieve High
00:35:41
sensitivity in detecting low abundance
00:35:44
protein using TMT based
00:35:45
proteomics so uh in prot is always a
00:35:49
challenge that you know
00:35:51
whatever entire complex protein we want
00:35:53
to analyze there are always some
00:35:55
abundant protein which will be having
00:35:57
more more peptides and they will always
00:35:59
mask our uh you know
00:36:01
maspic ionization followed by msms
00:36:04
analysis so you always see high abundant
00:36:07
protein much more as compared to low
00:36:08
abundant protein so it's actually you
00:36:11
know not the TMT or any quantitative
00:36:14
strategy which can enrich lot of low
00:36:16
burent protein but rather your sample
00:36:18
prep is the first place when you can
00:36:20
think about how to enrich your complex
00:36:23
proteum uh many samples like for example
00:36:26
you work on a plant sample it has the
00:36:27
plant leaf as Risco protein very
00:36:29
abundant protein you work on the saliva
00:36:32
protein you work on the plasma protein
00:36:34
all of the these samples like you know
00:36:36
amas or having the
00:36:38
Albin IGG all of these kind of abundant
00:36:41
proteins you need to remove them to then
00:36:43
try to enrich the getting the high
00:36:45
abundant protein and to be out and low
00:36:48
abundant to be seen in the m so sample
00:36:50
prep is the first step which can be
00:36:52
utilized then once you are let's say
00:36:55
done the extraction and done the uh your
00:36:57
proper ways of enrichment for lowan
00:37:00
protein then next thing is can you
00:37:02
fractionate those you know so that you
00:37:04
have from the one complex uh tube which
00:37:07
has all the 10,000 protein can you
00:37:09
separate in 10 tubes right and and with
00:37:11
different type of pH fraction other type
00:37:14
of chromatographic method or isric Point
00:37:16
method and fractionary do then each one
00:37:18
of those when you are going to run in
00:37:20
the MP you are going to see abundant
00:37:22
proteins which are low even abundant
00:37:24
will be enr now TMT will be D
00:37:28
very powerful because TMT is very
00:37:30
accurate for the quantification so if
00:37:33
you have done these kind of pre- sample
00:37:35
processing and the fractionation then
00:37:37
TMT based approaches will be able to in
00:37:39
like you know do more accurate
00:37:41
quantification of the low abundant
00:37:43
protein which you are not able to do
00:37:45
earlier but your sample preparation
00:37:47
strategy and fracturation strategy will
00:37:49
make a very important role for giving
00:37:52
you the broad
00:37:53
coverage so uh we'll be happy to take
00:37:56
more questions so please feel free to
00:37:58
ask uh on the Forum if you have more
00:38:00
query but let's just take you know kind
00:38:03
of to broaden the scope we talking about
00:38:05
multiomic I just talked about proteomic
00:38:07
and metabolomics think about genomics
00:38:09
why that is the most important like you
00:38:11
know the blueprint right so can we even
00:38:13
try to think about uh integrating the
00:38:16
data from genome and prum right and that
00:38:18
sometime especially for disease context
00:38:21
can be very important uh genomic is very
00:38:23
powerful for sure and very robust it can
00:38:26
you know very quickly with the Next
00:38:27
Generation sequencing can look at all
00:38:30
possible you know your Gene sequences
00:38:32
the various variant forms looking at
00:38:34
mutations all type of things and if you
00:38:36
were to look at from the same sample the
00:38:39
protein and the same sample the
00:38:40
metabolite you're building the layer by
00:38:42
layer information now from The genome
00:38:45
level you are able to see okay which are
00:38:46
all possible mutation from that given
00:38:48
cancer or given disease you are able to
00:38:51
see and now using protein you are able
00:38:53
to further enrich and say okay how many
00:38:56
modifications I can see what is the
00:38:58
correlation between these type of
00:39:00
genotypic changes which we see and
00:39:02
finally happening at the phenotypic
00:39:03
level by integrating the functional
00:39:05
information from the proteome and the
00:39:07
metabolome so this is where I think
00:39:09
integrating the piece of information is
00:39:11
very important and you are taking this
00:39:13
course on proteogenomics you have gone
00:39:15
through it and there is you know very
00:39:17
active effort with the proteogenomics
00:39:19
community to look at the especially for
00:39:21
the cancer program which wased by Joe
00:39:23
Biden about cancer moonshot to look at
00:39:26
integr the data of different cancer type
00:39:29
and provide much more powerful
00:39:30
information as a part of a big
00:39:32
initiative known as the ICPC
00:39:34
International cancer protogen Consortium
00:39:36
where India is also not part of the
00:39:38
cancer Moon short program so aish is
00:39:41
going to show you briefly in the lab
00:39:43
about one of the uh genome sequence and
00:39:46
and some brief overview about it then we
00:39:49
will shift to some of the Big Data
00:39:50
analysis part
00:40:00
hello everyone I Ai and I'll be giving
00:40:02
you a demo on the uh NGS instrument that
00:40:05
we have So currently we have iron
00:40:08
torrent S5 instrument that you can see
00:40:10
in your screen I'll give a brief
00:40:12
overview on the technology and then we
00:40:14
can talk about the instrumentation so if
00:40:16
anit can focus on the screen yeah so uh
00:40:20
I'm going to talk about the iron torrent
00:40:21
S5 so a brief overview on how you can
00:40:24
prepare your sample for the genomic so
00:40:26
first step is to prepare the library and
00:40:29
to prepare the library very very
00:40:31
initially you should have a good genomic
00:40:33
DNA or that or any kind of DNA that you
00:40:35
want to do for the sequencing you need
00:40:38
to have that DNA good quantity and you
00:40:40
quantify that DNA followed by the
00:40:44
amplification followed by the
00:40:46
amplification once the amplification of
00:40:48
the target is
00:40:50
done uh you have to digest the amplicons
00:40:53
digest the amplion such as that the
00:40:55
sticky end yes
00:40:58
uh I think probably your screen is
00:40:59
shared you have to stop the sharing of
00:41:01
your screen then we can see you in large
00:41:04
because right now it's very small panel
00:41:05
to really
00:41:08
view
00:41:10
okay from your system probably the Have
00:41:13
you shared the screen something that you
00:41:15
have to
00:41:17
stop I have not shared
00:41:20
anything and this Frame of anit has to
00:41:23
come on the full screen because you are
00:41:25
very
00:41:27
a small manely
00:41:29
scene
00:41:33
okay I should come on the full screen
00:41:35
right of your part then only it will be
00:41:38
visible now I I think it's
00:41:41
coming okay now you have to it's PPT is
00:41:44
coming
00:41:45
actually yeah so first I'll explain the
00:41:48
pp and then you can give a demo of the
00:41:49
instrument all
00:41:51
right yeah so uh as I was saying the
00:41:55
first step in the Next Generation
00:41:57
sequencing is to get a good quantity of
00:41:59
the DNA and once you have a good
00:42:01
quantity of the DNA next you can amplify
00:42:04
the targets there are multiple
00:42:05
techniques by which uh targets can be
00:42:07
Amplified one of the most common method
00:42:09
is the PCR and once the targets are
00:42:12
Amplified then the sticky ends are
00:42:14
created as you can see here in the
00:42:16
workflow the targets are Amplified
00:42:18
you'll get multiple copies of your
00:42:20
target DNA and the uh then the sticky
00:42:23
ends can be created using mild detergent
00:42:25
once those sticky ends are created
00:42:27
then the adapter barcodes which are
00:42:30
essential for the sequencing reaction
00:42:32
will be added to those sticky ends and
00:42:34
now these are your libraries which are
00:42:36
prepared the next step is to amplify
00:42:40
those Target libraries for that in our
00:42:43
uh iron torrent S5 we have an instrument
00:42:45
called iron OneTouch two instrument
00:42:47
which works on the principle of imulsion
00:42:49
PCR so uh this is how the instrument
00:42:53
looks like and you have to fill the
00:42:54
imulsion mixture here on this on this
00:42:58
setup and once it's there the imulsion
00:43:00
reaction will be started and the the the
00:43:03
libraries that you have prepare during
00:43:05
your first step of uh NGS will be
00:43:08
Amplified followed by The Next Step
00:43:11
would be to enrich those template
00:43:13
specific uh Target libraries for that we
00:43:16
have another instrument which is called
00:43:18
OneTouch es this is how the instrument
00:43:20
looks like so you will just enrich your
00:43:23
template specific Target libraries in
00:43:25
this step once the temp template
00:43:28
specific Target libraries are enrich in
00:43:30
this step you will remove those beads
00:43:31
which are not having any kind of uh uh
00:43:34
any kind of Library so those libraries
00:43:37
those beads will be removed in the step
00:43:39
and you will have proper um like proper
00:43:42
template specific library and those
00:43:44
libraries will be loaded onto this chip
00:43:47
like this as as it is shown here the
00:43:50
using one ml pip the libraries template
00:43:53
specific libraries will be loaded on the
00:43:55
chip I'll be giving a Dem of this chip
00:43:58
and everything in in a few minutes and
00:44:01
that's how you can once you have your
00:44:03
template specific librar are loaded you
00:44:05
can just load this chip and start the
00:44:08
sequencing run talking about the
00:44:10
principle how this iron to S5 works so
00:44:13
it works on identifying the uh this
00:44:16
proton ion which is released during the
00:44:18
formation of phosphor diaster bond so we
00:44:20
all have learned molecular biology where
00:44:22
we have learned that during the
00:44:23
formation of phosphor diester bond
00:44:25
whenever one nucleotide is added to the
00:44:28
um to the ongoing stand of DNA there is
00:44:31
formation of phosphodiester bond which
00:44:32
is something like this and there's a
00:44:34
release of proton ion so this instrument
00:44:36
basically detects this proton ion which
00:44:38
is released during this step and it
00:44:40
converts this chemical energy to the
00:44:42
electrical energy and that's how so this
00:44:45
chip has a lot of sensors here where
00:44:48
this proton ion is detected by the by
00:44:51
the sensor which is located below the
00:44:53
below the chip and it converts the
00:44:55
chemical signal to the electrical signal
00:44:58
that's how the instrument gets to know
00:44:59
that the sequencing is happening now if
00:45:02
I'm can show can show the instrument
00:45:04
I'll give a uh I'll give you the tour of
00:45:07
the uh
00:45:23
instrument right so if you can focus on
00:45:26
so this is the iron torrent S5
00:45:28
instrument where the sequencing happens
00:45:31
so before uh to start the sequencing
00:45:33
reaction you have to clean the
00:45:34
instrument so I'm going to perform that
00:45:36
step and I'll show you what all the
00:45:38
agents and what all cartridges goes
00:45:40
inside for your sequencing
00:45:50
so it will take some like just few uh
00:45:54
time so you can see here it will tell
00:45:56
you that these are all of the cartridges
00:45:59
and all of the washing Regents should
00:46:01
you should have before starting the uh
00:46:03
cleaning of the instrument and then
00:46:04
subsequently the sequencing run I'll
00:46:06
show you uh that's how the inside of the
00:46:09
instrument looks like
00:46:53
stopit so I'm not even there I have I
00:46:56
have left the meetings from
00:47:17
here all right so
00:47:20
uh she is figuring out you know way to
00:47:23
expand her screen uh what is important
00:47:26
here to think about uh the different
00:47:29
Technologies available whether for
00:47:31
looking at your complex pum or genome or
00:47:34
metabolome but can we really uh be
00:47:37
creative to look at from the same sample
00:47:39
and try to enrich that data right um and
00:47:42
how to really look at the entire thing
00:47:44
in more totality so now I think you can
00:47:46
see the IR instrument and I should
00:47:48
briefly please explain timing is as now
00:47:58
ai go ahead can
00:47:59
you okay so sorry for the glitch but now
00:48:03
I hope you can see the instrument here
00:48:06
so this is the iron torrent F5 setup
00:48:08
that we have in our phics lab and before
00:48:11
going for any sequencing back we have to
00:48:13
first clean the instrument so I'm going
00:48:14
to show you how to clean the instrument
00:48:16
and what all Regents start both inside
00:48:19
the sequencer so a you can focus on the
00:48:22
instrument now that's how the instrument
00:48:24
looks like and I can show you inside the
00:48:27
instrument where we have all these
00:48:29
cartridges and everything I'll just this
00:48:31
chip clamp where we have this chip which
00:48:34
is very important it some looks
00:48:36
something like this and there are here
00:48:38
are a lot of Bio sensors which I was
00:48:40
talking about and you have to load your
00:48:42
uh sequencing reaction onto here and
00:48:45
once your sequencing reaction is loaded
00:48:48
it will it should be equally spread
00:48:50
along this chip and then we can gently
00:48:52
place this chip um onto this chip
00:48:56
cartridge
00:48:58
like this it will go smoothly and you
00:49:00
have to close this chip clamp other than
00:49:03
chip this is the uh cartridge which is
00:49:07
the nucleotide cartridge here your all
00:49:09
the dntps are here and it will go like
00:49:12
this here like this in the instrument
00:49:14
this is the wash solution so every time
00:49:16
once your nucleotide is go uh like uh
00:49:19
goes to the goes to the instrument this
00:49:21
is a was solution so every time after
00:49:24
the one nucleotide the was solution is
00:49:26
uh will clean the tubes and everything
00:49:28
this is the cleaning solution which is
00:49:30
going to be used during the cleaning and
00:49:33
here we can also U for the um this is
00:49:38
the waste collecting uh thing where all
00:49:41
the uh waste of the sequencing reaction
00:49:44
will go here and you have to clean this
00:49:46
uh cartridge after the sequencing
00:49:48
reaction is complete so that's how you
00:49:50
have to you have to be very uh cautious
00:49:53
while checking all the things are in
00:49:55
place and once is
00:49:57
that sorry to stop you U I think we are
00:50:00
getting late thank you for this
00:50:03
demonstration um yeah so broadly now you
00:50:06
can see a lot of data can be generated
00:50:08
but how to analyze the data so timing is
00:50:10
bit short but we will give you at least
00:50:13
some flavor of uh looking at for the MPE
00:50:16
data and MPE data analysis can be very
00:50:19
complicated and the software which are
00:50:22
currently available they can take you
00:50:24
like you know good Ram of the inst your
00:50:26
MPE and it can take few days time but a
00:50:30
new software which is developed from
00:50:32
academic Lab Dr Alex's lab uh in
00:50:35
nashille uh in US Ms Fragger it is one
00:50:38
of the very highly recommended software
00:50:40
for you guys to try out even in know own
00:50:43
laptop you can run some you know complex
00:50:45
MP data and analyze that so ainash is
00:50:48
going to give you very quickly just the
00:50:51
kind of software
00:50:52
interface uh because we don't have time
00:50:54
to run the data right now uh but again
00:50:56
if you have really interest you can
00:50:58
always ask us we'll be happy to have a
00:50:59
separate session for you for the data
00:51:01
analysis part but ainash please uh
00:51:04
quickly give a demonstration of that
00:51:05
software yeah so sir can you unare your
00:51:10
screen yeah thank
00:51:19
you so uh this is a like sir I already
00:51:22
mentioned that so this is a your first
00:51:24
like after opening the software so this
00:51:26
is the frag pipe 22 version so you can
00:51:29
easily download this software by using
00:51:31
going in the Google and just type the
00:51:32
frag pipe you will easily uh get the all
00:51:35
the tutorials and the downloading links
00:51:37
so after downloading the links you will
00:51:39
get this kind of interface and this so
00:51:41
here we have to install the multiple
00:51:44
like like plugins for this software so
00:51:48
like Ms Fragger iron cont and D rure for
00:51:51
the different kind of analysis and the
00:51:52
Dian and the pythons and the and also
00:51:54
the for the different software for the
00:51:56
analysis so now we will go for the like
00:51:59
workflow like how we do the analysis so
00:52:01
after downloading all the the sessions
00:52:03
you can just click on the download and
00:52:05
you can download all the sessions after
00:52:07
downloading you can go to the workflow
00:52:09
and you can if so for this analysis I'm
00:52:11
just showing for the DDA analysis and so
00:52:13
this is the list of the different kind
00:52:15
of workflow has been given for the all
00:52:17
kind of analysis like DDA Dia tmtn you
00:52:19
can get all kind of data set So
00:52:21
currently I'm using the lfq MBR for the
00:52:23
DD files so just click on the lfq MBR
00:52:25
and you can click click on the load
00:52:27
workflow and it will get already loaded
00:52:29
and will load the all the parameters so
00:52:32
after that you can add the few files so
00:52:34
I already added here so you can just
00:52:36
click on this Ms data type you can just
00:52:38
click on ADD TI file and you can
00:52:40
download you can just select all the
00:52:42
files and you can upload here so U after
00:52:45
that after downloading the file it would
00:52:47
by default take the data type if it is
00:52:50
not taking the data type by the by
00:52:52
default you can just click on that and
00:52:54
you can change the data type and also
00:52:56
you can also add the different
00:52:58
biological group and so in this case we
00:53:01
have taken the three control and the
00:53:02
three tumor sample so likewise I have
00:53:04
annotated so you can just click on the
00:53:07
those samples and you can change by
00:53:09
using the uh conjugative Way by file
00:53:12
name and also you can do by the custom
00:53:14
way so there are multiple way of doing
00:53:16
this things so you can uh do do this
00:53:18
part in the same way you can uh if you
00:53:20
have the bio replicate and Technical
00:53:23
technical replicate you can mention this
00:53:25
part so this is the first part of
00:53:26
loading the database and after that you
00:53:28
can directly go to the uh database part
00:53:31
and you if you uh if you want to like uh
00:53:34
download or you can upload so there are
00:53:36
multiple way to uh upload your database
00:53:38
so some of the few database has been
00:53:40
already given for the human must mular
00:53:42
sensor and different other species and
00:53:45
also it was give the checkbox for the
00:53:47
review sequence add decoy and the add
00:53:49
common contamin so for this study this
00:53:52
is the human cell line so I'm just using
00:53:54
this by default one and I can just I
00:53:56
will just click on the okay so after
00:53:58
that it will be get downloaded and
00:54:00
suppose if you want to add your own
00:54:02
database you can just browse and you can
00:54:04
add your database here and after that
00:54:06
the next part is basically the all the
00:54:08
parameter the major parameter we set for
00:54:10
the analysis so the so you have already
00:54:12
selected the lfq MBR workflow and but if
00:54:15
you want to change a few parameters like
00:54:17
missing cleavage and the enzyme type you
00:54:19
can change this part so this is the like
00:54:22
showing the precursor M tolerance and
00:54:24
the fragment M tolerance so the by
00:54:26
default it give the 20 if you want you
00:54:28
can change according to your criteria so
00:54:31
and also you can also add like what kind
00:54:33
of tiation has been done so there are
00:54:35
different kind of drop down has been
00:54:37
given you can change your enzymes and
00:54:39
thetic cavage part and this section
00:54:41
basically you can add your modification
00:54:43
so by default they add like three
00:54:45
modification Al so two variable
00:54:46
modification and one fixed modification
00:54:49
so one is the oxidation methine and the
00:54:52
anal acation and the one another is a
00:54:55
fixed modification have to be quick yeah
00:54:57
yeah yeah so after that you can just
00:54:59
directly go to the qu Tams one you can
00:55:01
check just click on this part and if you
00:55:03
want to change anything you can change
00:55:05
here or otherwise you can just directly
00:55:07
go to the run and go to the browse and
00:55:09
select the directory where you want to
00:55:10
save the file and you can just start and
00:55:13
it will uh just click on the run so it
00:55:15
will get start running this way and
00:55:17
after the finishing this job it will
00:55:18
show this kind of interface to your job
00:55:20
is done and after running this file you
00:55:22
will get the output like this so this
00:55:25
will be output file and you will get the
00:55:27
combined protein peptide and anotation
00:55:28
file from this data sets now we will
00:55:31
move for the like we are rushing for the
00:55:33
time so we will move to the further
00:55:34
analysis so you can so currently they
00:55:37
have added the downstream analysis
00:55:40
analysis software as well which is the
00:55:42
track pipe anal if you just click on
00:55:43
this it will directly open up so I've
00:55:46
already opened up this software so it
00:55:48
the interface will be look like this so
00:55:50
yeah so this will be interface for the
00:55:52
frag type analyst you can just click on
00:55:54
the analysis you can select your data
00:55:56
type so our data type is lfq so we will
00:55:58
select that and if you can just browse
00:56:00
the what whatever the file was given so
00:56:03
you have to select the combined protein
00:56:06
folder combined protein so it will
00:56:09
already get uploaded after that you have
00:56:11
to give The annotation file so this is
00:56:13
our experimental annotation and after
00:56:16
that so yes is get downloaded here you
00:56:18
can select the max lfq intensity and if
00:56:21
you want to the change like various
00:56:23
other parameters like missing values and
00:56:25
the P value criteria and the full change
00:56:27
criteria and type of normalization so
00:56:29
you can check on that and also there are
00:56:31
different type of imputation method has
00:56:33
been given so Pur type K so those are
00:56:36
the basically commonly used methods and
00:56:39
also the Benjamin HB for the FDA
00:56:42
correction and you just click on the run
00:56:44
so yeah it will just take the few
00:56:46
seconds to complete this part and uh it
00:56:48
will show like all the QC parts so yeah
00:56:51
as you can see so the first part to
00:56:53
check the like uh to so these are the
00:56:56
the basically QC plot to check the data
00:56:58
quality so you can look for the PCA plot
00:57:00
sample correlation sample CVS and the
00:57:02
density plot and the missing value heat
00:57:04
map and you can also so overall you can
00:57:07
check all the type of QC plot from the
00:57:09
this data sets and after that you can
00:57:11
get the volcano plot and uh yeah so uh
00:57:16
so as you can see that in this there it
00:57:19
is showing a four five so we have not
00:57:21
added the missing value of criteria so
00:57:22
it will give you the list so you can
00:57:24
just sort on the basis of the like C
00:57:26
change criteria and uh like what F
00:57:28
change com so it is also giving the
00:57:30
volcano plot and you can download all
00:57:32
the plots and the tables which has been
00:57:34
given you can also add like what kind of
00:57:37
En enrichment you want so and also it if
00:57:40
you want you can look for the pathway
00:57:41
analysis this part so this is a very
00:57:43
convenient pathway like convenient like
00:57:46
for the all the for the downst strring
00:57:48
analysis and we can get the de is from
00:57:50
this and after that we will go for the
00:57:52
path analysis which like anit will talk
00:57:56
about that so that's all all right great
00:57:59
so uh thank you anash I think it was yes
00:58:02
quick demo but what users and the the
00:58:05
student who are joining today they can
00:58:07
see that even looking at uh please stop
00:58:10
sharing the screen yeah yes even uh now
00:58:13
analyzing the complex mpect data is very
00:58:16
much possible and earlier you know we
00:58:18
used to then look at different software
00:58:20
uh to do the plotting of the data
00:58:22
especially the u ms FR like you know
00:58:25
metab list and percs many other
00:58:29
software U but now I think you know you
00:58:31
have advantage that you can use the MS
00:58:33
frager and generate the from the same
00:58:36
software you can look at very simp of
00:58:37
output as well and very fast
00:58:40
Additionally you can always even do more
00:58:42
of the protog genomics data analysis
00:58:43
with the c bioportal and we just inst
00:58:46
you have already learned some of these
00:58:47
tools in the protog genomics course if
00:58:49
you're doing that for students of the
00:58:51
proteomic you can know that there's a
00:58:53
word Beyond this proteomic when you can
00:58:55
now start thinking about integrating
00:58:57
data and make it more multix you know
00:59:00
kind of picture and U given the short
00:59:03
time we will be uh skipping the demo of
00:59:06
other protog genomics tool U but what
00:59:09
you have to really uh understand about
00:59:11
you really want to emphasize that now
00:59:13
there is a field which is know as multix
00:59:15
or integrated omx which is now driving
00:59:18
the systems based approach of
00:59:20
understanding any complex system much
00:59:22
more
00:59:23
comprehensively that's the field which
00:59:25
is going to really make the difference
00:59:26
because in in our actual physiology
00:59:30
whether it's human or other kind of
00:59:31
living system we don't have the gene
00:59:34
protein RNA metabolite they don't work
00:59:36
separately or independently they all
00:59:38
work kind of like you know in in a very
00:59:41
relay race where every partner is very
00:59:44
important right so when we look at only
00:59:46
one side of the picture only genome or
00:59:48
only transcriptome or only proteum we
00:59:50
miss out the complete picture therefore
00:59:53
uh what is now much more with the robust
00:59:55
Technologies and instuments available
00:59:57
but is now much more possible to look at
01:00:00
different layer of information from the
01:00:01
same type of sample and that's really
01:00:03
more important for the complex disease
01:00:05
like you know for various type of human
01:00:06
cancer diabetes heart diseases now
01:00:09
people are trying to look at layer by
01:00:11
layer information and then now what you
01:00:13
realize is that since weting so much
01:00:15
amount of data it can't be only biology
01:00:18
project now we talking about the big
01:00:19
data so therefore people from the
01:00:21
statistical background becomes very
01:00:23
important to look at the significance of
01:00:25
the data people from the core
01:00:27
computational background becomes
01:00:28
important to look at the entire
01:00:31
programming and even from the chemical
01:00:33
background to look at the simulation of
01:00:35
the data and come up with the much more
01:00:36
systems modeling to understand a given
01:00:39
system much more comprehensively so all
01:00:41
this is really you know moving in the
01:00:43
direction of what we see like you know
01:00:44
the new era with the artificial
01:00:46
intelligence machine learning to really
01:00:48
make much more predictions but
01:00:50
predictions will only come if we have
01:00:51
the robust set of the data to build the
01:00:53
models so therefore right now this kind
01:00:55
of field is really uh playing an
01:00:58
important role to generate very
01:01:00
meaningful information and data sets
01:01:02
which can be utilized to make very
01:01:04
important hypothesis which can be
01:01:07
further tested with the specific type of
01:01:09
experiments I hope you got kind of a
01:01:11
gist of various technology which uh you
01:01:13
have been studying uh in bits and pieces
01:01:15
in the course but at least you had the
01:01:17
firstand opportunity today from the lab
01:01:20
to see some of these instruments and the
01:01:22
workflows so with this let me thank all
01:01:24
the t uh uh who have really given you
01:01:28
nice demonstration very limited time
01:01:29
what we had and uh also uh they have
01:01:33
been following upon all of your queries
01:01:35
on the Forum and giving you you know
01:01:38
some meaningful comments on your various
01:01:40
type of queries uh so thanks to entire
01:01:43
team and of course thanks to all of you
01:01:45
the students who are really showing good
01:01:47
interest into the course are asking some
01:01:50
very interesting questions to us and lot
01:01:51
of query which comes to us and hopefully
01:01:54
like some of you might be liking this
01:01:56
field to utilize it for your own
01:01:57
research or to take up the future job
01:02:00
opportunities and career opportunities
01:02:01
many student ask us these questions like
01:02:03
what kind of future opportunities we may
01:02:05
have in this area and of course you know
01:02:07
given the significance of this field uh
01:02:10
you will expect that okay you are going
01:02:12
to get lot of uh job opportunities in
01:02:15
this area because people who can uh
01:02:17
analyze these type of data sets will
01:02:19
been big demand uh with this I think we
01:02:22
will close today's live session which
01:02:24
was our second live session for this
01:02:26
smuk course and we really wish all of
01:02:29
you the best for your writing exams if
01:02:32
you do well in your uh this Muk course
01:02:36
you will have opportunity to come to it
01:02:37
Bombay uh spend some time in our lab or
01:02:40
do some internship or attend some
01:02:41
Workshop so uh the journey does not end
01:02:45
here with this kind of you know the
01:02:46
video courses but rather we want to have
01:02:49
to see some of you more live in the lab
01:02:52
and just you know on the lighter note
01:02:53
there has been several move propers who
01:02:55
have really you know eventually joined
01:02:57
our lab and I have been really you know
01:02:59
very proud to see their career
01:03:01
progression and our interaction with
01:03:02
them that they were virtual students who
01:03:05
you know then perform very well they
01:03:07
came to join our lab as a project
01:03:09
student or interns eventually they got
01:03:11
into the PHD programs or M tech programs
01:03:14
they finished the programs here and now
01:03:15
they're doing very well for their post
01:03:16
do and jobs in Us and other places so it
01:03:19
has been really you know remarkable uh
01:03:21
uh positive journey and experience to
01:03:23
see how the virtual interaction from all
01:03:26
over India and different parts of the
01:03:27
world brings people together and then
01:03:29
you know we are helping them to move
01:03:31
forward in their career trajectory so we
01:03:34
wish all of you the best and we'll be
01:03:35
very much available to take any other
01:03:38
query and your other kind of future uh
01:03:41
whatever you know apprehensions you may
01:03:43
have and questions you may have to give
01:03:45
you much more you know meaningful
01:03:46
insights of your query with this let me
01:03:49
thank the entire team and close today's
01:03:51
session okay bye everybody

Tag

multiomics
proteomics
single-cell analysis
biological research
data integration
genomics
metabolomics
proteogenomics
mass spectrometry
big data