Member Info for AngelMarbles

Down from 3.30 price was when I came in and I’ve lost all so it is the worst share I’ve held . So disappointing .

Would u say now is a good time to top up ? It has to be our time soon !

Patiently waiting for my rewards :) good luck all

What a dismal share Performance ; am so well under here and nothing seems like a progression in so much time
.Terrible director leadership to steer this . Let hope this changes

This must be the bottom and a great place to pick up more share

Hello .. yes information is coming out to show progress .. I am Holding tight

Yes , about time, we deserve some good news

Hello , I have had a moment of rest from posts.. lse then logged me out and I couldn’t post .. glad to see I’m missed :)
This is one definately for the long term .

All looks good .. a bargain still in the making and set to rise

New fields of energy oil n gas needed for the next 10 years to transition into the new energy technologies ..

Hehe. Absolutely agreed .. I’ve stayed away because of it .. the negative energies .. but I have faith in Sml and this one even though down in the dumps will rise majestically and catch up

Great post .. fir m/a I’d look at proteomics and diagnosis , the key to detect all diseases and ways to give personalised medicine .. check out proteome sciences #prm

It’s okay but I expected a better one . Good to see staff and machines increasing to meet level of demand . It would be nice to see more projects they are involved in . Again PR is imperative to promote your services . Good to see you are now going to the US for work and developing relationships where they are far ahead in innovative ideas .. with all these AI companies who have put the whole proteome library into computing can you see where you can be of value?
What areas have you identified that can be of value ? I’d like to be blown away ?

While use of a carrier sample allows for identification of low-abundance proteins in the target sample, the larger the amount of carrier sample that is used, the less time the mass spec is able to use for measuring ions from the target sample. And the fewer ions it measures from the target sample, the less accurate and reproducible the experiment's protein quantitation.

In the Nature Methods paper, the researchers arrived at a threshold of 20x carrier-to-target sample as ideal for ensuring high-quality data in most experiments, though they noted that users could potentially push that to 100x. Winter suggested that in some TMTcalibrator experiments some proteins might be more than 100-fold more abundant in the carrier tissue sample than in the plasma sample, which could impact quantitation.

Pike noted that "the amount of the [carrier] sample used for TMTcalibrator does indeed need careful consideration."

He said, though, that while he "would not be surprised if there are examples where there is 100-fold or greater difference for some peptides… in general this looks to be more in the range of two to 20-fold."

This limits the method's throughput, at a time when the proteomics field is perhaps more than ever prioritizing the ability to run large cohorts of samples.
Perhaps with this in mind, the company has recently begun working to streamline the approach, exploring a centrifuge-based depletion method that would provide significantly higher throughput than the current FPLC-based approach. It has also begun tackling larger sample cohorts, with several projects in the pipeline that will each analyze around 400 to 500 samples, Pike said. He said that those projects will take a few months, which is substantially longer than many newer plasma proteomic assays, but said that he believes it is in many cases worth the trade-off for the greater depth of coverage.
"I get why people want throughput," he said. "The statistics need to be powered by high numbers of samples, but I think with [TMTcalibrator] you're not taking that long to do it, and you're getting eight to 10 times the amount of coverage."
Beyond issues of assay complexity and throughput, there are also factors inherent in isobaric tagging experiments and TMTcalibrator-type experiments that can impact assay accuracy, noted Sebastian Virreira Winter, chief technology officer of Planegg, Germany-based plasma proteomics firm OmicEra. Winter developed the EASI-tag isobaric tagging reagent while a post-doc in the lab of Max Planck Institute of Biotechnology research Matthias Mann.
One of the major challenges for isobaric tagging is precursor interference, which can significantly impact the accuracy and precision of quantitative information generated in these experiments. In an isobaric tagging experiment, the mass spec isolates the target ion and fragments it, generating the isobaric tag reporter ions that correspond to the proportions of the different peptides in the tagged samples.
However, the isolation windows used to target a given precursor ion are typically wide enough that other non-target ions can slip through. Because these ions have also been labeled with isobaric tags, they also contribute to the reporter signal for the target peptide, decreasing the accuracy with which the actual target is measured.
One technique researchers have used to address this problem is to do quantitation at the MS3 level, which adds another level of ion isolation and fragmentation. Pike said Proteome Sciences does not typically use MS3 quantitation for its TMTcalibrator work, but that it has that capability. He added that the company is currently looking into using ion mobility to provide an additional level of separation that can help reduce the precursor interference issue.
Winter also highlighted the need to use an appropriate ratio of carrier sample to target sample. This has been an issue in single-cell proteomics, with researchers publishing a study last year in Nature Methods looking at what ratios would provide the best quantitative performance.
While use of a carrier sample allows for identification o

In 2019, the company and collaborators at the Centre for Neuroscience and Trauma at London's Blizard Institute published a study in Nature Scientific Reports that used TMTcalibrator to analyze plasma samples from 29 subjects with ALS. Using brain tissue from deceased ALS patients as the carrier sample, the researchers quantified 1,126 proteins in all samples.
Andrea Malaspina, formerly professor of neurology at the Blizard Institute and now a professor at University College London and senior author on the Nature Scientific Reports study, said the approach provided a much deeper look into the plasma samples than he was accustomed to from past work.
"My experience with large-scale analysis of proteins in a matrix like plasma was that you just pull out the most abundant proteins that make up 95 percent of proteins in blood," he said. "With the TMTcalibrator we were able to pull out signal from the low-abundance proteins derived from brain, including neurofilament isoforms and other proteins that are of interest in the ALS space."
Malaspina added that the quantitative nature of the data provided insight into "differential regulation between samples."
As an example of the approach's power, Pike cited work on idiopathic pulmonary fibrosis the company did in collaboration with researchers at the University of California, San Francisco and Pliant Therapeutics and presented at the American Thoracic Society annual meeting in 2018. Using the approach, they quantified roughly 5,600 proteins in all 30 plasma samples they analyzed.
That depth of coverage compares favorably with most any other approach to plasma proteome analysis. In March, for instance, Swiss proteomics firm Biognosys said that it expects by the end of the year to begin offering a new discovery proteomics workflow that will allow it to quantify around 2,700 proteins in a typical plasma study, and that it expects to manage around 3,300 proteins in large-scale discovery studies by the end of 2022.
Also in March, Qing Wang, CEO of Baltimore-based multi-omics company Complete Omics, said that the firm plans by the end of the year to launch a targeted mass spec assay that will quantify 4,550 proteins in plasma.
In an analysis last year of 141 plasma samples, proteomics firm Seer showed its Proteograph system could identify roughly 2,000 proteins.
SomaLogic's aptamer-based SomaScan platform can measure 7,000 proteins in plasma, while Olink is able to measure 1,500 proteins in plasma and expects to expand that figure to around 3,000 in coming years.
The downside of TMTcalibrator compared to many of these other workflows is its reliance on extensive depletion and fractionation. The depletion approach used can process only around 15 samples a day, while the depth of coverage reached in the pulmonary fibrosis study required splitting samples into 30 different fractions, each run on a two-hour liquid chromatography gradient. This limits the method's throughput, at a time when the proteomics fi

Via genome web
NEW YORK – As advances in technologies and workflows have sparked renewed interest in plasma proteomics, Proteome Sciences is positioning its TMTcalibrator product as a tool for plasma analysis.

The approach uses more extensive depletion and fractionation than many of the high-throughput plasma proteomic workflows that have gained prominence in recent years, but, said Proteome Sciences CSO Ian Pike, it offers great depth of coverage while retaining relatively good throughput due to the multiplexing capabilities of the TMT reagents.

Proteome Sciences has offered the TMTcalibrator workflow for more than a decade and for plasma analysis for several years but has recently begun exploring its feasibility for large-scale plasma experiments.

TMTcalibrator works by combining isobarically labeled peptides from both a sample of interest — plasma, for instance — and another sample (sometimes called a "carrier" sample) in which the proteins of interest are present at a relatively high concentration — a tissue digest, for example.

By combining the two types of samples, researchers can ensure that even analytes present only at low abundance in the samples of interest are present in relatively high abundance in the overall sample, making them more likely to be fragmented and detected by the mass spec.

The approach has seen uptake perhaps most prominently in the world of single-cell proteomics where methods like the Single Cell Proteomics by Mass Spectrometry (SCOPE-MS) workflow rely on the basic strategy underpinning the TMTcalibrator workflow, using a carrier sample consisting of many cells to enable analysis of proteins in single cells of interest.

Plasma proteomics is another area where researchers have struggled with challenges of sensitivity, and Proteome Sciences believes the TMTcalibrator approach can be similarly effective in this area.

Pike noted that the method allows researchers to reach deeper into plasma samples and prioritize proteins from the particular systems or disease states being studied. For instance, researchers looking for markers of liver disease in plasma could use a liver tissue sample as their carrier proteome, making it more likely that the mass spec would detect liver proteins present in plasma at low abundance.

"We can get really deep down into the relevant part of the plasma proteome and try to bias towards finding things that are part of the disease process so that we can align the biomarkers [discovered] more directly with the biology," he said.

Proteome Sciences has been offering a version of TMTcalibrator combined with extensive plasma depletion and fractionation for around four years, Pike said, working with collaborators and customers on projects including investigations of markers for diagnosing and monitoring amyotrophic lateral sclerosis and for diagnosing and selecting treatment for idiopathic pulmonary fibrosis.

In 2019, the company and collaborators at the Centre for Neuros

Hi guys .. I’ve always been here lol . Soon hopefully ours should be bolstered . We already have our golden tickets ?? .. keep the faith .. bargain atm

Yes they need to be innovative and jump on to it to progress or they stay stagnant or they merge and grow with other technologies and innovations AI , tech and computing , chemistry , vetinary , biotechs ..

Apart from those hycol they offer proteomics services for biotechs and medical companies analysing their projects

I really think we need to merge with AI platform technologies for personalised medicine , scans , diagnosis and individualised treatment plans

How about make a proteomics platform for personalised medicine #PRM like nautilus wants to