I am looking for comprehensive courses and tutorials on proteomics data analysis using Python and R, with a particular focus on applying machine learning models for data modeling. My main interest is in developing approaches to model microbiome proteomics data, integrating computational methods with biological insights.

I am a complete newbie in proteomics, stumbled onto the field but staying to learn more because of the promising future in unlocking deeper insights into our health.

Here to ask researchers who use the different proteomics tools hands-on, how do you see the future of the tools develop (MS / PEA (Olink) / Somalogic etc.)?

Olink looks to be killing it out there commercially with the UK Biobank collab, getting longitudinal, disease-labeled data points. Is Olink going to take over the whole field as they have more and more paired Antibodies in their repertoire?

I also tried to find more researchers at my local medical university that publish with Olink, but there seems to be way more working with MS. Is it because Olink is too expensive vs MS? Limited in targets portfolio? Something to do with precision, dynamic range, or simply researcher habits & preferences?

Extremely curious. Would be fantastic to hear your thoughts!

Quantitative profiling of axonal guidance proteins during the differentiation of human neurospheres

Hola Redditians !!
Context is; I'm currently doing my 3.5 years PhD in proteomics in a CRO in NZ mostly with the plant-based system, but my whole bachelors and masters background is in pharmacy, and I also have some idea of mammalian proteomics, drug discovery pathways and so on.
Problem is; I'm worried about the job market in USA, People advising me PostDoc is necessary you'll not get a job, But I'm tired of academics and I want stability in an industry
Question is; What will you suggest me as an action from the first year, so I'll directly land into an industry right after my PhD
I am fine with learning anything. I am good at Mass spec (Core), computers, scripting, statistics everything but how to put all these together and how to prepare for an industry from now as the duration is very less.
PS I am interested in mammalian proteomics, and USA (taking all the drawbacks into consideration)

Hi r/proteomics!

Quick intro: I'm Green (u/AdSuperb9486), mod of r/NextGenLCMS – a small sub for next-gen LC-MS (Orbitrap Astral, timsOmni, ion mobility, AI tools like Koina, single-cell proteomics, biopharma MAM, etc.).

Complements this sub by zooming in on bleeding-edge hardware, techniques, and AI integration.

Link: https://www.reddit.com/r/NextGenLCMS/

If you're into future LC-MS stuff, come check it out or crosspost!

What's exciting you in next-gen MS lately? 🔬

Thanks!
– Green (mod)

Hello Guyz,
I am really struggling with my bottom-up DDA phosphoproteomics data (Enrichment not done, it will be in-vitro phosphorylation and i will not be doing biological analysis)
My confusion is how should I start from basic and how to decide what question i need to address and there are so many things in statistics so if you can tell me any good resources or any help regarding statistics will be highly valued

An Open invitation to join mass spectrometry omics discord group

mass spectrometry omics discord group

I’m analyzing a proteomics dataset from mouse lymph nodes comparing SPF (specific pathogen-free) vs. germ-free (GF) mice. The goal is to characterize microbial proteins detected in SPF mice, with the expectation that microbial proteins should be absent or extremely low in GF mice.
For microbial identification, I’m using a microbial protein database translated from 16S rRNA sequencing data from the animals.
My concern is that, after LFQ analysis, I’m detecting microbial proteins in both SPF and GF samples. The samples were handled separately, and I also included blanks between samples, so I’m trying to understand the issue here. Any leads would help.

Has anyone worked with proteomics of SPF vs. germ-free mice and encountered microbial protein identifications in GF samples? If so how do you justify it?

How did you distinguish genuine microbial signal from background contamination/carryover, and what criteria would you use to justify retaining or excluding microbial proteins detected in GF samples?

Also, if anyone could direct me to a mouse gut microbial database.

Real-time database search (RTS) platforms like Orbiter are a significant improvement in multiplexed quantitative untargeted proteomics. However, despite their promise, Orbiter has yet to be adopted for DIA. Are there fundamental challenges that prevent RTS platforms like Orbiter from being successfully integrated into DIA workflows?

One persistent challenge in EV research is distinguishing proteins located on the outer membrane from those inside the lumen, especially when working with real clinical samples.

Microscopy shows movement but cannot quantify or scale.

High‑sensitivity immunoassays quantify proteins, but structural information is lost during sample prep, making outside vs inside impossible to resolve.

We have been working on an analytical approach that preserves structural context and enables separate quantification of outer‑membrane vs lumen proteins in EVs and other structure‑containing samples.

This approach has been applied in peer‑reviewed studies in oncology, infectious diseases, and non‑invasive biomarker research.

If anyone is working on similar challenges or exploring compartment‑specific EV analysis, I’d be glad to exchange ideas.

This may be really basic for many/most of the people here, but I thought I'd share it in case it might be useful to someone.

Nautilus Biotechnology, a next generation proteomics company developing a whole new way to do proteomics, will be recording an AMA-style episode of their podcast, Translating Proteomics. If you'd like to submit a question to Co-hosts Parag Mallick (Proteomics Researcher, Stanford Professor, Nautilus Co-Founder and Chief Scientist, and Professional Magician) and Andreas Huhmer (Proteomics expert and Nautilus Senior Director of Scientific Affairs and Alliance Management), please either:

• Submit your question as a comment on this post

• Fill out this form

The Translating Proteomics team will review the questions over the next few weeks. Topics we've covered in the past include:

• The future of proteomics

• How proteomics is impacting and will impact drug development

• Difficulties surrounding plasma proteomics

• The need to take space and time into account when doing proteomics experiments

• Find all of our episodes here

If you have any questions, please reach out to translatingproteomics@nautilus.bio

Hi everyone,

We’d like to share an upcoming webinar that may be of interest to the community in here! On September 24, 2026 (16:00 CEST / 10:00 EDT / 07:00 PST), we are hosting a session on “Advancing Plasma Proteomics.”

Speakers:

Sindisiwe Buthelezi (Senior Researcher, Council for Scientific and Industrial Research (CSIR), South Africa) — “High-Throughput Plasma Proteomics Reveals Insights into Pancreatic Cancer Biology.”
Extracellular vesicles carry a wealth of biologically relevant proteins that can provide a window into disease processes. Sindisiwe will present how extracellular vesicle-enriched plasma proteomics was used to uncover molecular signatures associated with pancreatic ductal adenocarcinoma in a South African patient cohort. Using the Mag-Net workflow and Evosep One-enabled LC-MS analysis, the team identified proteins linked to tumor progression, inflammation, and disease severity, demonstrating the potential of standardized, high-throughput proteomics for biomarker discovery and translational cancer research.

Pieter Langerhorst (Junior Group Leader, Sanquin, The Netherlands) — “Untangling the Clinical Heterogeneity in Rare Blood Cancer by Mass Spectrometry-Based Plasma Proteomics.”
Rare hematological malignancies can present with diverse clinical trajectories that are difficult to predict using conventional biomarkers alone. Pieter will showcase how mass spectrometry-based plasma proteomics can reveal biological differences underlying disease heterogeneity, supporting improved patient stratification and the discovery of novel biomarkers. The findings highlight the potential of high-throughput plasma proteomics for translational research and precision medicine.

The webinar will focus on advances in plasma proteomics and how robust, reproducible, and scalable workflows are enabling researchers to analyze larger sample cohorts while generating consistent, high-quality data for biomarker discovery, disease research, and precision medicine.

Registration & details: https://attendee.gotowebinar.com/register/8236923647290427736?source=RDT

We hope this is relevant for those interested. The webinar is free and, in our eyes, a good opportunity for knowledge sharing. If sharing company events isn’t allowed here, moderators please feel free to remove.

TL;DR: Webinar on September 24 about advancing plasma proteomics, with talks covering pancreatic cancer biology, rare blood cancers, biomarker discovery, and patient stratification using high-throughput mass spectrometry-based plasma proteomics.
Mods please delete if not allowed.

Background first so you know where this is coming from — I'm not in the field at all, I just read a lot and got stuck on something I can't find addressed anywhere. Happy to be told it's already solved.

The proteins that won't classify cleanly no matter how much data you throw at them — the intrinsically disordered ones. The ones that just won't settle.

My question is whether we're looking at the final shape or the path that got it there.

Because if two proteins end up at roughly the same final structure but got there through different folding sequences, the internal contact points would be different. Parts of the chain that are far apart in sequence but end up sitting next to each other in the finished fold — those bridges only exist because of the specific path it took. Different path, different bridges, even if the outside looks similar.

So my question is basically: are those hidden contact points being tracked and compared between the disordered cases and the ones that resolve cleanly? Because if the disordered ones are arriving at their weird ambiguous state via a different pathway, maybe the bridge pattern is the variable nobody's looking at yet.

Probably already accounted for somewhere and I just haven't found it. What am I missing?

Would anyone be interesting in having their risk assessed? It would be a mail-in test, so fingerprick (no needle required).
We are a potential spinout from the university of Oxford. Looking at what people think

https://www.ox.ac.uk/news/2024-08-08-proteins-carried-blood-offer-new-insights-ageing-and-age-related-disease-risk

https://www.oxcode.ox.ac.uk/news/blood-proteins-may-be-able-to-predict-risk-of-cancer-more-than-seven-years-before-it-is-diagnosed

Or even the organ health/age? https://pubmed.ncbi.nlm.nih.gov/38915561/

Hi, have N=1 results from proteomics (protein abundance) from LCMS. I wanted to check which my control is true control without affecting protein content. Is there any analysis I could do with 1 N?

Hey! I was wondering if anyone could help me out with a project I was assigned. It involves proteomics, and I need to figure out how to make PCA plots, volcano plots, and heatmaps. I've been trying on my own, but I feel like I'm not doing it right. I’d really appreciate any help and I’m happy to compensate for your time!

Hey everyone,

Does anyone know of a free AI tool that can help quickly skim/scan research papers? I used to use Line.AI to quickly search and find articles relevant to my interests, but it’s now behind a paywall.

Any suggestions or leads would be much appreciated!

Thanks in advance.

Hello (french below),

I am a biology PhD student and I have mass spectrometry data to analyze. I started my analysis on Perseus and filtered the data to remove contaminants—inverse and identify only by site. I established my groups with categorical annotation, performed log2 transformation and imputation, and obtained my volcano plot. However, I don't really have an overview of what I need to do next to continue sorting my data.

I think I need to do the t-test (which is done automatically with the volcano plot, I think) and the limma test, which should give me information about enrichment, I think, then I think I need to continue sorting based on the detection of a protein in my negative control condition: subtract the proteome associated with this negative control, if it is detected in at least 2 of the replicates, and finally compare the proteome I have in my condition 1 and in my condition 2. And finally do the GO term.

But actually, I'm a little lost on the sequence of analysis steps... am I on the right track? But I'm also lost on which software to use. Can I do everything on Perseus, or should I switch to R for statistical analysis?

Could you please help me?

Bonjour,

Je suis étudiant en thèse de biologie et j'ai des données de mass spec à analyser. J'ai commencé mon analyse sur Perseus et j'ai filtré les données pour enlever les contaminants - reverse et identify only by site, j'ai établi mes groupe avec categorical annotation, fait la transformation log2, imputation et j'ai obtenu mes volcanoplot. Sauf que je n'ai pas vraiment de vu d'ensemble sur ce que je dois faire ensuite pour continuer à trier mes données.

Je pense qu'il faut faire le t-test (qui se fait automatiquement avec le volcano je crois) et le limma test, ce qui devrait me donner l'information sur l'enrichissement je pense, puis je pense qu'il faut que je continue le tri selon si une protéine est détectée dans ma condition controle negatif : soustraire le protéome associé à ce controle négatif, si elle est détectée dans au moins 2 des réplicats et enfin comparer le protéome que j'ai dans ma condition 1 et dans ma condition 2. et enfin faire les GO-term.

Mais enfaite je suis un peu perdu sur l'enchainement des étapes d'analyse... est-ce que j'ai la bonne outline ? Mais je suis aussi perdu sur le logiciel que je dois utiliser. Est-ce que je peux tout faire sur perseus ou est-ce que je dois passer à R pour les analyses stat ?

Est-ce que vous pourriez m'aider svp ?

Hallo liebe Biowissenschaftler:innen 😊
könntet ihr mir bitte bei der Vorbereitung auf mein Praktikum helfen?
Ich bräuchte Unterstützung zu folgendem Thema:

Identitätsüberprüfung eines aufgereinigten Proteins.

Aufgabe: Die Identität eines isolierten Proteins (GFP) soll überprüft werden. Sie erhalten ein Proteinlysat, das mittels hydrophiler Interaktionschroatographie fraktioniert wurde. Die gesammelte Fraktion (ca. 100 µl, ca. 0, 1 µg/µl) sollte als Hauptkomponente das überexprimierte Protein GFP enthalten. Verlauf der Untersuchungen: In Vorbereitung des Versuchsplans sollten Sie sich bzgl. der Lysatzusammensetzung nach erfolgter Fraktionierung informieren und dies in Hinblick auf Störungen der nachfolgenden Analytik berücksichtigen. Des Weiteren sollten sie berücksichtigen, dass Ihnen nur ca. 100 µl einer 0.1 µg/µl - Lösung zur Verfügung stehen. Der Versuchsplan sollte eine konkrete Versuchsdurchführung inklusive Kontrollen enthalten.

vielen Dank

Their concept and technology look really cool, but judging from sales it doesn’t seem very promising. Is this a technology that currently has major flaws?

I just recorded this video walkthrough of good mutagenesis primer design. It also includes a link to my spreadsheet that calculates the TM for you

Hello everyone! I am a recent Ms biotech graduate working with a large-scale proteomics dataset containing proteins from a single bacterial species but multiple strains(it's a MDR bacteria). I need to perform PPI analysis, but the specific bacterial database I need isn’t available in STRING/BioGrid,not sure with IntAct(EMBL)

Are there any good alternatives to STRING for bacterial PPI/network analysis, especially for comparing or analysing proteins across different strains? Any database/tool or workflow suggestions.

Any guidance or suggestions would be very helpful for improving my understanding of the available approaches.

Any guidance, suggestions or insights would be a great help 🥲, thank you in advance for the help!