Integrated DIA Proteomics and Thermal Proteome Profiling Service

Q: When should I choose integrated analysis instead of thermal stability shift profiling alone?

Choose integrated analysis when abundance context is needed. A protein that shifts in thermal stability but also changes in abundance may require a different interpretation from a protein that shifts in stability without changing abundance.

Q: How are abundance-driven changes separated from thermal stability shifts?

DIA abundance changes are compared with thermal stability shift results protein by protein. The integrated analysis can classify proteins as abundance-only responders, stability-shift candidates, concordant responders, pathway-associated proteins, or possible off-target candidates.

Q: What follow-up validation is recommended after candidate ranking?

Follow-up depends on the project goal and may include single-target thermal stabilization validation, targeted MS confirmation, orthogonal binding analysis, affinity enrichment when feasible, activity-based profiling for compatible chemistries, or cell-based functional validation.

Integrated DIA Proteomics and Thermal Proteome Profiling Service combines DIA-based protein abundance profiling with proteome-wide thermal stability shift analysis to help drug discovery teams interpret compound response, target engagement-associated signals, target deconvolution candidates, off-target clues, and mechanism-of-action evidence from matched treatment and control samples.

When a compound changes a cell phenotype, abundance data alone may not tell the full story. A protein can stay at a similar level but change its stability, interaction state, or response to treatment.

Our integrated service helps you compare these two data layers and turn them into clearer evidence for target engagement-associated signals, target deconvolution candidates, off-target clues, and mechanism-of-action interpretation.

Key Advantages:

Compare abundance and thermal stability changes
Support target engagement interpretation
Prioritize candidate targets and off-targets
Connect protein shifts with MoA pathways
Receive QC-backed, review-ready data outputs

Discuss Project Fit

Integrated DIA proteomics and thermal stability shift profiling workflow for target engagement and MoA analysis.

Integrated Evidence Right Fit Workflow Deliverables Demo Results Sample Bioinformatics Comparison Case Study Publication FAQ References Discuss Compliance

Integrating Protein Abundance and Thermal Stability to Interpret Compound Response

A standard DIA proteomics study shows which proteins increase or decrease after treatment. That is valuable, especially for pathway analysis and drug-response profiling. But abundance changes do not always show whether a compound has changed a protein’s state, binding environment, or interaction network.

Thermal stability shift profiling adds that second view. It measures how proteins behave under controlled heat conditions after treatment. Some proteins show altered solubility patterns, which may reflect direct binding, complex remodeling, post-translational changes, pathway effects, or other protein-state changes.

We bring these two views together so your team is not left comparing separate spreadsheets. The goal is to help you read abundance response and stability-shift response in one framework.

For projects focused on the stability-shift layer alone, you may also review our Proteome-wide Thermal Stability Profiling service.

What DIA Proteomics Adds

DIA proteomics gives you the abundance-response layer. It helps show which proteins change after compound treatment, whether changes are dose- or condition-dependent, which pathways show coordinated abundance shifts, and whether observed effects are broad, targeted, or stress-related.

For drug mechanism studies, this layer helps describe how the cell responds to a compound. It can highlight changes in signaling proteins, metabolic enzymes, stress-response proteins, protein homeostasis factors, or pathway components connected to the observed phenotype.

What Thermal Stability Shift Profiling Adds

Thermal stability shift profiling gives you the protein-state layer. It helps identify proteins whose heat-solubility behavior changes after treatment.

These changes may point to target engagement-associated candidates, protein complex remodeling, altered protein interaction states, potential off-target candidates, pathway-linked thermal responders, and proteins that should move into follow-up validation.

For a related stability-focused approach, see Thermal Shift Proteomics.

Why Integrated Interpretation Matters

The strongest value comes from reading both layers together. A protein with a strong thermal stability shift but little abundance change may be a high-priority engagement-associated candidate. A protein with abundance change but no stability shift may be part of the downstream cellular response.

Our team organizes these patterns into practical interpretation categories so your scientists can decide which proteins, pathways, or compounds deserve the next experiment.

When Integrated DIA and Thermal Stability Shift Profiling Is the Right Fit

This service is a strong fit when your project needs more than a standard abundance profile. It is also useful when a stability-shift study needs abundance context before the results can be interpreted with confidence.

FIT 1

Target Engagement Support for Known Targets

If you already have a proposed target, we can help evaluate whether that target shows a treatment-associated thermal stability shift and whether the wider proteome response fits the expected biology.

Does the expected target show a stability-shift signal?
Does the compound affect related pathway proteins?
Are there additional proteins that may explain activity or liability?
Is the abundance response consistent with the expected mechanism?

We do not treat one signal as final proof. We help rank and interpret the evidence so your team can plan focused follow-up validation.

FIT 2

Target Deconvolution After Phenotypic Screening

For phenotypic hits, the biology may be visible before the target is known. Integrated DIA and thermal stability shift profiling can help narrow the search by combining proteins that change abundance after treatment with proteins that change thermal stability after treatment.

This helps separate broad downstream effects from proteins that may sit closer to the compound’s mechanism.

FIT 3

Compound Series and Dose-Response Comparison

Medicinal chemistry teams often need to compare compounds that look similar in one assay but behave differently in cells. We can design the integrated analysis around analogs, dose levels, treatment conditions, or reference compounds.

Abundance-response patterns across compounds
Thermal stability shift patterns across compounds
Candidate protein ranking by compound
Off-target clue comparison
Pathway response differences

For projects centered on dose-dependent protein stability behavior, our Dose-Response Thermal Stability Analysis service may also be relevant.

FIT 4

Mechanism-of-Action and Off-Target Clue Generation

A compound can affect cells through both direct and indirect mechanisms. DIA proteomics shows broader response patterns, while thermal stability shift profiling adds protein-state information.

MoA hypothesis generation
Pathway-level interpretation
Protein class annotation
Off-target clue prioritization
Follow-up validation planning

The result is not just a protein list. It is an organized evidence package that your biology, chemistry, and proteomics teams can review together.

For cell-context projects, this service can also connect naturally with Cell-based MS Drug Screening.

Our Integrated Workflow: From Compound Treatment to Interpretable Protein Candidates

We manage the project as a connected workflow from study design to final data delivery. Once samples enter the project, the technical steps and service steps are linked: sample review, preparation, LC-MS/MS acquisition, QC, data processing, integrated analysis, and interpretation-ready reporting.

Study Design and Feasibility Review

Before sample submission, we review compound identity, treatment goal, known or unknown target status, sample system, dose and treatment time, controls, biological replicate plan, phenotype readout, and the main project question.

This step matters because integrated analysis only works well when the treatment design, controls, and sample groups match the biological question.

DIA Proteomics Branch

The DIA branch measures protein abundance across your treatment and control groups. Typical steps include protein extraction, digestion, peptide preparation, LC-MS/MS acquisition, protein identification and quantification, differential abundance analysis, and QC review of sample consistency and replicate behavior.

Thermal Stability Shift Profiling Branch

The thermal stability branch measures protein solubility behavior after controlled heat challenge. Typical steps include matched sample preparation, controlled heat exposure, soluble protein fraction collection, protein digestion, LC-MS/MS analysis, stability-shift detection, filtering, and candidate ranking.

Integrated Data Analysis

After both data layers are generated, we compare abundance change, thermal stability shift, replicate consistency, dose or condition trend, pathway context, protein class, known target or off-target relevance, and feasibility of follow-up validation.

QC Checkpoints Across Both Data Layers

We review sample metadata, treatment consistency, sample integrity, protein recovery, digestion consistency, DIA acquisition quality, replicate correlation, missing value patterns, thermal stability shift consistency, candidate ranking, and biological plausibility.

Vertical workflow showing DIA proteomics and thermal stability shift profiling QC checkpoints.

What We Deliver: Data Package, QC Summary, and Interpretation Outputs

We focus on data your team can inspect, reuse, and discuss. A useful project should provide more than a short report; it should give you traceable result tables, QC summaries, and figures that support internal decision-making.

Raw LC-MS/MS data
Processed DIA protein quantification matrix
Differential abundance table
Thermal stability shift candidate table
Integrated abundance-stability classification table
Candidate target and off-target ranking table
Replicate-level QC summary
Visual result files for major plots
Pathway or protein class summary, when included
Method notes and parameter summary
Follow-up validation suggestions

Candidate Ranking Logic

Candidate ranking is based on multiple evidence layers, not one isolated score. We may consider strength of thermal stability shift, DIA abundance change context, treatment or dose consistency, replicate reproducibility, detectability across sample groups, pathway relevance, known target class or biology, relationship to expected phenotype, and suitability for follow-up validation.

This helps separate a protein that is mainly changing in abundance from one that may represent a stronger protein-state or target engagement-associated signal.

Interpretation Categories

To make the results easier to use, we organize findings into interpretation categories.

Category	What It May Suggest	How It Helps Your Team
Stability-shift candidate with little abundance change	Possible protein-state or engagement-associated response	Prioritize for targeted validation
Abundance-only responder	Downstream pathway or stress response	Support MoA interpretation
Concordant abundance and stability shift	Stronger compound-response signal	Review as a higher-priority biological clue
Pathway-associated thermal responder	Protein-state change within an affected pathway	Link stability data to MoA
Potential off-target candidate	Unexpected stability-shift protein	Evaluate selectivity risk

Demo Results: Reading Integrated DIA and Thermal Stability Shift Data

The following demo result types show how we usually make integrated data easier to read. They are representative result formats, not fixed claims about every project.

DIA and thermal stability integrated quadrant plot showing abundance and stability-shift classification.

DIA–Thermal Stability Integrated Quadrant Plot

A quadrant plot compares DIA abundance change on one axis and thermal stability shift on the other. This view helps separate abundance-only responders, stability-shift candidates, proteins changing in both layers, and proteins with little treatment-associated change.

Candidate ranking plot with thermal stability shift strength and DIA abundance overlay.

Candidate Ranking with Abundance Overlay

A ranked candidate view combines protein name, thermal stability shift strength, DIA abundance status, replicate consistency, pathway annotation, and priority category so biology, proteomics, and chemistry teams can review the same evidence together.

Pathway-Level MoA Interpretation Map

A pathway map connects candidate proteins with differentially abundant proteins and helps identify whether stability-shift candidates cluster in relevant pathways, support the same biological direction, or suggest nodes for follow-up validation.

Sample Requirements and Experimental Design Considerations

Sample planning is critical for this service. The most useful projects have matched treatment and control groups, consistent sample processing, complete metadata, and enough material for both data layers.

The table below gives practical planning guidance. Final input requirements should be confirmed during feasibility review because sample type, cell model, treatment design, and project scope can affect the amount needed.

Project Type	Recommended Sample	Input Guidance	Controls	Storage & Shipping	Key Metadata
Target-known compound engagement	Treated and vehicle-control cell pellets	For DIA proteomics, cell samples commonly use about 5×10⁶ cells for label-free input and about 1×10⁷ cells for DIA-size guidance	Vehicle control; known target information	Frozen storage; dry ice shipment where applicable	Compound, dose, time, cell model, target
Target-unknown phenotypic hit	Matched treated and control cell pellets or lysates	Confirm cell number or protein amount before submission; trace DIA cell samples may use about 200-5000 cells when the workflow is suitable	Vehicle control; phenotype-positive condition	Keep frozen; avoid repeated freeze-thaw cycles	Compound identity, phenotype, dose, exposure time
Compound series comparison	Matched samples across analogs, doses, or treatment conditions	Keep sample amount and processing consistent across all compounds	Vehicle control; optional reference compound	Consistent storage and shipment across groups	Compound series, dose levels, treatment time
Tissue-derived project	Tissue or tissue-derived protein sample	Trace DIA tissue guidance may use about 30-50 mg for general tissues; feasibility review is required for integrated profiling	Matched treatment/control groups	Low-temperature storage and dry ice shipment	Tissue type, collection method, treatment status
Lysate-based study	Matched treated or spiked lysates	Protein amount depends on project design and LC-MS/MS workflow	Matched lysate control	Keep cold during preparation; frozen storage	Lysis method, buffer, compound condition

Recommended Input Types

Common input types include treated cell pellets, matched vehicle-control cell pellets, cell lysates, tissue-derived protein samples, pilot feasibility samples, and compound series sample sets.

Design Variables to Confirm Before Submission

Before you send samples, we recommend confirming compound concentration, treatment time, number of conditions, sample matrix, biological replicate plan, vehicle control, known target or unknown target status, expected phenotype, storage condition, shipping condition, and whether follow-up validation is planned.

Sample Handling Notes

For protein-level studies, sample quality strongly affects result quality. Use consistent collection and processing conditions across groups. Record dose, treatment time, cell status, and sample metadata. Avoid repeated freeze-thaw cycles. Keep samples cold during preparation when appropriate. Store samples at low temperature before shipment. Ship frozen samples with sufficient dry ice when required. Tell us if samples contain special buffers, detergents, inhibitors, toxic compounds, polymers, or other unusual components.

Bioinformatics Analysis for Integrated DIA and Thermal Stability Shift Profiling

Bioinformatics is central to this service. Without integration, DIA and thermal stability shift profiling can become two separate datasets. Our analysis connects them into a practical interpretation framework.

Analysis Level	Included or Optional Outputs
Minimum Analysis	DIA protein quantification matrix; differential abundance analysis; thermal stability shift candidate table; integrated abundance-stability classification; candidate target/off-target ranking table; replicate-level QC summary; missing value review; visualization-ready result files; method and parameter summary
Optional Add-ons	Pathway enrichment analysis; protein class annotation; compound series comparison; dose-response thermal stability trend analysis; protein interaction network mapping; known target/off-target database annotation; custom report-ready figures; follow-up validation recommendation table

How We Separate Abundance Response from Stability-Shift Signals

A key part of the analysis is distinguishing what each signal means.

A protein may change in abundance because the cell is responding to treatment. A protein may shift in thermal stability because its protein state, interaction context, or binding environment has changed. Some proteins may show both signals.

Our integrated analysis helps organize proteins into interpretable groups, making it easier to decide what is likely downstream response, what may be closer to target engagement-associated biology, and what should be validated next.

DIA Alone, Thermal Stability Shift Profiling Alone, or Integrated Analysis: How to Choose

No single method answers every drug discovery question. The best choice depends on what you need to learn from the compound and sample system.

Method	Main Question Answered	Best-Fit Project	Compound Modification Needed	Proteome-Wide Capability	Key Strength	Key Limitation
DIA proteomics alone	Which proteins change in abundance?	Drug response, pathway analysis, MoA proteomics	No	Yes	Strong quantitative abundance comparison	Does not directly indicate protein-state or engagement-associated shifts
Thermal stability shift profiling alone	Which proteins change thermal stability after treatment?	Target engagement-associated profiling, protein-state analysis	No	Yes	Adds protein-state evidence beyond abundance	Direct and indirect effects must be separated carefully
Integrated DIA + thermal stability shift profiling	How do abundance and protein-state signals relate?	Target engagement interpretation, target deconvolution, off-target clues, MoA	No	Yes	Combines two evidence layers for better interpretation	Requires careful design and bioinformatics integration
Activity-based protein profiling	Which active or reactive proteins are engaged by a probe?	Enzyme families, covalent compounds, active-site biology	Often yes	Within probe scope	Strong for activity-state and reactive-site biology	Limited to compatible probes or chemistries
Affinity pull-down	Which proteins are enriched by compound capture?	Target identification when immobilization is feasible	Often yes	Depends on enrichment	Can support binding-enrichment evidence	Linker or immobilization may affect binding
Single-target thermal stabilization validation	Does one selected protein show a stability response?	Follow-up validation of a known candidate	No	No	Focused and easier to interpret	Not suitable for discovery-scale screening

Solution Selection Strategy

Choose DIA proteomics alone if your main question is abundance-level drug response.

Choose thermal stability shift profiling alone if your main question is protein-state or stability-shift response.

Choose integrated DIA + thermal stability shift profiling if you need to distinguish abundance-driven response from stability-driven candidate signals.

Choose Activity-based Protein Profiling if your compound biology is enzyme-family, active-site, covalent, or probe-compatible.

Choose affinity pull-down if compound immobilization is feasible and binding enrichment is your main question.

Choose Limited Proteolysis–MS when you need a protein-state or structure-sensitive method to complement abundance data and stability-shift findings.

Choose single-target thermal stabilization validation after discovery analysis to confirm selected candidates.

Case Study: Thermal Stability-Based Proteomics as a Functional Evidence Layer

Source: Thermal proteome profiling for interrogating protein interactions

Background

Protein abundance does not always explain protein function. A protein can remain at a similar abundance level while its binding state, interaction partners, modification state, or soluble fraction behavior changes.

Mateus and colleagues described how proteome-wide thermal profiling can be used to study protein states and interactions. The paper explains that proteins can change thermal stability when they interact with small molecules, nucleic acids, other proteins, or when their post-translational state changes.

This is directly relevant to integrated DIA and thermal stability shift profiling. DIA measures abundance response. Thermal stability shift profiling adds a protein-state evidence layer.

Methods

The paper describes a mass spectrometry-based proteomics workflow in which protein solubility is measured across heat conditions. The authors explain that a typical experiment includes selecting cellular material, applying a perturbation, applying heat treatment, collecting the soluble protein fraction, analyzing proteins by mass spectrometry-based proteomics, and interpreting thermal profiles across detected proteins.

The paper also describes different biological systems that can be used, including extracts, intact cells, tissues, and biological fluids.

Results

Figure 1 in the paper provides the key conceptual result for this service page. It shows that proteome-wide thermal profiles can provide information about protein states and interactions.

The article explains that protein thermal profiles can change when proteins interact with small molecules, nucleic acids, other proteins, or post-translational modifications. It also explains that proteome-wide profiling can be used to study targets and off-targets of drug-like molecules, that protein complex members can show related melting behavior, and that thermal stability behavior is not the same as protein half-life.

These observations support the logic of our integrated service: abundance data and stability-shift data answer different but complementary questions.

Conclusion

This publication supports the value of adding a protein-state evidence layer to standard abundance-based proteomics.

For drug discovery projects, integrated DIA proteomics and thermal stability shift profiling can help teams avoid relying on abundance data alone. The combined view can help prioritize target engagement-associated candidates, interpret pathway response, and choose proteins for follow-up validation.

Figure 1 from a published thermal proteome profiling study showing how thermal stability profiles support protein interaction analysis.

Published thermal stability-based proteomics study illustrating how proteome-wide thermal profiles can provide a functional layer for interpreting protein interaction states.

Related Publication

Thermal proteome profiling for interrogating protein interactions
Mateus, A., Kurzawa, N., Becher, I. et al. Molecular Systems Biology, 2020.

This publication supports the use of thermal stability-based proteomics as a functional evidence layer for interpreting protein state and interaction behavior.

FAQ

Frequently Asked Questions

Q: How is integrated DIA and thermal stability shift profiling different from standard DIA proteomics?

Standard DIA proteomics focuses on protein abundance changes. Integrated DIA and thermal stability shift profiling adds a second layer that measures treatment-associated thermal stability changes. This means we can help you see whether a protein changes in abundance, changes in stability behavior, shows both signals, or appears mainly as part of a downstream pathway response.

Q: Can integrated abundance and thermal stability data identify direct drug targets?

It can help prioritize target engagement-associated candidates, but it should not be treated as automatic proof of direct binding. Thermal stability shifts can reflect direct binding, protein complex changes, pathway effects, post-translational state changes, or other protein-state changes. We help classify and rank the evidence so your team can plan focused follow-up validation.

Q: When should I choose integrated analysis instead of thermal stability shift profiling alone?

Choose integrated analysis when you need abundance context. For example, if a protein shifts in thermal stability but also changes in abundance, the interpretation is different from a protein that shifts in stability without changing abundance. The integrated approach helps separate these patterns.

Q: Is this service suitable for target-unknown phenotypic hits?

Yes, it can be a strong fit for target-unknown projects when the sample design is clear. For phenotypic hits, the integrated workflow can rank stability-shift candidates, show abundance-level pathway response, and help identify proteins or pathways for follow-up studies.

Q: Can this workflow compare multiple compounds or doses?

Yes. The service can be designed for compound series, dose comparison, or condition comparison. This is useful when your team wants to compare analogs, evaluate selectivity clues, or understand whether a protein-state response strengthens with treatment level.

Q: What sample types are suitable for integrated DIA and thermal stability shift profiling?

Common sample types include treated cell pellets, matched control cell pellets, cell lysates, and selected tissue-derived protein samples. The best sample type depends on your biological question. We review the sample system, compound treatment, controls, and input amount before project start.

Q: How are abundance-driven changes separated from thermal stability shifts?

We compare DIA abundance changes with thermal stability shift results protein by protein. The integrated analysis can classify proteins as abundance-only responders, stability-shift candidates, concordant responders, pathway-associated proteins, or possible off-target candidates. This classification helps reduce over-interpretation.

Q: What follow-up validation is recommended after candidate ranking?

Follow-up depends on the project goal. Common next steps may include single-target thermal stabilization validation, targeted MS confirmation, orthogonal binding analysis, affinity enrichment if feasible, activity-based profiling for compatible chemistries, or cell-based functional validation.

References

Discuss Your Integrated DIA and Thermal Stability Shift Project

Working with a compound, cell model, lysate system, or phenotypic hit often raises a practical question: which protein-level signals are worth trusting first?

Our team can help you review whether integrated DIA and thermal stability shift profiling fits your project. We can also help think through sample design, controls, data layers, QC expectations, deliverables, and follow-up validation before the study begins.

Compliance Statement

This service is provided for Research Use Only. It is not intended for clinical diagnosis, medical decision-making, patient management, or therapeutic use.

Online Inquiry

Please submit a detailed description of your project. We will provide you with a customized project plan to meet your research requests. You can also send emails directly to for inquiries.