You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Specifications TableSubject area*Proteomics*More specific subject area*DIA-based mass spectrometric analysis of human brain tissue*Type of data*Raw files, msf files*How data was acquired*Mass spectrometry (Q Exactive HF mass spectrometer operated in data-dependent acquisition (DDA) mode performing HCD fragmentation)*Data format*Raw, unfiltered*Experimental factors*Data were obtained by mass spectrometric DDA measurements of human substantia nigra tissue spiked with iRT peptides for generation of a spectral library*Experimental features*Substantia nigra tissue was fractionated via gel electrophoresis and in-gel digested, resulting in 18 fractions. Bands were measured in triplicate, resulting in 54 files*Data source location*Bochum, Germany (51°26′43.4″N 7°15′27.9″E)*Data accessibilityThe data files are hosted in the public repository ProteomeXchange with identifier PXD011076.Related research article*Barkovits K., Linden A., Galozzi S., Schilde L., Pacharra S., Mollenhauer B., Stoepel N., Steinbach S., May C., Uszkoreit J., Eisenacher M., Marucs K.: Characterization of Cerebrospinal Fluid via Data-Independent Acquisition Mass Spectrometry*[@bib1]\
*Published in J Proteome Res.*[*2018*](mi:2018){#intref0010}**Value of the data**The provided data set represents a standard proteomic data set of human *substantia nigra* tissue measured in DDA mode. The samples were spiked with iRT peptides.•The spiked data set can be used as a spectral library for human brain tissue analysis in DIA mode, for example, in the context of Parkinson's disease.•The library can be used to map proteins in different brain regions.•The library may help to determine optimized parameters for the identification of proteins/peptides of interest.•The library can be used to model pathways located within specific brain regions.

1. Data {#sec1}
=======

The proteomic data set provided here includes mass spectrometric data generated from human *substantia nigra* tissue. The data set was generated from human *substantia nigra* tissue, that was lysed and fractionated by one-dimensional SDS polyacrylamide gel electrophoresis (see [Fig. 1](#fig1){ref-type="fig"}). The resulting lanes were cut into single bands and proteins were in-gel digested with trypsin. After peptide extraction the samples were spiked with iRT peptides and measured with a data-dependent mass spectrometric approach (DDA). The data set form human *substantia nigra* here provided is the most extensive and most advanced one regarding separation strategy and mass spectrometric analysis, compared to previous analyses, as e.g. from Licker et al. [@bib2], [@bib3].Fig. 1**Graphical workflow overview.***Substantia nigra* tissue was first homogenized, and the protein concentration in the resulting lysate was determined. For fractionation of the *substantia nigra* lysate, the lysate was completely fractionated (18 fractions) using two types of gradient SDS gels (3--8% Tris-acetate and 4--12% Bis-tris) to cover a width range of molecular weights. In total, 54 samples (3 replicates of each fraction) were generated for nanoHPLC-ESI-MS/MS analysis.Fig. 1

2. Experimental design, materials and methods {#sec2}
=============================================

For a detailed overview of the workflow see [Fig. 1](#fig1){ref-type="fig"}.

2.1. Sample preparation {#sec2.1}
-----------------------

*Substantia nigra* tissue was dissected as described by Plum et al. [@bib4]. For protein extraction, *substantia nigra* was first dissected into smaller pieces at 4 °C and transferred to 1.5 mL reaction tubes. Per 100 mg of tissue, 400 μL of precooled RIPA buffer (Cell Signalling, Germany) was added along with 0.35 g of precooled glass beads (Ø 1.25--1.65 nm; Ø 0.25--0.5 nm; Carl Roth, Germany). The samples were homogenized by sonication with an ultrasound probe (Potter S. Homogenizer, B. Braun, Germany) precooled at 4 °C with an amplitude of 90 and a power of 50% for 1 min for a total of four times. The supernatants were transferred to new 1.5 mL reaction tubes. The remaining glass beads in each tube were washed by adding 50 μL of precooled ultrapure water and vortexing, and the supernatants were merged with the samples transferred previously. The remaining reaction tubes and the glass beads were discarded, and the merged samples were again sonicated in an ice-cold ultrasonic bath (BANDELIN electronic GmbH & Co. KG, Germany) for 10 sec for a total of six times. To remove the insoluble components, the samples were centrifuged at 16.000 g for 15 min at 4 °C (centrifuge 5415R, Eppendorf GmbH, Germany). All supernatants were merged in a 5 mL reaction tube and vortexed. All remaining reaction tubes, along with the sediments, were discarded. To remove the remaining lipids, the merged sample underwent a further clean-up step involving acetone precipitation. For this, the tissue lysate was solved 1:4 (v/v) in acetone (J.T. Baker, the Netherlands) and incubated at −20 °C overnight. To remove the acetone from the precipitated protein, the sample was again centrifuged for 4 min at 5.000 g and 4 °C (centrifuge 5415R, Eppendorf GmbH). The supernatant was discarded, and the sediment was air dried in the opened reaction tube for 5 min on ice. Finally, the sediment was solubilized by adding LDS sample buffer (pH 8.5), which contained 26.5 mM Tris HCl, 35.25 mM Tris base, 2% LDS, 10% glycerol, 0.055 mM Coomassie blue G250 and 0.045 mM phenol red, and vortexing. The tissue lysate was subsequently aliquoted and stored at −80 °C. The protein concentration of the tissue lysate was determined with a Bradford assay (Sigma-Aldrich, Germany) that was operated according to the manufacturer's instructions. A total of 500 μg of the tissue lysate was transferred to a 0.5 mL reaction tube. For reduction of the disulphide bridges and denaturation, 1:10 (v/v) 2 mM DTT was added, followed by incubation for 10 min at 350 rpm and 90 °C in a thermomixer (Thermomixer comfort, Eppendorf GmbH). For removal of the insoluble components and condensation of the buffer in the lid of the reaction tube, the sample was again centrifuged for 4 min at5.000 g at room temperature (centrifuge 5415R, Eppendorf GmbH).

2.2. Complete SDS gel electrophoresis {#sec2.2}
-------------------------------------

For this, 40 μg of protein per lane was loaded on a NuPAGE™ 4--12% Bis-tris gel or on a NuPAGE™ 3--8% Tris-acetate gel (both from Fisher Scientific, Germany). The different types of gel were chosen to cover a large molecular weight range. The running buffers were prepared, and gel electrophoresis was performed according to the manufacturer's instructions.

2.3. Gel staining {#sec2.3}
-----------------

Next, the gels were stained with Coomassie blue (SimpleBlue^TM^SafeStain, Thermo Fisher Scientific) according to the manufacturer's protocol.

2.4. Trypsin in-gel digestion {#sec2.4}
-----------------------------

Lanes on the NuPAGE™ 4--12% Bis-tris gel were cut into 13 single bands, and lanes on the NuPAGE™ 3--8% Tris-acetate gel into 10 single bands (of which 5 were used for further analysis, because they separated proteins with a molecular weight, which was not covered by the NuPAGE™ 4--12% Bis-tris gel). Summing up the lanes of both gels lead to 18 bands. Each band was transferred into a glass vial, destained and pH adjusted by incubating the gel pieces first for 10 min in 50 mM ammonium bicarbonate (Sigma-Aldrich) and afterwards in 50% (v/v) 50 mM ammonium bicarbonate with 50% (v/v) 100% acetonitrile (Merck KGaA, Germany). The incubation cycle was repeated three times in total. For reduction and modification of the disulphide bridges after the second incubation with 50 mM ammonium bicarbonate, the ammonium bicarbonate buffer was discarded, and each glass vial was filled with 50 μL of 10 mM dithiothreitol (AppliChem GmbH, Germany), followed by incubation for 1 hour at 350 rpm and 56 °C in a thermomixer (Thermomixer comfort, Eppendorf GmbH). Then, the dithiothreitol solution was again discarded, and each glass vial was filled with 50 μL of 50 mM iodoacetamide (Merck KGaA) followed by incubation for 45 min at room temperature in the dark. Subsequently, the iodoacetamide solution was discarded, and the destaining protocol was continued with 50% (v/v) 50 mM ammonium bicarbonate with 50% (v/v) 100% acetonitrile.

2.5. Peptide extraction {#sec2.5}
-----------------------

The gel pieces were dried in a vacuum concentrator (RVC2-25CD plus, Martin Christ Gefriertrocknungsanlagen, Germany) and resuspended in 6 μL of trypsin solution (0.012 μg/μL, Promega Corp., Germany). Digestion was performed overnight. The digestion was stopped, and peptides were eluted by incubating the gel pieces twice for 15 min in 30 μL of a 1:1 solution containing 100% acetonitrile and 0.1% (v/v) trifluoroacetic acid (Merck KGaA) in an ice-cold ultrasonic bath. All of the peptide extract resulting from the bands of the complete SDS gel electrophoresis was used for nanoHPLC-ESI-MS/MS because the peptide concentration is, according to our experience, no higher than 500 ng per band and therefore does not exceed the capacity of the nanoHPLC. For the samples resulting from the whole *substantia nigra* lysates, 800 ng of each peptide extract was used for nanoHPLC-ESI-MS/MS. The peptide concentrations of these samples were determined by amino acid analysis.

2.6. Amino acid analysis {#sec2.6}
------------------------

The glass vials for the acidic gas-phase hydrolysis were first heated at 400 °C for 4 hours (muffle furnace, Carbolite CWF 1100, USA) to remove any contamination. From each sample, duplicate determinations were made. For acidic hydrolysis, 6 μL of each peptide extract (in the following referred to as samples) was transferred to a new glass vial, dried in a vacuum concentrator (RVC2-25CD plus) and then placed in an evacuation vessel. Afterwards, 400 μL of 6 M HCl and a phenol crystal were added to the samples, followed by alternating evacuation and aeration with argon four times. The hydrolysis was performed for 1 hour at 150 °C. To remove residual HCl, the peptide samples were evacuated again. For derivatization, 30 μL of AccQ-Fluor borate buffer with the internal standard Norleucine and 10 μL of AccQ-Fluor reagent (10 mM 6-aminoquinolyl-*N*-hydroxysuccinimidylcarbamate in acetonitrile) were added to the hydrolysed samples and incubated for 10 min at 55 °C. Primary and secondary amines were converted to stable derivatives. The derivatives were separated on a C18 reversed-phase separation column (2.1 mm × 100 mm in length, Waters GmbH, Germany). After derivatization with the derivatization reagent AccQ-Fluor, the hydrolysed peptides were dissolved in 10 μL of 20 mM HCl. A gradient system consisting of two solvents was used for the separation (solvent A: AccQ-Tag Ultra Eluent A; solvent B: AccQ-Tag Ultra Eluent B). Elution of the derivatives was performed at a flow rate of 0.7 mL/min and a column temperature of 55 °C by increasing the solvent A content in the solvent mixture (see [Table 1](#tbl1){ref-type="table"}). The amino acid derivatives were detected at an emission wavelength of 260 nm using a UV detector (Waters GmbH). Quantitative analyses were performed using different concentrations of an internal amino acid standard.Table 1Solvent gradient profile for the elution of amino acids.Table 1Time (min)% Solvent A% Solvent B099.90.10.5499.90.15.7490.90.17.7478.821.28.0440.459.68.0510.0908.6410.0908.7399.90.19.5099.90.1

2.7. Peptide spikeing {#sec2.7}
---------------------

The samples were transferred to glass vials, dried in a vacuum concentrator (Eppendorf GmbH) and resuspended in 15 μL of 0.1% (v/v) trifluoroacetic acid. Finally, the samples were spiked with one injection volume of iRT peptides (Biognosys, Switzerland) according to manufacturer's instructions and then analysed *via* nanoHPLC-ESI-MS/MS.

2.8. nanoHPLCsettings {#sec2.8}
---------------------

Prior to mass spectrometric analysis, the extracted peptides were first concentrated and cleaned-up using a nanoHPLC system (UltiMate 3000, Dionex, Germany) with an integrated capillary pre-column (100 μm × 2 cm, particle size 5 μm, pore size 100 Å; Thermo Scientific, USA), which was washed for 7 min with 0.1% trifluoroacetic acid. Then, the peptides were eluted from the pre-column to an analytical column (PepMap™ C18 75 μm × 50 cm, particle size 2 μm, pore size 100 Å; Thermo Fisher Scientific) before peptide separation was performed by a 1 min long segmented gradient of solvent A (0.1% formic acid) and solvent B (84% acetonitrile, 0.1% formic acid) (see [Table 2](#tbl2){ref-type="table"}). The concentration of solvent B was increased from 5% to 60%. The column oven temperature was set to 60 °C, and the flow rate was 400 nL/min. The nanoHPLC system was directly coupled to a nano electrospray ionization source (Thermo Fisher Scientific).Table 2Solvent gradient profile for the elution of peptides.Table 2Time (min)% Solvent B05752083010451275151051911321118231212512427127301293413237136431425214860149951549515551605

2.9. Mass spectrometry settings {#sec2.9}
-------------------------------

Subsequently, the peptides were ionized by electrospray ionization and injected into a Q Exactive™ HF mass spectrometer (Thermo Fisher Scientific, Germany) operating in data-dependent acquisition mode. The mass spectra were recorded between 350 and 1100 *m*/z with a resolution of 120,000 (AGC 3e6, 80 ms maximum injection time, 2.2 *m*/z wide isolation window). The capillary temperature was set to 250 °C, and the spray voltage was set to 1600 V. For internal recalibration, the lock mass of polydimethylcyclosiloxane (445.120 *m*/z) was used. HCD fragmentation of the top-10 abundant precursor ions at 27% NCE was performed, followed by fragment ion analysis with an orbitrap mass analyser with a resolution of 30,000 at 200 *m*/z (AGC 5e5, 120 ms maximum injection time). LC-MS/MS analysis of the *substantia nigra* fractions was performed with slight modifications. The HPLC gradient was a 141 min segmented gradient from 5 to 60% (v/v) solvent B. The mass range of the mass spectrometric analysis was set to 350--1100 *m*/z with a resolution of 120,000.

Transparency document {#appsec1}
=====================

The following is the transparency document related to this article:Multimedia component 1

This work was supported by WTZ Brasilien, a project of the BMBF, Germany (01DN14023), and P.U.R.E. (Protein Unit for Research in Europe), a project of Nordrhein-Westfalen, a federal German state. Moreover, it was supported by the HUPO Brain Proteome Project and the Deutsche Parkinson Gesellschaft.

Transparency document associated with this article can be found in the online version at <https://doi.org/10.1016/j.dib.2019.103711>.