1. Title of Dataset: Flow in fetoplacental-like microvessels in vitro enhances perfusion, barrier function, and matrix stability

2. Author Information
   Principal Investigator Contact Information
   Name: Kristina Haase
   Institution: European Molecular Biology Laboratory (EMBL)
   Address: Barcelona, Spain
   Email: kristina.haase@embl.es

3. Date of data collection (single date, range, approximate date): 2022-2023

4. Geographic location of data collection: Barcelona, Spain

5. Information about funding sources: This work was supported by funds from the European Molecular Biology Laboratory (EMBL) and is part of project number PID2020-116745GA-I00, funded by the Spanish Agencia Estatal de Investigación (AEI).

SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication

2. Links to publications that cite or use the data:

Cherubini M., Erickson S., Padmanabn P., Haberkant P., Stein F., Beltran-Sastre V. & Haase K. (2023). Flow in fetoplacental-like microvessels in vitro enhances perfusion, barrier function, and matrix stability.

1. Links to other publicly accessible locations of the data: None

2. Links/relationships to ancillary data sets: None

3. Was data derived from another source? No
   A. If yes, list source(s): NA

4. Recommended citation for this dataset:

Cherubini M., Erickson S., Padmanabn P., Haberkant P., Stein F., Beltran-Sastre V. & Haase K. (2023). Flow in fetoplacental-like microvessels in vitro enhances perfusion, barrier function, and matrix stability. Dryad Digital Repository https://doi.org/10.5061/dryad.jq2bvq8gg

DATA & FILE OVERVIEW

The following dataset contains an excel file ¨MS data.xlsx¨containing the complete list of proteins and a file ¨P2531_protein_1.tsv¨ which includes the relative statistical analysis using the limma package. These datasets are derived from the same 2 experiments with ≥ 2 replicates per static- or flow-conditioned microvascular in vitro devices. Please see code/software section for details on the methods employed on the microtissue samples and mass spec.

1. File List:

A) MS_data.xlsx
B) P2531_protein_1.tsv

1. Relationship between files, if important: File B encompasses a comprehensive list of peptides identified during the mass-spectrometry run, whereas File A comprises the proteins subjected to quantification and statistical analysisusing the limma package.

2. Additional related data collected that was not included in the current data package: None

3. Are there multiple versions of the dataset? No
   A. If yes, name of file(s) that was updated: NA
   i. Why was the file updated? NA
   ii. When was the file updated? NA

METHODS

This is a reduced version of methods information taken from the manuscript - please see the article for complete details.

Mass spectrometric characterization of matrix derived proteins
Tryptic digestion of hydrogel/tissue samples for their for mass spectrometry (MS) analysis was performed at day 7 after seeding as described by L.A. Sawicki et al. [1]. Two experiments were conducted, with each experiment having ≥ 2 replicates per static- or flow-conditioned devices. Samples were first decellularized to remove cellular structures and protein contribution. Gels were then dissolved and lyophilized samples were reconstituted as in [1]. Digested samples were concentrated to remove trypsin and collagenase.  Mixed peptides were subjected to a reverse phase clean-up step. Peptides were subjected to a reverse phase clean-up step prior their analysis by LC-MS/MS on an Orbitrap Fusion Lumos mass spectrometer (Thermo Scientific).

Peptides were separated using an Ultimate 3000 nano RSLC system and loaded onto the trap column at 30 µl per min over 2 h at 0.3 µl per min. The Orbitrap Fusion Lumos was operated in positive ion mode with a spray voltage of 2.2 kV and capillary temperature of 275° C. Full scan MS spectra with a mass range of 375–1.500 m/z were acquired in profile mode using a resolution of 120.000 with a maximum injection time of 50 ms, AGC operated in standard mode and a RF lens setting of 30%. Fragmentation was triggered for 3 s cycle time for peptide like features with charge states of 2–7 on the MS scan (data-dependent acquisition). Precursors were isolated using the quadrupole with a window of 0.7 m/z and fragmented with a normalized collision energy of 34%. Fragment mass spectra were acquired in profile mode and a resolution of 30,000. Maximum injection time was set to 94 ms and AGC target to custom. The dynamic exclusion was set to 60 s.

Acquired data were analyzed using FragPipe [2] and a Uniprot Homo sapiens database (UP000005640, ID9606 with 20594 entries, October 26th 2022) including common contaminants.  The following modifications were considered: Carbamidomethyl (C, fixed), TMT18plex (K, fixed), Acetyl (N-term, variable), Oxidation (M, variable) and TMT18plex (N-term, variable). The mass error tolerance for full scan MS spectra was set to 10 ppm and for MS/MS spectra to 0.02 Da. A maximum of 2 missed cleavages were allowed. A minimum of 2 unique peptides with a peptide length of at least seven amino acids and a false discovery rate below 0.01 were required on the peptide and protein level [3].

MS data processing (for those shown in the manuscipt)
Raw output files of FragPipe (protein.tsv – files*,* [2]) were processed using the R programming language. Contaminants were filtered out and only proteins that were quantified with at least two unique peptides were considered for the analysis. 333 proteins passed the quality control filters. Log2 transformed raw TMT reporter ion intensities were first cleaned for batch effects using the ‘removeBatchEffects’ function of the limma package [4] and further normalized using the vsn package (variance stabilization normalization, [5]). Proteins were tested for differential expression using the limma package. The replicate information was added as a factor in the design matrix given as an argument to the ‘lmFit’ function of limma. A protein was annotated as a hit with a false discovery rate (fdr) smaller than 5 % and a fold-change of at least 30 % and as a candidate with a fdr below 5 % with no fold-change threshold. Hit and candidate proteins were clustered into 2 clusters (method kmeans) based on the Euclidean distance between normalized TMT intensities divided by the 0 mm H₂O data point.

References
[1] L.A. Sawicki, L.H. Choe, K.L. Wiley, K.H. Lee, A.M. Kloxin, Isolation and Identification of Proteins Secreted by Cells Cultured within Synthetic Hydrogel-Based Matrices, ACS Biomater. Sci. Eng. 4(3) (2018) 836-845.
[2] A.T. Kong, F.V. Leprevost, D.M. Avtonomov, D. Mellacheruvu, A.I. Nesvizhskii, MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics, Nat Methods 14(5) (2017) 513-520.
[3] M.M. Savitski, M. Wilhelm, H. Hahne, B. Kuster, M. Bantscheff, A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets, Mol Cell Proteomics 14(9) (2015) 2394-2404.
[4] M.E. Ritchie, B. Phipson, D. Wu, Y. Hu, C.W. Law, W. Shi, G.K. Smyth, limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Res 43(7) (2015) e47.
[5] W. Huber, A. von Heydebreck, H. Sültmann, A. Poustka, M. Vingron, Variance stabilization applied to microarray data calibration and to the quantification of differential expression, Bioinformatics 18 Suppl 1 (2002) S96-104.

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DATA-SPECIFIC INFORMATION FOR: MS_data.xlsx

MS data worksheet

1. Number of variables: 14

2. Number of rows: 333

3. Variable List:

* Gene: gene nomenclature of all the quantified proteins

* Protein.ID: UniProt ID for the protein

* Protein.Description: name of the protein

* Organism: species of the protein

* found.in.files: number of times the protein was found in the original mass-spec file

* found.in.file_P2531_protein.tsv: number of times the protein was found in the P2531_protein.tsv file

* found.in.conditions: number of times the protein was found in the conditions (0 mmH2O, 3 mmH2O and 7 mmH2O)

* found.in.reps: number of times the protein was found in the condition replicates

* max.Unique.Peptides: maximum number of unique peptides detected

* average.Total.Intensity: average total intensity of the peptide signal detected

* reporter_intensity_: reporter intensity of the peptide detected in each replicate of each condition (18 columns: 6 replicates of 0 mmH2O condition, 7 replicates of 3 mmH2O condition and 5 replicates of 7 mmH2O condition)

* batchcl_reporter_intensity_: reporter intensity of the peptide detected in each replicate of each condition after batch effect removal (18 columns: 6 replicates of 0 mmH2O condition, 7 replicates of 3 mmH2O condition and 5 replicates of 7 mmH2O condition)

* norm_reporter_intensity_: reporter intensity of the peptide detected in each replicate of each condition after data normalization (18 columns: 6 replicates of 0 mmH2O condition, 7 replicates of 3 mmH2O condition and 5 replicates of 7 mmH2O condition)

* ctrl.ratio_: log2-transformed fold changes for each detected protein in each condition relative to the control condition (0 mmH2O condition)

1. Missing data codes: None

2. Specialized formats or other abbreviations used: None

Limma results worksheet
This worksheet shows the results of the limma analysis, which is a statistical method used to identify differentially expressed proteins between different conditions.

1. Number of variables: 26

2. Number of rows: 999

3. Variable List:

* Gene: gene nomenclature of all the quantified proteins

* Protein.ID: UniProt ID for the protein

* Protein.Description: name of the protein

* Organism: species of the protein

* found.in.files: number of times the protein was found in the original mass-spec file

* found.in.file_P2531_protein.tsv: number of times the protein was found in the P2531_protein.tsv file

* found.in.conditions: number of times the protein was found in the conditions (0 mmH2O, 3 mmH2O and 7 mmH2O)

* found.in.reps: number of times the protein was found in the condition replicates

* max.Unique.Peptides: maximum number of unique peptides detected

* average.Total.Intensity: average total intensity of the peptide signal detected

* logFC: log-fold changes

* AveExpr: average log-expression value for the protein across different conditions

* t: t-statistic values associated with each protein

* pvalue.limma: p-value associated with the limma analysis for each protein

* fdr.limma: false discovery rate-adjusted p-values for each protein

* B: logarithm of the odds that a protein is differentially expressed

* pvalue.fdrtool: adjusted p-values after applying the fdrtool correction to the limma results

* qval.fdrtool: q-values after applying the fdrtool correction to the limma results

* lfdr.fdrtool: local false discovery rates after applying the fdrtool correction to the limma results

* comparison: performed conditions comparison (e.g. 3mmH2O vs 0mmH2O)

* hit_annotation_method: method used to classify each protein based on their statistical significance and fold change values

* pvalue: p-value for each compared protein between conditions

* fdr: false discobery rate for each compared protein between conditions

* hit: classification of the result (TRUE or FALSE)

* hit_annotation: A protein is considered a hit, if the false discovery rate is smaller 0.05 and a fold change of at least 2-fold is observed. A protein is considered a candidate, if the false discovery rate is smaller 0.2 and a fold change of at least 1.5-fold is observed.

* Total.Intensity: overall expression intensity oof the protein across all conditions

1. Missing data codes: None

2. Specialized formats or other abbreviations used: None

DATA-SPECIFIC INFORMATION FOR: P2531_protein_1.tsv

1. Number of variables: 41

2. Number of rows: 825

3. Variable List:

* Protein: standard UniProt identification of the protein

* Protein.ID: UniProt ID for the protein

* Entry Name: gene name associated with the species

* Gene: gene name

* Length: length of the protein sequence

* Organism: species of identified protein

* Protein.Description: name of the protein

* Protein Existence: type of evidence that supports the existence of the protein

* Coverage: percent of protein sequence observed from the identified peptides

* Protein Probability: quantitative measure of the confidence associated with each identified protein

* Top Peptide Probability: best peptide probability of supporting peptides

* Unique Peptides: number of peptide sequences that are unique to a protein

* Razor Peptides: total number of peptides in support of the protein identification

* Total Spectral Count: total number of peptide-spectrum matches (PSMs) in support of the protein

* Unique Spectral Count: total number of PSMs that do not map to other identified proteins

* Razor Spectral Count: number of PSMs in support of the protein identification (unique + razor)

* Total Intensity: sum of the top 3 peptide abundances, including peptides that may map to another identified protein

* Unique Intensity: sum of the top 3 unique peptide abundances (only those that do not map to another identified protein)

* Razor Intensity: sum of the top 3 peptide abundances (unique + razor)

* Razor Assigned Modifications: modifications from supporting razor peptides

* Razor Observed Modifications: Delta Mass values from supporting razor peptides

* Indistinguishable Proteins: proteins that are equally supported by the evidence and cannot be distinguished from the identification in the Protein column

* channel_: TMT channel that contains relative reporter ion abundances (18 columns)

1. Missing data codes: None

2. Specialized formats or other abbreviations used: None