DOI: 10.5061/dryad.jq2bvq8gg
title: Flow in fetoplacental-like microvessels in vitro enhances perfusion, barrier function, and matrix stability

description: Proper placental vascularization is vital for pregnancy outcomes, but
 assessing it with animal models and human explants has limitations. Here,
 we present a 3D in vitro model of human placenta terminal villi that
 includes fetal mesenchyme and vascular endothelium. By co-culturing HUVEC,
 placental fibroblasts, and pericytes in a macro-fluidic chip with a flow
 reservoir, we generate fully perfusable fetal microvessels.
 Pressure-driven flow is crucial for the growth and remodeling of these
 microvessels, resulting in early formation of interconnected
 placental-like vascular networks and maintained longevity. Computational
 fluid dynamics simulations predict shear forces, which increase
 microtissue stiffness, decrease diffusivity and enhance barrier function
 as shear stress rises. Mass-spec analysis reveals the deposition of
 numerous extracellular proteins, with flow notably enhancing the
 expression of matrix stability regulators, proteins associated with actin
 dynamics, and cytoskeleton organization. Our model provides a powerful
 tool for deducing complex in vivo parameters, such as shear stress on
 developing vascularized placental tissue, and holds promise for unraveling
 gestational disorders related to the vasculature.
description: Please see the methods section in the manuscript for details on
 collection and processing.
description: 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: 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
 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 H<sub>2</sub>O
 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.
 \#########################################################################
 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) 4. Missing data codes: None 5.
 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 4.
 Missing data codes: None 5. 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) 4. Missing data codes: None
 5. Specialized formats or other abbreviations used: None