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Macroalgal genomics illuminate three paths to multicellularity - Supplementary Data - Tables - Data S4
<p>Macroalgae are multicellular, aquatic autotrophs that play vital roles in global climate maintenance and have diverse applications in biotechnology and eco-engineering, which are directly linked to their multicellularity phenotypes. However, their genomic diversity and the evolutionary mechanisms underlying multicellularity in these organisms remain uncharacterized. Here, we sequenced 112 macroalgal genomes from diverse climates and phyla, identifying key genomic features that distinguish them from their microalgal relatives. We found that macroalgae have expanded gene families related to cellular adhesion, extracellular matrix formation, cytoskeletal organization and signaling pathways. We discovered that many of these genes have viral origins and are lineage-specific or conserved among the three major macroalgal phyla: Rhodophyta (red algae), Chlorophyta (green algae) and Ochrophyta (brown algae). Our work reveals genetic determinants of convergent and divergent evolutionary trajectories that have shaped morphological diversity in macroalgae and provides genome-wide frameworks to understand photosynthetic multicellular evolution in marine environments.</p> <p><strong>Table S1.</strong> Functional annotation and metadata for macroalgal species. This is a multi-sheet Excel workbook containing the PFAM count matrix for decontaminated assemblies and their strain metadata, contamination estimates, assembly metrics including BUSCO and N50 scores, and source data for the ternary analysis. Related to Figs. 2 and 3.</p> <p><br><strong>Table S2.</strong> GO enrichment in macroalgal-specific genes. This is a multi-sheet Excel workbook containing enriched GO terms in macroalgal-specific PFAMs conserved in Rhodophyta, Ochrophyta, and Chlorophyta and response screening results comparing means in PFAM counts between divisions. Related to Fig. 3.</p> <p><br><strong>Table S3. </strong>Comparative genomics of micro- and macroalgae. This is a multi-sheet Excel workbook containing response screening tables comparing PFAM and GO variation in micro- compared to macroalgae. Related to Fig. 4.</p> <p><br><strong>Table S4.</strong> Unraveling the macroalgal adhesome. This is a multi-sheet Excel workbook containing macroalgal adhesome atlas and response screens among macroalgal phyla and between micro- and macroalgae. Related to Fig. 4.</p> <p><br><strong>Table S5. </strong>Endogenous viral elements in macroalgae. This is a multi-sheet Excel workbook containing VFAM count matrix and response screening results for comparisons of macroalgal VFAM counts by climate and habitat. This table also includes the EVOP matrix and response screening results comparing EVOPs found in the macroalgal genomes among climates and macroalgal tORFs with EsV-1-7 domains and their codomains. Related to Fig. 5.</p> <p> </p>
Data from: Phenotypic variation across the first steps of experimentally evolved multicellularity.
<p>This BBC_2024__README.txt file was generated on 2024-07-15 by Beatriz Baselga Cervera</p> <p>GENERAL INFORMATION</p> <p>1. Title of Dataset and code: Data from: Phenotypic variation across the first steps of experimentally evolved multicellularity.</p> <p>2. Author Information</p> <p> Corresponding Investigator</p> <p> Name: Dr Beatriz Baselga-Cervera</p> <p> Institution: University of Minnesota Twin cities, Minnesota, US.</p> <p> Email: <a href="mailto:bbaselga@umn.edu">bbaselga@umn.edu</a>; beabaselga@gmail.com</p> <p> Co-investigator 1</p> <p> Name: Dr Noah Gettle</p> <p> Institution: Wellcome Sanger Institute, Hinxton, UK</p> <p> Email: noah.gettle@sanger.ac.uk</p> <p> Co-investigator 2</p> <p> Name: Dr Michael Travisano</p> <p> Institution: University of Minnesota Twin cities, Minnesota, US.</p> <p> Email:travisan@umn.edu</p> <p> </p> <p>4.Data collector: Dr Beatriz Baselga-Cervera</p> <p> </p> <p>5. Date of data collection: 2021-2022</p> <p> </p> <p>6. Geographic location of data collection: Saint Paul, US</p> <p> </p> <p>5. Funding sources that supported the collection of the data: Fundación Alfonso Martín Escudero, Madrid, Spain.</p> <p> </p> <p>6. Recommended citation for this dataset: Baselga-Cervera et al. (2024), Data from: Phenotypic variation across the first steps of experimentally evolved multicellularity. Zenodo. Data set.</p> <p>DATA & FILE OVERVIEW</p> <p> </p> <p>1. Description of dataset</p> <p>In this study, we study the genotype to phenotype map that provides the basis for the diverse morphologies and unique adaptations observed in nascent multicellularity carried over from their unicellular ancestors under high and low nutrient availability conditions. We carried out a NetLogo agent-based model simulation of the multicellular snowflake yeast growth. Populations characterization was conducted with a Coulter Counter multisize 4 and FlowCam 3. The populations studied were the genetically uniform diploid wild-type <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T). The population data represent the six independent isolates per strain in two different media, YPD and SD.</p> <p>2. File list:</p> <ul> <li>NetLogo Agent Base model: </li> <ul> <li>File 1 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Netlogo%20Snowflake%203D.nlogo3d?versionId=6fc2e8c0-02be-4956-875e-b4e48dd37163">Netlogo Snowflake 3D.nlogo3d</a> </li> </ul> </ul> <p> File 1 Description: Netlogo code for the 3D model/</p> <ul> <ul> <li>File name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Data_NETLOGO_model.csv?versionId=bb3e52cd-aac2-4f3e-a395-40a4995a1192">Data_NETLOGO_model.csv</a> </li> </ul> </ul> <p> File 2 Description: Data obtained from the Netlogo model.</p> <ul> <li>Coulter Counter size distribution data of all the populations: </li> <ul> <li>File 3 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Counter_Counter_Raw_data.xls?versionId=389afe4d-46c3-4569-92cb-ea73355a4ed7">Counter_Counter_Raw_data.xls</a></li> <li>File 3 description: Data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD and SD at 24h growth. </li> <li>File 4 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Coulter_Counter_Raw_data_heterozygous_contructions.xlsx?versionId=e1fa6610-3f88-414c-b07a-03cc11b913e4">Coulter_Counter_Raw_data_heterozygous_contructions</a></li> <li>File 4 description: Size distributions of the heterozygous construct knockout (ACE2/<em>ace</em>2Δ) and homozygous missense (ACE2/<em>ace2Δ</em>) in YPD and SD at 24h growth. </li> </ul> <li>FlowCam image data:</li> <ul> <li>File 5 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/FlowCamPicturesData.zip?versionId=1e80636d-3022-4feb-828e-bc9552fef091">FlowCamPicturesData.zip</a></li> <li>File 5 description: Pictures generated by the FlowCam.</li> <li>File 6 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Flow_Cam_Raw%20data.xls?versionId=efd469dc-66f9-4504-8de3-f96c74f07f38">Flow_Cam_Raw data.xls</a></li> <li>File 6 desciption: FlowCam data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD and SD at 24h growth. </li> </ul> <li>Data generated statistically:</li> <ul> <li>File 7 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/bootstrapped_means.csv?versionId=48184a51-e3b5-407d-8ce1-ecdf5d7e1c99">bootstrapped_means.csv</a></li> <li>File 7 description: bootstrapped means from the Coulter Counter data to calculate the relative contributions to phenotypic variation.</li> <li>File 8 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/bootstrapped_vars.csv?versionId=3ff5b6aa-6644-4264-8a6b-448c8a733689">bootstrapped_vars.csv</a></li> <li>File 8 description: bootstrapped variance from the Coulter Counter data to calculate the phenotypic noise.</li> <li>File 9 name: overlapPairs_values.xlsx </li> <li>File 9 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data.</li> </ul> <li>R codes for data generated statistically:</li> <ul> <li>File 10 name: <a href="Code%20for%20bootstrapping.docx">R_code_bootstrapping.docx</a></li> <li>File 10 description: R code to obtain bootstrapped means and variance from the <a>Counter_Counter_Raw_data.xls</a> data to calculate the relative contributions to phenotypic variation.</li> <li>File 11 name: <a href="R%20code%20Overlap%20KDE%20distributions%20from%20the%20Coulter%20Counter%20data.docx">R_code_Overlap_KDE_distributions_from _the_Coulter_Counter_data</a></li> <li>File 11 description: R code to obtain overlapping indexes (η) of the Kernel density estimations (KDE) distributions from <a>Counter_Counter_Raw_data.xls</a></li> </ul> <li>R codes for figures:</li> <ul> <li>File 12 name: R_code_Fig2.docx</li> <li>File 12 description: R code for Figure 2 panels B and C. Panels are created from <a>Data_NETLOGO_model.csv</a>.</li> <li>File 13 name: R_code_Fig3.docx</li> <li>File 13 description: R code for Figure 3 panels A to E. Panels are created from <a>Counter_Counter_Raw_data.xls</a></li> <li>File 13 name: R_code_Fig4.docx</li> <li>File 13 description: R code for Figure 4, data from <a>Counter_Counter_Raw_data.xls</a></li> <li>File 14 name: R_code_FigS1.docx</li> <li>File 14 description: R code for Figure S1. Raw data from <a>Data_NETLOGO_model.csv</a> </li> <li>File 15 name: R_code_FigS2.docx</li> <li>File 15 description: R code for Figure S2. Raw data from <a>Counter_Counter_Raw_data.xls</a></li> <li>File 16 name: R_code_FigS3.docx</li> <li>File 16 description: R code for Figure S3. Raw data from <a>Coulter_Counter_Raw_data_heterozygous_contructions</a>.xlsx</li> <li>File 16 name: R_code_FigS4.docx</li> <li>File 16 description: R code for Figure S4. Raw data from <a>Counter_Counter_Raw_data.xls</a></li> </ul> </ul> <p> </p> <p>METHODOLOGICAL INFORMATION</p> <p>Strains: ancestral wildtype (Y55 strains), C1W8.1 and C1W8.2 multicellular derived strains isolated after 60 days of selection in YPD media, constructed ACE2 gene knockouts, and strains containing the ACE2 missense mutation (ACE2 c.1934 A>T).</p> <p>Media: Growth media used in this study were Yeast Peptone Dextrose media (YPD; 1% (v/w) yeast extract, 2% (v/w) peptone, 2% (v/w) D-glucose, pH 5.8) and Standard minimal (SD; Yeast nitrogen base with amino acids (YNB w/AA) 6.7 g L-1, 0.5% (v/w) D-glucose).</p> <p>Phenotypic characterization of the different strains was conducted in a Coulter Counter Multisizer 4 and FlowCam® 3.0 Fluid Imaging Technologies. Replicate populations of different individual isolates per strain were analyzed to obtain the population distributions in both YPD and SD media.</p> <p>Agent Base Model data was generated in Netlogo (https://ccl.northwestern.edu/netlogo/).</p> <p> </p> <p>3. Detailed description</p> <ul> <li>NetLogo Agent Base model: </li> <ul> <li>File 1 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Netlogo%20Snowflake%203D.nlogo3d?versionId=6fc2e8c0-02be-4956-875e-b4e48dd37163">Netlogo Snowflake 3D.nlogo3d</a> </li> </ul> </ul> <p>o File 1 Description: Netlogo code for the 3D model.</p> <p>o File name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Data_NETLOGO_model.csv?versionId=bb3e52cd-aac2-4f3e-a395-40a4995a1192">Data_NETLOGO_model.csv</a> </p> <p>o File 2 Description: Data obtained from the Netlogo model.</p> <p>§ Page 1: Agent Base Model data generated with Netlogo.</p> <pre> Column 1: run number</pre> <pre> Column 2: size-of-turtle(cells)</pre> <p> Column 3: [step]</p> <p> Column 4: N</p> <p> Column 5: ticks(generations)</p> <pre> Column 6:distance-turtle(cells)</pre> <p> Column 7: diameter cells</p> <p> Column 8: radius cells</p> <p> Column 9: diameter cluster</p> <p> Column 10: radius cluster</p> <p> Column 11: volume</p> <p> Column 12: SAVr (surface area/volume ratio)</p> <p> Column 13: packing</p> <p> Column 14: Ratio</p> <ul> <li>Coulter Counter size distribution data of all the populations: </li> <ul> <li>File 3 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Counter_Counter_Raw_data.xls?versionId=389afe4d-46c3-4569-92cb-ea73355a4ed7">Counter_Counter_Raw_data.xls</a></li> <li>File 3 description: Data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD and SD at 24h growth. </li> </ul> </ul> <p>§ Page 1: Coulter Coulter data runs at 24h</p> <p> Column 1: Volumen (um3)</p> <p> Column 2: Diameter (um2)</p> <p>Column 3: ace2Δ _k_1_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is SD media.</p> <p>Column 4: ace2Δ _k_1_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 5: ace2Δ _k_2_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate two is SD media.</p> <p>Column 6: ace2Δ _k_2_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate two is YPD media.</p> <p>Column 7: ace2Δ _k_3_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate three is SD media.</p> <p>Column 8: ace2Δ _k_3_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate three is YPD media.</p> <p>Column 9: ace2Δ _k_4_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate four is SD media.</p> <p>Column 10: ace2Δ _k_4_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate four is YPD media.</p> <p>Column 11: ace2Δ _k_5_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is SD media.</p> <p>Column 12: ace2Δ _k_5_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 13: ace2Δ _k_6_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is SD media.</p> <p>Column 14: ace2Δ _k_6_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is YPD media.</p> <p>Column 15: ace2Δ _m_1_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is SD media.</p> <p>Column 16: ace2Δ _m_1_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 17: ace2Δ _m_2_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate two is SD media.</p> <p>Column 18: ace2Δ _m_2_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate two is YPD media.</p> <p>Column 19: ace2Δ _m_3_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate three is SD media.</p> <p>Column 20: ace2Δ _m_3_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate three is YPD media.</p> <p>Column 21: ace2Δ _m_4_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate four is SD media.</p> <p>Column 22: ace2Δ _m_4_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate four is YPD media.</p> <p>Column 23: ace2Δ _m_5_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is SD media.</p> <p>Column 24: ace2Δ _m_5_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 25: ace2Δ _m_6_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is SD media.</p> <p>Column 26: ace2Δ _m_6_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is YPD media.</p> <p>Column 27: C1W8.1 _m_1_SD. C1W8.1 evolved strain isolate one is SD media.</p> <p>Column 28: C1W8.1 _m_1_YPD. C1W8.1 evolved strain isolate one is YPD media.</p> <p>Column 29: C1W8.1 _m_2_SD. C1W8.1 evolved strain isolate two is SD media.</p> <p>Column 30: C1W8.1 _m_2_YPD. C1W8.1 evolved strain isolate two is YPD media.</p> <p>Column 31: C1W8.1 _m_3_SD. C1W8.1 evolved strain isolate three is SD media.</p> <p>Column 32: C1W8.1 _m_3_YPD. C1W8.1 evolved strain isolate three is YPD media.</p> <p>Column 33: C1W8.1 _m_4_SD. C1W8.1 evolved strain isolate four is SD media.</p> <p>Column 34: C1W8.1 _m_4_YPD. C1W8.1 evolved strain isolate four is YPD media.</p> <p>Column 35: C1W8.1 _m_5_SD. C1W8.1 evolved strain isolate five is SD media.</p> <p>Column 36: C1W8.1 _m_5_YPD. C1W8.1 evolved strain isolate five is YPD media.</p> <p>Column 37: C1W8.1 _m_6_SD. C1W8.1 evolved strain isolate six is SD media.</p> <p>Column 38: C1W8.1 _m_6_YPD. C1W8.1 evolved strain isolate sic is YPD media.</p> <p>Column 39: C1W8.2 _m_1_SD. C1W8.2 evolved strain isolate one is SD media.</p> <p>Column 40: C1W8.2 _m_1_YPD. C1W8.2 evolved strain isolate one is YPD media.</p> <p>Column 41: C1W8.2 _m_2_SD. C1W8.2 evolved strain isolate two is SD media.</p> <p>Column 42: C1W8.2 _m_2_YPD. C1W8.2 evolved strain isolate two is YPD media.</p> <p>Column 43: C1W8.2 _m_3_SD. C1W8.2 evolved strain isolate three is SD media.</p> <p>Column 44: C1W8.2 _m_3_YPD. C1W8.2 evolved strain isolate three is YPD media.</p> <p>Column 45: C1W8.2 _m_4_SD. C1W8.2 evolved strain isolate four is SD media.</p> <p>Column 46: C1W8.2 _m_4_YPD. C1W8.2 evolved strain isolate four is YPD media.</p> <p>Column 47: C1W8.2 _m_5_SD. C1W8.2 evolved strain isolate five is SD media.</p> <p>Column 48: C1W8.2 _m_5_YPD. C1W8.2 evolved strain isolate five is YPD media.</p> <p>Column 49: C1W8.2 _m_6_SD. C1W8.2 evolved strain isolate six is SD media.</p> <p>Column 50: C1W8.2 _m_6_YPD. C1W8.2 evolved strain isolate sic is YPD media.</p> <p>Column 51: Y55_1_SD. Y55 ancestral strain isolate one is SD media.</p> <p>Column 52: Y55_1_SD. Y55 ancestral strain isolate one is YPD media.</p> <p>Column 53: Y55_2_SD. Y55 ancestral strain isolate two is SD media.</p> <p>Column 54 Y55_2_SD. Y55 ancestral strain isolate two is YPD media.</p> <p>Column 55: Y55_3_SD. Y55 ancestral strain isolate three is SD media.</p> <p>Column 56: Y55_3_SD. Y55 ancestral strain isolate three is YPD media.</p> <p>Column 57: Y55_4_SD. Y55 ancestral strain isolate four is SD media.</p> <p>Column 58: Y55_4_SD. Y55 ancestral strain isolate four is YPD media.</p> <p>Column 59: Y55_5_SD. Y55 ancestral strain isolate five is SD media.</p> <p>Column 60: Y55_5_SD. Y55 ancestral strain isolate five is YPD media.</p> <p>Column 61: Y55_6_SD. Y55 ancestral strain isolate six is SD media.</p> <p>Column 62: Y55_6_SD. Y55 ancestral strain isolate sic is YPD media.</p> <p> </p> <p> </p> <p>o File 4 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Coulter_Counter_Raw_data_heterozygous_contructions.xlsx?versionId=e1fa6610-3f88-414c-b07a-03cc11b913e4">Coulter_Counter_Raw_data_heterozygous_contructions</a></p> <ul> <ul> <li>File 4 description: Size distributions of the heterozygote construct knockout (ACE2/<em>ace</em>2Δ) and heterozygous missense construct (ACE2/<em>ace2Δ</em>) in YPD and SD at 24h growth. </li> </ul> </ul> <p>§ Page 1: Coulter Coulter data runs at 24h</p> <p> Column 1: Volumen (um3)</p> <p> Column 2: Diameter (um2)</p> <p>Column 3: ACE2/ace2Δ missense_1_SD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate one is SD media.</p> <p>Column 4: ACE2/ace2Δ missense_1_YPD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 5: ACE2/ace2Δ missense_2_SD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate two is SD media.</p> <p>Column 6: ACE2/ace2Δ missense_2_YPD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate two is YPD media.</p> <p>Column 7: ACE2/ace2Δ missense_3_SD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate three is SD media.</p> <p>Column 8: ACE2/ace2Δ missense_3_YPD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate three is YPD media.</p> <p>Column 9: ACE2/ace2Δ knockout_1_SD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate one is SD media.</p> <p>Column 10: ACE2/ace2Δ knockout _1_YPD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 11: ACE2/ace2Δ knockout _2_SD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate two is SD media.</p> <p>Column 12: ACE2/ace2Δ knockout _2_YPD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate two is YPD media.</p> <p>Column 13: ACE2/ace2Δ knockout _3_SD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate three is SD media.</p> <p>Column 14: ACE2/ace2Δ knockout _3_YPD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate three is YPD media.</p> <ul> <li>FlowCam image data:</li> <ul> <li>File 5 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/FlowCamPicturesData.zip?versionId=1e80636d-3022-4feb-828e-bc9552fef091">FlowCamPicturesData.zip</a></li> <li>File 5 description: Pictures generated by the FlowCam.</li> <li>File 6 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Flow_Cam_Raw%20data.xls?versionId=efd469dc-66f9-4504-8de3-f96c74f07f38">Flow_Cam_Raw data.xls</a></li> <li>File 6 desciption: FlowCam data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD and SD at 24h growth. </li> </ul> </ul> <p>§ Page 1: FlowCam data runs at 24 hours of Y55 strain.</p> <p> Column 1:Particle ID</p> <p> Column 2: Class. Classification as; single cells and mother-daughter(s).</p> <p> Column 3: Area ABD</p> <p> Column 4: Aspect Ratio (Width/Length)</p> <p> Column 5: Circle Fit</p> <p> Column 6: Area base Diameter (ABD)</p> <p> Column 7: Equivalent Spherical Diameter (ESD)</p> <p> Column 8: Elongation</p> <p> Column 9: Perimeter</p> <p> Column 10: Roughness</p> <p> Column 11: Volume ESD-based</p> <p> Column 12: Width</p> <p> Column 13: Source. Name of the sample.</p> <p> Column 14: Strain Y55.</p> <p> Column 15: media. Values: YPD and SD</p> <p> Column 16: clone. Isolate.</p> <p>§ Page 2: FlowCam data runs at 24 hours of C1W8.1 multicellular strain.</p> <p> Column 1: Particle ID</p> <p> Column 2: Area ABD</p> <p> Column 3: Aspect Ratio (Width/LEngth)</p> <p> Column 4: Circle Fit</p> <p> Column 5: Area base Diameter (ABD)</p> <p> Column 6: Equivalent Spherical Diameter (ESD)</p> <p> Column 7: Elongation</p> <p> Column 8: Perimeter</p> <p> Column 9: Roughness</p> <p> Column 10: Volume ESD-based</p> <p> Column 11: Width</p> <p> Column 12: Source. Name of the sample.</p> <p> Column 13: Strain C1W8.1.</p> <p> Column 14: media. Values: YPD and SD</p> <p> Column 15: clone. Isolate.</p> <p>§ Page 3: FlowCam data runs at 24hours of C1W8.2 multicellular strain.</p> <p> Column 1: Particle ID</p> <p> Column 2: Area ABD</p> <p> Column 3: Aspect Ratio (Width/LEngth)</p> <p> Column 4: Circle Fit</p> <p> Column 5: Area base Diameter (ABD)</p> <p> Column 6: Equivalent Spherical Diameter (ESD)</p> <p> Column 7: Elongation</p> <p> Column 8: Perimeter</p> <p> Column 9: Roughness</p> <p> Column 10: Volume ESD-based</p> <p> Column 11: Width</p> <p> Column 12: Source. Name of the sample.</p> <p> Column 13: Strain C1W8.2.</p> <p> Column 14: media. Values: YPD and SD</p> <p> Column 15: clone. Isolate.</p> <p> </p> <ul> <li>Data generated statistically:</li> <ul> <li>File 7 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/bootstrapped_means.csv?versionId=48184a51-e3b5-407d-8ce1-ecdf5d7e1c99">bootstrapped_means.csv</a></li> <li>File 7 description: bootstrapped means from the Coulter Counter data to calculate the relative contributions to phenotypic variation.</li> </ul> </ul> <p>§ Page 1: Bootstrap mean values.</p> <p> Column 1: row number</p> <p> Column 2: sample. ID of the sample from whom the value was generated.</p> <p> Column 3: diameter_um. Mean diameter (um) values.</p> <ul> <ul> <li>File 8 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/bootstrapped_vars.csv?versionId=3ff5b6aa-6644-4264-8a6b-448c8a733689">bootstrapped_vars.csv</a></li> <li>File 8 description: bootstrapped variance from the Coulter Counter data to calculate the phenotypic noise.</li> </ul> </ul> <p>§ Page 1: Bootstrap variance values.</p> <p> Column 1: row number</p> <p> Column 2: sample. ID of the sample from whom the value was generated.</p> <p> Column 3: diameter_um. Variance in diameter (um) values.</p> <ul> <ul> <li>File 9 name: <code>overlapPairs_values.xlsx</code> </li> <li>File 9 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data.</li> </ul> </ul> <p>§ Page 1: Overlapping indexes (η) of the KDE distributions by strain, media and isolate.</p> <p> Column 1: var1. Population 1.</p> <p> Column 2: var2. Population 2.</p> <p> Column 3: value. Overlap value.</p> <p>§ Page 2: Overlapping indexes (η) of the KDE distributions by strain and media.</p> <p> Column 1: var1. Strain 1.</p> <p> Column 2: var2. Strain 2.</p> <p> Column 3: value. Overlap value.</p> <p> Column 4: media.</p> <p> </p> <p> </p>
An environmentally induced multicellular life cycle of a unicellular cyanobacterium
<p>Data used in the manuscript by Tang, Pichugin, Hammerschmidt <a href="https://doi.org/10.1101/2021.09.29.462355">https://doi.org/10.1101/2021.09.29.462355</a></p>
Macroalgal deep genomics illuminate multiple paths to aquatic, photosynthetic multicellularity - ASSEMBLIES [INTERNATIONAL] - Data S1
<p>Macroalgae are a polyphyletic group of multicellular aquatic organisms vital to global climate maintenance and have a wide variety of commercial applications. The lack of genomic datasets and poor physiological records preclude understanding their ecological roles and industrial potential. We <em>de novo</em> sequenced 121 macroalgal genomes from various climates spanning five major latitude parallels. The resultant genomic datasets reveal genetic bases for niche habitation facilitated by morphological complexity in diverse and extreme regions and illuminate the evolutionary mechanisms behind macroalgal diversification and specialization. Adhesome genes (e.g., cadherins, integrins, and lectins), extracellular matrix enzymes, and cytoskeletal organization regulating genes (e.g., spondins, Rho-type GTPases) predominantly distinguished macroalgal genomes from their microalgae correlates. Deep neural networks could accurately classify an alga as micro- or macro- from set of significance-ranked genomic features (n = 251, entropy R<sup>2</sup> > 0.99, RASE = 0.001) as well as adhesome gene sets (n = 110, entropy R<sup>2</sup> > 0.86). By deciphering the macroalgal adhesome, a clear picture of the genetic basis for the development and maintenance of complex algal tissues could be resolved. Sequences from giant viruses were rampant in the macroalgal genomes and coded for zinc-finger transcription factors, ankyrins, Rieske proteins, and other exotic codomains. Lineage-specific retentions of transcription factors, cadherins, integrins, polysaccharide-acting enzymes, and receptor kinases, many with predicted viral origins, outline the divergent mechanisms facilitating multicellularity in these three macroalgal lineages. This work sheds new light on the evolution of multicellularity in three phyla (Rhodophyceae, Chlorophyceae, and Ochrophyceae v. Phaeophyceae) through the lens of large-scale genomics and paves the way for the genomic exploration of macroalgal biology.</p>
Cell-type transcriptomes of the multicellular green alga Volvox carteri yield insights into the evolutionary origins of germ and somatic differentiation programs
GEO Series GSE104835. Volvox carteri. 4 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic atlas of mushroom development reveals conserved genes behind complex multicellularity in fungi [Schizophyllum commune]
GEO Series GSE125198. Schizophyllum commune. 14 samples. Type: Expression profiling by high throughput sequencing.
Multi-omics analysis of aggregative multicellularity
GEO Series GSE249880. Dictyostelium discoideum. 24 samples. Type: Expression profiling by high throughput sequencing.
Single cell RNA-sequencing of iPS cells derived multicellular human liver organoids
GEO Series GSE130073. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Role of Epigenetics in Unicellular to Multicellular Transition in Dictyostelium
GEO Series GSE137604. Dictyostelium discoideum; Dictyostelium discoideum AX4. 117 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Single-Cell Transcriptomic Analysis of Human Lung Reveals Complex Multicellular Changes During Pulmonary Fibrosis II
GEO Series GSE122960. Homo sapiens. 17 samples. Type: Expression profiling by high throughput sequencing.
Data from: Predation and the formation of multicellular groups in algae
Open the record for dataset details and reuse information.
Data from: Implementation of complex biological logic circuits using spatially distributed multicellular consortia
Open the record for dataset details and reuse information.
Multicellular Transcriptional Analysis of Mammalian Heart Regeneration
GEO Series GSE95755. Mus musculus. 64 samples. Type: Expression profiling by high throughput sequencing.
Single-Cell Transcriptomic Analysis of Human Lung Reveals Complex Multicellular Changes During Pulmonary Fibrosis
GEO Series GSE121611. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
scRNA-Seq of iPS cells derived multicellular human liver organoids; RNA-Seq of multicellular human liver organoids derived from 3 different iPS cells
GEO Series GSE130075. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
An Engineered Multicellular Stem Cell Niche for the 3D Derivation of Human Myogenic Progenitors from iPSCs
GEO Series GSE201424. Mus musculus; Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Expression data from follicular lymphoma cells cultured either in suspension either as Multicellular aggregates of lymphoma cells (MALC)
GEO Series GSE41851. Homo sapiens. 6 samples. Type: Expression profiling by array.
Single-cell dissection of the multicellular ecosystem and molecular feature sunderlying microvascular invasion in hepatocellular carcinoma
GEO Series GSE242889. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic Analysis of Hepatic Cells in Multicellular Organotypic Liver Models
GEO Series GSE74424. Rattus norvegicus. 23 samples. Type: Expression profiling by array.
Macrophage containing multicellular lung organoids [bulkRNA-seq]
GEO Series GSE231466. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.