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Target Submission DateLast updatedStatuspreprint urlpaper urlReferenced Data DOIStatus of Data ProcessingGroupAuthor name(s)PoC namePoC emailTitleBrief descriptionNotes
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05/08/2020PublishedTTD-CalTechEng CL, Lawson M, Zhu Q, Dries R, Koulena N, Takei Y, Yun J, Cronin C, Karp C, Yuan GC, Cai L.Long Cailcai@caltech.eduTranscriptome-scale super-resolved imaging in tissues by RNA seqFISH.Published in Nature
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05/08/2020PublishedTTD-CalTechZhu Q, Shah S, Dries R, Cai L, Yuan GC.Guo-Cheng Yuangcyuan@jimmy.harvard.eduIdentification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data.Published in Nat Biotechnol
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05/08/2020PublishedTTD-CalTechDong R, Yuan GC.Guo-Cheng Yuangcyuan@jimmy.harvard.eduGiniClust3: a fast and memory-efficient tool for rare cell type identification.Published in BMC Bioinformatics
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01/19/2021Draft in ProgressTMC-UFMark Atkinsonatkinson@ufl.eduCirculation in the human spleenLittoral cell and arteriole mapping of red and white pulp in the human spleen
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01/19/2021Draft in ProgressTMC-UFMark Atkinsonatkinson@ufl.eduThymic lobule mapping across age3D map of the lobules
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01/19/2021Draft in ProgressTMC-UFMark Atkinsonatkinson@ufl.eduSpleen and LN mapping across age3D map of primary and secondary follicles with markers of T and B cell memory
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04/20/2021PublishedTTD-CalTechRuben Dries, Qian Zhu, Chee-Huat Linus Eng, Arpan Sarkar, Feng Bao, Rani E George, Nico Pierson, Long Cai, Guo-Cheng YuanGuo-Cheng Yuanguo-cheng.yuan@mssm.eduGiotto, a toolbox for integrative analysis and visualization of spatial expression dataGenome Biology
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10/20/2021Draft in ProgressTTD-PurdueHelminiak, YeDongHye Yedonghye.ye@marquette.eduDLADS 2.0Advanced development of DLADS
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10/20/2021Draft in ProgressTTD-PurdueLi, LaskinJulia Laskinjlaskin@purdue.eduDevelopment of a plastic microfluidic probe for nano-DESI MSI
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11/03/2021doi: https://doi.org/10.1021/acs.jproteome.1c00403)HIVE MC-NYGC
Bingjie Zhang, Avi Srivastava, Eleni Mimitou, Tim Stuart, Ivan Raimondi, Yuhan Hao, Peter Smibert, Rahul Satija
Rahul Satijarsatija@nygenome.orgCharacterizing cellular heterogeneity in chromatin state with scCUT&Tag-proIntroducing scCUT&Tag-pro (single-cell profiling of histone modifications and cell surface proteins), scChromHMM (inferring chromatin states in single cells), and an integrated reference of 9 molecular modalities in human PBMCPublished on bioRxiv, Under Review
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11/16/2021Published
https://arxiv.org/abs/1911.02995
AllHuBMAP ConsortiumMichael P Snydermpsnyder@stanford.eduThe human body at cellular resolutionPublished in Nature
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11/16/2021Draft in ProgressTMC-VU-kidneyElizabeth K. Neumann, Tina Tsui, N. Heath Patterson, Raf Van de Plas, Richard M. Caprioli, and Jeffrey M. Spraggins Jeff Spraggins/Elizabeth Neumannjeff.spraggins@vanderbilt.eduMultimodal Imaging Quality Assurance/Control Methods for Reproducible Chemical Imaging QA/QC Method for MALDI IMSTarget Journal: Analytical Chemistry or JASMS - Still in prep
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11/16/2021Publisheddoi: https://doi.org/10.1038/s42003-021-01797-9)HIVE MC-NYGC
Hao Y*, Hao S*, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zagar M, Hoffman P, Stoeckius M, Papalexi E, Mimitou EP, Jain J, Srivastava A, Stuart T, Fleming LB, Yeung B, Rogers AJ, McElrath JM, Blish CA, Gottardo R, Smibert P*, Satija R*
Rahul Satijarsatija@nygenome.org Integrated Analysis of Multimodal Single-Cell Data. Cell, 2021Introduces Azimuth and Seurat v4Published in Cell
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11/16/2021Publisheddoi: https://doi.org/10.1101/2020.12.23.424201HIVE MC-NYGCMarlon Stoeckius, Shiwei Zheng, Brian Houck-Loomis, Stephanie Hao, Bertrand Z. Yeung, William M. Mauck III, Peter Smibert, and Rahul SatijaRahul Satijarsatija@nygenome.orgCell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomicsPublished in Genome Biology
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11/16/2021Publishedhttps://biorxiv.org/cgi/content/short/2021.10.20.465151v1HIVE TC-CMUHurley K, Ding J, Villacorta-Martin C, Herriges MJ, Jacob A, Vedaie M, Alysandratos KD, Sun YL, Lin C, Werder RB, Huang J, Wilson AA, Mithal A, Mostoslavsky G, Oglesby I, Caballero IS, Guttentag SH, Ahangari F, Kaminski N, Rodriguez-Fraticelli A, Camargo F, Bar-Joseph Z, Kotton DN.Ziv Bar-Josephzivbj@cs.cmu.eduReconstructed Single-Cell Fate Trajectories Define Lineage Plasticity Windows during Differentiation of Human PSC-Derived Distal Lung Progenitors.Published in Cell Stem Cell
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11/16/2021Publishedhttps://biorxiv.org/cgi/content/short/2022.02.15.480384v1HIVE TC-CMUComponentZiv Bar-Josephzivbj@cs.cmu.eduInferring TF activation order in time series scRNA-Seq studies.Published in PLoS Xomput. Biol
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11/16/2021Publishedhttps://chemrxiv.org/engage/chemrxiv/article-details/60c74b12f96a001f0e287564HIVE TC-CMUDing J, Lin C, Bar-Joseph Z.Ziv Bar-Josephzivbj@cs.cmu.eduCell lineage inference from SNP and scRNA-Seq data.Published in Nucleic Acid Res.
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11/16/2021Publishedhttps://chemrxiv.org/engage/chemrxiv/article-details/60c753234c89192bdfad427bHIVE TC-CMULin C, Bar-Joseph Z.Ziv Bar-Josephzivbj@cs.cmu.eduContinuous State HMMs for Modeling Time Series Single Cell RNA-Seq Data.Published in Bioinformatics
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11/16/2021Publishedhttps://chemrxiv.org/engage/chemrxiv/article-details/610dc1ac45805dfc5a825394HIVE TC-CMURashid S, Shah S, Bar-Joseph Z, Pandya R.Ziv Bar-Josephzivbj@cs.cmu.eduDhaka: Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic Data.Published in Bioinformatics
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11/16/2021PublishedTMC-VU-kidneyMarissa Jones, Sung Hoon Cho, Nathan Heath Patterson, Raf Van de Plas, Jeff Spraggins, Mark Boothby, and Richard CaprioliJeff Spragginsjeff.spraggins@vanderbilt.eduDiscovering New Lipidomic Features Using Cell Type Specific Fluorophore Expression to Provide Spatial and Biological Specificity in a Multimodal Workflow with MALDI Imaging Mass Spectrometrymultimodal analysis of spleenPublished in Analytical Chemistry
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11/16/2021Draft in ProgressTMC-VU-kidneyElizabeth K. Neumann, Leonoor Tidman, Lukasz Migas, N. Heath Patterson, Jamie Allen, Maya Brewer, Dave Anderson, Danielle Gutierrez, Raymond Harris, Mark deCastacker, Agnes Fogo, Joana Goncalves, Richard Caprioli, Raf van de Plas, Jeff SpragginsJeff Spragginsjeff.spraggins@vanderbilt.eduA Multimodal Molecular Functional Tissue Unit Atlas of the Human KidneyOur V1 kidney atlas including MALDI IMS, AF microscopy, stained microscopy, CODEX, and RNAseq. Sptially driven data mining and analysis is based on larger functional tissue units including cortex, medulla, and glomeruli and span a wide range of molecular classes including lipidomics, proteomics and transcriptomics.Would like to include in the Consortium Package
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11/16/2021Draft in ProgressTMC-VU-kidneyN. Heath Patterson, Elizabeth K. Neumann, Dave Anderson, Jamie Allen, Maya Brewer, Mark deCastecer, Raf van de Plas, Richard Caprioli, and Jeff M. Spraggins Jeff Spraggins/Heath Pattersonjeff.spraggins@vanderbilt.edu3-D Multimodal IMS on Human Kidneyexperimental and computational tools for generating high resolution 3-D multimodal molecular imaging data including IMS and microscopyWould include as part of a consortium package if there are methods papers included for publication in secondary journals (e.g. Nature Methods). - Still in prep
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11/16/2021PublishedTMC-CalTechJunyue Cao, Malte Spielmann, Xiaojie Qiu, Xingfan Huang, Daniel M. Ibrahim, Andrew J. Hill, Fan Zhang, Stefan Mundlos, Lena Christiansen, Frank J. Steemers, Cole Trapnell & Jay ShendureJay Shendureshendure@uw.eduThe single-cell transcriptional landscape of mammalian organogenesisPublished in Nature
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11/16/2021SubmittedbioRxiv 2021.09.13.460120; doi: https://doi.org/10.1101/2021.09.13.460120HIVE MC-NYGCSaket Choudhary, Rahul SatijaRahul Satijarsatija@nygenome.orgComparison and evaluation of statistical error models for scRNA-seqWe provide recommendations for the usage of statistical error models in scRNA-seq analyses based on a meta-analyses of 58 datasets across technologies and systemsSubmitted to Genome Biology
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11/16/2021SubmittedTMC-VU-kidneyElizabeth Neumann, Emilio Rivera, Nathan Heath Patterson, Jamie Allen, Maya Brewer, Mark deCaestecker, Agnes Fogo, Richard Caprioli, Jeff SpragginsElizaabeth Neumann/Jeff Spragginsjeff.spraggins@vanderbilt.eduHighly Multiplexed Immunofluorescence of the Human Kidney using Co-Detection by Indexing.CODEX of human kidneyIn review at Kidney International. Preprint on bioRxiv https://doi.org/10.1101/2020.12.04.412429
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11/16/2021Draft in ProgressTMC-VU-kidneyJarod Fincher, Katerian Djambazova, Dustin Klein, Martin Dufresne, Kukasz Migas, Raf Van de Plas, Richard Caprioli, Jeff SpragginsJeff Spragginsjeff.spraggins@vanderbilt.eduMolecular Imaging of Neutral Lipids using Silicon Nanopost Arrays and Trapped Ion-Mobility Time-of-Flight Mass Spectrometry
Using Nanopost arrays and TIMS to visualize and differentiate lipid isobars and isomers in tissue Target Journal: Analytical Chemistry
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11/16/2021Draft in ProgressTTD-PurdueYang,Li, LaskinJulia Laskinjlaskin@purdue.eduCorrelative imaging of lipids and proteins
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11/16/2021PublishedHIVE TC-CMUJ Ding, Z Bar-Joseph.Ziv Bar-Josephzivbj@andrew.cmu.eduAnalysis of time series regulatory networks .
Current Opinion in Systems Biology. , 21, Pages 16-24, 2020
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11/16/2021PublishedHIVE TC-CMUH. Zafar, C. Lin, Z. Bar-Joseph.Ziv Bar-Josephzivbj@andrew.cmu.eduSingle-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data
Nature Communications , 11:3055, 2020
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11/16/2021PublishedHIVE TC-CMUC. Lin, J. Ding, Z. Bar-Joseph.Ziv Bar-Josephzivbj@andrew.cmu.eduInferring TF activation order in time series scRNA-Seq studies
PLoS Comput Biol. , 16(2):e1007644, 2020
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11/16/2021PublishedHIVE TC-CMUG. Songwei, H. Wang, A. Alavi, E. Xing and Z. Bar-JosephZiv Bar-Josephzivbj@andrew.cmu.eduSupervised Adversarial Alignment of Single-Cell RNA-seq Data
Journal of Computational Biology , Online ahead of print, 202
1
Original version appeared in Proceedings of the 24th Annual International Conference on Research in Computational Molecular Biology (RECOMB), pp 72-87, 2020
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11/16/2021HIVE TC-CMUY. Yuan and Z. Bar-Joseph. Ziv Bar-Josephzivbj@andrew.cmu.eduDeep learning of gene relationships from single cell time-course expression data. Briefings in Bioinformatics. In press, 2021
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11/16/2021ConceptTTD-PurdueLi, LaskinJulia Laskinjlaskin@purdue.edu
Nano-DESI Imaging with High THroughput and High Spatial Resolution
High throughput/high resolution nano-DESI imaging
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11/16/2021Publishedhttps://www.biorxiv.org/content/10.1101/2021.08.10.455720v1TTD-PNNL-ShiChia-Feng Tsai, Pengfei Zhang, David Scholten, Kendall Martin, Yi-Ting Wang, Rui Zhao, William B. Chrisler, Dhwani B. Patel, Maowei Dou, Yuzhi Jia, Carolina Reduzzi, Xia Liu, Ronald J. Moore, Kristin E. Burnum-Johnson, Miao-Hsia Lin, Chuan-Chih Hsu, Jon M. Jacobs, Jacob Kagan, Sudhir Srivastava, Karin D. Rodland, H. Steven Wiley, Wei-Jun Qian, Richard D. Smith, Ying Zhu, Massimo Cristofanilli, Tao Liu, Huiping Liu & Tujin ShiTujin ShiTujin.Shi@pnnl.govSurfactant-assisted one-pot sample preparation for label-free single-cell proteomicsThe developed surfactant-assisted one-pot sample preparation coupled with mass spectrometry (SOP-MS) method enables routine and precise quantitative single-cell proteomics without transfer of samples minimizing sample loss.Published: Commun Biol. 2021, 4, 265. doi: 10.1038/s42003-021-01797-9
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11/16/2021Publishedhttps://www.biorxiv.org/content/10.1101/2021.09.08.459490v1TTD-PNNL-ShiKendall Martin, Tong Zhang, Tai-Tu Lin, Amber N. Habowski, Rui Zhao, Chia-Feng Tsai, William B. Chrisler, Ryan L. Sontag, Daniel J. Orton, Yong-Jie Lu, Karin D. Rodland, Bin Yang, Tao Liu, Richard D. Smith, Wei-Jun Qian, Marian L. Waterman, H. Steven Wiley, Tujin ShiTujin ShiTujin.Shi@pnnl.govFacile one-pot nanoproteomics for label-free proteome profiling of 50-1000 mammalian cellsThe developed one-pot nanoproteomics enables a facile and robust sample preparation method towards deep proteome profiling of small numbers of cells and low-input samples.
Published: J. Proteome Res. 2021, 20, 9, 4452–4461 doi.: 10.1021/acs.jproteome.1c00403
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11/16/2021Submittedhttps://www.biorxiv.org/content/10.1101/507566v1.fullTMC-Stanford
Maria Brbic, Kaidi Cao, John Hickey, Yuqi Tan, Mike Snyder, Garry Nolan, Jure Leskovec
John Hickeyjwhickey@stanford.edu
Location-Aware Annotation of Spatially ResolvedSingle-cell Datasets with Geometric Deep Learning
Geometric deep learning for transferring cell type labels and discovering new ones in other spatial datasets
Would like to be a part of the package
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11/16/2021Publishedhttps://www.biorxiv.org/content/10.1101/576827v2TMC-StanfordJohn Hickey, Yuqi Tan, Yury Goltsev, Garry NolanJohn Hickeyjwhickey@stanford.edu
Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data
Comparison of normalization and unsupervised clustering techniques for CODEX Multiplexed imaging data
Published in Frontiers Immunology
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11/16/2021Publishedhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6941488/TMC-VU-kidneyElizabeth K. Neumann, Lukasz G. Migas, Jamie L. Allen, Richard M. Caprioli, Raf Van de Plas, and Jeffrey M. SpragginsJeff Spraggins/Elizabeth Neumannjeff.spraggins@vanderbilt.eduSpatial Metabolomics of the Human Kidney using MALDI Trapped Ion Mobility Imaging Mass SpectrometryMALDI IMS of small metabolites in human kdineyPublished in Analytical Chemistry
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11/16/2021Publishedhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6946663/TMC-VU-kidneyJosiah McMillen, Jarod Fincher, Dustin Klein, Jeff Spraggins, Richard CaprioliJeff Spragginsjeff.spraggins@vanderbilt.eduEffect of MALDI matrices on lipid analysis of biological tissues using MALDI-2 postionization mass spectrometryPublished in the Journal of Mass Spectrometry
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11/16/2021Submittedhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8553253/TMC-VU-kidneyJosiah Mc Millen, Danielle Gutierrez, Audra Judd, Jeff Spraggins, Richard CaprioliJeff Spragginsjeff.spraggins@vanderbilt.eduEnhancement of tryptic peptide signals from tissue sections using MALDI IMS post-ionization (MALDI-2)utalization of MALDI-2 for improving sensitivity of typtic peptide IMS experimentsIn review at Analytical Chemistry
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11/16/2021Draft in ProgressTMC-VU-kidneyNathan Heath Patterson, Elizabeth Neumann, Kavya Sharman, David Anderson, Jamie Allen, Maya Brewer, Mark deCastecker, Richard Caprioli, Raf Van de Plas, Jeff SpragginsHeath Patterson/Jeff Spragginsjeff.spraggins@vanderbilt.eduAutofluorescnec microscopy as a label free tool for renal histology and glomerular segmentationDeveloping methods for AF microscopy segmentation of fuctional tissue units in the human kidney. Submitting to Kidney International as a technical note in Feb 2021.
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11/16/2021PublishedTMC-VU-eye,pancreasPrentice BM, Hart NJ, Phillips N, Haliyur R, Judd A, Armandala R, Spraggins JM, Lowe CL, Boyd KL, Stein RW, Wright CV, Norris JL, Powers AC, Brissova M, Caprioli RM.Jeff Spragginsjeff.spraggins@vanderbilt.eduImaging mass spectrometry enables molecular profiling of mouse and human pancreatic tissue.published in Diabetologia
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11/16/2021PublishedTMC-VU-kidneyRyan DJ, Spraggins JM, Caprioli RM.Richard Capriolirichard.m.caprioli@Vanderbilt.EduProtein identification strategies in MALDI imaging mass spectrometry: a brief review.Published in Curr Opin Chem Biol
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11/16/2021PublishedTMC-VU-kidneyPrentice BM, McMillen JC, Caprioli RMRichard Capriolirichard.m.caprioli@Vanderbilt.EduMultiple TOF/TOF Events in a Single Laser Shot for Multiplexed Lipid Identifications in MALDI Imaging Mass Spectrometry.Published in Int J Mass Spectrom
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11/16/2021PublishedTMC-VU-kidneyEmilio S. Rivera, Kate V. Djambazova, Elizabeth K. Neumann, Richard M. Caprioli, Jeff M. SpragginsJeff Spragginsjeff.spraggins@vanderbilt.edu Integrating Ion Mobility and Imaging Mass Spectrometry for Comprehensive Analysis of Biological Tissues: A brief review and perspectiveReview on ion mobility IMSPublished in the Journal of Mass Spectrometry
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11/16/2021PublishedTMC-VU-kidneyElizabeth K. Neumann, Kate V. Djambazova, Richard M. Caprioli, Jeff M. Spraggins Jeff Spraggins/Elizabeth Neumannjeff.spraggins@vanderbilt.eduMultimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and MedicineReview on multimodal IMSPublished in the Journal of the American Society of Mass Spectrometry
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11/16/2021PublishedTMC-VU-eye,pancreasMirazul Islam, Bob Chen, Jeff Spraggins, Ryan Kelly, Ken LauJeff Spragginsjeff.spraggins@vanderbilt.eduUse of single cell-omic technologies to study the gastrointestical tract and diseases, from single cell identities to patient featuresreviewPublished in Gastroenterology
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11/16/2021SubmittedTMC-VU-kidneyLee HH, Tang Y, Xu K, Bao S, Fogo AB, Harris R, deCaestecker MP, Heinrich M, Spraggins JM, Huo Y, and Landman BA
Bennett Landman/Jeff Spragginsjeff.spraggins@vanderbilt.eduMulti-Contrast Computed Tomography Healthy Kidney Atlasaverage anatomical atlas of human kidneyIn review at IEEE Transactions on Biomedical Engineering
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11/16/2021SubmittedTMC-VU-kidneyTang Y, Bao S, Gao R, Mathurin C, Lee HH, Yu X, Nath V, Savoie BV, Huo Y, Xu Z, Harris R, de Caestecker MP, Spraggins JM, Fogo AB, and Landman BA
Bennett Landman/Jeff Spragginsjeff.spraggins@vanderbilt.eduAutomatic Segmentation of the Renal Cortex, Medulla and Pelvicalyceal System with Deep Neural Networks: Assessment of Quantitative Measurements and ReproducibilityMethods for segmenting histological regions of the kidney from CT scansIn review at Radiology: Artificial Intelligence
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11/16/2021Draft in ProgressTMC-VU-kidneyElizabeth Neumann, Kavya Sharman, Nathan Heath Patterson, Andy Weiss, Jamie Allen, Jessica Sheldon, Raf Van de Plas, Richard Caprioli, Eric Skaar, and Jeff SpragginsJeff Spragginsjeff.spraggins@vanderbilt.eduDeciphering Host Immune Responses with MALDI IMS and CODEX ImmunofluorescenceMultimdoal imaging (IMS, CODEX, AF, Stained Microscopy) of staph infected tissues. Using this analysis pipeline to uncover molecular profiles associated with specific cell types and FTUs in the tissue microenvironmentTarget Journal: Nature Methods
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11/16/2021Draft in ProgressTMC-UFMark Atkinsonatkinson@ufl.eduT follicular helper cells in human spleen and lymph nodesMapping of these cells in context of maturity of immune system has not been done in humans
53
11/16/2021PublishedHIVE TC-CMUY. Yuan, Z. Bar-JosephZiv Bar-Josephzivbj@andrew.cmu.edu
GCNG: Graph convolutional networks for inferring cell-cell interactions
Genome Biology , 21(1):300, 2020
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11/16/2021PublishedHIVE TC-CMUD. Li, J. Ding, Z. Bar-JosephZiv Bar-Josephzivbj@andrew.cmu.eduIdentifying signaling genes in spatial single cell expression data.
Bioinformatics , in press, 2020
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11/16/2021PublishedHIVE TC-CMUA. Alavi, Z. Bar-Joseph.Ziv Bar-Josephzivbj@andrew.cmu.eduIterative point set registration for aligning scRNA-seq data.
PLoS Comput Biol. , 16(10):e1007939, 2020
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11/30/2021Draft in Progresshttps://osf.io/z9gm3/HIVE TC-HMSHIVE teamNils Gehlenborgnils@hms.harvard.eduHuBMAP Data Portal: A Resource for Multi-Modal Spatial and Single-Cell Data of Human TissuesManuscript introducing the HuBMAP Portal
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12/03/2021Publishedhttps://www.nature.com/articles/s41592-019-0404-0TTD-HarvardKishi JY, Lapan SW, Beliveau BJ, West ER, Zhu A, Sasaki HM, Saka SK, Wang Y, Cepko CL, Yin P.Jocelyn KishiJocelyn.Kishi@wyss.harvard.eduSABER amplifies FISH: enhanced multiplexed imaging of RNA and DNA in cells and tissues.Published in Nat. Methods
58
12/06/2021Publishedhttps://www.sciencedirect.com/science/article/pii/S0092867420313908?via%3DihubTTD-Yale
Liu Y#, Yang M#, Deng Y#, Su G, Enninful A, Guo CC, Tebaldi T, Zhang D, Kim D, Bai Z, Norris E, Pan A, Li J, Xiao Y, Halene S, Fan R.
Rong Fanrong.fan@yale.edu
High-Spatial-Resolution Multi-Omics Atlas Sequencing of Mouse Embryos via Deterministic Barcoding in Tissue
Deterministic barcoding in tissue enables NGS-based spatial multi-omics mapping. DBiT-seq identified spatial patterning of major tissue types in mouse embryos. Revealed retinal pigmented epithelium and microvascular endothelium at cellular level. Direct integration with scRNA-seq data allows for rapid cell type identification
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12/06/2021Draft in Progresshttps://www.biorxiv.org/content/10.1101/2020.10.13.338475v2TTD-Yale
Liu Y, Enninful A, Deng Y, Fan R. Spatial transcriptome sequencing of FFPE tissues at the cellular level.
Rong Fanrong.fan@yale.eduSpatial transcriptome sequencing of FFPE tissues at cellular levelwe demonstrated spatial transcriptome sequencing of embryonic and adult mouse FFPE tissue sections at cellular level (25μm pixel size) with high coverage (>1,000 genes per pixel). Spatial transcriptome of an E10.5 mouse embryo identified all major anatomical features in the brain and abdominal region. Integration with singlecell RNA-seq data for cell type identification indicated that most tissue pixels were dominated by single-cell transcriptional phenotype.Currently under review in Nature Biotechnology 2021
60
12/06/2021Draft in ProgressTTD-YaleLiu Y, Fan R.Rong Fanrong.fan@yale.eduLiu Y, Fan R. High-plex antibody based spatial protein sequencing of human tonsilTo be sumitted to Nature Methods. 2021
61
12/06/2021Submittedhttps://doi.org/10.1101/2021.03.11.434985TTD-YaleDeng Y, Zhang D, Liu Y, Su G, Enninful A, Bai Z, Fan R. Rong Fanrong.fan@yale.edu
Spatial Epigenome Sequencing at Tissue Scale and Cellular Level
Currently under review
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12/06/2021Submittedhttps://doi.org/10.1101/2021.06.06.447244TTD-Yale
Deng Y, Bartosovic M, Ma S, Zhang D, Liu Y, Qin X, Su G, Xu M, Halene S, Craft J, Castelo-Branco G, Fan R.
Rong Fanrong.fan@yale.edu
Spatial-ATAC-seq: spatially resolved chromatin accessibility profiling of tissues at genome scale and cellular level
Currently under review
63
12/28/2021Publisheddoi: https://doi.org/10.1101/2021.07.16.452703HIVE MC-NYGCC Hafemeister†, R Satija†Rahul Satijarsatija@nygenome.org
Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
Published in Genome Biology
64
12/28/2021Publisheddoi: https://doi.org/10.1101/2021.07.28.454201HIVE MC-NYGC
T Stuart*, A Butler*, P Hoffman, C Hafemeister, E Papalexi, WM Mauck, M Stoeckius, P Smibert, R Satija†
Rahul Satijarsatija@nygenome.orgComprehensive Integration of Single-Cell DataPublished In Cell
65
12/28/2021Publishedhttps://arxiv.org/abs/2005.00595HIVE MC-NYGC
Rood J, Stuart T*, Ghazanfar S*, Biancalini T*, Fisher E, Butler A, Hupalowska A, Gaffney L, Mauck WM, Eraslan G, Marioni JC*, Regev A*, Satija R*
Rahul Satijarsatija@nygenome.orgTowards a Common Coordinate Framework for the Human BodyPublished In Cell
66
12/28/2021Submittedhttps://arxiv.org/abs/2102.12030HIVE MC-NYGC
Alsu Missarova, Jaison Jain, Andrew Butler, Shila Ghazanfar, Tim Stuart, Maigan Brusko, Clive Wasserfall, Henry Nick, Todd Brusko, Mark Atkinson, Rahul Satija, John Marioni
John Marionijohn.marioni@ebi.ac.uk
geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq
wW have developed a novel cluster-free, batch-aware and flexible approach, geneBasis, which takes scRNA-seq data and the number of genes to be selected and returns a ranked gene panel of the designated size. Importantly, we provide a comprehensive set of metrics – at multiple levels of granularity - that can be used to assess the completeness of the suggested panel. Submitted to Genome Biology
67
12/28/2021Publishedhttps://arxiv.org/abs/2112.02159HIVE MC-NYGC,
TTD-Caltech
T. Lohoff, S. Ghazanfar, A. Missarova, N. Koulena, N. Pierson, J. A. Griffiths, E. S. Bardot, C.-H. L. Eng, R. C. V. Tyser, R. Argelaguet, C. Guibentif, S. Srinivas, J. Briscoe, B. D. Simons, A.-K. Hadjantonakis, B. Göttgens, W. Reik, J. Nichols, L. Cai & J. C. Marioni
John Marionijohn.marioni@ebi.ac.ukIntegration of spatial and single-cell transcriptomic data elucidates mouse organogenesisWe apply an image-based single-cell transcriptomics method, sequential fluorescence in situ hybridization (seqFISH), to detect mRNAs for 387 target genes in tissue sections of mouse embryos at the 8–12 somite stage. By integrating spatial context and multiplexed transcriptional measurements with two single-cell transcriptome atlases, we characterize cell types across the embryo , and characterize fundamental steps in the patterning of the midbrain–hindbrain boundary (MHB) and the developing gut tube.Published; Nature Biotechnology (2021)
68
02/14/2022Published10.26434/chemrxiv.12494705.v1Cross-consortia
John W. Hickey, Elizabeth K. Neumann, Andrea J. Radtke, Jeannie M. Camarillo, Rebecca T. Beuschel,Alexandre Albanese, Elizabeth McDonough, Julia Hatler, Anne E. Wiblin, Jeremy Fisher, Josh Croteau, Anup Sood, Richard M. Caprioli, R. Michael Angelo, Garry P. Nolan, Kwanghun Chung, Stephen M. Hewitt, Ronald N. Germain, Jeffrey M. Spraggins, Emma Lundberg, Mike P. Snyder, Neil L. Kelleher, Sinem K. Saka
Sinem Sakasinem.saka@embl.de
Spatial mapping of protein composition and tissue organization:
A primer for multiplexed antibody-based imaging
A primer and guide for multiplexed antibody-based imagingarXiv: https://arxiv.org/abs/2107.07953 (posted on 16 July 2021)
Accepted to Nature Methods on 03.08.2021
69
2/15/22ConceptTMC-UCSD-ZhangGloria Pryhuber
gloria_pryhuber@urmc.rochester.edu
Description of process and outcome of Policy WG for HuBMAP ConsortiumLooking for co-authors
70
02/17/2022SubmittedTTD-UCSDXu Chen, Riccardo Calandrelli, John Girardini, Zhangming Yan, Zhiqun Tan, Xiangmin Xu, Annie Hiniker, Sheng Zhong Sheng Zhongszhong@ucsd.eduPHGDH expression increases with progression of Alzheimer’s disease pathology and symptomsCombining single-cell nucleus RNA-seq, spatial protein staining to reveal a consistent increase of phosphoglycerate dehydrogenase (PHGDH) mRNA and protein levels in two mouse models and four human cohorts in Alzheimer’s disease brains compared to age- and sex-matched control brains. Remarkably, the increase of PHGDH expression in human brain correlates with symptomatic development and disease pathology.Accepted
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02/23/2022Publishedhttps://www.nature.com/articles/s41587-019-0207-yTTD-HarvardSaka SK, Wang Yu, Kishi JY, Zhu A, Zeng Y, Xie W, Kirli K, Yapp C, Cicconet M, Beliveau BJ, Lapan SW, Yin S, Lin M, Boyden ES Boyden, Kaeser PS, Pihan G,
Church GM, Yin P.
Sinem Sakasinem.saka@wyss.harvard.eduImmuno-SABER enables highly multiplexed and amplified protein imaging in tissuesPublished in Nature Biotechnology
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03/07/2022PublishedTTD-PurdueDavid Helminiak, Hang Hu, Julia Laskin, DongHye YeDongHye Yedonghye.ye@marquette.eduDeep Learning Approach for Dynamic Sparse Sampling for High-Throughput Mass Spectrometry ImagingFirst paper on DLADSPublished In: Proceedings of the 2021 IS&T International Symposium on Electronic Imaging (EI 2021), January 18–22, 2021
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03/07/2022PublishedTTD-PurdueR. Yin, K. E. Burnum-Johnson, X. Sun, S. K. Dey, J. LaskinJulia Laskinjlaskin@purdue.eduHigh Spatial Resolution Imaging of Biological Tissues Using Nanospray Desorption Electrospray Ionization Mass SpectrometryNat. Protoc. 14,  3445–3470 (2019). DOI: 10.1038/s41596-019-0237-4featured on the cover of Nature Protocols
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03/07/2022PublishedTTD-PurduePaul D. Piehowski*, Ying Zhu*, Lisa M. Bramer, Kelly G. Stratton, Rui Zhao, Daniel J. Orton,
Ronald J. Moore, Jia Yuan, Hugh D. Mitchell, Yuqian Gao, Bobbie-Jo M. Webb-Robertson,
Sudhansu K. Dey, Ryan T. Kelly & Kristin E. Burnum-Johnson
Kristin Burnum-Johnsonkristin.burnum-johnson@pnnl.govAutomated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolutionNat Commun 11, 8 (2020). https://doi.org/10.1038/s41467-019-13858-z
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03/08/2022Publishedhttps://doi.org/10.1038/s41587-019-0290-0TMC-UCSDChen S, Lake BB, Zhang K.Song Chensochen@ucsd.eduHigh-throughput sequencing of the transcriptome and chromatin accessibility in the same cellPublished in Nature Biotechnology
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03/14/2022Publishedhttps://arxiv.org/abs/2112.02159https://doi.org/10.3389/frvir.2021.727344
Kidney, male, left: https://doi.org/10.48539/hbm792.npxx.335
RUI experiment + Luddy: https://doi.org/10.5281/zenodo.5658725
RUI control: https://doi.org/10.5281/zenodo.5189516
HIVE MC-IUBueckle, Andreas, Kilian Buehling, Patrick C. Shih, Katy BörnerAndreas Bueckleabueckle@iu.eduOptimizing Performance and Satisfaction in Matching and Movement Tasks in Virtual Reality with Interventions Using the Data Visualization Literacy FrameworkPublished in Frontiers in Virtual Reality: https://doi.org/10.3389/frvir.2021.727344
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03/15/2022Submittedhttps://osf.io/y8thv/HIVE TC-HMSNils Gehlenborgnils@hms.harvard.eduUser-Centric Process of Designing a Molecular & Cellular Query Interface for Biomedical ResearchDescription of the design process and results for the molecular and cellular query interface in the HuBMAP portal
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03/15/2022Submittedhttps://www.biorxiv.org/content/10.1101/2021.08.16.456524v2TMC-UCSD-Zhang
Brendon R Lutnick, David Manthey, Jan U Becker, Brandon Ginley, Katharina Moos, Jonathan E Zuckerman, Luis Rodrigues, Alexander J Gallan, Laura Barisoni, Charles E Alpers, Xiaoxin Wang, Komuraiah Myakala, Bryce A Jones, Moshe Levi, Jeffrey B Kopp, Teruhiko Yoshida, Seung Seok Han, Sanjay Jain, Avi Z Rosenberg, Kuang Yu Jen, Pinaki Sarder, for the Kidney Precision Medicine Project
Pinaki Sarderpinakisa@buffalo.edu
A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology
An automated online tool for end-users for segmentation of tissue micro-compartments from brightfield microscopy tissue biopsy images, with opportunity to improve segmentation via human AI interaction via using an interactive training method. Tool has been tested extensively in kidney tissues and in paraffin sections. It can be easily extended for frozen sections and other organ systems.

The source code is available on GitHub at https://github.com/SarderLab/Histo-cloud, and packaged as a pre-built Docker image https://hub.docker.com/r/sarderlab/histo-cloud. This data sharing allows for easy deployment on a remote server for use as well as further development by the community over the web. Additionally, a publicly available instance of Histo-Cloud is available for the community at: athena.ccr.buffalo.edu. All the models described are available in the <Collections> section in the <Segmentation models> folder on athena.ccr.buffalo.edu or at https://bit.ly/3ejZhab. Documentation for using this tool is available at https://bit.ly/3nNMpfH. A video overview of Histo-Cloud is available at https://bit.ly/3r5GrZr.
In revision
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03/17/2022Draft in ProgressTMC-CHOPTBD, representing the CHOP TMC heart teamLiming Peipeil@chop.eduA multimodal atlas of human heart at single-cell resolution
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03/17/2022Submittedhttps://www.biorxiv.org/content/10.1101/2021.01.25.427845v1TMC-CHOPPang M, Peng T, Chen GM, Tan KKai Tantank1@chop.edu
GLUER: integrative analysis of single-cell omics and imaging data by deep neural network
81
03/17/2022Publishedhttps://www.science.org/doi/10.1126/sciadv.abf1356?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmedTMC-CHOPHu Y, Peng T, Gao L, Tan KKai Tantank1@chop.edu
CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data
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03/17/2022Publishedhttps://genome.cshlp.org/content/31/1/101.longTMC-CHOPUzun Y, Wu H, Tan KKai Tantank1@chop.edu
Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data
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03/17/2022Publishedhttps://opg.optica.org/oe/fulltext.cfm?uri=oe-30-2-2453&id=468365TMC-CHOPXie H, Pan Z, Zhou L, Zaman FA, Chen DZ, Jonas JB, Xu W, Wang Y, Wu XXiaodong Wuxiaodong-wu@uiowa.edu
Globally optimal OCT surface segmentation using a constrained IPM optimization
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03/21/2022Submittedhttps://www.biorxiv.org/content/10.1101/2021.10.20.465151v1TMC-UF
Seth Currlin, Harry S. Nick, Marda Jorgensen, Jerelyn A. Nick, Maigan A. Brusko, Hunter Hakimian, Jesus Penaloza-Aponte, Natalie Rodriguez, Miguel Medina-Serpas, Mingder Yang, Irina Kusmartseva, Todd M. Brusko, Kevin Otto, Amanda L. Posgai, Clive H. Wasserfall, View ORCID ProfileMark A. Atkinson
Mark Atkinsonatkinson@ufl.eduInnervation of human spleen, thymus and LNlight sheet mapping of nerves and blood vessels
https://biorxiv.org/cgi/content/short/2021.10.20.465151v1
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04/04/2022Submittedhttps://osf.io/gzu27/HIVE TC-HMSNils Gehlenborgnils@hms.harvard.eduDrava: Disentangled Representation Learning as A Visual Analysis Approach for Concept-Driven Exploration of Small Multiples
86
04/04/2022Submittedhttps://osf.io/b76nt/HIVE TC-HMSNils Gehlenborgnils@hms.harvard.eduPolyphony: an Interactive Transfer Learning Framework for Single Cell Data Integration and Analysis
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04/04/2022Publishedhttps://arxiv.org/abs/2005.00595https://doi.org/10.1109/TVCG.2020.3028948HIVE TC-HMSNils Gehlenborgnils@hms.harvard.eduA Generic Framework and Library for Exploration of Small Multiples through Interactive PilingGeneralized visualization framework for small multiple visualization.
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04/14/2022SubmittedHigh-throughput Nano-DESI Mass Spectrometry Imaging of Biological Tissues Using an Integrated Microfluidic Probe | Analytical Chemistry | ChemRxiv | Cambridge Open EngageTTD-PurdueX. Li, H. Hu, R. Yin, Y. Li, X. Sun, S. K. Dey, and J. Laskin Julia Laskinjlaskin@purdue.eduHigh-throughput Nano-DESI Mass Spectrometry Imaging of Biological Tissues Using an Integrated Microfluidic Probe
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04/14/2022Publishedhttps://chemrxiv.org/articles/An_Integrated_Microfluidic_Probe_for_Mass_Spectrometry_Imaging_of_Biological_Samples/12275507TTD-PurdueX. Li, R. Yin, H. Hu, Y. Li, X. Sun, S.K. Dey, and J. LaskinJulia Laskinjlaskin@purdue.eduAn Integrated Microfluidic Probe for Mass Spectrometry Imaging of Biological Samples
Angew. Chem., 59, 22388-22391 (2020). DOI: 10.1002/anie.202006531submitted to Angew Chem and ChemRxiv: https://chemrxiv.org/articles/An_Integrated_Microfluidic_Probe_for_Mass_Spectrometry_Imaging_of_Biological_Samples/12275507
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04/14/2022Publishedhttps://chemrxiv.org/engage/chemrxiv/article-details/60c753234c89192bdfad427bTTD-PurdueHang Hu, Ruichuan Yin, Hilary M. Brown, Julia LaskinJulia Laskinjlaskin@purdue.eduSpatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate ThresholdingNew method for the automated segmentation of MSI dataPublished: Anal Chem, 2021 DOI:10.1021/acs.analchem.0c04798; Chemrxiv: https://chemrxiv.org/engage/chemrxiv/article-details/60c753234c89192bdfad427b
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04/14/2022Publishedhttps://chemrxiv.org/engage/chemrxiv/article-details/610dc1ac45805dfc5a825394TTD-PurdueHang Hu, Ruichuan Yin, Hilary M. Brown, Julia LaskinJulia Laskinjlaskin@purdue.edu
Self-supervised Clustering of Mass Spectrometry Imaging Data Using Contrastive Learning
New approach for self-supervised classification of mass spec imaging data using contrastive learningAdvance article in Chemical Science: https://pubs.rsc.org/en/Content/ArticleLanding/2021/SC/D1SC04077D, Chemrxiv: https://chemrxiv.org/engage/chemrxiv/article-details/610dc1ac45805dfc5a825394
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04/19/2022PublishedPhD thesis; no preprinthttps://www.doi.org/10.13140/RG.2.2.33214.00328
Kidney, male, left: https://doi.org/10.48539/hbm792.npxx.335
RUI experiment + Luddy: https://doi.org/10.5281/zenodo.5658725
RUI control: https://doi.org/10.5281/zenodo.5189516
HIVE MC-IUAndreas BueckleKaty Börnerkaty@indiana.eduOptimizing performance and satisfaction in virtual reality environments with interventions using the data visualization literacy framework (PhD thesis)Defended and published
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04/19/2022Draft in ProgressHIVE MC-IU
Quardokus|Caron|Balhoff|Hardi|Roncaglia|Musen|Herr|Osumi-Sutherland
David Osumi-Sutherlanddavidos@ebi.ac.uk
A general strategy for generating expert-guided, simplified views of ontologies
Abstract
The use
of common biomedical ontology standards within and across different communities improves data integration. Expanding to fit the needs of these multiple communities and to conform to good engineering practices required for scalable development, means that widely
used biomedical ontologies inevitably become larger, and more complex than the immediate requirements of individual communities and users. This can often make ontologies daunting for non-experts to use, even with tooling that lowers the barriers to searching
and browsing. We, therefore, need mechanisms to provide simple, tailored views of these ontologies - limiting the displayed classes and relationship types.

The ASCT+B
tables are an expert-curated resource detailing human anatomical structures, cell types, biomarkers, and their relationships to each other. These tables support the development of a 3D atlas of human anatomy, integrating diverse omics analysis datasets, many
at the single-cell level, using a combination of semantic and image/coordinate based approaches. Entries in these tables are mapped to the multi-species anatomy ontology Uberon, and the Cell Ontology (CL). Here we describe a method for validating these tables
against the CL and Uberon, using the results to harmonise and improve both tables and source ontologies and to drive the generation of a simplified, combined view of Uberon and CL in which all relationships are true according to the semantics of the source
ontologies.
Partial text
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04/19/2022Submittedhttps://osf.io/wd2gu/HIVE TC-HMSNils Gehlenborgnils@hms.harvard.eduViv: Multiscale Visualization of High-Resolution Multiplexed Tissue Data on the WebAdvanced imaging data visualization with modern web technologies.Submitted
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04/19/2022Publishedhttps://arxiv.org/abs/2102.12030https://doi.org/10.1371/journal.pone.0258103
https://doi.org/10.5281/zenodo.5189516
Kidney, male, left: https://doi.org/10.48539/hbm792.npxx.335
HIVE MC-IUAndreas Bueckle, Kilian Buehling, Patrick C. Shih Katy BörnerKaty Börnerkaty@indiana.edu3D Virtual Reality vs. 2D Desktop Registration User Interface Comparison Published in PLOS ONE
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04/19/2022Publishedhttps://www.biorxiv.org/content/10.1101/2021.05.31.446440v1https://www.nature.com/articles/s41556-021-00788-6Requested new DOI for 1st and 2nd ASCT+B and 3D ref organ release from IEC

DOIs for 11 ASCT+B tables and 26 organs see https://static-content.springer.com/esm/art%3A10.1038%2Fs41556-021-00788-6/MediaObjects/41556_2021_788_MOESM1_ESM.pdf
HIVE MC-IUKaty Börner, Sarah A. Teichmann, Ellen M. Quardokus, James Gee, Kristen Browne, David Osumi-Sutherland, Bruce W. Herr II, Andreas Bueckle, Hrishikesh Paul, Muzlifah A. Haniffa, Laura Jardine, Amy Bernard, Song-Lin Ding, Jeremy A. Miller, Shin Lin, Marc K. Halushka, Avinash Boppana, Teri A. Longacre, John Hickey, Yiing Lin, M. Todd Valerius, Yongqun He, Gloria Pryhuber, Xin Sun, Marda Jorgensen, Andrea J. Radtke, Clive Wasserfall, Fiona Ginty, Jonhan Ho, Joel Sunshine, Rebecca T. Beuschel, Maigan Brusko, Sujin Lee, Rajeev Malhotra, Sanjay Jain, Griffin WeberKaty Börnerkaty@indiana.eduAnatomical Structures, Cell Types, and Biomarkers of the Human Reference AtlasPublished in Nature Cell Biology
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04/28/2022Draft in ProgressTMC-PNNLSwensen AC, Chen J, Campbell-Thompson M, Mathews CE, Qian WWeijun QianWeijun.Qian@pnnl.govEndoplasmic Reticulum Dysfunction and Translational Deficiency in Islets of Subjects with Pre-symptomatic Stage 1 Type 1 Diabetes Revealed by Nanoproteomics ProfilingPrior to the onset of type-1 diabetes (T1D) we know little of the molecular changes that occur in human islets . Herein, we applied a nanoproteomics approach to identify proteomic changes in human islets of subjects with pre-symptomatic T1D. Islet sections were collected through laser microdissection (LMD) of frozen pancreatic tissues of organ donors positive for islet autoantibodies compared to age/sex-matched controls. The overall analyses revealed alterations associated with several major functions, including reduced translation and protein synthesis, ER dysfunction and stress, increased immune response, cell death, and increased compensatory upregulation in metabolism and exocytosis.
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05/19/2022Draft in ProgressTMC-UCSD-LaurentTMC-UCSD-LaurentScott Lindsay-Hewett, Cayla Mason, Bill Flynn, Valentina Stanley, Marni Jacobs, Ana Rodriguez-Soto, Chanond Nasamran, Elise Courtois, Santhosh Sivajothi, Rebecca Rakow-Penner, Paul Robson, Mana Parast, Louise Laurent, Kathleen FischKathleen Fischkfisch@health.ucsd.eduA spatially-resolved multimodal atlas of the human term placentaVersion 1 of the human placenta atlas includes bulk RNAseq, bulk ATACseq, 10x multiome, single cell RNAseq, IMC, GeoMX spatial transcriptomics, MRI, ultrasound, ECM proteomics from 4 regions from each placenta including maternal and fetal surface from two regions, balanced with fetal sexes and mode of delivery (cesarian vs vaginal)
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06/22/2022Publishedhttps://doi.org/10.1016/j.aca.2021.338522TMC-VU-kidneyLeonoor Tideman, Lukasz Migas, Katerina Djambazova, Nathan Heath Patterson, Richard Caprioli, Jeff Spraggins, Raf Van de PlasRaf Van de Plas/Jeff Spragginsraf.vandeplas@tudelft.nlAutomated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive ExplanationsInterpretive machinel learning for IMS data miningIn review at Analytica Chimica Acta. Preprint available on bioRxiv at https://doi.org/10.1101/2020.12.23.424201
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06/23/2022Submittedhttps://chemrxiv.org/engage/chemrxiv/article-details/62aa4ea5f70c21fa962fdb7cTTD-PurdueHu, Helminiak, Ye, LaskinJulia Laskinjlaskin@purdue.eduHigh-Throughput Mass Spectrometry Imaging with Dynamic Sparse SamplingExperimental implementation of DLADSSubmitted to ACS Measurement Science Au