Bio

Academic Appointments


  • Visiting Associate Professor, Genetics

Publications

All Publications


  • Spatial proteomics: a powerful discovery tool for cell biology NATURE REVIEWS MOLECULAR CELL BIOLOGY Lundberg, E., Borner, G. H. 2019; 20 (5): 285–302
  • Voices in methods development. Nature methods Anikeeva, P., Boyden, E., Brangwynne, C., Cissé, I. I., Fiehn, O., Fromme, P., Gingras, A. C., Greene, C. S., Heard, E., Hell, S. W., Hillman, E., Jensen, G. J., Karchin, R., Kiessling, L. L., Kleinstiver, B. P., Knight, R., Kukura, P., Lancaster, M. A., Loman, N., Looger, L., Lundberg, E., Luo, Q., Miyawaki, A., Myers, E. W., Nolan, G. P., Picotti, P., Reik, W., Sauer, M., Shalek, A. K., Shendure, J., Slavov, N., Tanay, A., Troyanskaya, O., van Valen, D., Wang, H. W., Yi, C., Yin, P., Zernicka-Goetz, M., Zhuang, X. 2019; 16 (10): 945–51

    View details for DOI 10.1038/s41592-019-0585-6

    View details for PubMedID 31562479

  • Deep learning is combined with massive-scale citizen science to improve large-scale image classification NATURE BIOTECHNOLOGY Sullivan, D. P., Winsnes, C. F., Akesson, L., Hjelmare, M., Wiking, M., Schutten, R., Campbell, L., Leifsson, H., Rhodes, S., Nordgren, A., Smith, K., Revaz, B., Finnbogason, B., Szantner, A., Lundberg, E. 2018; 36 (9): 820-+

    Abstract

    Pattern recognition and classification of images are key challenges throughout the life sciences. We combined two approaches for large-scale classification of fluorescence microscopy images. First, using the publicly available data set from the Cell Atlas of the Human Protein Atlas (HPA), we integrated an image-classification task into a mainstream video game (EVE Online) as a mini-game, named Project Discovery. Participation by 322,006 gamers over 1 year provided nearly 33 million classifications of subcellular localization patterns, including patterns that were not previously annotated by the HPA. Second, we used deep learning to build an automated Localization Cellular Annotation Tool (Loc-CAT). This tool classifies proteins into 29 subcellular localization patterns and can deal efficiently with multi-localization proteins, performing robustly across different cell types. Combining the annotations of gamers and deep learning, we applied transfer learning to create a boosted learner that can characterize subcellular protein distribution with F1 score of 0.72. We found that engaging players of commercial computer games provided data that augmented deep learning and enabled scalable and readily improved image classification.

    View details for PubMedID 30125267

  • How many human proteoforms are there? Nature chemical biology Aebersold, R., Agar, J. N., Amster, I. J., Baker, M. S., Bertozzi, C. R., Boja, E. S., Costello, C. E., Cravatt, B. F., Fenselau, C., Garcia, B. A., Ge, Y., Gunawardena, J., Hendrickson, R. C., Hergenrother, P. J., Huber, C. G., Ivanov, A. R., Jensen, O. N., Jewett, M. C., Kelleher, N. L., Kiessling, L. L., Krogan, N. J., Larsen, M. R., Loo, J. A., Ogorzalek Loo, R. R., Lundberg, E., MacCoss, M. J., Mallick, P., Mootha, V. K., Mrksich, M., Muir, T. W., Patrie, S. M., Pesavento, J. J., Pitteri, S. J., Rodriguez, H., Saghatelian, A., Sandoval, W., Schlüter, H., Sechi, S., Slavoff, S. A., Smith, L. M., Snyder, M. P., Thomas, P. M., Uhlén, M., Van Eyk, J. E., Vidal, M., Walt, D. R., White, F. M., Williams, E. R., Wohlschlager, T., Wysocki, V. H., Yates, N. A., Young, N. L., Zhang, B. 2018; 14 (3): 206–14

    Abstract

    Despite decades of accumulated knowledge about proteins and their post-translational modifications (PTMs), numerous questions remain regarding their molecular composition and biological function. One of the most fundamental queries is the extent to which the combinations of DNA-, RNA- and PTM-level variations explode the complexity of the human proteome. Here, we outline what we know from current databases and measurement strategies including mass spectrometry-based proteomics. In doing so, we examine prevailing notions about the number of modifications displayed on human proteins and how they combine to generate the protein diversity underlying health and disease. We frame central issues regarding determination of protein-level variation and PTMs, including some paradoxes present in the field today. We use this framework to assess existing data and to ask the question, "How many distinct primary structures of proteins (proteoforms) are created from the 20,300 human genes?" We also explore prospects for improving measurements to better regularize protein-level biology and efficiently associate PTMs to function and phenotype.

    View details for PubMedID 29443976

  • The Human Cell Atlas ELIFE Regev, A., Teichmann, S. A., Lander, E. S., Amt, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., Clevers, H., Deplancke, B., Dunham, I., Eberwine, J., Elis, R., Enard, W., Farmer, A., Fugger, L., Gottgens, B., Hacohen, N., Haniffa, M., Hemberg, M., Kim, S., Klenerman, P., Kriegstein, A., Lein, E. D., Linnarsson, S., Lundberg, E., Lundeberg, J., Majumder, P., Marioni, J. C., Merad, M., Mhlanga, M., Nawijin, M., Netea, M., Nolan, G., Pe'er, D., Phillipakis, A., Ponting, C. P., Quake, S., Reik, W., Rozenblatt-Rosen, O., Sanes, J., Satija, R., Schumacher, T. N., Shalek, A., Shapiro, E., Sharma, P., Shin, J. W., Stegle, O., Stratton, M., Stubbington, M. T., Theis, F. J., Uhlen, M., Van Oudenaarden, A., Wagner, A., Watt, F., Weissman, J., Wold, B., Xavier, R., Yosef, N., HUMAN CELL ATLAS MEETING 2017; 6

    Abstract

    The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

    View details for PubMedID 29206104

  • A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family NATURE COMMUNICATIONS Carreras-Puigvert, J., Zitnik, M., Jemth, A., Carter, M., Unterlass, J. E., Hallstrom, B., Loseva, O., Karem, Z., Calderon-Montano, J., Lindskog, C., Edqvist, P., Matuszewski, D. J., Blal, H., Berntsson, R. A., Haggblad, M., Martens, U., Studham, M., Lundgren, B., Wahlby, C., Sonnhammer, E. L., Lundberg, E., Stenmark, P., Zupan, B., Helleday, T. 2017; 8: 1541

    Abstract

    The NUDIX enzymes are involved in cellular metabolism and homeostasis, as well as mRNA processing. Although highly conserved throughout all organisms, their biological roles and biochemical redundancies remain largely unclear. To address this, we globally resolve their individual properties and inter-relationships. We purify 18 of the human NUDIX proteins and screen 52 substrates, providing a substrate redundancy map. Using crystal structures, we generate sequence alignment analyses revealing four major structural classes. To a certain extent, their substrate preference redundancies correlate with structural classes, thus linking structure and activity relationships. To elucidate interdependence among the NUDIX hydrolases, we pairwise deplete them generating an epistatic interaction map, evaluate cell cycle perturbations upon knockdown in normal and cancer cells, and analyse their protein and mRNA expression in normal and cancer tissues. Using a novel FUSION algorithm, we integrate all data creating a comprehensive NUDIX enzyme profile map, which will prove fundamental to understanding their biological functionality.

    View details for PubMedID 29142246

  • A proposal for validation of antibodies NATURE METHODS Uhlen, M., Bandrowski, A., Carr, S., Edwards, A., Ellenberg, J., Lundberg, E., Rimm, D. L., Rodriguez, H., Hiltke, T., Snyder, M., Yamamoto, T. 2016; 13 (10): 823-?

    View details for DOI 10.1038/NMETH.3995

    View details for Web of Science ID 000385194600015

    View details for PubMedID 27595404

  • Voices of biotech. Nature biotechnology Amit, I., Baker, D., Barker, R., Berger, B., Bertozzi, C., Bhatia, S., Biffi, A., Demichelis, F., Doudna, J., Dowdy, S. F., Endy, D., Helmstaedter, M., Junca, H., June, C., Kamb, S., Khvorova, A., Kim, D., Kim, J., Krishnan, Y., Lakadamyali, M., Lappalainen, T., Lewin, S., Liao, J., Loman, N., Lundberg, E., Lynd, L., Martin, C., Mellman, I., Miyawaki, A., Mummery, C., Nelson, K., Paz, J., Peralta-Yahya, P., Picotti, P., Polyak, K., Prather, K., Qin, J., Quake, S., Regev, A., Rogers, J. A., Shetty, R., Sommer, M., Stevens, M., Stolovitzky, G., Takahashi, M., Tang, F., Teichmann, S., Torres-Padilla, M., Tripathi, L., Vemula, P., Verdine, G., Vollmer, F., Wang, J., Ying, J. Y., Zhang, F., Zhang, T. 2016; 34 (3): 270-275

    View details for DOI 10.1038/nbt.3502

    View details for PubMedID 26963549

  • A Chromosome-centric Human Proteome Project (C-HPP) to Characterize the Sets of Proteins Encoded in Chromosome 17 JOURNAL OF PROTEOME RESEARCH Liu, S., Im, H., Bairoch, A., Cristofanilli, M., Chen, R., Deutsch, E. W., Dalton, S., Fenyo, D., Fanayan, S., Gates, C., Gaudet, P., Hincapie, M., Hanash, S., Kim, H., Jeong, S., Lundberg, E., Mias, G., Menon, R., Mu, Z., Nice, E., Paik, Y., Uhlen, M., Wells, L., Wu, S., Yan, F., Zhang, F., Zhang, Y., Snyder, M., Omenn, G. S., Beavis, R. C., Hancock, W. S. 2013; 12 (1): 45-57

    Abstract

    We report progress assembling the parts list for chromosome 17 and illustrate the various processes that we have developed to integrate available data from diverse genomic and proteomic knowledge bases. As primary resources, we have used GPMDB, neXtProt, PeptideAtlas, Human Protein Atlas (HPA), and GeneCards. All sites share the common resource of Ensembl for the genome modeling information. We have defined the chromosome 17 parts list with the following information: 1169 protein-coding genes, the numbers of proteins confidently identified by various experimental approaches as documented in GPMDB, neXtProt, PeptideAtlas, and HPA, examples of typical data sets obtained by RNASeq and proteomic studies of epithelial derived tumor cell lines (disease proteome) and a normal proteome (peripheral mononuclear cells), reported evidence of post-translational modifications, and examples of alternative splice variants (ASVs). We have constructed a list of the 59 "missing" proteins as well as 201 proteins that have inconclusive mass spectrometric (MS) identifications. In this report we have defined a process to establish a baseline for the incorporation of new evidence on protein identification and characterization as well as related information from transcriptome analyses. This initial list of "missing" proteins that will guide the selection of appropriate samples for discovery studies as well as antibody reagents. Also we have illustrated the significant diversity of protein variants (including post-translational modifications, PTMs) using regions on chromosome 17 that contain important oncogenes. We emphasize the need for mandated deposition of proteomics data in public databases, the further development of improved PTM, ASV, and single nucleotide variant (SNV) databases, and the construction of Web sites that can integrate and regularly update such information. In addition, we describe the distribution of both clustered and scattered sets of protein families on the chromosome. Since chromosome 17 is rich in cancer-associated genes, we have focused the clustering of cancer-associated genes in such genomic regions and have used the ERBB2 amplicon as an example of the value of a proteogenomic approach in which one integrates transcriptomic with proteomic information and captures evidence of coexpression through coordinated regulation.

    View details for DOI 10.1021/pr300985j

    View details for Web of Science ID 000313156300007

    View details for PubMedID 23259914