Auer, Manfred

English

eScholarship - University of California

Lawrence Berkeley National LaboratoryLBL PublicationsTitleUltrastructural cellular signatures: does cellular form follow function?Permalinkhttps://escholarship.org/uc/item/3dc786v8JournalNational Science Review, 6(5)ISSN2095-5138AuthorAuer, ManfredPublication Date2019-10-01DOI10.1093/nsr/nwz057 Peer reviewedeScholarship.org Powered by the California Digital LibraryUniversity of California© The Author(s) 2018. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the originalwork is properly cited.PERSPECTIVESBIOLOGY & BIOCHEMISTRYUltrastructural cellular signatures: does cellular form follow function?Manfred AuerIn his watershed 1896 essay The TallOffice Building Artistically Considered,protomodern architect Louis Sullivanfamously declared that ‘form followsfunction’. Could this concept also holdtrue in biology? Have cells, throughevolutionary optimization, adopted a3D architecture (‘form’) that reflectstheir function? Or, to keep with thearchitectural metaphor, how does ‘form’relate to the cell’s ‘intended functionor purpose’? Is there an ultrastructuralsignature that reflects the ‘constraintsand requirements’ of a cell’s function inits specific spatial and temporal context?To be fair, cells of similar function willvery likely never display the exact sameultrastructural 3D organization. Like inarchitecture and design there is not onesingle solution, but instead a variety of3D organizational solutions that satisfythe constraints of function. Just as themany houses in any given city are differ-ent from one another, there are also manyshared similarities such as doors, win-dows, roofs and walls. And just like officebuildings in their function and layout dif-fer from single-home residences, so willcells that face different ‘functional con-straints and requirements’, even thoughthey use the same components and dis-play an overall similar inventory.For example, epithelial cells—independent of the organ they residein—are highly similar to one another,e.g. in terms of cell polarity, cell–celladhesion and the 3D organization ofcytoskeletal filaments. Epithelial cells(Fig. 1A) are distinct from other cells thatconstitute nervous, muscle or connectivetissue, or from individual cells thatmigrate through the extracellular matrix(Fig. 1B). Moreover, each cell type willundergo changes throughout their lifecycle. Some mammary gland epithelialcells reside already in a tissue-likecontext before they undergo branchingmorphogenesis (Fig. 1C), e.g. duringorgan development [1] before theydifferentiate and undergo senescence.Yet, other mammary gland epithelialcells will transform from a healthy to apremalignant (Fig. 1D) and malignant(Fig. 1E) diseased stage. For each of allthese different stages, the cells will dis-play unique patterns of 3D organizationof organelles, like the nucleus, mito-chondria and endoplasmic reticulum(Fig. 1F), supramolecular complexessuch as microvilli-like membraneprotrusions (Fig. 1G), cell–cell adhesionsites/desmosomes (Fig. 1H) and thecytoskeleton (Fig. 1I) (see also [2]).One may argue that the combinationof their respective abundance, size, shape,3D distribution and other characteristicsconstitutes an ‘ultrastructural fingerprint’(see Fig. 2) or a ‘cellular signature’ thatuniquely characterizes each of these cellsin their respective spatiotemporal con-text. If one accepts this premise, how doesone go about determining such ‘cellularsignatures’?For centuries, observation of struc-ture has been key for an understand-ing of life, first at the levels of anatomy,then histology/neuroanatomy, and morerecently cell biology and biochemistry.The two disciplines that study of theorganization of cells and the 3D struc-tures of proteins are called structural cellbiology and structural molecular biology,respectively. Structural biology in its clas-sical sense aims to determine the pre-cise shapes of proteins and other macro-molecules, ideally at atomic resolution.In a well-received commentary calledWhither Structural Biology, Steven Harri-son [3] described the state of the art in3D-structure determination and laid outa path to expand structural biology to thehighly regulated complexity of cells, e.g.through the further development of cry-otomography and computational model-ing. However, in this vision, the goal ofstructural cell biology remains the visu-alization of cellular sceneries, albeit at asomewhat lower resolution, and to iden-tify and then dock protein and/or proteincomplex structures into such cryotomog-raphy density maps.In the last four decades, structuralmolecular biology [4] has strongly out-performed structural cell biology [5–7]through implicit or explicit averaging byX-ray crystallography, and more recentlyby cryoelectron microscopy, culminatingin the 2017 Nobel Prize in Chemistry.While tens of thousands of atomic struc-tures are known, only a few attempts havebeen made to map their precise loca-tion inside a cell’s ultrastructural densitymap [8,9], in part due to the fact thatonly very large complexes can be unam-biguously identified without the needfor a label. Correlative light and electronmicroscopy approaches aim to superim-pose fluorescence signals onto an elec-tron density map [10,11]; however, avariety of technical challenges have keptthe success rate to date somewhat low.To be clear, what is proposed hereis not a classical structural biology862 Natl Sci Rev, 2019, Vol. 6, No. 5 PERSPECTIVESFigure 1. Ultrastructure of cells in different cell and tissue contexts. (A) 2D TEM projection view inner of a sensory epithelium reveals apical–basolateralpolarity, with its characteristic distribution of apical microvilli and basolateral cell–cell adhesion complexes, which are separated by tight junctioncomplexes that seal off the luminal space from the basolateral space. (B) Block-surface SEM image of an individual fibrosarcoma cell attached to asubstrate surface. (C) Murine mammary gland cells forming organoids, undergoing FGF2-induced branching morphogenesis. (D) Human mammary glandcells grown in a 3D matrix, forming premalignant, growth-arrested acini. Note the sparse cell–cell adhesion and prominent presence of microvilli-likeapical cell protrusions, which are both reminiscent of the cell aggregates undergoing branching morphogenesis. (E) Mammary gland cells grown in a3D matrix forming malignant cell aggregates, which are no longer growth-arrested and do possess metastatic potential. (F) 3D rendering of segmentednucleus (color: navy blue), endoplasmic reticulum (color: salmon) and mitochondria (color: yellow) of 3D data set shown in (B). (G) Segmented microvilli-like protrusions residing between adjacent cells, frequently flanked on both sides by microvilli, typically seen in both (C) and (D), and to some extent in(E), but not in mature epithelial tissues. (H) Desmosomes (color: lime green) connecting adjacent cell–cell adhesion complexes. (I) 3D rendering of thecytoskeleton (color: blue) and mitochondria (color: golden) of data sets similar to the one shown in (D). Scale bars: (A, B) 2 μm, (C) 50 μm, (D, E) 5 μmand (F–I) 1 μm.approach. Instead, the goal here is toidentify which of several categories acertain ultrastructural feature belongs to,e.g. to the category of cell–cell adhesioncomplexes. Categorization is followedby the determination of its abundanceand 3D localization, by measurements ofits dimensions (i.e. the cell–cell contactsize) as well as the number and 3Dorganization of attached cytoskeletalfilaments. The relative strength ofcell adhesion for respective cells canbe inferred from these parameterswithout the knowledge of any atomic-resolution model for the individualproteins involved. Likewise, for thecytoskeleton, one may want to determineits abundance and distribution, thedegree of cross-connectivity and theregularity of its pattern, as well as theabundance, distribution and type ofall of its interaction partners (such asthe nucleus or adhesion complexes).Such cytoskeletal signatures mayeventually serve as a starting pointfor the computational simulation ofmechanical properties under different3D architectural scenarios [12].Given that every cell is unique, onekey challenge is to discriminate truly dif-ferent signatures from the heterogeneityinevitably inherent in biological systems.A promising route may be a rigorous sta-tistical analysis, which in turn requireslarge-scale 3D data sets, ideally of entirecells and small tissues/organoids at aresolution that allows the simultaneousidentification of the major supramolecu-lar assemblies and organelles.Focused Ion Beam Scanning ElectronMicroscopy is an ideal technique forimaging such large cellular volumes [13].Apart from instrument access anddata acquisition time, there are no lim-its on the size of volume it can cover ata resolution of ∼7–10 nm. Such inter-mediate resolution will be too coarseto identify individual proteins by shapealone (which is not the goal of cel-lular imaging anyway), but allows thediscrimination of supramolecular com-plexes such as the cytoskeleton andcell–cell adhesion complexes, as well asorganelles. With careful (e.g. cryogenic)sample preparation methods that avoidartifacts such as aggregation and extrac-tion, cells retain the 3D organizationof their supramolecular complexes andorganelles, even when samples are sub-jected to heavy metal staining, dehydra-tion and resin-embedding [14].The challenge of turning voxelinformation into knowledge arises inpart from the size of the data sets,which can reach tens of terabytes, andin part from the high feature densityPERSPECTIVESFigure 2. Ultrastructural signature of cellular state. (A) Superposition of original image data withmanually segmented image data (B). Note that the plasma membrane is shown in black, microvilli-like protrusions are shown in brown, nuclear membrane is shown in navy blue, endoplasmicreticulum is shown in salmon, mitochondria are shown in a golden color, the cytoskeleton is shownin light blue and desmosome-like cell–cell adhesion complexes are shown in lime green.combined with the high complexityof cellular constituent supramolecularcomplexes and organelles, which arein close contact with one another.Supervised machine learning, alsoknown as deep learning, requires alarge amount of so-called ‘ground truth’that typically needs to be establishedmanually (and thus painstakingly) by ahuman expert. Deep learning currentlylacks the accuracy that is needed to fullyautomate the process of segmentation.Deep learning may also be employed toassess the similarities between differentcells and discriminate cellular signaturesfrom sample heterogeneity.Furthermore, feature extraction andclassification is only the starting pointfor turning voxels into knowledge. Toreach that ultimate goal, resolution-faithful simplified volumetric modelsof supramolecular complexes andorganelles need to be built automatically,and subjected to rigorous measurementsof volumetric properties. Such a compre-hensive quantitative statistical analysiswill allow the creation of prototypicmodels that acknowledge and representthe ultrastructural heterogeneity inher-ent in biological data sets. Also missingis a semantic mathematical descriptionof cellular 3D architecture, capable ofproviding the respective relationships ofconstituents, and their hierarchy. Lastbut not least, prototypic models ideallywill reside in map-centered ‘biospatial’databases, not unlike ‘Google Maps’,which then could be queried for patterns.Such a ‘Google-Cell Biology’ databasewould become the platform to inte-grate—at the model level—additionalimaging, metabolic or proteomics data,as well as encyclopedic knowledge.In summary, what I am proposing herebuilds on, yet goes beyond, traditionalstructural molecular and cellular biology.Instead of determining the 3D structureof individual macromolecules orcellular sceneries, the objective is toquantify the geometric properties ofsimplified volumetric models. Instead ofdetermining a single, defined structure,we seek for each cellular state to identifythe range of such geo/volumetric modelproperties with sufficient commonalitiesto constitute an ultrastructural cellularsignature. Such cellular signatures willreflect the ‘form’ and the ultrastructuralheterogeneity that is inherent in eachstate, and will allows us to discriminateheterogeneity from truly altered statesand thus yield mechanistic insightinto the cell’s ‘intended function orpurpose’.ACKNOWLEDGEMENTSM.A. thanks former and current members of theAuer laboratory for their help in obtaining imagematerial for Fig. 1 and Fig. 2.FUNDINGM.A. would like to acknowledge grant support fromNIH/GMS P01GM051487.Conflict of interest statement. None declared.Manfred AuerMolecular Biophysics and Integrated BioimagingDivision, Lawrence Berkeley National Laboratory,USAE-mail: mauer@lbl.govREFERENCES1. Ewald AJ, Huebner RJ and Palsdottir H et al .J Cell Sci 2012; 125: 2638–54.2. Jorgens DM, Inman JL and Wojcik M et al .J Cell Sci 2017; 130: 177–89.3. Harrison SC. Nat Struct Mol Biol 2004; 11: 12–5.4. Cheng Y. Science 2018; 361: 876–80.5. Koning RI, Koster AJ and Sharp TH. Ann Anat2018; 217: 82–96.6. Pfeffer S and Mahamid J. Curr Opin StructBiol 2018; 52: 111–8.7. Weber MS, Wojtynek M and Medalia O. Cells2019; 8: E578. Asano S, Engel BD and Baumeister W. J MolBiol 2016; 428: 332–43.9. Robinson CV, Sali A and Baumeister W.Nature 2007; 450: 973–82.10. Schorb M and Briggs JA. Ultramicroscopy2014; 143: 24–32.11. Zhang P. Curr Opin Struct Biol 2013; 23: 763–70.12. Fletcher DA and Mullins RD. Nature 2010;463: 485–92.13. Titze B and Genoud C. Biol Cell 2016; 108:307–23.14. McDonald KL and Auer M. Biotechniques 2006;41: 137, 139, 141, 143.National Science Review6: 861–863, 2019doi: 10.1093/nsr/nwz057Advance access publication 16 May 2019Auer 863

Ultrastructural cellular signatures: does cellular form follow function?

Auer, Manfred,

Ezid

https://escholarship.org/uc/item/3dc786v8

Ultrastructural cellular signatures: does cellular form follow function?

Abstract

Similar works

Full text

Available Versions

eScholarship - University of California

Ezid