Session MOB. There are 4 abstracts in this session.

Session: Proteome Organization in Space and Time, time: 09:50 - 10:15 am

The spatial organisation of the transcriptome and proteome

Kathryn Lilley
University of Cambridge, Cambridge, United Kingdom

In biological systems, genome size does not correlate with organismal complexity. Indeed, the proteome of higher organisms is arguably too small for the complex functions that it is has to perform. Proteome functionality is enhanced by post translation modification, spatial restriction and the interactions proteins make with other biomolecules. The location of protein synthesis also plays a role in protein function, and the aberrant translation of proteins in the wrong location underpins multiple disease states. The relationship between the transcriptome and proteome both in terms of physical interaction and spatial location is therefore of great importance.

Here, we present emerging methodologies that capture the spatial relationship between the proteome and transcriptome.

Firstly, in order to characterise intracellular RNA location, efficient extraction of RNA species and their coordinating protein binding partners (RBPs) is essential. To date RBP capture has centred round UV crosslinking RNA to protein and enrichment with oligo(dT)-coupled beads. This approach is limited to RBPs that coordinate polyadenylated RNA. Here describe the orthogonal organic phase separation (OOPS), a highly efficient method, that enables reproducible recovery of RBPs or protein-bound RNA (PBR), compatible with downstream proteomics and RNA sequencing and independent of polyadenylation status of RNA (1). We demonstrate its application to a number of different studies revealing some surprising roles as RBPs for a variety of different metabolic proteins.

Secondly, we introduce a series of new spatial proteomics works workflows including, LOPIT-DC (2), a straightforward high-resolution spatial proteomics approach that uses differential centrifugation coupled with machine learning approaches to efficiently interrogate data 

Finally, we show how modification of subcellular fractionation methods are necessary for compatibility with OOPS, to generate spatial information for not only proteins but also RNA species on a cell wide scale.

1. Queiroz (2019) Nature Biotechnology – doi:10.1038/s41587-018-0001-2

2. Geladaki  (2019) Nature Communications  - doi:10.1038/s41467-018-08191-w

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Session: Proteome Organization in Space and Time, time: 10:15 - 10:35 am

Systems Biochemistry of the Metaphase Spindle

Martin Wuhr
Princeton University, Princeton, NJ

The metaphase spindle is composed of chromosomes, microtubules, and an unknown number of proteins. Spindle microtubules are dynamic with an average lifetime of ~20 seconds making isolation of native spindles extremely challenging. We developed methods to isolate metaphase spindles in less than 5 seconds from undiluted Xenopus egg extract via a rapid filtration approach. Using quantitative multiplexed proteomics, we determined the partitioning between spindle and cytoplasm for ~5,500 proteins. We observed over 100 new spindle proteins and confirmed the localization for a subset via microscopy. We were able to globally measure the spindle’s proteome turn-over by adding cell lysate from a different frog species and follow the equilibration of these proteins with quantitative proteomics. Lastly, we determined the absolute amount of each protein bound to the spindle. This systems level measurement allowed us to compare the concentration of spindle microtubules with bound microtubule associated proteins (MAPs). In contrast to standard textbook cartoons, microtubules seem to be saturated with MAPs. We demonstrate that MAPs are competing for microtubule binding sites in the spindle suggesting a simple model how spindle composition and morphology could adapt to the drastically changing cell sized in early embryonic development. Thus, we present the first measurement of the composition of a metaphase spindle with endogenous dynamics at molecular resolution. This generated new insight into spindle architecture and might provide a framework to understand how the spindle can adapt its size for different developmental contexts.

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Session: Proteome Organization in Space and Time, time: 10:40 - 10:55 am

Microprobe CE-ESI-HRMS Reveals Proteomic Reorganization in Spatially and Temporally Developing Cell Clones Directly in Live X. laevis Embryos

Camille Lombard-Banek1; Sally Moody2; Peter Nemes1
1University of Maryland, CP, College Park, Maryland; 2The George Washington University, Washington, DC

Establishment of cell-to-cell differences (single-cell heterogeneity) is critical to normal embryonic development and tissue formation. Quantitative proteomics with single-cell resolution has the potential to deepen our understanding of cell type specific gene expression during embryonic development. We here realize this potential by developing an approach to enable the proteomic analysis as an identified cell gives rise to a cell clone directly in the early developing vertebrate embryo. Our strategy integrated optically guided capillary microsampling, capillary electrophoresis (CE) nanoelectrospray ionization (nanoESI), and high-resolution tandem mass spectrometry (Q-Exactive +). The optimized technology accomplished 700 zmol lower limit of detection for model peptides. Moreover, it enabled the identification of ~750‒800 protein groups (PGs, <1% FDR) from only 5 ng of protein digest extracted from Xenopus laevis (frog) embryos. We validated the approach by identifying proteomic differences between the animal and vegetal poles of the zygote, in which molecular heterogeneity is known in space. The approach allowed us to reproducibly quantify ~460 PGs as the dorsal-animal cell (D11 cell) divided to give rise to its neural-fated clone in the 16-, 32-, 64-, and 128-cell embryo. Hierarchical cluster analysis (HCA) of quantitative proteomic data revealed reorganization of the single-cell proteome, revealing ~90 proteins with significant dysregulation (fold change > 1.3, p < 0.05, Student’s t-test). Our microanalytical single-cell proteomic approach opens new opportunities to study molecular mechanisms of cell differentiation directly in spatially and temporally evolving tissues in live vertebrate embryos and other biological models.

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Session: Proteome Organization in Space and Time, time: 10:55 - 11:10 am

Extensive Intratumor Proteogenomic Heterogeneity Revealed by Multiregion Sampling in a High-Grade Serous Ovarian Tumor Specimen

Allison Hunt3; Nicholas Bateman1, 2; Guisong Wang1; Brian Hood1; Julie Oliver1; Dave Mitchell1; Glenn Gist1; Ming Zhou3; Brian Blanton1; Kelly Conrads1; Kathleen Darcy1; Craig Shriver2; Yovanni Casablanca1; G. Larry Maxwell4; Thomas Conrads3
1Gynecologic Cancer Center of Excellence, Annandale, VA; 2John P. Murtha Cancer Center, Bethesda, MD; 3Inova Schar Cancer Institute, Falls Church, VA; 4Department of Obstetrics and Gynecology, Inova, Falls Church, VA

We generated 200 consecutive thin sections from a single high-grade serous ovarian carcinoma (HGSOC) tumor and laser microdissected (LMD) four spatially separated tumor “core” regions throughout the depth of the tissue block to examine proteogenomic intratumor heterogeneity (ITH). Tumor epithelium and stroma were LMD enriched at 150µm intervals and mixed epithelial and stromal (e.g. whole tumor) samples were harvested from adjacent thin sections; the remaining tissue was cryopulverized. Mass spectrometry-based proteomics quantified 6,053 proteins and 4,225 phosphosites and RNA sequencing mapped to 20,785 transcripts. Unsupervised hierarchical cluster analysis of 1,018 and 584 differentially proteins and transcripts demonstrated distinct sub-clusters of tumor cores and enriched tumor epithelium versus enriched stroma and whole tumor samples, with the cryopulverized proteome clustering independently of these sample groups. Comparison of protein and transcripts with historic prognostic molecular subtypes for HGSOC showed that enriched stroma, but not tumor epithelium collections was positively correlated with mesenchymal molecular subtype, which is associated with poor disease outcome. Further analyses revealed prognostic biomarkers predicting risk of suboptimal surgical debulking were also anti-correlated between enriched stroma and tumor epithelium. While there was concordance between protein and transcript abundance for each LMD collection type, transcript abundance from enriched stroma was most strongly correlated with cognate protein products from the mixed epithelial/stromal collection rather than enriched stroma, potentially explainable by the secretory nature of ovarian stromal cells and inclusion of extracellular matrix proteins in mixed LMD collections. Proteins measured from the cryopulverized tissue were overall negatively correlated with enriched tumor epithelium, and notably had limited detection of the ovarian tumor cell biomarker CA-125. This proteogenomic analysis reveals stark molecular heterogeneity in the cellular admixture of the HGSOC tumor microenvironment and underscores the need to account for compartmental ITH in molecular profiling analyses of cancer.

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