Simultaneous profiling of native-state proteomes and transcriptomes of neural cell types using proximity labeling Restricted; Files Only

Abstract

Deep molecular phenotyping of cells at transcriptomic and proteomic levels is an essential first step to understanding cellular contributions to development, aging, injury, and disease. Since proteome and transcriptome level abundances only modestly correlate with each other, complementary profiling of both is needed. We report a novel method called simultaneous protein and RNA -omics (SPARO) to capture the cell type-specific transcriptome and proteome simultaneously from both in vitro and in vivo experimental model systems. This method leverages the ability of biotin ligase, TurboID, to biotinylate cytosolic proteins including ribosomal and RNA-binding proteins, which allows enrichment of biotinylated proteins for proteomics as well as protein-associated RNA for transcriptomics. We validated this approach first using well-controlled in vitro systems to verify that the proteomes and transcriptomes obtained reflect the ground truth, bulk proteomes and transcriptomes. We also show that the effect of a biological stimulus (e.g., neuroinflammatory activation by lipopolysaccharide) can be faithfully captured. We also applied this approach to obtain native-state proteomes and transcriptomes from two key neural cell types, astrocytes and neurons, thereby validating the in vivo application of SPARO. Next, we used these data to interrogate protein-mRNA concordance and discordance across these cell types, providing insights into groups of molecular processes that exhibit uniform or cell type-specific patterns of mRNA-protein discordance.

Table of Contents

1.0          Introduction…………………………………………………………………………………………1

1.1          Central dogma of molecular biology………………………………………………………..1

Figure 1.7.1      Central dogma of molecular biology……………………………………………..  2

1.2          Cells of the central nervous system………………………………………………………..  2

1.3          Relevant models to study neural cell types……………………………………………… 9

1.4          Cell isolation approaches for cell type-specific phenotyping……………………… 12

Figure 1.7.2      Isolation-dependent approaches for cell type-specific molecular profiling……………………………………………………………………………………………………….. 14

1.5          Methods for RNA and protein profiling of neural cells in vivo and ex vivo………. 14

Figure 1.7.3      Methods for in vivo cell type-specific transcriptomics……………………… 17

Figure 1.7.4      Methods for in vivo cell type-specific proteomics……………………………  21

1.5          Methods for simultaneous transcriptomics and proteomics………………………. 22

1.6          Chapter 1 summary and dissertation research aims…………………………………. 23

Figure 1.7.5      Cytosolic TurboID biotinylates many proteins that interact with RNA, leading to the development of a simultaneous transcriptomics and proteomics approach…..         24

2.0          Simultaneous profiling of native-state proteomes and transcriptomes of neural cell types using proximity labeling………………………………………………………………………….   25

2.1          Introduction……………………………………………………………………………………….. 26

2.2          Results………………………………………………………………………………………………. 28

2.2.1     Validation of SPARO in BV2-TurboID cells in vitro………………………………………  28

2.2.2     Validation of SPARO in vivo using cortical astrocytes and neurons………………  31

2.2.3     The SPARO- and RiboTag-enriched astrocytic transcriptomes are similar……..  34

2.2.4     Correlation of the SPARO transcriptomes and proteomes of cortical astrocytes and neurons in vivo………………………………………………………………………………………………            36

2.3          Discussion…………………………………………………………………………………………. 39

2.3.1     Feasibility and validity of the SPARO approach…………………………………………  40

2.3.2     The SPARO with RiboTag transcriptomes are similar and complementary…….. 41

2.3.3     SPARO to assess the concordance between paired proteomes and transcriptomes                  ………………………………………………………………………………………………………… 42

2.3.4     Broader applications of SPARO…………………………………………………………….. 44

2.4          Materials and Methodology………………………………………………………………….. 46

2.5          Chapter 2 Figures……………………………………………………………………………….. 52

Figure 2.5.1      Simultaneous transcriptomics and proteomics profiling of BV2 microglia under homeostatic and LPS-stimulated conditions…………………………………………… 53

Figure 2.5.2      In vitro validation of SPARO in LPS-stimulated BV2-microglial cells….. 56

Figure 2.5.3      In vitro validation of the SPARO-enriched transcriptome in untreated and LPS-stimulated BV2-microglial cells……………………………………………………………….. 59

Figure 2.5.4      In vitro validation of the SPARO-enriched proteome in untreated and LPS-stimulated BV2-microglial cells. …………………………………………………………………….. 61

Figure 2.5.5      TurboID-based dual transcriptomic and proteomic analysis of cortical Aldh1l1-expressing astrocytes and Camk2a-expressing neurons in vivo……………….. 63

Figure 2.5.6      In vivo validation of SPARO in astrocyte-TurboID and neuron-TurboID models……………………………………………………………………………………………………….. 66

Figure 2.5.7      In vivo validation of SPARO to capture cell type enriched pulldown transcriptomes from astrocyte-TurboID and neuron-TurboID models…………………… 68

Figure 2.5.8      SPARO captures unique transcriptomic and proteomic signature of LPS-stimulated astrocytes in vivo…………………………………………………………………………. 70

Figure 2.5.9      In vivo validation of the astrocyte-RiboTag pulldown transcriptomes and differences with the SPARO enriched astrocyte-TurboID pulldown transcriptomes… 72

Figure 2.5.10   Correlation analysis of SPARO transcriptomes and proteomes in vitro and in vivo……………………………………………………………………………………………………………. 74

Figure 2.5.11   Correlation analysis of TurboID-based transcriptomes and proteomes of Aldh1l1-expressing astrocytes and Camk2a-expressing neurons in vivo………………. 77

Figure 2.5.12   GO term overlap of concordant and discordant mRNA-protein pairs of astrocyte-TurboID and neuron-TurboID SPARO pulldowns…………………………………. 80

Figure 2.5.13   Investigating transcript traits and protein half-life to identify processes contributing to discordance between mRNA and protein abundances………………… 82

3.0          Discussion and future directions…………………………………………………………. 83

3.1          Contributions to science……………………………………………………………………. 83

3.2          Future applications of SPARO……………………………………………………………… 85

Figure 3.2.1      The SPARO approach can capture microRNAs (miRNAs) enriched in cortical astrocytes………………………………………………………………………………………………….. 89

3.3          Limitations of SPARO…………………………………………………………………………. 96

4.0          Bibliography……………………………………………………………………………………… 99

About this Dissertation

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School	Laney Graduate School
Department	Biological and Biomedical Sciences
Subfield / Discipline	Neuroscience
Degree	Ph.D.
Submission	Dissertation
Language	English
Research Field	Biology, Neuroscience
Keyword	proteomics transcriptomics Proximity labeling mRNA-protein concordance neural cell types
Committee Chair / Thesis Advisor	Rangaraju, Srikant, Emory University Sloan, Steven, Emory University
Committee Members	Seyfried, Nicholas, Emory University Faundez, Victor, Emory University Feng, Yue, Emory University

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