In vivo FRET–FLIM reveals cell-type-specific protein interactions in Arabidopsis roots

Synapses in the brain exhibit cell–type–specific differences in basal synaptic transmission and plasticity. Here, we evaluated cell–type–specific differences in the composition of glutamatergic synapses, identifying Btbd11, as an inhibitory interneuron–specific synapse–enriched protein. Btbd11 is highly conserved across species and binds to core postsynaptic proteins including Psd–95. Intriguingly, we show that Btbd11 can undergo liquid–liquid phase separation when expressed with Psd–95, supporting the idea that the glutamatergic post synaptic density in synapses in inhibitory and excitatory neurons exist in a phase separated state. Knockout of Btbd11 from inhibitory interneurons decreased glutamatergic signaling onto parvalbumin–positive interneurons. Further, both in vitro and in vivo, we find that Btbd11 knockout disrupts network activity. At the behavioral level, Btbd11 knockout from interneurons sensitizes mice to pharmacologically induced hyperactivity following NMDA receptor antagonist challenge. Our findings identify a cell–type–specific protein that supports glutamatergic synapse function in inhibitory interneurons–with implication for circuit function and animal behavior.

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LIM Factor Lhx3 Contributes to the Specification of Motor Neuron and Interneuron Identity through Cell-Type-Specific Protein-Protein Interactions

Cell ◽

10.1016/s0092-8674(02)00823-1 ◽

2002 ◽

Vol 110 (2) ◽

pp. 237-249 ◽

Cited By ~ 223

Author(s):

Joshua P. Thaler ◽

Soo-Kyung Lee ◽

Linda W. Jurata ◽

Gordon N. Gill ◽

Samuel L. Pfaff

Keyword(s):

Motor Neuron ◽

Protein Interactions ◽

Specific Protein ◽

Cell Type ◽

Protein Protein Interactions ◽

Cell Type Specific

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Proximity labeling of protein complexes and cell-type-specific organellar proteomes in Arabidopsis enabled by TurboID

eLife ◽

10.7554/elife.47864 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 22

Author(s):

Andrea Mair ◽

Shou-Ling Xu ◽

Tess C Branon ◽

Alice Y Ting ◽

Dominique C Bergmann

Keyword(s):

Transcription Factor ◽

Protein Interactions ◽

Protein Complexes ◽

Cell Types ◽

Specific Protein ◽

Cell Type ◽

Subcellular Compartment ◽

Cellular Processes ◽

Highly Active

Defining specific protein interactions and spatially or temporally restricted local proteomes improves our understanding of all cellular processes, but obtaining such data is challenging, especially for rare proteins, cell types, or events. Proximity labeling enables discovery of protein neighborhoods defining functional complexes and/or organellar protein compositions. Recent technological improvements, namely two highly active biotin ligase variants (TurboID and miniTurbo), allowed us to address two challenging questions in plants: (1) what are in vivo partners of a low abundant key developmental transcription factor and (2) what is the nuclear proteome of a rare cell type? Proteins identified with FAMA-TurboID include known interactors of this stomatal transcription factor and novel proteins that could facilitate its activator and repressor functions. Directing TurboID to stomatal nuclei enabled purification of cell type- and subcellular compartment-specific proteins. Broad tests of TurboID and miniTurbo in Arabidopsis and Nicotiana benthamiana and versatile vectors enable customization by plant researchers.

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Ubiquitous and cell-type-specific protein interactions with human papillomavirus type 16 and type 18 enhancers

Virology ◽

10.1016/0042-6822(90)90382-2 ◽

1990 ◽

Vol 178 (1) ◽

pp. 92-103 ◽

Cited By ~ 10

Author(s):

Harikrishna Nakshatri ◽

Mary M. Pater ◽

Alan Pater

Keyword(s):

Human Papillomavirus ◽

Protein Interactions ◽

Specific Protein ◽

Cell Type ◽

Human Papillomavirus Type 16 ◽

Cell Type Specific

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Proximity labeling of protein complexes and cell type-specific organellar proteomes in Arabidopsis enabled by TurboID

10.1101/629675 ◽

2019 ◽

Cited By ~ 3

Author(s):

Andrea Mair ◽

Shou-Ling Xu ◽

Tess C. Branon ◽

Alice Y. Ting ◽

Dominique C. Bergmann

Keyword(s):

Transcription Factor ◽

Protein Interactions ◽

Protein Complexes ◽

Cell Types ◽

Specific Protein ◽

Cell Type ◽

Subcellular Compartment ◽

Cellular Processes ◽

Highly Active

AbstractDefining specific protein interactions and spatially or temporally restricted local proteomes improves our understanding of all cellular processes, but obtaining such data is challenging, especially for rare proteins, cell types, or events. Proximity labeling enables discovery of protein neighborhoods defining functional complexes and/or organellar protein compositions. Recent technological improvements, namely two highly active biotin ligase variants (TurboID and miniTurboID), allowed us to address two challenging questions in plants: (1) what are in vivo partners of a low abundant key developmental transcription factor and (2) what is the nuclear proteome of a rare cell type? Proteins identified with FAMA-TurboID include known interactors of this stomatal transcription factor and novel proteins that could facilitate its activator and repressor functions. Directing TurboID to stomatal nuclei enabled purification of cell type- and subcellular compartment-specific proteins. Broad tests of TurboID and miniTurboID in Arabidopsis and N. benthamiana and versatile vectors enable customization by plant researchers.

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Genoppi: an open-source software for robust and standardized integration of proteomic and genetic data

10.1101/2020.05.04.076034 ◽

2020 ◽

Author(s):

Greta Pintacuda ◽

Frederik H. Lassen ◽

Yu-Han H. Hsu ◽

April Kim ◽

Jacqueline M. Martín ◽

...

Keyword(s):

Open Source ◽

Protein Interactions ◽

General Pattern ◽

Genetic Data ◽

Specific Protein ◽

Specific Interactions ◽

Cell Type ◽

Statistical Framework ◽

Cell Type Specific ◽

Human Ipsc

AbstractCombining genetic and cell-type-specific proteomic datasets can lead to new biological insights and therapeutic hypotheses, but a technical and statistical framework for such analyses is lacking. Here, we present an open-source computational tool called Genoppi that enables robust, standardized, and intuitive integration of quantitative proteomic results with genetic data. We used Genoppi to analyze sixteen cell-type-specific protein interaction datasets of four proteins (TDP-43, MDM2, PTEN, and BCL2) involved in cancer and neurological disease. Through systematic quality control of the data and integration with published protein interactions, we show a general pattern of both cell-type-independent and cell-type-specific interactions across three cancer and one human iPSC-derived neuronal type. Furthermore, through the integration of proteomic and genetic datasets in Genoppi, our results suggest that the neuron-specific interactions of these proteins are mediating their genetic involvement in neurodevelopmental and neurodegenerative diseases. Importantly, our analyses indicate that human iPSC-derived neurons are a relevant model system for studying the involvement of TDP-43 and BCL2 in amyotrophic lateral sclerosis.

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Genoppi is an open-source software for robust and standardized integration of proteomic and genetic data

Nature Communications ◽

10.1038/s41467-021-22648-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Greta Pintacuda ◽

Frederik H. Lassen ◽

Yu-Han H. Hsu ◽

April Kim ◽

Jacqueline M. Martín ◽

...

Keyword(s):

Open Source ◽

Protein Interactions ◽

General Pattern ◽

Neuronal Cell ◽

Genetic Data ◽

Specific Protein ◽

Specific Interactions ◽

Cell Type ◽

Cell Type Specific ◽

Human Ipsc

AbstractCombining genetic and cell-type-specific proteomic datasets can generate biological insights and therapeutic hypotheses, but a technical and statistical framework for such analyses is lacking. Here, we present an open-source computational tool called Genoppi (lagelab.org/genoppi) that enables robust, standardized, and intuitive integration of quantitative proteomic results with genetic data. We use Genoppi to analyze 16 cell-type-specific protein interaction datasets of four proteins (BCL2, TDP-43, MDM2, PTEN) involved in cancer and neurological disease. Through systematic quality control of the data and integration with published protein interactions, we show a general pattern of both cell-type-independent and cell-type-specific interactions across three cancer cell types and one human iPSC-derived neuronal cell type. Furthermore, through the integration of proteomic and genetic datasets in Genoppi, our results suggest that the neuron-specific interactions of these proteins are mediating their genetic involvement in neurodegenerative diseases. Importantly, our analyses suggest that human iPSC-derived neurons are a relevant model system for studying the involvement of BCL2 and TDP-43 in amyotrophic lateral sclerosis.

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