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Spatial-omics goes 3D๐Ÿค—! Out at Cell Cell ๐Ÿ‘‰๐Ÿผ We developed DISCO-MS with Matthias Mann Lab, a spatial proteomics technology for specimens fully imaged in 3D. DISCO-MS is aided by robotics and enables the study of diseases at their early stages.

217,718 views โ€ข 3 years ago โ€ขvia X (Twitter)

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Ali Max Erturk's profile picture
Ali Max Erturk3 years ago

Summary: DISCO-MS is a spatial proteomics technology: it enables proteomics analysis of cleared tissues imaged in 3D. DISCO-MS is aided by AI and robotics and yields proteome similar to fresh or fixed samples. Have questions? Add them below๐Ÿ‘‡ we will answer๐Ÿ“–

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Ali Max Erturk3 years ago

DISCO-MS Step 1: We start by rendering a mouse body or human organ optically transparent using robust clearing methods such as vDISCO or SHANEL. Then, after their cell-level light-sheet microscopy scan, we can visualize fluorescently-labeled structures in 3D as complete.

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Ali Max Erturk3 years ago

Step 2: Next, we employ deep learning-based image analysis to identify regions associated with illnesses such as Alzheimerโ€™s disease, brain injury, or coronary artery disease in an unbiased manner.

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Ali Max Erturk3 years ago

Step 3: Small tissue regions are isolated with the help of a specialized robotic extraction system called DISCO-bot. Samples can currently be as small as 0.014 mm3. Smaller samples (down to 0.0005 mm3 or 60 cells) can be analyzed using laser capture microdissection.

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Ali Max Erturk3 years ago

Step 4: Next, the molecular composition of the tissue isolates is analyzed using high-sensitivity mass-spectrometry. The cellular proteome measured in cleared tissue is almost indistinguishable from the one obtained from fresh or fixed tissue controls.

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Ali Max Erturk3 years ago

Example application 1: To show the utility of DISCO-MS, we first characterized microglia activation after traumatic brain injury. We identified numerous proteins that were differentially regulated between equivalent regions along the optical tract in damaged and healthy brains.

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Ali Max Erturk3 years ago

Example application 2: Our technology enables us to characterize subtle changes, which would be easily missed using standard methods. To this end, we analyzed the first A-beta plaques appearing in the 5xFAD Alzheimerโ€™s disease mouse model from different brain regions.

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Ali Max Erturk3 years ago

Example application 3: Next, we studied immune cell heterogeneity in the bone marrow. To this end, we used Lsy-M-EGFP mice (labeling immune cells) and identified regional protein expressions in curvy bones: scapula and cranium.

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Ali Max Erturk3 years ago

Example application 4: DISCO-MS works equally well on clinical samples. We used DISCO-bot-aided DISCO-MS to study coronary artery disease. After identifying all atherosclerotic plaques in the human heart, we studied proteome composition related to plaque development.

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Ali Max Erturk3 years ago

Conclusions: DISCO-MS combines the complete 3D-imaging data of whole organs and organisms with their unbiased proteome signatures. It works well in preclinical and clinical samples, thus, can be used almost for any biomedical tissue analysis.

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Ali Max Erturk3 years ago

Limitations and future developments: Currently we cannot reach single cell resolution as such proteomics methods are limited. We are also working on improving DISCO-bot for smaller tissue isolation.

Ali Max Erturk's profile picture
Ali Max Erturk3 years ago

You want to learn more about DISCO-MS, and/or use it? Let us know!

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Ali Max Erturk3 years ago

Authors @HarsharanBhatia @ADBxMPIB @FurkOzturk @Sakethkapoor @zhouyi_rong @Hongcheng_Mai @marvinchth @mayarbali @rami96614090 @MihailMuc @jocpae @Dorie00 @ilginkolabas @MolbayM @moritz_negwer @Lu_h99 @NatalieKrahmer @Farida80168644 @IngoBechmann @fabian_theis @Bjoern @menze_group

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Ali Max Erturk3 years ago

Authors @labs_mann @erturklab Non-author acknowledgement @MarkusElsner1 @MarinBralo

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Ali Max Erturk3 years ago

Worked done at the @HelmholtzMunich, @MPI_Biochem, @LMU_Muenchen, @LMU_Uniklinikum, @ISD_Research, @TU_Muenchen, @DZNE_de, supported by @SyNergy_Cluster, @ERC_Research, @NOMIS and graduate programs @IMPRS_LS, GSN-LMU, and MMRS-LMU.

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