Proteomics of spatially identified tissues in whole organs

Hs, Bhatia; Brunner, Andreas-David; Rong, Zhouyi; H, Mai; Thielert, Marvin; Al-Maskari, Rami; Jc, Paetzold; Kofler, Florian; Mi, Todorov; M, Ali; Molbay, Muge; Zi, Kolabas; Kaltenecker, Doris; Müller, Stephan; Sf, Lichtenthaler; Menze, Bjoern; Fj, Theis; Mann, Matthias; Ertürk, Ali

doi:10.1101/2021.11.02.466753

Cited by 10 publications

(11 citation statements)

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“…Although mainly demonstrated here for single‐cell total proteome measurements, the sensitivity gain achieved in our workflow will be advantageous in any situation that is sample limited. This includes investigation of other compound classes such as metabolites or drugs, post‐translational modifications from small numbers of cells or from in vivo material, and measurements directly from paraffin‐embedded formalin‐fixed (FFPE) pathology specimens, which we are already pursuing (preprint: Bhatia et al , 2021; preprint: Mund et al , 2021).…”

Section: Discussionmentioning

confidence: 99%

Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation

Brunner

Thielert

Vasilopoulou

et al. 2022

Molecular Systems Biology

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Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing. However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow-rate chromatography, and a novel trapped ion mobility mass spectrometer, resulting in a more than 10-fold improved sensitivity. We precisely and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single-cell proteomes to transcriptome data revealed a stable-core proteome despite perturbation, while the transcriptome appears stochastic. Our technology can readily be applied to ultra-high sensitivity analyses of tissue material, posttranslational modifications, and small molecule studies from small cell counts to gain unprecedented insights into cellular heterogeneity in health and disease.

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Section: Discussionmentioning

confidence: 99%

Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation

Brunner

Thielert

Vasilopoulou

et al. 2022

Molecular Systems Biology

Self Cite

374

397

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“…Expansions of CCPLS toaddress these limitations are an intended focus of our future work. In addition, the responsive expression values can be replaced by the other omics data such as spatial metabolome or proteome data, which is also an intended focus of our future work (Alexandrov, 2020; Bhatia et al ., 2021; Dyring-Andersen et al ., 2020; Geier et al ., 2020; Liu et al ., 2020; Seydel, 2021).…”

Section: Resultsmentioning

confidence: 99%

CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

Tsuchiya

Hori

Ozaki

2022

Preprint

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Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

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“…This previously involved the painstaking definition and excision of a large number of cells but can now be achieved by a combination of state-of-the-art imaging, AI, automated single-cell isolation, and ultra-sensitive proteomics using the DVP technology. An obvious next step is to extend these technologies into the third spatial dimension, by stacking and connecting the results of many tissue slices, creating a true 3D multiomics map (Bhatia et al, 2021;Kiemen et al, 2020).…”

Section: Discussion and Outlookmentioning

confidence: 99%