Artem Baranovskii

Max Delbrück Center

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Bora Uyar

Max Delbrück Center

Vedran Franke

Max Delbrück Center

Altuna Akalin

Max Delbrück Center

Jan Dohmen

Max Delbrück Center

Jonathan Ronen

Max Delbrück Center

Samantha Mendonsa

Max Delbrück Center

Laura Breimann

Harvard University

Sayaka Dantsuji

Max Delbrück Center

Nadja Zerna

Max Delbrück Center

Marina Chekulaeva

Max Delbrück Center

Cooperative Institutions

Max Delbrück Center

Freie Universität Berlin

Humboldt-Universität zu Berlin

Friedrich Miescher Institute

European Molecular Biology Laboratory

Berlin Institute of Health at Charité - Universitätsmedizin Berlin

Charité - Universitätsmedizin Berlin

Harvard University

Institute of Molecular Biotechnology

Max Perutz Labs

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Additional file 2 of Identifying tumor cells at the single-cell level using machine learning

Figshare (2022)

Jan Dohmen Artem Baranovskii Jonathan Ronen Bora Uyar Vedran Franke

Additional file 2. Tables with data description and results.

10.6084/m9.figshare.19930512

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mRNA stability and m6A are major determinants of subcellular mRNA localization in neurons

Molecular Cell (2023)

Inga Loedige Artem Baranovskii Samantha Mendonsa Sayaka Dantsuji Niko Popitsch

For cells to perform their biological functions, they need to adopt specific shapes and form functionally distinct subcellular compartments. This is achieved in part via an asymmetric distribution of mRNAs within cells. Currently, the main model of mRNA localization involves specific sequences called "zipcodes" that direct mRNAs to their proper locations. However, while thousands of mRNAs localize within cells, only a few zipcodes have been identified, suggesting that additional mechanisms contribute to localization. Here, we assess the role of mRNA stability in localization by combining the isolation of the soma and neurites of mouse primary cortical and mESC-derived neurons, SLAM-seq, m6A-RIP-seq, the perturbation of mRNA destabilization mechanisms, and the analysis of multiple mRNA localization datasets. We show that depletion of mRNA destabilization elements, such as m6A, AU-rich elements, and suboptimal codons, functions as a mechanism that mediates the localization of mRNAs associated with housekeeping functions to neurites in several types of neurons.

P-bodies

Neurite

10.1016/j.molcel.2023.06.021

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Citations (38)

Additional file 3 of Identifying tumor cells at the single-cell level using machine learning

Figshare (2022)

Jan Dohmen Artem Baranovskii Jonathan Ronen Bora Uyar Vedran Franke

Additional file 3. Results of statistical analysis.

10.6084/m9.figshare.19930515

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Identifying tumor cells at the single-cell level using machine learning

Genome biology (2022)

Jan Dohmen Artem Baranovskii Jonathan Ronen Bora Uyar Vedran Franke

Abstract Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation—the assignment of cell type or cell state to each sequenced cell—is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts.

Single-Cell Analysis

Cell type

Single cell sequencing

10.1186/s13059-022-02683-1

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Citations (40)

Identifying tumor cells at the single cell level

bioRxiv (Cold Spring Harbor Laboratory) (2021)

Jan Dohmen Artem Baranovskii Jonathan Ronen Bora Uyar Vedran Franke

Abstract Tumors are highly complex tissues composed of cancerous cells, surrounded by a heterogeneous cellular microenvironment. Tumor response to treatments is governed by an interaction of cancer cell intrinsic factors with external influences of the tumor microenvironment. Disentangling the heterogeneity within a tumor is a crucial step in developing and utilization of effective cancer therapies. The single cell sequencing technology enables an effective molecular characterization of single cells within the tumor. This technology can help deconvolute heterogeneous tumor samples and thus revolutionize personalized medicine. However, a governing challenge in cancer single cell analysis is cell annotation, the assignment of a particular cell type or a cell state to each sequenced cell. One of the critical cell type annotation challenges is identification of tumor cells within single cell or spatial sequencing experiments.This is a critical limiting step for a multitude of research, clinical, and commercial applications. A reliable method addressing that challenge is a prerequisite for automatic annotation of histopathological data, profiled using multichannel immunofluorescence or spatial sequencing. Here, we propose Ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single cell level. We have tested ikarus on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.

Cell type

Limiting

Tumor Heterogeneity

Single-Cell Analysis

10.1101/2021.10.15.463909

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Citations (2)

Massively parallel identification of mRNA localization elements in primary cortical neurons

Nature Neuroscience (2023)

Samantha Mendonsa Nicolai von Kügelgen Sayaka Dantsuji Maya Ron Laura Breimann

Abstract Cells adopt highly polarized shapes and form distinct subcellular compartments in many cases due to the localization of many mRNAs to specific areas, where they are translated into proteins with local functions. This mRNA localization is mediated by specific cis -regulatory elements in mRNAs, commonly called ‘zipcodes’. Although there are hundreds of localized mRNAs, only a few zipcodes have been characterized. Here we describe a novel neuronal zipcode identification protocol (N-zip) that can identify zipcodes across hundreds of 3′ untranslated regions. This approach combines a method of separating the principal subcellular compartments of neurons—cell bodies and neurites—with a massively parallel reporter assay. N-zip identifies the let-7 binding site and (AU) n motif as de novo zipcodes in mouse primary cortical neurons. Our analysis also provides, to our knowledge, the first demonstration of an miRNA affecting mRNA localization and suggests a strategy for detecting many more zipcodes.

Neurite

10.1038/s41593-022-01243-x

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Citations (30)

Multi-Omics Alleviates the Limitations of Panel Sequencing for Cancer Drug Response Prediction

Cancers (2022)

Artem Baranovskii Irem B. Gündüz Vedran Franke Bora Uyar Altuna Akalin

Comprehensive genomic profiling using cancer gene panels has been shown to improve treatment options for a variety of cancer types. However, genomic aberrations detected via such gene panels do not necessarily serve as strong predictors of drug sensitivity. In this study, using pharmacogenomics datasets of cell lines, patient-derived xenografts, and ex vivo treated fresh tumor specimens, we demonstrate that utilizing the transcriptome on top of gene panel features substantially improves drug response prediction performance in cancer.

Pharmacogenomics

Drug response

Cancer drugs

Cancer cell lines

Ex vivo

10.3390/cancers14225604

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Multi-omics alleviates the limitations of panel-sequencing for cancer drug response prediction

bioRxiv (Cold Spring Harbor Laboratory) (2022)

Artem Baranovskii Irem B. Gündüz Vedran Franke Bora Uyar Altuna Akalin

Abstract Comprehensive genomic profiling using cancer gene panels has been shown to improve treatment options for a variety of cancer types. However, genomic aberrations detected via such gene panels don’t necessarily serve as strong predictors of drug sensitivity. In this study, using pharmacogenomics datasets of cell lines, patient-derived xenografts, and ex-vivo treated fresh tumor specimens, we demonstrate that utilizing the transcriptome on top of gene panel features substantially improves drug response prediction performance in cancer.

Pharmacogenomics

Drug response

Cancer drugs

Ex vivo

Cancer cell lines

10.1101/2022.06.15.496249

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Citations (1)

Massively parallel identification of zipcodes in primary cortical neurons

Figshare (2023)

Samantha Mendonsa Nicolai von Kügelgen Sayaka Dantsuji Maya Ron Laura Breimann

Processed NGS and proteomics data

Identification

10.6084/m9.figshare.21180571

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